The Pennsylvania State University
The Graduate School
Department of Chemical Engineering
DEVELOPMENT OF BIOLOGICAL PLATFORM FOR THE
AUTOTROPHIC PRODUCTION OF BIOFUELS
A Dissertation in
Chemical Engineering
by
Nymul Khan
 2015 Nymul Khan
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
May 2015
The dissertation of Nymul Khan was reviewed and approved* by the following:
Wayne Curtis
Professor, Chemical Engineering
Dissertation Advisor
Chair of Committee
Esther Gomez
Assistant Professor, Chemical Engineering
Manish Kumar
Assistant Professor, Chemical Engineering
John Regan
Professor, Environmental Engineering
Phillip E. Savage
Walter L. Robb Family Endowed Chair
Head of the Department of Chemical Engineering
*Signatures are on file in the Graduate School
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ABSTRACT
The research described herein is aimed at developing an advanced biofuel platform that has the
potential to surpass the natural rate of solar energy capture and CO2 fixation. The underlying concept is to
use the electricity from a renewable source, such as wind or solar, to capture CO2 via a biological agent,
such as a microbe, into liquid fuels that can be used for the transportation sector. In addition to being
renewable, the higher rate of energy capture by photovoltaic cells than natural photosynthesis is expected
to facilitate higher rate of liquid fuel production than traditional biofuel processes. The envisioned
platform is part of ARPA-E’s (Advanced Research Projects Agency - Energy) Electrofuels initiative
which aims at supplementing the country’s petroleum based fuel production with renewable liquid fuels
that can integrate easily with the existing refining and distribution infrastructure (http://arpae.energy.gov/ProgramsProjects/Electrofuels.aspx). The Electrofuels initiative aimed to develop liquid
biofuels that avoid the issues encountered in the current generation of biofuels: (1) the reliance of
biomass-derived technologies on the inefficient process of photosynthesis, (2) the relatively energy- and
resource-intensive nature of agronomic processes, and (3) the occupation of large areas of arable land for
feedstock production. The process proceeds by the capture of solar energy into electrical energy via
photovoltaic cells, using the generated electricity to split water into molecular hydrogen (H2) and oxygen
(O2), and feeding these gases, along with carbon dioxide (CO2) emitted from point sources such as a
biomass or coal-fired power plant, to a microbial bioprocessing platform. The proposed microbial
bioprocessing platform leverages a chemolithoautotrophic microorganism (Rhodobacter capsulatus or
Ralstonia eutropha) naturally able to utilize these gases as growth substrates, and genetically modified to
produce a triterpene hydrocarbon fuel molecule (C30+ botryococcenes) native to the alga Botryococcus
braunii. In addition to the genetic modification and bioreactor performance studies of these organisms for
the production of botryococcene or squalene, the research examined the potential economic feasibility of
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the proposed platform through the use of bioreactor, microbial energetic models and experimentally
measured growth yield and maintenance coefficients.
In order to carry out an economic analysis, a process model was created in Aspen with the
bioreactor at the center. This is presented in Chapter 2. The model looked at the effects of growth yield
and maintenance coefficients of R. capsulatus and R. eutropha, reactor residence time, gas-liquid masstransfer coefficients, gas composition and specific fuel productivity on the volumetric productivity and
fuel yield on H2. It was found that the organism with the lowest maintenance coefficient performed better
under very low growth rates evaluated in the model (based on residence time through the reactor)
performed the best. The optimum parameter values were then used to determine the capital and operating
costs for a 5000 bbl-fuel/day plant and the final fuel cost based on the Levelized Cost of Electricity
(LCOE). It was found that under the assumptions used in this analysis and crude oil prices, the LCOE
required for economic feasibility must be less than 2¢/kWh. While not feasible under current market
prices and costs, this work identifies key variables impacting process cost and discusses potential
alternative paths toward economic feasibility. This was the best case scenario of the two organisms
evaluated, and an optimally suited organism with high growth yield and low maintenance coefficient
should obviously improve the economics. This economic constraint will improve with the rise of fossil
fuel prices, which should occur if the environmentally detrimental effects of their use are factored into the
price, through higher taxation, for example.
A review of the current status of metabolic engineering of chemolithoautotrophs is carried out in
order to identify the challenges and likely routes to overcome them. This is presented in Chapter 3 of this
dissertation. The initial metabolic engineering and bioreactor studies was carried out using a number of
gene-constructs on R. capsulatus and R. eutropha. The gene-constructs consisted of Plac promoter
followed by the triterpene synthase genes (SS or BS) and other upstream genes. In R. capsulatus, by
genetically supplementing the methylerithrotol phosphate (MEP) pathway and supplementing the growth
with glucose, it was found that the triterpene synthase enzymes were substrate-limited i.e. depended on
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the carbon-flux to them. A comparison of the production of triterpenes were done in the different growth
modes that R. capsulatus was capable of growing – aerobic heterotrophic, anaerobic photoheterotrophic
and aerobic chemoautotrophic. Small-scale testing (<50 ml) under typical (un-supplemented) growth
conditions showed that the per-cell triterpene production levels were surprisingly similar in all the
different growth modes (around 5 mg/gDW). However, the results were much improved when tested in
controlled fed-batch bioreactors, capable of reaching significantly higher cell densities. In the
heterotrophic case, production was found to increase up to 40 mg/L (~11 mg/gDW), unfortunately
inhibited by some sort of toxic effect at OD660 around 12. Autotrophic growth on H2, O2 and CO2, on the
other hand, showed no such effect and growth occurred well up to an OD660 of 17 (corresponding to about
7 gDW/L), limited only by the mass-transfer of the gases and triterpene productivity increased
continuously to greater than 100 mg/L (16 mg/gDW) in the batch mode. Continuous autotrophic
operation further increased the specific titer to 23 mg/gDW, reaching a steady state. The specific
productivity was found to be around 0.5 mg/gDW-hr. This demonstrated that autotrophic productivity
could likely be improved much further by increasing the available mass-transfer of the reactor. These
efforts are presented in Chapter 4 of this dissertation.
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TABLE OF CONTENTS
List of figures ........................................................................................................................... ix
List of tables............................................................................................................................. xv
List of symbols and abbreviations ........................................................................................... xvi
Acknowledgements .................................................................................................................. xvii
Chapter 1 Background and outline........................................................................................... 1
I. Motivation..................................................................................................................... 1
II. My research ................................................................................................................. 2
III. Rationale for Selection of the Microbial Bioprocessing Platform ............................. 6
IV. Metabolic engineering strategy .................................................................................. 8
V. Dissertation outline ..................................................................................................... 10
Chapter 2 A process economic assessment of hydrocarbon biofuels production using
chemoautotrophic organisms ........................................................................................... 11
I. Preface .......................................................................................................................... 11
II. Specific contributions .................................................................................................. 11
III. Introduction ................................................................................................................ 12
IV. Model development ................................................................................................... 13
2.IV.I. Proposed electrofuels production process..................................................... 13
2.IV.II. Bioreactor modeling .................................................................................... 14
V. Results and discussion................................................................................................. 22
2.V.I. Sensitivity analysis: effect of reactor residence time (τ) ................................ 22
2.V.II. Sensitivity analysis: effect of specific productivity (Rfuel) ............................ 24
2.V.III. Sensitivity analysis: effect of gas-liquid mass transfer coefficient (kLa)..... 27
2.V.IV. Process economic analysis of capital and operating costs........................... 29
2.V.V. Alternative perspective ................................................................................. 33
2.V.VI. Targets based on the analysis ...................................................................... 34
VI. Conclusions ................................................................................................................ 36
Chapter 3 Literature review: Metabolic engineering in chemolithoautotrophic hosts for the
production of fuels and chemicals.................................................................................... 37
I. Preface .......................................................................................................................... 37
II. Specific contributions .................................................................................................. 38
III. Introduction ................................................................................................................ 38
IV. Background ................................................................................................................ 41
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3.IV.I. Carbon Fixation in Chemolithoautotrophs.................................................... 43
3.IV.II. Hydrogen Utilization by Chemolithoautotrophs ......................................... 47
3.IV.III. Microbial Electrosynthesis via Direct Electron Feeding............................ 49
V. Metabolic Engineering of Natural Chemolithoautotrophic Organisms...................... 50
3.V.I. Status and Constraints for Metabolic Engineering in Chemolithoautotrophs 50
3.V.II. Metabolic Engineering in Aerobic Chemolithoautotrophs ........................... 53
3.V.III. Metabolic Engineering in Acetogens .......................................................... 55
3.V.IV. Metabolic Engineering in Other Chemolithotrophs .................................... 58
VI. Using Metabolic Engineering to Improve / Creating New Chemolithoautotrophs ... 59
3.VI.I. Engineering Carbon Capture ........................................................................ 59
3.VI.II. Engineering improved H2-utilization........................................................... 62
3.VI.III. Metabolic Engineering of Alternative Electron Delivery Pathways .......... 64
VII. Metabolic Engineering Toolbox for Existing Autotrophs ........................................ 65
3.VII.I. Genetic Transformation ............................................................................... 65
3.VII.II. Plasmids, Promoters and Vectors of Particular Utility for
Chemolithoautotrophs ...................................................................................... 68
VIII. Status of productivities and scale-up considerations for metabolic engineering.... 71
IX. Conclusion ................................................................................................................. 76
Chapter 4 Triterpene hydrocarbon production engineered into a metabolically versatile host – R.
capsulatus......................................................................................................................... 78
I. Preface .......................................................................................................................... 78
II. Specific contributions .................................................................................................. 78
III. Introduction ................................................................................................................ 79
IV. Results........................................................................................................................ 83
4.IV.I. Achieving Substrate-Limited Triterpene Biosynthetic Constructs ............... 83
4.IV.II. Productivity Enhancement through Bioreactor Operational Strategies ....... 88
V. Discussion ................................................................................................................... 93
Chapter 5 Ongoing and future work and conclusion ............................................................... 96
I. Triterpene production in R. eutropha............................................................................ 96
5.I.I. Pathway enhancements .................................................................................... 100
II. Future work ................................................................................................................. 103
III. Conclusion ................................................................................................................. 107
Appendix A Bioreactor material balance equations for section process economic analysis
(section 2.III.II) ................................................................................................................ 108
Appendix B A comparison of experimental measurements and thermodynamic predictions of
true yield (
) ............................................................................................................ 111
Calculation of true yield by TEEM .......................................................................... 112
Modifications applied to the original TEEM to incorporate autotrophic growth, reversed
electron transport, and BPF synthesis for R. eutropha and R. capsulatus ........ 115
Comparison of original TEEM and Modified Electron Balance methods of predicting
............................................................................................................... 118
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Appendix C Measurement of growth yield and maintenance coefficient ............................... 121
Growth yield and maintenance coefficient ............................................................... 121
Theory ...................................................................................................................... 122
Experimental procedure and results ......................................................................... 124
Appendix D Materials and methods........................................................................................ 129
Bacterial Strains and Growth Conditions ................................................................. 129
Small Scale Phototrophic and Autotrophic Growth Conditions for R. capsulatus Strains130
Heterotrophic Bioreactor Growth Condition............................................................ 131
Autotrophic Bioreactor Growth Conditions ............................................................. 132
Cloning Procedures .................................................................................................. 132
Intergeneric Mating Between E. coli S17-1 and R. capsulatus SB1003 .................. 136
Hydrocarbon Analysis .............................................................................................. 137
Appendix E Background calculations for Figure 4-5. ............................................................ 139
IV. Aerobic heterotrophic growth on glucose .................................................................. 139
V. Anaerobic photoheterotrophic growth on malate ........................................................ 140
VI. Autotrophic growth on H2 .......................................................................................... 141
VII. Carbon balance ......................................................................................................... 141
Aerobic heterotrophic growth on glucose ................................................................ 141
Anaerobic photoheterotrophic growth on malate ..................................................... 142
Autotrophic growth on H2 ....................................................................................... 142
Appendix F Other miscellaneous work ................................................................................... 143
VIII. Small scale multiplexed autotrophic screening device............................................ 143
IX. 100-L plastic bag trickle-bed reactor. ........................................................................ 145
X. Secretion of hydrocarbon by R. capsulatus ................................................................. 146
References ................................................................................................................................ 148
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List of figures
Figure 1-1. System load with and without large (16 GW) PV system on two spring days
(reproduced from Denholm & Margolis 2006). ............................................................... 4
Figure 1-2. Conceptual depiction of an Electrofuels process. Energy from the sun is captured in
the form of renewable electricity (depicted as solar photovoltaics, PV) and is used to split
water. The resulting O2 and H2 is combined with CO2 and fed into a bioreactor where R.
capsulatus consumes these gases in stoichiometric requirement. R. capsulatus is genetically
engineered to produce a C30+ triterpene hydrocarbon which can be recovered as an
extracellular phase-separated oil. ..................................................................................... 5
Figure 1-3. Metabolic engineering strategy for the production of tritepenes in R. capsulatus.
9
Figure 2-1. Process flow diagram of the envisioned Electrofuels process. CO2, H2, O2, fresh
nutrient and recycled cells enter the reactor and cells+oil leave the reactor. The hydrophobic
top oil layer is separated in oil-water separator and pass through fuel filtration and processed
to final product. The remaining cells+water+residual oil from oil-water separator enters a
clarifier where the denser cell layer is split into two fractions, one recycled back to the
reactor and one sent to sludge processing. The top, less dense overflow from this clarifier
enters a second clarifier where cell debris are separated from the water, which is processed
through waste-water processing to be recycled to the reactor with fresh nutrients and makeup
water. ................................................................................................................................ 14
Figure 2-2. Framework for capturing microbial growth energetics, cellular maintenance and fuel
production. (A) Incorporating the experimentally measured biological yield and maintenance
coefficients into model reaction/stoichiometry equations suitable for reactor design. (B)
Concise version of metabolic pathway for calculating the minimum required Gibbs’ free
energy for fuel synthesis in a bacteria. ............................................................................. 17
Figure 2-3. Variation of volumetric productivity,
(A), fuel yield on H2, / 2 (B) and cell
density, (C) as a function of residence time ( ) through the reactor for R. capsulatus (●)
and R. eutropha (o). Simulation parameters: true growth yield ( 2 ) of R. capsulatus =
2.66, R. eutropha = 7.68 gDW/mol-H2, maintenance coefficients (
2) of R. capsulatus =
2.01x10-3, R. eutropha = 6.8x10-3 mol-H2/gDW.hr, kLa = 330/hr, specific productivity = 0.5
g fuel/g biomass.hr, cell recycle efficiency = 95%. ......................................................... 24
Figure 2-4. (A) Variation of electricity requirement for H2 generation (●) and volumetric
productivity (o) with specific fuel productivity representing an increasing fraction of energy
being converted into fuel instead of biomass (Simulation parameters: residence time = 15 hr;
other parameters same as in Figure 2-3). (B) Variation in reactor volume as a function of kLa
showing the reduction in culture process volume that is enabled as the mass transfer rate is
improved ( ). The range of electricity requirements that would be required to achieve the
specified kLa in different bioreactor configuration is shown as the shaded area between the
dashed lines. The high and the low value of the electricity requirement at each kLa
corresponds to the highest and the lowest P/V able to produce the specified kLa; the range of
P/V for a variety of bioreactor configurations are presented in Figure 2-5...................... 26
x
Figure 2-5. Gas-liquid mass transfer coefficient (kLa) vs. power/volume (P/V) for various reactor
types. Surface aerator 1 = wastewater treatment plant demonstration run, Chattanooga, TN
(Cosby & Gay, 2003); Surface aerator 2 = wastewater treatment plant demonstration run,
Yuba City, CA (Lewis & Gay, 2003); Stirred 1 = stirred tank reactors correlation for
electrolytes, (Van’t Riet, 1979), Vs = 0.005 m/s; Stirred 2 = stirred tank reactor correlation
for non-electrolytes (Van’t Riet, 1979), Vs = 0.005 m/s; Stirred 3 = stirred tank reactors
correlation for electrolytes (Linek et al., 1987), Vs = 0.005 m/s; TBR 1 = trickle-bed reactor
correlation set 1 (Reactor parameters: Roininen et al., 2009, kLa correlation: Goto & Smith,
1975, pressure-drop correlation: Larachi et al., 1991); TBR 2 = trickle-bed reactor
correlation set 1 (Reactor parameters: Roininen et al., 2009, kLa correlation and pressuredrop correlation: Reiss, 1967); Microbubble – various = experimental data from various
investigators (Bredwell & Worden, 1998; Hensirisak et al., 2002). This compilation of
literature values focuses on large-scale commercial units (except for microbubble
technology)....................................................................................................................... 28
Figure 2-6. (A) Capital costs of various process components for a 5000 bbl/day fuel production
plant. Results are grouped into high (black bards) and low (dashed bars) range capital costs.
(B) A breakdown of process costs (the dominant cost of electricity generation is not
presented). Top (grey) group represents the major operating costs which make up 36% of
total; middle (hashed) group represents the major non-photovoltaic capital costs which make
up 46% of total; bottom (black) group estimates the major non-PV fixed costs corresponding
to 17% of the total. (C) A sensitivity analysis of the final fuel cost based on LCOE of various
generation methods reported in two different sources: REN21, 2013 (black); IRENA, 2012
(dashed). CSP = Concentrate Solar Power, PT = Parabolic Trough, ST = Solar Tower. The
horizontal line indicates year 2020 crude oil price estimate (Gruenspecht, 2012). Off-grid
hydropower values were not available in IRENA, 2012. ................................................. 31
Figure 3-1. Analogy between direct photosynthetic growth and indirect photosynthetic growth
based on the feeding of electrons as reducing power to reduce CO2............................... 39
Figure 3-2. An examination of the CO2 reduction reactions from the perspective of the location of
the delivery of the reducing power to either the reactor, or to the inside of the cells as a
major determinant of the application of these for biotechnological purposes. Light cannot
penetrate dense cultures (Beer-Lambert Law), where gasses face limitations of solubility in
the liquid phase (Henry’s Law). Electrons are particularly challenging to deliver (Ohm’s
Law) either to a reactor electrode or into the cell although this can be facilitated by the
reduction of a ‘carrier’. .................................................................................................... 40
Figure 3-3. A summary of autotrophic growth modes with the common theme of reducing CO2 to
biomass (carbohydrate) using various forms of reducing power. These different autotrophic
growth modes are often associated with other classification names that are listed. These
common name classifications are useful, but also lead to problems typical of generalizations.42
Figure 3-4. Schematic illustrating the diversity of the cbb operons including paralogs of the
various accessory proteins in addition to the large and small subunit (cbbL/S). Form I
Rubisco is denoted by cbbLS and Form II by cbbM, both of which are found in R. capsulatus
and R. sphaeroides (Panel A&B). R. eutropha also contains two CBB operons, one on the
chromosome (Panel C), and one on the megaplasmid pHG1 with a defective transcriptional
activator (cbbR*, Panel D). The iron-oxidizing bacteria Acidithiobacillus ferooxidans
xi
contain four different CBB operons with two encoding Form I Rubisco subunits. Other
genes are: cbbF=SBPase/FBPase, cbbP=PRK, cbbT=TKT; cbbA=FBA/SBA, cbbE=PPE,
cbbG=GAPDH, cbbK=PGK, cbbZ=phosphoglycolate phosphatase), cbbBXYQ=unknown.
The presence of these functional operons in plasmids provided the historical basis of
elucidating autotrophic genes including CO2 fixation and hydrogen use, now adapted to
metabolic engineering of these phenotypes in chemolithoautotrophs.............................. 44
Figure 3-5. The various carbon fixing pathways. .................................................................... 46
Figure 3-6. Schematic representation of carbon and energy metabolism, regulation and gene
expression in a typical aerobic chemolithoautotroph. ...................................................... 48
Figure 3-7. (A) Maximum reported productivities in grams of carbon fixed in products per liter
(gC-fixed/L) in autotrophic systems. Native pathways: PHB by R. eutropha (I), acetate by
M. thermoacetica (II), ethanol by C. ljungdahlii (III); engineered pathways: botryococcene
by R. capsulatus (IV), methyl ketone by R. eutropha (V), butyrate by C. ljungdahlii (VI). All
are timecourses adapted from literature reports presented in Table 3 and reflect growth on
CO2 only (not chemoautotrophic hosts utilizing organic carbon source). (B) Maximum
theoretical yield on H2 for the native molecules. ............................................................. 75
Figure 4-1. R. capsulatus is a metabolically diverse organism that can utilize and interconvert the
energy provided from the sun in a variety of different ways. Metabolism within the dashed
line representing Rhodobacter cell are those reported in this study: aerobic chemoautrophic
growth consumes H2 and O2 while fixing carbon, with these gases being provided by
photovoltaic-driven electrolysis in this scenario (or other natural sources). Aerobic
heterotrophic growth is the typical consumption of sugars and associated aerobic respiration,
anaeobic photoheterotrophic growth consumes energy poor organic acids with photosystemII mediated ATP generation using light energy. Depiction of the evolution (green arrows) or
uptake (red arrows) of metabolic gases emphasizes the bioreactor requirements for gasexchange as a major constraint for process design and operation. The lower part of the
figure depicts the metabolic engineering strategy within the Rhodobacter cell to achieve the
production of the high energy terpenes botryococcene or squalene. The operon includes the
enzyme that commits carbon flux to the MEP pathway: (1-Deoxy-D-xylulose 5-phosphate
synthase, dxs) and the isopentenyl diphosphate (IPP) isomerase (idi) to enhance the ratio of
IPP to dimethylallyl pyrophosphate (DMAPP). This DNA construct also includes the gene
for farnesyl diphosphate synthase (fps) which utilizes one DMAPP and two IPP to produce
C15 farnesyl pyrophosphate, the substrate for the terpene synthases (botryococcene synthase,
BS; squalene synthase, SS). .............................................................................................. 80
Figure 4-2. Engineering of triterpene synthases with the native or engineered MEP pathways of E.
coli DH5α. Constructs were engineered as a single polycistron under the control of the PLac
promoter with triterpene synthase only (BS, botryococcene synthase; or SS, squalene
synthase); in combination with a prenyltransferase (fps); or with one plasmid-borne copy of
the engineered MEP pathway (dxs-idi-fps) or two plasmid-borne copies. Five biological
replicates of each strain were grown aerobically for 24 hours in 2xYT-1% glycerol and then
extracted with 1:1 acetone and hexane to determine triterpene content........................... 84
Figure 4-3. R. capsulatus triterpene metabolic engineering. (a) Constructs containing
botryococcene synthase (BS, upper panel) or squalene synthase (SS, lower panel) with
xii
increasing enhancements in metabolic flux by including the avian farnesyl pyrophospate
synthase (fps), and putative rate-limiting enzymes in the MEP pathway. (b) Accumulation
of triterpenes in R. capsulatus grown in standard growth medium (Std. medium) and that
supplemented with an additional carbohydrate source, 80 mM glucose (+Glu suppl.) for the
adjacent constructs: botryococcene for the upper panel, and squalene for the lower panel.
The level of enhancement beyond standard growth medium alone is indicated by the
extended bars (open bars). Experimental values were determined from five replicate
cultures; error bars depict standard error of the mean. ..................................................... 85
Figure 4-4. Shake flask autotrophic time-course of R. capsulatus harboring pBBR:Plac:BS-dxs-idifps and pBBR:Plac:BS only (Top panel) and pBBR:Plac:SS-dxs-idi-fps and pBBR:Plac:SS only
(Bottom panel). ................................................................................................................ 86
Figure 4-5. Exploring alternative trophisms for hydrocarbon production in Rhodobacter. (a)
Triterpene specific productivity of R. capsulatus with pBBR:BS-dxs-idi-fps (blue-left panel),
and pBBR: SS-dxs-idi-fps (red-right panel). (b) Reaction for hydrocarbon (HC) production
for given tropism. (c) Trophims are presented quantitatively in terms of thermodynamic
energy required to assimilate a mole of carbon into pyruvate as well as the energy required
to produce an ATP normalized to a molar carbon basis. The carbon balance is shown to
illustrate the relative magnitude of carbon produced by aerobic and photo-heterotrophic
growth as compared to autotrophic CO2 assimilation. ..................................................... 88
Figure 4-6. Heterotrophic bioreactor growth of R. capsulatus pBBR:Plac:BS-dxs-idi-fps. R.
capsulatus genetically engineered with the above expression vector was grown in a 5 L
BioFlo with glucose provided as the sole initial carbon and energy source, followed by fedbatch supplementation based on feedback control of dissolved oxygen (DO). Ammonia was
also added in a fed-batch manner for pH control. (a) Growth monitored as OD660 (black
circles) and botryococcene accumulation (blue squares) respectively. (b) The DO (red
squares) and cumulative glucose supplementation (black line), where glucose fed batch was
initiated at ~40 hr, and the approximate times for incremental increases in O2
supplementation (light blue arrows) are noted. Cultures stopped growing and accumulating
hydrocarbon at about 70 hr; respiration slowed considerably in stationary phase as indicated
by the sharp rise in DO which could not be recovered with additional glucose feed. ..... 89
Figure 4-7. Growth tests with heterotrophic bioreactor culture and culture supernatant. (a
and b) Replicate samples from the bioreactor inoculated into RCVB (-malate) minimal
media supplemented with 10 g/L glucose. (c) Sample from the bioreactor inoculated into
YCC complex medium supplemented with 10 g/L glucose. (d) Fresh culture of R. capsulatus
pBBR:Plac::BS-dxs-idi-fps inoculated into bioreactor supernatant supplemented with
concentrated YCC medium nutrients (to make the final concentration similar to YCC). (e
and f) Replicate fresh cultures of R. capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into
bioreactor supernatant supplemented with concentrated RCVB (-malate) medium nutrients
(to make the final concentration similar to RCVB). (g) Fresh culture of R. capsulatus
pBBR:Plac::BS-dxs-idi-fps inoculated into RCVB (-mal) medium supplemented with 27 g/L
glucose. (h) Fresh culture of R. capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into RCVB
(-mal) medium supplemented with 10 g/L glucose. ......................................................... 90
Figure 4-8. Performance of R. capsulatus pBBR:PLac:BS-dxs-idi-fps in an autotrophic bioreactor.
R. capsulatus expressing this plasmid was grown with gas phase feeding of H2, O2 and CO2
xiii
(for growth) and liquid phase feeding of ammonia (for pH control) in batch operation for the
first 110 hours and under continuous operation after that. Continuous flow was established
with 12.5 g/L flow of the CA medium, which corresponds to 10% of µmax of R. capsulatus.
(a) The inlet gas composition profile. During the batch growth mode, the compositions were
varied to accommodate the growth demand, based on outlet gas composition measurement.
Gas consumption did not vary greatly during continuous flow and the inlet composition was
essentially kept constant. (b) The OD660 and botryococcene profile during batch and
continuous operation. Cells grew exponentially to OD 5 and linearly after that reaching a
maximum of 17. Botryoccocene accumulation increased proportionally as the cells and
achieved a maximum volumetric accumulation of 110 mg L-1 botryococcene at the end of
batch growth. Under continuous operation, the cells reached a steady state within about 130
hrs of starting flow, reaching an OD of ~7 and maintaining a relatively constant level 60 mg
L-1 botryococcene. (c) The specific botryococcene productivity profiles. The specific
botryococcene levels increased steadily during batch growth to about 17 mg/gDW. Batch
specific productivities were around 0.5 mg/gDW-hr. Specific botryococcene continued to
increase during the continuous flow and reached 23 mg/gDW. However, specific
productivity decreased somewhat and reached a steady level of about 0.3 mg/gDW-hr. 92
Figure 5-1. Triterpene production by R. eutropha transformed with pRK:Plac:SS-fps. The three
different colored bars indicate three different clones. ...................................................... 97
Figure 5-2. Heterotrophic reactor growth of R. eutropha pRK:Plac:SS-fps. (A) Defined medium
fed-batch with fructose. (B) LB medium fed-batch with fructose. .................................. 98
Figure 5-3. Comparison between two backgrounds of R. eutropha: PHB- and wild-type in terms
of triterpene production, transformed with pRK:Plac-SS-FPS. The cultures are grown in LB
medium supplemented with indicated levels of fructose. ................................................ 99
Figure 5-4. Bioreactor growth of R. eutropha wild-type transformed with pRK:Plac-SS-FPS. 100
Figure 5-5. Triterpene production by R. eutropha PHB- pRK:Plac:SS-fps + pBBR:Plac:MevTMBIS (dual plasmid). ....................................................................................................... 103
Figure 5-6. Triterpene production by R. eutropha H16 wild-type transformed with pBBR:Plac:BSdxs-idi-fps. ........................................................................................................................ 101
Figure 5-7. Desired mega-plasmid containing all the MVA pathway genes, the triterpene synthase
and fps. ............................................................................................................................. 104
Figure A1. Bioreactor flow streams and mass-balance envelops. 1. Cell separator and recycle, 2.
bioreactor and cell separator, 3. bioreactor only. ............................................................. 108
Figure C1: Experimental setup for the measurement of growth yield and maintenance coefficient.125
Figure C2. Media reservoir weight throughout the experiment. The first derivative of y gives the
instantaneous media flow rate, while the second derivative gives the deceleration rate. Media
flow rates varies from 10.03 g/hr at the initial steady state to 6.92 g/hr at the final steady
state, with a deceleration rate of -0.0974 g/hr2. ................................................................ 127
xiv
Figure C3. Change of culture density with media flow rate. At the initial steady state the culture
density remains around 1 g/L, increasing with decrease of flow rate to about 1.4 g/L during
the final steady state. ........................................................................................................ 127
Figure C4. Plot of µ/Y vs. µ for two D-stat experiments covering two different ranges of dilution
rates. Inverse of the slope gives the growth yield and the intercept gives the maintenance
coefficients. ...................................................................................................................... 128
Figure F1. (Top) A multiplexed autotrophic growth device design for continuous monitoring of
growth using LED/phododiode assemblies at the base of the culture. Gas inlet to multiplexed
cultures is achieved with a very small sapphire orifice manifold. (Bottom) Examples of
online optical density collected from the screening device while growing different strains of
R. capsulatus. ................................................................................................................... 144
Figure F2. A 100-L low cost plastic bag trickle bed that has been assembled to explore regions of
high mass transfer efficacy operation for autotrophic growth. ........................................ 146
Figure F3. Hydrocarbon secretion in R. capsulatus autotrophic bioreactor cultures. Triterpene
levels in cells, media and total culture during late lag and stationary phase of two
independent batch cultures. .............................................................................................. 147
xv
List of tables
Table 2-1. Parameter values used in final scaled-up design.
29
Table 3-1. Catalytic properties of various hydrogenases (compiled from BRENDA).
65
Table 3-2. Comparison of productivities of different compounds in various autotrophic and nonautotrophic hosts.
75
Table D1. Strains and plasmids used in this study.
130
Table D2. Nucleotide sequences of the wildtype and R. capsulatus codon-optimized genes used.
133
xvi
List of symbols and abbreviations
a, b, c, d
bbl
stoichiometric coefficients in cell growth equation
US fluid barrel
concentration of fuel in the bioreactor, mol·L-1
liquid phase concentration of i-th gas component, mol·L-1
diffusion coefficient of i-th gas component, m2·s-1
rate of consumption of i-th gas component, mol·L-1·h-1
rate of substrate utilization for growth, fuel synthesis, maintenance requirements and total
respectively, mol·L-1·h-1
EROI
F
gDW
IGCC
LCOE
,
NGCC
PC
V
X
/
ε
µ, µmax
τ
Energy Return on Investment
rate of liquid feed into reactor, L·h-1
grams dry weight
gas transfer rate of i-th gas component, mol·L-1·h-1
Henry’s law coefficient of i-th gas component, atm·L·mol-1
Integrated Gasification Combined Cycle
gas-liquid mass transfer coefficient of the i-th component, h-1
Levelized Cost of Electricity
maintenance coefficient of cells on H2 and O2 respectively mol·gDW-1·h-1
Natural Gas Combined Cycle
Pulverized Coal
volumetric fuel productivity, g-fuel·L-1·h-1
total pressure, atm
specific fuel productivity, g-fuel·gDW-1·h-1
rate of growth of cells, gDW·L-1·h-1
reactor volume, L
cell density, gDW·L-1
yield of fuel on H2, g-fuel· (mol-H2)-1
gas phase mole fraction of i-th component
true growth yield of cells on H2, gDW·mol-1
cell recycle efficiency
specific growth rate and maximum specific growth rate of cells, h-1
residence time through reactor, V/F, h-1
xvii
Acknowledgements
I would like to thank Prof. Wayne Curtis for his excellent support and mentoring throughout my
PhD. Prof. Joseph Chappell at University of Kentucky provided valuable insights and direction for the
project. My colleagues Dr. S. Eric Nybo at University of Kentucky, Dr. Alex Rajangam and Ryan
Johnson at Penn State University were directly involved in the project and provided great assistance in
major aspects of the project. My PhD cohorts, classmates and friends Sergio Florez and Dr. Trevor
Zuroff, provided both intellectual and material help. I am immensely grateful to the numerous excellent
undergraduate students who voluntarily put their time and effort for the project - Stephanie Tran, Bill
Muzika, Andrew Barmasse, John Myers, Erik Wolcott and Justin Yoo. Microbial energetic model
modifications developed by Amalie Tuerk (Masters from Curtis lab, currently doing her PhD at
University of Delaware) were used in the process economic analysis. She also provided major inputs and
insights to help write the manuscript related to this. Ben Woolston (honors student from Curtis lab,
currently doing his PhD at Massachusetts Institute of Technology) provided valuable inputs for the
writing of the literature review on metabolic engineering. Dr. Steven Singer at Lawrence Berkeley
National Lab kindly provided two strains of R. eutropha. Financial assistance for this project was
provided by ARPA-E electrofuels award DE-AR0000092.
Chapter 1
Background and outline
I. Motivation
Energy crisis and environmental concerns for CO2 emission have been continuing
challenges for mankind in the 21st century. Fossil fuels have provided a readily accessible and
energy-dense fuel source to pave the way for science and technology advancements. But their
supply is limited, and their use contribute towards the increase greenhouse gas emissions,
adversely affecting global climate. Thus sustainable and renewable sources of biofuels are
needed.
Global carbon cycle is maintained by photosynthetic plants and microorganisms
sequestering CO2 into biomass using Sun’s energy, providing sources of food and energy for life
on earth. In the process of their consumption by humans and animals, the CO2 is released back
into the atmosphere. In the last hundred years or so, the exponential growth of human civilization
and technologies, and thus the use of fossil fuels, have significantly outpaced natural rate of
carbon fixation. Maintenance of economic growth has relied on using millions of years of natural
accumulation in fossil fuel reserves within a very short period of time. Further implication of this
is the increase of atmospheric CO2 by greater than 10%, which is believed to be causing global
adversities (Melillo, Jerry M., Terese (T.C.) Richmond, and Gary W. Yohe 2014). Estimates
indicate that the current rate of consumption may be sustained for a few more centuries (IEA
2013), but more catastrophic consequences of rising CO2 levels are prompting research towards
reduced greenhouse gas (GHG) emission and carbon capture and storage (CCS) technologies.
These technologies, however, offset the real problem of renewable use of carbon. In addition,
2
depleting reserves and requirement of advanced technologies for unlocking more difficult
underground resources mean that prices of fossil fuels will continue to rise in the long term. Only
recently have these problems attracted serious attention and concerns from general public and
policy-makers and as such, numerous approaches are being funded to develop transformative
technologies to address these problems.
Notable efforts in this direction include bioethanol and biodiesel, which are first
generation biofuels, i.e. produced directly from food crops (for example corn in US, sugarcane in
Brazil etc.). Corn-ethanol, currently the most widely produced biofuel in the United States, still
made up less than 5% of transportation fuels in 2011 (www.eia.gov). Furthermore, there is
ongoing controversy related to corn-ethanol contributing to higher food prices. Second
generation, non-food biofuels, such as cellulosic ethanol are only recently starting to be
commercialized but are limited by natural biomass supply. Third generation algae-biofuels are
under active research and development. Even with increasing degree of productivity and
sustainability, these technologies still rely on the natural rate of photosynthesis and carbon
capture, and this needs to be surpassed if renewable liquid fuels are to be reasonably inexpensive.
II. My research
My research aimed at developing a more advanced biofuel platform that could have the
potential to surpass the natural rate of solar energy capture and CO2 fixation. The underlying
concept is to use the electricity from a renewable source, such as wind or solar, to capture CO2
via a biological agent, such as a microbe, into liquid fuels that can be used for the transportation
sector. The envisioned platform is part of ARPA-E’s (Advanced Research Projects Agency Energy) Electrofuels initiative which aims at supplementing the country’s petroleum based fuel
3
production with renewable liquid fuels that can integrate easily with the existing refining and
distribution infrastructure (http://arpa-e.energy.gov/ProgramsProjects/Electrofuels.aspx).
In addition to being renewable, the higher rate of energy capture by photovoltaic cells
than natural photosynthesis is expected to facilitate higher rate of liquid fuel production than
traditional biofuel processes. A conservative estimate of efficiency of today’s photovoltaic cells is
around 10% (Peng et al. 2013). Using a maximum biomass yield of 7.68 gDW/mol-H2 for R.
eutropha (measured in this work), biomass heat content of 5.411 kcal/gAFDW (Ho & Payne
1979) and electrolyzer energy requirement of 53.4 kWh/kg-H2 (Ivy 2004), we can estimate that
the overall efficiency of a system converting CO2 into biomass using H2 should be around 4.3%
in terms of energy captured in biomass. Improved efficiency of photovoltaic or other solar energy
capture technologies (Bartels et al. 2010) in future should improve this efficiency further.
Compared to this, the best case of sugarcane production (based on a yield of 75 tons/hectare/yr)
has an efficiency of 0.38% in terms of total energy captured in biomass (Rosa 2005). Even with a
theoretical maximum yield of 280 tons/hectare/yr, the efficiency could only get to 1.4%.
Due to the inherent fluctuations of renewable energy sources, the future integration of
renewables into the power grid remains a challenge. Because of operational issues such as noncoincident peaks, non-dispatchability and the stability of the power supplied due to their inherent
fluctuating nature (Figure 1-1), there are very legitimate concerns about reliably operating an
electrical grid that derives a large fraction of its power renewable sources. Also, solar
photovoltaics produce low-voltage DC electricity, which incurs substantial loss in efficiency
when converting and stepping up to high voltage AC and subsequently stepping down to low
voltage DC for most of its use. Much of the renewable electricity generation is also quite
distributed and there is significant cost to create transmission and distribution infrastructure for
this electricity.
4
Figure 1-1. System load with and without large (16 GW) PV system on two spring days
(reproduced from Denholm & Margolis 2006).
The transportation sector is the largest consumer of liquid fossil fuels (13.44 million
bbl/day in 2011, www.eia.gov/forecasts/aeo/MT_liquidfuels.cfm) that cannot be readily replaced
by electricity. It is also the second largest source of CO2 emission (1.9 billion metric tons in 2008,
http://www.eia.gov/oiaf/1605/ggrpt/carbon.html) after power plants. Thus, storing the intermittent
and variable energy of renewable electricity into the chemical bonds of a fuel molecule may be a
viable alternative to other forms of storage technologies. Also, when comparing liquid fuel
products to other energy storage forms, it is apparent that a liquid hydrocarbon fuel is a much
more efficient energy carrier by weight and volume than thermal, mechanical, battery and
hydrogen energy storage.
The Electrofuels initiative aimed to develop liquid biofuels that avoid the issues
encountered in the current generation of biofuels: (1) the reliance of biomass-derived
technologies on the inefficient process of photosynthesis, (2) the relatively energy- and resourceintensive nature of agronomic processes, and (3) the occupation of large areas of arable land for
feedstock production. To address these issues, the Electrofuels initiative funded research efforts
that sought to develop biological processes that would convert distributed, off-grid, renewable
electricity into alternative liquid fuels. The logic of this approach rests in its ability to provide a
5
reliable energy source for the transportation sector by storing transiently-available electrical
energy in a chemical bond (
Figure 1-2). In addition, the Electrofuels approach is synergistic with advances in
photovoltaic cells.
SUN
Hydrocarbons
CO2
P.V.
CO2
Bioreactor
Engineering
RuBP
3GP
O2
Metabolic
Engineering
Botryococcene
Platform
Organism
Engineering
Squalene
Rhodobacter
MVA
eH20
IPP
H2
Process Modeling
& Economics
MEP
NADP
NADPH
H2
Figure 1-2. Conceptual depiction of an Electrofuels process. Energy from the sun is captured in
the form of renewable electricity (depicted as solar photovoltaics, PV) and is used to split water.
The resulting O2 and H2 is combined with CO2 and fed into a bioreactor where R. capsulatus
consumes these gases in stoichiometric requirement. R. capsulatus is genetically engineered to
produce a C30+ triterpene hydrocarbon which can be recovered as an extracellular phase-separated
oil.
Under the Electrofuels initiative, a range of approaches to this challenge were funded
(summary found in (A. Tuerk 2011)). The concept for our approach is depicted schematically in
Figure 1-2 and involves collecting and transporting electrons to a centralized bioreactor
for biological capture of the reducing power in the chemical bonds of a hydrocarbon fuel. This
proceeds by: (1) the capture of solar energy into electrical energy via photovoltaic cells (with
demonstrated laboratory efficiencies upwards of 40%, a seven-fold improvement on
photosynthesis), (2) the use of the generated electricity to split water into molecular hydrogen
(H2) and oxygen (O2), and (3) feeding these gases, along with carbon dioxide (CO2) emitted from
point sources such as a biomass or coal-fired power plant, to a microbial bioprocessing platform.
6
Our proposed microbial bioprocessing platform leverages a chemolithoautotrophic
microorganism (R. capsulatus or R. eutropha) naturally able to utilize these gases as growth
substrates, and genetically modified to produce a triterpene hydrocarbon fuel molecule (C30+
botryococcenes) native to the alga Botryococcus braunii. The details and rationale for these
choices are discussed below.
III. Rationale for Selection of the Microbial Bioprocessing Platform
Biological approaches for producing alternative fuels are benefitting from advances in
molecular biology. These developments have increased the range of fuel molecule targets that can
be synthesized by living organisms (Kim et al. 2006; Farmer & Liao 2001), as well as expanded
the selection of alternative hosts for production through the development of new genetic
engineering tools (Steinbrenner & Sandmann 2006). However, current approaches most
frequently use E. coli or yeast as the microbial host with carbohydrate (i.e. fixed-carbon) based
substrates. Our approach to the Electrofuels initiative leverages developments in alternative hosts
capable of autotrophic growth on H2 as well as developments in fuel targets.
1.III.I.I Microbial Host Selection
For my work, I studied two different chemolithoautotrophs (microbes growing on H2, O2
and CO2) based on specific differences in their physiology, metabolism and energetic yields – R.
capsulatus and R. eutropha. I have primarily looked at R. capsulatus, a purple non-sulfur
facultative phototroph, as a candidate because of its capability of diverse metabolic modes
(including autotrophic), ability to utilize a range of growth substrates, and innate high level
production of carotenoids (Hunter et al. 2009) indicating that pathways for isoprenoid
7
biosynthesis are active in this organism. On the other hand, the chemoautotrophic growth mode
was developed as an alternative approach to bioprocessing using R. eutropha (Tanaka et al.
1995). R. eutropha is a well-studied chemoautotroph, whose use was initially motivated by its
robust growth and the production of the industrially relevant biopolymer poly-hydroxy butyrate
(PHB). High-density growth (40 – 90 gDW/L) of R. eutropha on gaseous substrates was achieved
in high gas-liquid-mass-transfer bioreactors with controlled addition of inorganic nutrients,
demonstrating the technical feasibility of such a process. As will be discussed later, R. capsulatus
is less efficient in utilizing H2 for growth, but has lower maintenance requirements than R.
eutropha, differences which provide economic tradeoffs in an Electrofuels process. The range of
yield and maintenance parameters between R. capsulatus and R. eutropha is likely representative
of most chemoautotrophs and we were interested in quantifying how these contrasting energetic
needs affect the overall economics of the process.
1.III.I.II Hydrocarbon Fuel Molecule Selection
One of the largest barriers faced by alternative energy technologies is poor economics
relative to fossil fuels. Having been the most widely developed biofuel to date, ethanol has
achieved the most success competing economically with gasoline. However, ethanol’s relative
competitiveness results in part from governmental subsidies and mandates that ensure lower cost
of ethanol feedstocks and higher selling prices (Solomon et al. 2007). Estimates of the Energy
Return on Investment (EROI) of corn-derived ethanol currently range from a mere 0.7 to 1.65,
while those estimated for cellulosic ethanol range from 4.40 to 6.61 (Hammerschlag 2006; de
Castro et al. 2014).
We have chosen the hydrocarbon botryococcene as the product of the Electrofuels
process as a means of addressing issue of energy-intensive industrial processing, which is a major
8
drawback to ethanol production. In choosing this fuel molecule target, the project leveraged our
experience in working with the alga Botryococcus braunii (Khatri et al. 2014) and the associated
discovery of the enzymes responsible for the synthesis of C30 squalene and botryococcene (Okada
et al. 2004). C30+ triterpenes include methylated botryococcenes and squalenes, and are produced
by B. braunii race B, a colonial green algae that accumulates these hydrocarbon oils up to 30
percent of its dry weight composition (Metzger & Largeau 2005). Ancestors of this alga have
been implicated in producing up to 1.4 percent of the total hydrocarbon in Maar-type oil shales
based on the unique carbon footprint of C34 botryococcene oil (Derenne et al. 1997). They have
properties similar to crude oil (Hillen et al. 1982), a higher energy density than ethanol (38.1 vs.
23.5 kJ/L) (A. Tuerk 2011) and can be easily separated from an aqueous phase by decantation
(Khatri et al. 2014). Given these advantages, it was anticipated that the production of
botryococcenes could achieve a higher EROI than ethanol.
IV. Metabolic engineering strategy
The synthesis of triterpenes occur via the terpenoid synthesis pathways. There are two
distinct natural terpenoid pathways – the mevalonic acid (MVA) patway and the methylerithritol
phosphate (MEP) pathway. The metabolic engineering strategy relied on introducing one or both
of these pathways into the autotrophic host in addition to the specific triterpene synthase –
squalene or botryococcene synthase (SS or BS). The strategy is schematically depicted in Figure
1-3.
9
This is the preferred means
for engineering high levels of
FPP biosynthesis into
Rhodobacter
Operon 2
Operon 1
Pro
ERG10
Acetyl-CoA
tHMGR
HMGS
Acetoacetyl-CoA
Pro
HMG-CoA
ERG12
Mevalonate
ERG8
Mev-P
Operon 4
Idi1
MVD1
Mev-PP
IPP
Mevalonate (Mev) pathway (eukaryotic)
FPS
Pro
SS/BS
TMT
DMAPP
TRITERPENES
2x
FPP
Methyl erythritol phosphate (MEP) pathway (prokaryotic)
METHYLATED
TRITERPENES
2x
Pyruvate
+
DXP X
MEP
CDP-ME
CDP-ME2P
ME-2,4cPP
HMB4PP
IPP
DMAPP
G3P
Knockout mutation
to be introduced
into the DXP
reductoisomerase
locus
Pro
DXS
Idi2
FPS
Operon 3
This pathway exists and
operates in Rhodobacter
natively – but is subject
to endogenous regulatory
mechanisms controlling carbon
flux down the pathway.
This is the alternative approach to engineering high FPP levels in Rhodobacter.
But this is not the preferred approach because it relies on too many of the “native”
Rhodobacter genes, which will be subject to endogenous regulatory mechanisms.
Figure 1-3. Metabolic engineering strategy for the production of tritepenes in R. capsulatus.
MEP pathway is naturally found in many bacteria including the ones relevant for this
work – E. coli, R. capsulatus and R. eutropha. Therefore, the natural metabolic flux of these
bacteria can be channeled into the target triterpene molecule by introducing only the key limiting
enzymes of this pathway. This is designated as Operon 3 in Figure 1-3. On the other hand, all the
enzymes of the MVA pathway must be installed in order for this pathway to be functional in the
hosts of our choice. This involves eight enzymes in total and therefore the pathway is broken
down into two synthetic operons – Operon 1, converting acetyl-coA to MVA and Operon 2,
converting MVA into farnesyl pyrophosphate (FPP). Farnesyl pyrophosphate synthase (FPS) is
technically not part of either of these pathways but it is also found to be a rate-limiting enzyme
and therefore are included in both of these pathways. More details can be found in Chapter 4.
10
V. Dissertation outline
The project can be divided into three major aspects: 1. Modeling and economic
assessment of the electrofuels process, 2. Engineering of the organisms and pathways for the
production of biofuel, 3. Bioreactor design and scale-up to improve productivity. The economic
assessment provides the potential for economic feasibility of the electrofuels process under some
given scenarios as well as identifies the areas for improvement that will have the highest impact.
The electrofuels scenario is quite different from the existing biofuel (e.g. corn or cellulosic
ethanol) production processes. No model for this process thus exists for the prediction of the
performance and cost of the fuel from this process. In Chapter 2, I describe a framework for
integrating the different aspects of the electrofuels process as well as develop bioreactor and
organism model that are used for carrying out a process economic assessment. This is published
as an article in Bioresource technology (Khan et al. 2014).
Chapter 3 is a literature review of the current status of metabolic engineering of
chemolithautotrophic hosts. It briefly covers chemolithoautotrophic metabolism in terms of
electron capture and carbon fixation and more in-depth the recent advancements in genetic
engineering of these organisms to produce fuels and chemicals, performance of the native and
engineered systems and finally the major current and future challenges. It is submitted as an
invited review in the journal Metabolic engineering. Chapter 4 describes our efforts to genetically
engineer R. capsulatus to produce C30 triterpene hydrocarbons and studying its performance
under different conditions including autotrophic.
Chapter 2
A process economic assessment of hydrocarbon biofuels production using
chemoautotrophic organisms
I. Preface
In this chapter, the economic analysis of an ARPA-e Electrofuels process is presented,
utilizing metabolically engineered R. capsulatus or R. eutropha to produce the C30+ hydrocarbon
fuel, botryococcene, from hydrogen, carbon dioxide, and oxygen. This is published as an article
in the journal Bioresource Technology. The analysis is based on an Aspen plus® bioreactor
model taking into account experimentally determined R. capsulatus and R. eutrohpha growth and
maintenance requirements, reactor residence time, correlations for gas-liquid mass-transfer
coefficient, gas composition, and specific cellular fuel productivity. Based on reactor simulation
results encompassing technically relevant parameter ranges, the capital and operating costs of the
process were estimated for 5000 bbl-fuel/day plant and used to predict fuel cost. Under the
assumptions used in this analysis and crude oil prices, the Levelized Cost of Electricity (LCOE)
required for economic feasibility must be less than 2¢/kWh. While not feasible under current
market prices and costs, this work identifies key variables impacting process cost and discusses
potential alternative paths toward economic feasibility.
II. Specific contributions
I acted as first author, wrote major portion of the text and facilitated submission of the
manuscript to Bioresource Technology. I carried out the measurement of the growth yield and
12
maintenance coefficients for R. capsulatus and R. eutropha, refined the microbial energetic model
for fuel production, developed the bioreactor model in Aspen plus® to integrate the
experimentally measured parameters with the energetic model of fuel production to predict the
operating cost of the process. Amalie Tuerk assisted in refinement of writing and energetic
analysis. John Myers facilitated the capital cost estimation of the process and had done
considerable writing associated with his thesis. Erik Wolcott helped with the compilation of the
gas-liquid mass-transfer coefficients for the various reactor systems.
III. Introduction
This exercise of using process economic analyses to research priorities is an important
aspect of the ARPA-E program. An initial analysis of the economic feasibility of the microbial
process, based on microbial energetic theory, can be found in an extensive thesis (A. Tuerk
2011). A preliminary process economic model can also be found in (Myers 2013). In this part of
the work, I expand upon these previous analyses by constructing a more detailed bioreactor
model in Aspen Plus® (described in section 2.IV.II). Furthermore, I examined specific scenarios
based on reactor residence time (τ), specific fuel productivity (
) and gas-liquid mass transfer
coefficient (kLa) to predict their effect on the overall process volumetric fuel productivity ( ) and
fuel cost ($/bbl-fuel). The ultimate goal of this analysis was to identify limiting parameters for the
process and ranges of these variables needed to achieve economic feasibility.
13
IV. Model development
2.IV.I. Proposed electrofuels production process
The process flow diagram of the proposed Electrofuels production process is depicted in
Figure 2-1. Production of the C30+ hydrocarbon fuel by the host organism occurs in the bioreactor,
which is fed the gaseous substrates H2, O2 and CO2. The relative amount of these gases fed is
equal to the ratio that supports growth and fuel production by the bacteria. The determination of
this stoichiometric ratio is discussed in further detail under Section 2.IV.II.IV. Other nutrients
required for cell growth, such as salts and vitamins, are fed to the bioreactor via a liquid stream.
The bioreactor is operated continuously and the bioreactor effluent contains biomass,
botryococcene fuel product, and spent culture medium. An underlying assumption critical to this
process configuration is that the botryococcene fuel is excreted and deposited as a hydrophobic
layer on top of the culture, which can be separated by decantation (oil-water separator). The
decanted hydrocarbon layer then flows from the oil-water separator to a fuel filtration system
where the residual aqueous phase is removed, and the fuel molecules can then exit the process as
the crude hydrocarbon product. We did not include subsequent processing, such as
hydrocracking, within the scope of this economic analysis, since the point of comparison is crude
oil, which would require similar downstream processing (Hillen et al. 1982). The aqueous phase
that exits the oil-water separator flows to a cell clarifier. This concentrates the biomass phase
before returning a portion of the biomass to the bioreactor, and the remaining biomass to sludge
processing to avoid accumulation of dead cells and cellular waste (perfusion mode operation).
The spent media passes through a waste treatment process (a second clarifier or filter) to remove
cellular debris and waste material, and is recycled as fresh medium after supplementing with salts
and other nutrients.
14
Figure 2-1. Process flow diagram of the envisioned Electrofuels process. CO2, H2, O2, fresh
nutrient and recycled cells enter the reactor and cells+oil leave the reactor. The hydrophobic top oil
layer is separated in oil-water separator and pass through fuel filtration and processed to final
product. The remaining cells+water+residual oil from oil-water separator enters a clarifier where
the denser cell layer is split into two fractions, one recycled back to the reactor and one sent to
sludge processing. The top, less dense overflow from this clarifier enters a second clarifier where
cell debris are separated from the water, which is processed through waste-water processing to be
recycled to the reactor with fresh nutrients and makeup water.
2.IV.II. Bioreactor modeling
The biological processes taking place in the bioreactor (cell growth, maintenance and fuel
molecule synthesis) determine the rate of substrate consumption (H2, CO2, O2), and therefore the
gas-liquid mass-transfer (kLa) requirements, which are the most critical components of the
bioreactor operating cost. The bioreactor volumetric fuel productivity (dependent on the specific
fuel productivity, residence time and kLa) and the scale of the process (bbl-fuel/day) determine
the required size of all associated process equipment, and thus the capital and fixed costs. The
15
capital investment at that plant size can then be amortized at a nominal internal rate of return and
combined with the operating costs to arrive at an estimated cost for crude fuel production ($/bblfuel). A detailed model of the bioreactor processes associated with hydrocarbon fuel production,
which includes cell growth and maintenance, fuel synthesis, and gas transport, is therefore a
critical prerequisite to accurately estimating the capital and operating costs. The assumptions
related to the bioreactor and development of the associated material balance equations are
described in Appendix A.
In aerobic H2-utilizing chemoautotrophic metabolism (referred to as ‘autotrophic’
metabolism from here on), electrons are transferred from H2 to O2 through a series of steps which
generate the energy needed for various cellular processes, including CO2 fixation for growth and
fuel synthesis. Thermodynamic models (Mccarty 1971; McCarty 2007; A. Tuerk 2011) can be
used to calculate these requirements (examples of these methods for calculating the true yield are
demonstrated in Appendix B). The microbial substrate requirements ultimately reduce to
stoichiometric equations that can be incorporated in a bioreactor model. To integrate the
bioreactor model with the full model of the Electrofuels process, we used Aspen plus® software.
We selected Aspen plus® as it can solve complicated material and energy balance equations with
rigorous physical property determinations, and we selected the RCSTR reactor model to
incorporate the microbial energetic equations described below in sections 2.IV.II.I through
2.IV.II.IV.
2.IV.II.I Substrate requirement for cell growth
Figure 2-2A depicts the conceptual framework for modeling cell growth and maintenance
processes. In this context, growth processes refer to the use of energy and carbon substrates for
the production of new cellular biomass, whereas cellular maintenance is the use of energy
16
substrates for non-growth processes such as motility, ion pumping, futile cycling etc. (van
Bodegom 2007; Pirt 1965). For autotrophic metabolism, the process of cell growth (excluding
energy demands for maintenance) can be represented by a stoichiometric relationship, the
‘growth equation’ as shown below in Equation 2-1.
CO + aH + bNH + cO → Cell CN H
O(
)
+ dH O + bH
(2‐1)
In this relationship, the inorganic carbon source (CO2), electron donor (H2), electron
acceptor (O2), and nitrogen source (ammonium, NH ) are consumed, resulting in the formation of
cell biomass, water (H2O), and hydrogen ions (H+). Consequently, the rate of consumption of
, where i represents H2, CO2, or O2) is
each of the gases due to growth processes
related to the rate of cell growth
r
=
[ ]
=
[ ]
by Equation 2-2,
=
=
(2‐2)
The indices for the elements N, H and O in the cell biomass term of Equation 2-1 [ ,
2 + 3 − 2 and 2 + 2 − , respectively] were obtained from literature reports of the
empirical elemental formula for cell biomass for each R. capsulatus and R. eutropha (Hoekema,
R. D. R. D. Douma, et al. 2006; Ishizaki & Tanaka 1990), resulting in three of the four equations
needed to calculate the four coefficients a, b, c and d. The fourth equation needed to complete the
set is from the true growth yield (substrate requirement for growth that could occur in the absence
of cellular maintenance requirements) of the bacteria on the energy substrate H2 (
) and is
related to the coefficient a by Equation 3,
Y
=
(2‐3)
17
where
is the formula weight of one ‘mole’ of cell according to the empirical
formula for cell biomass. Because
is the quantity of H2 needed for growth-only processes
(and does not include energy requirements for maintenance), this parameter can be obtained
through the use of microbial energetic models or experimental measurements (Pirt 1965).
A
Energetics of bacterial
growth
Energetics of fuel synthesis
B
Growth yield
(YT)
Maintenance
coeffcient (m)
20ATP
10NADH
CO2
9ATP
6NADPH
RuBP
Glyceraldehyde-3phosphate (G3P)
2ATP
2NADH
H2 + ½O2  ½H2O
Maintenance equation
ΔGfuel
3phosphoglycolate
(PGA)
2ATP
2NADH
CO2 + aH2 + bNH4+ + cO2 Cell + dH2O + bH+
Growth equation
Acetyl CoA
2NADPH
Mevalonate
22ATP
11NADPH
34CO2 + (29+f)H2 + f/2O2  C34H58 + fH2O
Fuel synthesis equation
Botroycoccene
Figure 2-2. Framework for capturing microbial growth energetics, cellular maintenance and fuel
production. (A) Incorporating the experimentally measured biological yield and maintenance
coefficients into model reaction/stoichiometry equations suitable for reactor design. (B) Concise
version of metabolic pathway for calculating the minimum required Gibbs’ free energy for fuel
synthesis in a bacteria.
18
2.IV.II.II Substrate requirement for cellular maintenance
Additional consumption of H2 and O2 occurs in order to generate energy for cellular
maintenance. Because cellular maintenance requirements are defined as independent of energy
requirements for growth, the simplest way to conceive of these requirements is to consider them
on a per-time, per mass of cell basis, and thus the maintenance coefficients (
and
) are a
measure of the rate of substrate required to satisfy the maintenance needs of one unit mass of
cells. Cells are also known to utilize the already formed cellular material under energy-starved
condition. This is termed endogenous respiration and is not specifically taken into account in this
model. It is assumed that in active, albeit suboptimal, lithotrophic growth with abundance of H2,
this quantity would be relatively small. The rate of consumption of H2 and O2 due to cellular
maintenance is therefore proportional to the total biomass concentration in the reactor, X, as
shown in Equation 4.
m X=
is related to
= m X
(2‐4)
by a factor of two because of the stoichiometry of water formation,
which is the reaction for energy generation in autotrophic metabolism since electrons from H2 are
transferred to O2. We initially presented values of
and
based on literature reports and
used them in conjunction with microbial energetic theory to predict net growth and fuel yields
under variety of conditions (A. Tuerk 2011). To confirm these estimates towards improving the
accuracy of this economic analysis, we have recently measured the values of
and
under
19
identical conditions for both R. capsulatus and R. eutropha using an adaptation of the traditional
methods using continuous chemostat cultures under autotrophic conditions (Pirt 1965).
2.IV.II.III Substrate requirement for fuel synthesis
To incorporate botryococcene fuel synthesis into the bioreactor model, we first defined a
parameter,
, the specific fuel productivity. The rate of fuel synthesis is therefore given by
Equation 5.
=R
X
(2‐5)
The botryococcene fuel molecule, C34H58, is energetically expensive to synthesize.
Therefore, additional H2 and O2 must be consumed to provide energy for fuel synthesis, beyond
the requirements for cell growth. We designate the parameter
to account for the additional H2
which must be oxidized in order to provide this energy; this is above and beyond the hydrogen
incorporated into the botryococcene molecules themselves. The overall fuel synthesis reaction is
summarized in Equation 6. This treatment is analogous to cell growth and maintenance described
in sections 2.IV.II.I and 2.IV.II.II.
34CO + (29 + f)H + O → C H
+ fH O
(2‐6)
The minimum energetic requirement for botryococcene synthesis from CO2 via the
Calvin-Benson-Bassham (CBB) and mevalonic acid (MVA) pathways can be calculated by
summing the total Gibbs energy required for generating the cofactors (mainly ATP, NAD(P)H
and NADH) in the metabolic steps. A simplified version of the relevant metabolic pathways is
shown in Figure 2-2B. The cofactor requirement for going from mevalonate to botryococcene has
been previously elucidated by (Okada et al. 2004). The total Gibbs energy required for
20
synthesizing one mol of botryococcene from CO2 was converted to the amount of H2 (f in
Equation 6) that must be oxidized to provide this energy. The rate of consumption of H2, O2 and
CO2 for fuel synthesis can then be calculated through Equation 2-7,
R
X=
=
The fuel mass yield on H2 (
=
/
(2‐7)
) is defined as the quantity
, where
is the
formula weight of botryococcene.
2.IV.II.IV Mass transfer limitations for gas transport and optimization
The total rate of consumption of each of the gases is the sum of the contributions from
cell growth, cellular maintenance, and fuel production (Equation 8),
=
+
+
(2‐8)
Based on this premise, the volumetric productivity of botryococcene is ultimately
constrained by the mass transfer rate of the limiting gas substrate into liquid. At steady state
conditions in the bioreactor, where liquid-phase gas concentrations are constant, the uptake rate
of the i-th gas substrate by the organism
component (
is equal to the gas transfer rate of this gas
). For gaseous component i, these rates can be calculated from the gas phase
fractional composition ( ), the total gas pressure (
(
), the gas-liquid mass transfer coefficient
,), Henry’s law coefficient ( ) and the liquid phase concentration ( ), as shown in
Equation 9:
= GTR = k a
−C
(2-9)
21
Thus, this equation illustrates that the
by the organisms, are limited by the
, and therefore the total possible gas uptake
that is possible in a particular reactor type.
To simultaneously satisfy the organism’s H2, O2, and CO2 demands, there is only one set
of
at which all the gases are transported at a stoichiometrically balanced rate. At any other gas
composition, only the limiting component will transport at the maximum rate ([ ] = 0), while the
other components are transported at a sub-maximal rate ([ ] > 0). Therefore, the bioreactor must
be operated at the gas composition that results in maximum transport rate of each of the gases to
achieve the highest volumetric productivity. In practice, this could be achieved by controlling the
gas phase composition via feedback control loops. In our simulations, we used the optimization
functionality in Aspen plus® to converge on the optimal gas composition. Because the bioreactor
also affects the
, as described above, we constrained the optimization by limiting
to values correlated for bioreactor configurations. Fixing
, as these are related to
then fixes
and
by the diffusion coefficient correction to mass transfer
(Equation 10),
/
k a =k a
(2‐10)
An inter-conversion based on diffusion coefficient to 2/3 power is used. This is because
correlations in literature are typically reported for
as a function of operating conditions
(rpm, gas rate etc.) and mass transfer coefficients have an inherent dependence on gas diffusion in
the associated boundary layer, which needed to be captured in the analysis.
22
V. Results and discussion
Continuous operation provides for the high productivity required for fuel production.
Extracellular botryococcene accumulation allows for the recycle of biomass for a higher
operating concentration and higher process volumetric productivity. Since achieving economic
feasibility is anticipated to be challenging, these operational enhancements are assumed as
baseline requirements.
2.V.I. Sensitivity analysis: effect of reactor residence time (τ)
In a perfusion bioreactor, the residence time through the reactor system ( = V/F) and cell
retention efficiency (ε) determine the required cell proliferation rate (µ) and operational cell
concentration (X). The associated equations relating these quantities are developed in Appendix
A. These subsequently affect the rate at which fuel will be produced for a given specific fuel
productivity (
). The economics of this process are highly dependent on the volumetric fuel
productivity ( ; this sets the required reactor size and therefore the capital cost) and the fuel
yield on H2, (
/
; the major operating cost). Therefore, we performed an initial set of
simulations to evaluate the effect of reactor residence time on these two variables (Figure 2-3).
The steady state cell density in the reactor is also presented to assist in interpretation (Figure
2-3C) and to establish the operational cell concentration that must be achievable using the host
organism. For these simulations experimentally determined values for microbial energetic
parameters were used (unpublished data): true growth yield (
(
) and maintenance coefficient
) of both R. capsulatus (2.66 gDW/mol-H2 and 2.01x10-3 mol-H2/gDW·hr, respectively) and
R. eutropha (7.68 gDW/mol-H2 and 6.8x10-3 mol-H2/gDW·hr, respectively). Figure 2-3 presents
23
the results for the chosen process variables:
= 330/hr,
= 0.5 g-fuel/gDW·hr and ε =
95%.
The results indicate that X,
and
/
all increase with residence time, asymptotically
approaching maximum values. This behavior results from the opposing effects of X and : in a
continuous flow bioreactor, = growth rate of the bacteria ( ) =
(given <
).
Therefore, as increases, specific growth rate decreases. Since the volumetric rate of substrate
availability is fixed by gas mass transfer (kLa), the reduction in specific growth rate and reduced
gas consumption per cell permit an increased steady state cell concentration with an associated
greater fuel production per unit volume. Based on the assumption that metabolism can be directed
to produce fuel instead of cell mass results in operating at lower growth rates. At lower growth
rates less of the available H2 must be partitioned to cell growth, enabling more of the H2 to be
used towards synthesizing fuel, and enabling a higher
/
. This is reflected in Figure 2-3C.
The preceding discussion also explains the important observation that at higher residence
times (and lower growth rates) the organism with lower maintenance requirement (R. capsulatus,
in this case) is predicted to perform better despite its less efficient utilization of H2 for growth.
This is because at a lower growth rate, a smaller fraction of the available energy substrate is used
for maintenance, leaving more H2 available for fuel synthesis. The assumption that mass transfer
limitations are the dominant constraint on bioreactor operation is central to this argument.
Because it is desirable to operate at higher fuel yield and volumetric productivity,
R. capsulatus and = 15 hr were chosen for further simulations.
and
of
24
A
Volumetric
productivity
(kg fuel/m3.hr)
1
0.8
Rhodobacter capsulatus
Ralstonia eutropha
0.6
0.4
B
Yield on H2
(g fuel/mol H2)
3.0
2.0
Cell density
(gDW biomass/L)
1.0
10
C
8
6
4
0
5
10
Residence time (hr)
15
Figure 2-3. Variation of volumetric productivity, (A), fuel yield on H2, / (B) and cell density,
(C) as a function of residence time ( ) through the reactor for R. capsulatus (●) and R. eutropha
(o). Simulation parameters: true growth yield ( ) of R. capsulatus = 2.66, R. eutropha = 7.68
gDW/mol·H2, maintenance coefficients (
) of R. capsulatus = 2.01x10-3, R. eutropha = 6.8x103
mol-H2/gDW·hr, kLa = 330/hr, specific productivity = 0.5 g fuel/g biomass.hr, cell recycle
efficiency = 95%.
2.V.II. Sensitivity analysis: effect of specific productivity (Rfuel)
Specific fuel productivity is an intrinsic biological property of a genetically engineered
production strain, and affects the volumetric productivity as well as the fuel yield on H2. The
range of specific fuel productivities examined in this analysis spanned from 0.1 to 2 gfuel/gDW.hr (Figure 2-4A). This is a wide range covering conservative to very optimistic specific
25
productivity targets. As a reference, the ethanol specific productivities of E. coli growing on
various fixed-carbon substrates reported in the literature range from about 0.3 to 0.8 g·gDW-1·hr-1
(estimated from Ohta et al, 1991; Hildebrand et al, 2013). The resulting fuel yields on H2 were
converted to electricity required (kWh/bbl fuel), based on an average electrolytic efficiency of
80% for H2 generation (Myers 2013). This is the major component of the operating cost of the
process. In the range of low specific productivities, the electricity requirement for H2 generation
initially decreases sharply as specific productivity increases. This is attributed to the fact that
when specific productivity is low, the majority of the energy is expended in the growth of cells
rather than ending up in the fuel molecule, so small improvements in specific productivity give
large electricity cost savings. However, the electricity requirement remains essentially unchanged
above specific productivities of 1 g-fuel·gDW-1·hr-1. This asymptotic minimum is the sum of the
energy fixed in the fuel molecule and the minimum loss that is associated with cell growth and
maintenance.
Specific productivity is the target associated with genetic engineering and development
of the host organism. Obviously, higher is better, but the more important question is: at what
range of specific productivity will the process achieve economic viability? Initial efforts to
introduce the botryococcene synthesis pathway via metabolic engineering approaches have
successfully produced the botryococcene molecule (Khan et al. 2013); however, the observed
production levels are more than an order of magnitude lower than the low-range values used in
this simulation. For the current, experimentally observed productivity levels, it was calculated
that the electricity cost would be about 175 times that of the 0.1 g-fuel·gDW-1·hr-1 specific
productivity case. The genetic engineering efforts are well justified because a small increase in
specific productivity (which is possible at the early stages of development) translate to large
savings in electricity cost. However, after a significant productivity has been attained, and
electricity requirements level off, other aspects of the process become increasingly important for
26
achieving economic feasibility. For our subsequent analysis, the moderately optimistic level of
0.5 g-fuel·gDW-1·hr-1 was used.
Figure 2-4. (A) Variation of electricity requirement for H2 generation (●) and volumetric
productivity (o) with specific fuel productivity representing an increasing fraction of energy being
converted into fuel instead of biomass (Simulation parameters: residence time = 15 hr; other
parameters same as in Figure 2-3). (B) Variation in reactor volume as a function of kLa showing
the reduction in culture process volume that is enabled as the mass transfer rate is improved ( ).
The range of electricity requirements that would be required to achieve the specified kLa in different
bioreactor configuration is shown as the shaded area between the dashed lines. The high and the
low value of the electricity requirement at each kLa corresponds to the highest and the lowest P/V
able to produce the specified kLa; the range of P/V for a variety of bioreactor configurations are
presented in Figure 2-5.
27
2.V.III. Sensitivity analysis: effect of gas-liquid mass transfer coefficient (kLa)
The mass transfer rate affects the volumetric productivity and therefore determines the
reactor volume required at a given production capacity, as discussed in section 2.IV.II.III.
Increasing
comes at the expense of increased operating costs for mass transfer, but this
facilitates a decrease in the capital cost due to a reduction in the required reactor volume. Figure
2-4B shows the decrease in reactor volume and increase in energy cost (kWh/bbl-fuel) for an
increase in kLa from 330 to 1000 hr-1 for a range of bioreactor configurations. To serve as a guide
in determining relevant reactor costs and realistic kLa values, the ranges of kLa used in Figure
2-4B and the associated power-per-unit-volume (P/V) are based on values for various gas-liquid
contacting reactors reported in the literature. The compilation is presented in Figure 2-5. In
performing the literature review, we focused on systems that can be operated at the large scale
required for this process. The key is to choose a bioreactor where high mass transfer can be
sustained at relatively low power levels. In this respect, trickle-bed reactors were found to
perform the best at commercial scales. While microbubble reactors have similar performance,
they have only been demonstrated at the laboratory scale. We have therefore used trickle-bed
reactor costs in our final analysis. Slight biofilm formation on reactor wall is expected and
observed experimentally, but operational strategies such as pulsatile flow can be implemented to
keep this to a minimal and is not expected to be a problem.
Comparing the energy costs for kLa enhancements in Figure 2-4B to the costs of energy
for H2 production in Figure 2-4A, energy required for mass transfer is only 1.5 to 4.5% of the
minimum energy required for H2 generation (~4000 kWh/bbl-fuel). This result emphasizes the
dominance of H2 generation requirements in overall energy requirements. Optimizing kLa,
therefore, will only become important in the later stages of plant design when more accurate
28
equipment costs are available. Therefore in determining the total capital and operating cost of the
process, we selected a kLa of 1000 hr-1.
Surface aerator 1
Stirred 1
Stirred 3
TBR 2
10000
Surface aerator 2
Stirred 2
TBR 1
Microbubble - various
kLa (1/hr)
1000
100
10
1
10
100
1000
10000
P/V (W/m3)
Figure 2-5. Gas-liquid mass transfer coefficient (kLa) vs. power/volume (P/V) for various reactor
types. Surface aerator 1 = wastewater treatment plant demonstration run, Chattanooga, TN
(Cosby & Gay, 2003); Surface aerator 2 = wastewater treatment plant demonstration run, Yuba
City, CA (Lewis & Gay, 2003); Stirred 1 = stirred tank reactors correlation for electrolytes,
(Van’t Riet, 1979), Vs = 0.005 m/s; Stirred 2 = stirred tank reactor correlation for nonelectrolytes (Van’t Riet, 1979), Vs = 0.005 m/s; Stirred 3 = stirred tank reactors correlation for
electrolytes (Linek et al., 1987), Vs = 0.005 m/s; TBR 1 = trickle-bed reactor correlation set 1
(Reactor parameters: Roininen et al., 2009, kLa correlation: Goto & Smith, 1975, pressure-drop
correlation: Larachi et al., 1991); TBR 2 = trickle-bed reactor correlation set 1 (Reactor
parameters: Roininen et al., 2009, kLa correlation and pressure-drop correlation: Reiss, 1967);
Microbubble – various = experimental data from various investigators (Bredwell & Worden,
1998; Hensirisak et al., 2002). This compilation of literature values focuses on large-scale
commercial units (except for microbubble technology).
29
2.V.IV. Process economic analysis of capital and operating costs
The preceding analysis, based on specific productivity and mass-transfer requirements,
helps define the realistic range to conduct a more detailed analysis of capital and operating costs.
Table 2-1 shows the values selected for parameters used in this section of the work, all of
which are based on the calculations described above. The scaled-up process design is based on a
daily production rate of 5,000 barrels of oil per day (795,000 L/d). As a reference point, in 2012
the largest ethanol production facility in the United States had a nameplate capacity of slightly
over 27,000 bpd, while the average facility produced around 4,500 bpd (Nebraska 2014).
Table 2-1. Parameter values used in final scaled-up design.
Parameter
Value
Plant size
5000 bbl/day
Specific productivity
0.5 gfuel/gDW.hr
Oxygen mass-transfer coefficient (kLa)
1000/hr
Cell recycle
95%
The results of the capital cost analysis are presented in Figure 2-6A; as can be seen, the
costs of the process components varied over a range of three orders of magnitude. We grouped
the equipment into categories of lower and higher cost, and note that the capital cost of
photovoltaics included in H2 generation costs corresponds to the highest capital expense. The
capital cost of the platform (excluding H2 production) is $24.44/bbl-fuel, assuming an after-tax
rate of return of 15% (B). The fixed cost component was much smaller than the capital cost
component at $8.81/bbl-fuel. The operating cost of the process is highly dependent on the cost of
electricity and the LCOE of electricity is quite variable for the various types of renewable energy
30
technologies. However, the cost of nutrient feed-water, wastewater, cooling water, and CO2 are
relatively insensitive to electricity cost, and amount to ~$18.9/bbl-fuel. Although we expected
CO2 cost to be minimal due to it being a waste product, surprisingly, this cost was found to be the
most significant cost after electricity. In this analysis we assume that CO2 from a power plant or a
similar point source will be purified, compressed, and transported to this facility for use. The
magnitude of this cost varies substantially with the types of point source, carbon capture
technology employed, and the means of transport.
The average of the cost of capture and compression of CO2 for IGCC, NGCC and PC
power plants is reported to be $31/tonne-CO2 (Metz et al. 2005). Transportation costs for CO2 via
pipeline or tanker transport varies from 1 to 5 USD/tonne-CO2/250 km (Metz et al. 2005). Using
an average cost of $3/tonne-CO2/250 km and an average distance of 500 km between the CO2
point source and the Electrofuels plant, we predict CO2 transportation costs of $6/tonne-CO2.
Thus, using the total predicted CO2 cost of $37/tonne-CO2 results in a platform cost of $17/bblfuel. As shown in Figure 2-6B, the overall cost of the Electrofuels production platform (excluding
the cost of H2 and the much smaller bioreactor operational requirements for mass transfer) is
therefore estimated to be around $54.3/bbl-fuel, a price well below the target sales price of
$127/bbl-fuel (based on the DOE’s estimate of the 2020 price of oil (Gruenspecht 2012)).
31
A
Electrolyzers
Reactors
Reactor Packing
Heat Exchangers
Pumps
Feed and Product Vessels
0
20
40
60
80
Cell Waste Clarifier
Cell Clarifier
100
120
Values in Millions
of 2010 Dollars
Fuel Filtration
Oil Water Separation
0.0
0.2
0.4
0.6
0.8
1.0
CO2
B
Feed Water
Waste Water
Cooling Water
Electrolyzers
Reactors
Reactor Packing
Heat Exchangers
Pumps
Feed and Product Vessels
Cell Waste Clarifier
Cell Clarifier
Fuel Filtration
Oil Water Separator
Property Tax
SG&A
Rep & maint.
Overhead
Insurance
Labor
$18.9
$8.8
$24.4
Capital Costs (Minus electricity generation)
Operating Costs (Minus electricity)
Fixed Costs
0
3
6
9
12
Cost per bbl ($/bbl)
15
18
C 2500
IRENA
1500
1000
500
Hydropower
(Off-grid)
CSP (ST)
CSP (PT)
Solar PV
wind
(offshore)
0
Wind
(onshore)
Fuel cost (US$/bbl)
REN21
2000
Figure 2-6. (A) Capital costs of
various process components for
a 5000 bbl/day fuel production
plant. Results are grouped into
high (black bards) and low
(dashed bars) range capital
costs. (B) A breakdown of
process costs (the dominant cost
of electricity generation is not
presented). Top (grey) group
represents the major operating
costs which make up 36% of
total; middle (hashed) group
represents the major nonphotovoltaic capital costs which
make up 46% of total; bottom
(black) group estimates the
major non-PV fixed costs
corresponding to 17% of the
total. (C) A sensitivity analysis
of the final fuel cost based on
LCOE of various generation
methods reported in two
different sources: REN21, 2013
(black); IRENA, 2012 (dashed).
CSP = Concentrate Solar Power,
PT = Parabolic Trough, ST =
Solar Tower. The horizontal line
indicates year 2020 crude oil
price estimate (Gruenspecht,
2012). Off-grid hydropower
values were not available in
IRENA, 2012.
32
The challenge is therefore to determine the true effect of renewable electricity costs on
this technology, which is complicated by the range of various technologies available and the large
variability of their costs in North America and around the world. The International Renewable
Energy Report (IRENA 2012) shows that the worldwide LCOE of various renewable energy
technologies varies from as low of 4 cents/kWh for onshore wind power to a high of >30
cents/kWh for large-array solar PV. For North America the averages range from 4 cents/kWh for
hydropower to 50 cents/kWh for solar PV (REN21 2013; IRENA 2012). The range of final fuel
costs, based on the potential range of renewable electricity costs, is shown in Figure 2-6C. As can
be seen, the range of final fuel costs spans a very wide range, which reflects the wide variation
found in the renewable electricity sources. Furthermore, the lowest projected cost (187.2748.8/bbl-fuel; based on onshore wind electricity) is still about two to six times higher than the
2020 crude oil price estimate ($127/bbl-fuel) (Gruenspecht 2012). The highest costs are generally
for solar photovoltaics, which are about twenty times that of this baseline crude oil price.
To place the cost of these electricity generation technologies in perspective, we are
presenting a brief comparison based on two traditional, fossil fuel based technologies for H2
generation: coal gasification and natural gas reforming. In a recent study, (Bartels et al. 2010)
reported hydrogen production costs of 0.36-1.83 $/kg and 2.48-3.17 $/kg from coal and natural
gas respectively. This produces ranges of 68.8-205.4 and 265.8-330 $/bbl-fuel respectively based
on the Electrofuels scenario. Although much lower in cost than some of the renewable
technologies for H2 generation, both coal gasification and natural gas reforming generate a much
higher ratio of CO2 to H2 than can be consumed by the process. From our analysis, we found that
the maximum ratio of consumption of CO2 to H2 in the electrofuels process is 0.2 mol-CO2/molH2. On the other hand, the best of coal gasification and natural gas reforming technologies would
generate 1.08 and 0.42 mol-CO2/mol-H2. According to these numbers, at least 81% and 52% of
33
the CO2 generated by these technologies respectively would be either released to the atmosphere
or have to captured by some other process.
It is obvious that at the current crude oil prices and renewable electricity costs, the
Electrofuels process is not economically favorable. There are several opportunities that were not
taken into account in this analysis that could provide for somewhat improved economics. These
include cost reductions for carbon credit, use of low-cost plastic fermentation systems (T. Hsiao
et al. 1999), use of the waste biomass from the process for heating, etc. Furthermore, some of the
LCOE costs used in the analysis include capital and operating costs for transmission and
distribution of the electricity to the grid, which is not necessary if a direct electrolysis process is
adopted. Nonetheless, the overall analysis provides quantitative evidence that even with
improvements and meeting cellular productivity metrics, the proposed process does not achieve
economic feasibility in terms of producing a fuel product. It is not entirely unexpected that an
electrofuel would have difficulty competing with crude oil at the current price, as the energy
content of crude oil is a result of millions of years of accumulation.
2.V.V. Alternative perspective
A potential advantage of the hypothetical electrofuels process that is not captured in this
analysis is the potential for using this microbial platform to make alternative, high-value products
by leveraging the high specificity of synthesis that is an inherent capability of biological systems.
Through the use of genetic engineering, the autotrophic host could be designed to produce
biochemical products with very high degree of specificity. For example, in parallel to the genetic
engineering efforts to produce botryococcene, we have also demonstrated squalene production in
R. capsulatus (unpublished data). The current bulk price of squalene is $15-20/kg, which
translates to about $2027-2702/bbl-fuel. Other isoprenoids, such as lycopene, valencene, beta-
34
carotene, etc., are also high value products whose production are demonstrated in non-native
hosts (Alper et al. 2005; Beekwilder et al. 2014a; Verwaal et al. 2007). Wax ester (a health
product commonly sold as Jojoba oil) is a fatty ester for which we have also demonstrated
production in R. capsulatus (unpublished data). At ~$68/L, the retail price of Jojoba oil translates
to a price of $8064/bbl-fuel. These examples demonstrate the potential value of alternative
products able to be generated by a biological production platform using a sustainable, renewable
substrate. This alternative paradigm for Electrofuels commercialization could facilitate the
simultaneous production of multiple high value products (different reactors employing different
genetically modified strains) using remote sources of electricity/energy. This could be an
alternative use of the electrofuels process during the technology development phase until
reductions in the cost of renewable electricity and/or the rise in the price of crude oil make
Electrofuels production economically feasible.
2.V.VI. Targets based on the analysis
One of the primary goals of this analysis was to first establish the effect of the key
process parameters –
and
of the bacteria, τ,
and kLa – on the cost of fuel produced
by an Electrofuels process, and subsequently define the target productivity metrics for potential
feasibility. The range of values for
and kLa used in this analysis were selected based on
realistic engineering considerations of what is actually feasible, while
and
are measured
physiological values. The value of the residence time parameter (τ) is subsequently constrained
by these parameters.
It was found that the cost of H2 generation (i.e. the cost of electricity) accounts for >90%
of the current cost of fuel from the Electrofuels process. The next largest costs are the capital
investment required for other non-H2 process equipment and the operating costs for providing
35
mass transfer and supplying CO2. Therefore, steps that decrease H2 consumption would
drastically improve process economics. Critical steps include using a host organism with high
and low
, and improving the specific fuel productivity to achieve a target >0.3 g-fuel·gDW-
1
.hr-1. The sharpest change in H2 cost occurs at specific productivities below 0.5, where H2 costs
escalate rapidly. This means that incremental changes in productivity in this range have a large
impact on the process economics. Also, since the cost of kLa is low relative to the H2 generation
cost, and because increases in mass transfer have the potential to drastically decrease the required
reactor volume (and thus reduce capital cost), it is advantageous to use a bioreactor that can
achieve large kLa, even at the expense of increased energy costs for achieving a target kLa.
The other major goal of this analysis was to assess the overall economic feasibility of an
Electrofuels process. It was found that above a specific fuel productivity of 0.3, the process can
be feasible at an LCOE <2¢/kWh at the current crude oil prices; this LCOE is about 2-20 times
lower than the cost of electricity by current renewable electricity technologies. Given that the
economic feasibility is the primary concern, producing fuels via the Electrofuels process is not a
viable option at the moment. However, the various technologies considered in this analysis
(renewable electricity, autotrophic host, engineering and energetics of hydrocarbon pathways) are
in early developmental phase and have the potential to drastically change the economics in future.
Alternative higher-value biochemical targets such as lubricants may be pursued during this phase
of development to act as economic motivator for near-term profitability, so that the technologies
can be developed for application when the rising crude oil prices (due to the depletion of
reserves) allow acceptable economics for this process.
36
VI. Conclusions
Cost of electricity (for H2 generation) was found to have the largest impact on process
economics. Given the assumptions made and current crude oil prices, the process is expected to
be feasible at an LCOE ≤2¢/kWh. Performance parameters examined in this analysis included
both organism-specific physiological parameters, and parameters for process operational modes.
A critical requirement for economic feasibility is an autotrophic host with a high yield on
hydrogen, low metabolic maintenance energy requirements, and able to meet a specific fuel
productivity metric of >0.3 g-fuel·gDW-1·hr-1. Gas-liquid mass transfer capabilities of the
bioreactor were not found to be critical.
37
Chapter 3
Literature review: Metabolic engineering in chemolithoautotrophic hosts for
the production of fuels and chemicals
I. Preface
This chapter presents a comprehensive review of the literature from the perspective of the
metabolic engineering of chemolithoautotrophic hosts and written in this form as an invited
review for the journal Metabolic Engineering. The use of chemolithoautotrophic hosts as
production platforms has been renewed by the prospect of metabolically engineered commodity
chemicals and fuels. Efforts such as the ARPA-E electrofuels program highlight both the
potential and obstacles that chemolithoautotrophic biosynthetic platforms provide. This review
surveys the numerous advances that have been made in chemolithoautotrophic metabolic
engineering with a focus on hydrogen oxidizing bacteria such as the model chemolithoautotrophic
organism (Ralstonia), the purple photosynthetic bacteria (Rhodobacter), and anaerobic acetogens.
Two alternative strategies of microbial chassis development are considered: (1) introducing or
enhancing autotrophic capabilities (carbon fixation, hydrogen utilization) in model heterotrophic
organisms, or (2) improving tools for pathway engineering (transformation methods, promoters,
vectors etc.) in native autotrophic organisms. Unique characteristics of autotrophic growth as they
relate to bioreactor design and process development are also discussed in the context of
challenges and opportunities for genetic manipulation of organisms as production platforms.
38
II. Specific contributions
This review was a collective effort among Dr. Wayne Curtis, Dr. S. Eric Nybo, Ben
Woolston and myself. Dr. Curtis provided an overall guidance as well as critical input for the
writing of this review. He also helped write the introduction and background sections. Dr. Nybo
provided useful material and helped with the writing of sections 3.IV.I.I, 3.V.II and 3.VI.I.I. Ben
Woolston provided critical input as well as wrote the entirety of sections 3.V.III and 3.VII.I.II.
Although not first author, the vast majority of writing, figures and technical assembly of
references was conducted by myself including the logistics of submission. A large fraction of the
highly biological focus of initial writing by Eric nybo was displaced in various re-organizations,
however, it was decided that his overall contribution to the Electrofuels effort and academic
carrier focus warranted first authorship.
III. Introduction
The vast majority of biological activity on our planet derives its energy from the sun.
Heterotrophic organisms rely on the primary productivity of photosynthetic organisms to provide
reduced carbon. This photosynthetic activity is divided into the process of photon capture to
release high energy electrons (light reactions) and the channeling of those electrons to their use in
the reduction of carbon dioxide (dark reactions). Electrons can be made available by a variety of
sources (wind, hydroelectric, etc.) where the most analogous to photosynthesis is the production
of electricity from photovoltaics. In Figure 3-1, this analogy is used to illustrate the concept of
‘replacing’ the light reactions of biological photosynthesis with photovoltaics and utilizing those
electrons to reduce CO2. The path traversed by these electrons defines broad classes of
microorganisms. Since free electrons are rapidly reacted, the reducing power is often shuttled by
39
other compounds. The prevalence of dihydrogen (H2) from numerous biological or geochemical
reactions has provided motivation for biological utilization of this form of reducing power to fix
CO2, and can be viewed simplistically as a form of autotrophic growth where H2 is used as a
substitute for light, and the dark reactions of CO2 fixation proceed as they do in photosynthetic
organisms. Not surprisingly, nature has found numerous other pathways to fix CO2 (Berg 2011);
however, the basic concept of capturing available reducing power into central metabolism
remains the basis of autotrophic growth.
Figure 3-1. Analogy between direct photosynthetic growth and indirect photosynthetic growth
based on the feeding of electrons as reducing power to reduce CO2. DEF: direct electron feeding,
Fd: ferredoxin.
Included in the ‘dark reactions’ of Figure 3-1 is also the process of splitting water, which
can be achieved external to the cell by electrolysis, or with the assistance of electron carriers that
40
might be reduced either inside or outside the cell. As will be discussed in more detail below,
some organisms can directly accept the reducing power from an electrode (Nevin et al. 2010), and
the nature of configuring bioreactors to accommodate this broad range of electron delivery
becomes an integral component of an eventual process to use the reducing power within
metabolically engineered organisms to produce biochemicals (conceptually depicted in Figure
3-2). The scope of metabolic engineering of autotrophs therefore spans from improving the ability
of an organism to accept electrons into metabolism and fix CO2 to the introduction of
biosynthetic pathways into this diverse class of organisms that already possesses
chemolithoautotrophic metabolism. Since the metabolic engineering of facile heterotrophic
organisms such as E. coli or yeast is highly advanced, it is tempting to pursue introducing
autotrophic metabolism into these model chassis. This remains an exciting possibility,
particularly if the host chassis is an extremophile or industrially robust organism, however this is
currently limited by the tremendous complexity of both the associated enzyme systems and their
regulation.
ehv
H2, CO2,
CO, O2
I = V/
Ohm’s law
Beer‐Lambert law
Henry’s law
Reactor
Boundary
X-H
Electron
acceptors
Photosynthetic
apparatus
H2-ases,
carboxylating
enzymes
Cell Boundary
Figure 3-2. An examination of the CO2 reduction reactions from the perspective of the mode of
transport of the reducing power to either the reactor, or to the inside of the cells as a major
41
determinant of the application of these for biotechnological purposes. Light cannot penetrate
dense cultures (Beer-Lambert Law), where gas transfer rates (GTR) face limitations of solubility
in the liquid phase (Henry’s Law). Electrons are particularly challenging to deliver (Ohm’s Law)
either to a reactor electrode or into the cell although this can be facilitated by the reduction of a
‘carrier’ (X-H).
IV. Background
By definition, autotrophic growth encompasses any organism that can ‘feed itself’ from
non-organic carbon sources – namely CO2. Since there are microorganisms that can utilize
essentially any reduced chemical as a component of their metabolism, it is useful to briefly
review this classification to define the scope of this review. Aerobic photoautotrophs utilize light
to extract hydrogen from water (Figure 3-3), and are not the focus of this review. Many
organisms can utilize H2 including the anaerobic methanogens and acetogens, as well as aerobic
hydrogen oxidizing bacteria that are often referred to as Knallgas bacteria (Schwartz et al. 2013).
The methanogens are excluded from this review because their ubiquitous presence in association
with anoxic breakdown of organic material (sediments, wastewaster, animal gut) require its own
specific review (Demirel & Scherer 2008; Thauer et al. 2008). Acetate production from CO2 and
H2 is of interest in this review in part because acetate can readily be converted to alternative
biochemicals.
The aerobic hydrogen-oxidizing bacteria are of particular interest for this review because
of their demonstrated potential as production platforms. The model organism R. eutropha (used
here in preference to the new classification of Cupriavidus necator) has been the subject of study
for over 100 years (Kaserer 1906). Its ability to accumulate biomass and poly-3-hydroxybutyrate
(PHB) to very high levels both heterotrophically (Ryu et al. 1997) and autotrophically (Tanaka et
al. 1995) demonstrated its ability to be competitive with E. coli as a production platform.
Considerable efforts have been made to conduct scale-up and optimization studies. R. capsulatus
42
is also a noteworthy chemolithoautotrophic (M. Madigan & Gest 1979) candidate due to its study
as a model organism and diverse growth modes (Hunter et al. 2009). Obligate anaerobic
acetogens, like Clostridium ljungdahlii, are attractive hosts because of their ability to use
synthesis gas (syngas) or CO2 and H2 for growth. C. ljungdahlii naturally produces acetate,
ethanol, butanol, and 2,3-butanediol; companies such as INEOS, Coskata and LanzaTech are
actively commercializing it for production of variety of chemicals from syngas (Köpke, Mihalcea,
Bromley, et al. 2011). A more in-depth examination of some of the characteristics of these model,
sequenced organisms provides some additional perspectives on considerations for metabolic
engineering of biochemicals production in these organisms. The autotrophs which can utilize
reduced inorganic compounds (NH4+, H2S, Fe2+) are also of notable interest because of either
utilization of an industrial waste stream or the potential of the substrate to undergo cyclic
reduction at an anode as the basis of delivering the reducing power. Finally, the potential for
direct electron feeding from a biocathode (sometimes referred to as microbial electrosynthesis)
represents the ultimate example of delivery of reducing power from a renewable electrical
generation plant to store that reducing power in chemical bonds of a microorganism (Lovley &
Nevin 2013).
Photoautotrophs
CO2
Knallgas bacteria
CO2
Acetogens
CO2
Nitrifying, Sulfur, Iron
Oxidizing bacteria
CO2
Biocathode
CO2
hv
H2O
H2
CO
H2
XH
eH2O
CH2O
CH2O
CH2O
CH2O
CH2O
Figure 3-3. A summary of autotrophic growth modes with the common theme of reducing CO2 to
biomass (carbohydrate) using various forms of reducing power. These different autotrophic
43
growth modes are often associated with other classification names that are listed. These common
name classifications are useful, but also lead to problems typical of generalizations.
3.IV.I. Carbon Fixation in Chemolithoautotrophs
3.IV.I.I Calvin-Benson-Bassham (CBB) Pathway and Rubisco
The predominant mechanism for CO2 capture in aerobic chemolithoautotrophs is via the
photosynthetic dark reactions of the Calvin-Benson-Bassham (CBB) pathway (Figure 3-4). This
pathway is present in many of the model autotrophic organisms including R. eutropha,
Rhodobacter, and some species of Nitrosomonas, and Acidithiobacillus. The first committed step
of the CBB pathway is a carboxylation reaction involving CO2 and ribulose-1,5-bisphosphate
(RuBP) by ribulose bisphosphate carboxylase/oxygenase (Rubisco), which many autotrophs
compartmentalize into organelles called carboxysomes. Regeneration of RuBP involves enzymes
of central metabolism, a sedoheptulose bisphosphatase (SBPase), that converts a 7-carbon
sedoheptulose-1,7-bisphosphate to sedoheptulose-7-P, and phosphoribulokinase (PRK), that
converts ribulose-5-P to RuBP. Enzymes Rubisco, SBPase and PRK are considered to be unique
to the CBB pathway. Rubisco is responsible for most of the CO2 captured on the planet and is the
CO2-fixing enzyme in the majority of aerobic chemolithoautotrophs. The extensive studies of
energetic and catalytic efficiency of Rubiscos are of particular interest to metabolic engineering.
Three forms of Rubisco exist, which differ in catalytic efficiency (kcat) and CO2-specificity (SCO2)
–important properties which generally represent trade-offs in functionality. Variants of these
forms are found in chemolithoautotrophic bacteria (Badger & Bek 2008). Sometimes more than
one variant exist simultaneously in a single bacteria and are generally differentially regulated in
response to CO2 concentration (Berg 2011).
44
An affinity for O2 is the biggest disadvantage of the Rubisco enzymes and causes the
phenomenon called photorespiration in plants, which is the tendency of Rubisco to oxidize RuBP,
rather than carboxylating it. The resulting need to ‘recover’ the oxidized product is energetically
very expensive and involves loss of CO2 and consuming the amino acid glycine to give
ammonium. Photorespiratory CO2 loss is estimated to be 21% of the net CO2 assimilation in
plants (Berg 2011). In bacteria, the product of Rubisco oxidation is metabolized more efficiently
than plants, where 2-phosphoglycolate is dephosphorylated to glycolate, which is either secreted
from the cell or recycled back to central metabolism (Berg 2011). Both of these mechanisms
waste carbon and energy, thus there is room for improvement in chemolithoautotrophic bacteria
as well.
Figure 3-4. Schematic illustrating the diversity of the cbb operons including paralogs of the
various accessory proteins in addition to the large and small subunit (cbbL/S). Form I Rubisco is
denoted by cbbLS and Form II by cbbM, both of which are found in R. capsulatus and R.
sphaeroides (Panel A&B). R. eutropha also contains two CBB operons, one on the chromosome
(Panel C), and one on the megaplasmid pHG1 with a defective transcriptional activator (cbbR*,
Panel D). The iron-oxidizing bacteria Acidithiobacillus ferooxidans contain four different CBB
operons with two encoding Form I Rubisco subunits. Other genes are: cbbF=SBPase/FBPase,
cbbP=PRK, cbbT=TKT; cbbA=FBA/SBA, cbbE=PPE, cbbG=GAPDH, cbbK=PGK,
cbbZ=phosphoglycolate phosphatase), cbbBXYQ=unknown. The presence of these functional
operons in plasmids provided the historical basis of elucidating autotrophic genes including CO2
fixation and hydrogen use, now adapted to metabolic engineering of these phenotypes in
chemolithoautotrophs.
45
The Rubisco forms have evolved for optimized performance (by a balance of kcat and
SCO2) in their respective niches with varying degrees of CO2 and O2 availability; therefore the
target platform organism may not have the appropriate properties for the intended bioreactor
environment. Therefore the choice of the Rubisco form for metabolic engineering will be largely
dictated by the O2 and CO2 operational conditions. For example, choice for a Form II Rubisco
(having lower SCO2 but higher kcat) can be made in an autotroph if high catalytic rate is desired by
increasing CO2 and limiting O2 reactor concentrations. Carbon concentrating mechanisms (CCM)
and carboxysomes found in bacteria are a reminder that the bioreactor operating conditions can be
significantly modified by the microbial chassis. Rubisco and associated genes needed for
functional CO2 fixation are nearly always located in cbb operons (Figure 3-4). These operons
reflect that a functional CBB pathway requires at least four genes including a transcriptional
regulator (CbbR) while at the same time a tremendous diversity has evolved. These carbonfixing operons tend to be ‘complete’ by the principle of the ‘selfish operon’ (Lawrence & Roth
1996), where the likelihood of a gene being passed along to subsequent host is enhanced by the
clustering of genes in an operon that encodes a complete function. This clustering of genes by
function continues to be a significant asset to metabolic engineering efforts that has been
accelerated by next generation sequencing and associated reverse genetics.
3.IV.I.II Other Carbon-fixation Pathways
Due to the diverse habitats of chemo-/photoautotrophic bacteria, various other CO2
fixation pathways have evolved. In addition to CBB, at least five other CO2 fixation pathways
have been characterized (Figure 3-5): (1) the reductive tricarboxylic acid cycle (rTCA), (2)
reductive acetyl-CoA or Wood-Ljungdahl pathway (rAC), (3) 3-hydroxypropionate bicycle
(3HP), (4) 3-hydroxypropionate-4-hydroxybutyrate cycle (HP/HB), and (5) dicarboxylate-4-
46
hydroxybutyrate cycle (DC/HB) (Berg 2011). rTCA cycle has a wide distribution in
chemolithoautotrophic and photosynthetic bacteria. It involves running the TCA cycle in reverse.
Wood-Ljungdahl pathway is limited mostly to obligate anaerobic acetogens of Clostridial genera
and methanogenic Archaea and fixes CO2 as well as CO via a linear pathway. Distribution of
3HP pathway is very limited to within green non-sulfur bacteria with Chloroflexus aurantiacus
being the most heavily studied. The DC/HB and HP/HB are found in archaea - mostly
aerobic/microaerobic thermophiles. Although the HB branch is common to both the cycles, the
enzymes appear to have evolved independently of each other. With the interest in using CO2 as a
substrate for biochemical production, these pathways are being intensely studied including efforts
to enhance or even transplant these pathways to create new autotrophic chassis for metabolic
engineering.
CBB
CO2
3-Phosphoglycerate
Ribulose-1,5-2P
1
Ribulose-5-P
Glyceraldehyde-3-P
Sedoheptulose-7-P
Sedoheptulose-1,7,-2P
CO
2
CO2
Citrate
Pyruvate
Wood-Ljungdahl
(acetogen)
5-MethylCorrinoid
Acetyl-CoA
6
HCO3-
8
5,10-Metylene-THF
Citramalyl-CoA
Glyoxylate
L-Malyl-CoA
Malonyl-CoA
Phosphoenol
pyruvate
cis-Aconitrate
HCO3-
Acetoacetyl-CoA
Methylmalyl-CoA
Propionyl-CoA
HCO3-
7
5,10-Metyenyl-THF
3-HPA
8
(S)-Methylmalonyl-CoA
4-Hydroxybutyrate
10-Formyl-THF
DC/HB
Oxaloacetate
rTCA
Isocitrate
5
Succinyl-CoA
Malate
4
2-Oxoglutarate
CO2
CO2
Formate
HP/HB
3
CO2
H2
Figure 3-5. Representation of various CO2-fixation pathways in relation to each other: CBB
(black ≡), Wood-Ljungdahl (red =), Reverse TCA (rTCA; yellow - -), 3-hydroxypropionate (3-
47
HPA; blue —), 3-hydroxypropionate/4-hydroxybutyrate (HP/HB; green ▪▪▪), dicarboxylate/4hydroxybutyrate (DC/HB; purple — ▪ ▪). The carboxylating enzymes are indicated by numbered
circles: 1, ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco); 2, bifunctional carbon
monoxide dehydrogenase/acetyl-CoA synthase (CODH/ACS); 3, formate dehydrogenase; 4, 2oxoglutarate synthase (Fd-dependent); 5, isocitrate dehydrogenase; 6, pyruvate synthase; 7,
phosphoenolpyruvate carboxylase; 8, bifunctional acetyl-CoA/propionyl-CoA carboxylase.
3.IV.II. Hydrogen Utilization by Chemolithoautotrophs
Hydrogenases are metal-containing enzymes that are common in bacteria (Vignais &
Billoud 2007). They are classified based on the H2-binding sites as the [FeFe] and [NiFe], with a
third rare [Fe]-only type found only in methanogens (Schick et al. 2012). [FeFe] hydrogenases,
have simple monomeric forms, consisting of only the catalytic subunit, which in principle would
be easier to introduce into a heterologous host. [NiFe] hydrogenases are composed of small and
large subunits, and generally have relatively large number of maturation and assembly accessory
proteins. Hydrogenases are also subdivided as membrane bound (MBH) uptake or H2-evolving
and bidirectional soluble (SH) forms (Vignais & Billoud 2007). The uptake MBH of interest for
generating an autotroph is generally attached to cytochrome b complex and ultimately involved in
ATP generation. The SH contains subunits that bind to soluble cofactors such as NAD or NADP.
They are able to catalyze the evolution or consumption of H2, subsequently oxidizing or reducing
the cofactors, depending on the physiological conditions (i.e. NAD(P)/NAD(P)H ratio, H2 partial
pressure etc.). Some SH also act as H2 sensors, being part of the regulatory mechanism and not H2
activation. Further discussion of the details of hydrogenases is beyond the scope of this review
and are thoroughly reviewed elsewhere (Lubitz et al. 2014). A schematic representation of an
aerobic chemolithoautotroph in terms of energy generation, utilization, CO2-fixation and
regulation is presented in Figure 3-6.
The genes for hydrogenase synthesis, assembly and maturation are usually
conveniently organized onto operons which facilitates their heterologous expression (Rousset &
48
Liebgott 2014). In fact, Alcaligenes hydrogenophilus contains the hydrogenase activity (Hox+) on
a mega-plasmid that allowed early identification of hydrogenase genes by mobilization into other
species which conferred ability to grow on hydrogen (Yagi et al. 1986). The transitions of this
work into the current bioinformatic era is discussed further in Section 4.3. These historical studies
on the genetic transfer of the Hox+ phenotype, were followed by a period of intense study of
hydrogenases for the production of bio-hydrogen (Lubitz et al. 2014; Rousset & Liebgott 2014),
providing considerable genetic resources and experience in heterologous hydrogenase expression.
Figure 3-6. Schematic representation of carbon and energy metabolism, regulation and gene
expression in a typical aerobic chemoautotroph. Both membrane bound (MBH) and soluble (SH)
49
hydrogenases generate reducing equivalents. MBH also channels electrons to the membranebound Electron Transport Chain for the production of ATP. SH facilitates ATP production via the
NADH oxidoreductase (Complex I). The energy and reducing power are used by the carbon
fixation pathway (typically CBB). The Regulatory Network is a complex interaction among
external substrates as well as internal metabolites and cofactors, which eventually controls the
transcription of the hydrogenase operons (hox, hup, hyp etc.) and CBB operons. There is direct
regulation of these operons as well (e.g. H2).
3.IV.III. Microbial Electrosynthesis via Direct Electron Feeding
Where the ability of microorganisms to donate electrons to the anode of a microbial fuel
cell (MFC) has been extensively studied, the acceptance of electrons at a biocathode to reduce
CO2 falls within the scope of this review. The ability of hydrogenase to reduce NAD+ mediated
by electrode potential was demonstrated over 20 years ago (Cantet et al. 1992). Biohydrogen and
biomethane production subsequently dominated the study of electrosynthesis where the typical
feedstock included organic-laden wastewater streams. Microbial electrosynthesis (MES) of
organic compounds is recognized and recently reviewed (Rabaey & Rozendal 2010; Lovley &
Nevin 2013). While most of microbial electron feeding has involved undefined consortia, (Nevin
et al. 2011) recently reported a number of pure culture of acetogens (belonging to Spormusa,
Clostridium and Moorella genera) are capable of accepting electron from a cathode and fix CO2
into acetate (on the order of 0.1-1 mM over several days). Autotrophic electrosynthesis of acetate,
methane, and hydrogen gas was recently reported by a microbial consortia (Marshall et al. 2012).
The cathode was populated predominantly by the archaebacteria Methanobacterium spp. and the
eubacteria Acetobacterium spp., and peak yields of methane reached 7 mM per day, acetogenesis
was over 4 mM per day (28.5 mM in total over 12 days), and hydrogen production was over 11
mM per day. Similarly, a mixed culture of bacteria achieved rates of 129 mL per day of CH4 and
94.72 mg/day of acetic acid, further demonstrating the capability for carbon fixation directly from
50
electrical power (Jiang et al. 2013). However, to date we are not aware of any genetically
engineered microbial electrosynthetic system.
V. Metabolic Engineering of
Natural Chemolithoautotrophic Organisms
The diversity of strategies for metabolic engineering in chemolithoautotrophs is as
diverse as the range of autotrophic hosts. The status of genetic engineering is very organismdependent with the two major categories of aerobic H2-oxidizing bacteria and anaerobic
acetogens. The current focus of metabolic engineering study is largely defined by considerations
for the level of basic science study, existing metabolism, genetic engineering tools and
ease/danger of culture conditions (using H2 and CO). Model chemolithoautotrophic organisms
including R. eutropha, Rhodobacter sp. and C. ljungdahlii, provide a useful basis for discussing
key characteristics of autotrophic metabolisms. An assessment of performance under autotrophic
conditions is of particular relevance, but is often not tested since these model organisms can grow
heterotrophically.
3.V.I. Status and Constraints for Metabolic Engineering in Chemolithoautotrophs
R. eutropha is a model chemolithoautotroph that has very high aerobic autotrophic
productivity when grown on H2, and accumulates large quantities of PHB where its knockout in
principle could provide extensive flux within central metabolism via acetyl-CoA. Combined with
relatively mature genetic engineering (transformation, chromosomal modifications, plasmids),
this organism has been the basis of the full array of commodity biochemicals and biofuels. R.
capsulatus is also robust hydrogen-oxidizing chemolithoautotroph that benefits from the
51
extensive study of its closely related species R. sphaeroides, which can also be coerced into
chemolithoautotrophic growth as well (Paoli & Tabita 1998). The study of its diverse metabolism
including anaerobic photoheterotrophy has resulted in extensive understanding of its basic
physiology and genetics. Rhodobacter also has moderate capacity for PHB accumulation as well
as isoprene metabolism associated with photosynthetic pigments. Due to the anaerobic nature of
acetogens, these organisms display natural accumulation of fermentative end-products of
industrial interest from CO2, including ethanol, butanol and even acetone and 2,3-propanediol. C.
ljungdahlii has emerged as a front-runner for further development as a metabolically engineered
biochemical platform in part since the genetic tools are becoming available relative to other
candidates. Acetogenic bacteria also have a high autotrophic flux to acetyl-CoA which makes
them attractive candidates for autotrophic production of a variety of fuels and chemicals (Hu et al.
2013).
This range of model organisms represents a gradual change of metabolism from highly
O2 tolerant to highly O2 sensitive. This distinction is quite important from the standpoint of
metabolic engineering of chemolithoautotrophs due to energetic considerations. Anaerobic
organisms are generally more efficient at energy capture because of the inherently smaller
available thermodynamic free energy to drive heterologous metabolism (Bar-Even et al. 2012).
For this same reason the anaerobic rate of energy capture is quite constrained particularly for the
production of highly reduced molecules (Fast & Papoutsakis 2012).
Acetogenesis from H2/CO2 proceeds via the stoichiometry
4H2 + 2CO2  CH3COOH + 2 H2O (ΔG = -95 kJ/mol acetate)
and is thus one of the most constrained modes of energy conservation known (Drake &
Daniel 2004). Energy is conserved through the Wood-Ljungdahl pathway by a combination of
substrate level phosphorylation (production of acetate from acetyl-CoA) and chemiosmosis
(Mayer & Müller 2014). Diverting acetyl-CoA flux away from acetate production therefore puts
52
severe energy limitations on the cell, and stoichiometric models predict that this will drastically
limit the yield of the target molecule (Fast & Papoutsakis 2012). This may explain why attempts
to produce highly reduced chemicals through the Wood-Ljungdahl pathway to-date have met with
limited titers and yields. Unlike genetic systems, which can be gradually improved and refined
over time, these thermodynamic considerations are immutable, and may ultimately constrain the
commercialization of these microbes.
On the other hand, aerobes are capable of capturing large amount energy from an electron
donor because of the ability to use O2 as an electron acceptor, and thus drive high rates of
productivity of energy-dense molecules at the expense of energy capture efficiency. In addition to
the inherently less efficient energy capture in aerobic chemolithoautotrophs, the presence of O2
poses numerous additional problems (see Section 3.VI.I.II for further discussion). Almost all the
key enzymes for CO2-fixation and H2-activation are either permanently or temporarily
deactivated by O2 or have undesired side reactions that are energetically wasteful. Natural
systems have solved this problem by evolving a balance among these antagonistic effects of O2.
Where light is available in high quantity, higher photoautotrophs use the abundance of ATP to
circumvent the wasteful photorespiratory branch of photosynthesis and use carbon concentrating
mechanisms to exclude O2 from reaction centers. Bacteria that do not have this option carry out a
slightly more efficient (but still wasteful) cycling mechanism to counteract this non-specific
reaction of Rubisco. Rubiscos and hydrogenases in nature have also evolved highly ‘optimized’
forms based on the organism’s natural habitats. Catalytically faster Rubiscos are less specific
towards CO2. Catalytically faster [FeFe] hydrogenases are highly sensitive to O2 and deactivate
permanently, while [NiFe] SH and MBH display gradual tolerance towards O2 and corresponding
reduced catalytic activity. The goal of metabolic engineering is to develop organisms to produce
chemicals at high rates of productivity and using low energy as both of these contribute towards
the cost – biofuel being the most extreme example where both of these conditions have to be
53
simultaneously met. From this standpoint, the engineering needs to capture the best of both
worlds – through genetic, metabolic and bioreactor engineering.
3.V.II. Metabolic Engineering in Aerobic Chemolithoautotrophs
The native biosynthetic capacity of R. eutropha to produce PHB at greater than 70% of
dry weight, even in presence of carbon monoxide (Volova et al. 2002), has motivated the
engineering of this organism for even higher levels of PHB and other biopolymers. Fukui and coworkers engineered non-native poly[(R)-3-hydroxybutyrate-co-3-hydroxypropionate] polyesters
in R. eutropha by introducing malonyl-CoA reductase and 3-hydroxypropionyl-CoA synthetase
genes from C. aurantiacus (Kichise et al. 1999). Most metabolic engineering strategies of
heterologous pathways in R. eutropha have similarly incorporated some form of knock-out of the
PHB synthesis pathway, to divert a portion of that carbon flux. However, in recent work
producing methyl ketones from a truncated lipid -oxidation pathway, there was surprisingly no
enhancement observed for the ±knockout comparison, and the highest level of
chemolithoautotrophic production was only 180 mg/L. Hydrocarbon biosynthesis engineering in
R. eutropha using decarboxylation of fatty acids also used -oxidation mutants, and was executed
in a manner to explore promoters, origins of replication and ribosomal binding (Bi et al. 2013).
Where broad host range pBBR and arabinose-inducible PBAD outperformed other typical vectors,
an unexpected combination of a high copy number mutant of pCM62 combined with a synthetic
RBS gave 6 mg/L in this generally low expression testing.
Only R. eutropha PHB knockout strains were tested under heterotrophic growth
conditions in efforts to produce branched-chain isobutanol and 3-methyl-1-butanol production
(Lu et al. 2012). Batch production was over 300 mg/L and repeated media removal for 50 days
achieved a cumulative production totaling 14 g/L. In a subsequent effort from the Sinskey
54
laboratory, the systematic optimization of isopropanol production demonstrated substantial
effects for codon usage, and gene dosage and was able to achieve 3.44 g/L in batch cultures with
less than 1 gDW/L. This represents one of the highest specific productivities achieved to date for
metabolic engineering of R. eutropha (Grousseau et al. 2014). However, this was achieved on
fructose, and remains to be demonstrated under chemolithoautotrophic conditions. As part of a
demonstration of integrated electrolytic reduction of CO2 to formate (with subsequent uptake and
formation of CO2 and NADH), Liao laboratory (Li et al. 2012) executed an autotrophic
fermentation (80:10:10, H2, O2, CO2) to produce 1 g/L alcohols (~50:50 isobutanol:3-methyl-1butanol). This work included a PHB knockout and the ‘Ehrlich pathway’ in conjunction with
enhanced α-keto acid flux toward the branched valine/leucine amino acid synthesis pathway. The
elevated production is partly assisted by the ability of autotrophic growth to achieve high cell
concentrations of ~12 gDW/L (OD600=24).
Purple non-sulfur bacteria, such as R. sphaeroides and R. capsulatus, naturally synthesize
large amounts of carotenoids as well as increased lipid metabolism during intracellular membrane
production under O2/light limitation (Beekwilder et al. 2014b). Utilizing the endogenous
IPP/DMAPP flux of R. sphaeroides, valencene production (57 mg/L) was achieved by simply
expressing Callitropsis nootkatensis valencene synthase (CnVS) which was 40-fold higher than
the same construct in Saccharomyces cerevisiae. Further enhancement to 352 mg/L was observed
upon introduction of the mevalonate (MVA) pathway from Paracoccus zeaxanthinifaciens,
indicating that the CnVS enzyme was substrate limited. We took an alternative approach of
enhancing the endogenous R. capsulatus methylerithrotol phosphate (MEP) pathway to produce
the C30 triterpenes, squalene and botryococcene, using the triterpene synthases from the colony
algae Botryococcus braunii race B (Niehaus et al. 2011). This was accomplished by expressing
high activity avian FPS to catalyze conversion of IPP/DMAPP to farnesyl diphosphate (FPP),
along with expressing the rate-limiting MEP enzymes 1-deoxy-D-xylulose 5-phosphate synthase
55
(DXS) and isoprenyl diphosphate isomerase (IDI) from pBBR vectors (Khan et al. n.d.). As
observed for most other biochemicals tested, autotrophic production was comparable to screening
results on complex heterotrophic media, indicating a robust metabolism under fully
chemolithoautotrophc growth conditions. Autotrophic bioreactors achieved 110 mg/L when
biomass reached 6.6 gDW/L and subsequent continuous operation stabilized at 60 mg/L (at 2.6
gDW/L). This represents a specific productivity of 0.5 mg·gDW-1·h-1 which is comparable to the
average alcohol specific productivity of 0.67 mg∙gDW-1∙hr-1 (though this work of Liao displayed a
growth-dissociated burst of 3.3 mg∙gDW-1∙hr-1 after 3 days of culture). These results are still an
order of magnitude lower than the 93 mg∙gDW-1∙hr-1 isopropanol reported for growth on fructose
(Grousseau et al. 2014). Comparative interpretations of productivity are often difficult to interpret
in the absence of details of bioreactor operational strategy, which is addressed in some additional
detail in Section VIII of this review.
3.V.III. Metabolic Engineering in Acetogens
Metabolic engineering of acetogens is in its infancy, largely due to the limited genetic
tools available for these microbes. In contrast, the native productivity of these organisms for
producing their final electron acceptors is well beyond metabolic engineering efforts in aerobic
chemolithoautotrophs. Since acetate is the precursor to a variety of important chemical
compounds, for example polyvinyl acetates, the ability to generate it autotrophically instead of
from petrochemicals continues to be developed, including the use of genetic engineering. For
example, the acetogenic capacity of Acetobacteium woodii was recently improved by
overexpression of native pathway enzymes. Transformation of plasmids carrying either the
phosphotransacetylase (PTA) and acetate kinase (ACK) or the four THF-dependent enzymes in
the Wood-Ljungdahl pathway was achieved by methylating these vectors using an E. coli strain
56
expressing the C. ljungdahlii DNA methylation genes prior to electroporation (Straub et al. 2014).
The PTA-ACK strain had significantly higher specific productivity of 0.9 g∙gDW-1∙hr-1 and final
acetate titer 50.5 g/L compared to the already impressive ‘wild-type’ production strain
performance (0.85 g∙gDW-1∙hr-1 and 44.7 g/L). In addition to acetate, syngas-fermenting
organisms natively produce several other compounds, including ethanol, butyrate, butanol, and
2,3-butanediol (Liew et al. 2013; Daniell et al. 2012; Köpke, Mihalcea, Liew, et al. 2011). Three
companies: Coskata, INEOS and LanzaTech are commercializing autotrophic ethanol production
(Schiel-Bengelsdorf & Dürre 2012). LanzaTech is also working on 2,3-butanediol production
(Köpke, Mihalcea, Liew, et al. 2011) and although no results are yet available in the literature,
numerous patents have been filed in the last year by LanzaTech in this space.
The first example of an engineered acetogen for non-native production involved
transplanting the butanol biosynthesis pathway from the heterotrophic acetogen Clostridium
acetobutylicum into C. ljungdahlii via the pIMP1 shuttle vector. The recombinant strain produced
0.15 g/L (5 kJ/L) butanol from synthesis gas in mid growth-phase, but this was subsequently
consumed by the end of the fermentation (Köpke et al. 2010). More recently Derek Lovley’s
group improved the transformation protocol for C. ljungdahlii, reporting high efficiency
replicating plasmids, as well as integrative vectors and the ability to generate knockouts (Leang et
al. 2013). The genetic toolkit was further expanded by the discovery that the lactose-inducible
promoter and glucuronidase reporter on the pAH2 vector originally designed for Clostridium
perfringens functioned in C. ljungdahlii. The inducible system was used to drive expression of
genes coding for acetone biosynthesis, leading to the successful production of acetone from
syngas at a titer of ~0.87 g/L (26.8 kJ/L) (Banerjee et al. 2014), a substantial improvement over 9
mg/L acetone production previously obtained in C. aceticum (Schiel-Bengelsdorf & Dürre 2012).
Butyrate production in C. ljungdahlii was recently enhanced using plasmid-based
expression of the 8-gene butyrate synthesis pathway from C. acetobutylicum resulted in 0.74 g/L
57
for H2/CO2 and 1.24 g/L for CO/CO2 (Ueki et al. 2014). By chromosomal integration of the
butyrate pathway and shutting down Pta-dependent acetate, AdhE1-dependent ethanol and Ctfdependent fatty acid synthesis pathways, the production improved to 1.35 g/L for H2/CO2 and
1.48 g/L for CO/CO2. A notable result of this effort is that chromosomal knockouts predicted to
eliminate ethanol and acetate could not abolish accumulation, indicating the existence of
additional unidentified genes/pathways for these competing molecules. In addition, for the initial
plasmid introduction the acetate/ethanol levels were essentially unchanged from wild type at
roughly 7 g/L, where the 0.64 g/L enhancement in butyrate was accompanied by a 5.9 g/L decline
in these alternative acetate/ethanol fluxes. This indicates that much needs to be learned about the
pathways and compensatory energetics of these organisms to effectively utilize them as
biochemical production platforms.
Genetic systems have also recently been developed for the promising acetogen Moorella
thermoacetica. First, a pre-methylated suicide vector was used to generate a uracil auxotroph by
inactivation of the pyrF gene, selected for with 5-FOA. Uracil prototrophy was then restored
along with integration of a lactate dehydrogenase gene behind the GAPDH promoter, that
allowed production of lactate at 0.61 g/L from fructose (Kita et al. 2012). Later, a kanamycin
resistance gene was integrated into the same locus, conferring resistance to 300 g/mL (Iwasaki
et al. 2013). A functional antibiotic resistance marker opens up the ability to perform integration
into any site within the genome. Earlier this year, Evonik (in collaboration with LanzaTech)
announced the production of 2-HIBA, the precursor for Plexiglas, from syngas using an
engineered strain (http://corporate.evonik.com/en/media/focus/Pages/bacteria-syngas.aspx).
Although the details of the engineering are not publically available, the work likely takes
advantage of a recently discovered CoA-carbonyl mutase enzyme (Rohwerder & Müller 2010).
Recently, a series of studies by Tyurin and co-workers at Syngas Biofuels Energy, Inc.
reported engineering several Clostridium sp. strains for the continuous high level production of
58
several heterologous compounds such as acetone, n-butanol, mevalonate etc. (Kiriukhin & Tyurin
2014; Berzin et al. 2013; Kiriukhin & Tyurin 2013; Berzin et al. 2012). By elimination of key
processes in the cell, such as spore formation, acetate and acetaldehyde production, they have
reported very high specific productivity of these molecules in continuous syngas or CO2/H2
fermentations of recombinant systems. However, strong doubts have been expressed on the
validity of these results, and they should be considered with caution (Bengelsdorf et al. 2013).
3.V.IV. Metabolic Engineering in Other Chemolithotrophs
Nitrosomanas europaea is an example of a chemolithoautotroph that utilizes the reducing
power of ammonium to reduce CO2. The Scott Banta group received an ARPA-E award to
engineer isobutanol into N. europaea (Khunjar et al. 2012); however, this exemplified the
challenge of working with non-model organisms. Although preliminary genetic tools were
developed by the group and GFP expression and isobutanol production were achieved, the slow
growth and excessive energetic penalty for transport and oxidation of ammonia by this bacteria
ultimately resulted in this effort being abandoned in favor of an iron-oxidizing chemolithotroph
(personal communication). Utilizing the bioreactor concept of an electrochemical reactor to
repeatedly reduce an electron carrier, this effort has transitioned to ferric-ferrous reduction at a
cathode as basis of autotrophic CO2 reduction by Acidithiobacillus ferrooxidans (Li et al. 2014).
Although this work is still quite new with initial work demonstrating introduction of isobutyrate
production (Banta & West 2014); the work will continue as an NSF-funded project with
preliminary demonstration of autotrophic heptadecane hydrocarbon production.
59
VI. Using Metabolic Engineering to
Improve / Creating New Chemolithoautotrophs
While genetic engineering in existing autotrophic systems has shown promise and
demonstrated a wide range of compounds produced directly from CO2, the limitations of tools
development combined with the likelihood of desirable characteristics for an industrial platform
from a non-autotrophic host has motivated the alternative approach of improving or transplanting
pathways for carbon and reducing power capture. Besides avoiding homologous metabolic
regulation, heterologous expression often provides insights into scientific details obscured in the
native host.
3.VI.I. Engineering Carbon Capture
3.VI.I.I Engineering CBB pathway
The limitations of rate and CO2/O2 specificity of Rubisco have made this the target for
improving primary productivity in agriculture by extensive research efforts that are chronicled in
many reviews (Mueller-Cajar & Whitney 2008). Although these efforts have largely ‘failed’ as
stated in a recent evaluation “ … although the challenge of making a ‘better Rubisco’ has
exceeded the grasp of career of many scientists” (Whitney et al. 2011) this must be qualified by
understanding that the context of the goal of agricultural productivity is very different from the
metabolic engineering goal that is the basis of this review. There is both tremendous natural
diversity associated with evolution ranging from aerobic to anaerobic conditions, as well as
presence / absence of carbon concentrating mechanisms. Screening assays have improved both
kcat, affinity and specificity where tradeoffs have largely precluded improved overall
performance. R. capsulatus has been used as a host to screen for improved Rubisco by creating a
60
large subunit deletion mutant with impaired photoautotrophic growth, that was then used in
complementation studies with a mutated cyanobacterial Rubisco (Smith & Tabita 2003). The
defined conditions of a bioreactor provide a clearly defined objective to exploit the extensive
research on Rubisco. For example, an improved affinity for CO2 can allow a higher H2 partial
pressure with associated improved gas transport rates. Similarly, there is an opportunity to
manipulate wasteful RuBP oxidation through bioreactor operation and control, particularly under
the continuous steady-state operation that is pragmatically the only means of achieving economic
feasibility for an autotrophic process (Khan et al. 2014). This emphasizes the importance of
integration of metabolic engineering efforts with bioprocess design. It seems highly likely that an
improved Rubisco would be component of an autotrophic production platform.
In addition to Rubisco, other components of the CBB pathway can also be limiting. It
was observed that overexpression of SBPase from the cyanobacteria Synechococcus and from
Arabidopsis thaliana in tobacco (Miyagawa et al. 2001; Lefebvre et al. 2005) and from
Chlamydomonas reinhardtii in Dunaliella bardawil (Fang et al. 2012) improved photosynthetic
CO2 fixation by 20-100%. A gene found on bacterial cbb operons, cbbZ, encodes for
phosphoglycolate phosphatase (PGP) that dephosphorylates phospohglycolate (formed due to the
oxidation of RuBP; analogous to photorespiration in plants). This allows glycolate to recycle back
to CBB cycle via the D-glycerate pathway while also relieving phosphoglycolate inhibition of
triosephosphate isomerase (Berg 2011). By defining a production platform organism and
operating condition, the relevance of such potentially rate-limiting steps within the CBB pathway
becomes a much clearer optimization objective.
61
3.VI.I.II Engineering Other Carbon Capture Pathways
Engineering the CBB pathway may not be the most logical choice from biotechnological
perspective where kinetic and energetic efficiency are critical criteria. The CBB pathway in algae
and plants evolved under conditions where the supply of ATP and NADPH were not typically
limiting. In aerobic systems, robustness of CBB enzymes to O2 appears to be a dominant
criterion, where co-evolution of carbon concentrating mechanisms further protected the kinetic
shortcomings of this pathway. The five other known carbon fixation pathways have varying
degrees of kinetic and energetic efficiencies and O2 tolerance (Bar-Even et al. 2012; Fast &
Papoutsakis 2012) and computational analysis suggests that superior pathways may be possible
based on several quantitative criteria (such as pathway specific activity, ATP and NADPH cost,
number and compatibility of the enzymes with existing network etc.) These existing alternative
pathways have also been subject to the various natural selective pressures for their respective
niche (Berg 2011), which may require modifications for optimal biotechnological application.
Reconstituting a functional heterologous carbon fixation pathway in a non-autotrophic
organism will be the next major step towards creating a synthetic chemolithoautotrophic
production platform. The 3-HPA bicycle (from Chloroflexus aurantiacus) has been largely
installed into into E. coli by Pam Silver laboratory (Mattozzi et al. 2013). Although each
component of the pathway was demonstrated to be functional, the complete pathway failed to
achieve autotrophic growth. By introduction of a portion of the HP/HB pathway from
Metallosphaera sedula into the hyperthermophile Pyrococcus furiosus, partial
chemolithoautotrophy was conferred to this archaeon to produce 3-hydroxypropionic acid, which
represents a major advance toward a process-rationalized host rather than a convenient genetic
model (Keller et al. 2013). As an example of altering carbon fluxes through expression of
heterologous CO2 fixation pathways, an “autotrophic bypass” was engineered into yeast by
62
introducing Rubisco and PRK from the chemolithoautotroph Thiobacillus denitrificans
(Guadalupe-Medina et al. 2013). The associated CO2 fixation created an electron sink to decrease
glycerol production by 90% and increase ethanol production by 10%. These recent results,
combined with the broadened emphasis toward using biological metabolism of CO2 as a
component of reducing GHG emissions is poised to utilize metabolic engineering to create more
carbon efficient biochemical production processes.
3.VI.II. Engineering improved H2-utilization
In contrast to the challenge of transplanting CO2 fixation, conferring the ability to utilize
H2 is comparatively easy, with the initial successes preceding modern genomics as a result of the
transmission of plasmids containing complete functional hydrogen utilization operons (Umeda et
al. 1986). This does not mean that hydrogenase function is by any means simple, as it involves
metal incorporation and maturation accessory proteins that vary considerably by hydrogenase
type and organism (Lubitz et al. 2014). The role of metabolic engineering is parsing through the
available hydrogenase diversity, including the focus on biohydrogen production, to identify the
appropriate platform candidate.
In chemolithoautotrophic bacteria, oxidation of molecular H2 provides the reducing
equivalents and ATP needed for CO2-fixation. The mechanism for energy conservation in MBH
are generally less efficient than SH. SH can provide the reducing equivalents directly to the redox
carriers such as NAD(P) or ferredoxin (Fd) and ATP is generated via proton-motive force. On the
other hand, although ATP generation is facilitated more directly by MBH through membrane
linked electron transport chain, less efficient reverse electron transport is generally required to
generate reducing equivalents (Vignais & Billoud 2007). A practical example of the relative
effect of SH and MBH is R. eutropha, where doubling time increased significantly for SH
63
knockout mutants (12 h vs. 3.6 h for wild-type) while only slightly for MBH mutants, indicating
that reductant supply is growth-limiting. Furthermore, transfer of R. eutropha SH genes into
Pseudomonas facilis (containing MBH) reduced its doubling time from 12 to 9 h (Friedrich &
Schwartz 1993). This is likely also reflected in apparent H2 yield of R. eutropha of 6.4 gDW/molH2 (Bongers 1970) versus only 2.7 for R. capsulatus (Siegel & Ollis 1984). Energetic efficiency
is also affected the specific redox carrier used. For example, Fd is more efficient than others
because of its lower reduction potential (Bar-Even et al. 2012).
The catalytic properties of hydrogenases are of relatively minor importance compared to
carboxylating enzymes where some approach ‘kinetic perfection’ (Berg et al. 2002), being limited
by the diffusion rates of H2 (see Error! Reference source not found. for rates). On the other
hand O2 tolerance is an important factor, [NiFe] MBH are the most O2 tolerant, followed
respectively by [NiFe] SH and [FeFe] hydrogenases, with the catalytic properties generally
reflecting an evolution that varies inversely with their oxygen tolerance. Therefore, choices of
hydrogenases and cofactors are ultimately dictated by the consideration of whether the
chemolithoautotrophic platform is being designed for aerobic or anaerobic conditions. Although
[FeFe] hydrogenases are easier to genetically engineer (due to relatively small number of
maturation factors) and are also generally faster and more efficient, both the hydrogenases and
the cofactor Fd are very sensitive to O2. However, if bioreactor operating conditions permit
maintaining low dissolved O2 (e.g. to avoid explosion hazard), there may be added advantage to
using [FeFe] versus [NiFe] hydrogenases.
Much of the work related to engineering hydrogenases in heterologous hosts was
motivated by the production of hydrogen (Rousset & Liebgott 2014) rather than the creation of
better autotrophic organisms. None-the-less, the tools, and in many cases, the outcomes of these
efforts are equally useful for improvement to organisms as autotrophic production platforms. In
fact, at bioreactor operating conditions where partial pressure of H2 is anticipated to be above 0.5
64
atm (Bongers 1970), it is likely that most hydrogenases will act as uptake hydrogenases, because
at typical ratios of cellular redox pairs (NAD(P)H/NAD(P) or Fdred/Fdox) the equilibrium partial
pressure for H2 uptake appears to be quite low (<1000 Pa) (Veit et al. 2008). Therefore, studies
seeking to increase the efficiency of H2 production via improved O2 tolerance (Rousset &
Liebgott 2014) or channeling substrate and reductant flow (Kontur et al. 2012) may directly
translate to enhanced H2 uptake rates.
Table 3-1. Catalytic properties of various hydrogenases (compiled from BRENDA).
Organism
Type of
Substrate
H2ase
R. eutropha
[NiFe]
Redox
Km
partner
(mM)
Kcat (1/s)
Km/kcat
(M-1 s-1)
H2
0.037
187.1±108.4
5.06x106
NAD+
0.293
126±24
4.3x105
NADH
0.048
196.5±36
4.11x106
H2
0.063
-
-
NAD+
0.137
-
-
3.3x10-
133
4.03x109
483
5.49x109
soluble
hydrogenase
R. opacus
C.
[FeFe]
acetobutylicum
soluble
H2
Fd
5
hydrogenase
H2
Flavodoxin 8.8x105
3.VI.III. Metabolic Engineering of Alternative Electron Delivery Pathways
Although there is no demonstration of an engineered organism directly accepting
electrons from a cathode to produce non-native chemicals, components of this system have been
demonstrated separately, namely electron delivery to autotrophic organisms (Nevin et al. 2011)
that have been genetically modified to produce various compounds (Ueki et al. 2014). However,
65
the volumetric rates of product formation are extremely slow (compared to the performance of the
same organisms in other systems), likely due to the very small densities (depending on the surface
area of the electrode only) and/or the rate of electron delivery to the cells. The mechanism for
electron delivery to the cell is not yet fully understood. Genetic engineering has been used to
improve the electron transfer in microbial fuel cells (MFC) via overexpression of electron carriers
or synthesis of novel electron carriers (Sydow et al. 2014), an approach which may be applicable
to microbial electrosynthesis. A summary of the status, challenges and potential for improving the
electrosynthesis platform is available (Rabaey & Rozendal 2010). The choice and tradeoffs
between aerobic versus anaerobic is reiterated in terms of productivity and yield. The field is very
new, however, with much to be learned about the potential of microbial electrosynthesis for the
autotrophic production of fuels and chemicals.
VII. Metabolic Engineering Toolbox for Existing Autotrophs
For those trained in molecular techniques using only facile organisms such as E.
coli, transitioning to work with most autotrophs requires patience, persistence and attention to
detail as there are fewer tools and methods are less forgiving and reproducible. As details were
largely omitted in the review of metabolic engineering advances (Section 3), the critical
importance of the metabolic engineering toolbox warrants some additional discussion.
3.VII.I. Genetic Transformation
Transformation by mating and counter-selection against the conjugal donor is
still a dominant method utilized for transformation of both Ralstonia and Rhodobacter. Relative
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to heat shock or electroporation in E. coli, this method increases the time required for a given
transformation by five or more. Transformation procedures also often require a period of
developing mastery, which adds additional delay in achieving results that would otherwise seem
rather mundane. Although recent successes provide methods to achieve metabolic engineering in
chemolithoautotrophs, these tools are still under active development, where new methods are
slow to be adopted. It is therefore useful to compile some of the recent advances for manipulating
chemolithoautotrophic organisms – particularly for acetogens that have only very recently yielded
to reliable transformation.
3.VII.I.I Genetic Transformation of Aerobic Chemolithoautotrophs
Relatively routine techniques are available for transformation of Ralstonia and
Rhodobacter. Both species are amenable to conjugal plasmid transformation, electroporation and
homologous chromosomal transformation. One of the seminal developments in engineering a
wide range of chemolithoautotrophs was the establishment of intergeneric conjugation from
specialized E. coli donor strains in a bi- or tri-parental mating (Ditta et al. 1985). These systems
take advantage of broad host range vectors that feature an origin of transfer (oriT), which can be
mobilized from E. coli strains harboring the RK2 mobilization machinery, either integrated into
the chromosome (i.e. E. coli S17-1, diparental mating) or harbored on the pRK2013 plasmid,
(triparental mating). The recipient strain preferably exhibits a drug resistance to counter-select the
E. coli host, such as rifampicin resistance for R. capsulatus SB1003 or gentamicin for R.
eutropha. This adds considerable additional constraints on vector design. Homologous
recombination in gram-negative bacteria has been available for over 20 years utilizing the
efficient sacB counter-selection (Quandt & Hynes 1993). These methods provide both for
deletion, gene modification and insertion chromosomal insertion (Jaschke et al. 2011) as noted
67
for the most recent high-level expression systems based on the T7 polymerase vectors described
in the next section.
One of the ongoing challenges concerning genetic engineering of chemolithoautotrophs is
the recalcitrance of many host strains to electroporation of foreign DNA, which if successful
would circumvent the need for time-consuming conjugation protocols. Improved vectors that
utilize electroporation continue to be developed for R. eutrophoa (Solaiman et al. 2010).
Restriction systems are believed to be responsible for recalcitrance by rapidly degrading foreign
DNA. Recently, the rsh1 endonuclease gene of R. sphaeroides was knocked out, which
subsequently made the strain amenable to electroporation (Jun et al. 2013), a method which is
described in more detail below for acetogens.
3.VII.I.II Genetic Transformation of Acetogens
The primary limiting factor for acetogen metabolic engineering is the lack of robust
genetic tools. Transformation in these organisms is frequently hindered by extensive restriction
modification systems (Leang et al. 2013; Straub et al. 2014; Kita et al. 2012; Tsukahara et al.
2014). There are a number of strategies to combat this. In a strategy named Plasmid Artificial
Modification Systems (PAMS), the transformation vector is purified from an E. coli host
heterologously expressing the native methyltransferases, increasing transformation efficiency
(Yasui et al. 2009; Suzuki & Yasui 2011). The methylation domains of these enzymes are readily
identified bioinformatically, thus the strategy can be implemented based solely on the sequenced
genome of the target organism. Alternatively, the specific methylation pattern, or ‘methylome’ of
the target organism can be established via Single Molecule Real Time (SMRT) sequencing, and
then avoiding these sites in the design of transformation vectors (Murray et al. 2012; Flusberg et
al. 2010). Even when DNA digestion has been abated, the plasmid must either successfully
68
replicate in the host, or integrate into the genome. Toward the first aim, a modular Clostridia
plasmid system, with multiple origins of replication and antibiotic resistance markers has been
generated by the Minton lab (Heap et al. 2009) that facilitates rapid screening of potential vectors.
Integration via the universal homologous recombination mechanism occurs with low efficiency in
Clostridia (Al-Hinai et al. 2012), hypothesized to be partially due to the absence of the resolvase
enzyme in many of these species (Rocha et al. 2005). This scheme as well as generally
implementing knock-outs and knock-ins is limited by the availability and effectiveness of
negatively selectable markers. A summary of the breadth of transformation methods currently
being used for chemolithotrophs is provided as Supplemental Table S2.
In extensive work with homologous recombination in C. ljundalhii, the Lovley lab has
noted an inability to get any of the three negative selections to work (e.g. pyrF, galK, mazF)
(Ueki et al. 2014) thereby preventing marker reuse of the limited selectable markers using the
Cre-LOX excistion procedures (Marx & Lidstrom 2002). Despite the noted limitations,
transformation systems now exist for the three model acetogens: Moorella thermoacetica,
Clostriidum ljungdahlii, and Acetobacter woodii, facilitating both metabolic engineering and
studies to expand and improve the genetic systems.
3.VII.II. Plasmids, Promoters and Vectors of Particular Utility for
Chemolithoautotrophs
The most heavily utilized broad host range plasmids for engineering of gram-negative
bacteria are the IncP-based pRK plasmids (Ditta et al. 1985) and the pBBR1MCS vectors
(Kovach et al. 1995). Most IncP-based vectors rely on light-sensitive tetracycline selection,
although pRKD418 employs a tetrahydrofolate reductase gene for trimethoprim resistance
(Mather et al. 1995). The largest drawback of most plasmid-based expression in a production
69
environment is the need for antibiotic supplementation for constant selection pressure. Recently,
the narrow-host range plasmid, pIND4, was maintained in R. sphaeroides without selection
pressure (Ind et al. 2009). This vector incorporates several attractive features, including an IPTGinducible lacIq cassette and the repressible hybrid PA1/A0/A3 promoter and stable MG160 replicon
(Inui et al. 2003). This plasmid has also been used to build a BioBricks® toolbox for R.
sphaeroides with expression levels (of DsRed fluorescent protein) comparable to an E. coli Plac
system (Tikh et al. 2014). In our hands, pIND4 displayed poor stability in R. capsulatus; we
observed that R. capsulatus transformants appeared to express the kanamycin marker poorly and
replacement of KanR with the neomycin phosphotransferase of pBBR1MCS-2 doubled the
kanamycin resistance phenotype to a level of 20-25 μg/mL suggesting that this plasmid might be
improved for the more robust chemolithoautotroph R. capsulatus which would very useful for
dual plasmid testing of operons.
A hoxA-mediated ‘plasmid addiction’ system was recently developed specifically for R.
eutropha lithoautotrophic growth (Lütte et al. 2012). The chromosomal gene for the HoxA
hydrogenase operon transcriptional regulator is essential for utilization of hydrogen. The
transgenes of interest are provided on the pLO11 plasmid which complements the hoxA- mutation
and renders the plasmid indispensable, thereby eliminating the need for a selectable marker under
chemolithoautotrophic conditions. A similar obligatory plasmid system based on the KDPG
aldolase (eda) system (Voss & Steinbüchel 2006) is not applicable to chemolithoautotrophic
growth because the enzyme, KDPG aldolase, is part of the Entner-Douderoff pathway which is
only active during heterotrophic growth – although this demonstrates the generality of the
approach. The mobilization efficiency and stability of four different plasmid systems (based on
incompatibility groups IncP, IncQ, IncW and pBBR) capable of replicating in R. eutropha were
recently assessed (Gruber et al. 2014). They showed that RP4 and RSF1010 based mobilization
sequences are respectively about 50000 and 5000 times more efficient than pBBR1 mobilization
70
sequence. Notably, the inclusion of a 2.3 kb par region of RP4 onto other plasmids nearly
completely prevented plasmid loss for up to 96 hours in the absence of antibiotic selection. This
par region apparently has broad utility for plasmid retention as it previously conferred a high
degree of stability to E. coli plasmids in absence of antibiotic selection for up to 200 generations
(Gerlitz et al. 1990). It is now believed that par region encodes for proteins that stabilize plasmid
retention based on site-specific recombination that can resolve plasmid multimers. A similar new
high-stability plasmid for R. eutropha has been generated by cloning oriV28 and parABS28
regions from pMOL plasmid of Ralstonia metallidurans into an E. coli cloning vector which also
permitted transformation by electroporation (Sato et al. 2013). The relative copy number for the
four plasmids having common mob and par regions was assessed and the pBBR based vector was
found to be about 2 and 4 times higher than those of RSF1010 and pSa respectively.
Early promoter engineering efforts in R. eutropha developed native promoters to drive
expression of acetoin, polyhydroxyalkanoate, and pyruvate biosynthetic genes (Delamarre & Batt
2006). While most of these fusions with an artificial polyhydroyalkanoate operon succeeded in
producing PHA, the overall yields were less than 10%, reflecting an innate limitation of this
method. Orthogonal promoters for R. eutropha gene expression under chemolithoautotrophic
conditions have the potential to bypass the host’s innate genetic regulation machinery, where
many of the transcription initiation signals were adapted from E. coli. In comparison of
heterologous promoters it was found that the T5 phage-derived promoter Pj5 enhanced expression
in R. eutropha by 4 to 5-fold relative to Ptac and Plac (Gruber et al. 2014). The PcbbL promoter
which is induced under autotrophic conditions (Kusian et al. 1995), was effective in increasing
flux to the amino acid oligomer cyanophycin (Lütte et al. 2012).
The T7 RNA polymerase (T7 RNAP) system that is used to drive high level expression in
E. coli vectors has been adopted to R. eutropha and R. capsulatus, although most metabolic
engineering efforts may not require ultra-high protein expression. In R. eutropha the T7 RNAP
71
gene was placed under the control of phaP promoter of the PHA biopolymer operon and the oph
gene for organophosphohydrolase (OPH) target protein under the control of T7 promoter
(Barnard et al. 2004). This approach expressed OPH at 6% to 18% of total soluble protein. A
similar T7 RNAP expression system has been developed for R. capsulatus resulting in upwards
of 80 mg/L of yellow fluorescent protein (Katzke et al. 2010).
The anhydrotetracycline-inducible promoter system based on high-affinity binding to the
tetracycline repressor/operator (TetR/O; Lutz and Bujard 1997), has recently been adapted for
tunable gene expression in R. eutropha by screening for variable expression of TetR (Li & Liao
2014). An order of magnitude dynamic range of tunable expression was achieved. Although this
is two orders of magnitude smaller range than the analogous E. coli system, it was sufficient to
alleviate toxicity associated with an intermediate accumulation in the isobutanol metabolic
engineering in R. eutropha. These recent advances in genetic toolbox development should
accelerate progress in metabolic engineering which is just beginning to tap into the potential of
chemolithoautotrophic organism as illustrated in the next section.
VIII. Status of productivities and
scale-up considerations for metabolic engineering
Design of metabolic engineering strategies are affected by scale-up considerations, where
there are additional challenges for autotrophs as compared to traditional organisms. Downstream
factors such as product separation, genetic stability, induction of gene expression and
growth/non-growth associated product formation, all affect the choices for metabolic engineering
approach. The choice of platform organism is also quite critical and is coupled with the energetic
efficiency (growth yield and maintenance coefficients) (Khan et al. 2014), aforementioned scaleup considerations and various other factors such as product toxicity. Many of these issues relate
72
specifically to the target product and are beyond the scope of this review. In this section we will
briefly discuss the performance of autotrophs during bioreactor culturing and the relevance to
metabolic engineering.
Since R. eutropha was known to produce large amounts of PHB, early developments of
autotrophic bioreactor design to achieve high density and titer focused on this organism. A
limited number of recent studies addressed scale-up of engineered lines as well (see Error!
Reference source not found.). The volumetric and specific productivities reflect the
performance of the bioreactor and the cells respectively. This compilation represents the best
productivities reported in the literature for autotrophic hosts for the conditions noted and includes
native production of metabolites as a comparative benchmark. E. coli hosting the PHB pathway is
also included for comparison.
The production of native compounds by autotrophs represents the high capacity of these
organisms for producing biochemicals from CO2. The volumetric productivity reflects not only
the biosynthetic capacity, but the permissiveness of the organism to be cultured at high density. It
is interesting to note that for autotrophic culture conditions the continuous provision of carbon
from CO2 is analogous to heterotrophic fed-batch and the maximum biomass cell densities
represent non-carbon limitations on growth. It is noteworthy that the highly productive PHB (Ahn
et al. 2000) and ethanol (Dumsday et al. 1999) could be largely replicated in E. coli for which the
most advanced metabolic engineering tools exist. By comparison, metabolic engineering efforts
are generally two to three orders of magnitude lower in volumetric and specific productivity in
autotrophs. This is true even in the high density (~25 gDW/L) autotrophic productivity of R.
eutropha for cyanophycin (Lütte et al. 2012). This situation is also generally not improved for the
metabolically engineered strains even for heterotrophic conditions, suggesting that the current
ability to manipulate fluxes in autotrophs is still tapping into only a fraction of the potential of
these organisms. This is expected, since in contrast to heterotrophic metabolism,
73
chemolithoautotrophic metabolism is much more complex due to sensitive coupling of energy
generation and utilization. Thus more global perspectives are needed while designing metabolic
strategies. Strategies that integrate the target product formation tightly with the existing cellular
machinery for energy generation and regulation, while eliminating non-essential competing
pathways are much more likely to succeed.
In an effort to place these comparisons on a more common basis, the incorporation of
carbon into natural and metabolically engineered metabolites is presented in Figure 6. The time
courses represent best case data presented in the literature with the case of PHB (Tanaka et al.
1995) outpacing other chemolithoautotrophs by a large margin (and comparable to the best results
of even E. coli). Acetate and ethanol production by M. thermoacetica and C. ljungdahlii are also
quite impressive as compared to engineered metabolites (Hu et al. 2013). The best case for
metabolically engineered butyrate production is still very low. Figure 3-7 also presents a final
reminder of the fundamental differences between aerobic and anaerobic chemolithoautotrophic
organisms as production platforms, where much more hydrogen is required to fuel the rapid
production rates – resulting in a much lower theoretical energetic yield. However, at this time the
capability of metabolic engineering in autotrophs is so far from realizing these yield differences
that making such arguments for excluding one chemolithoautotrophic platform over the other is
not warranted.
74
Table 3-2. Comparison of productivities of different compounds in various autotrophic and nonautotrophic hosts. Productivities have been calculated using data reported in the cited works.
Substrate
Organism
Compound
Mode of
operation
PV, avg
(max)
[g/L-h]
PS, avg
(max)
[g/gDW
-h]
Density,
biomass
(product)
[g/L]
Reference
Autotrophic hosts – engineered systems
H2/O2/CO2
R. eutropha
Methyl
ketones
Batch
0.0017
(0.0036)
0.0019
(0.0046)
1*
H2/O2/CO2
R. eutropha
Cyanophycin
Fed-batch
0.0097
0.00041
25.4*
R.
capsulatus
C.
ljungdahlii
Botryococcene
(C30H50)
Continuou
s
0.001
(0.0023)
0.0003
(0.0006)
7 (0.11)
Butyrate
Batch
0.011
0.028
0.8*
Acetate
Fed-batch
0.56
(1.2)
0.24
(0.31)
0.28
(0.9)
0.0026
(0.0034)
H2/O2/CO2
CO/CO2
H2/CO2
A. woodii
Glucose
R. eutropha
OPH
Fed-batch
Fructose
R. eutropha
Isopropanol
Batch
0.076
0.093
1.25*
R.
sphaeroides
C.
ljungdahlii
Valencene
(C15H24)
Batch
0.0049
-
-
Acetone
Batch
0.016
0.019
0.885
Yeast extract
Fructose/Yea
st extract
2 (51)
113
(Müller et
al, 2013)
(Lütte et al,
2012)
(Khan et al,
2015)
(Ueki et al,
2014)
(Straub et al,
2014)
(Barnard et
al, 2004)
(Grousseau
et al, 2014)
(Beekwilder
et al, 2014)
(Banerjee et
al, 2014)
Autotrophic hosts – non-engineered systems
H2/O2/CO2
CO/CO2
H2/CO/CO2
R. eutropha
M.
thermoaceti
ca
C.
ljungdahlii
PHB
Fed-batch
1.55 (5)
0.05
(0.18)
91.3
(61.9)
(Tanaka et
al, 1995)
Acetate
Fed-batch
0.55
0.14
(0.4)
3.92
(Hu et al,
2013)
Ethanol
Continuou
s
0.322
0.184
4
3.14
(9.6)
4.94
(5.13)
0.06
(0.14)
0.38
(0.9)
Glucose
R. eutropha
PHB
Fed-batch
Sucrose
Alcaligenes
latus
PHB
Fed-batch
281 ()
111.7 ()
(Phillips et
al, 1993)
(Ryu et al,
1997)
(Wang &
Lee, 1997)
75
Model, non-autotrophic systems
E. coli
(engineered)
E. coli
(engineered)
S. cerevisiae
(nonengineered)
Glucose (LB)
Glucose (LB)
Glucose
PHB
Fed-batch
4.63
0.088
194.1
(141.6)
Ethanol
Continuou
s
0.57
0.18
3.2*
Ethanol
Continuou
s
12.7
1.41
9 (106)
(Choi et al,
1998)
(Dumsday et
al, 1999)
(Bayrock &
Ingledew,
2005)
PV = volumetric productivity, PS = specific productivity.
Non-engineered systems indicate production of native compounds by hosts that have not been
otherwise genetically engineered. Engineered systems indicate production of heterologous
compounds by hosts that have been genetically engineered. Non-autotrophic, engineered systems
indicate production of heterologous compounds by non-autotrophic hosts that have been
genetically engineered.
* Estimated using a conversion factor of 0.5 g/L/OD
A
B
Engineered
productivities
0.4
0
0
200
400
0
600
Butanol
0.2
IV
V
PHB
10
0.2
Ethanol
0.4
II
0.3
Acetate
0.6
20
0
0
100
200
0.1
mol-C fixed/mol H2
0.8
Butanol
VI
III
0.5
Acetate
I
30
1
Ethanol
gC-fixed/L
40
Substrate: H2, CO2 +/- O2
gC-fixed/L
Native
productivities
Anaerobic
(Wood-Ljungdahl pathway)
1
Aerobic
(CBB pathway)
Time (hr)
Figure 3-7. (A) Maximum reported productivities in grams of carbon fixed in products per liter
(gC-fixed/L) in autotrophic systems. Native pathways: (I) PHB by R. eutropha, (Tanaka et al,
1995); (II) acetate by M. thermoacetica, (Hu et al, 2013); (III) ethanol by C. ljungdahlii, (Phillips
et al, 1993). Engineered pathways: (IV) botryococcene by R. capsulatus, (Khan et al., 2015), (V)
methyl ketone by R. eutropha (Müller et al, 2013); (VI) butyrate by C. ljungdahlii, (Ueki et al,
2014). All are autotrophic timecourses adapted from literature reports presented in Table 3. (B)
Maximum theoretical yield (using values reported by Fast & Papoutsakis, 2012) on H2 of acetate,
ethanol, PHB and butanol using aerobic CBB and anaerobic Wood-Ljungdahl (acetogen)
pathways.
76
IX. Conclusion
The status of metabolic engineering of chemolithoautotrophs is very dependent
on the specific organism where the genetic tools range from reasonably well-developed to nonexistent. The results of metabolic engineering efforts can be generally characterized as falling
short of tapping into the native fluxes of these organisms by several orders of magnitude. On one
hand the genetic tools developed in model organisms have provided for rapid advances of
methods, where opportunities to take advantage of unique characteristics of
chemolithoautotrophic behavior such as the Hox-addiction plasmid have been developed, but
have limited use since the majority of metabolic engineering testing is carried out under the
convenient heterotrophic conditions. At this early stage, metabolic engineering strategies are
understandably driven by proof-of-concept studies. But the next level of strategies and strain
development must be designed with autotrophic operating conditions and scale-up in mind and
performances tested in autotrophic conditions.
While transplanting H2-utilization to more facile model organisms is straight-forward, the
goal of conferring either CO2-fixation or enhanced direct electron uptake has not yet been
reported. Creation of an ‘optimal’ chemolithoautotrophic production platform should include
considerations for bioreactor operating conditions where the enzymes chosen can reflect
achieving high gas transport rates. Oxygen concentrations are of particular importance because of
this and appears to be one of the dominant evolutionary drivers for the key chemolithoautotrophic
enzymes. The efficiency advantage of anaerobes appears to come with an inherent constraint on
rates of metabolism, the impact of which is also dependent upon operations strategy where
continuous high density culture provides the highest productivity at minimum cost.
Chemolithoautotrophic systems have the potential to utilize an inexpensive carbon source as well
as interface to thermochemical deconstruction of renewable biomass with associated
77
environmental benefits. This opportunity will continue to drive commercialization efforts and
overcome the scientific and technical challenges presented by these useful metabolic pathways.
Chapter 4
Triterpene hydrocarbon production engineered into a metabolically versatile
host – R. capsulatus
I. Preface
This chapter presents genetic engineering of R. capsulatus to produce squalene and
botryococcene. Genes of triterpene hydrocarbon biosynthesis – squalene and botryococcene
synthase, were obtained from the ancient algae B. braunii. R. capsulatus was used as the host
because of its existing mechanisms for terpenoid synthesis and use, as well as its capability to
grow in diverse trophic modes and substrates – utilizing carbohydrate, sunlight or hydrogen (with
CO2 fixation) as alternative energy feedstocks. Engineering an enhanced MEP pathway was also
used to augment triterpene accumulation. The hetero-, photo- and autotrophic growth modes were
tested in small-scale (<50 mL) cultures. Fed-batch heterotrophic and continuous autotrophic
bioreactor studies were performed in order to test the performance of the strains under scaled-up
(1 and 5 L) heterotrophic and autotrophic conditions. Productivities were found to be improved
under the controlled bioreactor conditions, autotrophic conditions in fact being the best. This
work is accepted for publication in the journal Biotechnology and bioengineering.
II. Specific contributions
All the growth studies – including small-scale screening in the different trophic modes
and reactor operations – batch and continuous, hydrocarbon extraction and analyses were done by
myself. William Muzika helped set up some of the growth and reactor capabilities as well as help
79
with sampling. I have also facilitated the writing, figures, technical assembly of references,
logistics of submission and revision of the manuscript. Dr. S. Eric Nybo generated the gene
constructs used in this work. Dr. Alex Rajangam and Stephanie Tran helped with the conjugation
of the constructs into R. capsulatus.
III. Introduction
Feedstock flexibility is being targeted to improve the economic feasibility and
sustainability of the production of biofuels and chemicals. Corn-ethanol and the ongoing
transitioning to cellulosic-ethanol or biobutanol are examples of allowing plants to capture
photonic energy in chemical bonds, and subsequently selectively releasing that energy to produce
a more convenient liquid transportation fuel. Similarly, the production of biofuels by algae
reflects an even more direct use of the sun that relies on the storage of triacylglycerol lipids by
these photosynthetic organisms. These oils can then be converted to biodiesel with minimum
processing requirements. The use of these plant-derived energy sources, is inherently competitive
with their alternative use as food and adds an additional dimension to the interaction of energy
and food production costs. A more desirable alternative would be a host organism capable of
chemical production from a variety of energy and carbon sources. To demonstrate this, we have
targeted the production of two C30 hydrocarbons, potential fuel and chemical – botryococcene and
squalene, in R. capsulatus – a host capable of diverse metabolism and robust growth.
Nature has devised extensive options to tap into the energy of the sun, with equally
diverse carbon balances. This presents alternative approaches for biofuels production, where
Figure 4-1 illustrates the metabolic scenarios addressed in the presented work. Heterotrophic
growth is the familiar consumption of carbohydrates, where aerobic growth takes advantage of
metabolic efficiencies to produce CO2 and water. When oxygen is constrained, anaerobic
80
heterotrophic metabolism can result in the production of alcohols and organic acids and
dihydrogen gas. Photoheterotrophic metabolism utilizes a carbon source such as malic or
succinic acid from the TCA cycle and achieves tremendous carbon use efficiency by generating
most of its energy by ATP production from anaerobic photosynthesis. The final R. capsulatus
trophism illustrated in Figure 4-1 is aerobic chemolithotrophic metabolism, where the photonic
energy generates hydrogen (via electrolytic splitting of water), and this energy can then be used to
fix carbon dioxide. In nature the CO2 and H2 can be derived from anaerobic metabolism.
Figure 4-1. R. capsulatus is a metabolically diverse organism that can utilize and interconvert the
energy provided from the sun in a variety of different ways. Metabolism within the dashed line
representing Rhodobacter cell are those reported in this study: aerobic chemoautrophic growth
consumes H2 and O2 while fixing carbon, with these gases being provided by photovoltaic-driven
electrolysis in this scenario (or other natural sources). Aerobic heterotrophic growth is the typical
consumption of sugars and associated aerobic respiration, anaeobic photoheterotrophic growth
consumes energy poor organic acids with photosystem-II mediated ATP generation using light
81
energy. Depiction of the evolution (green arrows) or uptake (red arrows) of metabolic gases
emphasizes the bioreactor requirements for gas-exchange as a major constraint for process design
and operation. The lower part of the figure depicts the metabolic engineering strategy within the
Rhodobacter cell to achieve the production of the high energy terpenes botryococcene or
squalene. The operon includes the enzyme that commits carbon flux to the MEP pathway: (1Deoxy-D-xylulose 5-phosphate synthase, dxs) and the isopentenyl diphosphate (IPP) isomerase
(idi) to enhance the ratio of IPP to dimethylallyl pyrophosphate (DMAPP). This DNA construct
also includes the gene for farnesyl diphosphate synthase (fps) which utilizes one DMAPP and two
IPP to produce C15 farnesyl pyrophosphate, the substrate for the terpene synthases (botryococcene
synthase, BS; squalene synthase, SS).
The choice of the biofuel product molecule is the critically important complement to the
opportunities presented by feedstock flexibility. Our work focuses on the triterpene hydrocarbons
(C30+), squalene and botryococcene, because of their higher energy density than ethanol (A. Tuerk
2011) and ability to be processed nearly identically to that of crude oil (Hillen et al. 1982). Most
notable from a process design perspective is that the hydrocarbon fuels are sufficiently
hydrophobic to phase-separate, thereby eliminating the energy-intensive distillation step that
constrains ethanol production processes. The specific choice of the fuel molecule botryococcene
from the colonial algae Botryococcus braunii race B was purposeful because this is one of a very
few biological derived molecules known to directly contribute up to 1% of all oil shales (Derenne
et al. 1997; Glikson et al. 1989). In its native algae host, botryococcene oils are unique as
compared to other photosynthetic algae associated with biofuels production (Metzger & Largeau
2005). These oils are not for energy storage; their apparent use for floatation results in their
production to exceeding high levels > 30% of the carbon budget that is independent of the growth
rate (Khatri et al. 2014). As a result, they accumulate at all stages of growth, and not only during
storage conditions that characterize most algae biofuels scenarios. We previously isolated the
genes for the biosynthesis of botryococcene and characterized their unusual biosynthetic
mechanisms (Wu et al. 2012; Niehaus et al. 2011). This understanding has led to the generation
of the functional chimeric botyrococcene synthase (BS) gene used in this work. The more direct
biosynthetic route to squalene, another linear triterpene analog to botryococcene, was also
82
compared using the squalene synthase (SS) gene from the same B. braunii host as illustrated in
Figure 4-1.
The work presented here combines the concept of feedstock flexibility with the
production of advanced hydrocarbon biofuels. The multi-trophic utilization of feedstocks for
hydrocarbon biofuels production introduces constraints and opportunities for bioprocess
engineering. Within the production bioreactor, gas exchange is a dominant consideration due to
the low solubility of gases in aqueous solution and the energy required to transport these gases
across the interface. The familiar aerobic heterotrophic reactor has oxygen uptake and CO2
evolution, which is the opposite of the oxygenic photosynthetic algae. The less familiar
anaerobic photoheterotrophic condition has minimal gas transport considerations due to only
minor CO2 evolution, but has a large photon flux requirement. Aerobic chemolithoautotrophic
metabolism fixes CO2, while also taking up H2 and O2, and therefore has the greatest
requirements for gas transport of the presented options. At the same time, since gas transport is
unidirectional (into the culture), chemolithoautotrophic growth can be performed with 100%
conversion of the gas phase (Bongers 1970), and thereby enables the most efficient utilization of
the feedstocks presented. Our choice of the purple photosynthetic bacteria R. capsulatus was
thereby planned to give great latitude in examining diverse modes of feedstock metabolism in the
same organism for triterpene production.
In this study, we generated genetic constructs to produce C30 squalene and botryococcene
in R. capsulatus that are shown to have sufficient activity to be limited by substrate availability.
We then characterize the productivity for these triterpene oils for the three trophic modes of
aerobic heterotrophic, anaerobic photoheterotrophic and aerobic chemoautotrophic metabolism.
Finally, we demonstrate the ability to further amplify productivity by operating under fed-batch
and continuous production conditions in heterotrophic and autotrophic stirred tank bioreactors.
83
IV. Results
4.IV.I. Achieving Substrate-Limited Triterpene Biosynthetic Constructs
To probe the productivity of R. capsulatus in various trophic modes, codonoptimized genetic constructs were assembled based on the triterpene target and known ratelimiting steps in biosynthesis of terpenes. Our recent work on botryococcene biosynthesis
revealed a remarkable biochemical evolution where the triterpene botryococcene is synthesized
by two separate enzyme homologs (Niehaus et al. 2011). This led to the generation of a chimeric
fusion of these two sequentially acting enzymes, which provided the functional botryococcene
synthase (BS) used in this work. For comparative purposes, the B. braunii squalene synthase (SS)
is included which utilizes the same precursor as BS – farnesyl diphosphate (FPP) (Figure 4-1).
The avian FPP synthase (fps) gene was included in these constructs to ensure the robust
production of the common FPP substrate (Wu et al. 2006). The genes encoding for the microbial
equivalent of 1-deoxy-D-xylulose 5-phosphate synthase (dxs) and isoprenyl diphosphate
isomerase (idi) were also included because of their known enhancement of carbon flux in the
MEP pathway and to improve the stoichiometry for FPP formation (Miller et al. 2000).
The initial construction and functional testing of these constructs was carried out
in E. coli, which achieved successively higher titers when the dxs, idi and fps genes were added
to the constructs (Figure 4-2). Their performance in R. capsulatus for both squalene and
botryococcene in heterotrophic mode is shown in Figure 4-3, confirming a requirement for fps,
and the expected improved performance of the native squalene enzyme. However, the addition of
fps was not sufficient to pull large carbon flux out of central metabolism. This limitation was
relieved when the rate-limiting enzymes from the MEP pathway - dxs and idi- were added.
Productivity increased by 20 to 100 fold over fps-only constructs. Further increases in
84
productivity of 1.5 to 2 fold were observed when the medium (YCC) was supplemented with 80
mM glucose (Figure 4-3b). This demonstrated that the introduced triterpene synthase activities
were sufficient to be limited by general metabolic rates within central metabolism. The constructs
containing the triterpene synthase and the enhanced MEP pathway components
(pBBR:Plac::BS/SS-dxs-idi-fps) were further utilized to probe for major differences in available
flux from central metabolism under different trophic modes of growth.
Figure 4-2. Engineering of triterpene synthases with the native or engineered MEP pathways of E.
coli DH5α. Constructs were engineered as a single polycistron under the control of the PLac
promoter with triterpene synthase only (BS, botryococcene synthase; or SS, squalene synthase); in
combination with a prenyltransferase (fps); or with one plasmid-borne copy of the engineered
MEP pathway (dxs-idi-fps) or two plasmid-borne copies. Five biological replicates of each strain
were grown aerobically for 24 hours in 2xYT-1% glycerol and then extracted with 1:1 acetone
and hexane to determine triterpene content.
85
a
b
Std.
medium
PLac
BS
PLac
BS
FPS
PLac
BS
DXS
PLac
SS
PLac
SS
FPS
PLac
SS
DXS
+Glu
suppl.
Botryococcene
Squalene
IDI
FPS
IDI
FPS
0
5
10
mg Triterpnes/g DW
15
Figure 4-3. R. capsulatus triterpene metabolic engineering. (a) Constructs containing
botryococcene synthase (BS, upper panel) or squalene synthase (SS, lower panel) with increasing
enhancements in metabolic flux by including the avian farnesyl pyrophospate synthase (fps), and
putative rate-limiting enzymes in the MEP pathway. (b) Accumulation of triterpenes in R.
capsulatus grown in standard growth medium (Std. medium) and that supplemented with an
additional carbohydrate source, 80 mM glucose (+Glu suppl.) for the adjacent constructs:
botryococcene for the upper panel, and squalene for the lower panel. The level of enhancement
beyond standard growth medium alone is indicated by the extended bars (open bars).
Experimental values were determined from five replicate cultures; error bars depict standard error
of the mean.
The specific productivity at stationary phase for the different trophic growth modes were
compared in small scale (<50 mL) cultures (Figure 4-5a). Photoheterotrophic growth mode
improved the transition to autotrophic growth, through induction of the Calvin-Benson-Bassham
pathway for carbon fixation (McKinlay & Harwood 2010), and therefore sequentially preceded
autotrophic growth. The production levels paralleled growth over time (Figure 4-4) and were
86
found to be quite similar for all three trophic growth modes for either triterpene product, with
squalene levels being two-fold higher than the botryococcene levels. Squalene productivity was
greater than botryococcene probably because of the enzymatic efficiencies of the enzymes.
Conversion of FPP to squalene is catalyzed by a single active site in the enzyme, where the
conversion of FPP to PSPP to squalene occurs without the substrate leaving the active site. In
contrast, BS chimeric enzyme requires two catalytic sites, one converting FPP to PSPP, the
second, PSPP to botryococcene, which would entail the release of PSPP from the active site of
one enzyme and its re-binding at the second active site. These mechanisms provide an
explanation for the different rates observed at similar substrate levels. Since there is a
corresponding reduced accumulation of botryococcene, a degree of feedback regulation known to
occur for the endogenous MEP pathway (Banerjee & Sharkey 2014) appears to be functional in
Botryococcene
(mg L-1)
conjunction with the heterologous pathway enhancements.
4
BS+Op3
BS
2
0
Squalene
(mg L-1)
SS+Op3
SS
4
2
0
0
20
40
60
80
100
Time (hours)
120
140
Figure 4-4. Shake flask autotrophic time-course of R. capsulatus harboring pBBR:Plac:BS-dxs-idifps and pBBR:Plac:BS only (Top panel) and pBBR:Plac:SS-dxs-idi-fps and pBBR:Plac:SS only
(Bottom panel).
87
Independent of the precise rate-limiting mechanisms, obtaining similar productivities in
the different trophic modes was unexpected, because the three trophic conditions represent very
different physiological conditions, each having different energetic requirements. The differences
for typical growth conditions in these diverse trophic modes had less impact than simple glucose
supplementation. This result contrasts with the dramatically different metabolic modes (Figure
4-5b) that have been quantified in the “trophism snapshots” compiled in Figure 4-5c. The
trophisms were quantified in terms of their carbon balance, with net release of about 50% of the
carbon to CO2 in aerobic heterotrophic mode, and net capture of 100% of the CO2 in the
autotrophic mode. By comparison, about 80% of the carbon from malate ends up in the biomass
for photoheterotrophic metabolism, since the ATP generation is facilitated largely by light and
not from malate catabolism. The contrasting energetic features of these trophic modes are
depicted in the energy ‘source’ and ‘sink’ values. ‘Source’ was calculated as the efficiency with
which input energy is captured as ATP. These inputs were sugar, light and hydrogen for
heterotrophic, anaerobic photoheterotrophic and aerobic autotrophic modes of growth
respectively. To place these numbers on a common carbon basis with ‘sink’ energy requirements,
a conversion factor of 2.28 mol-ATP/mol-biomass-carbon was used (A. Tuerk 2011). The basis
of these calculations is presented in Appendix E. The most important features of these
calculations is that the sugar, malate and H2 are all products of photonic energy produced
externally to the production host for those alternative trophic production modes (Figure 4-1). The
comparable results for these dramatically different metabolisms and their associated fluxes of
carbon and reducing power suggest a strong homeostatic regulation of the precursor pools of the
central metabolism that is largely independent of the trophic growth mode.
88
a
b
CO2
Balance
c
Energy
(kJ/mol C)
(g-Cg-CO
biomass )
Botryococcene
Squalene
2
-100%
Aerobic
Heterotrophic
0%
+100%
Source
0
250
photons
(RCOOH)  HC + CO2
Aerobic Chemoautotrophic
CO2 + H2 + O2  HC + H2O
-100
5 0
5
mg triterpene / gDW
10
100
malate
H2
0
0
(CH2O)n
(CH2O)n + O2  HC + CO2 + H2O
Anaerobic Photoheterotrophic
Sink
500
Reductant
(e- → ATP)
CO2
Assimilation
(C → pyruvate)
Figure 4-5. Exploring alternative trophisms for hydrocarbon production in Rhodobacter. (a)
Triterpene specific productivity of R. capsulatus with pBBR:BS-dxs-idi-fps (blue-left panel), and
pBBR: SS-dxs-idi-fps (red-right panel). (b) Reaction for hydrocarbon (HC) production for given
tropism. (c) Trophims are presented quantitatively in terms of thermodynamic energy required to
assimilate a mole of carbon into pyruvate as well as the energy required to produce an ATP
normalized to a molar carbon basis. The carbon balance is shown to illustrate the relative
magnitude of carbon produced by aerobic and photo-heterotrophic growth as compared to
autotrophic CO2 assimilation.
4.IV.II. Productivity Enhancement through Bioreactor Operational Strategies
To determine if the growth-associated specific productivity could be maintained
while increasing culture density through fed-batch addition of carbon and nitrogen, a typical
feeding strategy of concentrated glucose feed (540 g/L) based on dissolved oxygen (DO)
feedback control was implemented (Gleiser & Bauer 1981). NH4OH (8 N) feed was also provided
for pH control while providing nitrogen (Figure 4-6). The heterotrophic bioreactor culture
reached an OD660 of 12.7 and accumulated a total of about 40 mg/L botryococcene with an
average specific titer (over the entire run) of 11.7±3.0 mg/gDW. This represents a 2-fold
improvement over small scale YCC cultures with glucose supplementation, which indicates
controlled pH and higher aeration significantly improved the substrate utilization of the cells to
89
generate a greater relative flux in the MEP pathway. However, we expected to reach a much
higher density, but repeatedly encountered abrupt growth cessation at relatively low culture
densities of less than 5 gDW/L, which are over an order-of-magnitude lower than E. coli cultures
that routinely reach densities upwards of 100 gDW/L (Lee 1996). We suspect that this may be a
result of some form of metabolic inhibition or a quorum sensing behavior that Rhodobacter is
known to display (Schaefer et al. 2002; Curtis 2011). To test further, aliquots of media from the
reactor stationary phase were tested for continued growth in subsequent batch flask culture
(Figure 4-7). Cultures inoculated into fresh media grew robustly, whereas cultures provided with
concentrated nutrients from the stationary phase bioreactor supernatant displayed minimal
growth. More work would be required to determine if this is typical homoserine lactone quorum
sensing behavior or a different metabolic inhibition.
Growth (OD660)
12
30
8
15
4
Triterpene (mg/L)
45
16
a
0
0
50
50
25
0
0
0
50
Time (hr)
Cumulative
glucose fed (g/L)
Dissolved O2
(% of air saturation)
b
Gas phase O2 increased
100
100
Figure 4-6. Heterotrophic bioreactor growth of R. capsulatus pBBR:Plac:BS-dxs-idi-fps. R.
capsulatus genetically engineered with the above expression vector was grown in a 5 L BioFlo
90
with glucose provided as the sole initial carbon and energy source, followed by fed-batch
supplementation based on feedback control of dissolved oxygen (DO). Ammonia was also added
in a fed-batch manner for pH control. (a) Growth monitored as OD660 (black circles) and
botryococcene accumulation (blue squares) respectively. (b) The DO (red squares) and
cumulative glucose supplementation (black line), where glucose fed batch was initiated at ~40 hr,
and the approximate times for incremental increases in O2 supplementation (light blue arrows) are
noted. Cultures stopped growing and accumulating hydrocarbon at about 70 hr; respiration
slowed considerably in stationary phase as indicated by the sharp rise in DO which could not be
recovered with additional glucose feed.
14
12
a
b
c
d
e
f
g
h
OD660
10
8
6
4
2
0
0
50
100
150
Time (hr)
Figure 4-7. Growth tests with heterotrophic bioreactor culture and culture supernatant. (a
and b) Replicate samples from the bioreactor inoculated into RCVB (-malate) minimal media
supplemented with 10 g/L glucose. (c) Sample from the bioreactor inoculated into YCC complex
medium supplemented with 10 g/L glucose. (d) Fresh culture of R. capsulatus pBBR:Plac::BSdxs-idi-fps inoculated into bioreactor supernatant supplemented with concentrated YCC medium
nutrients (to make the final concentration similar to YCC). (e and f) Replicate fresh cultures of R.
capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into bioreactor supernatant supplemented with
concentrated RCVB (-malate) medium nutrients (to make the final concentration similar to
RCVB). (g) Fresh culture of R. capsulatus pBBR:Plac::BS-dxs-idi-fps inoculated into RCVB (mal) medium supplemented with 27 g/L glucose. (h) Fresh culture of R. capsulatus
pBBR:Plac::BS-dxs-idi-fps inoculated into RCVB (-mal) medium supplemented with 10 g/L
glucose. These tests were performed in order to confirm if something accumulating in the
91
heterotrophic bioreactor prevented further growth of R. capsulatus. Freshly growing cultures of
R. capsulatus with the said construct inoculated into reactor supernatant supplemented with
concentrated media components failed to grow, while reactor cultures inoculated into fresh media
(both complex and defined) displayed proper growth. Freshly growing cultures inoculated into
fresh media with high and low glucose concentrations emulating the reactor media are provided
as control and displayed proper growth.
Sustainable biofuel production is dependent on CO2 fixation and we were
interested in evaluating Rhodobacter’s capacity to produce triterpene under chemoautotrophic
growth. To sufficiently characterize the metabolic capacity of the R. capsulatus platform for
triterpene production, we scaled up to a 1 L autotrophic bioreactor. R. capsulatus pBBR:Plac::BSdxs-idi-fps was grown in batch and continuous flow operation with gas phase H2, O2, CO2 feeding
and NH4OH based pH control (Figure 4-8). It was found to be much more robust in terms of
growth and triterpene accumulation in the scaled-up autotrophic reactor compared to
heterotrophic. The inlet gas composition was manually adjusted via precise mass flow controllers
to promote the high gas transport rate into the liquid (Figure 4-8a). The culture reached an OD660
of about 17 (7 gDW/L), comparable to that attained for heterotrophic growth, yet accumulated a
total of about 110 mg/L botryococcene (Figure 4-8b), more than double that observed with
heterotrophic reactor runs. Interestingly, in contrast to the heterotrophic bioreactor, there was a
steady increase in specific triterpene level during the course of batch growth up to about 16.7
mg/gDW (Figure 4-8c). The specific productivity of botryococcene calculated during batch
growth ranged between 0.17 and 0.5 mg/gDW-hr. Because steady state operation unambiguously
quantifies the specific productivity, we switched the reactor to continuous operation by flowing
12.5 g/hr CA medium (inorganic salts and vitamins) into the reactor and removing culture at the
same rate. This corresponded to a dilution rate of 9.62x10-3 hr-1, roughly 10% of the maximum
growth rate of R. capsulatus (µmax = 0.112 hr-1). The culture density came to steady-state around
2.75 gDW/L and the specific triterpene level was found to increase further and became relatively
constant at about 23 mg/gDW. The inlet gas composition was kept constant during continuous
92
operation (Figure 4-8a) to allow the culture to reach steady state. Constant outlet gas composition
was obtained when the culture reached a steady state (data not shown). The steady-state triterpene
specific productivity was found to be 0.32 mg/gDW-hr, thereby achieving significant
improvements based on all productivity measures as compared to heterotrophic small scale and
reactor cultures.
0.4
H2
0.8
0.3
0.6
O2
0.2
CO2
0.1
0.4
0.2
0
0
20
60
10
40
5
20
BATCH
CONTINUOUS
Specific Productivity
(mg/gDW∙hr)
0
0
0.5
20
0.4
15
0.3
10
0.2
Productivity
Titer
0.1
0
5
Specific titer
(mg/gDW)
Growth (OD660)
80
15
Botryococcene (mg / L)
100
b
c
O2 and CO2
Concentration
1
H2
Concentration
a
0
0
50
100
150
200
Time (hr)
Figure 4-8. Performance of R. capsulatus pBBR:PLac:BS-dxs-idi-fps in an autotrophic bioreactor.
R. capsulatus expressing this plasmid was grown with gas phase feeding of H2, O2 and CO2 (for
growth) and liquid phase feeding of ammonia (for pH control) in batch operation for the first 110
hours and under continuous operation after that. Continuous flow was established with 12.5 g/L
flow of the CA medium, which corresponds to 10% of µmax of R. capsulatus. (a) The inlet gas
composition profile. During the batch growth mode, the compositions were varied to
accommodate the growth demand, based on outlet gas composition measurement. Gas
consumption did not vary greatly during continuous flow and the inlet composition was
93
essentially kept constant. (b) The OD660 and botryococcene profile during batch and continuous
operation. Cells grew exponentially to OD 5 and linearly after that reaching a maximum of 17.
Botryoccocene accumulation increased proportionally as the cells and achieved a maximum
volumetric accumulation of 110 mg L-1 botryococcene at the end of batch growth. Under
continuous operation, the cells reached a steady state within about 130 hrs of starting flow,
reaching an OD of ~7 and maintaining a relatively constant level 60 mg L-1 botryococcene. (c)
The specific botryococcene productivity profiles. The specific botryococcene levels increased
steadily during batch growth to about 17 mg/gDW. Batch specific productivities were around 0.5
mg/gDW-hr. Specific botryococcene continued to increase during the continuous flow and
reached 23 mg/gDW. However, specific productivity decreased somewhat and reached a steady
level of about 0.3 mg/gDW-hr.
V. Discussion
Although biofuels like ethanol and other small chain alcohols are readily
obtained from genetically modified microbial hosts supplied with photosynthetic derived sugars
and cellulosic feedstocks, questions remain about the sustainability and efficiency of such
processes, including carbon balance and net energy generated. These observations have led to
efforts aimed at coupling the production of higher energy fuel molecules to a variety of solar and
geochemical energy sources. Utilizing the considerable knowledge that has become available
about the bacterial MEP pathway and the biosynthesis of high-energy triterpenes, hydrocarbon
biosynthetic pathways were introduced into R. capsulatus such that alterations in substrate
availability within central metabolism through glucose feeding were reflected in enhanced
triterpene accumulation. In choosing R. capsulatus there was a hope that the large differences in
carotenoid metabolism in these different growth modes – particularly photoheterotrophic – that
might provide a more favorable trophism for terpenoid flux. However, the observation that the
levels of triterpene production were similar for fundamentally different modes of growth
suggested a persistent degree of ‘homeostasis’ existed where the pools for metabolites of central
metabolism were comparable between the different trophic modes of growth and no one mode
was more advantageous relative to another for hydrocarbon production. It is possible that some of
94
the inherent potential of the alternative trophisms is obscured by endogenous regulation of the
MEP pathway, and work is planned to follow up by testing a heterologous MVA (mevalonate)
pathway that should lack such regulation. This strategy has been successful for the production of
various isoprenoids in E. coil (Kim et al. 2006; Martin et al. 2003).
Productivity can be enhanced considerably through bioreactor operational
strategies which increase cell density while maintaining a relatively fixed triterpene content.
However, this is largely limited to an order of magnitude enhancement and compliments
improvements in metabolic engineering strategies. A comparison of this work to other efforts at
terpene hydrocarbon metabolic engineering is very informative as compiled in Table 1, most of
which are based on complex carbon source or sugars. The maximum specific productivity in this
work of 0.5 mg/gDW/hr is on par with several lycopene (C40) tetraterpene efforts, however, the
distinct advantage of high-throughput screening for colored compounds has allowed for selecting
production levels of both lycopene and carotene to about 3 mg/gDW/hr. By comparison, the
production rates of ethanol are on the order of 500 mg/gDW/hr (estimated from (Ohta et al.
1991)), which illustrates the challenge of competing with a pathway that is a natural obligatory
final electron acceptor. What is most surprising, is a comparison to the native Botryococcus
braunii algae, where recent continuous culture work has provided very accurate specific
productivity levels at 4 mg/gDW/hr (Khatri et al. 2014) which represents photosynthetic
conditions from an organism that is notoriously slow growing (doubling times of 4+ days). This
terpeneoid flux has only been exceeded by the recent work using a modified MVA pathway to
produce C5 isoprene at 9 mg/gDW/hr in E. coli fed glucose and beef extract (Yang et al. 2012).
Combined with the comparative achievements of modest production levels, suggests that there
are critical aspects of metabolic engineering of hydrocarbons that remain to be discovered. It is
also interesting that the native algae biochemistry produces nearly exclusively tetra-methylbotryococcene. This suggests that there is subcellular management of carbon flux that will likely
95
require intricate recapitulation in heterologous hosts to achieve high stable hydrocarbon
production. Although organisms with alternative trophic modalities are more difficult to work
with experimentally, the results presented here demonstrate a comparable potential for
biochemicals production that do not rely on sugar as a carbon source.
96
Chapter 5
Ongoing and future work and conclusion
I. Triterpene production in R. eutropha
We have prioritized R. capsulatus for our initial studies of triterpene production using
enhanced MEP pathway because of its already existing enzymes for terpenoid synthesis and use
(in light-harvesting machinery) as well the ability to grow in diverse trophic modes and
substrates. This was described in Chapter 4. R. eutropha is another chemolithoautotroph that is
very widely studied due to its robust growth and natural biopolymer (PHB) production. R.
capsulatus and R. eutropha are, however, very different in their metabolism. The former is more
widely classified as a facultative anaerobic phototroph, while the latter as a classic “knall-gas”
bacteria. The most recent studies are the exploration of triterpene production in R. eutropha in
order to carry out a comparison between the two bacteria in terms of their ability to produce C30
hydrocarbons. These studies are not yet completed at the time of submission of the dissertation
but is expected that they will be soon after and generate sufficient data for a publication.
5.I.I. R. eutropha PHBFor the initial set of studies I chose a strain of R. eutropha that is knocked-out in its
ability to produce PHB (R. eutropha PHB-). PHB is a storage compound for these bacteria. Once
a nutrient limitation other than carbon (nitrogen or phosphorous for example) is encountered, R.
eutropha channels its carbon flux into PHB, for use later as a carbon source as other nutrients
become available. In removing its ability to make PHB during nutrient limitation, it is expected
that there will be additional flux to channel towards our target triterpene production.
97
First, to observe the triterpene production without any enhancement in the pathway, R.
eutropha was transformed with pRK:Plac:SS-fps. This is a construct containing gene for farnesyl
pyrophosphate synthase FPS (converting IPP and DMAPP into FPP) and the triterpene synthase
SS (converting FPP into squalene) (see Figure 1-3 for more details). With only these genes, there
was small amount of triterpene production (Figure 5-5). These levels are very similar to those
observed with R. capsulatus using the same construct but in a different vector backbone (i.e.
pBBR:Plac:SS-fps). Supplementation of fructose clearly improved production, as a result of both
increased biomass and increased substrate availability (shown later).
3
Squalene (mg/L)
2.5
2
1.5
1
0.5
0
LB
LB+Fru 5g/L
Medium
Figure 5-1. Triterpene production by R. eutropha transformed with pRK:Plac:SS-fps. The three
different colored bars indicate three different clones.
To study its performance under controlled conditions, the best performing line (based on
Figure 5-1) was grown in a bioreactor with heterotrophic carbon sources (Figure 5-2). Figure
5-2A shows growth on defined medium and Figure 5-2B shows growth on LB medium, both
supplemented with fructose (fed-batch). Their growth performance was not very good, that is
they did not reach the high densities normally reached according to reports for growth of the
wild-type strain of R. eutropha (Ryu et al. 1997). In both the current bioreactor efforts growth
ceased midway through the run and it is not clear at this point what caused this. It is very likely
98
because of the PHB knockout that limited its ability to regulate internal redox. It was observed
that pH changes had very strong effect on growth. Any time acid or base was added to stabilize
pH, growth would cease (apparent from the subsequent rise in DO).
pH
OD600
Squalene
DO
12
pH, OD600, Squalene (mg/L)
10
100
90
80
70
8
6
50
40
4
DO (%)
60
30
20
2
10
0
0
B
25
OD600, pH, Squalene (mg/L)
0
20
20
40
Time (hr)
60
80
100
90
80
70
pH
OD600
Squalene
DO
15
10
60
50
40
DO (%)
A
30
5
20
10
0
0
0
20
40
60
80
100
Time (hr)
Figure 5-2. Heterotrophic reactor growth of R. eutropha pRK:Plac:SS-fps. (A) Defined medium
fed-batch with fructose. (B) LB medium fed-batch with fructose.
99
5.I.II. R. eutropha wild-type
In comparison to the PHB- lines the wild-type without any knockout, performed much
better, both in terms of total and per-cell productivity. Figure 5-3 is a comparison between the
two backgrounds grown in LB supplemented with different amounts of fructose. On a per-cell
basis the wildtype performs about two-times better than the PHB-. The wildtype line also
performs much better than the PHB- during bioreactor growth as well – readily reaching OD600
greater than 120 (corresponding to 50 gDW/L) and good triterpene production Figure 5-4. This is
further proof that the PHB knockout line is highly constrained in its ability to grow well. Further
work is needed to elucidate the exact nature of the issues with this line but it is quite likely
because of its limited ability to control internal redox due to the knockout.
Re PHB- pRK
4.0
2.0
0.0
5
Squalene (mg/gDW)
6.0
Squalene (mg/L)
6.0
10
15
Re wildtype pRK
8.0
20
4.0
2.0
0.0
25
3.0
5
10
15
20
25
3.0
2.0
1.0
0.0
5
Squalene (mg/gDW)
Squalene (mg/L)
8.0
10
15
20
25
Fructose added (g/L)
(+LB)
2.0
1.0
0.0
5
10
15
20
25
Fructose added (g/L)
(+LB)
Figure 5-3. Comparison between two backgrounds of R. eutropha: PHB- and wild-type in terms
of triterpene production, transformed with pRK:Plac-SS-FPS. The cultures are grown in LB
medium supplemented with indicated levels of fructose.
140
100
120
80
100
60
80
OD600
Squalene
DO (% air sat)
60
40
DO (%)
OD600, pH, Squalene (mg/L)
100
20
40
0
20
0
-20
0
20
40
60
80
100
Time (hr)
Figure 5-4. Bioreactor growth of R. eutropha wild-type transformed with pRK:Plac-SS-FPS.
5.I.III. Pathway enhancements
5.I.III.I MEP pathway
In parallel to the above studies, I have also chosen to use the wild-type of R. eutropha to
see the effect of the enhanced MEP pathway. Based on our results for the different constructs
with R. capsulatus, pBBR-Plac:SS-dxs-idi-fps was chosen for transforming R. eutropha for
triterpene production. Figure 5-5 shows triterpene production by four ex-conjugants picked from
the plate and grown on LB medium supplemented with 20 g/L glucose and antibiotic (500 µg/ml
kanamycin). Specific production levels are somewhat less than those seen for R. capsulatus with
the same construct and glucose supplementation. However, comparison based on complex
101
medium is slightly ambiguous and bioreactor studies using defined medium as well as autotrophic
growth will clarify any obvious difference between Ralstonia and Rhodobacter.
Re wildtype pBBR-Plac-SS-dxs-idi-fps
6.0
Medium: LB+Glucose (20 g/L)
Squalene (mg/gDW)
Squalene (mg/L)
10.0
Re wildtype pBBR-Plac-SS-dxs-idi-fps
8.0
6.0
4.0
2.0
Medium: LB+Glucose (20 g/L)
4.0
2.0
0.0
0.0
1
2
3
Colony
4
5
1
2
3
Colony
4
5
Figure 5-5. Triterpene production by R. eutropha wildtype transformed with pBBR:Plac:SS-dxsidi-fps, grown on LB medium with 20 g/L glucose – total (left) and specific (right) titer levels.
5.I.III.II MVA pathway
The other pathway supplementation is via the MVA pathway. So I studied
supplementation of the basic triterpene synthesis pathway (SS-fps) with an enhancement of the
flux to IPP/DMAPP (substrates for FPS) using the MVA pathway genes. One way of achieving
this was to transform the lines discussed above (i.e. R. eutropha wildtype or PHB- pRK:Plac:SSfps) with the MVA pathway genes (see Figure 1-3). However, there have been considerable
difficulty in cloning the codon-optimized versions of Operons 1 and 2 (R. capsulatus and R.
eutropha codon-usage are approximately similar). Considerable effort was expended to obtain
codon-optimized genes where postdoc Alex Rajangam delegated the assembly to Justin Yoo.
Amplification of the long operons with high GC content presented substantial problem and PCR
polymerases and conditions needed to be optimized. Repetitive sequences in the operons
interfered with primer design and caused delays and needed to be troubleshot. Working with
wrong DNA for a period of about two months also contributed to a lot of wasted efforts.
102
Therefore, while that work was in progress, we have attempted to proceed with a construct
developed by Greg Stephanopoulos lab (pBbA5c:Plac:MevT-MBIS; available from Addgene), that
is codon-optimized for E. coli. This is essentially the same as Operons 1 and 2 driven by a single
promoter Plac. The construct is then cloned out from the pBbA5c backbone into the pBBR vector
for expression in R. eutropha and R. capsulatus, generating pBBR:Plac:MevT-MBIS.
This vector was transformed into the pRK line discussed above. Figure 5-6 shows the
results of growth screening of five clones on LB medium with and without fructose. Results are
slightly higher on average than those with only SS-fps but almost on the same level as the best
performing line above. This was surprising as it was expected that the addition of the MVA
pathway would provide a substantial flux to the triterpene synthesis. This could be the result of
the very different codon-usage of E. coli vs. R. eutropha. However, we cannot definitively answer
this question until the codon-optimized version of Operons 1 and 2 are successfully cloned.
3.50
Squalene (mg/L)
3.00
2.50
2.00
1.50
1.00
0.50
0.00
LB
LB+Fru 5g/L
Medium
103
Figure 5-6. Triterpene production by R. eutropha PHB- pRK:Plac:SS-fps + pBBR:Plac:MevTMBIS (dual plasmid).
II. Future work
There are immense opportunities to take this platform to the next level in terms of
metabolic engineering, protein engineering, improvements in host organism and bioreactor. Much
of this is described in Chapter 3. The task that can be finished in the near future without the
involvement of much more additional resources is the study of the comparison of the MEP and
MVA pathway in R. capsulatus and R. eutropha, in heterotrophic as well as autotrophic. The goal
of this would be to select the best combination of host and constructs to move forward to the next
level of improvements. Some potential work that will require much more additional resources are
described below.
5.II.I.I Generation of a mega-plasmid
A dual plasmid system is problematic in that it takes much longer for the conjugations
due to the added step of conjugating the first plasmid, selecting and screening for hydrocarbon
production before the next plasmid can be conjugated. Once the bacterial lines are obtained, it
must be propagated under two to three antibiotic selections, which tend to negatively affect
growth (and therefore triterpene production). Extent of plasmid loss is also more in this case. In
case of R. capsulatus, we have generally seen fair degree of plasmid loss with the pBBR based
constructs, and although measurements are not done for dual-plasmid systems, it can only be
expected to be worse. Furthermore, due to the possibly different copy numbers of the two
plasmids, the relative gene dosage is also variable. It is quite difficult to obtain accurate copy
104
number information, particularly because it varies with the growth stages. These problems
prompted us to attempt to construct a single plasmid with all the genes of interest of the MVA
pathway as well as the triterpene synthase and FPS (Figure 5-7). This will yield a proper
comparison between the enhanced MEP and MVA pathways. However, the genetic engineering
of this ‘mega-plasmid’ has proven particularly difficult to obtain as a result of these long operons,
not only because of length (~3-5 kb) but also because of high GC content (>65%) and repetitive
sequences in the intergenic regions. Restriction digest based cloning is also not possible because
of the lack of compatible sites. These problems have been partially overcome by the use of
several different polymerases and optimization of PCR conditions, but there are still some way to
go before this construct can be entirely constructed and tested for functionality.
e
pBBR-Plac:Op1-Plac:Op2-Plac-SS-FPS
17032 bp
Figure 5-7. Desired mega-plasmid containing all the MVA pathway genes, the triterpene synthase
and fps.
105
5.II.I.II Promoter
The promoter used in the majority of our work, Plac, is a common, moderately strong and
constitutive promoter in R. capsulatus and R. eutropha. But there are much stronger inducible
promoters available that can increase gene expression and potentially improve the triterpene
level. Some of these include Ptac, PBAD, Ptet etc. Much of the recent advancements regarding
promoters is described in Section 3.VII.II. However, the highest level of expression of all the
genes is probably not optimal for the desired pathway engineering. Much more important would
be the relative level of expression of the genes and their respective enzyme activity within the
engineered pathway. Furthermore, the gene expression should be inducible in such a way that it is
regulated with respect to the growth, energy or carbon metabolism by the organism. Some such
important host-specific inducible systems in R. capsulatus that respond to environmental factors
are puc, puf, cbb, hydrogenase operon promoters. The first two respond to light levels and oxygen
(increasing expression with low levels of both), cbb promoters respond to CO2 and O2 levels and
hydrogenase operon promoters respond to H2. All of these promoters also affected by the cellular
redox state and global regulatory network. Similarly complicated gene regulatory systems exist
for R. eutropha as well. As a result, it is not a straight-forward choice as to which environmental
cue will be most suitable to use. For example, oxygen tension can be used based on a bioreactor
operating condition that is also suitable from a safety standpoint. However, it is not directly
related to growth so may not be so well-suited in that respect.
In the same way, fine tuning the relative expression level of the genes that result in
maximal production is not a straight-forward objective, as a change in the expression level of one
will require a change in all the others. A screening strategy could be designed in such a way that
each of the genes (or at least each operon) has its own inducible promoter and/or operator.
Common promoters such as Plac, PBAD, Pfru, Ptet etc. could be used where each responds to a
106
different chemical substance over a substantial range. Growth tests could be performed where
different levels of the inducer substance are used and transcript levels of the genes for the best
triterpene producing line could be selected. Unless a high-throughput promoter modification
method such as MAGE can be combined with rapid product screening (e.g. lycopene (Alper et al.,
2005)) optimization of productivity is a daunting task. In addition, since the proximity of the
genes from the promoter also affects its expression level, the genes could be shuffled around
within a particular operon using a strategy that maximizes the product of that operon.
5.II.I.III Other potential areas of improvement
Further factors controlling gene expression such as plasmid stability, gene dosage etc.
must also be improved. Plasmid stability has been a significant problem in our studies. A strategy
to make the plasmid indispensable to the cell - for example having a key gene in the hydrogenase
or cbb operon on the plasmid while knocking out the corresponding copy in the genome is a
useful strategy that has been used successfully. Gene expression is not the only factor
determining the product formation through an engineered pathway. Strategies such as knocking
out major non-essential pathways (such as PHB in R.eutropha) to divert the carbon flux will
prove to be important at a later stage. Protein engineering, such as creating tethers or scaffolds for
the proteins, is also a major area that can vastly improve product formation by channeling the
substrate or preventing the loss of the substrate to other competing pathways in the cell. Much of
this is discussed in detail in Chapter 3 and so is not repeated here.
107
III. Conclusion
In my PhD work, I tried to lay the foundation of the different aspects of the specific
Electrofuels paradigm we worked with, starting from analyzing the process economic feasibility
and identifying major areas that need to be improved. I worked with two different classes of
bacteria that are promising in different ways, although R. capsulatus is prioritized more than the
R. eutropha to begin with. Their comparison in terms of growth yield and maintenance
parameters was completed but a proper comparison in terms of triterpene production still needs to
be captured. I believe this work would be the basis of next level of engineering to improve biofuel
production in autotrophic hosts.
108
Appendix A
Bioreactor material balance equations for section process economic analysis
(Section 2.V.I)
In performing simulations the reactor is modeled as a continuous-flow perfusion with cell
recycle. Since product is in the media steady operation can be characterized by the mean
residence time of the liquid, however the amount of biomass present is determined from cell
recycle.
2
1
XR
F
R
3
Sep
XR
F
X=0
XR, V
XR
F+R
Figure A1. Bioreactor flow streams and mass-balance envelops. 1. Cell separator and recycle, 2.
bioreactor and cell separator, 3. bioreactor only.
Symbols:
F = Feed flow rate (L/h)
R = Recycle flow rate (L/h)
V = Volume of reactor (L)
XR = Cell density in the reactor (gDW/L)
Rfuel = specific productivity of the fuel (g-fuel/gDW-h)
= Ratio of cell density of recycle stream to reactor outlet stream
109
= Ratio of cell density of the separator outlet stream to reactor outlet stream
The cell recycle efficiency is defined as,
=
(
)
=
(
(A1)
)
From a biomass balance around the separator (1), the recycle ratio is,
=
(A2)
From the overall biomass balance (2) at steady state,
=
−
where
=
=
(A3)
is the growth rate of the bacteria in the reactor.
=
(A4)
Thus, in a perfusion type system such as this, the actual growth rate of the organism is
decreased as the cell recycle efficiency increases. Lower growth rates decreases the metabolic
demand for growth and therefore increases the impact of maintenance coefficient.
Mass balance of the dissolved H2 in the liquid phase of the reactor (3),
=
[
−( + )
/
where
/
+
]+
−
−
is the true growth yield of biomass on H2,
coefficient of biomass on H2,
=
(
)
(A5)
is the maintenance
is the mass transfer coefficient of H2.
,
and
are
110
the liquid phase concentration, gas phase partial pressure and Henry’s law coefficient of H2
respectively.
In a simplified form assuming
[
/
−( + )
+
= 0 (limiting reactant),
]+
=
(A6)
Similar equations can be written for O2 and CO2. These equations combined with the
growth, cell maintenance and fuel synthesis equations discussed in the main body of the text for
all three gases and biomass can be solved to get the stream values at a given gas composition. The
optimization routine in Aspen can solve this set of equations within the gas composition range of
each gas and the constraints of kLa to find the stoichiometric mix.
Volumetric fuel productivity is defined as,
=
(A7)
Fuel yield on H2 is defined as
/
=
(A8)
111
Appendix B
A comparison of experimental measurements and thermodynamic
predictions of true yield (
)
A modified form of the Thermodynamic Electron Equivalents Model (TEEM), developed
and presented by McCarty (McCarty 2007; McCarty 1971; Rittman & McCarty 2001), was used
as the basic framework for predicting the true yields attainable for cell growth and botryococcene
product fuel (BPF) production by R. capsulatus and R. eutropha as a function of process
conditions (Tuerk, 2011). Abbreviated here as the modified Electron Balance (EB) method, this
approach incorporates knowledge about the carbon fixation and energy-capturing processes of
Rba. capsulatus and R. eutropha into the previously formulated TEEM model. By accounting for
these differences, we attempted to improve the estimate of the metabolic efficiency factor (ε) for
each organism, and thus improving the estimates of true yield on hydrogen (
) for each
organism, with the ultimate aim of improving projections of actual cell growth and botryococcene
product fuel (BPF) yield across a range of process conditions.
Extensive details can be found in (A. L. Tuerk 2011); here we outline a simplified
version of the calculations producing
values. This includes a summary of the original
formulation of TEEM (McCarty 1971; McCarty 2007) and the subsequent modifications
implemented in the modified EB method (Tuerk, 2011). Finally, the values of the cellular
efficiency model parameter ε that most closely matched experimentally determined yield and
maintenance values are highlighted.
112
Calculation of true yield by TEEM
For simplicity and demonstration purposes, we are here assuming an empirical biomass
formula of C5H7O2N for both R. capsulatus and R. eutropha. Gibbs free energy values for half
reactions on a per-electron equivalent (eeq) basis were obtained from (Rittman & McCarty 2001).
First, the electron donor half reaction (red) is constructed:
:
+
→
,
∆
=
.
/
(B1)
along with the electron acceptor half reaction (rea):
:
+
+
→
,
∆
=−
.
/
(B2)
These are combined to form the overall energy-generating reaction at standard
biological conditions:
=
−
:
+
→
, ∆
=∆
−∆
=−
.
/
(B3)
Adjustments to the Gibbs free energy of this reaction should be made for
deviations of culture conditions (temperature, pH, activity of products/reactants) relative
to standard biological conditions; for simplicity here, we are only invoke energy values
under standard biological conditions.
The cell synthesis half reaction (rsynth) is constructed based on the empirical
biomass formula:
:
+
+
+
→
+
(B4)
113
Unlike the half reactions for the electron donor and the electron acceptor, the
Gibbs free energy associated with the cell synthesis half reaction is not explicitly known.
Under TEEM, it is assumed that the energy required for cell synthesis comes from the
energy necessary to reduce the carbon source to the level of a metabolic intermediate
(Acetyl-CoA), using electrons from the electron donor. This is predicted to be an
exergonic reaction at standard biological conditions):
=
−
∆
:
+
=∆
+
−∆
=
→
. −
+
.
+
=− .
(B5)
/
Additionally, ATP generation is another substantial component of the energy
demands for cell synthesis. This is envisioned as a second step in the cell synthesis
process, where ATP is used to synthesize cellular components from the intermediate
Acetyl-CoA:
∆
=
The value of
/
×
=
/
from Rittman and McCarty, 2001,
the empirical biomass formula, and
.
×
=
.
/
(B6)
(cost of ATP synthesis per eeq cell) was obtained
is the formula weight of biomass according to
is the number of electron eeq required to produce
one mol of cell from CO2 (obtained from the cell synthesis half reaction).
The overall Gibbs free energy of cell synthesis (∆
) is assumed to be the
sum of the energy required for these two steps (using values adjusted for non-standard
114
conditions); to account for the irreversibility of cellular metabolism, the energetic of the
individual steps must be adjusted for cellular inefficiency (ε). Thus, exergonic reactions
are penalized so that less energy is available to the cell than thermodynamics predict, and
endergonic reactions are penalized so that more energy is required:
∆
= ∆
+
∆
∆
<
(B7)
Finally, an electron balance on the electrons from the electron donor substrate
partitions the fraction of electrons from the ED into those required to be used for energy
generation (
), and those able to be used for cell synthesis (
). These fractions can be
calculated from the relative energetic of cell synthesis and energy generation as follows:
=
(B8)
=
(B9)
=−
∆
(B10)
∆
The values of
and
are employed to construct the overall stoichiometric
equation for growth:
=
+
(B11)
From the overall stoichiometric equation for growth, true yield can be calculated
from the stoichiometric coefficients ( ) for electron donor and cell biomass. For this
particular case,
115
=
=
×
(B12)
Modifications applied to the original TEEM to incorporate autotrophic growth,
reversed electron transport, and BPF synthesis for R. eutropha and R. capsulatus
A number of modifications were made to the original formulation of TEEM to account
for chemolithoautotrophic metabolism and botryococcene hydrocarbon synthesis, as TEEM was
primarily was developed for heterotrophic organisms. Accounting for autotrophic growth and
reversed electron transport was accomplished by altering the method for calculating the Gibbs
free energy of cellular synthesis (∆
), although the overall stoichiometry of
is
unchanged. The modifications are summarized in Figure S2.1 below, with equations S2.13 and
S2.14 providing the details of this altered computation; these can be compared to their equivalent
in the original method, equation S7.
116
Figure B1: A step-by-step comparison of the
calculation by the original TEEM method,
and the modified version developed specifically for Electrofuels process calculations. (Figure
reproduced from Tuerk, 2011).
for R. eutropha (without Reversed Electron Transport):
=
Step:
e- from H2 to NADH
+
+
e- from
NADH to
acetyl-coA
(B13)
+
e- from
acetyl-coA
cell and BPF
utilization of
ATP for cell
and BPF
synth.
117
(B14)
for R. capsulatus (with Reversed Electron Transport):
=
Step:
+
e- from H2
to
ubiquinone
pool
The factor
+
e- from
ubiquinone
pool to
NADH
+
e- from
NADH to
acetyl-coA
+
e- from
acetyl-coA
cell and BPF
utilization of
ATP for cell
and BPF
synth.
is +1 for endergonic reactions, and -1 for exergonic reactions. The use of
NADH as an intermediate electron carrier of reducing energy for CO2 fixation is based in
physiology, as this is the true electron donor for carbon fixation via the Calvin-Benson-Bassham
pathway. In reversed electron transport (RET), NAD+ is not reduced directly by the electron
donor, as it is for R. eutropha (no RET). Instead, electrons from the ED must first reduce
ubiquinone (E0’=+113 mV (Thauer et al. 1977); ∆
= -10.9 kJ/eeq), and then NAD+ is reduced
by expending the proton motive force to force electrons from ubiquinone uphill (E0’ = -320 mV
(Thauer et al. 1977); ∆
= 30.9 kJ/eeq). In addition, the baseline value of
/
was increased to 5.36 kJ/g cell (28.5 kJ/eeq) for calculations in the Electrofuels scenario, based
on data provided in (Kelly 1990), to account for the observation that autotrophy is energetically
more demanding than heterotrophy.
To account for BPF production, two additional modifications were applied. First, the
selected ratio of BPF synthesis to cell synthesis was used to adjust the empirical cell biomass
formula. The effect of this was to increase the degree of reduction of the cell, requiring more
electrons from the electron donor to enable synthesis. Secondly, the additional energy required
for synthesizing BPF in addition to cellular components was accounted for by increasing the
118
value of
in accordance with the ratio of BPF to cell, based on an experimentally
/
determined value for the heat of combustion of botryococcene. For details, see Tuerk, 2011.
A summary of the Gibbs free energy values employed for calculating
are provided in
Table B1 below; for further details of the calculations, see (A. L. Tuerk 2011). For simplicity, the
values in this table are based on calculations at standard conditions, and have not been corrected
for predicted process conditions. For illustration, we used the empirical biomass formula
(C5H7O2N) reported above, without the production of BPF.
Table B1: Calculation steps using modified TEEM
∆ of electron transport steps
∆
=
∆
=
∆
∆
(with RET)
(no RET)
−∆
∆
−50.8
)
0.25
−∆
≡stoichiometric coefficient of CO2 in
biomass (cell + fuel) half reaction
∆
∆
R. eutropha
−∆
(∆
=
R. capsulatus
−8.97
41.8
0.25
(33.3 − 30.9) = 0.12
0.25 × 20
/
≈0
5.36
/
/
= 28.5
Comparison of original TEEM and Modified Electron Balance methods of predicting
Using the methods and values described above,
was calculated for a range of cellular
efficiency values at standard biological conditions. The results are shown in Tables B2 (TEEM)
and B3 (Modified Electron Balance) below. It should be noted that for simplifying these
119
calculations, a single baseline elemental composition of R. capsulatus was used for both R.
capsulatus and R. eutropha; the use of empirical biomass formulas specific for each species
yields slightly different values.
as a function of ε, calculated using original TEEM method
Table B2:
ε
0.20
0.25
0.30
0.35
0.37
0.40
0.45
0.50
0.55
0.60
0.65
0.70
∆
92.00
72.79
59.84
50.45
47.38
43.31
37.65
33.03
29.17
25.88
23.03
20.52
A
3.879
2.455
1.682
1.216
1.080
0.913
0.705
0.557
0.447
0.364
0.299
0.247
0.205
0.289
0.373
0.451
0.481
0.523
0.586
0.642
0.691
0.733
0.770
0.802
0.795
0.711
0.627
0.549
0.519
0.477
0.414
0.358
0.309
0.267
0.230
0.198
2.32
3.27
4.21
5.10
5.43
5.91
6.63
7.26
7.81
8.29
8.70
9.06
Table B3:
as a function of ε, calculated using the modified Electron Balance method
R. capsulatus
R. eutropha
ε
0.20
∆
341.9
A
14.42
0.065
0.935
0.73
ε
0.20
∆
141.3
A
5.96
0.144
0.856
1.62
0.25
269.0
9.07
0.099
0.901
1.12
0.25
112.2
3.79
0.209
0.791
2.36
0.30
219.5
6.17
0.139
0.861
1.58
0.30
92.7
2.61
0.277
0.723
3.13
0.35
183.4
4.42
0.185
0.815
2.09
0.35
78.6
1.89
0.345
0.655
3.90
0.37
171.5
3.91
0.204
0.796
2.30
0.37
74.0
1.69
0.372
0.628
4.21
0.40
155.7
3.28
0.233
0.767
2.64
0.40
68.0
1.43
0.411
0.589
4.65
0.45
133.6
2.50
0.285
0.715
3.22
0.45
59.6
1.12
0.473
0.527
5.34
0.50
115.4
1.95
0.339
0.661
3.83
0.50
52.8
0.89
0.529
0.471
5.98
0.55
100.1
1.53
0.395
0.605
4.46
0.55
47.1
0.72
0.581
0.419
6.56
0.60
86.9
1.22
0.450
0.550
5.09
0.60
42.3
0.59
0.627
0.373
7.09
0.65
75.3
0.98
0.506
0.494
5.72
0.65
38.2
0.50
0.669
0.331
7.56
0.70
65.0
0.78
0.561
0.439
6.34
0.70
34.6
0.42
0.706
0.294
7.98
At a ‘typical’ cellular efficiency of 0.37 (McCarty, 2007), the
values calculated by
the modified EB method for R. capsulatus and R. eutropha were 2.30 and 4.21 gDW/mol-H2
respectively, compared to the value of 5.43 gDW/mol-H2 by the original TEEM method.
120
Comparing these values to experimentally determined values (Table B4 below), it is clear that the
original TEEM method substantially overestimates true yield for R. capsulatus, which is expected
since the energetically demanding process of reversed electron transport is not explicitly
accounted for by the original TEEM method. Accounting for reversed electron transport via the
modified EB method predicts true yield values closer to experimentally determined values; an
efficiency factor of 0.40-0.45 would be a more appropriate parameter choice for R. capsulatus in
this model. For R. eutropha, the choice of ε=0.37 underestimates true yields using both methods
(compared to experimentally determined values in this work). This suggests that R. eutropha may
have a higher inherent cellular efficiency than the ‘typical’ value, on the order of 0.65.
Table B4: Comparison of the true yield and maintenance coefficient values from various
sources
R. capsulatus
R. eutropha
∙ℎ
2.86
-
2.66
0.002
2.30
-
5.43
-
References
(Siefert & Pfennig
1979)
Experimental (this
work)
Calculated by
modified EB
(ε=0.37)
Calculated by
original TEEM
(ε=0.37)
∙ℎ
4.1
4.5
6.4
7.68
0.001
0.025
0.007
0.0068
4.21
-
5.43
-
References
(Siegel & Ollis 1984)
(Bongers 1970)
Experimental (this
work)
Calculated by
modified EB (ε=0.37)
Calculated by
original TEEM
(ε=0.37)
The power of this type of modeling is the ability to use experimentally determined values
to project outcomes for future research directions - namely the ability to predict BPF yields based
on what is known about cell growth and behavior in the absence of fuel production.
121
Appendix C
Measurement of growth yield and maintenance coefficient
Growth and maintenance processes of an organism are the major sinks for the energy
substrate, H2 in this case. Since the cost of H2 is also the most significant fraction of the fuel cost
from the electrofuels process, it was hypothesized that the organism with higher growth yield and
lower maintenance coefficient will ultimately result in lower fuel cost. For these reasons, the
growth yield and maintenance coefficient of R. capsulatus and R. eutropha were determined
under similar autotrophic conditions to provide an unbiased comparison between the two hosts.
The process model described in Chapter 2 of this thesis utilizes these parameters to predict the
relative effect of the two parameters on the fuel cost. In this appendix, I describe the general
background and procedure adopted for their determination.
Growth yield and maintenance coefficient
For a microbe, growth yield is defined as the energy needed for biomass generation and
cell division only and not for the processes related to general maintenance of the cell such as
homeostasis, motility, futile cycling etc. (Pirt 1965). The growth yield and maintenance
coefficient of R. capsulatus on H2, are not reported in the literature. In fact, values of these
parameters are generally very scarce for any organism under autotrophic conditions. Only those
of R. eutropha can be found on multiple instances in the literature. Siegel & Ollis (1984) noted
that the maintenance coefficient of R eutropha is a strong function of dissolved O2 concentration.
As observed by other researchers (Howells 1982), O2 can be both inhibitory and limiting for the
growth of a bacteria at different concentrations and is strongly strain dependent. Under
continuous culture conditions, the steady state in between inhibitory and limiting concentrations
122
is unstable and tends to move either to washout at higher concentration or towards O2 limitation
(Siegel & Ollis 1984). In general, high O2 concentration is problematic for most chemoautotrophs
because of the deactivation (permanent or temporary) of the hydrogenase enzymes (this is
described in more detail in Section 3.IV.II). Therefore, lower O2 concentration is expected to
improve the values of these parameters. O2 concentration is a very important parameter from not
only this perspective, but also from a safety standpoint, because a mixture of H2 and O2 is
generally explosive at an O2 concentration greater than about 6.9%. These factors ultimately
largely dictate a low (and/or limiting) O2 concentration in the reactor. I have thus used O2-limited
conditions while measuring the growth yield and maintenance coefficients.
Theory
In principle, maintenance coefficient and growth yield can be calculated if the yield on
the substrate is known as a function of the growth rate. (Pirt 1965) derived the following equation
as the inter-relationship among specific growth rate ( ) and yield (
maintenance coefficient (
=
and
+
) and true yield (
) and
).
(6)
are both experimentally measurable parameters. Therefore plotting
a straight line can be obtained whose slope and intercept would be equal to the
against
and
respectively. This has been traditionally used in the determination of true yield and maintenance
coefficient. However, in a batch system the growth rate varies continuously, therefore an accurate
measure of instantaneous growth rate and yield are not possible. At the same time, the culture
goes through physiological changes as it progresses through the different growth stages and
therefore the yields obtained this way may not refer to the same condition. So, true yield and
123
maintenance are measured in continuous systems, in which the growth rate of an organism can be
fixed at a particular value by fixing the dilution rate (inverse of residence time in a reactor).
Cell balance on a continuous reactor:
=
where
−
(7)
= volume of reator,
= growth rate,
= flow rate,
and
= inlet and outlet
cell concentration respectively. With 0 inlet cell concentration at steady state,
=
(8)
From the definitions of specific growth rate, μ =
=
and dilution rate,
= , we have μ
in a steady state continuous flow reactor.
Multiple steady states are established by varying the dilution rate and yield values are
measured at these steady states. The corresponding yield and dilution rates are plotted on a 1/
vs. 1/ graph to get the true yield and maintenance coefficient. However, this method is very
time consuming and produces only a limited number of data points. Increasingly, these
parameters are being measured in deceleration-stat or acceleration-stat (D-stat or A-stat) (Barbosa
et al. 2005) in contrast to a chemostat. Instead of going from one dilution rate to the next in a step
and waiting for a steady state to be established, the dilution rate is varied smoothly from one to
the next. If the rate is slow enough for the cells to adapt physiologically, the culture goes through
a succession of pseudo steady-states and the yield and dilution rate within this slowly changing
dynamic period is used to calculate the maintenance and true yield. The D-stat method is
followed in my determination of the yield and maintenance coefficient.
124
Experimental procedure and results
The experimental setup and schematic of the autotrophic bioreactor is shown below
(Figure C1). H2, O2 and CO2 gases are mixed via three mass flow controllers (SLA 5850, Brooks
Instruments, CA;
http://www.brooksinstrument.com/downloads/Product%20Documentation/Thermal%20Mass%20
Flow%20Meters%20Controllers%20Digital%20Gas/Data%20Sheets/ds-tmf-sla5800-mfceng.pdf) and delivered to the reactor after sparging through a humidifying column. The outlet gas
passes through a drying column, then through a mass flow meter (M-50SCCM, Alicat Scientific;
http://www.alicat.com/products/mass-flow-meters-and-controllers/mass-flow-meters/) that
measures the flow rate and a process GC (8610, SRI Instrument;
http://www.srigc.com/2005catalog/cat38-39.htm) that measures the composition. The
composition data is collected once every hour. The reactor used in this work is a 1 L reactor
(Celligen, New Brunswick). The liquid flow rate into and out of the reactor is controlled via a
highly precise peristaltic pump (120 U, Watson-Marlow), whose rotation can be controlled to
within 0.01 rpm by a 0-5 V analog input. This is very critical in maintaining an accurate and
consistent flow rate and smoothly varying it for D-stat. The actual flow rate of the inlet media is
calculated by periodically recording the weight of the media bottle which sits on a balance and
fitting appropriate regression lines through the associated dynamics. The culture is recirculated
via a second peristaltic pump for online optical density measurement. Samples are taken
periodically from the outlet for offline OD and dry weight measurements. The media used for
growth is based on a previous work in the literature (M. T. Madigan & Gest 1979) where the
autotrophic growth of R. capsulatus was first demonstrated. A pH probe continuously measures
the pH of the culture. As discussed before, the culture is allowed to come to a steady state at a
particular dilution rate and then the dilution rate is decreased slowly. The deceleration rate is
125
based on previous literature work on R capsulatus (Hoekema, R. D. Douma, et al. 2006) and
experience with running the experiment multiple times with various difficulties over a period of
more than six months.
Figure C1: Experimental setup for the measurement of growth yield and maintenance coefficient.
Figure C2: Watson-Marlow 120 U three-channel precision peristaltic pump used for media
delivery to and removal from the reactor. A 15-pin D-sub connector was used to interface with
computer to change the flow rates and log the actual rotor speed.
126
Another critical aspect of the experiment is the measurement of gas flow rate and
composition, which is much more difficult to do accurately than liquid phase because of strong
temperature and pressure effects and leaks. Mass flow meters, although capable of measuring
flow rate of a single gas very accurately, are not very accurate for a mixture of gases and at low
flow rates. A soap-bubble meter (1-10-100 ml meter, Bubble-O-Meter, Dublin, OH;
http://www.bubble-o-meter.com/bom.php?curPos=BOM1-10-100ml) was therefore placed at the
end of the line to calibrate the outlet flow based on manual measurements. This was checked
against the flow rate based on composition correction of the MFM and they compared very well.
Furthermore, a state-of-art GC was used to analyze these gases continuously in a single pass for
composition measurement.
Figures C2 through C4 shows representative data from an experiment for the
measurement of growth yield and maintenance coefficient of R. capsulatus. Similar methods were
used for R. eutropha as well (data not shown). Figure C3 is the total media consumption with
time at various stages of the experiment. The instantaneous flow rates are calculated by the first
derivative of the fitting equations. As can be seen, this parameter could be maintained very
accurately using the precision peristaltic pump. The deceleration rates used in these experiments
were based conservatively on a previous work of (Hoekema, R. D. Douma, et al. 2006) where
they measured the yield and maintenance of R. capsulatus in the photoheterotrophic growth
mode, producing H2. Plot of culture dry weight against time (Figure C4) shows that the expected
variation with media flow rate deceleration. The apparent yields calculated in two such D-stat
experiments for R. capsulatus is plotted in Figure C5, with µ/Y vs. µ. Inverse of the slope then
gives the growth yield and intercept the maintenance coefficient. The values obtained in these
experiments are shown in Table C1.
127
1900
Initial steady state
Media consumed (g)
1700
Deceleration
y = 6.9181x - 1068.2
Final steady state
1500
1300
y = -0.0487x2 + 41.799x - 7312.4
1100
y = 10.026x - 2131.5
900
700
500
300
320
340
360
380
Time (hr)
400
420
Figure C3. Media reservoir weight throughout the experiment. The first derivative of y gives the
instantaneous media flow rate, while the second derivative gives the deceleration rate. Media
flow rates varies from 10.03 g/hr at the initial steady state to 6.92 g/hr at the final steady state,
with a deceleration rate of -0.0974 g/hr2.
1.5
10.5
10
1.4
9.5
9
1.2
1.1
Biomass dry weight
8.5
Media flow rate
8
7.5
1
7
0.9
Flow rate (g/hr)
Dry weight (g/L)
1.3
6.5
0.8
6
300
320
340
360
Time (hr)
380
400
Figure C4. Change of culture density with media flow rate. At the initial steady state the culture
density remains around 1 g/L, increasing with decrease of flow rate to about 1.4 g/L during the
final steady state.
128
30
μ/Y x 1000 (mol/g.hr)
25
H2 (Expt. 1)
O2 (Expt. 1)
H2 (Expt. 2)
O2 (Expt. 2)
CO2 (Expt. 1)
CO2 (Expt. 2)
6
5
y = 372.99x + 2.6521
20
15
4
3
y = 377.81x + 1.3736
y = 43.457x + 0.4721
10
2
y = 42.378x + 0.1957
5
1
y = 76.632x + 0.9908
y = 95.132x + 0.5096
0
0
0
0.02
0.04
0.06
0.08
μ (1/hr)
Figure C5. Plot of µ/Y vs. µ for two D-stat experiments covering two different ranges of dilution
rates. Inverse of the slope gives the growth yield and the intercept gives the maintenance
coefficients.
Table C1: Growth yield and maintenance coefficients of R. capsulatus and R. eutropha.
Growth yield (gDW/mol-H2) Maintenance coefficient
(mol-H2/gDW.hr)
R. capsulatus
2.66
2.01x10-3
R. eutropha
7.68
6.8x10-3
These results indicate that in the non-growing conditions, R. eutropha requires more than
3x more enerfy for maintenance. In the ideal production system, growth would be minimized
since it represents loss of substrate to biomass rather than conversion to product. Therfore despite
the fact that biomass yield of R. eutropha is twice that of R. capsulatus it may not be the best
platform. It comes down to the efficiency with which the reducing power can be delivered to the
heterologously introduced pathways.
129
Appendix D
Materials and methods
Bacterial Strains and Growth Conditions
E. coli DH5α (Invitrogen) was used as the host for routine cloning experiments
and for E. coli triterpene engineering. E. coli DH5α was grown on LB agar or in LB broth at
37°C for cloning and routine maintenance. For triterpene engineering, E. coli DH5α was
transformed with the plasmid constructs using standard heat shock protocols (Schmidt et al.
1999). R. capsulatus SB1003 (ATCC BAA-309) was used as a second prokaryotic host for
triterpene engineering experiments. For heterotrophic growth and maintenance, R. capsulatus
was grown in YCC liquid medium (5 g/L yeast extract, 6 g/L casamino acids) and on YCC agar
plates at 34oC. When phosphate buffered saline was required, phosphates were added to a final
concentration of 6.60 mM K2HPO4, 3.30 mM KH2PO4 just before inoculation. Glucose
supplementation was accomplished by, adding a sterilized 1M glucose solution to a final
concentration of 20-100 mM just before inoculation. The bacterial cell lines are described in
Table D1. When appropriate, E. coli strains were grown with 50 µg mL-1 kanamycin and 100 µg
mL-1 ampicillin. R. capsulatus strains were grown in the presence of 20 µg mL-1 kanamycin and
10 µg mL-1 rifampicin.
For heterotrophic production of triterpenes, both E. coli and R. capsulatus were picked as
a single colony from a plate and grown in 1 mL of LB or YCC respectively and were incubated
for 20 hours at 37°C or 35°C. This allowed for normalization of growth between biological
replicates the same growth-stage before performing the time course and terminal endpoint
fermentations. Terminal endpoints for heterotrophic and photoheterotrophic samples were taken
after 48 hours for five replicate cultures.
130
Table D1. Strains and plasmids used in this study.
Strain
Characteristics and Relevance
References
E. coli DH5α
E. coli S17-1
Host for routine cloning and operon expression
Host for mobilization of broad host range plasmids
into R. capsulatus; recA pro hsdR RP4-2-Tc::MuKm::Tn7
Rhodobacter expression host for triterpene
production.
Characteristics
Plasmid cloning vector; pMB1 replicon, lacZ, ApR
Plasmid cloning vector for TA-cloning of PCR
products; pMB1 replicon, lacZ, ApR
Broad Host Range E. coli expression vector; pBBR
replicon, KmR, oriT, PLac promoter
SS from Botryococcus braunii race B with the Cterminal membrane spanning domain deleted; pBBR
replicon, KmR, oriT, PLac promoter
SS and fps from chicken expressed as a single operon
under PLac promotion; pBBR replicon, KmR, oriT, PLac
promoter
SS, dxs, idi, and fps expressed as a single operon under
PLac promotion; pBBR replicon, KmR, oriT, PLac
promoter
A gene fusion of SSL-1 and SSL-3 botryococcene
synthase enzymes from Botryococcus braunii race B
under PLac promotion; pBBR replicon, KmR, oriT, PLac
promoter
BS gene fusion and fps expressed as a single operon
under PLac promotion; pBBR replicon, KmR, oriT, PLac
promoter
BS gene fusion and dxs, idi, and fps expressed as a
single operon under PLac promotion; pBBR replicon,
KmR, oriT, PLac promoter
Invitrogen™
R. capsulatus SB1003
Plasmid
pBlueScript II SK(+)
pGEM-T Easy Vector
pBBR1MCS-2
pBBR:PLac:SS
pBBR:PLac:SS+fps
pBBR:PLac:
SS+dxs+idi+fps
pBBR:PLac:BS
pBBR:PLac:BS+fps
pBBR:PLac:BS+dxs+
idi+fps
References
Agilent
Promega
This study
This study
This study
This study
This study
This study
Small Scale Phototrophic and Autotrophic Growth Conditions for R. capsulatus
Strains
To induce photoheterotrophic growth, R. capsulatus SB1003 wildtype strain and
its derivatives were picked as single colonies directly from YCC plates conjugation plates
131
(supplemented with antibiotics when appropriate) and inoculated into 13 mm screw cap culture
tubes filled completely with RCVB medium (M. Madigan & Gest 1979). This
photoheterotrophic growth phase was found to be necessary to efficiently transition R. capsulatus
SB1003 from chemoheterotrophic growth to autotrophic growth. The cells were grown
anaerobically under light to saturation and inoculated to a starting OD660 of about 0.1 (Beckman
DU520 spectrophotometer) into CA media (M. Madigan & Gest 1979) with antibiotics (M.
Madigan & Gest 1979). After the lag period, 2 mL samples are removed every 24 hrs to measure
OD660 and analyze for product.
The input gases for autotrophic growth (H2, CO2, and O2) were mixed via three mass flow
controllers (SLA5850, Brooks Instruments, Hatfield, PA) to a set composition of 80:10:10. The
autotrophic growth setup consists of 125 mL baffled Erlenmeyer flasks, each with a gas inlet and
outlet, and a sampling port (5” 16G needle extending to the bottom of flask with 3 mL syringe).
The flasks are connected in series for gas delivery and are shaken at 200 rpm (G2 shaker, New
Brunswick). The entire setup is contained in an incubated chamber kept at 34°C.
Heterotrophic Bioreactor Growth Condition
Cells were grown in a 5 L bioreactor (BioFlo, New Brunswick, Edison, NJ) with a two
Rushton impellers in glucose fed-batch mode. The working liquid volume of the reactor was 4 L.
The pH of the culture was maintained at 7 by feeding 8 N NH4OH solution. The O2 fraction in the
gas feed and the agitation was increased manually initially to maintain dissolved oxygen (DO)
around 20% of air saturation until an RPM of 250 and dissolved O2 fraction of 15%. Temperature
was maintained at 34 oC. The reactor was inoculated from a 100 mL shake-flask culture grown
aerobic heterotrophically in RCVB(-M) media+80 mM glucose+25 µg/mL kanamycin to a
starting OD660 of around 0.1 in the reactor of the same media (M. Madigan & Gest 1979). 2-3 mL
132
sample was removed from the reactor at regular interval for the measurement of OD660 and
hydrocarbon content.
Autotrophic Bioreactor Growth Conditions
Cells were grown in a 1 L bioreactor (BioFlo, New Brunswick, Edison, NJ) with a celllift impeller in batch and continuous growth mode. The working liquid volume of the reactor was
900 mL. The inlet gas stream was mixed using three high-precision mass flow controllers
(SLA5850, Brooks Instruments, Hatfield, PA) and the outlet gas flow rate and compositions were
measured using a mass flow meter (M-50SCCM-D, Alicat Scientific, Tucson, AZ) and two gas
flow sensors for H2 (BCP-H2, BlueSens, Herten, Germany) and O2/CO2 respectively (BlueInOne,
BlueSens, Herten, Germany). The pH of the culture was maintained at 7 by feeding 25% NH4OH
solution. The RPM and temperature were maintained at 1000 and 34 oC. The inlet gas flow rate
and compositions were varied during batch growth but kept constant during continuous growth
mode (discussed in the results section). The reactor was inoculated from an autotrophically grown
flask culture (described above) to a starting OD660 of 0.1 in CA media with antibiotic (Imam et al.
2013). After about 15 hrs from inoculation, 2 mL sample was removed from the reactor at a
regular interval for the measurement of OD660 and hydrocarbon.
Cloning Procedures
Nucleotide sequences for the native and codon optimized versions of genes used in this
report are included in Table D2. The Rhodobacter codon optimized genes were synthesized by
Genscript. These genes were cloned into the pUC57 cloning vector and were the source of
template for PCR amplification. All gene combinations were amplified from pUC57 templates by
133
PCR according to standard procedures (Schmidt et al. 1999). Genes were amplified and spliced
by overlap extension (SOE) into polycistronic cassettes, which were then cloned into pGEM-Teasy vector for sequencing (Promega). The outermost primer set for amplifying the fully spliced
region was modified to have a unique 5’- BamHI restriction site and a 3’-XbaI restriction site.
The cassettes for SS, BS, SS-fps, BS-fps, SS-dxs-idi-fps, and BS-dxs-idi-fps were cut with unique
BamHI and XbaI restriction sites and spliced into the same sites of pBBR1MCS-2 under the
control of the PLac promoter, which is a strong constitutive promoter in R. capsulatus due to the
lack of the lacI repressor.
Table D2. Nucleotide sequences of the wildtype and R. capsulatus codon-optimized genes used.
Wildtype squalene synthase (SS) nucleotide sequence
ATGGGGATGCTTCGCTGGGGAGTGGAGTCTTTGCAGAATCCAGATGAATTAATCC
CGGTCTTGAGGATGATTTATGCTGATAAGTTTGGAAAGATCAAGCCAAAGGACGA
AGACCGGGGCTTCTGCTATGAAATTTTAAACCTTGTTTCAAGAAGTTTTGCAATCG
TCATCCAACAGCTCCCTGCACAGCTGAGGGACCCAGTCTGCATATTTTACCTTGTA
CTACGCGCCCTGGACACAGTCGAAGATGATATGAAAATTGCAGCAACCACCAAGA
TTCCCTTGCTGCGTGACTTTTATGAGAAAATTTCTGACAGGTCATTCCGCATGACG
GCCGGAGATCAAAAAGACTACATCAGGCTGTTGGATCAGTACCCCAAAGTGACAA
GCGTTTTCTTGAAATTGACCCCCCGTGAACAAGAGATAATTGCAGACATTACAAAG
CGGATGGGGAATGGAATGGCTGACTTCGTGCATAAGGGTGTTCCCGACACAGTGG
GGGACTACGACCTTTACTGCCACTATGTTGCTGGGGTGGTGGGTCTCGGGCTTTCC
CAGTTGTTCGTTGCGAGTGGACTACAGTCACCCTCTTTGACCCGCAGTGAAGACCT
TTCCAATCACATGGGCCTCTTCCTTCAGAAGACCAACATCATCCGCGACTACTTTG
AGGACATCAATGAGCTGCCTGCCCCCCGGATGTTCTGGCCCAGAGAGATCTGGGG
CAAGTATGCGAACAACCTCGCTGAGTTCAAAGACCCGGCCAACAAGGCGGCTGCA
ATGTGCTGCCTCAACGAGATGGTCACAGATGCATTGAGGCACGCGGTGTACTGCCT
GCAGTACATGTCCATGATTGAGGATCCGCAGATCTTCAACTTCTGTGCCATCCCTC
AGACCATGGCCTTCGGCACCCTGTCTTTGTGTTACAACAACTACACTATCTTCACA
GGGCCCAAAGCGGCTGTGAAGCTGCGTAGGGGCACCACTGCCAAGCTGATGTACA
CCTCTAACAATATGTTTGCGATGTACCGTCATTTCCTCAACTTCGCAGAGAAGCTG
GAAGTCAGATGCAACACCGAGACCAGCGAGGATCCCAGCGTGACCACCACTCTGG
AACACCTGCATAAGATCAAAGCTGCCTGCAAGGCTGGGCTGGCACGCACAAAAGA
TGACACCTTTGACGAATTGAGGAGCTAA
Wildtype botryococcene synthase fusion, SSL-1 and SSL-3, (BS) nucleotide sequence
ATGACTATGCACCAAGACCACGGAGTCATGAAAGACCTTGTCAAGCATCCAAATG
AATTTCCATACTTGCTCCAACTAGCTGCAACAACGTACGGCTCACCGGCTGCACCG
ATCCCCAAGGAACCGGACCGAGCTTTCTGCTACAATACTCTTCACACCGTTTCGAA
GGGGTTCCCCAGATTTGTTATGAGACTTCCGCAGGAACTCCAAGATCCGATATGCA
TATTCTACCTCCTGTTGCGAGCACTAGACACGGTGGAGGATGATATGAACCTCAAA
134
AGTGAGACGAAGATTTCACTCCTACGCGTTTTCCATGAACACTGTTCAGACAGGAA
CTGGAGTATGAAAAGTGATTATGGCATATATGCAGATCTGATGGAAAGATTCCCC
CTGGTCGTATCCGTCTTAGAGAAGCTCCCTCCCGCCACACAGCAGACTTTCAGGGA
GAATGTCAAATACATGGGCAATGGCATGGCAGATTTTATTGATAAGCAGATCCTG
ACAGTGGATGAGTACGACCTCTACTGCCACTATGTGGCCGGCAGTTGCGGCATTGC
TGTCACCAAGGTCATTGTGCAGTTCAACCTTGCCACGCCTGAAGCTGACTCCTACG
ACTTTTCCAACAGTCTGGGCCTCTTGCTTCAGAAGGCCAACATCATCACTGACTAC
AATGAAGACATCAATGAAGAGCCCAGGCCCAGGATGTTCTGGCCCCAGGAGATTT
GGGGGAAGTACGCGGAGAAGTTGGCTGACTTCAATGAACCCGAAAATATTGATAC
AGCCGTGAAGTGCTTGAACCACATGGTCACAGATGCAATGCGGCACATTGAGCCT
TCCCTCAAAGGCATGGTTTATTTCACAGACAAGACAGTCTTTCGGGCGCTCGCTCT
TCTGCTGGTCACAGCCTTTGGCCATTTGTCCACTTTGTACAACAACCCCAATGTCTT
TAAAGAGAAAGTGAGACAGCGGAAGGGAAGGATTGCACGGCTGGTCATGTCATCC
AGGAATGTACCAGGCCTCTTCCGTACATGCCTCAAACTCGCAAACAACTTCGAGTC
CAGGTGCAAGCAAGAGACGGCAAATGATCCCACTGTGGCCATGACTATCAAGCGC
TTGCAATCTATTCAAGCTACATGCAGAGATGGCCTGGCCAAGTATGACACACCCTC
TGGGCTGAAATCTTTCTGCGCAGCCCCAACTCCCACCAAGGGTGGTTCTGGTGGTG
GTTCTGGTGGTGGTTCTGGTATGAAACTTCGGGAAGTCTTGCAGCACCCGGGTGAG
ATTATCCCTCTCCTGCAAATGATGGTCATGGCCTACCGCAGGAAGAGGAAGCCTCA
AGATCCCAATTTGGCCTGGTGCTGGGAGACGCTGATTAAAGTTTCGAGAAGTTACG
TTCTAGTCATTCAGCAGCTTCCTGAAGTACTTCAGGACCCTATCTGCGTCAACTATC
TTGTTCTTCGAGGCTTGGACACACTGCAGGATGACATGGCAATTCCCGCAGAGAA
GCGGGTTCCACTCCTCCTCGACTACTACAACCATATTGGAGACATAACTTGGAAGC
CGCCTTGCGGATATGGGCAGTATGTGGAGCTGATTGAGGAGTATCCAAGGGTGAC
AAAAGAGTTCTTGAAACTCAACAAGCAAGATCAGCAGTTTATCACGGACATGTGC
ATGCGGCTGGGAGCGGAGATGACAGTATTTCTCAAGAGGGACGTGTTGACAGTTC
CTGACTTGGATCTGTATGCCTTCACTAATAACGGGCCAGTTGCTATCTGCCTGACC
AAGTTATGGGTGGACAGAAAGTTTGCAGACCCAAAGCTTCTGGACCGGGAGGACC
TATCGGGCCACATGGCCATGTTCTTGGGCAAGATTAACGTCATCCGCGACATCAAG
GAGGATGTCTTGGAGGATCCTCCTCGCATCTGGTGGCCGAAGGAGATCTGGGGAA
AGTACCTCAAGGACCTGAGGGACATCATCAAGCCTGAGTATCAAAAGGAAGCGCT
GGCCTGTCTCAATGACATCCTCACAGATGCACTGCGCCATATCGAGCCCTGCCTTC
AGTACATGGAGATGGTTTGGGACGAGGGCGTTTTTAAGTTCTGCGCCGTGCCAGA
GCTCATGTCCTTGGCTACCATCTCGGTGTGTTACAACAATCCGAAGGTCTTCACAG
GTGTTGTCAAGATGAGGAGGGGCGAAACAGCAAAGCTGTTTCTGAGCGTAACAAA
TATGCCAGCTCTGTACAAGAGTTTTTCAGCCATTGCTGAAGAAATGGAGGCCAAGT
GTGTGAGGGAGGATCCCAACTTTGCACTCACAGTCAAGCGGCTTCAGGATGTCCA
GGCGTTATGCAAGGCAGGCCTAGCAAAATCAAATGGAAAGGTTTCAGCTAAGGGT
GCTTAG
Rhodobacter codon optimized SS
GATGTCGACTAGGAGGAATATAAAATGGGCATGCTGCGGTGGGGCGTCGAGAGCC
TCCAGAACCCGGACGAGCTGATCCCCGTGCTGCGGATGATCTATGCGGACAAGTT
CGGCAAGATCAAGCCGAAGGACGAGGACCGCGGCTTCTGCTACGAGATCCTGAAC
CTCGTCTCGCGGAGCTTCGCGATCGTGATCCAGCAGCTGCCGGCCCAGCTCCGCGA
CCCCGTCTGCATCTTCTATCTGGTGCTCCGGGCGCTGGACACGGTCGAGGACGACA
TGAAGATCGCCGCGACCACGAAGATCCCGCTGCTCCGCGACTTCTACGAGAAGAT
CTCGGACCGCAGCTTCCGGATGACCGCCGGCGACCAGAAGGACTATATCCGCCTG
CTCGACCAGTACCCGAAGGTGACGTCGGTCTTCCTGAAGCTCACCCCCCGCGAGCA
GGAGATCATCGCGGACATCACGAAGCGGATGGGCAACGGCATGGCCGACTTCGTG
135
CACAAGGGCGTCCCGGACACCGTGGGCGACTACGACCTGTATTGCCATTACGTCG
CGGGCGTGGTCGGCCTGGGCCTCTCGCAGCTGTTCGTGGCCAGCGGCCTGCAGTCG
CCCAGCCTCACGCGCTCGGAGGACCTGAGCAACCACATGGGCCTGTTCCTCCAGA
AGACCAACATCATCCGGGACTATTTCGAGGACATCAACGAGCTGCCGGCGCCCCG
CATGTTCTGGCCGCGGGAGATCTGGGGCAAGTACGCGAACAACCTGGCCGAGTTC
AAGGACCCCGCCAACAAGGCCGCGGCCATGTGCTGCCTGAACGAGATGGTCACGG
ACGCGCTGCGCCATGCCGTGTATTGCCTCCAGTACATGTCGATGATCGAGGACCCG
CAGATCTTCAACTTCTGCGCGATCCCCCAGACGATGGCCTTCGGCACCCTGTCGCT
CTGCTATAACAACTACACGATCTTCACCGGCCCGAAGGCGGCCGTGAAGCTGCGT
CGCGGCACCACGGCGAAGCTCATGTATACCTCGAACAACATGTTCGCGATGTACC
GCCACTTCCTGAACTTCGCCGAGAAGCTCGAGGTCCGGTGCAACACGGAGACCTC
GGAGGACCCCAGCGTGACCACGACCCTGGAGCACCTCCATAAGATCAAGGCCGCG
TGCAAGGCGGGCCTGGCGCGGACCAAGGACGACACGTTCGACGAGCTGCGGAGCT
GAGATGTCGACT
Rhodobacter codon optimized BS
GATGTCGACTAGGAGGAATATAAAATGACCATGCACCAGGACCACGGCGTGATGA
AGGACCTCGTCAAGCACCCCAACGAGTTCCCGTATCTGCTCCAGCTCGCGGCCACC
ACCTATGGCAGCCCGGCCGCGCCGATCCCCAAGGAGCCCGACCGCGCCTTCTGCT
ACAACACCCTGCACACGGTGTCGAAGGGCTTCCCGCGCTTCGTCATGCGGCTGCCG
CAGGAGCTCCAGGACCCCATCTGCATCTTCTATCTGCTCCTGCGCGCGCTGGACAC
CGTGGAGGACGACATGAACCTCAAGAGCGAGACGAAGATCAGCCTCCTGCGCGTC
TTCCACGAGCATTGCTCGGACCGGAACTGGTCGATGAAGAGCGACTACGGCATCT
ATGCCGACCTGATGGAGCGCTTCCCCCTCGTGGTCTCGGTGCTGGAGAAGCTCCCG
CCCGCCACCCAGCAGACGTTCCGGGAGAACGTCAAGTACATGGGCAACGGCATGG
CGGACTTCATCGACAAGCAGATCCTGACCGTGGACGAGTACGACCTCTATTGCCAT
TACGTGGCCGGCAGCTGCGGCATCGCGGTCACCAAGGTGATCGTCCAGTTCAACCT
GGCCACGCCGGAGGCGGACTCGTATGACTTCTCGAACAGCCTCGGCCTCCTGCTCC
AGAAGGCCAACATCATCACGGACTACAACGAGGACATCAACGAGGAGCCGCGCC
CCCGGATGTTCTGGCCGCAGGAAATCTGGGGCAAGTATGCCGAGAAGCTGGCGGA
CTTCAACGAGCCCGAGAACATCGACACCGCCGTGAAGTGCCTGAACCACATGGTC
ACGGACGCGATGCGCCATATCGAGCCGTCGCTCAAGGGCATGGTGTATTTCACCG
ACAAGACGGTCTTCCGGGCCCTGGCGCTGCTCCTGGTGACCGCGTTCGGCCACCTG
TCGACGCTCTACAACAACCCGAACGTGTTCAAGGAGAAGGTCCGCCAGCGGAAGG
GCCGCATCGCCCGGCTGGTGATGTCGAGCCGCAACGTCCCCGGCCTCTTCCGGACC
TGCCTGAAGCTCGCGAACAACTTCGAGAGCCGCTGCAAGCAGGAGACGGCCAACG
ACCCGACCGTGGCGATGACGATCAAGCGCCTCCAGTCGATCCAGGCCACCTGCCG
GGACGGCCTGGCGAAGTATGACACGCCGTCGGGCCTCAAGAGCTTCTGCGCCGCG
CCGACCCCCACGAAGGGCGGCTCGGGCGGCGGCAGCGGCGGCGGCTCGGGCATG
AAGCTGCGCGAGGTGCTCCAGCACCCGGGCGAGATCATCCCCCTCCTGCAGATGA
TGGTCATGGCCTACCGCCGGAAGCGCAAGCCGCAGGACCCCAACCTGGCGTGGTG
CTGGGAGACCCTCATCAAGGTGTCGCGGAGCTATGTGCTGGTCATCCAGCAGCTGC
CGGAGGTCCTGCAGGACCCCATCTGCGTGAACTACCTGGTCCTCCGCGGCCTGGAC
ACCCTCCAGGACGACATGGCCATCCCGGCGGAGAAGCGGGTGCCCCTCCTGCTCG
ACTACTATAACCATATCGGCGACATCACGTGGAAGCCGCCCTGCGGCTATGGCCA
GTACGTGGAGCTCATCGAGGAGTATCCCCGCGTCACCAAGGAGTTCCTGAAGCTC
AACAAGCAGGACCAGCAGTTCATCACGGACATGTGCATGCGCCTGGGCGCGGAGA
TGACCGTGTTCCTGAAGCGGGACGTGCTCACGGTCCCGGACCTGGACCTCTACGCC
TTCACCAACAACGGCCCCGTGGCGATCTGCCTGACGAAGCTCTGGGTCGACCGCA
136
AGTTCGCCGACCCGAAGCTGCTCGACCGGGAGGACCTGTCGGGCCACATGGCGAT
GTTCCTCGGCAAGATCAACGTGATCCGCGACATCAAGGAGGACGTCCTCGAGGAC
CCGCCCCGGATCTGGTGGCCGAAAGAAATCTGGGGTAAGTACCTGAAGGACCTCC
GCGACATCATCAAGCCCGAGTACCAGAAGGAGGCCCTGGCGTGCCTCAACGACAT
CCTGACCGACGCCCTCCGCCATATCGAGCCGTGCCTGCAGTATATGGAGATGGTGT
GGGACGAGGGCGTCTTCAAGTTCTGCGCCGTGCCGGAGCTGATGAGCCTCGCGAC
CATCTCGGTGTGCTACAACAACCCCAAGGTCTTCACGGGCGTGGTCAAGATGCGCC
GGGGCGAGACCGCCAAGCTGTTCCTCTCGGTGACGAACATGCCGGCGCTGTACAA
GTCGTTCAGCGCCATCGCGGAGGAGATGGAGGCCAAGTGCGTGCGCGAGGACCCC
AACTTCGCGCTGACCGTGAAGCGGCTCCAGGACGTCCAGGCGCTGTGCAAGGCCG
GCCTCGCGAAGAGCAACGGCAAGGTGTCGGCCAAGGGCGCGTGAGCTGTCGACT
Rhodobacter codon optimized fps
GCTGTCGACTAGGAGGAATATAAAATGCAGCCGCACCACCACCATAAGGAGGGC
CGGATGCACAAGTTCACGGGCGTCAACGCCAAGTTCCAGCAGCCCGCGCTCCGCA
ACCTGTCGCCCGTGGTGGTGGAGCGCGAGCGGGAGGAGTTCGTCGGCTTCTTCCCG
CAGATCGTGCGCGACCTCACCGAGGACGGCATCGGCCACCCGGAGGTGGGCGACG
CCGTGGCCCGGCTGAAGGAAGTGCTCCAGTACAACGCCCCGGGCGGAAAGTGCAA
CCGCGGCCTGACCGTGGTGGCGGCCTATCGGGAGCTCTCGGGCCCGGGCCAGAAG
GACGCGGAGAGCCTGCGCTGCGCGCTCGCCGTCGGCTGGTGCATCGAGCTGTTCC
AGGCGTTCTTCCTCGTGGCCGACGACATCATGGACCAGTCGCTGACCCGTCGCGGC
CAGCTCTGCTGGTACAAGAAGGAAGGCGTCGGCCTGGACGCGATCAACGACAGCT
TCCTGCTCGAGTCGAGCGTCTACCGCGTGCTCAAGAAGTATTGCCGCCAGCGGCCG
TACTATGTGCACCTGCTCGAGCTGTTCCTCCAGACCGCGTATCAGACGGAGCTGGG
CCAGATGCTGGACCTCATCACGGCCCCGGTCTCGAAGGTGGACCTCTCGCATTTCA
GCGAGGAGCGCTACAAGGCGATCGTGAAGTATAAGACGGCCTTCTACTCGTTCTA
TCTGCCCGTGGCGGCCGCCATGTACATGGTGGGCATCGACAGCAAGGAAGAGCAC
GAGAACGCGAAGGCCATCCTGCTCGAGATGGGCGAGTACTTCCAGATCCAGGACG
ACTATCTGGACTGCTTCGGCGACCCCGCCCTCACGGGCAAGGTCGGCACGGACAT
CCAGGACAACAAGTGCTCGTGGCTGGTCGTGCAGTGCCTGCAGCGCGTGACCCCC
GAGCAGCGGCAGCTGCTCGAGGACAACTACGGCCGCAAGGAGCCCGAGAAGGTC
GCGAAGGTGAAGGAGCTCTATGAGGCGGTGGGCATGCGGGCCGCCTTCCAGCAGT
ACGAGGAGTCGAGCTATCGTCGCCTGCAGGAGCTCATCGAGAAGCATTCGAACCG
GCTGCCCAAGGAGATCTTCCTCGGCCTCGCCCAGAAGATCTACAAGCGCCAGAAG
TGAGATGTCGACT
Intergeneric Mating Between E. coli S17-1 and R. capsulatus SB1003
E. coli S17-1 (ATCC 47055) was used as a host for intergeneric conjugation of
pBBR1MCS-2 derivatives into R. capsulatus SB1003 (Simon et al. 1983). In brief, E. coli S17-1
strains harboring the pBBR1MCS-2 derivative constructs was grown from a single colony in 1
137
mL of lytic broth (LB) (supplemented with 50 µg mL-1 kanamycin) to early log phase. In
parallel, R. capsulatus SB1003 was grown in 1 mL of YCC (supplemented with 10 µg mL-1
rifampicin) for 24 hours before the conjugal mating experiment. The cells were spun down and
washed twice with 1 mL of sterile LB, then the E. coli S17-1 donor and R. capsulatus recipient
cells were each resuspended in 100 µL of sterile LB and mixed. The entire mating mixture was
spot plated (10 µL) onto dried YCC agar plates and allowed to mate for 8-12 hours. Matings
were scraped with a toothpick, resuspended in 200 µL of sterile LB, and serially diluted onto
YCC plates (supplemented with 10 µg mL-1 rifampicin and 20 µg mL-1 kanamycin) and incubated
for 48 to 72 hours before exconjugants appeared. Exconjugants were picked with a sterile
toothpick and inoculated in 1 mL of YCC supplemented with antibiotics for 20 hours so that all
the strains were in early to middle stationary phase. These cultures were used to inoculate the
shake flask and culture tube experiments.
Hydrocarbon Analysis
Bacterial culture and acetone were mixed in 1:1 volumetric ratio, either in a 4 mL or 2
mL HPLC vial and vortexed for 30 seconds to lyse the cells and to extract the triterpenes. 0.5 mL
isooctane was then added to the mixture and vortexed for another 30 seconds to extract the
triterpenes. The culture-acetone-isooctane mixture is then centrifuged for 5 minutes at >3000g.
Cell debris pellets at the bottom and liquid phases separate into acetone-water (bottom) and
acetone-iso-octane (top) layers. 100 to 300 µL of the top layer is collected into glass inserts for
injection (1 µl) into GC-FID for analysis. The injection port is equipped with an inlet liner having
silica wool (deactivated) to trap the non-volatile components.
138
GC-FID analysis was conducted on an Agilent 7820A GC. Squalene standard was
analyzed in concentrations ranging from 1 ng to 100 ng to generate standard curves.
139
Appendix E
Background calculations for Figure 4-5.
The calculations below provide the basis for carbon and energy balance depicted in
Figure 3. The three different trophic modes described in this work encompass very distinct carbon
and energy sources. Aerobic heterotrophic uses glucose as both energy and carbon source to
generate biomass and release CO2. Anaerobic photoheterotrophic growth uses light as the energy
source to generate ATP which is used to assimilate the more oxidized carbon source malate into
biomass. There is a small net release of CO2. Chemoautotrophic growth uses H2 as the energy
source to fix CO2 and therefore causes a net uptake of CO2.
To place the source of energy on a common basis with assimilation, the energy utilized
per ATP is calculated, then a conversion factor of 10.5 mol ATP consumed per gram-dry-weight
and a biomass composition of 50% by weight carbon yields 2.28 mol-ATP/mol-C assimilated
into biomass.
The energy sink is evaluated as simple heat of reaction of the carbon source to pyruvate,
which is the most immediate common metabolite of central metabolism.
Heats of combustion are obtained from (Domalski 1972).
IV. Aerobic heterotrophic growth on glucose
Source – the energy content of the energy source per mol ATP
The oxidative phosphorylation of 1 mole of glucose via the electron transport chain
generates 30 to 38 moles of ATP.
The heat of combustion of glucose = 2800.35 kJ/mol (Domalski 1972).
140
Therefore, energy required for 1 mol of ATP = 73.68 to 93.34 kJ/mol-ATP (Average =
83.51 kJ/mol-ATP)
Using a value of 10.5 mol-ATP/gDW (A. Tuerk 2011) (=2.28 mol-ATP/mol-C-fixed) we
have 190.4 kJ/mol-C-fixed for aerobic heterotrophic growth on glucose.
Sink – energy released/consumed per mole of C fixed in pyruvate
→2
+2
∆Hr = 2*(-587.02) – (-1271) = 96.96 kJ/mol-Glucose = 16.16 kJ/mol-C-pyruvate
V. Anaerobic photoheterotrophic growth on malate
Source – the energy content of the energy source per mol ATP
2 photons yield 1 mol of ATP (Berg et al. 2002)
Energy of 1 mol of 800 nm photon (hc/λ) =
.
×
. × .
×
= 149.8 kJ/mol-
photon.
Energy required for 1 mol ATP = 299.6 kJ/mol-ATP
Using a value of 10.5 mol-ATP/gDW(A. Tuerk 2011) (=2.28 mol-ATP/mol-C-fixed) we
have 683.1 kJ/mol-C-fixed for anaerobic photoheterotrophic growth on malate.
Sink – energy released/consumed per mole of C fixed in pyruvate
→
+
∆Hr = (-587.02 – 393.51) – (-1103.66) = 123.13 kJ/mol-pyruvate = 41.04 kJ/mol-Cpyruvate.
141
VI. Autotrophic growth on H2
Source – the energy content of the energy source per mol ATP
Hydrogenases can generate 1 NADH/NADPH molecule upon oxidation of 1 H2 (Lubitz
et al. 2014). The number of ATPs generated by NADH and NADPH range from 2.5 to 3.5
(Lodish et al. 2000; Van Walraven et al. 1996). For simplicity we are assuming it roughly equal
to 3 i.e. 3ATP/H2.
Heat of combustion of H2 = 286 kJ/mol-H2
Energy consumed per ATP = 95.33 kJ/mol-ATP
Using a value of 10.5 mol-ATP/gDW(A. Tuerk 2011) (=2.28 mol-ATP/mol-biomass-C)
we have 217.4 kJ/mol-C-fixed for autotrophic growth on H2.
Sink – energy released/consumed per mole of C fixed in pyruvate
3
+2
→
+
3
2
∆Hr = (-587.02 – 0) – (-393.51*3) = 593.5 kJ/mol-Pyr = 197.8 kJ/mol-C
VII. Carbon balance
Aerobic heterotrophic growth on glucose
The carbon yield on glucose (g-C-biomass/g-C-glucose) varies from 0.4 to 0.6 (McCarty
2007). Assuming an average of 0.5 g-C-biomass/g-C-glucose, the rest must leave as CO2.
Therefore, C-CO2-released/C-consumed = 0.5 or 50%.
142
Anaerobic photoheterotrophic growth on malate
Under photoheterotrophic growth we have measured the yield of biomass on malate to be
0.84±0.045 gC-biomass/gC-malate. Therefore, C-CO2-released/C-consumed = 0.26 or 26%.
Autotrophic growth on H2
Since there is no other sink for CO2 consumed than end up in biomass, essentially 100%
of the CO2 consumed is captured into biomass. That is, C-CO2-released/C-consumed = -100%.
143
Appendix F
Other miscellaneous work
This section includes works that had been started but discontinued due change of
priorities and lack/limitation of resources.
VIII. Small scale multiplexed autotrophic screening device.
It was desirable to have the ability to grow many small autotrophic cultures at once to
allow for screening of multiple genetic constructs. A volume of one mL was sufficient for
chemical hydrocarbon screening, therefore the design was implemented in test tubes with a
working culture volume of less than 5 mL. We developed a robust optical density monitoring
system based on an LED and a photodiode that was placed in a solid rack in which the test-tubes
sat (Figure F1A). CO2, H2 and O2 gasses were precisely mixed using a bank of mass flow
controllers, and this gas was bubbled into the test tubes through a stainless steel tubing. To
minimize problems of daisy-chaining of gas delivery, a means of manifolding gas delivery was
devised based on pressure drop through precise ruby orifices (RB#22104, Bird Precision,
Waltham, MA). Since the pressure drop in the orifices was larger than in the device, the source
pressure (and flow through each orifice) could be made reasonably accurate at a minimum cost
compared other means of multiplexed gas delivery. To improve the accuracy of OD
measurements, the gas delivery could be stopped briefly with a solenoid just prior to making
measurements of the LED-photodiode voltage. All of this was interfaced through a multi-channel
analog input card with monitoring and control provided by custom programming in LabView
software. A multiplexed growth assessment is shown in Figure F1B illustrating the ability to
144
monitor the kinetics of growth online for growth under autotrophic conditions. Ryan Johnson,
Reed Taylor and Bill Muzika helped extensively with this effort.
Figure F1. (Top) A multiplexed autotrophic growth device design for continuous monitoring of
growth using LED/phododiode assemblies at the base of the culture. Gas inlet to multiplexed
cultures is achieved with a very small sapphire orifice manifold. (Bottom) Examples of online
optical density collected from the screening device while growing different strains of R.
capsulatus.
145
IX. 100-L plastic bag trickle-bed reactor.
To demonstrate the low capital and operating cost at a higher scale, we constructed a 100L capacity plastic trickle-bed reactor. Our literature review on large-scale reactors showed that
trickle-bed reactors have the lowest power-per-volume requirements for gas-liquid mass transfer
coefficient (kLa) and can achieve substantially high kLa at large scales (Figure 2-5). Combined
with the low capital and operating cost, scalability and low explosion hazard, plastic trickle-beds
appear to be ideal for such a process. Dr. Wayne Curtis already has a patent for such a reactor
system (T. Y. Hsiao et al. 1999; Curtis 2004). Therefore, in anticipation of much higher levels of
production with current genetic engineering efforts, we constructed a 100 L plastic bag tricklebed reactor (working volume of 10 L; Figure F2) for demonstration purposes. It is made almost
entirely of plastic materials, which not only reduces the explosion hazard, but is extremely low
cost by several orders of magnitude compared to traditional stirred tank bioreactors. It is built
within an explosion bay in Fenske laboratory and all the necessary safety features in place, in
order to prevent damages in case of explosion. We have been able to significantly improve the
kLa of this reactor by altering the packing, spray nozzles etc.
146
Figure F2. A 100-L low cost plastic bag trickle bed that has been assembled to explore regions of
high mass transfer efficacy operation for autotrophic growth.
X. Secretion of hydrocarbon by R. capsulatus
We also had the interesting observation that when R. capsulatus was grown to these
densities (> 2 gDW/L) we could detect accumulation of botryococcene in the aqueous phase of
the culture (Figure F3). This was observed in repeated experiments. It is, however, deemed highly
unlikely that a hydrophobic molecule such as botryococcene would simply be excreted by the
cells and remain in the aqueous phase. Our hypothesis in this case was that botryococcene is
somehow incorporated into the carotenoid-based pigment-protein complex of Rhodobacter and
released as particles in the aqueous phase. Part of the reason for this idea is that at relatively
higher density the culture was highly pigmented and the hydrocarbon extract (from the regular
extraction method used throughout the project) became increasingly pigmented with increasing
cell density. We could also found a previous report in the literature where the incorporation of a
non-native hydrocarbon species, β-carotene, was shown to be incorporated in the light harvesting
147
complex of R. sphaeroides. However, we did not pursue this observation because of other
priorities.
Figure F3. Hydrocarbon secretion in R. capsulatus autotrophic bioreactor cultures. Triterpene
levels in cells, media and total culture during late lag and stationary phase of two independent
batch cultures. Cultures were spun down and supernatant and pellet were separately analyzed.
Samples of the whole culture was also analyzed separately. The blue columns indicate the amount
of triterpene found in the supernatant of the cultures while the pink portion represents the
triterpene in the culture pellet. The red columns indicate the total triterpene. Error bars indicate
deviation of two replicates.
148
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Nymul Khan
Pennsylvania State University, 226 Fenske Lab • University Park, PA, 16802 • (336) 790 8954
[email protected]
Education
Bangladesh University of Engineering and Technology
Bachelor of Science in Chemical engineering
Dhaka, Bangladesh
2001 - 2006
Greensboro, NC, USA
2007-2010
North Carolina A&T State University
Masters of Science in Chemical engineering
University Park, PA, USA
2010-2015
The Pennsylvania State University
Ph.D. in Chemical Engineering
Research Publications / Presentations
Journal

Nybo, S. E., Khan, N. E., Woolston, B. M., & Curtis, W. R. (2015). Metabolic Engineering in
Chemolithoautotrophic Hosts for the Production of Fuels and Chemicals. Metabolic Engineering. (Invited
review; co-first author)

Khan, N., Nybo, S. E., Chappell, J., & Curtis, W. R. (2015). Triterpene Hydrocarbon Production
Engineered Into a Metabolically Versatile Host–Rhodobacter capsulatus. Biotechnology and bioengineering.
DOI: 10.1002/bit.25573.

Khan, N. E., Myers, J. A., Tuerk, A. L., & Curtis, W. R. (2014). A process economic assessment of
hydrocarbon biofuels production using chemoautotrophic organisms. Bioresource technology, 172, 201-211.

Adewuyi, Y. G., & Khan, N. E. (2012). Modeling the ultrasonic cavitation‐enhanced removal of nitrogen
oxide in a bubble column reactor. AIChE Journal,58(8), 2397-2411.

Khan, N. E., & Adewuyi, Y. G. (2011). A new method of analysis of peroxydisulfate using ion
chromatography and its application to the simultaneous determination of peroxydisulfate and other common
inorganic ions in a peroxydisulfate matrix. Journal of Chromatography A, 1218(3), 392-397.

Khan, N. E., & Adewuyi, Y. G. (2010). Absorption and oxidation of nitric oxide (NO) by aqueous solutions
of sodium persulfate in a bubble column reactor.Industrial & Engineering Chemistry Research, 49(18), 87498760.

Rahman, M. A., & Khan, N. E. (2010). Study of an Evaporation System for Sodium Hydroxide
Solution. Journal of Chemical Engineering, 24, 35-36.
Conference

Nymul Khan, Alex Rajangam, Eric Nybo, Joe Chappell, Wayne Curtis. Rhodobacter as a platform for
autotrophic biofuel production. AIChE annual meeting, Pittsburgh, PA, 2012.

Nymul Khan, Yousuf Adewuyi. Modeling of the sonochemical removal of nitric oxide using Raleigh Plesset
bubble dynamics equation, AIChE annual meeting, Nashville, TN, 2009.

Nymul Khan, Yousuf Adewuyi. Removal of nitric oxide using aqueous solution of sodium persulfate in a
bubble column reactor. AIChE Annual Meeting, Nashville, TN, 2009.