University of Nebraska - Lincoln
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Biological Systems Engineering--Dissertations,
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Biological Systems Engineering
7-2014
Characterization of Genetically Modified High
Biomass Producing Tobacco Plant
Pankaj Singh Kuhar
University of Nebraska-Lincoln, [email protected]
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Characterization of Genetically Modified High Biomass Producing Tobacco Plant
by
Pankaj S. Kuhar
A THESIS
Presented to the Faculty of
The Graduate College at the University of Nebraska
In Partial Fulfillment of Requirements
For the Degree of Master of Science
Major: Agricultural and Biological Systems Engineering
Under the Supervision of Professor Deepak R. Keshwani
Lincoln, Nebraska
July 2014
Characterization of Genetically Modified High Biomass Producing Tobacco Plant
Pankaj S. Kuhar, M.S.
University of Nebraska, 2014
Advisor: Deepak R. Keshwani
Global warming and peak oil has clouded our energy security. In light of this situation,
bioethanol as emerged as one of the most amenable solutions to the problem. However
bioethanol has its own shortcomings and transgenics seem imperative to exploit its full
potential. A high biomass producing line in transgenic tobacco (Nicotiana tabacum cv.
Xanthi) was identified during a routine genetic transformation, termed giant recombinant
(GR). To characterize the phenotype of the giant line, growth rate and lignocellulosic
composition was analyzed relative to the non-transgenic control line. The GR line
accounted for 240% more biomass than the untransformed line within 135 days of its
germination. Furthermore, there were significant differences in chemical composition
within GR line relative to a control line. The GR characteristics are likely due to
disruption or activation of an unknown plant gene. Identification of the GR gene
responsible for increased biomass productivity could lead to improvement of other plant
species
to
develop
feed-stocks
for
bio-based
energy.
iii
ACKNOWLEDGEMENTS
I would like to express my heartfelt regards to my mentors Dr. Deepak R. Keshwani and
Dr. Amitava Mitra for their constant support, patience and wisdom. I would like thank
Dr. George Meyer for his help and guidance, and my loved ones for their irreplaceable
support.
iv
TABLE OF CONTENTS
Pages
CHAPTER 1: A Review of Plant Genetic Engineering as a New Tool for Cellulosic
Ethanol……………..………………………………………………………………… ….6
Abstract……………………………………………….......................................................6
Introduction ………………………………………………………………………………7
Cellulosic ethanol overview………………………………………………………………9
Genetic manipulation of biomass feedstock……………………………………………..11
Reducing pretreatment cost.……………………………………………………………..13
In planta enzyme production …………………………………………………………....15
Conclusion……………………………………………………………………………….18
References ………………………………………………………….…………………...19
CHAPTER 2: Characterization of Genetically Modified High Biomass Producing
Tobacco Plant..…..……………………………………………………………………....25
Introduction …………………………………………………………..............................26
v
Material and Methods …………………………………………………………………...28
Result and Discussion …………………………………………………………………...32
Conclusion …..…………………………………………………………………………..49
References …...…………………………………………………………………………..50
APPENDIX
Sample SAS code.………………………………………………………………………..56
Files included in CD-ROM………………………………………………………………57
6
Chapter1
A Review of Plant Genetic Engineering as a New Tool for Cellulosic Ethanol
Abstract
Global warming and peak oil price and availability have threatened our energy security.
In light of this situation, bioethanol as emerged as a possible amenable solution to the
problem. Developments in sugarcane and corn ethanol have established a potential
market for biofuels; however, production is limited due to competition for food and feed.
Whereas cellulosic ethanol is a more favorable solution with abundant supplies of nonfood based lignocellulosic biomass around the world. However, due to limitations of the
current conversion technology and in vitro enzyme production for cellulosic ethanol,
cellulosic ethanol becomes an expensive alternative compared to other solutions.
Fortunately with recent developments in plant genetic engineering, it is possible to
produce cellulases and hemicellulases in plant systems, improve crop biomass
production, and modify its secondary cell wall structure such that would in turn improve
ethanol yield per se.
7
Introduction
Global warming and fluctuating oil prices have created an international unrest with
regard to the future energy security. For instance, rapid industrialization in countries like
India and China have increased their dependence on crude oil imports, consequently
affecting their economic growth. Even developed nations like United States of America
are not left alone, increasing their dependence on foreign petroleum reserves while
competing with the international demand undermining economic stability and future
energy security1,4. For example, China needs a clear air policy with rising carbon dioxide
levels globally. No nation either developing or developed is untouched by the ill effects
of incautious fossil fuels usage. Biofuels such as ethanol are being adapted globally as a
green alternative for petroleum-based transportation fuels. Biofuels can be produced from
abundant supplies of biomass using various agricultural crops such as sugar cane and
starchy crops, oil crops, and even animal manure. Additionally, biofuels can have
positive environmental impacts such as lower emissions compared to petroleum. Biofuels
promise a step toward sustainable and carbon neutral renewable fuels, through reduced
greenhouse gas emissions by fixating atmospheric carbon dioxide via photosynthesis27.
Starch or sugar based food crops such as corn or sugarcane have been primarily used for
production of ethanol globally. Their contribution to global energy market has been small
but profitable due to rising crude oil prices16. Sugar cane based bioethanol has been
successful in Brazil. Brazil has adapted cost effective approaches to produce bioethanol
form sugarcane which fulfills one quarter of its total requirements for transportation fuel.
Corn based ethanol in the United States has also been successful. In 2013 alone, the US
produced 13 billion gallons of corn based ethanol. However, corn would still only be
8
able fulfill 15% of the total required transportation fuel, even if all the corn grown were
converted to ethanol15. On the other hand, corn grain usage for renewable fuels
potentially undermines the nation’s food and feed security. For instance, 50.8% of the
total US corn grain produce is currently used in livestock feed and nutrition. Increased
demand for corn ethanol has already started to impact the food industry due to increased
prices in meat and dairy products16.
In light of this situation, using cellulosic feedstock for ethanol has been proposed as an
alternative. There is an abundance of non-food based lignocellulosic biomass in nature,
and, usage of second generation bioethanol promises reduction in greenhouse gases.
Additionally, cellulosic ethanol would be sustainable even in countries where climate
conditions do not support sugarcane or corn grain production. For example, rice straw
can be used as a potential lignocellulosic biomass source which constitutes more than
50% of world’s agronomic biomass, as rice is the staple diet globally.
The cellulosic ethanol market is fast growing; significant investment is being provided to
support its large scale development, from governmental programs, various venture
capitalists, and even traditional fossil fuel companies like British petroleum. Companies
like Abengoa, POET and Iogen are building refineries which would process cellulosic
biomass into ethanol.
However, like all renewable energy sources, cellulosic ethanol has its own limitations.
First and foremost is the high cost of production of feedstock, and the development of
degrading enzymes.
Secondly, and most important, there is not enough cultivable
agricultural land to produce sufficient amounts of feedstock to meet increasing demands
9
for liquid fuels, using current industrial conversion technologies14. These two limitations
together make the price of cellulosic ethanol less competitive compared to distilled corn
grain ethanol. Fortunately with recent developments in plant genetic engineering, it may
be possible to improve crop biomass production and modify its secondary cell wall
structure that would in turn improve ethanol yield per se16,32. Others developments in
plant engineering technology could possibly produce cellulases and hemicellulases within
the crop biomass which would reduce or completely eliminate need of external source of
enzyme needed for scarification.
Cellulosic Ethanol Overview
Bioethanol is primarily divided in two main categories: first-generation and secondgeneration bioethanol. Sugar beet, sorghum, corn, sugarcane are first generation sugar or
starch bioethanol feed stocks. Second-generation cellulosic ethanol is primarily produced
from lignocellulosic biomass such as perennial grasses like switchgrass, miscanthus,
eucalyptus, and willow and hybrid poplar.
10
Figure 1: Cellulosic ethanol production Overview
Flow diagram showing the essential steps in the production of cellulosic ethanol from
biomass feedstock to ethanol recovery
11
Genetic Manipulation of Potential Biomass Feedstock
The selection of a suitable bioenergy crop is region and climate specific. There are
several criteria for selecting biomass feedstock before recommending it to a local farmer
for production: a) potential yield in the given climate conditions, b) agricultural land
availability for the bioenergy crop; c) irrigation and cropping system management; d)
mode of transportation available and the distance to a functional bio refinery; e) USDA
regulations if it is a transgenic crop; f) and most importantly, the level of crop
development which is usually determined by its agronomic traits such as biomass yield
and lignocellulosic composition. The most important among these selection criteria are
the agronomic traits of a lignocellulosic biomass crop. These traits include a)
photosynthetic efficiency: (C4 plants are preferred over C3 plants due their high weather
tolerance and better water use efficiency); b) canopy duration: longer canopy duration
with a short perennial life span; c) translocation of photosynthesized carbohydrate into
structural lignocellulose. Such traits are common in perennial species such as miscanthus
and switch grass21,32.
However, implementation of wild populations of switchgrass and miscanthus as an
agricultural crop is slow; unlike traditional food crops, years of traditional breeding has
not been performed on these plant species. In light of this situation, plant genetic
engineering is an imperative tool to improve agronomic traits of bioenergy feed stocks.
The primary tools for plant genetic transformation are Agrobacterium mediated
transformation, and gene gun mediated gene transfer. These tools has proven worthwhile
in transforming many agricultural crops such as rice, corn, wheat, barley, sorghum and
many other for agriculturally relevant traits. However, the effectiveness of these tools on
12
biomass energy crops such as switchgrass and miscanthus has been limited. It has been
shown that among all switchgrass genotypes, only few cultivars have been efficiently
genetically engineered. The recalcitrance of perennial crops to Agrobacterium mediated
transformation is not well understood and needs additional research11. An alternative
approach is functional genomics and mutation studies on model plant systems such as
tobacco and Arabidopsis. In these studies, genes involved in phytohromone metabolism,
cell-cycle machinery or cellulose and hemicellulose biosynthesis pathways are identified,
targeted and altered for enhanced plant growth and byproducts.
Plant hormones are responsible for regulating growth and development of plants
throughout their life span. They interact with each other and other signaling pathways
which effect plant growth, in recent literature Gibberellins (GA) and Brassinosteroids
have been reported to play a vital role in growth associated with stem elongation and
thickness27. In one such study transgenic tobacco was engineered to express GA20oxidase gene from Arabidopsis, showed enhanced biomass accumulation owing to
increased plant height and early flowering with increased levels of GA. Similarly
transgenic hybrid poplar with increased GA biosynthesis reflected higher growth and an
increased biomass accumulation, In another such study, elevated growth rate in tobacco
has been reported by over expressing D-type cyclin (CycD2) gene from Arabidopsis
responsible for cell division. The transgenic tobacco was reported to have taller stem and
elevated overall growth rate with early attainment of flowering8. There have been
numerous attempts in the last decade or so to understand the cellulose and hemicellulose
biosynthetic pathways by exploiting functional genomics, but still the knowledge in this
field of study is limited. Although feedstock biomass can potentially be increased by
13
changing the environmental growth conditions such as carbon allocation in the form of
carbon dioxide, oxygen, water supply and soil nutrient content. For instance, a study on
maize showed 20% increase in plant biomass by supplementing the environment with
higher levels of carbon dioxide29. However it is hard to predict that if this higher yield
can be directly co related with higher photosynthetic rate in maize; as photosynthetic rate
is highly dependent on synchronization between plant circadian clock11 and external
change in light–dark cycle29. Increasing plant biomass by regulating growth rate holds
potential for bioenergy crops.
Reducing Pretreatment cost
Lignocellulosic biomass is primarily composed of long chain carbohydrate polymers
mainly cellulose and hemicellulose, and phenolic compounds such as lignin. Cellulose
and hemicellulose make up majority of the cell wall dry matter, and are the carbohydrates
used for production of bioethanol. Cellulose and hemicellulose are hydrolyzed via
enzymatic saccharification to sugars that are then fermented to ethanol. Cellulose
contains cellobiose dimers linked by ß-1, 4 glycosidic bonds forming crystalline fibrous
polymers made up of individual glucose units. Hemicellulose, another component of
lignocellulosic feedstock, is a short chain polymer primarily composed of C-5 and C-6
sugars, like xylose, arabinose and galactose, glucose, mannose respectively. The third
most common constituent biomass feedstock is polyphenolic lignin. The primary function
of lignin is to provide structural integrity to plants. These three compounds are
intertwined and this complex nature of lignocellulosic biomass makes it recalcitrant to
enzymatic hydrolysis and adds up the cost of pretreatment processing. However recent
developments in plant genetic engineering allows us to better understand lignin
14
biosynthetic pathways, to down regulate lignin composition and modify chemical
structure of lignin components9,3. This can potentially bring down the cost of
pretreatment processing and increase amenability to enzymatic scarification.
Lignin primarily has three precursors: paracoumaryl, coniferyl and sinapyl alcohol, which
form the phenylpropanoid unit parahyrdroxuphenyl, guaiacyl and syringyl units
respectively through a complex and interconnected lignin biosynthetic pathway17. Similar
to cellulose and hemicellulose biosynthetic pathways there is limited knowledge about
lignin biosynthetic pathways20. However with recent development in functional genomics
and mutant studies plant scientists are able identify novel genes involved in lignin
biosynthetic pathways. Also studies have been able to down regulate these genes in
various plant species using antisense technology or using RNA interference technology9.
For instance in a recent study using annotated expressed sequence tags from xylogenic
tobacco culture, gene expression was analyzed for transgenic tobacco lines down
regulated for lignin biosynthetic enzymes namely Cinnamate-4-hydroxylase(se4h),
Cinnamoyl-C0A-reductase (asccr) and lignin specific peroxidase (asprx), and compared
with UDP glucuronic acid decarboxylase (asuxs) involved in nucleotide sugar pathway of
Xylan biosynthesis. It was shown that lines down regulated in asprx showed 3 fold
improvement in enzymatic saccharification, and lines down regulated in as asuxs
improved saccharification by 50% when compared to wild type9. In a similar study,
transgenic alfalfa was down regulated for of six different lignin biosynthetic pathway
enzymes namely cinnamate 4‑hydroxylase (C4H), hydroxyl cinnamoyl transferase
(HCT), 4‑hydroxycinnamate 3‑hydroxylase (C3H), S‑adenosyl-methionine-caffeoyl-
15
CoA/5-hydroxyferuloyl-CoA-O-methyltransferase (CCoA-OMT), ferulate 5-hydroxylase
(F5H), or caffeate O-methyltransferase (COMT ), and it was shown that down regulation
of these enzymes could completely eliminate need of pretreatment processing in alfalfa6,7.
In both alfalfa and tobacco modification of lignin biosynthesis pathway enzymes has
been shown to reduce the pretreatment process and improve the efficiency of enzymatic
scarification. However, neither alfalfa nor tobacco is a viable bioenergy feedstock.
Therefore further investigations are needed to confirm these effects and evaluate the
usefulness of down regulating lignin biosynthesis pathway enzymes on potential
bioenergy crops such switchgrass or miscanthus. In significant other mutational studies,
lignin biosynthetic enzyme, O-methyl transferase (OMT) has been reported to increase
overall biomass accumulation in transgenic tobacco without any changes in lignin
deposition2,1. In another such study, Cinnamoiyl-COA-reductase (CCR) was down
regulated in transgenic tobacco resulting in an overall drop in lignin composition and
simultaneous increase in xylan and cellulose composition in tobacco secondary cell
wall5,19. In another recent development, transgenic switchgrass was down regulated for
caffeic acid 3-O-methyltranferase (COMT) which resulted in lower lignin content with
reduced S/G ratio and increased sugar and conversion yield37. In another attempt in
switchgrass overexpression of transcription factor PvMYB4 resulted in suppression of
lignin biosynthetic pathways genes with enhanced enzymatic scarification efficiency of
up to 300% compared to wild type28.
In Planta enzyme production
Production of second-generation cellulosic ethanol involves use of cellulase enzymes for
hydrolysis followed by fermentation. Hydrolysis is the chemical reaction that converts
16
the complex polysaccharides present in the biomass in to simple sugars using a cocktail
of cellulose enzymes. There are typically of three kinds of enzymes needed to hydrolyze
cellulose into glucose: endoglucanase, exoglucanase and β‑glucosidase. During the
hydrolysis process, endoglucanase first randomly cleaves the crystalline cellulose
molecule internally; producing additional reduced chain ends thereafter exoglucanase
attaches itself to the chain end, results in small cellobiose units. Exoglucanase also
attaches itself to amorphous cellulose with exposed chain ends and directly producing
small cellobiose units. In the final step β‑glucosidase breaks cellobiose subunits into
simple monomers of glucose25. These enzymes work synergistically. For example,
increase in cellobiose can hinder exoglucanase activity through feedback inhibition,
making β-glucosidase irreplaceable. Therefore, the ratio of these enzymes is critical to
ensure complete and efficient hydrolysis.
Cost of production of hydrolysis enzymes currently high. They are produced at
commercial scale in a microbial bioreactor via fermentation. Research is being carried
out to produce hydrolysis enzymes in planta. This approach could lower the energy input
by eliminating the cost of industrial production and separation.
An important development in this regard has been the characterization of microbes most
suitable for synthesizing cell wall degrading enzymes. Among these well characterized
microorganisms are either bacterial origins or fungal origins such as Trichoderma
reesei30. Most of these micro-organisms share common characteristics such as being able
to produce a large variety of cell wall hydrolyzing enzymes that efficiently breakdown
recalcitrant lignocellulosic biomass22.
17
This characterization of cell-wall hydrolysis enzymes has made it convenient for plant
molecular biologists to target specific species of bacteria to express their microbial
protein in planta is using codon alteration, as many of the identified enzymes are of either
bacterial or fungal origin. The key issue associated with heterologous expression of cell
wall hydrolyzing enzyme are a) in planta storage b) stability c) level of activity and
extraction of enzyme from plant biomass. There are numerous ways in which these
issues are being dealt with such as by expressing the recombinant enzyme directly into
seed endosperm so that biomass and seeds can be easily separated during harvest and
used separately. This methodology has been tried on corn and rice grain using seed
specific promoters such as glutein and globulin17. In a similar way E1 endocellulase was
expressed in tobacco seeds which stayed viable even after a year of storage at room
temperature10. This stability is a marketable feature of In Planta enzymes since it
simplifies the value chain of storage, extraction and transportation of the enzyme to a
biorefinery and companies like Syngenta and Edenspace are working towards exploiting
this technology on a commercial scale32. Furthermore, numerous efforts are being taken
to produce enzyme within the plant cytosol so that under optimum temperature hydrolysis
can occur automatically and pretreatment process can be reduced or eliminated. For
example, E1 endocellulase has been expressed at 5% total soluble protein in rice and 2%
total soluble protein in maize, however these levels are significantly low to substitute any
pretreatment process23,24. In another approach several transgenic enzymes have been
targeted to be expressed directly into plant subcellular compartments such as apoplast,
chloroplast and endoplasmic reticulum, instead of the plant cytosol. For example
increased level of enzymes have been reported to achieved by genetically engineering the
18
chloroplast genome in poplar, which also ensured bioconfinment of transgene as
chloroplast genome is maternally inherited. (1) Another potential subcellular
compartment for enzyme production is the endoplasmic reticulum as it provides excellent
enzyme stability due to its oxidizing environment and presence of minimal proteases
compared to cytosol13. Recent literature shows that transgenic enzyme had 10 fold
greater activity when retained endoplasmic reticulum then secreted in the cytosol18. In a
similarly fashion plant apoplast has been targeted to accumulate transgenic enzyme as
this plant subcellular compartment is the largest and can store large amounts of enzyme.
Apoplast has also been preferred for expression of thermophilic transgenic enzymes
which allows cellulase to cleave plant cell wall internally by just increasing temperature
to optimal levels for enzyme activation32.
Although in planta enzyme production seems very promising; there is substantial
research pending before this technology can be realized on a commercial scale. Most
importantly there are significant regulatory hurdles before implementation of these
technologies outside laboratory settings.
Conclusion
Even with all this development in plant genetic engineering to create dedicated bioenergy
crops, significant work remains. In general we have identified few challenges which
hinder further development in this field. First and for most problem is the recalcitrant
nature among wild type perennials crops towards genetic transformation. There needs to
be development of a unified genotype-nonspecific genetic transformation technology for
bioenergy feedstocks. Secondly we still need to produce sufficient amounts of in planta
19
celluslase enzymes. There is promise in potentially accumulating cellulase in the
apoplast, but this technology needs further development. Additional research is also
needed to lower lignin content and increase carbohydrate content in the plant cell wall
without negatively impacting the physical viability of the feedstock.
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genetically modified switch grass using simultaneous saccharification and
fermentation and a consolidated bioprocessing approach. Biotechnology for
Biofuels 5(1): 81.
38. Ziegelhoffer, T., J. Raasch and S. Austin-Phillips. 2001. Dramatic effects of
truncation and sub-cellular targeting on the accumulation of recombinant
microbial cellulase in tobacco. Molecular Breeding 8(2): 147-158.
25
Chapter 2
Characterization of Genetically Modified High Biomass Producing Tobacco Plant
Abstract
During a routine transformation experiment, we fortuitously obtained a transgenic
tobacco plant that grew to the size of a small tree , The high biomass producing
transgenic tobacco (Nicotiana tabacum cv. Xanthi) line, termed giant recombinant (GR),
has the potential for a new class of energy crops by converting normal plants to high
biomass producing plants.. To characterize the giant line, we analyzed its growth rate and
lignocellulosic composition relative to the non-transgenic control line. The growth rate of
the plants were measured by tracking stem height, width, and leaf count, length, and
width for 126 days. The chemical composition (cellulose, hemicellulose and lignin) of the
GR line relative to the control line was quantified using standardized NREL protocol.
The GR line accounted for 240% more biomass than the untransformed line within 135
days of its germination. Furthermore we found there were significant differences in
chemical composition within GR line, and relative to control line. The GR characteristics
are likely due to a disruption or activation of an unknown plant gene. Identification of the
GR gene responsible for increased biomass productivity could lead to its utilization in
other plant species to develop feed-stocks for bio-based energy.
26
Introduction
The World Commission on Environment and Development states sustainable
development as “development that meets the needs of the present without compromising
the ability of future generations to meet their own needs”. This definition emphasizes on
the concepts of protecting needs of the future generation and overcoming technological
limitations generated on our environment to meet these needs. However, incautious
usages of fossil fuels possess a serious threat to our environmental health and future
energy security20.
In the last decade scientists and policy makers all over the world have tried to resolve this
problem by exploiting various avenues in renewable sources of energy. Bioethanol is an
attractive alternative to gasoline; it is environmental friendly, renewable and can be
produced on a large scale13. The United States primary production of ethanol is currently
corn grain based, and the US government has target to supply 36 billion gallons of
biofuels annually by 2022. However, it is projected that corn ethanol itself would be
inadequate to meet this overwhelming demand21.
Among other alternatives, energy from cellulose based ethanol has been developed as a
favorable solution. There is abundance of non-food based lignocellulosic biomass in
nature and at the same time usage of second generation bioethanol promises reduction in
greenhouse gases20. However like all renewable energy sources cellulosic ethanol has its
own limitations. It is estimated that there is not enough cultivable agricultural land to
produce sufficient amounts of liquid fuel in support to increasing demands for gasoline,
using current conversion technologies12. Fortunately with recent developments in plant
27
genetic engineering it is possible to improve crop biomass production and modify its
secondary cell wall structure that would in turn improve ethanol yield per acre13,24.
Doing a routine genetic transformation experiment with Nicotiana tabacum cv. Xanthi, a
high biomass producing giant recombinant (GR) line of tobacco was identified. We have
established that the transgenic GR tobacco phenotype is T-DNA linked and segregates in
a Mendelian fashion. Preliminary analysis indicates that the transgenic event contains one
copy of T-DNA insert. This T-DNA insertion most likely disrupted the function of a gene
or activated a nearby gene10 resulting in the dramatic enhancement in plant growth. In
this study we characterized the GR to identify (a) unknown physicochemical differences
between GR and control line, (b) to explore the potential avenues for identification of GR
gene based on our results. The specific objectives of this study are:
1) To measure growth of GR line relative to control, by tracking change in stem
height, width, and leaf count, length, width, and cumulative increase plant dry
weight.
2) The second objective is to determine if there were any changes in biomass
composition (cellulose, hemicellulose and lignin) of the GR line relative to the
control line during plant growth.
The phenotypic characterization of the transgenic GR tobacco line may provide clues of
the nature of the GR gene. This in turn will be valuable in development of a dedicated
energy crop with increase biomass production.
28
Materials and Methods
Plant Material and Growth Conditions
Nicotiana tabacum cv. Xanthi (control) and giant recombinant (GR) tobacco seeds were
obtained from Dr. Mitra, Department of Plant Pathology, University of NebraskaLincoln. Seeds were sterilized with 70% alcohol and 10% house hold bleach. Control
seeds were germinated in petri plates containing 3% sucrose, 0.8% agar at pH 5.8 and
10 ml L-1 Amphotericin, whereas growth medium for GR line seeds were supplemented
with 50 mg L-1 Kanamycin for selection of T2 GR line. Following germination and
selection in growth chamber, 5 seedlings from GR line 3 and 3 control seedlings were
transferred to a greenhouse for plant growth measurement. Using the same procedure, 9
seedlings from each GR line 1,2,3,4 and 9 control seedlings were transferred to a
greenhouse for composition analysis. Constant growth conditions were maintained in the
green house.
Plant Growth Measurements:
The plants were kept in growth medium up to 1 week after germination so that they were
stable and survived the process of transplantation. Seedlings obtained from growth
chamber were then transferred from petri-plates to uniform size pots. These pots were
then transferred to the green house as seen in image 1, where constant growth conditions
were maintained.
29
Image1: Individual plant from control line on the left and GR line on the right growing
under constant greenhouse conditions.
Five individual plants from GR line 3 and 3 plants from the control line were measured
for specific growth parameters. Measurements for specific growth parameters were
initiated when plants were 1 months old from the point of germination in growth
chamber. Readings were taken on a regular interval of 8 days until plants were 126 days
old.
Stem Height Measurements: Each individual plant height was measured using a thread
from the point of origin of stem above soil. The thread was kept as close to the stem as
possible. The start and end point of stem were marked on thread and then measured using
measuring tape.
30
Stem Width Measurements: The widest point of plant stem was identified and marked,
and change in stem thickness was measured using a Vernier caliper at a regular interval
of 8 days.
Leaf Length and Width Measurements: The largest leaf for individual plant was
identified and marked. The measurements were taken using a measuring tape on a regular
interval of 8 days for the identified leaf for its length and width. Leaf petiole lengths were
excluded from the measurements, as petioles were absent from the GR line.
Number of leafs: Simple count of the number of leafs was kept on an interval of 8 days
for each plant.
The data represented for the plant growth comparison was computed by averaging every
data point from individual plant, and representative curves were plotted from the data set.
Each graph shows change in respective growth factor within the limits of experimental
error. Standard deviation bars associated with each data set are plotted to show variation
between individual plants. The points of observation are represented in GR by square and
in control by circle. It is important to note that as plants aged, variation within individual
plants became more pronounced.
Chemical composition analysis
Plants were harvested (excluding root system) from green house at three time points. A)
When the plants were 30 days old, B) When plants were 90 days old, and C) When plants
were 135 days old. Following each harvest, the plants were measured for total fresh
weight and were then dried in oven at 45± 3 °C, until constant weight was obtained. The
plant’ dry weights so obtained were used for measuring growth rate in terms of total
31
biomass accumulation. Dried plant tissue samples of GR and control line were ground to
pass through 2.00 mm mesh, test sieve (fisher brand). Samples were analyzed for
lignocellulosic components: cellulose, hemicellulose and lignin according to the National
Energy Research Energy (NREL) protocol23. Klason lignin of the plant biomass was
measured with two step acid hydrolysis methodology. Uniformly sized biomass was acid
hydrolyzed in a hot water bath at 30 °C with 72% hydrochloric acid for 30 minutes. The
hydrolysate was diluted with distilled water and autoclaved at 120°C for 60 minutes.
After bringing the hydrolysate to room temperature, it was filtered using Pyrex 30 ml
crucibles to separate acid insoluble lignin from acid soluble components in the filtrate.
The pre-weighed crucibles and supernatant were then dried at 105°C for four hours and
weighed for lignin estimation.
Carbohydrates in the acid soluble component of acid hydrolysate were analyzed
according to NREL laboratory method # 223. Acid hydrolysate was first neutralized with
calcium carbonate, and then filtered through HPLC grade filters into HPLC sample vials.
An HPLC (Thermo scientific), fitted with an inline Bio-Rad HPX-87P analytical column
with a refractive index detector (Shodex) was used to detect various concentration of
carbohydrates present in plant samples. The samples were analyzed at 6000 KPa
pressure, 85 °C column temperature and a sample flow rate of 0.6 ml min -1.The
polymeric sugars concentrations were calculated from the corresponding concentration of
monomeric sugars, using an anhydro correction of 0.88 for C-5 sugars and a correction of
0.90 for C-6 sugars. Following which the percentage of each sugar concentration on an
extractives free basis was calculated using NREL protocol23, so obtained sugar
concentrations on percent basis were used for statistical analysis.
32
Data Analysis
Repeated measures Analysis of covariance (ANCOVA) was used to differentiate between
GR and control line based on chemical composition (percent basis) and time of harvest.
Data from 30 days and 90 days was used to observe differences within GR line and
relative to control line. Date from 135 days was not used in the analysis due to limited
number of replicates.
Optimal Statistical model was selected from CS (Compound symmetry), AR (1)
(Autoregression (1)), TOEP (Toeplitz), Unstructured and ANTE (1) (Ante-dependence)
based on lowest values generated for AIC (Akaike information criterion) and BIC
(Bayesian information criterion)22. ANTE (1) was configured optimal for our data
analysis, due to its ability to estimate both covariance and residual variance within a
given experimental unit17. ANTE (1) also gave us lowest values for our selection criteria.
Result and Discussion
The growth of GR line relative to control line was studied in detail; it is primarily known
that the growth of tobacco can be divided into three stages from point of seed
germination to flowering25.The first stage is the time period in which the seedlings
germinate and establishes itself in the soil. Once in the green house, plants took 1-2
weeks to establish in the soil; this period of adaptation was observed by loss of existing
leaves and initiation of newer leaves. Growth measurements were started at the end of the
first period when plants appeared well established.
33
Figure1: Change in Stem height for GR (n=5) and control (n=3) line between 30 and
126 days. At time equals zero plants are 1 month old from the point of germination. On X
axis time is represented in days and on Y axis height is represented in inches.
Figure2: Change in stem thickness for GR (n=5) and control (n=3) line between 30 and
126 days. At time equals zero plants are 1 month old from the point of germination. On X
axis time is represented in days and on Y axis thickness is represented in inches.
34
Figure3: Change in leaf number for GR (n=5) and control (n=3) line between 30 and
126 days. At time equals zero plants are 1 month old from the point of germination. On X
axis time is represented in days and on Y axis leaf number is represented in numeric
values.
Figure4: Change in leaf width for GR (n=5) and control (n=3) line between 30 and 126
days. At time equals zero plants are 1 month old from the point of germination. On X axis
time is represented in days and on Y axis leaf width is represented in inches.
35
Figure 5: Change in leaf length for GR (n=5) and control (n=3) line between 30 and 126
days. At time equals zero plants are 1 month old from the point of germination. On X axis
time is represented in days and on Y axis leaf length is represented in inches.
In the first stage of growth some morphological differences could be observed between
the GR and control line, leaf petioles were absent in GR line and leafs appeared broader
and longer compared to control. It was observed that within the first month of initiation
from nodes, GR leafs were at 9.2”(wide)x13.16”(long), whereas the control leafs were at
2.31”x 3.5” as seen in figures 4 and 5. Also the GR stem originated to be wider than the
control; GR stem was as wide as 0.6” whereas as control stem was 0.2’’wide as seen in
figure2. The second stage was marked by rapid growth in all aspects of the control line
from 30 days up till 78 days, however this period of rapid growth extended for GR line
until the end of the experiment. During the early second stage from 30 to 54 days leafs
for both GR and control line grew to its full potential at 12”x25” and 5”x8” respectively
as seen in figure4 and 5. The total number of leafs for both control and GR continued at a
similar rate. The most noticeable change in this period was increased accumulation of
above ground biomass in terms of stem height and thickness. Growth in control stem
36
increased steadily up till 78 days, thereafter plants started branching profusely. The
increase in control height after 78 days was only attributed by branching as seen in
figure1, and it also showed signs of budding, which can be marked as the beginning of
reproductive stage in control. On the other hand GR line showed steady increase in its
stem height and thickness with no signs of budding or branching.
Third or the reproductive stage of tobacco plant was marked by flowering in all of
individual control plants. The control plants showed branching with no or little increase
in stem thickness a seen in figure2. However GR line showed a steady increase in stem
height and thickness, and by the end of 126 days GR line showed 3 fold increase in its
overall stem height and thickness compared to control line as seen in figure1 and 2.
Furthermore GR line showed no signs of decrease in its growth until 126 days. The only
similarity observed between GR and control line in all the three stages was the
concomitant increase in the total number of leafs as seen in figure3.
These changes observed in growth rate of GR line are presumably due to mutation caused
by T-DNA insertion; however, we have little information of the disrupted or activated
gene in GR genome at this time. In the past, increased growth rate in tobacco plants has
been achieved exploiting phytohromone metabolism or altering cell-cycle related genes.
Plant hormones are responsible for regulating growth and development of plants
throughout their life span. They interact with each other and other signaling pathways
which effect plant growth. In recent literature Gibberellins (GA) and Brassinosteroids
have been reported to play a vital role in growth associated with stem elongation and
thickness19. In a similar study, transgenic tobacco was engineered to express GA20-
37
oxidase gene from Arabidopsis, showed enhanced biomass accumulation, higher levels of
lignin and early flowering with increased GA production. (1) However GR line showed
no signs of flowering during the control growth cycle. Therefore possibilities of alteration
in GA production in GR line can be ruled out.
In another such study elevated growth rate in tobacco has been reported by over
expressing D-type cyclin (CycD2) gene from Arabidopsis responsible for cell division.
The transgenic tobacco was reported to have taller stem and elevated overall growth rate
with early attainment of flowering7. However in GR line no flowering was observed
during control growth cycle. Therefore possibilities of alteration in GR cell cycle
machinery can also be ruled out.
Growth Rate
Figure6: Change in total biomass accumulation for GR line (n=3), and Control line
(n=3) between 30 and 135 days. On Y axis total biomass is represented in grams and on
X axis time period represented in months.
38
Plant growth is a complex phenomenon; the carbon dioxide assimilated during
photosynthesis may be stored in various plant organelles or used to support growth. The
plant growth rate depends upon both environmental and genetic factors9. The total
carbohydrate assimilated during plant growth can be directly associated to increase in its
total biomass over time. To quantify the plant growth rate usually a parameter R is used.
R may be described as the rate of production of dry matter per unit initial dry weight. R
can be used as an indicator of efficiency of a given plant to produce biomass per unit of
time. R = lnW1 - lnW2 / T2 – T1, Where W1 and W2 are oven dry weight in grams over a
discrete growth interval T1 and T2. (9)
We calculated R for both GR and control line as .007382 and 0.00444 respectively, and
the relative efficiency of GR line to produce biomass was found out to be 1.6 times that
of control line. It can also been seen in figure6, that the GR line produces 240% more
biomass than control line within 135 days of its germination. These observations reflect
the GR potential to produce tremendous amount of biomass in a relatively short duration
of time.
Chemical composition
The plant cell wall in tobacco is broadly classified as primary and secondary cell walls.
These are easily distinguishable by the presence or absence of lignin deposition. The
primary focus of our study is based on the change in secondary cell wall composition in
tobacco leaf and stem during plant growth. The major accumulation of biomass
associated with tobacco plants occurs in the secondary cell wall in the form of cellulose,
lignin and hemicellulose18.
39
We looked at change in biomass accumulation in four GR lines with respect to control
line between 30 days and 90 days. On the basis of our statistical analysis between these
two harvest points we found that GR line (1,2,3,4) had higher rate of biomass
accumulation compared to control line (5) as seen in figure7. Furthermore we found
statistically significant differences between GR lines as summarized in table 1. It was
observed that GR line 4 was significantly different from GR lines (2, 3) and it had the
highest rate of biomass accumulation within GR lines. Whereas no significant differences
occurred between GR line 4 and GR line1 at p>0.05, however at p>0.10 they are
different, as can be seen in figure7.
Figure7: Change in biomass accumulation for GR lines 1, 2, 3 and 4(n=3), and Control
line 5(n=3) between 30 and 90 days. On Y axis total biomass is represented in grams and
on X axis time period represented in months.
40
Table1: Statistically significant differences for total biomass accumulation in secondary
cell wall of GR and control line between 30 & 90 days.
To further understand this change in rate of biomass accumulation in GR lines and
control line we compared the change in their respective cellulose, hemicellulose and
lignin composition (percent basis) between 30 and 90 days. The major chemical
constituents in the secondary cell wall of leaf and stem were similar in GR and control
line. However the percentage of structural carbohydrates varied significantly between
them.
Table 2 shows the Glucan composition in the stems. The analysis indicated that GR line 1
was similar to control line, whereas GR line 2, 3 and 4 were different from control.
Furthermore it can be observed that GR line 2 was different from GR line 3 and 4 and no
difference occurred between GR line 3 and 4. Whereas no statistically significant
differences could be observed in leaf Glucan concentration between GR lines and control
41
line as seen in table 3. However it is important to note that glucan composition dropped
in leafs of all the plant lines between 30 and 90 days as seen in figure9. This can be
explained by the fact that during rapid growth in tobacco it has been reported that
carbohydrates get translocated from leafs to stem to support increasing growth25.
Therefore this drop is more prominent in GR line on account of its higher growth rate
compared to control .Furthermore it is also important to note that between 30 and 90 days
GR lines 2, 4 and 4 stem showed rapid increase in cellulose concentration as seen in
figure8.
Figure8: Change in cellulose deposition for GR lines 1, 2, 3 and 4(n=3), and Control
line 5(n=3) stem between 30 and 90 days. On Y axis Glucan is represented on percent
basis and on X axis time period represented in months.
42
Figure9: Change in cellulose deposition for GR lines 1, 2, 3 and 4(n=3), and Control
line 5(n=3) leaf between 30 and 90 days. On Y axis glucan is represented on percent
basis and on X axis time period represented in months.
Table2: Statistically significant differences for change in lignocellulosic composition in
secondary cell wall of GR and control line stem between 30 & 90 days
43
Table3: Statistically significant differences for change in lignocellulosic composition in
secondary cell wall of GR and control line leaf between 30 & 90 days.
Table 2 summarizes the results for xylan composition in the stem. The analysis indicated
that all the GR lines were different from control, and there were significant differences
within GR lines; GR line 1 was different from 2, 3 and 4 and similarly GR line 2 was
different from GR line 1, 3, 4, and no difference occurred between GR line 3 and 4. It
was also observed the xylan concentration increased in GR lines 2, 3, and 4, whereas it
decreased in control and GR line 1 between 30 and 90 days as seen in figure10.
44
Furthermore on the basis of change in leaf xylan, all GR lines were statistically different
control line as summarized in table 3 and there was concomitant drop in xylan and glucan
in all GR lines as seen in figure9 and figure11.
Figure10: Change in hemicellulose deposition for GR lines 1, 2, 3 and 4(n=3), and
Control line 5(n=3) stem between 30 and 90 days. On Y axis Xylan is represented on
percent basis and on X axis time period represented in months.
Figure11: Change in hemicellulose deposition for GR lines 1, 2, 3 and 4(n=3), and
Control line 5(n=3) leaf between 30 and 90 days. On Y axis Xylan is represented on
percent basis and on X axis time period represented in months.
45
On the basis of change in lignin in GR and control stem, statistically analyzed results are
summarized in table 2. The results prove that all GR lines were different from control
lines, and there were significant differences between GR lines; GR line 1 was different
from GR line 3 and 4 and GR line 4 was different from GR line 1, 2, and 3. Whereas no
difference occurred between GR line 2 and 3, and GR line 1 and 2. Furthermore on the
basis of change in leaf lignin, all GR lines were statistically different from control line,
and there were differences within GR line as well; GR line 4 was different from GR line
2 and 3, and no differences observed between GR line 1 and GR lines (2, 3, 4) as
summarized in table 3. It was also observed that leaf lignin concentration in all plant lines
increased between 30 and 90 days as seen in figure13. This can explained by the fact that,
as plants leafs get older lignin composition tends to increase18. However it was also
observed that stem lignin concentration (percent basis) dropped in GR line 1, 2, and 3,
whereas it increased in control line and GR line 4 as seen in figure12.
Figure12: Change in lignin deposition for GR lines 1, 2, 3 and 4(n=3), and Control line
5(n=3) stem between 30 and 90 days. On Y axis total lignin is represented on percent
basis and on X axis time period represented in months.
46
Figure13: Change in lignin deposition for GR lines 1, 2, 3 and 4(n=3), and Control line
5(n=3) stem between 30 and 90 days. On Y axis Glucan is represented on percent basis
and on X axis time period represented in months.
These changes in Glucan and Xylan concentration indicate that there was an overall
increase in cellulose and hemicellulose in secondary cell wall of GR line relative to
control line. Whereas as the lignin dropped in GR lines 1, 2 and 3 compared to control
line. These changes may be somehow associated with higher growth rate in GR line and
needs further analysis. What we already know about the GR line is that the T-DNA
insertion somehow disrupted or activated an unknown plant gene which resulted in such
radical changes in GR physiology. It is possible that the GR gene disrupted regulators
responsible for plant cell wall biosynthesis. There have been recent developments in
functional genomics related to transgenic tobacco specifically altered in its
lignocellulosic composition to better understand changes in cellulose, hemicellulose and
lignin biosynthetic pathways8,4. It has been established that cellulose, hemicellulose and
lignin biosynthetic pathways are interdependent and any increase or decrease in
concentration of lignocellulosic components in tobacco secondary cell wall can be
47
directly correlated with concomitant changes in expression of associated gene at
transcriptional level8.
In a recent study lignin biosynthetic enzyme, O-methyl transferase (OMT) has been
reported to increase overall biomass accumulation in transgenic tobacco without any
changes in lignin deposition2,4. In another such study Cinnamoiyl-COA-reductase (CCR)
was down regulated in transgenic tobacco which resulted in overall drop in lignin and
simultaneous increase in xylose and cellulose concentration in tobacco secondary cell
wall5,15. These results resonate with our finding in GR line as it showed an overall
increase in biomass accumulation with concomitant increase in xylose and cellulose
concentration with respect to control line. It was also observed that in GR lines (1, 2, and
3) lignin deposition drops between 30 and 90 days. Therefore there is a possibility that
expression of OMT and COMT related genes is being altered in GR line.
However it is hard to speculate how lignin concentration changes within GR line stem
after 90 days due to experimental limitations, but it was observed that on average lignin
concentration in GR line stem at 135 days was higher than control line as seen in
figure14. This brings us to another possibility that there may be a delay in in lignin
deposition in GR line 1, 2 and 3 between 30 and 90 days. This hypothesis can be
correlated with a recent finding where lignin accumulation was delayed in transgenic
tobacco with altered expression of phenylpropanoid pathway associated enzyme class II
cinnamate 4-hydroxylase3. However to better understand these changes in GR line we
need further analysis using proteomics of cell wall related genes.
48
Figure14: Glucan, Xylan and Lignin concentration in GR (n=4) and Control (n=3) line
stem at 135 days.
Figure15: Glucan, Xylan and Lignin concentration in GR (n=4) and Control (n=3) line
stem at 135 days.
49
Conclusion
The giant recombinant was obtained after a routine Agrobacterium mediated genetic
transformation.GR line was characterized relative to control line; it was concluded that
the relative efficiency of GR line to produce biomass was found out to be 1.6 times that
of control line. GR line also accounted for 240% more biomass relative to control line
within 135 days of its germination. It was also observed that there were significant
differences in lignocellulosic composition within GR lines (1, 2, 3, 4) and compared to
control line during plant growth which need further analysis and may lead to
identification of unknown GR gene. Identification of GR gene with enhanced biomass
productivity can provide a breakthrough that will help secure future energy resource. The
GR gene could conceivably turn any other plant species and eco-friendly fuel source that
can help alleviating global dependence on fossil fuels
50
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54
APPENDIX
Sample SAS CODE:
Data Glucan;
do plantline= 1 to 5;
do rep=1 to 3;
input subject @@;
do time=30,90;
input Glucan @@;
thr=time/30;
th2=thr*thr;
output;
end;
end;
end;
datalines;
1
33.22640489
33.50265384
2
34.16762699
31.90636455
3
35.55962435
33.89666505
4
24.36385764
34.53505701
5
29.71597434
33.79750621
6
28.29800559
32.16857203
7
22.91810191
35.32355045
8
22.56839771
34.44263457
9
22.90929911
34.06263443
10
23.91101249
33.58624802
11
18.31847571
32.89844168
12
19.31516726
33.7919419
13
32.22565317
32.15704768
14
31.66298303
32.12943025
15
30.87524484
32.8612922;
proc print;
run;
title 'Compound Symmetry with full model';
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=phenotype thr phenotype*thr th2 phenotype*th2/htype=1 ddfm=kr;
random _residual_/subject=subject type=cs;
run;
title 'Unstructured with full model';
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=diet thr phenotype*thr th2 phenotype*th2/htype=1 ddfm=kr;
random _residual_/subject=subject type=un;
run;
title 'AR(1) with full model';
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=phenotype thr phenotype*thr th2 phenotype*th2/htype=1 ddfm=kr;
55
random _residual_/subject=subject type=ar(1);
run;
title 'Toeplitz with full model';
run;
proc glimmix data=glucose;
class phenotype subject;
model Glucan=phenotype thr phenotype*thr th2 phenotype*th2/htype=1 ddfm=kr;
random _residual_/subject=subject type=toep;
run;
title 'Ante-dependence with full model';
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=phenotype thr phenotype*thr th2 phenotype*th2/htype=1 ddfm=kr;
random _residual_/subject=subject type=ante(1);
run;
title 'Ante-dependence with full model';
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=phenotype thr phenotype*thr th2 phenotype*th2/htype=1 ddfm=kr solution;
random _residual_/subject=subject type=ante(1);
run;
title 'Ante-dependence with separate estimates for slope and quadratic by treatment';
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=phenotype thr(phenotype) th2(phenotype)/htype=1 ddfm=kr solution noint;
random _residual_/subject=subject type=ante(1);
ods output parameterestimates=solf;
run;
data solf;
drop phenotype;
set solf;
pheno=phenotype;
run;
proc print data=solf;
run;
proc sort data=solf;
by pheno;
run;
proc transpose data=solf (keep=pheno effect estimate) out=solpl;
by pheno;
id effect;
var estimate;
run;
proc print data=solpl;
run;
data ploteq;
set solpl;
by pheno;
do x=1 to 3 by 1;
Glucan=phenotype+thr_phenotype_*x+th2_phenotype_*x*x;
output;
end;
run;
56
proc print data=ploteq;
run;
title 'Glucan by phenotype over time';
run;
goptions colors=(red) i=join;
symbol1 v=star;
symbol2 v=circle;
symbol3 v=square;
symbol4 v=triangle;
symbol5 v=z;
symbol6 v=x;
proc gplot data=ploteq;
plot Glucan*x=pheno;
run;
plot Glucan*x=pheno/
autohref lhref=2
chref=lime
autovref lvref=5
cvref=pink
caxis=blue
ctext=red ;
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=phenotype thr(phenotype) th2(phenotype)/htype=1 e1 noint solution;* ddfm=kr solution
noint;
random _residual_/subject=subject type=ante(1);
run;
proc glimmix data=Glucan;
class phenotype subject;
model Glucan=phenotype thr(phenotype) th2(phenotype)/htype=1 e1 noint solution;* ddfm=kr solution
noint;
random _residual_/subject=subject type=ante(1);
contrast 'variety 1 vs 2 linear' thr(phenotype) 1 -1 0 0 0 th2(phenotype) 1 -1 0 0 0 ;
contrast 'variety 1 vs 3 linear' thr(phenotype) 1 0 -1 0 0 th2(phenotype) 1 0 -1 0 0 ;
contrast 'variety 1 vs 4 linear' thr(phenotype) 1 0 0 -1 0 th2(phenotype) 1 0 0 -1 0 ;
contrast 'variety 1 vs 5 linear' thr(phenotype) 1 0 0 0 -1 th2(phenotype) 1 0 0 0 -1 ;
contrast 'variety 2 vs 3 linear' thr(phenotype) 0 1 -1 0 0 th2(phenotype) 0 1 -1 0 0 ;
contrast 'variety 2 vs 4 linear' thr(phenotype) 0 1 0 -1 0 th2(phenotype) 0 1 0 -1 0 ;
contrast 'variety 2 vs 5 linear' thr(phenotype) 0 1 0 0 -1 th2(phenotype) 0 1 0 0 -1 ;
contrast 'variety 3 vs 4 linear' thr(phenotype) 0 0 1 -1 0 th2(phenotype) 0 0 1 -1 0 ;
contrast 'variety 3 vs 5 linear' thr(phenotype) 0 0 1 0 -1 th2(phenotype) 0 0 1 0 -1 ;
contrast 'variety 4 vs 5 linear' thr(phenotype) 0 0 0 1 -1 th2(phenotype) 0 0 0 1 -1 ;
contrast 'variety 1 vs 2 quadratic' th2(phenotype) 1 -1 0 0 0 ;
contrast 'variety 1 vs 3 quadratic' th2(phenotype) 1 0 -1 0 0 ;
contrast 'variety 1 vs 4 quadratic' th2(phenotype) 1 0 0 -1 0 ;
contrast 'variety 1 vs 5 quadratic' th2(phenotype) 1 0 0 0 -1 ;
contrast 'variety 2 vs 3 quadratic' th2(phenotype) 0 1 -1 0 0 ;
contrast 'variety 2 vs 4 quadratic' th2(phenotype) 0 1 0 -1 0 ;
contrast 'variety 2 vs 5 quadratic' th2(phenotype) 0 1 0 0 -1 ;
contrast 'variety 3 vs 4 quadratic' th2(phenotype) 0 0 1 -1 0 ;
contrast 'variety 3 vs 5 quadratic' th2(phenotype) 0 0 1 0 -1 ;
contrast 'variety 4 vs 5 quadratic' th2(phenotype) 0 0 0 1 -1 ;
run;
57
Files included in CD-ROM
a) Complete Statistical Analysis
b) Calculations EXCEL files