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Research
Cite this article: Harrison JP, Dobinson L,
Freeman K, McKenzie R, Wyllie D, Nixon SL,
Cockell CS. 2015 Aerobically respiring
prokaryotic strains exhibit a broader
temperature– pH– salinity space for cell
division than anaerobically respiring and
fermentative strains. J. R. Soc. Interface 12:
20150658.
http://dx.doi.org/10.1098/rsif.2015.0658
Received: 23 July 2015
Accepted: 17 August 2015
Subject Areas:
astrobiology, bioenergetics, environmental
science
Keywords:
cell division, fermentation, habitability,
limits for life, metabolism, respiration
Author for correspondence:
Jesse P. Harrison
e-mail: [email protected]
†
Present address: School of Earth, Atmospheric
and Environmental Sciences, University of
Manchester, Manchester, UK.
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rsif.2015.0658 or
via http://rsif.royalsocietypublishing.org.
Aerobically respiring prokaryotic strains
exhibit a broader temperature– pH –
salinity space for cell division than
anaerobically respiring and
fermentative strains
Jesse P. Harrison, Luke Dobinson, Kenneth Freeman, Ross McKenzie,
Dale Wyllie, Sophie L. Nixon† and Charles S. Cockell
UK Centre for Astrobiology, School of Physics and Astronomy, University of Edinburgh, James Clerk Maxwell
Building, King’s Buildings, Edinburgh EH9 3FD, UK
JPH, 0000-0002-4877-4684
Biological processes on the Earth operate within a parameter space that is
constrained by physical and chemical extremes. Aerobic respiration can
result in adenosine triphosphate yields up to over an order of magnitude
higher than those attained anaerobically and, under certain conditions, may
enable microbial multiplication over a broader range of extremes than other
modes of catabolism. We employed growth data published for 241 prokaryotic
strains to compare temperature, pH and salinity values for cell division
between aerobically and anaerobically metabolizing taxa. Isolates employing
oxygen as the terminal electron acceptor exhibited a considerably more extensive three-dimensional phase space for cell division (90% of the total volume)
than taxa using other inorganic substrates or organic compounds as the
electron acceptor (15% and 28% of the total volume, respectively), with all
groups differing in their growth characteristics. Understanding the mechanistic
basis of these differences will require integration of research into microbial
ecology, physiology and energetics, with a focus on global-scale processes.
Critical knowledge gaps include the combined impacts of diverse stress
parameters on Gibbs energy yields and rates of microbial activity, interactions between cellular energetics and adaptations to extremes, and relating
laboratory-based data to in situ limits for cell division.
1. Introduction
The biosphere can be defined as the physical and chemical parameter space
that sustains all biological processes on the Earth. Determining how different
stress parameters limit microbial multiplication within extreme habitats, and
the mechanisms by which life adapts to biologically hostile environments, is of
significance to several fields including ecology, food security, biotechnology
and astrobiology [1 –5]. Additionally to research into the in situ activities of
microbial communities under extreme conditions [6 –8], the isolation and
physiological characterization of microbial strains has been essential to our
knowledge of the physico-chemical limits for life, as it has enabled us to investigate the growth phenotypes of extremophilic taxa in detail. Based on cardinal
growth data (minimal, optimal and maximal conditions for cell division), several
efforts to characterize the global-scale boundary space for bacterial and archaeal
cell division have now been made, using two- and three-dimensional maps of
habitability [9–12].
Although mapping the boundaries of the biosphere has advanced our understanding of how diverse parameters (such as temperature, pH, salinity and
hydrostatic pressure) can limit cell division both individually and in combination,
& 2015 The Author(s) Published by the Royal Society. All rights reserved.
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Table 1. Proportions of fully observational, mean-based, median-based and forced cardinal growth values employed for data analysis (see §2.1 for details).
total no.
values
fully
observational
meanbased
medianbased
median-based
(forced)
aerobic respiration
954
84
2
13
1
anaerobic respiration (inorganic terminal
electron acceptors)
648
82
3
14
1
anaerobic respiration and fermentation
630
84
2
13
1
(organic terminal electron acceptors)
several of the mechanisms that control the habitability of extreme
environments are yet to be identified. Life within these environments is energetically demanding, and thermodynamic limits to
habitability have been proposed [13–23]. For example, the Gibbs
free energy (and, consequently, adenosine triphosphate (ATP))
yields generated during dissimilatory metabolism have been
suggested to determine the ability of several prokaryotic taxa
to multiply within high-salt environments [19]. Adaptation to
low-energy conditions has additionally been proposed as a
factor that distinguishes between the temperature and the pH
preferences of thermophilic bacteria and archaea [23,24].
Global-scale limits to cell division, however, have not been
determined for individual modes of catabolism, particularly
with reference to multiple-stress conditions that characterize
the majority of extreme environments on the Earth [12,25,26].
For instance, as aerobic respiration frequently results in high
cellular growth yields [27] and can enable ATP yields up to an
order of magnitude higher than those attained anaerobically
(with up to 36 ATP molecules for each oxidized glucose molecule), could this type of metabolism enable cell division over
a broader range of physical and chemical extremes than other
catabolic pathways?
In simple terms, energy-yielding reactions can be divided
into those relying on ion-gradient-driven phosphorylation
and those based on substrate-level phosphorylation [28].
Aerobic and anaerobic respiration use the former, whereas
both types of phosphorylation can be involved in fermentation
[29–32]. These categories (and anaerobic respiration in particular) encompass an incredible diversity of metabolic reactions
that widely differ in terms of their Gibbs energy yields
[33–35]. For example, nitrate reduction can result in energy
yields close to those obtained by aerobic respiration (with
redox potentials ranging up to þ0.75 V for reaction pairs involving nitrate and þ0.82 V for O2/H2O), while energy yields
from the reduction of organic compounds can be nearly an
order of magnitude lower [36]. Although the redox potentials
of certain anaerobic respiratory pathways are similar to those
involving oxygen, both ATP yields and cellular growth
yields are relatively low for the former, partially because of
differences in the efficiency of aerobic and anaerobic electron
transport systems [36]. Furthermore, energy yields from fermentation are frequently lower than those attained through
aerobic and anaerobic respiration [30–32,37].
Environmental conditions (including temperature, pH
and salinity, as well as substrate availability and geochemical
composition) can significantly influence the activity rates and
energy yields of microbially mediated reactions [34,37–40],
which hinders our ability to predict global-scale growth
limits for different modes of catabolism, especially when
multiple-stress interactions are considered. Even so, under conventional reference conditions (i.e. 258C, neutral pH, an ionic
strength of 0.1 M and atmospheric pressure; see [39]), both the
volumes of three-dimensional windows for cell division [12]
and group-specific tolerance ranges for a given stress parameter
can be expected to differ in the order aerobic respiration .
anaerobic respiration . fermentation. Here, we use growth
data published for 241 prokaryotic strains to systematically
compare the temperature, pH and salinity (NaCl) values for
cell division between catabolic groups (based on the terminal
electron acceptor used during growth range experiments).
While the groups are found to differ with reference to their
temperature–pH–salinity space for cell division, particularly
between aerobic and anaerobic strains, these patterns are
shown to be in limited agreement with simple predictions of
energy yields under conventional conditions. To facilitate investigation of the mechanisms underpinning our results, we highlight
key areas of research into the ecology, physiology and energetics
of extremophilic microorganisms, and the habitability of
extreme environments.
2. Experimental methods
2.1. Data retrieval and construction of
three-dimensional maps
Previously published cardinal growth data were collected for
241 prokaryotic extremophilic or extremotolerant strains (132
genera and two unclassified isolates), consisting of minimal,
optimal and maximal conditions supporting cell division
with reference to temperature (8C), pH and salinity (% w/v
of NaCl) (electronic supplementary material, table S1).
Depending on the experimental conditions used for determining cardinal values for cell division, each strain was allocated to
a catabolic group consisting of either taxa with O2 as the terminal electron acceptor (aerobic respiration), other inorganic
substrates (Fe(III), S0, S compounds or NO2
3 ) as terminal electron acceptors (anaerobic respiration using inorganic electron
acceptors) or organic compounds as the electron acceptor
(anaerobic respiration using organic electron acceptors and fermentation). A stricter classification of anaerobically respiring
versus fermentative strains was precluded because of challenges associated with the extraction of data from previously
published articles. Regardless, as Gibbs energy yields from
using organic compounds as terminal electron acceptors
during anaerobic respiration are often more comparable with
J. R. Soc. Interface 12: 20150658
catabolic group
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data type (%)
2
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(a)
3
20
10
0
0
temperature (°C)
NaCl (% w/v)
NaCl (% w/v)
30
120
30
20
10
0
0
tem 40
pera 80
ture 120 12
(°C
)
0
)
40
0 ture (°C
8
12 120 era
p
tem
8
pH
4
40
0
0
0
4
pH 8
10
20 w/v)
0
3
12 0
l (%
4
NaC
4
pH 8
10 )
20
/v
12 0 30 (% w
l
4
C
Na
4
pH 8
10 )
20
/v
12 0 30 (% w
l
4
C
Na
4
pH 8
10
20 w/v)
0
3
12 0
l (%
4
NaC
0
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4
8
pH
80
(b)
40
30
20
10
0
0
120
temperature (°C)
NaCl (% w/v)
NaCl (% w/v)
40
30
20
10
0
4
8
pH
0
tem 40
pera 80
ture 120 12
(°C
)
0
)
40
0 ture (°C
8
12 120 era
p
tem
8
pH
4
80
40
0
0
0
0
(c)
40
30
20
10
0
0
120
temperature (°C)
NaCl (% w/v)
NaCl (% w/v)
40
30
20
10
0
4
8
pH
0
tem 40
pera 80
ture 120 12
(°C
)
0
)
40
0 ture (°C
8
12 120 era
p
tem
8
pH
4
80
40
0
0
0
0
(d)
40
30
20
10
0
0
120
temperature (°C)
NaCl (% w/v)
NaCl (% w/v)
40
30
20
10
0
4
8
pH
0
)
40
0 ture (°C
8
12 120 era
p
tem
0
tem 40
pera 80
ture 120 12
(°C)
8
pH
4
0
80
40
0
0
0
Figure 1. Three-dimensional boundary spaces for the multiplication of aerobically respiring, anaerobically respiring and fermentative prokaryotic strains, determined
as a function of temperature (8C), pH and NaCl concentration (% w/v). (a) Total dataset; (b) strains with O2 as the terminal electron acceptor (aerobic respiration);
(c) strains with inorganic substrates other than O2 as the terminal electron acceptor (anaerobic respiration); and (d ) strains with organic compounds as the terminal
electron acceptor (anaerobic respiration and fermentation). Views of the boundary spaces are provided from three arbitrarily selected angles for each group. The
habitable spaces were estimated using cardinal growth data (see §2.1 for details). A total of 241 strains were used (electronic supplementary material, table S1),
with each polyhedron corresponding to an individual strain.
those obtained via fermentation than to other respiratory pathways, the approach employed is likely to provide a meaningful
distinction between the three groups examined in this work.
A lack of sufficient data prevented the inclusion of other
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40
40
types of anaerobic catabolism (e.g. manganese, arsenate and
selenite reduction and methanogenesis [19]) in our study.
Strains for which cardinal growth values were determined
for several electron acceptors were allocated to more than a
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NaCl (% w/v)
12
(b)
pH
8
4
0
0
(c)
40
30
20
10
40
80
120
temperature (°C)
0
0
40
80
120
temperature (°C)
4
40
30
20
10
0
4
8
12
Figure 2. Two-dimensional boundary spaces for the multiplication of aerobically respiring, anaerobically respiring and fermentative prokaryotic strains, determined
as functions of temperature (8C), pH and NaCl concentration (% w/v). Limits for cell division are shown for (a) temperature and pH, (b) temperature and salinity,
and (c) pH and salinity. The data are split into three catabolic groups (AR, aerobic respiration; ANR (inorg. e2), anaerobic respiration using inorganic terminal
electron acceptors; ANR þ F (org. e2), anaerobic respiration using organic terminal electron acceptors, and fermentation). Boundaries for overlapping groups
are indicated by outlines. The habitable spaces were estimated using cardinal growth data (see §2.1 for details). A total of 241 strains were used, with a full
list of strains available in the electronic supplementary material, table S1.
0.2
of a previously described method [12]. To enable plotting,
the mean of the strain-specific growth minimum and maximum was used in the absence of optimal growth data. If data
on minimal and/or maximal growth conditions were unavailable, the catabolic group-specific median was used. To prevent
the occurrence of artefacts in the three-dimensional maps, this
value was forced to meet the expected order of strain-specific
cardinal values for cell division (i.e. minimum optimum maximum) where necessary, using the nearest available
observed value. For example, if the median optimal value
was higher than the observed maximal value (or if the calculated minimum exceeded the observed optimum), the
median was adjusted so that the two values were equivalent.
With the mean-based, median-based and forced values taken
into account, the dataset consisted of 2232 growth values.
A summary of the various types of growth values employed
for data analysis is provided in table 1.
mode of catabolism
AR
ANR (inorg. e–)
ANR + F (org. e–)
CAP2
0.1
0
–0.1
–0.2
–0.2
–0.1
0
CAP1
0.1
0.2
Figure 3. Canonical analysis of principal coordinates (CAP) ordination of aerobically respiring, anaerobically respiring and fermentative prokaryotic strains.
The data are split into three catabolic groups (AR, aerobic respiration; ANR
(inorg. e2), anaerobic respiration using inorganic terminal electron acceptors;
ANR þ F (org. e2), anaerobic respiration using organic terminal electron
acceptors, and fermentation). The ordination was derived from a Euclidean
similarity matrix calculated from normalized cardinal growth data (n ¼ 29
for each grouping). Randomly selected strains were used for the ordination
(see §2.2 for details). (Online version in colour.)
single group where appropriate, resulting in a total of 248
data entries (n ¼ 106 for aerobic respiration; n ¼ 72 for
anaerobic respiration using inorganic terminal electron acceptors; n ¼ 70 for fermentation and anaerobic respiration using
organic terminal electron acceptors). A total of 1863 cardinal growth values were collected (n ¼ 705 for temperature;
n ¼ 681 for pH; n ¼ 477 for salinity). While culture-based
data are characterized by several biases, other types of
data are currently insufficient for estimating global-scale
boundaries for cell division, with in situ limits to microbial
activity only having been described for a small selection of
environments (see [12] and Discussion section).
Three-dimensional approximations of the window for cell
division on the Earth were constructed using a modification
2.2. Data analysis and statistics
Volumes of the three-dimensional maps corresponding to
specific catabolic groups (% of the total volume estimated for
all strains) were calculated by Monte Carlo integration, using
107 integration steps. Using randomly selected strains for
which data for all growth variables were available (n ¼ 29 for
each group), a Euclidean similarity matrix of normalized cardinal growth values was constructed using the PRIMER statistical
package (v. 6.1.13) with the PERMANOVAþ add-on (v. 1.0.3)
[41,42]. Randomly selected strains were used in order to
obtain a balanced design for statistical analysis. Following
unconstrained and constrained ordinations of these data
(non-metric multi-dimensional scaling and canonical analysis
of principal coordinates, respectively [43]), a one-way permutational analysis of variance (PERMANOVA) was performed
with ‘mode of catabolism’ as the factor (Type III sums of
squares, 9999 unrestricted permutations of the raw data) [44].
PERMANOVA is conceptually related to traditional analyses
of variance (ANOVA), and is based on a non-parametric and
distribution-free approach that enables reliable comparisons
of multivariate data between sample groups. Post hoc pairwise
comparisons were performed using the same PERMANOVA
settings. The SIMPER routine was used to calculate the relative
contributions (%) of each cardinal growth variable to differences between catabolic groups. Variation in within-group
J. R. Soc. Interface 12: 20150658
pH
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mode of catabolism
AR
ANR (inorg. e–)
ANR + F (org. e–)
NaCl (% w/v)
(a)
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Table 2. Pairwise comparisons (PERMANOVA) of temperature, pH and salinity values for cell division by aerobically respiring, anaerobically respiring and
fermentative prokaryotic strains (grouped by terminal electron acceptors).
t
p-value
unique permutations
aer. resp. versus anaer. resp. (inorganic e2 acceptors)
aer. resp. versus anaer. resp. and ferm. (organic e2 acceptors)
3.507
3.469
,0.001
,0.001
9947
9947
anaer. resp. (inorganic e2 acceptors) versus anaer. resp. and ferm. (organic e2 acceptors)
1.937
0.020
9956
3. Results
3.1. Three-dimensional volumes and limits of the
parameter space for cell division
To determine the impacts of temperature, pH and salinity on
the window for cell division between aerobically and anaerobically metabolizing prokaryotic isolates, cardinal growth data
were employed to construct three-dimensional approximations
of the habitable space on the Earth (Experimental methods
and electronic supplementary material, table S1). Aerobically
respiring strains occupied 90% of the total boundary space
(figure 1a,b). Taxa using inorganic electron acceptors other
than oxygen and those using organic electron acceptors occupied 15% and 28% of the boundary space, respectively, with
considerable overlap between catabolic groups (figures 1
and 2). Despite this overlap, strains from the three groups for
which data were available for all growth variables (Experimental methods) differed significantly with reference to their
temperature, pH and salinity preferences (one-way PERMANOVA: pseudo-F2,84 ¼ 10.132, p , 0.001, 9930 unique
permutations) (figures 2 and 3; electronic supplementary
material, figure S1). Each group occupied a unique position
within the total parameter space for cell division (figures 2
and 3; table 2), with temperature, pH and salinity exhibiting distinct contributions (%) to the observed differences (electronic
supplementary material, table S2). Multivariate dispersions
varied between groups (PERMDISP: F2,84 ¼ 11.145, p ,
0.001), with the aerobic respiration group exhibiting a higher
degree of dispersion than other modes of catabolism (figure 3;
electronic supplementary material, table S3). No differences in
multivariate dispersion were detected between the two anaerobic
groups (electronic supplementary material, table S3).
In support of these data, we next performed a comparison
of growth minima, optima and maxima between the three
catabolic groups. The mean temperature minimum for cell division by aerobically respiring strains (x ¼ 22 + 28C s.e.; n ¼
101) was approximately twofold lower than the mean temperature minima observed for other modes of catabolism (figure 4;
see electronic supplementary material, figure S2 for
medians). The lowest minimum temperature within the aerobic
respiration group (2158C) corresponded to the bacterium
Planococcus halocryophilus [47,48], constituting the lowest temperature within the entire dataset (figures 1 and 2; table 3). By
contrast, strains employing organic terminal electron acceptors
displayed the highest mean temperature maximum for cell
division (x ¼ 77 + 38C s.e.; n ¼ 67) (figure 4; also see electronic supplementary material, figure S2), although the
highest overall temperature (1218C) was attributed to strains
using inorganic electron acceptors other than oxygen (figures 1
and 2; table 3). While the most acidic and alkaline pH values
for cell division (0 and 14, respectively) were represented
by aerobically respiring isolates, the other two catabolic
groups also exhibited cell division under extremes of pH (minimum of 1 and maximum of 11.1; figures 1 and 2; table 3).
Moreover, the mean pH minima, optima and maxima for
cell division were relatively similar between all three catabolic groups, with the aerobic respiration group exhibiting
the highest degree of variability in comparison with other
groups (figure 4; electronic supplementary material, figure
S2). The highest mean salinity maximum for cell division
(x ¼ 17 + 1% s.e. w/v of NaCl; n ¼ 74) corresponded to aerobically respiring strains (figure 4). Although the highest NaCl
concentration within the dataset (36% w/v) also corresponded
to this group, this value was only 2% higher than the upper salinity limit for cell division by strains using organic terminal
electron acceptors (figures 1 and 2; table 3).
3.2. Temperature, pH and salinity ranges for cellular
multiplication
Strain-specific temperature ranges for cell division differed
between the catabolic groups (one-way ANOVA: F2,198 ¼
11.89, p , 0.001), with a Tukey’s HSD test indicating that
temperature ranges for the anaerobic respiration group were
lower ( p , 0.05) than those observed for the other groups
(figure 5a). Temperature ranges for cell division did not differ
significantly between aerobically respiring strains and strains
using organic terminal electron acceptors (figure 5a). Similar
results based on one-way ANOVA (F2,195 ¼ 21.20, p , 0.001)
and Tukey’s HSD comparisons were observed with reference
J. R. Soc. Interface 12: 20150658
dispersion was assessed by a test for the homogeneity of multivariate dispersions (PERMDISP using distances from centroids
and 9999 permutations) [45]. The PERMDISP routine is used as
an accompaniment to PERMANOVA and provides information
on whether significant differences between sample groups are
due to differences in location (in multivariate space), dispersion
(within-group variability) or both.
Strain-specific temperature, pH and salinity (% w/v of
NaCl) ranges were calculated as the difference between maximal and minimal values for cell division. Strains for which
these values were unavailable were omitted from statistical
analysis. One-way ANOVA and post hoc Tukey’s honestly significant difference (HSD) tests for comparing ranges for cell
division were performed with ‘mode of catabolism’ as the
factor, using the R software package (v. 3.1.0) [46]. The tests
were conducted using balanced designs based on the lowest
common number of values for a given environmental parameter
(n ¼ 67 for temperature; n ¼ 66 for pH; n ¼ 44 for salinity), with
values randomly selected from each group where necessary.
The data for comparing salinity ranges for cell division were
square-root-transformed to achieve normality.
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catabolic groups
5
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(a)
ANR (inorg. e–)
AR
ANR + F (org. e–)
mean temperature (°C)
60
4. Discussion
40
min. opt. max.
(b)
AR
n = 67
n = 67
n = 67
n = 71
n = 66
n = 67
n = 103
n = 102
n = 101
0
min. opt. max.
min. opt. max.
ANR (inorg. e–)
ANR + F (org. e–)
10.0
mean pH
7.5
5.0
min. opt. max.
(c)
min. opt. max.
ANR (inorg. e–)
AR
n = 66
n = 68
n = 66
n = 67
n = 64
n = 66
n = 96
0
n = 96
n = 98
2.5
min. opt. max.
ANR + F (org. e–)
15
n = 46
n = 40
n = 48
n = 46
n = 47
n = 74
n = 66
5
n = 39
10
n = 71
mean salinity (% w/v of NaCl)
20
0
min. opt. max.
min. opt. max.
min. opt. max.
Figure 4. Growth values for aerobically respiring, anaerobically respiring and
fermentative prokaryotic isolates. Data are shown for minimal (‘min.’), optimal (‘opt.’) and maximal (‘max.’) conditions for cell division, corresponding to
(a) temperature (8C), (b) pH and (c) salinity (% w/v of NaCl). The data are split
into three catabolic groups (AR, aerobic respiration; ANR (inorg. e2), anaerobic
respiration using inorganic terminal electron acceptors; ANR þ F (org. e2),
anaerobic respiration using organic terminal electron acceptors, and fermentation). The growth values are presented as means + s.e., with frequencies of
strains indicated within or above the bars.
to pH ranges (figure 5b). In contrast with these findings, strainspecific salinity ranges for cell division differed between the
catabolic groups (one-way ANOVA: F2,129 ¼ 17.56, p , 0.001).
A Tukey’s HSD test showed that salinity ranges for aerobically
respiring isolates were significantly broader than those for the
J. R. Soc. Interface 12: 20150658
20
Energetic factors have been proposed as a determinant of habitability within extreme environments [15–17,19–23]. Our
understanding of this topic, however, has been limited by a
shortage of research into the ability of different catabolic
groups to tolerate physico-chemical extremes [12,25]. Here,
we analysed 2232 cardinal growth values corresponding to
241 prokaryotic isolates (electronic supplementary material,
table S1) to compare the temperature, pH and salinity values
for cellular multiplication between aerobically respiring
taxa (oxygen as the terminal electron acceptor), strains using
inorganic electron acceptors other than oxygen during respiration, and taxa employing organic electron acceptors (either
fermentation or anaerobic respiration). Aerobically respiring
strains covered a higher relative volume of the total temperature–pH–salinity space for cell division than either anaerobic
group (figures 1 and 2). Each group occupied a unique position
within the overall three-dimensional space, with differences
between the aerobic respiration group and the other modes
of catabolism being partially attributable to the high degree
of multivariate dispersion exhibited by this group (figure 3;
electronic supplementary material, table S3). The significant
differences observed between all three groups demonstrate
that these categories, despite the broad spectrum of catabolic
pathways they encompass, are meaningful from the viewpoint
of understanding how different types of metabolism map onto
the global boundary space for life.
While the difference observed between aerobically and
anaerobically metabolizing strains was in agreement with simplified energetic predictions (see Introduction section), the
volume for cell division by isolates using organic compounds
as terminal electron acceptors exceeded that of strains using
inorganic electron acceptors other than oxygen (figures 1
and 2). This finding, combined with a comparison of cardinal
growth values (figure 4 and table 3) and ranges (§3.2;
figure 5; electronic supplementary material, figure S3), strongly
suggests that predictions of Gibbs energy yields under a limited set of reference conditions (258C, neutral pH, an ionic
strength of 0.1 M and atmospheric pressure) are insufficient
for predicting limits for prokaryotic cell division within multiple-stress environments. This conclusion is in agreement
with previously published research which has shown that
Gibbs energy yields depend on several physico-chemical
factors (including temperature, pH, salinity, pressure and the
surrounding chemical composition), as well as substrate availability [15,34,38,39]. For example, Amend & Shock [39]
calculated Gibbs free energies for over 300 reactions as a
function of temperature and concluded that accounting for
extremes of this parameter (as well as other conditions
beyond the typical bioenergetic reference frame) is essential
to understanding the ecology of thermophilic taxa. As our
dataset encompassed temperatures ranging from 215 to
1218C, pH values from 0 to 14 and NaCl concentrations of
0–36% (w/v), this study highlights the need for research into
microbial bioenergetics across the entire range of conditions
6
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80
other two groups (figure 5c), despite some individual anaerobic taxa exhibiting salinity ranges of greater than 15% (w/v
of NaCl) (electronic supplementary material, figure S3). No
difference in salinity ranges was detected between the two
anaerobic modes of catabolism (figure 5c).
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Table 3. Temperature, pH and salinity limits of cell division for aerobically respiring, anaerobically respiring and fermentative prokaryotic strains (grouped by
terminal electron acceptors).a
limit
strain
reference
all data
min. temp. (8C)
max. temp. (8C)
215
121
Planococcus halocryophilus
strain 121
[47,48]
[49]
Acidiplasma aeolicum
Ferroplasma acidarmanus
[50]
[51]
Picrophilus oshimae
[52,53]
Nocardiopsis valliformis
Haloquadratum walsbyi
[54]
[55]
as for total dataset
Pyrobaculum aerophilum
[56]
min. pH
max. pH
max. NaCl (% w/v)
aer. resp.
min. temp. (8C)
max. temp. (8C)
2
anaer. resp. (inorganic e acceptors)
a
215
104
0
14
as for total dataset
as for total dataset
max. NaCl (% w/v)
36
as for total dataset
min. temp. (8C)
22
Desulfuromonas svalbardensis
Desulfuromusa ferrireducens
max. temp. (8C)
min. pH
121
1
as for total dataset
Acidianus infernus
[58]
Acidianus manzaensis
[59]
10.3
Stygiolobus azoricus
Desulfurivibrio alkaliphilus
[60]
[61]
24
Dethiobacter alkaliphilus
Desulfohalobium utahense
[62]
Marinilactibacillus psychrotolerans
Hyperthermus butylicus
[63]
[64]
Pyrococcus yayanosii
[65]
max. NaCl (% w/v)
anaer. resp. and ferm. (organic e acceptors)
14
36
min. pH
max. pH
max. pH
2
0
min. temp. (8C)
max. temp. (8C)
21.8
108
[57]
min. pH
max. pH
2
11.1
Acidilobus aceticus
Clostridium paradoxum
[66]
[67]
max. NaCl (% w/v)
34
Haloanaerobium lacusroseus
[68]
For a complete list of strains, see electronic supplementary material, table S1.
reported. In particular, this work shows the need to consider
interactions between multiple-stress parameters in order to formulate accurate predictions of cellular tolerance to extremes
[39]. Crucially, to correctly apply these predictions to natural
ecosystems, any calculations of Gibbs energy yields would
also need to account for the concentrations of all reactants
and products corresponding to a given reaction. Anabolic factors are additionally likely to be important to estimating limits
to cell division, and are similarly dependent on environmental
conditions. For example, the energetic cost of synthesizing biomass has been found to be lower under anaerobic conditions
than in aerobic environments [69,70], which could partially
underpin the results reported in our study.
Further to these considerations, several other variables
may influence the physico-chemical growth limits of different
catabolic modes. For example, experiments using sediment
microbial consortia have showed that biological factors (e.g.
cellular free-energy efficiency, population viability and mutualism) can exert a greater degree of control over rates of
microbial catabolism than the availability of usable energy
([71] and references therein). Given our results, it is possible
that such factors can also influence the distribution of diverse
modes of catabolism within the global-scale parameter space
for life. Indeed, a number of physiological mechanisms could
explain the ability of several anaerobically metabolizing
taxa to multiply under hypersaline conditions (figures 4c,
5c; electronic supplementary material, figures S2 and S3), as
discussed by Oren [13,14,19]. The ‘salt-in’ mechanism of osmoadaptation, for example, provides an energetically efficient
alternative to synthesizing compatible solutes, which may
enable certain isolates (such as Haloanaerobium lacusroseus) to
carry out fermentation at extreme salinity (table 3) [13,14,19].
Energy-conserving strategies, which include diverse structural,
biochemical and ecological adaptations, can also facilitate
cellular maintenance and multiplication under extreme temperatures and pH [21,23,24,72–77]. For example, the lipid
structure of the archaeal cell membrane is likely to be adapted
for life under nutrient-poor conditions that are common
within biologically hostile environments [23,24]. Modifications
of the cytoplasmic membrane (along with other adaptations)
are additionally known to help sustain pH homeostasis under
extremes of pH [72,76].
J. R. Soc. Interface 12: 20150658
parameter
rsif.royalsocietypublishing.org
group
7
Downloaded from http://rsif.royalsocietypublishing.org/ on June 14, 2017
(a)
40
b
20
n = 67
R
(in A
or N
g. R
e–
A )
N
(o R
rg + F
.e –
)
A
4
a
3
b
2
n = 66
1
(in
A
R
0
or AN
g. R
e–
A )
N
(o R
rg + F
.e –
)
n = 95
mean strain-specific
pH range
a
n = 66
(b)
20
a
15
10
b
b
n = 45
or AN
g. R
e–
A )
N
(o R
rg + F
.e –
)
(in
A
R
0
n = 44
5
n = 68
mean strain-specific
salinity range (% w/v of NaCl)
(c)
Figure 5. Growth ranges for aerobically respiring, anaerobically respiring and
fermentative prokaryotic isolates. Data are shown for (a) temperature (8C),
(b) pH and (c) salinity (% w/v of NaCl). The data are shown as untransformed
means + s.e., with frequencies of strains indicated under the sample labels
(AR, aerobic respiration; ANR (inorg. e2), anaerobic respiration using inorganic terminal electron acceptors; ANR þ F (org. e2), anaerobic
respiration using organic terminal electron acceptors, and fermentation).
Different letters above the bars indicate significant differences between individual modes of catabolism ( p , 0.05 (Tukey’s HSD test following one-way
ANOVAs with balanced designs; see §3.2 for ANOVA results)).
J. R. Soc. Interface 12: 20150658
0
n = 67
10
n = 101
mean strain-specific
temperature range (°C)
30
8
rsif.royalsocietypublishing.org
a
a
While physiological and/or evolutionary factors are likely
to have played a key role in determining the position of each
catabolic group within the total parameter space for cell division (tables 2 and 3, figures 1–5), it is possible that some of
the results reported in our study were influenced by purely
physical or chemical processes. The low solubility of oxygen
at high temperatures and the presence of reducing conditions
within hyperthermophilic environments, for example, could
contribute to the tendency of aerobically respiring taxa to
exhibit lower temperature preferences compared with anaerobically respiring and fermentative strains (figure 4a) [78].
Although the known temperature window for cell division
may expand as a result of further research, ultimately the thermal boundaries for life are set by limits to macromolecular
stability and access to liquid water [16,20,79]. A common
water-activity limit (approx. 0.61) to cellular multiplication
has been proposed for all three domains of life [80]. Improvements in the availability of suitable growth data (e.g. via the
targeted isolation and characterization of extremophilic strains)
are therefore needed to improve our ability to determine the
extent to which such factors affect the known limits for cell
division on the Earth (table 3, figures 1 and 2).
Metagenomic analyses have previously demonstrated a
strong correlation between environmental conditions and
microbial strategies for energy conversion and conservation
[81,82]. While such analyses would complement culturebased attempts to advance our knowledge of the distribution
of different metabolic pathways within the temperature –
pH –salinity space for prokaryotic multiplication on the
Earth (figure 1a), they would require an unprecedented
sampling effort. Indeed, global metagenome surveys to date
have focused on pelagic marine habitats [83 –85]. Additionally, attempts to define the habitable parameter space on
the Earth have necessarily been restricted to data obtained
for isolates [9–12]. As the vast majority of microorganisms
are unculturable in the laboratory [86], it remains unclear
how representative the taxa employed in our study are of
those that define the temperature, pH and salinity limits for
cell division within natural environments. Furthermore, as
laboratory experiments are typically conducted under nutrient-rich conditions [87] and in the absence of several stresses
encountered in the environment (e.g. predation, competition
and physico-chemical parameters including extremes of radiation or pressure), results from strain-based investigations are
likely to be poorly correlated with in situ patterns of microbial
activity and limits to cell division [12,23,88]. Indeed, rates of
microbial metabolism are often likely to be lower within natural environments than under laboratory conditions, with
important implications for microbial growth and persistence.
While aerobically respiring communities can occur within
marine sediments to a depth of up to 75 m, for example, both
cell numbers and rates of microbial activity are very low
within these environments [89].
Regardless of uncertainties associated with strain- and laboratory-based attempts to determine the boundaries of the
biosphere [12], the results of this study have broad implications
for our knowledge of ecosystem processes within extreme
environments, and the global-scale physico-chemical limits
for microbial cell division. The data presented herein are consistent with the theory that the evolution of an oxygen-rich
atmosphere may have enabled microorganisms to exploit a
broader global niche space compared with an early and
oxygen-poor Earth [90], but they also highlight the ability of
Downloaded from http://rsif.royalsocietypublishing.org/ on June 14, 2017
By systematically comparing minimal, optimal and maximal
values for cell division between 241 prokaryotic strains, we
have shown that aerobically respiring isolates are characterized
by a broader temperature–pH–salinity window for cell division than strains which employ other inorganic substrates or
organic compounds as the terminal electron acceptor. We
also found that taxa employing organic terminal electron
acceptors displayed a broader habitable window in comparison with anaerobically respiring strains that used inorganic
substrates as the electron acceptor, which was not anticipated
by simple energetic predictions (relative Gibbs energy yields
under the conventional reference conditions of 258C, neutral
pH, an ionic strength of 0.1 M and atmospheric pressure).
Our findings, therefore, highlight the need for global-scale
models of habitability to account for the impacts of environmental conditions (and in particular, interactions between
extremes) on cellular energetics [94]. Such models will also
require consideration of anabolic reactions, as well as substrate
availability and rates of microbial activity (substrate consumption). Further to these issues, it is possible that one of the factors
underpinning our results is the ability of physiological and/or
evolutionary adaptations to extremes to enable low-ATP-yielding modes of catabolism (such as fermentation) to operate
Data accessibility. Supplementary figures and tables, including a full
list of the strains used in this work, are available in the electronic
supplementary material.
Authors’ contributions. J.P.H. and C.S.C. designed the study; all authors
assembled data for analysis; L.D. constructed the three-dimensional
maps and performed Monte Carlo integrations; L.D. and J.P.H.
constructed the two-dimensional maps; J.P.H. performed statistical
tests and wrote the initial draft of the manuscript; all authors
contributed to and approved the final version of the manuscript.
Competing interests. We declare we have no competing interests.
Funding. This work was supported by the Science and Technology
Facilities Council (STFC consolidated grant no. ST/1001964/1).
Acknowledgements. We thank Mark Fox-Powell, Samuel Payler and four
anonymous reviewers for their constructive feedback on the
manuscript.
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