ABRIL GUEVARA A.Pdf

GENETIC IMPROVEMENT OF CELL WALL COMPOSITION AND WATERLOGGING TOLERANCE OF SORGHUM BICOLOR

ALEJANDRA ABRIL GUEVARA

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2019

To my parents and sister for all their unconditional love and support

ACKNOWLEDGMENTS

I would like to thank my advisor Dr. Wilfred Vermerris for his guidance, training, support, and trust. I also want to thank Dr. Diane Rowland for providing me with equipment, time, revisions to my water-table experiments, Dr. Thomas Colquhoun and Dr. Claudio Gonzalez for allowing me to use their lab facilities and equipment for some of the C4H purification related experiments, and Dr. Matias Kirst for allowing me to use some of his lab equipment for experiments and sample processing. In addition, I would like to thank all my committee members mentioned above for all their contributions, collaborations, comments and help which made my manuscripts and work of higher quality.

I also would like to thank Dr. Chulhee Kang and from Washington State University and

Dr. Scott Sattler from the USDA-ARS in Lincoln, NE for providing me with the recombinant sorghum cytochrome P450 reductase, which was essential part of my experiments on C4H activity. I am also grateful to Dr. Luisa Trindade, Behzad Rashidi and Dr. Andres F. Torres from

Wageningen University, The Netherlands, for their help with the ion chromatography experiments, and to Dr. Lauren McIntyre for her guidance on the statistical analysis of my data.

Dr. Felix Fritschi and Dr. Sougata Bardhan from the University of Missouri were instrumental for the experiments in the flooding channels and the selection of parents for the waterlogging mapping populations.

And finally, I want to thank all my lab mates and undergraduate students whose work made an important contribution to all the field, lab experiments and publications result of my research.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 7

LIST OF FIGURES ...... 8

LIST OF ABBREVIATIONS ...... 11

ABSTRACT ...... 13

CHAPTER

1 LITERATURE REVIEW ...... 16

2 MAGNESIUM IN ROOTS CONTRIBUTES TO ENHANCING STEM BIOMASS ACCUMULATION IN SORGHUM BICOLOR (L.) MOENCH UNDER HIGH- WATER-TABLE CONDITIONS ...... 32

Introduction ...... 32 Materials and Methods ...... 35 Screening for Variation in Response to Waterlogging Under Field Conditions ...... 35 Evaluation of Responses to High Water Table in Greenhouse Conditions ...... 36 Experimental design ...... 36 Root morphology ...... 37 Root mineral analysis: inorganic content in the roots ...... 38 Chlorophyll a fluorescence and concentration ...... 39 Height, root and stem biomass ...... 40 Statistical analysis ...... 40 Results...... 43 Root Mineral Analysis: Inorganic Content in Roots ...... 44 Chlorophyll a Fluorescence and Concentration ...... 45 Root Morphology ...... 46 Data Analysis ...... 47 Discussion ...... 48

3 PURIFICATION OF ACTIVE FORMS OF TWO RECOMBINANT SORGHUM BICOLOR TRANS-CINNAMATE 4-HYDROXYLASES ...... 65

Introduction ...... 65 Materials and Methods ...... 73 Identification of Sorghum bicolor C4H Orthologs and Their Expression Profile ...... 73 Hydropathy Analyses of SbC4H2 (505 aa) and SbC4H1 (501 aa) Protein Sequences ...76 SbC4H1 and SbC4H2 cDNA Sequence Optimization ...... 76 Gene Synthesis, Cloning and Validation ...... 77

Protein Expression and Purification ...... 78 SbC4H Sequencing ...... 80 Enzymatic Activity ...... 83 Results...... 85 Identification of Sorghum bicolor C4H Orthologs and Their Expression Profile ...... 85 Hydropathy Analysis of SbC4H2 (505 aa) and SbC4H1 (501 aa) Protein Sequences ...86 SbC4H1 and SbC4H2 Sequence Optimization ...... 88 Gene Synthesis, Cloning and Validation ...... 88 Protein Expression and Purification ...... 88 Enzymatic Activity ...... 91 Discussion ...... 93

4 GENERATION OF A BIPARENTAL MAPPING POPULATION FOR GENETIC ANALYSIS OF WATERLOGGING TOLERANCE IN SORGHUM BICOLOR ...... 119

Introduction ...... 119 Materials and methods ...... 121 Screening for Response to Waterlogging Under Field Conditions ...... 121 F1 Generation and Developing of The Mapping Populations ...... 122 Phenotypic Screening of Subset of Waterlogging Population in Flooding Channels ...123 Results...... 124 Discussion ...... 125

5 CONCLUSIONS ...... 136

APPENDIX: FINAL AND MODIFIED CDNA SEQUENCES FOR SORGHUM C4H1 AND C4H2 ...... 140

LIST OF REFERENCES ...... 143

BIOGRAPHICAL SKETCH ...... 165

LIST OF TABLES

Table page

2-1 Root tips mineral correlations in control (C) and waterlogged (W) plants...... 64

2-2 Traits separated by effect. Statistical significance was calculated using model in Equation 2-2...... 64

3-1 SbC4H primers used for gene expression analysis. Fragment sizes correspond to the cDNA lengths...... 116

3-2 mSbC4H2 and Sb03C4H-NCH2 cloning primers (5'-> 3') into EK pET32 LIC ...... 116

3-3 mSbC4H2 and Sb03C4H-NCH2 sequencing primers ...... 116

3-4 Dilution series used to generate a calibration curve to measure SbC4H expression levels. pET28 refers to the vector in which fragments of cDNA to be quantified by qRT-PCR were cloned, 0910 (SbC4H1), 8160 (SbC4H2), 7460 (SbC4H3) and UBI3 (Sb01g030340)...... 117

3-5 Quantitative real time PCR program used to determine the expression of sorghum C4H genes ...... 118

3-6 Ingredients of the enzymatic activity assay and their corresponding concentrations ...... 118

4-1 Initial biparental population crosses made with tolerant (T) by sensitive (S) parental lines...... 131

4-2 Development of the biparental population of IS 29314  IS 12883. Number of families per year per generation are shown...... 131

4-3 Development of the biparental population of IS 7131  IS 22799...... 132

4-4 Field planting map of F7 individuals from the biparental population IS 29314  IS 12883...... 133

LIST OF FIGURES

Figure page

2-1 Average height at week 6 of plants under waterlogged or well-watered (control) conditions...... 53

2-2 Dry stem weight vs. genotype after seven weeks under waterlogged or well-watered (control) conditions...... 54

2-3 Plant growth rate from week 1 to week 6 of the six selected genotypes...... 55

2-4 Mineral concentrations in root tips (thickness ≤ 1.5mm) vs. genotype after seven weeks under waterlogged or well-watered (control) conditions...... 56

2-5 Dissipated energy flux per reaction center (DIo/RC) was affected by genotype (p- value <0.001), but not by treatment...... 57

2-6 Relative chlorophyll content of the flag leaf measured after six weeks of waterlogging treatment vs. well-water controls...... 58

2-7 Sorghum roots composition and changes over a period of five weeks...... 59

2-8 Principal component analysis (PCA) of data collected with the rhizotron camara...... 60

2-9 Net change of total average root diameter ( 5-1) for the six selected genotypes...... 61

2-10 Root growth and senescence of the six selected genotypes...... 62

2-11 The arrow marks a root growing under water, outside the pot, that is exposed to light. Photo courtesy of author...... 63

3-1 Sequence alignment of the 60 N-terminal amino acids of Sorghum bicolor (Sb) C4H1(Sb02g010910) and C4H2 (Sb03g038160) and Arabidopsis thaliana (At) C4H...... 97

3-2 SbC4H2 amino acid sequence (505 aa) analyzed with ProtScale (software) (Gasteiger et al., 2005) ...... 97

3-3 SbC4H1 amino acid sequence (501 aa) analyzed with ProtScale (software) (Gasteiger et al., 2005) ...... 98

3-4 Hydropathy scale based on the Kyte & Doolittle scale of polarity. Individual values for the 20 amino acids are displayed (Kyte and Doolittle, 1982)...... 98

3-5 Prediction of transmembrane domains and signal peptides in SbC4H1...... 99

3-6 Prediction of transmembrane domains and signal peptides in SbC4H2...... 100

3-7 Validation of a 100% protein identity of mSbC4H1 and mSbC4H2 with SbC4H1 and SbC4H2 respectively, despite the corresping cDNA optimizations...... 101

3-8 Initial expression and validation of modified and recombinant SbC4H...... 102

3-9 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying protein expression and degradation of SbC4H...... 103

3-10 Nitrocellulose membrane displaying western blots following incubation with anti- 6×His-antibodies...... 104

3-11 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying purified mSbC4H2: 6×His-TRX-SbC4H (MW ~71kDa)...... 104

3-12 Cinnamate 4-hydroxylase (C4H) converts trans-cinnamic acid (left) into p-coumaric acid (right)...... 105

3-13 RNA samples and cDNA samples after PCR amplification with SbC4H1 primers...... 105

3-14 SbC4H1 (left) and SbC4H2 (right) qRT-PCR amplicon calibration curves for quantification of m RNA transcripts...... 106

3-15 Melting curve from 55-95˚C with the PCR products obtained from the reaction with (A) SbC4H1 and (B) SbC4H2 primers...... 106

3-16 Summary of constructs used for analysis...... 107

3-17 UV-absorbance spectrum of the different compounds used in the enzymatic activity assay...... 108

3-18 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying total protein extract from E. coli BL21(DE3) Rosetta cells incubated through time ...... 109

3-19 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying purified and dialyzed mSbC4H1 and mSbC4H2...... 109

3-20 SbC4H1 and SbC4H2 differencial expression quantified in different tissues and developmental stage of sorghum plants in the field...... 110

3-21 GC/MS-MS-generated total ion current (TIC) chromatograms of reactions containing mSbC4H1...... 111

3-22 GC/MS-MS-generated total ion current (TIC) chromatograms of reactions containing mSbC4H2...... 112

3-23 GC/MS-MS generated chromatogram of enzymatic reactions containing mSbC4H1 or mSbC4H2...... 113

3-24 GC/MS-MS-generated total ion current (TIC) chromatograms of enzymatic reaction controls...... 114

3-25 SbC4H protein identification based on sequencing...... 115

4-1 Correlation of dry stem biomass between waterlogged replicates in the left (L) and right (R) side of the flooded channel ...... 127

4-2 Phenotyping of 87 F7 lines originated from IS 29314  IS 12883 biparental population...... 128

4-3 Average of dry stem biomass of two waterlogged replicates in the left (L) and the (R) side of the channel and the rain-watered controls...... 129

4-4 Average of aerenchyma formation scored on a scale from 0 (no aerenchyma) to 5 (all plants displaying noticeable aerenchyma of two waterlogged replicates in the left (L) and the (R) side of the channel and the rain-watered controls...... 130

LIST OF ABBREVIATIONS

C4H Cinnamate 4-hydroxylase

CA trans-cinnamic acid

CPR Cytochrome P450 reductase

CS Excited cross section

DIo/CSo Dissipated energy flux per cross section

DIo/RC Dissipated energy flux per reaction center

JABS/CSm =Fm Absorbed photon flux per cross section (or also, apparent PSII antenna size)

JABS/RC Average absorbed photon flux per PSII reaction center (or also, apparent antenna size of an active PSII)

JOET2/CS Electron transport flux from QA to QB per cross section

JOET2/RC Electron transport flux from QA to QB per PSII

JOTR/CS Maximum trapped exciton flux per cross section

JOTR/RC Maximum trapped exciton flux per PSII

LHC light-harvesting complex a/b

Mg Magnesium

OEC oxygen evolving complex pCA p-coumeric acid

PIABS Performance index for energy conservation from photons absorbed by PSII antenna, to the reduction of QB

PITABS Performance index for energy conservation from photons absorbed by PSII antenna, until the reduction of PSI acceptors

PITCSm Performance index on cross section basis

PSI (II) Photosystem I (Photosystem II)

QA primary quinone acceptor of PSII

QB secondary quinone acceptor of PSII

RC Reaction center

RC/CSm The number of active PSII RCs per cross section

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

GENETIC IMPROVEMENT OF CELL WALL COMPOSITION AND WATERLOGGING TOLERANCE OF SORGHUM BICOLOR

Alejandra Abril Guevara

May 2019

Chair: Wilfred Vermerris Major: Plant Molecular and Cellular Biology

Climate change and increasing energy demand due to human population growth necessitate the improvement of food and bioenergy crops production. Additionally, changes in the patterns of rainfall can create periodic droughts as well as periodic flooding. Flooding in agricultural areas constitutes a threat for numerous hectares of farm land causing billions of dollars in losses to government and farmers. Sorghum (Sorghum bicolor (L.) Moench) is an attractive bioenergy crop due to its potential high yield and tolerance to a wide range of environmental conditions. In the context of climate change and the need for sustainable agricultural development, it is necessary to favor selection of traits that increase resilience, resulting in constant yields in frequently and unpredictably changing weather. The use of low- productivity land can sustainably expand the acreage available for agriculture, especially for bioenergy crops. Low-productivity lands include drought-prone, low-fertility land, as well as flood plains of major rivers and flood-prone land. Tolerance of sorghum to waterlogging has been observed in numerous experiments under field and greenhouse conditions. Although some physiological and morphological responses to waterlogging stress have been described, the mechanisms of response and subsequent acclimation to waterlogging conditions in sorghum are not fully understood.

As a result of this research we have identified multiple genotype-dependent response mechanisms to waterlogging of six sorghum genotypes and reported that their responses vary depending on the severity of the stress. High phenotypic plasticity was observed in all the genotypes given that regardless of being previously classified as waterlogging-tolerant or - sensitive, all genotypes enhanced biomass accumulation under high-water-table conditions. An increased magnesium concentration in the root tips was highly correlated (~45%) with enhanced stem biomass accumulation without having an observable effect on the photosynthetic performance. We also determined that ‘tolerance’ depended upon the stress context. The results of this experiment contribute to a better understanding of how to enhance biomass production of

Sorghum bicolor when cultivated in low-productivity land and, along with two waterlogging biparental populations that we have generated, will contribute to a more detailed understanding of the genetic variation of this trait.

In order to improve the biomass conversion properties of sorghum, cell wall architecture, especially lignin content and/or composition can be modified. Lignin is an aromatic polymer that forms a physical barrier to cellulolytic enzymes used to convert cellulose to fermentable sugars.

While the impact of modifying the expression of several genes involved in monolignol biosynthesis has been determined in sorghum, the role of trans-cinnamate 4-hydroxylase (C4H) remains to be elucidated. C4H, a cytochrome P450 hydroxylase anchored to the membrane of the endoplasmic reticulum, is a pivotal enzyme that converts cinnamic acid to p-coumaric acid, a precursor for several classes of compounds, including monolignols/lignin, flavonoids, isoflavonoids, tannins, anthocyanins, and benzoic acid. The membrane-bound nature of native

C4H has made it difficult to elucidate its structure, catalytic mechanism and potential interactions with other enzymes. This dissertation research resulted in the expression and

purification of active, soluble forms of SbC4H1 and SbC4H2. The availability of these enzymes enables detailed structural and kinetic characterization that can ultimately be used to reroute metabolic flux through the monolignol biosynthetic pathway with the aim of modifying lignin composition or lignin content.

CHAPTER 1 LITERATURE REVIEW

The human population is expected to reach between 9 and 10 billion people by the middle of the century. Coupled with an increasing standard of living in southeast Asia, the global demand for food, water, and energy will increase substantially (Jackson, 2005; Mano et al.,

2005b; Lauer, 2008; Colmer and Voesenek, 2009; Ahmed and Rafii, 2012). The Food and

Agriculture Organization of the United Nations (FAO) has calculated that the demand for food is expected to increase by 60% by 2050 (FAO, 2012). However, studies accounting for agricultural areas around the globe have evaluated the trends of crop yield production and have demonstrated that although yield has continually increased in several areas - especially in the developed countries - it has been reported that in 24-39% of maize-, rice-, wheat- and soybean-growing areas, yields have either stagnated or collapsed. At the same time, the human population has increased by 60% jeopardizing food security (Ray et al., 2012). Variation on crop yield can be attributed to multiple factors including fertilizer use, water availability and climate change resulting from an increase in the concentration of greenhouse gases due to human activity since the industrial revolution.

The link between crop yield climate change has been studied in many crops were the effects of temperature, precipitation, drought, water and fertilizer management, soil preparation and maintenance have been analyzed. Although in some scenarios higher concentrations of atmospheric CO2 can increase crop yields up to 5% in many countries (Sakurai et al., 2014;

Deryng et al., 2016), the effects of temperature, indirect effect of higher concentration of atmospheric CO2, can be significantly detrimental for food crops. For the United States alone, it has been predicted that warmer temperatures will decrease average yields of corn, soybean and

cotton by 30–46% before the end of the century under the slowest warming scenario and by 63–

82% under the most rapid warming scenario (Schlenker and Roberts, 2009).

Already, due to climate change related yield loses, the world-wide production of maize and wheat has declined 3.8% and 5.5%, respectively (Lobell et al., 2011).

Maize, rice, wheat, and soybeans together account for over 60% of the world’s food

(Tilman et al., 2011). Yield loses in these crops signify losses for governments, farmers, and put in danger the food availability for consumers. In 2012 alone, more than $1.6 billion worth of maize and soybean was lost due to flooding in the mid-west of the United States (Bailey-Serres et al., 2012).

Furthermore, integrated assessment models have been used to model the costs of climate change in terms of economics and energy consumption. It has been estimated that an 11% increment in energy will be required to cool and/or heat homes at the end of the century as a consequence of both lower or higher temperatures (Deschênes and Greenstone, 2011).

Changes in the patterns of rainfall can create periodic droughts as well as periodic flooding. According to the National Oceanic and Atmospheric Administration of the United

States (NOAA), approximately 75% of all presidential disaster declarations are associated with flooding. In 2012, Southern California, the North East, Alaska, the Gulf Coast, and Florida were identified as having the highest flood frequencies regions in the United States (Michel-Kerjan et al., 2012). Flooding can be caused by different phenomena, such as flash flooding, overflowing rivers, intense rainfalls over short periods of time, jams of snow or ice causing rivers to overflow, or failure of water control systems such as levees or dams. Flooding in these areas constitutes a threat for many hectares of farmland causing billions of dollars in losses to government and farmers. According to the most recent data from the National Weather Service,

most of the agricultural areas of the United States face  10% risk of flooding due to proximity to rivers (NOAA, 2018). Estimated crop losses in the U.S. due to excess of water amounted to

$3.1 billion (reviewed by Voesenek and Bailey-Serres (2013)) and 70% reduction in harvest in the same year (Bailey-Serres et al., 2012).

Even though there has been an increase in the incidence of flooding, soil water resources and availability have been predicted to be limited in the next few decades due to climate change and population growth generating a major shift in vegetation, ecosystems and agriculture

(Gerten et al., 2007; Schewe et al., 2014; Schlaepfer et al., 2017). Many countries will experience water scarcity, and others with water-rich areas will struggle with the quality of the water, causing public health complications. Scarcity of water will be associated with fast growing cities as well as agricultural areas that do not receive adequate rainfall. On the other hand, more severe drought events are expected in arid and semi-arid regions, further increasing the challenge of water availability, distribution, and usage (Vörösmarty et al., 2007; Elliotta et al., 2014).

Anticipated changes in temperature combined with the difficulty of predicting the weather and limited water resources, necessitate the development of crop plants that are able to acclimate quickly to within-season environmental changes. In this context, technification of farms and crop improvement through traditional breeding and/or genetic engineering will be necessary to achieve the required amounts of food and energy in the future reliably.

Excessive amounts of water associated with flooding deplete soil oxygen, compromising root respiration and gas exchange forcing plants to obtain ATP through anaerobic metabolism

(Mustroph et al., 2010). Anaerobic metabolism for production of energy is not as efficient as aerobic respiration, which is why crop performance is usually compromised (Lauer, 2008).

However, some plants have developed mechanisms to survive and in some cases even morphological, physiological and biochemical adaptations to normally grow and develop under the excess of water (Gunawardena et al., 2001; Mano et al., 2005b; Xu et al., 2006; Sasidharan and Voesenek, 2015; Takahashi et al., 2016). Our research aimed at a better understanding of the effects of water submergence on sorghum and the development of flooding-tolerant lines will enable crop production in flood-prone areas. In this dissertation, the identification of multiple genotype-dependent response mechanisms to waterlogging stress is documented, which offers the potential to utilize some of the land not suitable for other crops.

The burning of fossil fuels is the main source of greenhouse gas accumulation and climate change. Transportation alone is responsible for 23% of the world’s energy-related greenhouse gas emissions where 95% of it is derived from fossil fuels, mostly diesel (31%) and gasoline (47%) (Kahn Ribeiro et al., 2007). EPA (2018) reported that transportation accounted for 28% of the greenhouse gas emissions in 2016, most of it derived from burning petroleum based fuels as diesel and gasoline (EPA, 2018). During the 20th century, materials such as dyes, inks, plastics, clothing and, synthetic fibers were made from plant sources. However, their primary source of this materials has been shifting towards petroleum by-products up to 70% in

1989 (Ragauskas et al., 2006)

With the need to mitigate the environmental impact caused by extraction, processing, and usage of petroleum, biofuels have been proposed as an alternative and sustainable way to produce energy, chemicals, and materials currently produced from hydrocarbons. Ethanol is by far the most produced biofuel worldwide. According to the Renewable Fuels Association, in

2015, The United States alone accounted for the production of over 60% of the bioethanol used

in the world, with a total volume of ~60 billion liters, mostly from corn starch, in 2017 (Batres-

Marquez, 2018).

Currently, ~99% of bioethanol is blended with gasoline for use as vehicle fuel in the

United States (Rusco, 2012). It has been demonstrated that E10 (10% ethanol, 90% gasoline) can be used in most spark ignition engines, as a way to enhance the octane number (Abdel-Rahman and Osman, 1997) while reducing the carbon monoxide emissions by 30% (Reviewed by Yüksel and Yüksel, 2004).

In 2010 the Environmental Protection Agency (EPA) approved the use of E15 (15% ethanol, 85% gasoline) in vehicles built after 2001. The highest blend available is E85 (85% ethanol, 15% gasoline), but it can only be utilized in so-called flex-fuel vehicles (RFA, 2011).

Biodiesel can be produced via trans-esterification of oils and fast for use in diesel engines

(Rahman et al., 2017).

To date, the majority of fuel ethanol is produced by microbial fermentation of monosaccharides derived from corn starch, sugar cane, and sugar beets, whereas the majority of the biodiesel is derived from soybeans, sunflowers, canola, cottonseed, peanuts and animal fats.

All these crops are used for food production, which has raised ethical concerns, known as the

‘food vs. fuel debate’. Due to biofuel production, some projections suggest that food prices could increase by 8%, 13%, 7% and 35% for wheat, grains, oilseeds, and vegetable oil, respectively, in order to meet policies of gasoline blending or biofuels in energy use. This demonstrates that a shift of existing agricultural production towards biofuels will aggravate food security by decreasing food availability and increasing their prices (FAO et al., 2011; Suweis et al., 2015). In addition, concerns have been raised over the environmental impact associated with the use of these crops, due to the need for fertilizer and irrigation (Rosenzweig et al., 2014; Stehle and

Schulz, 2015; Spera et al., 2016). Environmental sustainability is now an important consideration associated with the commercial production of biofuels. The sustainability is affected by the organisms from which the fuel is generated (plants, bacteria, algae), their cultivation and processing. The European Commission has reported that 81% of biofuels reduce the CO2 emissions by 30%. However, almost half of them have a higher net environmental impact than fossil fuels. In order to have a sufficient area to cultivate soybeans in Brazil, areas of the Amazon forest need to be deforested causing increasing concentrations of CO2 and major negative impacts on biodiversity. However, depending on the crop inputs and management biofuels can in principle be produced in an environmentally friendly manner (Hischier et al., 2007).

To decrease the competition between food and fuel, second-generation biofuels have been proposed as an alternative. Second-generation biofuels are also called lignocellulosic biofuels or advanced biofuels. Lignocellulosic biomass consists primarily of plant cell walls. Its name refers to the complex of polymers that make up the cell wall: cellulose, hemicellulose, and lignin. Cellulose is a linear polymer of -1,4-linked D-glucopyranose (glucose) synthesized from

UDP-activated glucose molecules by membrane bound enzymes called cellulose synthases

(CesA’s) (Morgan et al., 2013; Kumar and Turner, 2015). Hemicellulosic polysaccharides are not homogeneous and their composition varies within plant species and cell types.

Hemiceluloses include xyloglucans, xylans, mannans and glucomannans (present in all plants in different ratios), and β-(1→3,1→4)-glucans (only present in grasses). Their primary function is to provide structure and support to the cell wall, interacting with cellulose and lignin (Scheller and Ulvskov, 2010).

As part of the processing of lignocellulosic biomass to fuels, cellulose is broken down into its individual glucose molecules through enzymatic saccharification. The resulting glucose

molecules are fermented to ethanol, with Saccharoyces cerevisiae (baker’s yeast) the most commonly used microbe (Sedlak and Ho, 2004). The biomass conversion to ethanol takes place in biorefineries, where this process is integrated with power and heat production and value-added chemicals from biomass (Fernando et al., 2006). There are facilities with different capacities and they are generally located in close proximity to the bioenergy feedstocks in order to avoid transportation and reduce production costs.

In the United States, most of the biorefineries are currently located in very close proximity to corn production areas in the Midwest region of the United States (Rusco, 2012).

According to the Renewable Fuels Association, there are 168 ethanol biorefineries in the

U.S. as of January 2018 across ten states dedicated exclusively to the conversion of corn starch:

Iowa (43), Nebraska (26), Minnesota (22), South Dakota (15), Illinois (14), Indiana (14), Kansas

(12), Wisconsin (9), Ohio (7), and Missouri (6). In contrast, there are only four biorefineries dedicated to the conversion of cellulosic material to ethanol located between Iowa (2), Kansas, and Florida with a total capacity of 314.2 million liters per year. Biorefineries that process corn and grain sorghum are located in Kansas (3) and California (3) with a total capacity of 708 million liters per year. There are two biorefineries located in Iowa able to convert starch as well as cellulosic material from corn with a capacity of 655 million liters per year (RFA, 2018).

The University of Florida Institute of Food and Agricultural Sciences (IFAS), owned The

Stan Mayfield Biorefinery Pilot Plant located in Perry, FL, in association with Buckeye

Technologies, Inc. The plant was managed by the Florida Center for Renewable Chemicals and

Fuels from the Department of Microbiology and Cell Science at The University of Florida. This pilot plant was established with the objective of investigating microbial metabolic engineering for biocatalysts development, environmental microbiology to reduce hazardous chemical and

biological substances from soils and water, enzyme development for depolymerization of cellulose and hemicellulose, biomass processing, and hydrogen research to understand hydrogen metabolism and development of applications in production of biofuels and chemicals

(https://fcrc.ifas.ufl.edu/stan-mayfield-biorefinery/).

The United States Energy Independence and Security Act mandates production of 80 billion liters of biofuels from cellulosic biomass and other non-grain sources in the United States by 2022 (U.S. Congress, 2007). For 2017, The Environmental Protection Agency final volume requirements standards under the renewable fuel standards program for 2017 mandated the production of 1.17 billion liters of cellulosic ethanol. Based on the U.S. Bioenergy statistics of the USDA, in 2017, the~60 billion liters of bioethanol produced were derived for the most part

(94%) from corn starch (https://www.ers.usda.gov/data-products/us-bioenergy-statistics/us- bioenergy-statistics/).

The lag in cellulosic ethanol production has been attributed to the higher cost of production, which has reduced the competitiveness with fuels derived from petroleum, and to the need for changes in infrastructure associated with the production and processing of biomass.

The higher cost of production is the result of biomass recalcitrance, which reduces the efficiency of enzymatic saccharification. Recalcitrance can be reduced by thermochemical pretreatments that take place prior to saccharification of cell wall polysaccharides. The addition of this extra step requires equipment, chemicals, high temperature and pressure and trained personnel. Pre-treatments can be classified in four major groups: 1) physical (particle size reduction, milling); 2) chemical (alkaline, acid, ionic liquid, or organosolv pretreatments) ,3) physico-chemical (liquid hot water, steam explosion, ammonia fiber expansion (AFEX),

oxidative, and SPORL-sulfite pretreatments); and 4) biological (pretreatments with enzymes or microorganisms) (Seidl and Goulart, 2016).

Different pretreatments use different mechanisms and have different effects on the lignocellulosic biomass. Some of them are used to help solubilize cellulose (e.g. ionic liquid pretreatment), reduce cellulose crystallinity (alkaline, oxidative, organosolv, ionic liquid), reduce particle sizes of cellulose (steam explosion, organosolv, SPORL and biological), whereas other pretreatments solubilize lignin (acid, alkaline, oxidative, organosolv, AFEX) (Chen et al., 2009;

Krishnan et al., 2010; Saritha et al., 2012; Qu et al., 2017). Pretreatments are selected depending on the feedstock, cost of operation, and local regulations concerning the operation of high- pressure equipment or the use of hazardous chemicals. The choice of pretreatment determines the quality, nature and yield of cellulose, hemicellulose and lignin, affects the amount of energy and water required for conversion, influences the type of end-by-products, and determines the levels of production of CO2, (Seidl and Goulart, 2016). Together with the cellulolytic enzymes, pretreatment represents an expensive step in the production of cellulosic biofuels that has limited their ability to compete with gasoline (Van Rijn et al., 2018).

Transitioning from sugar- and starch-based to mostly lignocellulosic biofuels to meet the energy demand in the near future will require major changes in infrastructure. For example, strategies for the production, harvest, and transportation of biomass will need to be developed and rural economic development closely assessed (Richard, 2010).

In 2000, it was estimated that more than 20% of the world's population inhabits what are called ecological hotspots, areas rich in endemic species and also threatened by human activities such as agriculture (Cincotta et al., 2000). Keeping up with food and energy demands will require intensification of agriculture and big human-induced ecological changes in biodiversity

are expected. However, the environmental impacts will depend on how this intensification is done.

In order to keep the agricultural advances sustainable, it becomes necessary to consider land use management (Chaplin-Kramer et al., 2015; Fischer et al., 2017). Based on data from

91% of the world’s countries, Tilman et al. (2011) made projections for 2050 taking into consideration global average yield, global nitrogen use, area of land clearing, and greenhouse gas emissions based on the intensification (increase productivity on a given area) or extensification

(increase area of production) required to increase crop yield to satisfy the necessary future food/energy demand. It was estimated that more than one billion hectares would need to be cleared to accommodate new crop land. However, they demonstrated that by intensifying already existing agriculture in places where it is inefficient (poorer countries or low-productivity land) the environmental effect on intensification can be lessened. The authors concluded that a combination of technology transfer and crop improvement needs to be implemented in order for this approach to be successful (Tilman et al., 2011).

Low-productivity (‘marginal’) land is being considered for the cultivation of bioenergy crops in order to limit impacts on biodiversity, endangered species and native forests. Low- productivity lands include drought-prone, low-fertility land, as well as flood plains of major rivers. These conditions pose new challenges for geneticists, plant physiologists and plant breeders to develop crops that are not only able to survive, but that also generate sufficiently high yields to be economically sustainable. In the context of climate change and the need for sustainable agricultural development, it is necessary to favor selection of traits that increase resilience, resulting in constant yields in frequently and unpredictably changing weather conditions over short periods of time (Howden et al., 2007; Binju Abraham et al., 2014). For

example, it is known that root diameter, root length and density specially at depths in soil with available water are some of the traits that favor rice and corn to growth and development under periods of drought (Comas et al., 2013).

Sorghum bicolor (L.) Moench is a C4 grass that originated in Africa (De Wet and Harlan,

1971), it is an attractive crop due to its ability to tolerate a wide range of environmental conditions, including environments not suitable for other crops such as drylands, lands prone to flooding, high-salinity soils, and alkaline soils. The Sorghum bicolor genome sequence was released in 2009 (Paterson et al., 2009). At 730 Megabasepair (Mbp), the sorghum genome is approximately 1/3 of the maize genome (2.3 Gbp) (Schnable et al., 2016), which, combined with its diploid nature (2n = 20) makes it an attractive genetic model for the Poaceae.

Four classes of sorghum are cultivated worldwide: grain, forage, sweet and biomass sorghum. This classification reflects different end uses, and has led to divergent phenotypes. The different classes of sorghum are, however, all part of the same species and can be readily crossed with each other.

Grain sorghum is cultivated for the seed and it is commonly used as feed for livestock as bird seed. In equatorial Africa, India and China, sorghum grain is also as a staple food. Recently, grain consumption in developed countries has increased due to the high concentrations of anthocyanins in the seeds whose antioxidant properties offer potential benefits for human health

(Reviewed by Awika and Rooney, 2004). Grain sorghum was first introduced to the United

States during the slave trade, when it arrived as a grain sorghum, known as ‘Guinea corn'. It is also known as milo. In 2017, sorghum was the third most produced grain crop in the United

States after corn, wheat, and rice, with a production of 9,241,330 Mg of grain (USDA and

NASS, 2018).

Recently, given sorghum’s adaptability and wide range of stress tolerance, there has been a growing interest to utilize sorghum varieties for animal feed, syrup production, and eventually biofuels.

Forage sorghum is cultivated for livestock consumption. Brown midrib (bmr) varieties are of particular interest for this type of sorghum given their lower lignin content which facilitates rumen digestibility (Porter et al., 1978; Oliver et al., 2005; Xin et al., 2008). Forage sorghum has a higher yield potential than corn under limited supplies of water and fertilization, making it a suitable alternative as scarcity of resources increases with climate change (Getachew et al., 2016).

Sweet sorghums, which accumulate soluble sugars in their stems, are cultivated for the production of syrup and, more recently, for biofuel production (Prasad et al., 2007). Sweet sorghums are assessed based on the refractometer value of the juice (measured in ºBrix), a measurement of the soluble solids in solution, which can be used as an approximation for the concentration of sucrose, glucose and fructose in the juice. Two classes of sweet sorghum exist, one with sucrose as the predominant sugar in the juice, and one with a mix of sucrose, glucose and fructose (Murray et al., 2009; Wang et al., 2009), although the sugar composition can also depend on developmental stage (Almodares et al., 2008). Refractometer values between 12-24

ºBrix are considered sweet sorghum varieties (Reviewed by Regassa and Wortmann, 2014).

Sweet sorghum varieties, were independently introduced into the United States in the 1850s, and this crop became the basis of the American syrup industry and expanded after disrupted production of cane syrup during The Civil War (Winberry, 1980). However, the availability of cheaper glucose syrups from sugar cane destabilized the sorghum syrup industry, causing a sharp decline by the end of the 1870s (Winberry, 1980).

Biomass sorghum is grown exclusively for bioenergy purposes. It is a relatively new use of sorghum developed for long growing seasons. Most commercially available biomass sorghums are photoperiod-sensitive, whereby flowering is triggered primarily based on shortening day length. It has been determined that the time to floral initiation is regulated by four main Maturity loci, Ma1, Ma2, Ma3, and Ma4 that together will determine maturity, with a range between 40 and >100 days to flowering (DTF) (Quinby, 1974). Biomass sorghums are of particular interest for lignocellulosic biofuel production given their high nitrogen use efficiency, and long vegetative growth resulting in large amounts of biomass accumulated in the stalks

(Olson et al., 2013; Truong et al., 2017).

To improve the biomass conversion of sorghum, cell wall architecture, especially lignin concentration and/or subunit composition can be modified. Lignin is a cell wall polymer synthesized from phenylpropanoid precursors. The general phenylpropanoid pathway is of great importance in plants as it produces numerous chemicals involved in physiological responses, plant structure, reproduction, UV protection, and pathogen-defense responses (Pontin et al.,

2010; Fleck et al., 2011; Gallego-Giraldo et al., 2011; Watanabe et al., 2013; Yokawa and

Baluška, 2015). The hydroxycinnamoyl-CoA esters that are synthesized via the general phenylpropanoid pathway are subsequently reduced to hydroxycinnamyl alcohols, and the phenolic ring can be substituted with hydroxyl and methoxyl groups, resulting in monolignols that can polymerize to lignin. In grasses, the three monolignols that give rise to lignin are p- coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, with the latter two being predominant

(Vanholme et al., 2010). These monolignols are synthetized in the cytosol then transported to and across the plasma membrane and finally into the cell wall. Within the cell wall, the monolignols undergo dehydrogenation of their hydroxyl groups through the action of

peroxidases and hydrogen peroxide, or laccases and molecular oxygen (Sterjiades et al., 1992;

O’Malley et al., 1993; Zhao et al., 2013). The result are radicals that can undergo oxidative coupling. Newly formed radicals are added to the growing lignin polymer. When assembled in the polymer, the above mentioned monolignols give rise to p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) subunits, respectively (Ralph et al., 2004; Vermerris and Abril, 2015). The lignin present in the plant cell wall creates a barrier for hydrolytic enzymes that can break down cellulose and hemicellulose. This is advantageous to the plant as a defense mechanism against pathogens, but it also limits the enzymatic saccharification and subsequent conversion to ethanol.

Many efforts have been made to develop plants with reduced recalcitrance, because lignin modification improves the yield of fermentable sugars for biofuel production (Chen and Dixon,

2007).

Reduction in lignin concentration and changes in lignin composition reduces cell wall recalcitrance, which translates into additional available cellulose and hemicellulose for enzymatic hydrolysis. In sorghum, brown midrib (bmr) mutants, genotypes where genes encoding enzymes of the general phenylpropanoid or monolignol biosynthetic pathway are mutated, have a reduced concentration of lignin content and an altered subunit composition

(Saballos et al., 2008; Saballos et al., 2012; Sattler et al., 2012). These, genotypes with reduced lignin content are desired for higher ruminant digestibility and higher glucose yields from biomass with and without pretreatment, enhancing the ethanol production process (Oliver et al.,

2005; Saballos et al., 2008; Sattler et al., 2012).

Enhancement of total fermentable sugar yield in lignin-modified plants have been reported in several plants relevant for bioenergy production, including sugar cane, switchgrass,

poplar, eucalyptus and sorghum (Ranocha et al., 2002; Saballos et al., 2008; Fu et al., 2011; Jung et al., 2012; Sattler et al., 2012; Sykes et al., 2015).

As part of this dissertation research, efforts were focused on the enzyme trans-cinnamate

4-hydroxylase (C4H). In plants, C4H converts trans-cinnamic acid to p-coumaric acid, a precursor for several classes of compounds, including monolignols/lignin, flavonoids, isoflavonoids, tannins, anthocyanins, and benzoic acid. The biochemistry and structure of C4H, and its potential interactions with other enzymes remain poorly understood. A better understanding of the structure and catalytic mechanism of C4H is expected to enable protein engineering with the aim of manipulating metabolic flux and hence cell wall architecture to improve biofuel production. This study attempts the expression, characterization, and preparation for crystallization of sorghum C4H.

Structural biology is a growing field of molecular biology and biochemistry which attempts to elucidate three-dimensional structures of biological macromolecules (e.g. proteins and nucleic acids) to understand their function and the mechanisms by which they work and interact with other molecules. There are currently several methods to study molecule structures including X-ray crystallography which is among the most accurate methods to model protein structures. It calculates probabilities of atomic positions based on measures of angles and intensities of the diffracted beams upon interaction with the crystallized protein. Given the importance of proteins in cell function and structure, determining their conformation has enable critical advances in medicine and agriculture based on detailed understanding of protein- substrate interactions that led to the generation of drugs (Bray et al., 2014) and herbicides (Funke et al., 2006).

Lignin constitutes one of the major waste in biorefineries. In some cases, the polymer itself is burned to generate energy to sustain biorefinery labor. However, lignin-based products have been proposed as a strategy to increase the competitiveness of biofuels relative to fossil fuels and chemicals. Industrial lignin is reactive and has antimicrobial and antioxidant properties, that make it an ideal candidate to develop lignin-fused-polymer materials (Thakur et al., 2014).

Development of lignin-based materials has potential environmental and economic benefits of lignin-based products, lignin-based products like plastics and carbon fibers can be used to replace materials which currently originate from crude oil. This will not only alleviate the exhaustion of crude from the planet but also will make the biomass conversion for biofuel a much more efficient process. The lignin-based products from which more revenue can be obtained are called value-added products. It is know that physicochemical properties of materials derived from lignin depend on both lignin composition and lignin extraction methods (Ten et al.,

2014).

Current research in lignin-based materials shows development in polymers as thermoplastics, rubber and, foams (Thakur et al., 2014). Lignin-based materials can be found in pharmaceutical applications as well, lignin-based capsules have been developed to encapsulate either hydrophobic or hydrophilic drugs (Tortora et al., 2014; Yiamsawas et al., 2014). In the same manner, porous materials have been developed. It is possible to find lignin-based membranes for water purification and aerogels with potential applications with insulating and adsorbent properties (Reviewed by Duval and Lawoko, 2014). Similarly, lignin present in the waste of biorefineries can be used as a source to generate lignin nanotubes (LNTs), which have the ability to penetrate into the nucleus of human HeLa cells. All features may enable the use of

LNTs a method for gene therapy (Ten et al., 2014).

CHAPTER 2 MAGNESIUM IN ROOTS CONTRIBUTES TO ENHANCING STEM BIOMASS ACCUMULATION IN SORGHUM BICOLOR (L.) MOENCH UNDER HIGH-WATER- TABLE CONDITIONS

Introduction

Increasing demand for food, feed, fodder and energy due to human population growth necessitate increased crop production. The Food and Agriculture Organization of the United

Nations (FAO) reported that worldwide demand for food is expected to increase by 60% by 2050

(FAO et al., 2012). There is evidence that climate change has a negative impact on global crop production. For example, due to yield losses, the total production of maize and wheat has declined 3.8% and 5.5%, respectively (Lobell et al., 2011). Studies to determine how climate change in particular will affect food availability projected that extreme climate and weather, including floods, will reduce food production (Porter et al., 2014). Based on eleven climate models, flood frequency is estimated to increase in certain regions of all six populated continents

(Hirabayashi et al., 2013). Flooding constitutes a threat for numerous hectares of farmland, causing billions of dollars in losses to government and farmers around the world (Porter et al.,

2014). In order to meet the anticipated demand, there is a need for improving the yield of existing crops, developing more resilient crops, and expanding production to marginal areas, such as low-fertility soils and floodplains.

Sorghum bicolor is a C4 grass that originated in Africa (De Wet and Harlan, 1971). It is an attractive food, feed and fodder crop due to its potential high yield and tolerance to a wide range of environmental conditions. It is used worldwide predominantly as livestock feed, but it is cultivated as a food crop in Africa and India, and is gaining interest in Western countries following its reported health benefits. Sorghum grain, the soluble sugars in the juice of sweet sorghums, and vegetative parts of the plant can serve as feedstock for the production of

renewable fuels and chemicals (reviewed by Awika and Rooney (2004); Regassa and Wortmann,

2014). In 2017, sorghum was the fourth most produced grain in the United States after corn, wheat and rice, with a production of 9,241,330 Mg (USDA and NASS, 2018).

Excessive amounts of water in the soil have negative effects on crop performance by limiting growth and development (Lauer, 2008). One of the major consequences of waterlogging is the depletion of soil oxygen (anoxia). Diffusion of oxygen through water is 104 fold slower than in air (Armstrong and Drew, 2002). This reduction jeopardizes root respiration and gas exchange necessary for photosynthesis, forcing plants to obtain ATP through mainly anaerobic metabolic pathways, such as glycolysis, fermentation, and alternative respiration (Mustroph et al., 2010). Anaerobic metabolism in roots results in stomatal closure, a reduction of the CO2 exchange rate, and a significant reduction in mineral uptake and hence mineral concentration in leaves. All these responses decrease the plant’s photosynthetic capacity, measured as the quantum efficiency of photosystem II (PSII) (Ashraf et al., 2011; Caudle and Maricle, 2012;

Verma et al., 2014). Cell membrane permeability (uptake of water and solutes) is mainly regulated by aquaporins, water channel proteins associated with the cell plasma and vacuolar membranes (Reviwed by Javot and Maurel, 2002). Anoxia in roots has been demonstrated to decrease the capability of roots to take up water, resulting in a reduced cytoplasmic pH and a disturbance of the cell’s biochemical balance (Tournaire-Roux et al., 2003; Törnroth-Horsefield et al., 2006).

Plants have developed several adaptations to enable survival in excess of water.

Morphological adaptations such as the development of aerenchyma (air pores within roots and stems that allow gas exchange) enhances the assimilation of nitrogen, phosphorus, and potassium up to 70% (Postma and Lynch, 2011; Yin et al., 2013). In some species, anatomical,

morphological and biochemical waterlogging responses (e.g. reduced Rubisco activity and stomatal position in leaves) provide an adequate physical environment for gas exchange and photosynthesis while roots are immersed in water (Mommer et al., 2005). Waterlogging has been shown to trigger production of the phytohormones abscisic acid and gibberellins that in turn stimulate ethylene production (Xu et al., 2006; Chen et al., 2011b; Sasidharan and Voesenek,

2015). In maize, it has been demonstrated that ethylene biosynthesis is strongly correlated with aerenchyma development, since this hormone promotes programmed cell death inside roots and stems (Arora et al., 2017). Many of these adaptations are regulated primarily by ethylene transduction cascades at different levels (reviewed by Voesenek and Bailey-Serres (2013)).

Interactions between auxin and ethylene signaling induce adventitious roots, allowing roots to form above the water and carry out gas interchange in the air (Ahmed and Rafii, 2012). These complementary processes generate an efficient ventilation system by which O2 and CO2 can be transported through the plant even when the main root system is under water. Moreover, waterlogging stress forces plants to activate metabolic pathways to manage or re-route compounds such as ethanol and lactate, which are generated during anaerobic metabolism. In canola (Brassica napus L.) this ability has been highly correlated with waterlogging tolerance

(Xu et al., 2016). Similarly, excess water activates transcription of ARE genes, named after the anaerobic responsive elements (AREs) they contain. These genes share conserved sequences within the promoter regions, suggesting the waterlogging-inducible nature of these genes as observed in the Alcohol dehydrogenase1 and -2 (Adh1 and Adh2) genes in maize and rice

(Marshall et al., 1973; Walker et al., 1987; Dennis et al., 1988; Dolferus and Jacobs, 1994; Fukao et al., 2006; Mustroph et al., 2010; Arora et al., 2017). In sorghum, gene expression studies comparing genotypes with contrasting responses to waterlogging indicated that selected

aquaporins play an important role in managing the response to waterlogging in roots (Kadam et al., 2017).

Although the physiological and morphological responses to waterlogging stress have been described in some species, the mechanisms of response and subsequent acclimation to waterlogging stress in sorghum are not fully understood. The purpose of this study was to evaluate the response to waterlogging in six different sorghum genotypes. During a period of six weeks plants were cultivated in pots that were submersed in water and monitored using non- invasive methods such as chlorophyll a fluorescence and root growth and development. After 6 weeks, stem and root biomass, plant height, and the concentrations of various ions in the roots were measured. Additionally, the relationships between waterlogging-stress responses in the roots versus the above-ground parts of the plant were evaluated, as well as the relationship between root morphology and root functioning (nutrient uptake). The ultimate goal was to obtain a more detailed understanding of the effects of water submergence on sorghum plants to aid the development of waterlogging-tolerant lines that will enable crop production in flood-prone areas.

Materials and Methods

Screening for Variation in Response to Waterlogging Under Field Conditions

In order to identify sorghum genotypes with different responses to waterlogging, two replicates of the sorghum minicore, a collection consisting of 242 diverse genotypes (Upadhyaya et al., 2009), were seeded at the University of Missouri Horticulture & Agroforestry Research

Center (HARC) near New Franklin, MO on 20 July 2013. This facility contains channels of 100 m long  5 m wide that can be inundated in a controlled manner. The seeds were placed in rows of 2 m long, running parallel to the long axis of the channel, and spaced 76 cm apart; a 1.2-m alley separated blocks of five adjacent rows. Two checks, inbred lines BTx623 and M81E, were included as border rows to minimize edge effects on the genotypes of the minicore. Two rows of

each of these genotypes were also included in random positions inside each of the channels in order to be able to compare growth and development across different channels. The waterlogging treatment was initiated on 18 September 2013 and maintained for a period of 5 weeks. The plants’ response to waterlogging was monitored by visual observation of yellowing of the plants

(no yellowing, some yellowing, complete yellowing) and the degree of lodging (no, some, complete) and measuring plant height (before and the initiation of waterlogging and at three time points after initiation). A follow-up screening was performed in 2014 with the 12 genotypes from the minicore, that represented the six most and the six least sensitive to waterlogging in 2013.

These were seeded in three replicates on 15 July 2014 inside a single channel, with cultivars

‘Atlas’ and M81E used controls. They were subjected to waterlogging on 12 September 2014 for a period of 5 weeks. In this case propensity to lodging and leaf discoloration (yellowing) were recorded after 2 and 4 weeks as a measure of susceptibility to waterlogging. On 3 January 2015, a greenhouse follow-up study was carried out with the seven most contrasting genotypes from the 2014 field replicate. These sorghum genotypes were tested for tolerance or sensitivity to waterlogging conditions for four weeks. For each genotype three pots with three plants per pot were grown in the greenhouse for 11 weeks, until they reached developmental stage 3

(Vanderlip, 1993). Then two pots were randomly selected and placed in four tubs filled with water, while the remaining pot was used as control. The entire root system was under water.

Height, total number of leaves and percentage of yellow leaves were recorded for each plant once a week.

Evaluation of Responses to High Water Table in Greenhouse Conditions

Experimental design

Six genotypes from the minicore collection that were classified as either tolerant or sensitive based on their response during the field screenings in 2013 and 2014 were selected: IS

29314, IS 7131, IS 12883, IS 4092, IS 19389, IS 22799. Follow-up experiments were carried out in the greenhouse where four pots per genotype were prepared with a combination of potting soil and perlite (Miracle-Gro® Potting Mix 0.21-0.11-0.16). Nine seeds per genotype were planted, and germination was observed 3 days after planting for all genotypes in at least one of the pots.

The placement of the pots was randomized to minimize confounding effects between genotype/treatment and environmental variation (light, temperature) typically present in greenhouse. Plants were thinned to three plants per pot. Pots designated to be under high-water- table treatment were located in large tubs. When seedlings reached stage 2 according to the sorghum developmental guide (Vanderlip, 1993), two pots per genotype were flooded. Tubs were filled with water to the 25-cm mark and this water level was maintained for the entire duration of the experiment, effectively submerging 30% of the root system. The position of the pots in the tubs and the position of the tubs in the greenhouse were randomized at the start of the experiment. Two pots per genotype were used as controls and placed in tubs, but maintained under well-watered conditions.

Root morphology

Pictures of the root system were taken once per week for five weeks (five sessions) after application of the treatment. Root images were taken with a BTC5-2009 Rhizotron camera

(Bartz Technology Corp., Santa Barbara, CA) inserted in the pots through a transparent plexiglass tube in the bottom of the pot. The external sections of the tube had been painted black, and the tubes were capped to prevent light from influencing the root system inside the pot. The camera was designed to lock in the tube to ensure that images were always taken in the same location. A total of five sessions were recorded with an average of 18 positions per tube; images were taken at 1.3-cm intervals. A total of 2,300 images were captured and analyzed with

WinRhizoTron 2011 software (Regent Instruments Inc., Québec, QC, Canada). Live and dead

root tissue could be distinguished based on color (white vs. red or brown). Parameters selected for analysis were: Total length (mm), 0 < Length  1.5 mm, length > 1.5 mm (Iijima et al.,

2003), total surface area (SA), 0 mm < SA  1.5 mm, SA > 1.5 mm, surface area of live roots, length of dead roots (mm), surface area of dead roots, average diameter of dead roots (mm/10), average diameter of all roots and of live roots (mm/10).

Root mineral analysis: inorganic content in the roots

Root samples were collected from root tip and root base in each of the pots. Root tips corresponded to 2-cm long segments from the root tip upwards. The root base was a 2-cm-long root segment adjacent to the stem. Samples were quickly washed and immediately frozen in liquid nitrogen. Roots were freeze-dried (FreeZone 6 Liter Console-freeze dry system,

LABCONCO, Kansas City, MO) for 72h at -70˚C and ground in a 2000 Geno/Grinder® with three series of 60 seconds at ~1400 strokes/minute or until samples were completely pulverized.

From each pot, an aliquot of 25-50 mg dried and ground sample was weighed in a glass tube.

Tubes were heated in a muffle furnace (Carbolite CWF 1100; Carbolite Gero Ltd., Hope, UK) for 10 h at 575˚C. Samples were cooled down in a desiccator to avoid re-moistening, 1 ml of 3M formic acid was added to each tube. The capped tubes were shaken and incubated at 99˚C for 15 min. and allowed to cool down to room temperature. When the ash was completely dissolved, 9 ml of ultrapure water prepared with a Milli-Q® Integral Water Purification System (Billerica,

MA, USA) was added to create a 10× diluted sample. In IC tubes (11 ml polypropylene tubes

(Metrohm, Zofingen, Switzerland)), samples were filtered with a 0.45 µm filter, an aliquot of

100 µL from the 10× diluted samples was added to 9.9 ml ultrapure water to create a 1000× diluted sample for analysis. Solutions for calibration were prepared using the certified multi- element ion chromatography anion and cation standard solutions (Sigma-Aldrich, St. Louis, MO,

USA) in ultrapure water. Anions and cations were analyzed separately. The anion running buffer consisted of 2 mM Na2CO3, 2 mM NaHCO3, 1% (v/v) acetone, and cation running buffer consisted of 3 mM HNO3, and 1% (v/v) acetone. A linear calibration curve was developed with serial dilutions of the standard solutions using concentrations of 0.5, 1, 2, 4 and 10 ppm.

- 2- 3- Concentrations of select anions (chloride (Cl ), sulfate (SO4 ), phosphate (PO4 )) and cations

(sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+)) were quantified. Samples were analyzed using an 881 Compact IC pro–Anion and Cation Metrohm® Ion Chromatography system using the Start Magic Net 3.0 software. For the analysis of anions a Metrosep A 150,

150/4.0 mm column equipped with a Metrosep C5/5 Supp 4/6 Guard column was used. For the analysis of cations a Metrosep C4 Supp 4, 250/4.0 mm column equipped with a Metrosep A

Supp 4/6 Guard column was used. The total concentration of minerals was calculated in µg/mg of dry weight based on the linear calibration curve mentioned above.

Chlorophyll a fluorescence and concentration

The chlorophyll a fluorescence induction curve was evaluated in dark-adapted sorghum leaves (Kautsky effect). Leaves were dark-adapted for one hour prior to taking measurement with an OS30+ fluorometer (Opti-Sciences Inc., Hudson, NH, U.S.A.) with power set at 50%, actinic light of 3500 µmol for 35 seconds. Measurements were taken at 12 PM (to ensure steady- state in photosynthesis) once a week for seven weeks after the initiation of high-water-table treatment. The measurements were recorded for the center of the uppermost fully developed leaf of each plant in each pot. Calculations of the JIP-test parameters were calculated based on Stirbet and Govindjee (2011) and dissipation parameters were calculated based on Strasser et al. (2004).

Only parameters that revealed a relationship between reaction centers and cross sections were utilized for further analysis (JABS/CSm, JABS/RC, JOET2/CS, JOET2/RC, JOTR/CS,

JOTR/RC, PIABS, PITABS, PITCSm, RC/CSm, DIo/CSo, DIo/RC). The relative concentration

of chlorophyll was measured in the mid-part of the uppermost fully developed leaf of each plant with a SPAD. The relative chlorophyll concentration was measured just once, in week six after the initiation of the high-water-table treatment.

Height, root and stem biomass

Height was measured weekly for each individual plant as the distance from the soil surface to the whorl or to the flag leaf if it developed. At the end of the experiments, roots were washed, cleaned and collected in paper bags, oven-dried for 7 days at 40˚C and weighed. Three stems per pot were cut, stripped and chopped in 10-cm segments, collected in paper bags then oven-dried for 7 days at 40˚C and weighed.

Statistical analysis

Multivariate correlation (Pearson Product-Moment Correlation) and principal component analyses were applied to identify the correlation between parameters of chlorophyll a fluorescence (JIP-test) and root mineral analysis. Highly correlated parameters were grouped and only one or two were selected for further analysis. Root morphology measurements were condensed into five informative parameters that summarized the performance of the roots over time. Cook’s D test and residuals observations were used to remove outliers.

Root morphology. The coefficient of variation (CV) for total root length was used as a measurement of root-growth rate to compress the five time points into one informative number.

The difference in total average diameter between week 5 and week 1 was used to represent the change in diameter. Positive values indicate an increment in root thickness, whereas negative values indicate an increment in thin roots over time.

Root mineral analysis. Given the biological importance of Mg2+ and Ca2+ ions in photosynthesis and root growth, these two minerals were analyzed separately. A principal component analysis (PCA) was performed to evaluate the relationship between the different

concentrations of the remaining minerals. A representative subset, including K+ and sulfates

2- (SO4 ), was selected based on the PCA.

Chlorophyll a fluorescence and concentration. The JIP-test data was subjected to principal component analysis to define how these variables could be grouped to explain the variance observed. SPAD measurements were analyzed independently.

Plant height, root and stem biomass. The coefficient of variation of height measurements was used as a representation of change in stem height (growth) over time (Height-

CV) and final height (Height6) was used for further analysis. The variables flowering time

(DAP, days after planting) and stem biomass were used independently.

Multivariate analysis. A total of fifteen parameters (traits) were selected for further

2+ 2+ + 2- analysis: DIo/CSo, DIo/RC, JOET2/RC, Mg and Ca , K and sulfate (SO4 ), total root length-

CV, total length5 (final length), dead length-CV (root senescence measured in root length; CV: coefficient of variation), and total average diameter 5-1, Height-CV, Height6, SPAD, and dry stem weight. All statistical analyses were performed using JMP® Pro 13.0.0 (SAS, Cary, NC,

USA)

To evaluate evidence for genotype-by-treatment interaction, two models were fit: a model including genotype and treatment main effects and genotype-by-treatment interaction effect shown in Equation 2-1 and a model with only genotype and treatment main effects shown in

Equation 2-2.

푦푖푗 = 푡푖 + 푔푗 + 푡푖 × 푔푗 +  (2-1)

푡ℎ where 푦푖푗 is the observed trait value for the 푗 genotype (푗 =IS29314, IS7131, IS12883, IS4092,

푡ℎ IS19389, and IS22799), in the 푖 treatment (푖 = high water table, control), 푡푖 is the fixed

treatment effect, 푔푗 is the fixed genotype effect and 푡푖 × 푔푗 is the fixed genotype by treatment interaction effect.

푦 = 푡 + 푔 +  푖푗 푖 푗 (2-2)

푡ℎ where 푦푖푗 is the observed trait value for the 푗 genotype (푗 =IS29314, IS7131, IS12883, IS4092,

푡ℎ IS19389, and IS22799), in the 푖 treatment (푖 = high water table, control), 푡푖 is the fixed treatment effect and 푔푗 is fixed genotype effect.

The two models were compared using the Bayesian information criterion (BIC; Neath and Cavanaugh, 2012). Parameters were considered to make a significant contribution when p- values < 0.05

In order to determine the predictability of stem dry weight, each parameter was fitted in model 3 (presented in Equation 2-3) treated as covariant along with treatment and genotype as follows:

푦 = 푡 + 푔 + 퐶 +  푖푗 푖 푗 푘(푖푗) (2-3)

푡ℎ where 푦푖푗 is the observed dry stem value for the 푗 genotype (푗 =IS29314, IS7131, IS12883,

푡ℎ IS4092, IS19389, and IS22799) in the 푖 treatment (푖 = high water table, control), 푡푖 is the fixed

푡ℎ 푡ℎ treatment effect, 푔푗 is fixed genotype effect and, 퐶푘(푖푗) is the 푘 trait value for the 푗 genotype

푡ℎ 2+ 2+ + 2- in the 푖 treatment (푘 = DIo/CSo, DIo/RC, JOET2/RC, Mg and Ca , K and sulfate (SO4 ), total root length-CV, total length5 (final length), dead length-CV, and total average diameter 5-1,

Height-CV, Height6, SPAD).

Finally, phenotypic correlations between stem dry weight (g) and each one of the fifteen parameters selected were calculated using the Fit Y by X platform using the default settings in

JMP® Pro 13.0.0.

Results

Tolerance of sorghum to waterlogging has been observed in numerous experiments under field and greenhouse conditions (Pardales et al., 1991; Singla et al., 2003; Jain et al., 2010;

Promkhambut et al., 2011; Kadam et al., 2017). This finding has been particularly intriguing given the fact that sorghum originated in the dry African savannas (De Wet and Harlan, 1971).

Based on initial greenhouse and field studies where 100% of the root system and part of the stem were submerged, six genotypes of the sorghum minicore were selected because of their contrasting phenotypes. Genotypes IS7131, IS29314, and IS19389 were considered tolerant to waterlogging, whereas genotypes IS22799, IS12883, and IS4092 were considered sensitive to waterlogging. During the initial screening, most of waterlogged plants either did not grow as fast as the controls or stopped growing altogether. The rate of senescence of the lower leaves was higher for waterlogged plants than for controls. All waterlogged plants generated aerial roots from higher nodes above the water. Similarly, waterlogged plants generated denser root systems with higher numbers of lateral roots. When comparing sensitive and tolerant genotypes, sensitive genotypes had between 40 and 100% yellow leaves, or were dead, whereas the tolerant genotypes showed values between 0 and 20%, similar to the controls (Data not shown). In contrast, during high water table experiments only 30% of the soil was under water, reducing the severity of the waterlogging stress that the plants experienced.

The purpose of this study was to evaluate the mechanism(s) used by these genotypes to maintain biomass production while their roots are partially submersed. Biomass yield is a relevant trait for sorghum cultivated as a feedstock for the production of renewable fuels and chemicals. During a period of six weeks, the six selected genotypes were cultivated in the greenhouse under control (well-watered) and high-water-table conditions and various physiological and morphological changes were monitored. Plant height was measured to evaluate

plant growth. A rhizotron camera was used to monitor root growth inside the pots during the course of the experiment, and chlorophyll a fluorescence was measured to monitor photosynthetic activity during the period of waterlogging. Stem and root biomass, flowering time and the concentrations of ions in the roots were measured at the end of the experiment.

After seven weeks under high-water-table conditions, this treatment was observed to stimulate plant growth in all six genotypes: the waterlogged plants were ~30% taller compared to the controls (Figure 2-1) and dry biomass yield was 84% greater. Plants subjected to high water table displayed a significant increase in stem biomass accumulation (p = 0.0004) (Figure 2-2).

This can be explained by a 22% increase in the growth rate (Height-CV) under high-water-table conditions. Height–CV was shown to be affected by treatment and genotype, with p-values of

0.0002 and < 0.0001, respectively (Figure 2-3). When height6 was added as a covariate in model presented in Equation 2-3, it showed a significant effect (p < 0.0001), indicating that some of the variation in dry stem biomass is explained by the height at week 6.

Root Mineral Analysis: Inorganic Content in Roots

One of the major consequences of waterlogging is the depletion of oxygen (anoxia). This depletion jeopardizes root respiration and gas exchange necessary for nutrient absorption and transport (Armstrong and Drew, 2002), and subsequently photosynthesis, plant growth and development. In order to evaluate the effects of high-water-table conditions on root functioning,

- 2- 3- the concentrations of anions [chloride (Cl ), sulfate (SO4 ), phosphate (PO4 )] and cations

[sodium (Na+), potassium (K+), magnesium (Mg2+), calcium (Ca2+)] in root samples were quantified.

A principal component analysis revealed that minerals could be classified into three groups (M1, M2, M3) based on the changes in concentrations in response to high-water-table

2+ 2+ + + + 3- conditions. Group M1 included Mg and Ca , group M2 K , Cl , Na , PO4 and group M3

2- 2+ 2+ +, 2- SO4 . Minerals in group M2 were highly correlated (Table 2-1). Mg , Ca , K and SO4 were selected as representative root minerals for inclusion in the integrated data analysis.

Different mineral profiles were observed in samples collected at the root base and root tips of waterlogged and well-watered control plants. To estimate ion uptake by the roots, concentrations in root tips were quantified (Zhang and Taylor, 1989; Lazof et al., 1994)).

Concentrations of K+ or Ca2+ in root tips did not appear to be affected by genotype or treatment.

However, when analyzed with the model presented in Equation 2-3, Ca2+ explained some of the variation in the stem biomass (p = 0.0194). The total concentration of Mg2+ in root tips was found to be significantly different not only among genotypes (p-value 0.0001), but also between treatments (p = 0.0003) (Figure 2-4). Mg2+ displayed a correlation of 45% with the dry stem weight (g). These results suggest that Mg2+ plays an important role in the enhanced stem biomass

2- accumulation. Similarly, the concentration of sulfates (SO4 ) in root tips was significantly different between the waterlogged plants and the controls (p = 0.0102) (Figure 2-4).

Chlorophyll a Fluorescence and Concentration

It has been demonstrated that the efficiency of photosynthesis plays an important role in the survival and performance of plants under waterlogging stress. The analysis of the ‘Kautsky effect’, which is the rise of chlorophyll a fluorescence in dark-adapted leaves, can be used to determine the state, integrity, and structure of the photosynthetic machinery, especially of PSII

(reviewed by Stirbet and Govindjee, 2011).

Chlorophyll a fluorescence was measured once a week for 7 weeks using the JIP-test

(Stirbet and Govindjee, 2011). A principal component analysis of JIP-test parameters suggested that the variables can be clustered in three main groups. Dissipated energy flux per reaction center (DIo/RC) was selected to represent Group F1 given the large number of strong correlations within the group. DIo/RC was negatively correlated with most of the other variables. Electron

transport flux from QA to QB per PSII (JOET2/RC) was selected from Group F2, because it has the lowest coefficient of variation in both waterlogged and control measurements, and dissipated energy flux per cross section (DIo/CSo) was selected from Group F3. The selected parameters from each of these three groups were subjected to further analysis. Additionally, the number of active reaction centers per cross section (RC/ CSm) were analyzed separately. All parameters at individual time points were fit in models represented in Equations 2-2 and 2-3. Dissipated energy flux per reaction center (DIo/RC) was affected by genotype (p < 0.001), but not by treatment.

Similarly, JOET2/RC and RC/CSm were different among genotypes with p-values of 0.0048 and

<0.001 respectively. Dissipated energy flux per cross section (DIo/CS), however, was not affected by either genotype or treatment (Figure 2-5).

Relative chlorophyll content was measured at the end of the experiment to determine whether the high-water-table treatment changed the chlorophyll content in leaves. It was observed that regardless of the treatment, the relative chlorophyll concentration did not change

(Figure 2-6). However, the SPAD measurements showed that there was a significant difference in relative chlorophyll content among genotypes (p < 0.001). As expected, SPAD measurements had a correlation between 20-30% with the JIP-test parameters and their interactions were significant.

Root Morphology

Submerging plant roots in water reduces phloem transport efficiency, leading to accumulation of photosynthetic metabolites in leaves, while reducing the amount of carbohydrates in roots. The combined result is compromised growth and development (Sloan et al., 2015). To understand the effects of high-water-table conditions on root growth and morphology of sorghum plants, pictures of the roots were taken over time.

The length of thin and thick roots progressed across time following the same trend

(Figure 2-7); total length appeared to be a reasonable representation of the growth over time of thin (0 < Length  1.5(mm) and thick roots (length > 1.5m). With few exceptions, root senescence (dead length (mm)) remained constant over time and close to 0 in most of the pots, as shown in Figure 2-7. Additionally, ‘Total length (mm)’ showed a correlation between 80 and

92% with the rest of the variables within the group.

Based on the PCA (Figure 2-8), total root length (mm), dead root length (mm) and alive root average diameter (mm/10) from groups R1, R2 and R3, respectively, were selected as a representation of the variables from the same group. Total root length-CV, total root length5

(final length), dead root length-CV, and total root average diameter 5-1 were the parameters chosen for further analysis. The root growth rate (total root length-CV) and total final root length

(total root length5) were different among genotypes (p = 0.0430 and 0.0053, respectively), but unaffected by treatment (Figure 2-10). Similarly, alive root average diameter (mm/10) was not affected by either genotype or treatment (Figure 2-9). However, the rate of root senescence has a correlation of 57% with the dry stem weight (g) despite not being affected by either genotype or treatment. The initial establishment of the roots (initial total lenghth) was not correlated with the accumulation of stem biomass. Additionally, ‘total root length (mm)’ acquired using the rhizotron camera showed a correlation of 65% with the total root biomass weight (g), direct measurement of the root biomass. For this reason only rhizotron values were used for further analysis.

Data Analysis

A multivariate analysis with all the selected parameters was performed in order to identify their contribution to the observed acclimation under high-water-table conditions. Table

2-2 summarizes the parameters and the treatment or genotype effects. For all parameters, the model in Equation 2-2 (genotype and treatment main effects) had a lower Bayesian information criterion (BIC), indicating that that model captured more of the observed variance (Neath and

Cavanaugh, 2012). No evident genotype×treatment effects were observed. Parameters not shown in the table were affected by neither genotype nor treatment. This analysis revealed that magnesium concentration in the root tips had a significant impact on the final stem biomass accumulation. Additionally, it was found that calcium concentration in the root tips has a significant effect on the biomass accumulation independently of treatment and genotype.

Discussion

Multiple morphological and physiological responses have been identified in relation to waterlogging tolerance and to root anoxia caused by submergence in sorghum. However, the underlying mechanisms by which this tolerance is achieved have not been fully identified. The objective of this study was to investigate the mechanism(s) underlying this tolerance in sorghum taking into consideration multiple genotypes.

After seven weeks of high-water-table treatment, stem biomass was significantly higher independent of the genotype. Additionally, our results show that magnesium ions are more concentrated in waterlogged roots tips compared to the controls. The role of this ion in the tolerance response is not clear, but its concentration is affected by treatment and it is significantly correlated with biomass (~45%). Given the mobile nature of Mg2+ ions and their importance in the photosynthetic machinery, it was unexpected that the physiological parameters related to the efficiency of photosynthesis and relative content of chlorophyll did not appear to be different. To validate the transport and uptake of Mg2+, the concentration of this ion will be measured in the stems. However, since the concentration in the root tips is significantly different between treatments, we hypothesize that there may be a mechanism where submersed roots take

up more Mg2+ without an observable impact on photosynthesis however has a benefitial impact in biomass accumulation.

Magnesium has multiple functions in the plant. A substantial amount of magnesium in plants is associated with the chlorophyll molecule. Magnesium is also a co-factor in enzyme- substrate interactions, participates in carbon partitioning and it is involved in regulation of cellular pH and cation-anion balance (reviewed by Cakmak and Kirkby (2008)). For example, enzymes involved in the fixation of CO2 in photosynthesis in crassulacean acid metabolism

(CAM) and C4 plants like phosphoenolpyruvate (PEP) carboxylase and ribulose-1,5- bisphosphate (RuBP) carboxylase/oxygenase are activated or highly influenced by Mg2+

(Wedding and Black, 1988; Portis, 1992). Association of Mg2+ ions and biomass has been reported in barley, where magnesium deficiencies resulted in a 34% reduction in biomass

(Tränkner et al., 2016). In arabidopsis roots specifically, Mg2+ ions are involved in the regulation of genes that enhance waterlogging tolerance by elevating transcripts encoding photosynthesis- chloroplast-associated proteins (Van Veen et al., 2016). Higher expression of these genes indicates the presence of chloroplasts in the roots, which is only possible in the presence of light

(Kobayashi et al., 2012). Given the fact that in our experiment, some of the roots were exposed to light (Figure 2-11), we hypothesize that, similar to these studies, light promoted the formation of chloroplasts in the roots. This might have contributed to the overall photosynthetic capability of the plant; hence resulting in an increment of carbon accumulation in the stems.

We observed that the efficiency of the photosynthesis (Figure 2-5) was not different between the waterlogged plants and the controls. We hypothesize that this observation reflects the ability of Mg2+ to regulate acclimation to high-water-table conditions. Mg2+ plays a crucial role in photosynthesis; it is an essential element in the structure of the chlorophyll molecule as

well as being required for its synthesis. Mg2+ is responsible for counterbalancing the proton gradient produced in the electron transport chain between the lumen and the stroma and this electrochemical potential is fundamental for the formation of ATP. It has been documented that increasing concentration of Mg2+ was positively correlated with the production of ATP (Lin and

Nobel, 1971). Similarly, it has been reported that higher concentration of Mg2+ in the stroma enhances the efficiency of RuBP carboxylase activity increasing the overall photosynthesis efficiency (reviewed by Cakmak and Kirkby, (2008)).

During the screening for consistenly tolerant or sensitive genotypes, not only the severity of the stress was varied, but also the developmental stage at which the waterlogging treatment was applied, and the environment (field vs. greenhouse). The fact that the selected six genotypes evaluated in this study were initially consistenly either waterlogging-sensitive or -tolerant across environments and developmental stages suggest that the genotypes may actually differ in their genetic pre-disposition to tolerance for this trait.

When comparing the response to waterlogging between the initial field-based screening and the greenhouse experiments, tolerance to waterlogging appears to depend on the severity of the stress that is applied. For example, under high-water-table conditions, where effectively 30% of the soil was fully submerged, we were not able to clearly differentiate between lines identified as tolerant and sensitive during the initial screening in the field. All genotypes were able to acclimate and outperform the well-watered control plants, regardless of whether they had been previously classified as sensitive or tolerant based on different field and greenhouse waterlogging treatments. These results show that sorghum genotypes have high phenotypic plasticity, and have very different physiological responses under different waterlogging conditions.

Given the higher accumulation of stem biomass, we can conclude that the stress was reduced or eliminated in the high-water-table conditions in the greenhouse as compared to the conditions in the field where genotypes were first selected. We hypothesize that, under high- water-table conditions, the plants had time to acclimate and that by the end of the experiment they outperformed the well-watered controls. Analysis of root tips of maize subjected to anoxia and analyzed by mass spectrometry revealed that gradual acclimation of the roots to anoxia is crucial for developing a tolerance. The majority of the proteins involved in anaerobic metabolism are produced during the acclimation phase and maintained throughout the anoxia stress (Chang et al., 2000). In the same way, ethylene plays a critical role in waterlogging-related responses including shoot elongation and cell death (Sasidharan and Voesenek, 2015). In rice, internode elongation has been reported as an ethylene-mediated response in tolerant lines (Fukao et al.,

2006).

In rice, protein levels of expansin-like OsEXP4 are responsible for enhancing seedling growth. In plants overexpressing the gene encoding OsEXP4, 31 and 97% increments in length of coleoptile and mesocotyl respectively were observed. In contrast, in seedlings of plants in which OsEXP4 is down-regulated following introduction of an antisense construct, coleoptile and mesocotyl length were reduced by 28 and 43%, respectively (Choi et al., 2003). It has been reported that magnesium depletion in Arabidopsis causes down-regulation of At2g40610, a gene encoding alpha-expansin 8 (EXP8) (Hermans et al., 2010). Expansins are proteins involved in plant developmental processes where cell wall loosening is necessary for cell growth and elongation (reviewed by Cosgrove (2000)). Based on previous studies and our findings reported here, we hypothesize that at least some expansin genes might be involved in the mechanism for waterlogging acclimation possibly regulated by Mg2+.

Waterlogging has been demonstrated to be a combinatory stress. We observed a large variation in the response mechanisms within the six genotypes tested (e.g. improved photosynthetic performance, aerenchyma formation, increase ion uptake, increase root biomass).

Plant organs exposed to excess of water have to deal with hypoxia/anoxia, deficiencies in nutrient uptake and gas exchange, and reduced light available for photosynthesis among others.

As a result, there is a diverse and compound matrix of responses; our data suggest the physiological responses are significantly different among genotypes. Similarly, this phenomenon has also been observed at the genetic level. Genome-wide association studies and transcriptomic analysis in Arabidopsis (86 accessions) suggest the involvement of multiple genes in waterlogging tolerance. Previous reports described that at least 145 genes may be associated with the tolerance response (Vashisht et al., 2016). With the attempt to understand underlying mechanisms related to waterlogging tolerance, we propose that magnesium ions contribute to the molecular signaling/regulation by which molecular and genetic responses are triggered, allowing sorghum plants to achieve partial-submergence tolerance under high-water-table conditions and accumulate larger amounts of stem biomass. The results of this experiment contribute to a better understanding of how to enhance biomass production and biomass conversion efficiency in sorghum when cultivated on flood-prone land.

Figure 2-1. Average height at week 6 of plants under waterlogged or well-watered (control) conditions. The height values correspond to the mean from the total of plants under the same treatment across genotypes. Flooded plants were ~30% taller than controls and the treatment effect was significant using the statistical model presented in Equation 2-2 (p-value = 0.0002). Error bars represent the standard error.

Figure 2-2. Dry stem weight vs. genotype after seven weeks under waterlogged or well-watered (control) conditions. Stem weight corresponds to the sum of three plants. Error bars indicate standard error. Differences between treatment and genotypes were significant with p-values of 0.0004 and 0.0332, respectively using the statistical model presented in Equation 2-2.

Figure 2-3. Plant growth rate from week 1 to week 6 of the six selected genotypes. Values correspond to the height CV of height data. A large coefficient of variation (CV) value indicates rapid growth between weeks and a small CV value indicates slower growth of waterlogged plants and controls. Height CV was significantly affected by treatment and genotype with p-values of 0.0002 and < 0.0001 respetively using the statistical model presented in Equation 2-2. Error bars represent the standard error.

Figure 2-4. Mineral concentrations in root tips (thickness ≤ 1.5mm) vs. genotype after seven weeks under waterlogged or well-watered (control) conditions. The concentration of 2- sulfates (SO4 ) were different between treatments (p-value = 0.0102). The concentration of Mg2+ showed significant differences between genotypes (p-value = 0.0001) and treatments (p-value = 0.0003). Statistical significance was calculated using the model presented in Equation 2-2. Error bars represent the standard error.

Figure 2-5. Dissipated energy flux per reaction center (DIo/RC) was affected by genotype (p- value <0.001), but not by treatment. Similarly, JOET2/RC and RC/CSm were different among genotypes with p-values of 0.0048 and < 0.001respectively. Dissipated energy flux per cross section (DIo/CS), however, was not affected by either genotype or treatment. Statistical significance was calculated using the model presented in Equation 2-2. Error bars represent the standard error.

Figure 2-6. Relative chlorophyll content of the flag leaf measured after six weeks of waterlogging treatment vs. well-water controls. The values correspond to the mean of six plants per genotype. Error bars represent the standard error.

Figure 2-7. Sorghum roots composition and changes over a period of five weeks. From top to bottom are displayed the length of thin roots (0 < Length  1.5mm), the length of thick roots (length > 1.5mm), total root length (Total length), and the length of dead roots.Values correspond to roots from three plants and are separated by pot. Pots under waterlogging treatment are: 1, 3, 4, 7, 8, 11, 12, 14, 15, 16, 20, and 22. Pots with well-watered control plants are: 2, 5, 6, 9, 10, 13, 17, 18, 19, 21, and 23.

Eigenvalue 20 40 60 80 1.0 7.7830 10 2.5204 1.7184 TotAvgDiam(mm/10) 0.5128 AliveAvgDiam(mm/10) DeadSurfArea(mm2) 0.2794

) 5 DeadAvgDiam(mm/10) 0.0812

% 0.5

0.0808 .

0.0194 (

0.0048 t 0

)

0.0000 e

n SA.>1.5

0.0000 o

p . TotSurfArea(mm2)

(

C AliveSurfArea(mm2)

-5 t 0.0 n L.>1.5

o p TotLength(mm)

-10 C 0<.L.<=1.5(mm)

-10 -5 0 5 10 Component 1 (59.9 %) -0.5

-1.0 -1.0 -0.5 0.0 0.5 1.0 Component 1 (59.9 %)

Figure 2-8. Principal component analysis (PCA) of data collected with the rhizotron camara. PCA suggested that root morphology variables could be arranged into three main groups. Group R1: Total root length (mm), 0 < Length  1.5(mm), length > 1.5mm, total surface area, 0 < SA  1.5, SA > 1.5mm and alive surface area. Group R2: dead length (mm), dead surface area, dead average diameter (mm/10). Group R3: total average diameter and alive average diameter (mm/10). Principal components, 1 and 2 together explain 80% of the variance of the data collected with the rhizotron. The variance (%) explained by the individual components is presented within parentheses next to each component.

Figure 2-9. Net change of total average root diameter ( 5-1) for the six selected genotypes. Each bar represents the change of diameter from week 5 relative to week 1. Positive values indicate increment in diameter and negative numbers indicate decrease of diameter. Error bars represent standard error.

Figure 2-10. Root growth and senescence of the six selected genotypes. A) Root growth rate, B) final root length and C) root senescence rate. Rates correspond to the coefficient of variation of data collected during five consecutive weeks. A large coefficient of variation (CV) value indicates a fast growth/senescence between weeks and a small CV value indicates slower growth/senescence of waterlogged plants and controls. Total length at week 5 corresponds to the final root length. Error bars represent standard error

Figure 2-11. The arrow marks a root growing under water, outside the pot, that is exposed to light. Photo courtesy of author.

Table 2-1. Root tips mineral correlations in control (C) and waterlogged (W) plants. 2+ - + 2+ + 3- 2- Treatment Mineral Ca Cl K Mg Na PO4 SO4 C+W Ca2+ 1.000 -0.183 -0.217 0.482 -0.089 -0.364 0.005 C+W Cl- -0.183 1.000 0.790 0.131 0.663 0.339 0.108 C+W K+ -0.217 0.790 1.000 0.401 0.582 0.490 0.147 C+W Mg2+ 0.482 0.131 0.401 1.000 0.258 0.017 0.312 C+W Na+ -0.089 0.663 0.582 0.258 1.000 0.570 0.044 3- C+W PO4 -0.364 0.339 0.490 0.017 0.570 1.000 -0.089 2- C+W SO4 0.005 0.108 0.147 0.312 0.044 -0.089 1.000

Table 2-2. Traits separated by effect. Statistical significance was calculated using model in Equation 2-2. Traits affected by Treatment Genotype Dry stem weight (g) Total root length (1-5)-CV [Mg2+] Total root length (mm) 5 2- [SO4 ] Height-CV Height week 6 Height week 6 Dio/RC JOET2/RC SPAD_week6 [Mg2+]

CHAPTER 3 PURIFICATION OF ACTIVE FORMS OF TWO RECOMBINANT SORGHUM BICOLOR TRANS-CINNAMATE 4-HYDROXYLASES

Introduction

The general phenylpropanoid pathway is of great importance in plants as it produces numerous chemicals such as flavonoids, anthocyanins, hydroxycinnamic esters, lignin and suberin, involved in physiological responses, plant structure, reproduction, UV protection, and even pathogen-defense responses. This pathway results in the conversion of L-phenylalanine and

L-tyrosine, products of the shikimic acid pathway, to a series of hydroxycinnamoyl CoA esters that form precursors for flavonoids, stilbenes and lignin. There are at least seven different enzymatic activities involved in the general phenylpropanoid pathway, including two cytochrome P450-dependent monooxygenases: trans-cinnamate 4-hydroxylase (C4H; E.C

1.14.13.11; Russell and Conn, 1967)) and p-coumarate 3'-hydroxylase ( C3'H; EC:1.14.13.36;

Schoch et al., 2001; Franke et al., 2002)

Cytochrome P450-dependent monooxygenases, commonly called P450s, make up a large family of heme-containing enzymes in eukaryotic organisms that are typically membrane- localized. This super family of enzymes was first discovered when characterizing a pigment in rat liver microsomes (Garfinkel, 1958; Klingenberg, 1958). The pigment’s ability to bind carbon monoxide was described several years later (Omura, Tsuneo; Sato, 1964). P450s require molecular oxygen and NADPH (not interchangeable with NADH) for activity and their reaction is inhibited by carbon monoxide (CO), although this inhibition can be reversed by exposure to light with a wavelength of 450 nm. Measuring the absorbance at this wavelength of solutions containing cytochrome P450-dependent monooxygenases in the presence of CO has become the principal mechanism to qualitatively determine enzyme activity. To complete the enzymatic reaction, most cytochrome P450 enzymes require an electron donor to reduce the heme-

prosthetic group in charge of the chemical reaction. NADPH-cytochrome P450 oxidoreductase

(CPR) transports electrons from NADPH through cofactors into the central heme-group of the

P450s (Hass et al., 1940; Jensen and Møller, 2010).

C4H was the first reported cytochrome P450-dependent monooxygenase in plants.

Russell et al. (1967) first discovered C4H activity in microsomal fractions of pea seedlings and later explained its role in the metabolism of aromatic compounds of higher plants (Russell,

1971). C4H was first identified as a member of the cytochrome P450 family involved in the general phenylpropanoid pathway upon isolation of fragments of the protein from manganese- induced microsomes and cDNA cloning from the Jerusalem artichoke (Helianthus tuberosus L.)

(Teutsch et al., 1993). Subsequent molecular cloning of cDNA enabled the investigation of C4H gene expression in response to environmental stresses and developmental stages. Expression of

C4H has shown to be regulated by pathogen elicitors in alfalfa (Medicago sativa L.) (Fahrendorf and Dixon, 1993), light in etiolated pea seedlings (Pisum sativum L.) (Benveniste et al., 1978), and by wounding, chemical treatments and xenobiotics in Jerusalem Artichoke (Reichhart et al.,

1980; Batard et al., 1997).

Recombinant Helianthus tuberosus C4H has been expressed and enzymatically characterized in yeast in the presence of human CPR. The authors determined that C4H had an apparent spectral dissociation constant Ks = 7.9µM, a high affinity for cinnamate (Km = 4µM) and a high turnover of 400 nmol•nmolP450-1• min-1; recombinant C4H had no detectable affinity for p-coumaric acid suggesting that the reaction is not reversible, and the CPR-C4H interaction was essential to observe C4H activity (Urban et al., 1994)

Genes encoding cinnamate 4-hydroxylase are considered to be members of the same

P450 family, CYP73, subfamily A. Members of the CYP73A subfamily share more that 40%

sequence similarity and are grouped in 16 groups, numbered CYP73A1 through CYP73A16

(Chapple, 1998). If members of the same subfamily share 97% identity or more, they are considered allelic variants of the same gene, unless demonstrated otherwise (Nelson et al., 1996)

In plants, C4H is associated with the membrane of the endoplasmic reticulum through an

N-terminal anchor where it catalyzes the second reaction of the general phenylpropanoid pathway, as depicted in Figure 3-12.

The substrate of C4H is generated from the enzymatic conversion of phenylalanine by phenylalanine ammonia lyase (PAL; EC 4.3.1.5). This enzyme is present in dicotyledonous as well as in monocotyledonous plants. Experiments in transgenic tobacco plants (Nicotiana tabacum) using co-expression of epitope-tagged versions of tobacco PAL1, PAL2 and C4H genes have demonstrated that at least PAL1 is localized in the cytocolic and microsomal fractions when

C4H is overexpressed. In contrast, PAL2 is localized in the cytosol regardless of the level of

C4H expression. The results of this study indicated that PAL1 and C4H are colocalized in the

ER and interact with each other in close proximity, but based on calculations of their distance, this interaction is not a consequence of tight physical interaction between the two enzymes

(Achnine et al., 2004). This phenomenon is referred to as metabolic channeling. Metabolic channeling reduces the reliance on diffusion of biochemical intermediates to the enzyme that will use a particular compound as a substrate (Lee et al., 2012). The locally higher substrate concentration and close proximity of the substrate as it is passed from one enzyme to the next increases the rate of conversion and reduces the occurrence of competing reactions. Metabolic channeling has also been reported in Populus trichocarpa, where multiple membrane-protein- heterodimer complexes have been detected between C4H and C3ʹH (Chen et al., 2011a).

In Arabidopsis thaliana it has been reported that even though the P450-dependent monooxygenases C4H, C3′H and F5H are in close proximity with each other, they are not covalently bound or experience tightly physical interactions (consistent with previous reports in other species). Instead, two membrane steroid binding proteins (MSBPs) organize and assemble the P450s. The two MSBPs form protein-protein complexes with the P450s and with each other, creating a metabolon essential for the monolognols biosynthesis (Gou et al., 2018). Using bimolecular fluorescence complementation assays (BiFC) in Nicotiana benthamiana leaf epidermal cells, the authors confirmed arabidopsis C4H, C3′H and F5H spactial proximity in vivo. However, mating-based split-ubiquitin yeast two-hybrid (mbSUS-Y2H) system experiements confirmed that there is not physical interaction between C4H, C3′H and F5H directly. Physical interactions of the MSBP1 and MSBP2 was revealed through mbSUS-Y2H and their interaction with the three P450s was observed through reciprocal coimmunoprecipitation assays. With 61% amino acid similarity, MSPB1 and MSBP2 were hypothesized to be redundant. RNAi-based MSPB1 knock-down mutants in an mspb2 mutant background results in growth defects when compared to the wild type (Col-0). MSPB1

RNAi/mspb2 double mutants showed smaller and weaker inflorescence stems, smaller rosette leaves, lignin desposition was reduced in the vascular bundles, acetyl bromide lignin was reduced by 16-19%, and there was a reduction in S- and G-subunits of 54 and 27%, respectively.

In the double mutants, the accumulation of sinapoyl malate and synapoyl glucose was reduced

55 and 66%, respectively. In contrast, the amount of flavonols increased from 54 to 88%. These results demonstrated that MSPB proteins control specifically the monolignol-biosynthesis branch of the phenylpropanoid pathway while not affecting flavonoid biosynthesis (Gou et al., 2018).

Grasses also display tyrosine ammonia lyase (TAL) activity (EC 4.3.1.5, Havir et al.,

1971, Beaudoin-Eagan and Thorpe, 1985), which results in the biosynthesis of 4-coumarate from

L-tyrosine, effectively bypassing C4H. PAL affinity for tyrosine was first reported in maize (Zea mays L.) where evidence for a common catalytic site was demonstrated (Havir et al., 1971), however Vmax with phenylalanine was eight times higher than with tyrosine. Almost two decades later, Rösler et al. (1997) demonstrated that the TAL activity of maize resides in PAL and that the activity towards both substrates is similar. The importance of TAL activity appears to vary among different grasses, with a major role in Brachypodium dystachion based on an analysis of the products made from radiolabeled tyrosine (Barros et al., 2016), and a more limited role in sorghum (Sorghum bicolor (L.) Moench) (Jun et al., 2017). A detailed structural and kinetic analysis of the major sorghum PAL, SbPAL1, showed that PAL and TAL activities depended on specific amino acids. Site-directed mutagenesis (H123F-SbPAL1) increased SbPAL1 activity 6.2 times while almost entirely eliminating TAL activity (Jun et al., 2017).

In order to study the enzymatic activity and kinetic mechanism of C4H, recombinant

C4H enzymes from various species have been expressed in yeast, insect cells and plants (Urban et al., 1994; Bell-Lelong et al., 1997; Mizutani et al., 1997; Nedelkina et al., 1999; Ro et al.,

2001). In Arabidopsis, C4H is encoded by a single gene whose expression has been evaluated through mRNA hybridization assays. AtC4H is expressed in all tissues, but the highest levels are present in roots and stems (Bell-Lelong et al., 1997). In maize, the hybridization signal in micro- array analysis showed the expression of two C4H genes (C4H1 and C4H2) throughout all organs. However, C4H1 was mostly expressed in roots, whereas C4H2 was expressed in all tissues of young plants, at the four- to five-leaf stage (Guillaumie et al., 2007). In poplar, C4H is expressed in young (at the four- to five-leaf stage) and mature leaves, developing green stems

and xylem, with the highest expression levels in developing xylem (Ro et al., 2001). Using confocal microscopy and a poplar C4H::GFP fusion protein in transgenic arabidopsis seedlings,

Ro et al. (2001) confirmed the localization of C4H in the endoplasmic reticulum in planta.

Among the products derived from phenylpropanoid precursors, the aromatic cell wall polymer lignin has received the most attention, largely due to its negative effect on both the digestibility of forage crops (Moore and Jung, 2001; Pei et al., 2016; Casler and Jung, 2017; Sato et al., 2018) and on the enzymatic saccharification of plant biomass, which yields sugars that can be fermented to biofuels and building blocks for renewable chemicals (Kavousi et al., 2010; Van

Acker et al., 2013; Marriott et al., 2014; Zeng et al., 2014). Lignin makes up 15-30% of plant biomass and is a major component of the secondary cell wall of vascular plants, where its main role is to provide structural support to plant tissues and water impermeability to the walls of water-conducting xylem cells (reviewed by Vanholme et al. (2010). Lignin also provides protection against pathogens and can be associated to physiological responses to combat stress

(reviewed by Ma and Yamaji (2006)).

Lignin is formed in the plant cell wall from the oxidative coupling of monolignol radicals with the growing lignin polymer. The monolignols are p-coumaryl alcohol, coniferyl alcohol, and sinapyl alcohol and are synthesized in the cytosol from hydroxycinnamoyl CoA esters by the enzymes cinnamoyl-CoA reductase ( CCR; E.C 1.2.1.44; Gross and Kreiten, 1975; Goffner et al., 1994), cinnamyl alcohol dehydrogenase (CAD; EC 1.1.1.16 , Wyrambik and Grisebach,

1975), caffeic acid O-methyltransferase (COMT; EC 2.1.1.6; Gowri et al., 1991) and ferulic acid

5-hydroxylase (F5H; Grand, 1984; Meyer et al., 1996). Upon synthesis, the monolignols are transported to and across the plasma membrane and finally into the cell wall. Within the cell wall, the monolignols undergo dehydrogenation of their hydroxyl groups through the action of

peroxidases and hydrogen peroxide, or laccases and molecular oxygen (Sterjiades et al., 1992;

O’Malley et al., 1993; Liu et al., 1994; Zhao et al., 2013). The result are radicals that can undergo oxidative coupling and that can be added to the growing lignin polymer. When assembled in the polymer, the above mentioned monolignols give rise to p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) subunits, respectively (Ralph et al., 2004; Vermerris and Abril,

2015).

Even though C4H is an enzyme of the general phenylpropanoid pathway, much of the 4- coumarate it generates gets used for the biosynthesis of monolignols. This is evident from the phenotype of transgenic tobacco (Nicotiana tabacum) with reduced C4H activity as a result of down-regulation of the C4H gene using antisense constructs or sense suppression. These plants contained up to 20% less lignin and displayed changes in the lignin subunit composition by reducing the S/G ratio (Sewalt et al., 1997). Transgenic down-regulation of the gene encoding

CYP73A15, cinnamate 4-hydroxylase, in tobacco, resulted in reduction of lignin of up to 30% and reduction of the S/G ratio from 0.9 to 0.5 (Blee et al., 2001).

Three reduced epidermal fluorescence3 (ref3) mutants (ref3-1, ref3-2, ref3-3) were identified in Arabidopsis, each of them harboring a mis-sense mutation in the C4H gene

(At2g30490) (Ruegger and Chapple, 2001). Both ref3-1 and ref3-2 show severe phenotypes, including dwarfism, male sterility and swelling of branch junctions. All three mutants also showed modified cell wall architecture; ref3-1 and ref3-2 displayed reduced Klason lignin content and ref3-3 had an increase in the S/G ratio compared the wild type and Klason lignin content was unchanged (Schilmiller et al., 2009).

C4H down-regulation though RNA interference via Agrobacterium-mediated transformation of Eucalyptus resulted in a decreased S/G ratio (Sykes et al., 2015). Similarly,

Kumar et. al. (2016) down-regulated the C4H gene in sweet wormwood (Artemisis annua) using

RNA interference and observed that trans-cinnamic acid accumulated and the concentration of p- coumaric acid was reduced. The overall production of phenolics, including anthocyanins, was reduced by ~40%. However, the concentrations of artemisinin and salicylic acid were increased.

These transgenic plants also displayed reduction in plant growth, had thinner stems, and repressed xylem development (Kumar et al., 2016).

C4H plays a crucial role in the biosynthesis of lignin, not only for mediating chemical reactions in the monolignol biosynthetic pathway, but because it establishes the potential for monolignol coupling. The addition of the para-hydroxyl group on the benzene ring of cinnamic acid catalyzed by C4H becomes the site of radical formation that is the basis for oxidative coupling to the lignin polymer. To reduce the degree of lignin polymerization, Zhang et al.

(2012) engineered a monolignol 4-O-methyltransferase, expressed in arabidopsis, which etherifies the para-hydroxyl groups of monolignols (intially added by C4H), preventing a fraction of the produced monolignols from being able to participate in oxidative coupling. As a result of this activity, lignin subunit composition changed, lignin content was reduced, and the biomass conversion to fermentable sugars became more efficient (Zhang et al., 2012). Similar improvements in enzymatic saccharification were reported for the Arabidopsis ref3 mutants.

Saccharification of dried tissue from Arabidopsis c4h mutants (ref3-2 and ref3-3) resulted in at least 21% increase in the yield of fermentable sugars compared to wild type (Vanholme et al.,

2012; Van Acker et al., 2013).

Since 1990, when the first plant cytochrome P450-dependent monooxygenase-encoding gene was cloned from avocado fruit (Persea americana) (Bozak et al., 1990), noticeable advances in gene discovery and understanding of plant metabolic pathways have been made. The

relatively late start in P450s enzymology studies, their low abundance, and limited stability during purification (reviewed by Chapple (1998)) have limited the progress in our understanding of the kinetic properties of this class of enzymes. In fact, only a few enzymes of this family have been crystalized and their tertiary structure revealed. The cytochrome P450-dependent monooxygenase1A2 is expressed in human liver and plays an important role in oxidation of a variety of polynuclear aromatic hydrocarbons (including several drugs) (Sansen et al., 2007). In

2011, p-coumarate 3′-hydroxylase (C3′H) was successfully expressed, purified and enzymatically and structurally characterized; part of the success was attributed to the genetic engineering of the C3′H N-terminal transmembrane domain, a modification which reportedly increased protein solubility (Kim et al., 2011).

While the activity of C4H has been measured in several plant species using crude plant extracts (Russell and Conn, 1967; Potts et al., 1974; Urban et al., 1994; Nedelkina et al., 1999;

Chen et al., 2011a), and partial peptide isolation from mung bean seedlings has been achieved and sequenced (Mizutani et al., 1993), the enzyme has never been successfully solubilized, purified and structurally characterized.

The objective of this study was express, purify and to elucidate substrate-enzyme interaction of Sorghum bicolor trans-cinnamate 4-hydroxylase (C4H) and prepare enzyme for structural studies such as X-ray crystallography or cryogenic electron microscopy (cryo-EM).

This was accomplished by heterologous protein expression in Escherichia coli, purification and enzymatic activity assays.

Materials and Methods

Identification of Sorghum bicolor C4H Orthologs and Their Expression Profile

C4H orthologs were identified by using the basic local alignment search tool, BLAST

(Altschul et al., 1997) available at NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi) using as query

the cDNA sequence of the Arabidopsis C4H gene (At2g30490) (Mizutani et al., 1997). Three sorghum orthologs were identified: Sb02g010910, Sb03g038160, and Sb04g017460.

Sb02g010910 (NCBI Reference Sequence: XM_002461894.2), Sb03g038160 (NCBI Reference

Sequence: XM_002458638.2, and Sb04g017460 (NCBI Reference Sequence:

XM_002451999.2). However, only two of them are utilized in this study given their predominant expression in stem tissue, where they are likely involved in lignification. Orthologs of maize

C4H have been already identified and annotated (Guillaumie et al., 2007), and the sorghum C4H genes were numbered to match the numbering of their maize orthologs. Sb02g010910,

Sb03g038160, and Sb04g017460 correspond to SbC4H1, SbC4H2, and SbC4H3, respectively.

To examine SbC4H gene expression, mRNA was extracted from roots, the stem, leaf whorl, and flag leaf from the sweet sorghum cultivar ‘Rio’ (IS 9606), growing under field conditions at the UF Suwannee Valley Agricultural Extension Center near Live Oak, FL.

Actively growing root tissue of approximately 10 cm in length, the upper half of the fourth stem internode counted from the bottom, 15 cm of the leaf whorl, and the entire flag leaf were collected from three plants (biological replicates) sampled at two different developmental stages

(50 and 70 days after planting) and placed in 50-mL polypropylene tubes. The harvested tissue was instantly frozen in liquid nitrogen in order to avoid mRNA degradation. Samples were stored at -80oC until they were processed.

Samples were ground in liquid nitrogen using a mortar and pestle. Total RNA was isolated using the Plant RNeasy Mini kit from QIAGEN (Gaitersburg, MD). The iScript™ cDNA Synthesis Kit (BioRad; Hercules, CA) was utilized to synthesize first-strand cDNA using

0.5 µg total RNA and the protocol recommended by the manufacturer. Three technical replicates per tissue sample were used and each technical replicate was quantified in triplicate. Thus, a total

of nine values were obtained for each tissue. Prior to quantitative real-time PCR (qRT-PCR), a subset of samples was tested for genomic DNA contamination of the cDNA samples. PCR primers (Table 3-1) were ordered from Sigma-Aldrich (St. Louis, MO) and were designed to distinguish between genomic DNA and cDNA amplicons by length. A three step PCR program was used: the first denaturation step, 1 min at 95˚C, followed by 30 cycles of 10 sec at 95˚C, 20 sec at 58˚C, and 30 sec at 72˚C, with a final extension of 2 min at 72˚C using a Bio-Rad thermal cycler. Each PCR reaction contained: 0.5 µM Forward/Reverse primers, 1 µL cDNA (previously

TM described), 10 µL Ready Mix RedTaq®PCR reaction mix with MgCl2 (Sigma-Aldrich) and 7

µL of Nuclease free H2O for a total of 20 µL reaction.

Gene expression was measured using real-time quantitative RT-PCR. SbC4H expression was analyzed using iCycler Software (BioRad) and transcripts were quantified using the total

RNA quantification method (Dorak, 2007). For quantification all the amplicons were cloned into pET28a(+) plasmid (Millipore-Sigma) and transformed into E. coli Zymo 5 ‘Mix and Go’ competent cells (Zymo Research, Irvine, CA). Plasmids were isolated from transformed E. coli

Zymo 5 cells using the ZyppyTM plasmid Miniprep kit (Zymo Research) and concentration quantified using a NanoVueTM Plus Spectrophotometer (GE Healthcare Life Sciences).

The total amount of transcript in each sample was determined with a calibration curve obtained with known amounts of pET28a(+) plasmids in which the SbC4H amplicons had been cloned. Each construct was quantified, and nine dilutions were prepared. Table 3-4 displays the values and calculations for each dilution for each gene evaluated.

The qRT-PCR was conducted with an iCycler iQTM optical Module (BioRad) using the protocol indicated in Table 3-5. A total of 20 ng (5 µL) of cDNA was mixed with 12.5 µL of iQ™ SYBR® Green Supermix (BioRad), 1.6 µM of each primer (Forward/ Reverse), 6.5 µL of

nuclease free H2O for a total of 25 µL reaction. A heat disassociation analysis in step 6 (melting curve) was performed at the end of the PCR to verify the amplification of a single product per sample. Total concentration of mRNA was calculated based on the calibration curves built taking into consideration Ct values and their corresponding concentration for each amplicon.

Hydropathy Analyses of SbC4H2 (505 aa) and SbC4H1 (501 aa) Protein Sequences

Hydropathy analyses were carried out using the ProtScale program (Gasteiger et al.,

2005). While long stretches of hydrophobic amino acids can be indicative of transmembrane domains, they can also serve as signal peptides. In order to distinguish between membrane domains and signal peptides, an analysis was performed using Phobius, a combined transmembrane protein topology and signal peptide predictor (Käll et al., 2004). The predictor is based on a hidden Markov model (HMM) capable of discerning between the two.

SbC4H1 and SbC4H2 cDNA Sequence Optimization

SbC4H1 and SbC4H2 were subjected to codon optimization for E. coli using the

Integrated DNA Technologies online platform (https://www.idtdna.com/CodonOpt) and further optimized by hand for optimal expression in E. coli. Quality of the optimization was measured using the codon adaptation index (CAI), which is based on highly expressed genes of a particular organism to assess the relative frequency of usage of codons (Sharpl and Li, 1987). Calculating

CAI (ranging between 0 and 1) helps predict the success of heterologous gene expression (Gouy and Gautier, 1982). For E. coli, CAI values >0.8 are considered adequate (Sharpl and Li, 1987).

The names of the optimized SbC4H cDNA sequences C4H2 and C4H1 were changed to mSbC4H2 and mSbC4H1, respectively. In the names, Sb: Sorghum bicolor, m: modified.

Gene Synthesis, Cloning and Validation

Both mSbC4H1 and mSbC4H2 cDNA sequences were synthetized by Genewiz, Inc.

(South Plainfield, NJ). mSbC4H1 and mSbC4H2 cDNA sequences were originally cloned in the pET28a(+) vector (Millipore-Sigma), but expression using this vector was not successful.

mSbC4H2 was subsequently cloned into pET-32 Ek/LIC Vector (Millipore-Sigma) following the manufacturer’s protocol with the primers shown in Table 3-2. Sb03C4H-NCH2 is a modified version of mSbC4H2 and contains a C-terminal 6×-Histidine tag for purification purposes. The cloned mSbC4H2 and Sb03C4H-NCH2 cDNA fragments were sequenced with primers shown in Table 3-3 to verify the DNA sequence and reading frame.

mSbC4H1 was directly synthetized and cloned into pET-32 Ek/LIC vector by Genewiz,

Inc. mSbC4H1 gene was cloned using restriction enzymes, KpnI at the 5 end and NcoI at the 3 end. Final and modified cDNA sequences for each protein are shown in APPENDIX A

Figure 3-16 provides a summary and a simplified representation of the recombinant and modified C4H constructs used during this study.

E. coli Zymo 5 ‘Mix and Go’ competent cells were transformed with plasmids containing either mSbC4H1 or mSbC4H2 and plated on selective medium with ampicillin (50

µg/mL). Plasmids were isolated from transformed E. coli Zymo 5 cells using the ZyppyTM plasmid Miniprep kit (Zymo Research) sequenced and introduced into E. coli BL21Rosetta and

BL21(DE3) Rosetta cells (Millipore-Sigma), which are engineered for the expression of recombinant proteins from pET vectors. In all transformations, protocols provided by the manufacturer were followed.

Protein Expression and Purification

E. coli BL21Rosetta and BL21(DE3) Rosetta cells (Millipore-Sigma) were used for protein expression. Five mL of Luria-Bertani (LB) medium supplemented with 20 µg/mL chloramphenicol and 50 µg/mL carbenicillin were inoculated with a single colony of E. coli

BL21-Rosetta cells harboring either plasmid pET32-mSbC4H1 or pET32-mSbC4H2 and grown overnight at 37˚C at 260 rpm. One liter of ZYP5052 auto-inducible media (Studier, 2005) was supplemented with 20 µg/mL chloramphenicol and 50 µg/mL carbenicillin and inoculated with 5 mL of overnight culture and incubated at 37˚C and 260 rpm until the OD600 reached 0.3 (~3h).

The medium was then supplemented with 0.5 mM of -aminolevulenic acid (heme-biosynthesis precursor) and returned to the incubator. When the culture reached an OD600 of 0.6, it was subjected to a heat shock of 20 min at 47˚C without shaking. Shaking was resumed at 210 rpm for an additional 20 min. before the temperature was reduced to 17˚C. The cultures were then incubated for 20 h at 100 rpm. Cells were collected by centrifugation at 8,000 rpm for 30 min using an Avanti J-26XP centrifuge (Beckman Coulter; Indianapolis, IN) equipped with a JLA-

8.1000 rotor. The resulting cell pellet was resuspended in 30 mL binding buffer and a half tablet of protease inhibitor (Pierce TM Protease inhibitor mini tablets, EDTA-Free) was added, and the sample was completely homogenized by vortexing. Cells were lysed using three cycles in a

French® pressure cell press (Thermo Scientific) at 1,000 psi. The resulting cell lysate was centrifuged at 17,000 rpm for 30 min at 4˚C in a JA-25.50 Beckman rotor. The supernatant

(soluble fraction) was mixed with 5 mL Ni2+-NTA resin (Millipore-Sigma) and 15 mL binding buffer, mixed thoroughly and allowed to settle in a chromatography column for at least one hour.

The column was washed with 400 mL wash buffer and mSbC4H1 or mSbC4H2 were eluted with

15 mL elution buffer and collected in 2-mL fractions. The entire protein purification protocol

was performed at 4˚C. Immediately after purification elution fractions 2 and 4 were pooled and dialyzed for ~20 h at 4˚C in 1 L of dialysis buffer. mSbC4H2 and mSbC4H1 were observed following sodium dodecyl sulfate polyacrylamide gele electrophoresis (SDS-PAGE) using a 12% gel (BioRad). The proteins were transferred to a nitrocellulose membrane using and the Trans-

Blot® transfer system (BioRad) for 7 min. The membranes were blocked for 1h with 20 mL of

1PBS blocking buffer containing 0.1% (v/v) Tween-20 and 20% (w/v) fat-free powdered milk

(Publix, Lakeland, FL). Forty µL of mouse monoclonal anti-6×His antibodies (Invitrogen;

Carlsbad, CA) were added to a new preparation of 20 mL of 1PBS blocking buffer and incubated overnight at 4˚C. Upon incubation, the membrane was washed three times for 5 min with 40 mL 1PBS containing 0.1% (v/v) Tween-20. Four µL of peroxidase-conjugated rabbit anti-mouse IgG secondary antibodies (GE Healthcare Life Sciences; Marlborough, MA) was added in 20 mL of 1PBS containing 0.1% (v/v) Tween-20 for 1 h. The membrane was washed twice for 2 min with 40 mL of 1PBS containing 0.1% (v/v) Tween-20. To prepare the membrane for colorimetric visualization of proteins, it was washed twice for 5 min with 20 mL of 1PBS. The Horseradish Peroxidase Conjugate Substrate kit from BioRad was used as a colorimetric method for detection of bound antibodies following the manufacture protocol.

Heterologous mSbC4H2 was engineered to contain an enterokinase site, which allows the cleavage of the tags (TRX (thioredoxin), S (S-peptide derived from ribonuclease A and 6×His

(histidine)) for posterior enzymatic assays. Tags were cleaved using the Enterokinase Cleavage

Capture Kit (Novagen) using a ratio of 4 mg of mSbC4H1 or mSbC4H2: 10 units enterokinase per µL at 4˚C for 12 h in a volume of 1 mL. Protein was recovered and concentrated using an

Amicon® centrifugal filter-30K (Merck Millipore Ltd., Tullagreen, Ireland) at 5,000 g for 20 min. at 4˚C in a JA-25-50 Beckman rotor.

Composition of buffers and media. ZYP5052 auto-inducible media (Studier, 2005) contained 50 mM Na2HPO4, 50 mM KH2PO4, 25 mM (NH4)SO4, 2 mM MgSO4, 0.2× Trace metals (TEKNOVA, Kristiansand, Norway), 0.5% (v/v) glycerol, 0.05% (w/v) glucose, 0.2%

(w/v) lactose, 1% (w/v) N-Z amine, and 0.5% (w/v) yeast extract. Buffers used for protein purification and dialysis were binding buffer (500 mM NaCl, 10% (v/v) glycerol, 50 mM Tris-

HCl pH=8.0, 5 mM imidazole, 0.5% (v/v) Lubrol); wash buffer (500 mM NaCl, 10% (v/v) glycerol, 50 mM Tris-HCl pH=8.0, 25 mM imidazole, 0.5% (v/v) Lubrol); elution buffer (500 mM NaCl, 10% v/v) glycerol, 50 mM Tris-HCl pH=8.0, 250 mM imidazole, 0.5% (v/v) Lubrol), and dialysis buffer (20% (v/v) glycerol, 10mM Tris-HCl pH=8.0, and 0.5mM of fresh tris (2- carboxyethyl) phosphine (TCEP).

SbC4H Sequencing

The presence of recombinant sorghum C4H in the purified fractions was validated though liquid chromtograpgy mass spectrometry, LC MS sequencing of protein extracted from the SDS- polyacrylamide gel . The mass spectrometry data acquisition was performed on an EASY-nLC

1200 ultraperformance liquid chromatography system (Thermo Scientific Inc., San Jose, CA) connected to an Orbitrap Q-Exactive Plus instrument equipped with a nano-electrospray source

(Thermo Scientific Inc., San Jose, CA). Protein sequencing was performed at the Proteomics and

Mass Spectrometry Facility at the University of Florida Interdisciplinary Center for

Biotechnology Research, ICBR using the methods presented bellow.

Sample preparation for mass spectrometry. The protein pellets were solubilized in 50 mM ammonium bicarbonate, pH 8.5, and then reduced by 10 mM tris (2-carboxyethyl) phosphine at 37 ˚C for 1 h, followed by alkylation by 20 mM iodoacetamide in the dark for 30 min. Proteins were digested with Trypsin (Promega, Fitchburg, WI) (w/w for enzyme : sample =

1 : 100) overnight at 37 °C. The trypsin digestion was quenched by the addition of 0.1%

trifluoroacetic acid (TFA, Sigma-Aldrich). The digested peptides were desalted using micro

ZipTip mini-reverse phase (Millipore). In brief, after wetting (with 50% acetonitrile) and equilibrating a ZipTip pipet tip (with 0.1% TFA), the peptides were bound to C-18 material. A subsequent washing step with 0.1% formic acid was followed by a final elution with 60% acetonitrile and 0.1% formic acid. The samples were lyophilized to dryness at 160 mBar using a speedvac (Centrivap, Labconco Inc., USA). The resulting peptide pellets were stored at -20°C until further use.

Nano-LC-MS. Peptides derived from the protein samples were resuspended in 0.1% formic acid for mass spectrometric analysis. The mass spectrometry data acquisition was performed on an EASY-nLC 1200 ultraperformance liquid chromatography system (Thermo

Scientific Inc., San Jose, CA) connected to an Orbitrap Q-Exactive Plus instrument equipped with a nano-electrospray source (Thermo Scientific Inc., San Jose, CA). The peptide samples were loaded to a C18 trapping column (75 μm i.d. × 2 cm, Acclaim PepMap® 100 particles with

3 μm size and 100 Å pores) and then eluted using a C18 analytical column (75 μm i.d. x 25 cm, 2

μm particles with 100 Å pore size). The flow rate was set at 250 nL/minute with solvent A (0.1% formic acid in water) and solvent B (0.1% formic acid and 99.9% acetonitrile) as the mobile phases. Separation was conducted using the following gradient: 2% of B over 0 - 2 min; 2 - 35 % of B over 2 - 48 min, 35 - 98 % of B over 48 - 50 min, and isocratic at 98% of B over 50-60 min, and then from 98 – 2% of B from 60 – 62 min. The equilibration at 2% B is from 62 to 75 min.

The full MS1 scan (m/z 350 - 1800) was performed on the Orbitrap with a resolution of

70,000. The automatic gain control (AGC) target is 3 106 with 250 ms as the maximum injection time. Peptides bearing +2 - 6 charges were selected with an intensity threshold of 1 

104. Dynamic exclusion of 30 s was used to prevent resampling the high abundance peptides.

The MS/MS was carried out in the Orbitrap, with a quadrupole isolation window of 1.3 Da.

Fragmentation of the top 10 selected peptides by high-energy collision dissociation (HCD) was performed at 27% of normalized collision energy. The MS2 spectra were acquired at a resolution of 17,500 and detected through Fourier transformation of image current with the AGC target as 5

 105 and the maximum injection time as 50 ms.

Data searching and analysis. Database searching - The tandem mass spectra were extracted from the Xcalibur.raw files and converted into mgf files using Proteome Discoverer 2.1

(Thermo Fisher Scientific Inc., San Jose, CA). Charge state deconvolution and deisotoping were not performed. All MS/MS samples were analyzed using Mascot (Matrix Science, London, UK; version 2.4.1). Mascot was set up to search the NCBInr_20130403 database (selected for

Bacteria, unknown version, 14961948 entries) assuming the digestion enzyme trypsin. Mascot was searched with a fragment ion mass tolerance of 0.0100 Da and a parent ion tolerance of 10.0

PPM. Carbamidomethyl of cysteine was specified in Mascot as a fixed modification. Gln->pyro-

Glu of the N-terminus, deamidated of asparagine, glutamine and arginine, and oxidation of methionine were specified in Mascot as variable modifications.

Criteria for protein identification. - Scaffold (version Scaffold 4.2.1, Proteome

Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than

95.0% probability by the Peptide Prophet algorithm (Keller et al., 2002) with Scaffold delta-mass correction. Protein identifications were accepted if they could be established at greater than

95.0% probability and contained at least one identified peptide. Protein probabilities were assigned by the protein prophet algorithm (Nesvizhskii et al., 2003). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to

satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters.

Enzymatic Activity

To initially detect p-coumaric acid (pCA) formation, 300 µL reactions containing either purified mSbC4H2 or mSbC4H1 with components in Table 3-6 were incubated at 30˚C for 1.5 h and A260, A310 and, A340 were recorded for 1.5 h at 5 min intervals. The experiment was carried out at two different pH values, 7.3 and 8.0, in 100 mM HEPES buffer. The reactions were carried out in a Synergy HT microplate reader (Biotek, Winooski, VT, USA) using the Kinetic Analysis mode.

To measure the production of p-coumaric acid (pCA) from trans-cinnamic acid (CA) catalyzed by either purified mSbC4H2 or mSbC4H1, two concentrations of substrate were tested in a 300 µL reaction in a 96-well microtiter plate incubated for 1.5 h in the dark at 30˚C in 100 mM sodium phosphate buffer pH 7.0 containing 0.06 mM or 0.6 mM CA (SigmaAldrich), 0.06 mM or 0.6 mM NADPH (SigmaAldrich), 0.6 µmol of recombinant sorghum cytochrome P450 reductase, and 0.6 µmol of either mSbC4H2 or mSbC4H1. Samples without SbC4H were used as negative controls and samples without CPR were used to validate the requirement of this enzyme to enable activity of SbC4H. Each reaction condition was replicated twice. After 1.5h, the total volume of the reaction (300 µL) was transferred to a 1.5-mL screw cap tube. To quench the reaction and to extract CA and pCA from the reaction mixture to enable quantification, 300

µL ethylacetate containing 0.06 mM vanillin (Fisher Scientific, Hampton, NH, USA) was added.

Vanillin was used as an internal standard for the extraction protocol. Upon addition of ethylacetate, samples were vortexed two times 20 s, centrifuged at 14,000 g for 2 min., the organic phase (upper layer) was transferred to a 1.5-mL tube and the content was dried under a gentle stream of nitrogen for approximately 15 min. A volume of 90 µL of ethanol was added to

dissolve the compounds, and 10 µL of 25% (w/v) tetramethylammonium hydroxide (TMAH;

Sigma-Aldrich) in methanol was added to each sample, followed by an incubation of 30 min. at

60˚C. TMAH methylates the hydroxyl moieties on C9 (CA, pCA) and C4 (pCA, vanillin) to make them compatible with gas chromatography.

A GC-MS-MS protocol (multiple reaction monitoring; MRM) was created for the analysis of the enzymatic reactions with the trans-methylated standards of CA, pCA and vanillin

(0.6 mM each). One μL was injected in a Bruker Scion 456 gas chromatograph (Bremen,

Germany), equipped with a Restek Rxi®-5ms column (30 m, 0.25 mm i.d., 0.25 µm film thickness (Restek Corp, Bellefonte, PA, U.S.A)), and coupled to a Bruker Scion triple quadrupole mass spectrometer. Helium was used as carrier gas with a column flow rate of 1.1 mL/min. The injector temperature was 220°C; the split ratio was 1:50. The GC program consisted of a hold at 70°C (2 min.), followed by temperature increases to 170°C (40°C/min),

240°C (15°C/min), and 300°C (50°C/min) with a final hold of 1 min. The transfer line was heated to 280°C. Electron impact ionization with 70 eV electrons generated ions that were subjected to MRM using argon with a pressure of 1.5 mTorr as collision gas. For the methyl ester of trans-cinnamic acid (retention time 6.02 min.), the molecular ion m/z 162 [M]+ was selected as precursor ion, and m/z 131 [M-31]+ as the product ion. The collision energy was varied by adjusting the voltage of Q2 and maximized at 10 V. The scan time was set at 400 ms.

For the double methyl ester of p-coumaric acid (retention time 7.65 min.) the molecular ion m/z

192 [M]+ was selected as the precursor ion, and m/z 161 [M-31]+ as the product ion. The optimal collision energy was also obtained when Q2 was set at 10 V. The scan time was 800 ms. For the methyl ester of vanillin (3,4-dimethoxybenzaldehyde; retention time 6.49 min.) the molecular ion m/z 166 [M]+ was selected as the precursor ion and m/z 165 [M-1]+ was selected as the product

ion. The optimal collision energy was obtained when Q2 was set at 3.3 V. The scan time was 400 ms.

Results

Identification of Sorghum bicolor C4H Orthologs and Their Expression Profile

In this chapter the C4H nomenclature is based on the degree of homology of sorghum

C4H genes to the C4H maize genes already annotated. The genes Sb02g010910, Sb03g038160, and Sb04g017460 correspond to SbC4H1, SbC4H2, and SbC4H3 respectively.

Based on available gene expression data, ESTs and the MOROKOSHI: transcriptome database (Makita et al., 2014) in Sorghum bicolor, SbC4H2 was considered the gene most likely to encode the C4H enzyme involved in monolignol biosynthesis in vegetative tissues of sorghum. Expression analysis showed that SbC4H2 is expressed in all tissues sampled (panicle, seed and stem), but highly expressed in anthers, inflorescences and shoots under stress (Makita et al., 2014). Transcripts of SbC4H3 were observed only under nitrogen stress and the expression profile of SbC4H1 was not reported in this database (Makita et al., 2014). However, McKinley et al (2016) reported the expression of SbC4H1 in samples from Sorghum bicolor stem internodes undergoing lignification and its highest expression levels were observed at 16 days prior to antithesis (McKinley et al., 2016). Based on these results, it was considered possible that

SbC4H1 is also involved in cell wall lignification.

To validate the information present in transcriptome databases, a gene expression analysis was developed using the genotype ‘Rio’. Flag leaf, root, leaf and stem tissue were collected at two different time points (50 and 70 days after planting) and C4H expression was measured. Figure 3-13 shows that the only PCR product generated corresponded to the expected size of the cDNA fragment, indicating the cDNA samples are free of genomic DNA. Figure 3-14 shows the calibration curves for SbC4H1 and SbC4H2, respectively. Threshold values (Ct) were

graphed against their corresponding known concentration with which the calibration curve was

2 built. The R values for all of them were above 0.9. Ct values are inversely proportional to the amount of cDNA present in the sample.

Figure 3-15 shows the presence of the SbC4H1 and SbC4H2 amplicons. For SbC4H3 the heat dissociation analysis showed two peaks suggesting two PCR products hence excluding this amplicon from expression analysis. Given the Tm for the first peak (~80˚C) this product may correspond to primer dimers. It was observed that the expression of SbC4H1 was higher in the flag leaf and the roots at 70 days after planting (DAP). However, in the stems the expression of the two genes were not noticeably different (Figure 3-20). The results of these experiments in combination with the full transcriptome analysis described previously led us to proceed with the heterologous expression, purification and characterization of both mSbC4H1 and mSbC4H2.

Hydropathy Analysis of SbC4H2 (505 aa) and SbC4H1 (501 aa) Protein Sequences

Evidence concerning the structure of membrane proteins suggests that motifs interacting with membranes (lipid bilayers) are compounded of mostly non-polar (hydrophobic) amino acids

(reviewed by Engelman et al., 1986). Cellular localization assays indicated that C4H is embedded in the membrane of the endoplasmic reticulum (ER) of plants (Potts et al., 1974), making it likely they contain one or more transmembrane domains. Based on the characterization of p-coumarate 3′-hydroxylase (C3′H) (Kim et al., 2011), we hypothesized that removal of the transmembrane domain of SbC4H would enhance the solubility of the enzyme and facilitate the purification. In order to predict the position within the SbC4H protein sequence where these transmembrane domains are located, the protein sequences were analyzed with different algorithms.

Figure 3-1 displays a protein alignment showing the first 60 amino-acids of C4H in sorghum and Arabidopsis in order to identify the N-terminal transmembrane domain based on

sequence homology. Based on this alignment, it is evident that there is greater conservation in the class of amino acid rather than the identity in each domain of the polypeptide. In the AtC4H protein sequence (Figure 3-1), the first thirty amino acids correspond to a hydrophobic domain, which has been identified as a signal-anchor for retaining P450s on the cytoplasmic surface of the ER membrane (Nelson and Strobel, 1988). A proline-rich region follows the N-terminal signal-anchor sequence and it is thought to be involved in the process of the accurate folding of microsomal P450s and also essential in heme cofactor incorporation into P450s (Yamazaki et al.,

1993).

Protein sequences were also subjected to an individual analysis using the Kyte &

Doolittle scale of polarity shown in Figure 3-4. The Kyte and Doolittle scale assigns large numbers (usually positive values) to non-polar (hydrophobic) amino acids and small numbers

(usually negative) to the hydrophilic ones (Kyte and Doolittle, 1982). Figure 3-2 and Figure 3-3 present the hydropathy analysis of SbC4H2 (505 aa) and SbC4H1 (501 aa), respectively. These analyses confirm the presence of major hydrophobic regions at the N- and C-termini. This coincides with the transmembrane domain identified based on sequence similarity with AtC4H.

Figure 3-5 and 3-6 depict the position of the different motifs predicted by Phobius. SbC4H1 protein is predicted to have a signal peptide at the N-terminus (Figure 3-5). In contrast, SbC4H2 protein is predicted to have two transmembrane motifs, between amino acids 6-23 and close to the C-terminus (Figure 3-6). Figures 3-3, 3-4, 3-5 and 3-6 show the predicted hydrophobic regions at the N-termini of both proteins. Based on these predictions, the similarity with the annotated and characterized AtC4H, and considering the importance of the proline-rich sequence essential for correct folding and heme incorporation (Yamazaki et al., 1993), the cDNA sequences encoding the first 23 amino acids of both sequences were removed in the engineered

versions of SbC4H1 and SbC4H2 to improve solubility of SbC4H for purification purposes and further analysis.

SbC4H1 and SbC4H2 Sequence Optimization

Upon removing the N-terminal hydrophobic residues, the SbC4H nucleotide sequences for both genes SbC4H1 and SbC4H2 were codon optimized to enhance heterologous expression in E. coli. The final cDNA sequence for SbC4H2 had its GC-content reduced from 65% to 49%, while its CAI value increased from 0.64 to 0.82; 70% of the codons selected were had a frequency of use of 91-100%, and only 5% of the codons had a frequency of use of less than

30%. Similarly, SbC4H1 was optimized for E. coli. The optimized SbC4H1 cDNA sequence had a GC content of 54%, a CAI of 0.87 and 100% of the codons had a 70% frequency of use in E. coli.

Gene Synthesis, Cloning and Validation

Both mSbC4H1 and mSbC4H2 cDNA sequences were synthetized by Genewiz, Inc.

Figure 3-7 validates the exact match of the peptide sequences of the endogenous SbC4H1 and

SbC4H2 with their corresponding recombinant modified versions mSbC4H1 and mSbC4H2, respectively.

Protein Expression and Purification

The SbC4H cDNA in plasmid pET32 is under control of the T7lac promoter, which is inducible with lactose, IPTG, or infection with CE6 lambda bacteriophage. SbC4H in pET28(a) was expressed in E. coli BL21 (DE3) Rosetta cells (Novagen) that supply tRNAs for AGG,

AGA, AUA, CUA, CCC, GGA codons, which are used in a very low frequency in E. coli, facilitating expression of eukaryotic proteins in the bacterial vector (Gustafsson et al., 2004).

SbC4H in pET32 results in a fusion protein containing a thioredoxin (TRX) tag at the protein N- terminus that is expected to increase the concentration of C4H soluble in the bacterial cytoplasm

(LaVallie et al., 1993). To reduce protein degradation and/or protein aggregation different strategies were tested. It has been reported that addition of co-factors can positively influence heterologous protein expression levels and protein stability when added to the bacterial growing cultures (Tolia and Joshua-Tor, 2006). Hence, different concentrations of aminolevulenic acid

(heme-biosynthesis precursor) were added to bacterial cultures. Additionally, heat shock procedures and media preparations (e.g. sorbitol and betaine) that simulate/increase osmotic stress in E. coli and increase the activity of chaperones involved in proper folding or that reduce protein aggregation and/or degradation due to misfolding (Oganesyan et al., 2007).

mSbC4H2 expression was first accomplished by infecting E. coli BL21 Rosetta cells with bacteriophage λCE6. Protein expression was observed after 4 h at 37oC as shown in Figure 3-8

A. However, mSbC4H2 was not soluble. Western blot analysis with anti-His antibodies identified a protein of ~58kDa that corresponds to the expected size of recombinant mSbC4H2

(Figure 3-8B). The smaller fragment below the most abundant protein (indicated with an arrow) could be the result of protein degradation. SbC4H was presumed to be aggregated and located in inclusion bodies or associated with a bacterial membrane and therefore targeted for degradation.

SbC4H was successfully expressed with 6×His-tags at both the N- and C-termini. His- tagged mSbC4H2 was purified using immobilized metal affinity chromatography (IMAC) using

Ni-NTA His-Bind resin, which relies on the interaction of Ni2+ ions and the 6×His-tag. His- tagged proteins were eluted in Tris-HCl pH 8.0 buffer containing 250 mM imidazole. To avoid protein degradation, protease-inhibitor cocktails were tested. Similarly, various reducing agents were added to dialysis buffers in order to avoid protein precipitation, increase protein stability and ensure preserved protein structure and function. Stabilization reagents like tris (2-

carboxyethyl) phosphine (TCEP), dithiothreitol (DTT) and non-ionic detergent Lubrol were added to the purification buffers and protein dialysis and evaluated independently.

Upon testing multiple growth media, temperatures and induction methods, it was determined that the most effective and efficient expression of mSbC4H1 or mSbC4H2 was obtained using auto-inducible media ZYP5052 and subjecting E. coli cells to the heat shock protocol described in the methods. In figure 3-9A, a protein present among all samples, with an approximate molecular weight of 75 kDa is observed. This molecular weight coincides with the expected molecular weight of the recombinant mSbC4H2 (71 kDa) including the TRX, S and

6×-His-tag. In figure 3-9A the arrow indicates potential smaller fragments of mSbC4H2. Intact and degraded His-tagged mSbC4H2 was detected with mouse monoclonal anti-6×His antibodies

(Invitrogen) (Figure 3-9B). Most of the smaller fragments are seen in the Sb03C4H-NCH2 with a 6×His-tag at both N and C-termini suggesting that double tag might be compromising protein structure or folding concluding in protein degradation (Figure 3-9B). Figure 3-10 shows different elution fractions during purification with and without the protease inhibitor. The smaller fragments are eliminated in the presence of the inhibitor (Figure 3-10B). However, some of the small fragments remained, indicating that either protein degradation had not been eliminated or the antibodies are unspecific.

In addition to the protease inhibitors, reduction of protein degradation was also accomplished by the addition of 0.5% Lubrol in all the purification buffers (Figure 3-11).

Furthermore, bacterial cultures were exposed to a heat shock, followed by incubation at 17˚C.

Figure 3-18 displays how the expression of mSbC4H2 is only accomplished after 8h of incubation at 37˚C and followed by incubation at 17˚C after heat shock in ZYP5052 media.

Upon dialysis, 4 mg/mL of protein in approximately 3.5 mL was recovered

SbC4H Sequencing

Validation of SbC4H purified from bacterial extracts was demonstrated based on protein sequencing. The peptides originated from the proteins extracted from the SDS-polyacrylamide gel were identified as: hypothetical protein SORBIDRAFT_03g038160 [Sorghum bicolor]

(gi|242059075)-trans-cinnamate 4-hydroxylase (Figure 3-25).

Enzymatic Activity

In order to identify the optimal pH for C4H activity, enzymatic assays were performed with buffers with pH range between 7.0 and 8.0. Enzyme activity was first observed at pH 7.3, close to the pH measured in endoplasmic reticulum of Arabidopsis (pH 7.1) (Shen et al., 2013).

Initially, spectrophotometric assays utilizing the differential absorbance of compounds at particular wavelengths were used to determine the presence, absence and relative amounts of CA and pCA in the enzymatic reaction. Figure 3-17 depicts the absorbance of trans-cinnamic acid, p-coumaric acid and NADPH and their corresponding wavelengths where peak(s) of absorbance are detected. The experiment was designed to measure the relative abundance of trans-cinnamic acid, p-coumaric acid and NADPH in samples containing purified mSbC4H1 and mSbC4H2.

Theoretically, when the enzymes are active, the absorbances at 340 nm (NADPH) and 260 nm

(trans-cinnamic acid) are expected to decrease with time, whereas the absorbance at 310 nm (p- coumaric acid) is expected to increase. The experiment was carried out at two different pH values, 7.3 and 8.0, in 100 mM HEPES buffer. The concentrations and components of the reaction are displayed in Table 3-6. Although it is possible to measure absorbance of CA and

NADPH in the reaction, the overlap in absorbance between CA and pCA made it impossible to trace pCA formation with this method (Figure 3-17).

To validate the production of p-coumaric acid (pCA) as a consequence of the enzyme activity of purified mSbC4H2 and mSbC4H1, a GC-MS-MS protocol (multiple reaction

monitoring; MRM) was used. Using this protocol, in combination with trans-methylation of acidic hydroxyl groups on the phenolic compounds, it was possible to unequivocally identify the methyl esters of trans-cinnamic acid and p-coumaric acid in the reactions, even when only small amounts of product were generated.

Using the ethylacetate extraction method, it was possible to identify p-coumaric acid in the reactions, indicating that mSbC4H2 and mSbC4H1 are enzymatically active.

Fugure 3-21 and Figure 3-22 show the chromatograms generated using the MRM protocol for mSbC4H2 and mSbC4H1 at two different substrate concentrations (0.06 mM and

0.6 mM). Methylated derivatives of trans-cinnamic acid, vanillin, and p-coumaric acid had retention times of 6.02, and, 6.49, and 7.65 min., respectively. As expected for an organic solvent extraction, peak sizes of 3,4-dimethoxybenzaldehyde (derivatized vanillin) varied among samples due to variations in the exact volume of ethylacetate that was removed. CA and pCA peaks were, therefore, normalized to vanillin.

The observed peak of the double methyl ester of p-coumaric acid was approximately 103 lower compared to observed peak of the methyl ester of trans-cinnamic acid and the double methyl ester of vanillin. However, the relative abundance of p-coumaric acid was directly proportional to the initial concentration of trans-cinnamic acid as shown in Figure 3-21 and 3-22.

When comparing the production of p-coumaric acid between the two enzymes, it is evident that reactions containing mSbC4H1 (Figure 3-21) generate larger amounts of p-coumaric acid compared to reactions containing mSbC4H2 (Figure 3-22) under the same the experimental conditions.. Additionally, the requirement of cytochrome P450 reductase (CPR) in the reaction was validated, since reactions without CPR failed to accumulate p-coumaric acid (Figure3-23 and Figure 3-24C, D). The data were consistent across replicates.

Small traces of (the double methyl ester of) p-coumaric acid were detected in samples lacking mSbC4H2 and mSbC4H1 (Figure 3-24A,B). This was determined to be due to residual compound lingering in the injector. A column cleaning protocol with no sample was able to remove this carry over from prior runs. Figure 3-23 demonstrates that the presence of p-coumaric acid is in fact due to the action of SbC4H given that significant and consistent differences are observed between the controls (No-CPR or No-C4H) and the samples containing either mSbC4H2 or mSbC4H1.

Discussion

Membrane proteins play a crucial role in metabolism in all organisms. They are involved in transport of molecules or ions across membranes, respiration, photosynthesis, and catalysis of chemical reactions.

It has been predicted that around 20-30% of genes in all genomes known until now, encode membrane proteins (Krogh et al., 2001). In humans for example, membrane proteins constitute ~20% of the coding genes and interestingly, around 60% of drugs target membrane proteins (Reviewed by Overington et al., 2006). Despite their importance, there are only a few membrane proteins with a known 3-D structure. This phenomenon is caused by their relative low quantities, and the difficulty to isolate them in a manner that is representative of their native state. Recombinant membrane proteins are often not soluble and unstable when purified.

In order to evaluate kinetic parameters and elucidate the sorghum C4H structure, relatively large amounts of stable, soluble, non-denatured purified protein are required. This requires both efficient expression of soluble protein and effective purification strategies. The latter required different combinations of buffers and additives to identify optimal purification conditions. We have determined that every protein requires unique conditions to achieve activity

outside its natural context and finding suitable conditions requires optimization of multiple parameters.

In this study, we have successfully determined conditions that enable heterologous expression in E. coli and subsequent purification of active, soluble SbC4H. We have demonstrated that the modified versions of SbC4H, mSbC4H1 and mSbC4H2 are enzymatically active, albeit at very low levels based on the small amount of product that is formed after 90 min.

The fact that the production of p-coumaric acid was directly proportional to the concentration of substrate suggests that the enzyme may require larger amounts of trans-cinnamic acid and/or

NADPH. Cytochrome P450 reductase (CPR) is a membrane-bound enzyme that transfers electrons from NADPH to the heme-group of all known microsomal cytochromes P450 to initiate enzyme activity (Lu et al., 1969; Wang et al., 1997). As an electron donor, CPR plays a major role in the efficiency of p-coumaric acid production in this experiment. The sorghum CPR protein used in this study, was a soluble, recombinant, heterologously expressed protein, with truncated N-terminus to increase solubility.

Based on structural and enzymatic analysis, it has been reported that the N-terminal transmembrane domain of CPR is necessary not only for anchoring the enzyme to the membrane, but for protein stability and proper spatial interaction for electron transfer. Enzymatic assays with rat liver CPR expressed in E. coli and solubilized, showed that the enzyme was capable of transferring electrons to cytochrome c and artificial acceptors, but that it was incapable of transferring electrons to cytochrome P450 (Wang et al., 1997). Hence, although active, the activity of truncated CPR may be low, and may, therefore, reduce the activation efficacy of mSbC4H1 and mSbC4H2, which explains the small amounts of p-coumaric acid observed.

However, no activity of mSbC4H1 and mSbC4H2 was observed unless SbCPR was present in

the reaction. These results are consistent with previously described C4H activity in microsomal fractions of yeast co-expressing C4H/CPR poplar enzymes where activity of C4H increased 10- fold in the presence of CPR relative to the C4H-only control strain (Ro et al., 2002).

The small amounts of p-coumaric acid can be also explained by the fact that both SbC4H and CPR are naturally membrane-bound, and, when truncated and solubilized, they lose stability and proximity, both features required for efficient protein activity (Nisimoto, 1986; Shen and

Kasper, 1995). The need for proximity would be consistent with the high amounts of enzyme required to observe activity using concentrations of 640 nM and 2.25 µM of SbCPR and mSbC4H1/mSbC4H2 per reaction, respectively. It was also observed that mSbC4H1 produced higher amounts of p-coumaric acid under all tested conditions (Figure 3-21). The reason of this apparent difference may be related to differences in the purity of the two enzyme preparations, or might be related to the heme-group co-factor (iron-containing molecule) binding in a larger proportion of the mSbC4H1 protein. The latter possibility is supported by the observation that the eluted fractions of mSbC4H1 are significantly redder in color than those of mSbC4H2.

However, this difference needs to be investigated further through kinetic analyses.

The cellular environment provides all the factors and variables necessary for the enzymatic reactions to take place in an effective manner. A large limitation and challenge when studying recombinant heterologous enzymes is that most of these variables cannot be replicated identically in test tubes. Additionally, it is important to mention that the three enzymes utilized in this study, mSbC4H1, mSbC4H2 and, SbCPR, are naturally occurring plant proteins being produced in a prokaryotic system (E. coli) and this might have implications that can jeopardize the enzymatic activity as well. In some scenarios, the cytoplasm of a prokaryotic cell is an inhospitable environment for eukaryotic-membrane proteins, and the reason why most of

mSbC4H1 and mSbC4H2 is found in the inclusion bodies in the insoluble fraction of bacterial cells. Additionally, in some cases, proper folding and activity of eukaryotic enzymes can be determined by different post-translational modifications (e.g. glycosylation) that prokaryotic organisms are unable to execute (Loll, 2003).

In plants, most of membrane proteins remain structurally uncharacterized, including the other two P450s involved in the phenylpropanoid pathway (C4H and F5H). Despite the number of advances in the understanding of the phenylpropanoid pathway, recent discoveries of interactions among proteins and this proteins with membranes and other molecules have generated many questions. It has been reported that C4H either closely interacts or forms complexes with other proteins, such as PAL and C3′H (Achnine et al., 2004; Chen et al., 2011a;

Bassard et al., 2012), but the underlying mechanisms and implication for lignin biosynthesis require further investigation. It appears the formation of certain complexes is species-specific, as demonstrated by Gou et al. (2018) in arabidopsis, where membrane steroid-binding proteins

(MCBPs) act as scaffolds to assemble the cytochrome P450s C4H, C3′H and F5H in the membrane of the endoplasmic reticulum. This interaction was demonstrated to be important for monolignol biosynthesis, and disruption of MCBPs affected lignin subunit composition.

The successful purification of active sorghum C4H paves the way to answer questions of protein-protein interactions and understanding of metabolic channeling within the pathway. The possibility to study SbC4H independently contributes to the scientific understanding the phenylpropanoid pathway. It also opens the potential for future engineering of plant cell wall architecture through genetic engineering, supporting the efforts to improve biofuel production from biomass and the invention of novel value-added products from lignin polymers.

Figure 3-1. Sequence alignment of the 60 N-terminal amino acids of Sorghum bicolor (Sb) C4H1(Sb02g010910) and C4H2 (Sb03g038160) and Arabidopsis thaliana (At) C4H. Amino acid color key: blue: acidic, red: hydrophobic, green: polar and pink: basic. The alignment was built using CLUSTAL OMEGA (1.2.4) multiple sequence alignment.

Figure 3-2. SbC4H2 amino acid sequence (505 aa) analyzed with ProtScale (software) (Gasteiger et al., 2005) . Scores are assigned to each amino acid based on the Kyte & Doolittle scale of polarity; the greater the number, the more hydrophobic the amino acid.

Figure 3-3. SbC4H1 amino acid sequence (501 aa) analyzed with ProtScale (software) (Gasteiger et al., 2005) . Scores are assigned to each aminoacid based on the Kyte & Doolittle scale of polarity; the larger the number the more hydrophobic the aminoacid.

Figure 3-4. Hydropathy scale based on the Kyte & Doolittle scale of polarity. Individual values for the 20 amino acids are displayed (Kyte and Doolittle, 1982).

Figure 3-5. Prediction of transmembrane domains and signal peptides in SbC4H1. A signal peptide is predicted, consisting of amino acids 1-24. A transmembrane domain is predicted close to the C-terminus with a probability of ~20%. The prediction was generated using Phobius software (Käll et al., 2004).

Figure 3-6. Prediction of transmembrane domains and signal peptides in SbC4H2. Two transmembrane domains were predicted. First located between 6-23rd amino acids and second close to the C-terminal. The prediction model was generated using Phobius software (Käll et al., 2004).

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Figure 3-7. Validation of a 100% protein identity of mSbC4H1 and mSbC4H2 with SbC4H1 and SbC4H2 respectively, despite the corresping cDNA optimizations. Protein alignment of SbC4H2 (right) and SbC4H1 (left) with their corresponding modified recombinant versions mSbC4H2 and mSbC4H1, respectively. The alignment confirms amino acid sequence identity despite the changes in codon usage. Both alignments display the truncated N-terminal amino acids 6-23) compared to the endogenous sorghum C4H, SbC4H2 (right) and SbC4H1 (left). Alignments were constructed using CLUSTAL multiple sequence alignment by MUSCLE (3.8)

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A B

Figure 3-8. Initial expression and validation of modified and recombinant SbC4H. A) 12% SDS- polyacrylamide gel stained with Coomassie Brilliant Blue displaying protein extracts from E. coli BL21 Rosetta infected cells with two different multiplicity of infection (MOI) of lambda CE6 (λCE6) bacteriophage. U, uninduced culture. I, Induced culture. E.V., empty vector as a negative control. Pellet and soluble fraction after cell lysis through French® pressure cell press are indicated. Arrows indicate 6×His- mSbC4H2. B) Western blot following incubation with anti-6×His-antibodies. The estimated molecular weight of mSbC4H2 including the 6×His-tag is 58kDa.The arrow indicates the band corresponding to 6×His-mSbC4H2. Bands below may indicate mSbC4H2 degradation given that the protein is found in the pellet.

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A B

KDa U1 SF1 P1 U2 SF2 P2 SF3 P3 +Control

Figure 3-9. 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying protein expression and degradation of SbC4H. A). 12% SDS- polyacrylamide gel with protein extracts from E. coli, BL21-Rosetta (DE3). A protein of approximately 75 kDa can be observed. Thick bands marked with the arrow indicate possible SbC4H degraded protein residues. B). Western blot following incubation with anti-6×His- antibodies. Estimated molecular weight of SbC4H including the 6×His-tag, S-tag and TRX tag is 71kDa. U, uninduced culture. I, induced culture. Pellet and SF, soluble fraction after cell lysis are indicated. Numbers accompanying letters in B: 1. mSbC4H2 2. Sb03C4H-NCH2, 3. λ-MOI6 (see Figure 3-8). NH2, mSbC4H2 with 6×His-Tag at N-termini. NCH2, Sb03C4H-NCH2 with His-Tag at N and C-termini, +Control, positive control (6×His-tagged GFP)

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A B

Figure 3-10. Nitrocellulose membrane displaying western blots following incubation with anti- 6×His-antibodies. Different elutions obtained from the purification of mSbC4H2 via immobilized metal affinity chromatography are presented. The estimated molecular weight of mSbC4H2 including the 6×His-tag, S-tag and TRX tag is 71kDa. A). E. coli cells resuspended in binding buffer without protease inhibitor B). E. coli resuspended in binding buffer with a protease inhibitor prior to cell lysis. U, uninduced culture. I, Induced culture. Pellet and SF, soluble fraction, W1, Wash 1, W2, W3, wash 3, E2-7, eluted fractions, +Control, positive control (6×His-tagged GFP).

kDa 1 2 3 4 5 6

Figure 3-11. 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying purified mSbC4H2: 6×His-TRX-SbC4H (MW ~71kDa) 1 Crude lysate 2 Elution fraction # 2. 3 Elution fraction # 3. 4 Elution fraction # 4. 5 Elution fraction # 5. 6 Elution fraction #6. Purification with Ni2+ column with 0.5% lubrol from auto- inducible media ZYP5052.

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+ + O2 + NADPH + H H2O + NADP

Figure 3-12. Cinnamate 4-hydroxylase (C4H) converts trans-cinnamic acid (left) into p- coumaric acid (right). C4H uses molecular oxygen and NADPH +H+ as cofactors.

bp 1 2 3 4 5 6 7

500

250

Figure 3-13. RNA samples and cDNA samples after PCR amplification with SbC4H1 primers. The 2% (W/V) agarose gel stained with GelRedTM (Biotium Inc., Hayward, CA ) and visualized under UV-light displays that the RNA and cDNA samples were not contaminated with genomic DNA. Lanes 1, 2, and 3 contain RNA samples. Lanes 4,5, and 6 contain cDNA samples, 7. Negative control. SbC4H1 cDNA amplicon has an expected size of 246bp.

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Figure 3-14. SbC4H1 (left) and SbC4H2 (right) qRT-PCR amplicon calibration curves for quantification of m RNA transcripts. The x-axis corresponds to the concentration of constructs containing the amplicons (transformed into log10 units), the y-axis corresponds to the cycle threshold (Ct) generated through qRT-PCR.

A B

Figure 3-15. Melting curve from 55-95˚C with the PCR products obtained from the reaction with (A) SbC4H1 and (B) SbC4H2 primers (Table 3-1). Each color represents a single reaction. The peaks correspond to the melting temperature of the amplicons in the samples and their respective fluorescence.

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Figure 3-16. Summary of constructs used for analysis. Cartoon representation of sorghum recombinant and modified C4H with fused tags, tag-cleavage sites, molecular weight and individual features. Constructs 1,2, and 4 marked with red “X” were not successful for protein expression. Construct 3 is referred as either mSbC4H1 or mSbC4H2 depending on the cDNA cloned.

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Figure 3-17. UV-absorbance spectrum of the different compounds used in the enzymatic activity assay. The assay was performed in 0.1 M potassium phosphate buffer (blue); 0.06 mM trans-cinnamic acid standard (red); 0.06 mM p-coumaric acid standard (green); 0.06 mM NADPH standard (purple); The reaction containing 0.06 mM trans- cinnamic acid, mSbC4H1, 0.06 mM NADPH (orange); same as before but with membrane-dialyzed mSbC4H1 and 0.06 mM NADPH (black).

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Figure 3-18. 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying total protein extract from E. coli BL21(DE3) Rosetta cells incubated through time to evaluate protein expression in ZYP5052 auto-inducible media at two different temperatures. OD600 after 20 h = 1.0 at 17˚C and 1.4 at 37˚C. Red box highlights the expression of heterologous mSbC4H2.

kD 1 2 3 4 5 1 2 3 5

Figure 3-19. 12% SDS-polyacrylamide gel stained with Coomassie Brilliant Blue displaying purified and dialyzed mSbC4H1 and mSbC4H2. Lane 1 mSbC4H1 elution fraction #3. Lane 2 mSbC4H1 elution 2+3 dialyzed within membrane O/N (2.4 mg/mL). Lane 3 mSbC4H2 elution fraction 3 Lane 4 mSbC4H1 dialyzed with Amicon®-30kD (0.8 mg/mL). Lane 5 mSbC4H2 dialyzed with membrane O/N (1.4 mg/mL). First 5 wells contain 15 µl of protein and last four contain 5 µl of protein sample.

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Figure 3-20. SbC4H1 and SbC4H2 differencial expression quantified in different tissues and developmental stage of sorghum plants in the field. Expression of SbC4H1 (Blue) and SbC4H2 (Red) in stems, leaves, roots and flag leaves at 50 (left) and 70 (right) days after planting (DAP) of sorghum plants of the genotype ‘Rio’. Concentrations are expressed in log10 units of copy number/µL

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A B

D C

Figure 3-21. GC/MS-MS-generated total ion current (TIC) chromatograms of reactions containing mSbC4H1. A and B are replicates of the reaction containing 0.06 mM CA; C and D are replicates of the reaction containing 0.6 mM CA. Retention times (RT) for the methyl ester of trans-cinnamic acid, methyl ester of vanillin, and double methyl ester of p-coumaric acid are 6.02, 6.49, and 7.65 min., respectively. The insets (with a different scale) show the peak corresponding to the double methyl ester of p- coumaric acid. To adjust for variation in the efficiency of the ethylacetate extraction, all peaks were normalized to the average total ion current (TIC) for the methyl ester of vanillin (6 106).

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A B

D C

Figure 3-22. GC/MS-MS-generated total ion current (TIC) chromatograms of reactions containing mSbC4H2. A and B are replicates of the reaction containing 0.06 mM CA; C and D are replicates of the reaction containing 0.6 mM CA. Retention times (RT) for the methyl ester of trans-cinnamic acid, methyl ester of vanillin, and double methyl ester of p-coumaric acid are 6.02, 6.49, and 7.65 min., respectively. The insets (with a different scale) show the peak corresponding to the double methyl ester of p- coumaric acid. To adjust for variation in the efficiency of the ethylacetate extraction, all peaks were normalized to the average total ion current (TIC) for the methyl ester of vanillin (6 106).

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Figure 3-23. GC/MS-MS generated chromatogram of enzymatic reactions containing mSbC4H1 or mSbC4H2. The peak representing the double methyl ester of p-coumaric acid, with a retention time of 7.65, is compared across reactions containing either 0.06 mM or 0.6 mM of trans-cinnamic acid and either mSbC4H1 or mSbC4H2. Negative controls lacking C4H and lacking CPR are also included.

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A B

C D

Figure 3-24. GC/MS-MS-generated total ion current (TIC) chromatograms of enzymatic reaction controls. A and B are replicates of the reaction containing 0.6 mM CA without SbC4H; C and D are replicates of the reaction containing 0.6 mM CA without SbCPR. Retention times (RT) for the methyl ester of trans-cinnamic acid, methyl ester of vanillin, and double methyl ester of p-coumaric acid are 6.02, 6.49, and 7.65 min., respectively. The insets (with a different scale) show the peak corresponding to the double methyl ester of p-coumaric acid. To adjust for variation in the efficiency of the ethylacetate extraction, all peaks were normalized to the average total ion current (TIC) for the methyl ester of vanillin (6 106).

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Figure 3-25. SbC4H protein identification based on sequencing. MS/MS samples were analyzed using Mascot (Matrix Science, London, UK; version 2.4.1) after liquid chromtograpgy mass spectrometry, LC MS. Protein identification probability is shown is green. The 60 and 70 numbers in the top right correspond to two diffentent bands isolated from the SDS-PAGE gel. The two bands may correpond to SbC4H isomers present in the sample. With 47% coverage, the bands extracted were identified with 100% identitiy to the cluster presented in the picture. The identification with other grass species is due to protein conservation across species. Data is visualided using the Scafford viewer (version 4.2.1, Proteome Software, Inc.)

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Table 3-1. SbC4H primers used for gene expression analysis. Fragment sizes correspond to the cDNA lengths Gene name Primer Sequence 5' → 3' Tm Fragment direction size (bp) Sb01g030340 Forward GGTTCGGGAGGTGGCATAGGT 60.00 130 (Ubiquitin) Reverse AGCATGTACATTCCCAGCGGTAG 60.00 SbC4H2 Forward AGACACGCCTCAAGCTCTTC 60.00 256 Reverse AGACACGCCTCAAGCTCTTC 60.00 SbC4H1 Forward GGACAACTTCGTCCAGGAAC 60.00 246 Reverse AGCACCGAGTCCATCTCCT 60.00 SbC4H3 Forward ACCGTCGGCGGGAAGGTGGACTT 71.30 279 Reverse ATGTCAAAATGACGCGCCGCATT 66.10

Table 3-2. mSbC4H2 and Sb03C4H-NCH2 cloning primers (5'-> 3') into EK pET32 LIC Primer direction Sequence 5' → 3'

Forward GACGACGACAAGATGTCCAAAATGCGTGGCCG

Reverse GAGGAGAAGCCCGGTTAGAAGGTACGCGGCTTGC

Reverse (NCH2) GAGGAGAAGCCCGGTGAGAAGGTACGCGGCTTGC

Table 3-3. mSbC4H2 and Sb03C4H-NCH2 sequencing primers Primer name Primer sequence (5' → 3')

SbC4H_seq212_F TGTGGCCGCTATTGAAACGA

SbC4H_seq843_R AGAACCAAATTCCACGCCCT

Standard T7 TAATACGACTCACTATAGGG

Standard T7 terminator GCTAGTTATTGCTCAGCGG

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Table 3-4. Dilution series used to generate a calibration curve to measure SbC4H expression levels. pET28 refers to the vector in which fragments of cDNA to be quantified by qRT-PCR were cloned, 0910 (SbC4H1), 8160 (SbC4H2), 7460 (SbC4H3) and UBI3 (Sb01g030340). Dilution Plasmid name Copy stock conc. cDNA H2O Vol to number number/µL (ng/µL) volume (µL) RT-PCR (µL)-0.5X 0 pET28-0910 3000000000 93.5 9.6 90.4 2 1 pET28-0910 300000000 9.35 9.6 90.4 2 2 pET28-0910 30000000 0.935 9.6 90.4 2 3 pET28-0910 3000000 0.0935 9.6 90.4 2 4 pET28-0910 300000 0.00935 9.6 90.4 2 5 pET28-0910 30000 0.000935 9.6 90.4 2 6 pET28-0910 3000 0.0000935 9.6 90.4 2 7 pET28-0910 300 0.00000935 9.6 90.4 2 8 pET28-0910 30 0.000000935 9.6 90.4 2 0 pET28-8160 3000000000 84.5 10.7 89.3 2 1 pET28-8160 300000000 8.45 10.7 89.3 2 2 pET28-8160 30000000 0.845 10.7 89.3 2 3 pET28-8160 3000000 0.0845 10.7 89.3 2 4 pET28-8160 300000 0.00845 10.7 89.3 2 5 pET28-8160 30000 0.000845 10.7 89.3 2 6 pET28-8160 3000 0.0000845 10.7 89.3 2 7 pET28-8160 300 0.00000845 10.7 89.3 2 8 pET28-8160 30 0.000000845 10.7 89.3 2 0 pET28-7460 3000000000 87.5 10.3 89.7 2 1 pET28-7460 300000000 8.75 10.3 89.7 2 2 pET28-7460 30000000 0.875 10.3 89.7 2 3 pET28-7460 3000000 0.0875 10.3 89.7 2 4 pET28-7460 300000 0.00875 10.3 89.7 2 5 pET28-7460 30000 0.000875 10.3 89.7 2 6 pET28-7460 3000 0.0000875 10.3 89.7 2 7 pET28-7460 300 0.00000875 10.3 89.7 2 8 pET28-7460 30 0.000000875 10.3 89.7 2

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Table 3-4. Continued. Dilution Plasmid name Copy stock conc. cDNA H2O Vol to number number/µL (ng/µL) volume (µL) RT-PCR (µL)-0.5X 0 pET28-UBI3 3000000000 78.5 11.2 88.8 2 1 pET28-UBI3 300000000 7.85 11.2 88.8 2 2 pET28-UBI3 30000000 0.785 11.2 88.8 2 3 pET28-UBI3 3000000 0.0785 11.2 88.8 2 4 pET28-UBI3 300000 0.00785 11.2 88.8 2 5 pET28-UBI3 30000 0.000785 11.2 88.8 2 6 pET28-UBI3 3000 0.0000785 11.2 88.8 2 7 pET28-UBI3 300 0.00000785 11.2 88.8 2 8 pET28-UBI3 30 0.000000785 11.2 88.8 2

Table 3-5. Quantitative real time PCR program used to determine the expression of sorghum C4H genes Step Duration Temperature (C˚) Cycles 1 2:00min 50 2 30 sec 95 3 5:00 min 95 4 Real time PCR 45 4.1 20 sec 95 4.2 50 sec 63 4.3 30 sec 73 4.4 1:00 min 55 5 Melting curve 10 sec 80

Table 3-6. Ingredients of the enzymatic activity assay and their corresponding concentrations Component Final concentration Human cytochrome P450 reductase (CPR) 2.25 µM mSbC4H1/ mSbC4H2 2.25 µM trans-cinnamic acid 0.06 mM NADPH 0.06 mM HEPES buffer pH 7.3 or 8.0 100 mM

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CHAPTER 4 GENERATION OF A BIPARENTAL MAPPING POPULATION FOR GENETIC ANALYSIS OF WATERLOGGING TOLERANCE IN SORGHUM BICOLOR

Introduction

Based on the results shown in the Chapter 2 of this dissertation, we have identified multiple response mechanisms of six sorghum genotypes to water-saturated soil. However, the genetic basis for these mechanisms in sorghum are not yet understood. This chapter describes the development of two biparental populations created with the aim of mapping loci associated with waterlogging tolerance.

Quantitative trait locus (QTL) mapping it is a method used to link phenotypic information (quantitative measurements of traits) with genotypic data (e.g. molecular and genetic markers). Such an analysis can determine the locus and/or genetic basis for a trait of interest

(Geldermann, 1975; Stuber et al., 1987; Tanksley and Hewitt, 1988; Paterson et al., 1990;

Kearsey, 1998) when a biparental population is generated from parents that are clearly distinct for the trait of interest. The higher the degree of genetic variation within the parental lines, the higher the likelihood of identifying genetic or molecular markers linked to the phenotype of interest. Genotypic data (markers) can be obtained using different techniques including restriction fragment length polymorphisms (RFLPs, Botstein et al., 1980), randomly amplified polymorphic DNA (RAPDs; Williams et al., 1990), simple sequence repeats (SSRs; Zietkiewicz. et al., 1994), amplified fragment length polymorphism (AFLPs; Vos et al., 1995) and, single nucleotide polymorphisms (SNPs) (LaFramboise; 2009).

All of the F1 progeny of a biparental cross are genetically and phenotypically identical, and heterozygous at all (marker) loci for which the parents differed. With each generation generated via self-pollination, the proportion of heterozygotes decrease by 50% (50% in the F2,

25% in the F3, 12.5% in the F4, etc.). Ideally, advanced generations (F5 or higher) are utilized for

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phenotypic screening and genotyping due to the greater level of homozygosity and the smaller linkage blocks that have been formed as a result of recombination, which increases the mapping resolution (Mackay and Powell, 2007). Alternatively, a large number (several hundred) of F2 individuals will be required in order to detect QTL (Mackay and Powell, 2007). An additional disadvantage of using early generations is that replication across years and locations is more difficult.

In general terms, phenotypes that are measured are then associated with each marker identified during the genotyping process. To find the correlation between the two, both variables need to be statistically analyzed simultaneously to evaluate if they co-segregate. Several software packages have been developed to facilitate this task such as R-qtl, Rascual and QTLNetwork

(Broman et al., 2003; Yang et al., 2008; Kumasaka et al., 2016).

Efforts to identify and understand the genetic basis of waterlogging tolerance in different grasses (Poaceae) show promising results. Mano et al. (2006) identified QTL on chromosomes 4 and 8 for adventitious root formation in two different maize varieties (Mano et al., 2005a; Mano et al., 2005b). Qui et al. (2007) conducted experiments validated in two different years with an

F2:3 (F2-derived F3, i.e. an F3 population derived from self-pollinating F2 individuals) population of 288 individuals and reported QTL on chromosomes 4 and 9 associated with root length, root dry weight, plant height, shoot dry weight, total dry weight consistently in experiments done in two separate years. In the same study, chromosomes 1, 2, 3, 6, 7, and 10 were also identified as secondary QTLs associated with tolerance to waterlogging (Qiu et al., 2007). In rice, quantitative trait loci (QTL) studies with strongly flooding-tolerant cultivars as O. sativa ssp. Indica. The authors defined tolerance as the ability of 8-10 d-old seedlings to survive and continue to normally develop during a period of de-submergence upon being completely under water for 14

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days. Under these conditions, Submergence1 (SUB1) was identified as a the major locus conferring the flooding resistance trait (Xu et al., 2006). The SUB1 locus was identified as a cluster of three genes encoding putative ethylene response factors (Xu et al., 2006). Furthermore,

QTL mapping in rice identified the SNORKEL1 and SNORKEL2 loci. SNORKEL genes encode ethylene response factors involved in ethylene signaling, and under deepwater conditions (water up to 70% of the plant height) ethylene accumulates and triggers the expression of these two genes that mediate the elongation of internodes creating hollow structures that function as snorkels allowing gas exchange (Hattori et al., 2009). In both studies, SUB1 and SNORKEL1 and

SNORKEL2, introduction of QTLs from tolerant into sensitive lines allows the latter to develop under flooded conditions. Recently, a QTL mapping study based on three biparental sorghum populations identified the gene SbNAC_D, encoding a transcription factor expressed in the stems as a regulator of genes involved in programmed cell death resulting in aerenchyma formation during internode development (Casto et al., 2018).

These combined findings contribute to the efforts of identifying loci that will enable the development of acclimated crops for cultivation on flood-prone land with minimal yield losses, thus contributing to more efficient use of available land.

Materials and methods

Screening for Response to Waterlogging Under Field Conditions

In order to identify sorghum genotypes with different responses to waterlogging, two replicates of the sorghum minicore collection, consisting of 242 diverse genotypes (Upadhyaya et al., 2009) were seeded at the University of Missouri Horticulture & Agroforestry Research

Center (HARC) near New Franklin, MO on 20 July 2013. This facility contains channels of 100 m long × 5 m wide that can be inundated in a controlled manner. The seeds were placed in rows of 2 m long, running parallel to the long axis of the channel, and spaced 76 cm apart; a 1.2-m

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alley separated blocks of five adjacent rows. Two checks, inbred lines BTx623 and M81E, were included as border rows to minimize edge effects on the genotypes of the minicore. Two rows of each of these genotypes were also included in random positions inside each of the channels in order to be able to compare growth and development across different channels. The waterlogging treatment was initiated on 18 September 2013 and maintained for a period of 5 weeks.

The plants’ response to waterlogging was monitored by visual observation of yellowing of the plants (no yellowing, some yellowing, complete yellowing) and the degree of lodging (no, some, complete) and measuring plant height (before and the initiation of waterlogging and at three time points after initiation). A follow-up screening was performed in 2014 with the 12 genotypes from the minicore, that represented the six most and the six least sensitive to waterlogging in 2013.

These were seeded in three replicates on 15 July 2014 inside a single channel, with cultivars

Atlas and M81E used controls. They were subjected to waterlogging on 12 September 2014 for a period of 5 weeks. In this case propensity to lodging and leaf discoloration (yellowing) were recorded after 2 and 4 weeks as a measure of susceptibility to waterlogging.

F1 Generation and Developing of The Mapping Populations

Initial crosses were made with the genotypes that represented the six most and the six least sensitive to waterlogging in 2013. These crosses were made during the field season in the summer of 2014 at the UF Suwannee Valley Agricultural Extension Center near Live Oak, FL.

The genotypes used as parents are listed in Table 4-1.

Crosses were made using an emasculation method in which the sorghum panicle is covered with a plastic bag sealed tightly around the peduncle (the stem on which the panicle forms) just before the initiation of flowering. In warm and humid conditions (such as in Florida), the resulting heat and humidity prevent pollen dehiscence while not affecting the viability of the styles and ovules (Schertz and Clark, 1967). When the panicle has completely flowered, the

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plastic bag is removed, and pollinated with the pollen from a selected male parent. The panicle is then covered immediately with a water-proof paper bag until harvest.

Based on the follow-up field experiment in 2014 described above, only the progenies from crosses between parental lines that were consistently tolerant or sensitive to waterlogging in

2013 and 2014 were continued. Those progenies are marked with an asterisk in Table 4-1, of which two were selected to generate biparental mapping populations. The history of their development along with the total number of individuals at each generation are presented in

Tables 4-2 and 4-3.

Phenotypic Screening of Subset of Waterlogging Population in Flooding Channels

To test the waterlogging conditions suitable for a large population phenotyping, a preliminary study was performed in the flooding channels at the University of Missouri described above. On 18 August 2017 three replicates of a subset of 87 F7 individuals from the biparental population IS 29314  IS 12883 were planted. Each replicate set also contained two replicates of the parental lines IS 29314 and IS 12883. The three replicates were planted in two flooding channels. One replicate was planted in a rain-watered channel (control) and two replicates in channel 2, which was to be subjected to waterlogging. The genotypes were planted in a randomized order within each channel, as shown in Table 4-4. Waterlogging treatment was applied on 11 October 2017 when plants were about 45 cm in height and maintained for two weeks. The water level was maintained up to 7 cm above ground level to keep the entire root system under water. A killing frost occurred on 28 October 2017. On 1 November five representative plants from each row were harvested and average height and aerenchyma formation in the stem was recorded. Aerenchyma formation was scored on a scale from 0 (no aerenchyma) to 5 (all plants displaying noticeable aerenchyma). After harvesting the plants,

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leaves were removed from stems, stems were cut in small pieces and dried at 60˚C for 6 days and dry weight was measured. Statistical differences between replicates and controls were evaluated using JMP® Pro 13.0.0 (SAS, Cary, NC, USA).

Results

From the mapping population IS 29314  IS 12883, the following number of families per generation were generated F7: 87, F6: 147, F5: 24. For the mapping population IS 7131  IS

22799, 88 F5 families were generated.

To determine whether the mapping population IS 29314  IS 12883 was segregating for response to waterlogging in the field, a subset of 87 F7 individuals (genotypes) were planted in the flood channels at the University of Missouri and subjected to waterlogging for a period of two weeks. Based on the plant height, dry stem biomass and aerenchyma formation data, the environmental variation appeared to be greater than the phenotypic variation within the population. This is evident from the lack of correlation between the dry stem weights of the two replicates of waterlogged plants (left versus right side of the channel) (Figure 4-1)

In order to further examine the environmental variation, the dry stem biomass was plotted against the row number in which the individual genotypes where planted within each replicate.

The Figure 4-2 shows that dry stem biomass varies as a function of the position in the channel where the individuals were planted.

All of the observations per channel were combined to determine the overall effect of waterlogging on dry stem biomass (Figure 4-3). On average, plants subjected to waterlogging accumulated more biomass, although the dry stem biomass data from the two waterlogged replicates were not consistent. All pairwise comparisons using Student’s t-test validated that the dry stem biomass accumulated by the waterlogged plants in the left side of the channel was

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signigficantly different from the rain-watered controls and the waterlogged plants in the right side of the channel with p-values of <0.0001 and 0.0003, respectively.

Similarly, on average, plants under waterlogging treatment developed more aerenchyma than plants in the rain-watered control (Figure 4-4). Aerenchyma formation was significantly different between plants under waterlogging treatment compared to the rain-watered controls (p- value < 0.0001). No significant difference was observed between the two waterlogged replicates.

Discussion

In this study, two mapping populations derived from waterlogging-tolerant and -sensitive parents were developed. Both populations are currently at advanced stages, F7 and F5, respectively, and are the foundation for future identification of genomic regions responsible for the trait through quantitative trait loci (QTL) mapping. Even though the initial phenotyping of the mapping population under field conditions was not possible, the effect of soil gradients need to be eliminated in order to carry out the phenotyping of the entire population. This can be accomplished by changes in the soil preparation and using a different planting design in which more channels are deployed, and whereby replicates are planted in the front, middle or rear third of each channel. However, consistent with the data on biomass accumulation from chapter 2, plants subjected to waterlogging showed enhanced stem biomass accumulation compared to the controls. Nonetheless, the observation that plants subjected to waterlogging treatment had on average significantly more aerenchyma than the control plants serves as evidence that the waterlogging was experienced as stress.

The diffusion of oxygen in the water is 104 times slower than in the air (Mommer et al.,

2005) exposing waterlogged roots to reduced oxygen concentrations and compromising respiration. This reduces the amount of energy available for adequate root functioning.

Formation of aerenchyma, air pores generated through programmed cell death in the stems and

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roots, has been demonstrated to be triggered by increased levels of endogenous ethylene caused by excess of water in the root and stems of maize, rice and sorghum (Drew et al., 1979; Justin and Armstrong, 1991; Gunawardena et al., 2001; Promkhambut et al., 2011). Aerenchyma enables oxygen flow from stems to roots and vice versa to mitigate the hypoxia/anoxia that roots are exposed to under water-saturated environments (Laan et al., 1990; Postma and Lynch, 2011).

The responses of the parental lines chosen for the creation of the mapping populations have been closely monitored under different waterlogging treatments, including field and greenhouse environments. Since the responses were treatment dependent, we anticipate that the tolerance-associated QTLs will also vary with the severity of the stress under which the plants are phenotyped. Under this scenario, the results of this research will contribute to more accurately determine the QTL’s that can be useful for crop development based on the stresses these crops may experience.

Additionally, the results of this study pave the way for understanding the genetic basis of waterlogging stress in sorghum that will contribute to a better understanding of the plant mechanisms to overcome the consequences of submergence. This will contribute to the efforts of generating more resilient crops able to acclimate to flooding events.

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Dry stem biomass_L(g) vs. Dry stem biomass_R(g) Dry stem biomass_L(g) 100 R²: 0.093

) 60

(

D 40

0 0 10 20 30 40 50 60 70 80 Dry stem biomass_R(g)

Figure 4-1. Correlation of dry stem biomass between waterlogged replicates in the left (L) and right (R) side of the flooded channel

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B A Rain-watered Waterlogged-left Waterlogged-right

Figure 4-2. Phenotyping of 87 F7 lines originated from IS 29314  IS 12883 biparental population. A) Dry stem biomass vs. row number and B) height vs. row number of waterlogged replicates and rain-watered controls. Dots indicate the average of five plants and the lines indicate the trend across the field.

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40 Mean

)

( 25

m 20

D 15

0 Rain-watered Waterlogged_L Waterlogged_R Rep

Figure 4-3. Average of dry stem biomass of two waterlogged replicates in the left (L) and the (R) side of the channel and the rain-watered controls. Values represent the average of five plants per each one of the 87 rows planted and the bars indicate standard error.

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4.5 Mean

4.0

3.5

3.0

)

u 2.5

(

h 2.0

A 1.5

1.0

0.5

0.0 Rain-watered Waterlogged_L Waterlogged_R

Figure 4-4. Average of aerenchyma formation scored on a scale from 0 (no aerenchyma) to 5 (all plants displaying noticeable aerenchyma of two waterlogged replicates in the left (L) and the (R) side of the channel and the rain-watered controls. Values represent the average of five plants and the bars indicate standard error.

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Table 4-1. Initial biparental population crosses made with tolerant (T) by sensitive (S) parental lines. Year Location Cross (Female  Male) Parental phenotype 2014 Live Oak IS 473 IS 22799 T/ S 2014 Live Oak IS 7131  IS 22799 T/ S * 2014 Live Oak IS 7131  IS 4092 T/ S 2014 Live Oak IS 8777  IS 22799 T/ S 2014 Live Oak IS 29314  IS 12883 T/ S * 2014 Live Oak IS 4092  IS 7131 S/ T 2014 Live Oak IS 7131  IS 22799 T/ S 2014 Live Oak IS 7131  IS 10969 T/ S 2014 Live Oak IS 7131  IS 22799 T/ S 2015 Live Oak IS 29314  IS 12883 T/ S * 2015 Live Oak IS 29314  IS 12883 T/ S * 2015 Live Oak IS 29314  IS 12883 T/ S * 2015 Live Oak IS 29314  IS 12883 T/ S * (*) Crosses selected for generation of advance populations.

Table 4-2. Development of the biparental population of IS 29314  IS 12883. Number of families per year per generation are shown. Generations grown in Puerto Rico were grown during the winter time in Florida (November) and harvested in the spring of the following year. Year Planting Location Source Planted Harvested # of families

2014-2015 Puerto Rico Live Oak 2014 F1 F2 1 2015 Live Oak PR14-15 F2 F3 6 2015-2016 Puerto Rico Live Oak 2015 F3& F1 F4& F2 92&4

2016 Live Oak PR15-16 F4&F2 F5& F3 91&20 2016-2017 Puerto Rico Live Oak 2016 F5& F3 F6& F4 87&172 2017 Live Oak PR16-17 F6& F4 F7& F5 87&174 Live Oak 2017 2018 Live Oak &PR16-17 F5& F4 F6& F5 147&24

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Table 4-3. Development of the biparental population of IS 7131  IS 22799. Number of families per year per generation are shown. Generations grown in Puerto Rico were grown during the winter time in Florida (December), which is why they are listed with two years. Year Planting Location Source Planted Harvested Families planted

2014-2015 Puerto Rico (PR) Live Oak 2014 F1 F2 1

2015 Live Oak PR14-15 F2 F3 89 2015-2016 Puerto Rico Live Oak 2015 F3 F4 89 2016 Live Oak PR15-16 F4 F5 88 .

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Table 4-4. Field planting map of F7 individuals from the biparental population IS 29314  IS 12883. Genotypes of the inbred lines are numbered based on the row number in the breeding nursery where the seed was produced (PR2016-2017). Parental lines are present in every replicate, including the rain-watered control. Treatment Waterlogging Rain-watered CHANNEL 1, LEFT CHANNEL 1, RIGHT CHANNEL 2, LEFT Row Genotype Row Genotype Row Genotype 1 edge row 1 edge row 1 edge row 2 edge row 2 edge row 2 edge row 3 PR16-17- 259 3 PR16-17- 224 3 PR16-17- 273 4 PR16-17- 257 4 PR16-17- 205 4 PR16-17- 233 5 IS 29314 5 PR16-17- 262 5 PR16-17- 275 6 IS 12883 6 PR16-17- 255 6 PR16-17- 267 7 PR16-17- 261 7 PR16-17- 279 7 PR16-17- 212 8 PR16-17- 222 8 PR16-17- 200 8 PR16-17- 226 9 PR16-17- 241 9 PR16-17- 246 9 IS 29314 10 PR16-17- 226 10 PR16-17- 218 10 IS 12883 11 PR16-17- 267 11 PR16-17- 252 11 PR16-17- 203 12 PR16-17- 216 12 PR16-17- 207 12 PR16-17- 204 13 PR16-17- 205 13 PR16-17- 232 13 PR16-17- 202 14 PR16-17- 247 14 PR16-17- 268 14 PR16-17- 240 15 PR16-17- 242 15 PR16-17- 245 15 PR16-17- 222 16 PR16-17- 282 16 PR16-17- 250 16 PR16-17- 276 17 PR16-17- 235 17 PR16-17- 239 17 PR16-17- 264 18 PR16-17- 229 18 PR16-17- 251 18 PR16-17- 248 19 PR16-17- 278 19 PR16-17- 272 19 PR16-17- 270 20 PR16-17- 220 20 PR16-17- 227 20 PR16-17- 262 21 PR16-17- 248 21 PR16-17- 256 21 PR16-17- 266 22 PR16-17- 250 22 PR16-17- 230 22 PR16-17- 236 23 PR16-17- 218 23 PR16-17- 233 23 PR16-17- 235 24 PR16-17- 284 24 IS 29314 24 PR16-17- 272 25 PR16-17- 238 25 IS 12883 25 PR16-17- 268 26 PR16-17- 255 26 PR16-17- 273 26 PR16-17- 257 27 PR16-17- 271 27 PR16-17- 277 27 PR16-17- 205 28 PR16-17- 231 28 PR16-17- 226 28 PR16-17- 214 29 PR16-17- 249 29 PR16-17- 259 29 PR16-17- 274 30 PR16-17- 219 30 PR16-17- 225 30 PR16-17- 217 31 PR16-17- 240 31 PR16-17- 270 31 PR16-17- 230 32 PR16-17- 273 32 PR16-17- 214 32 PR16-17- 281 33 PR16-17- 201 33 PR16-17- 263 33 PR16-17- 221

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Table 4-4. Continued. Treatment Waterlogging Rain-watered CHANNEL 1, LEFT CHANNEL 1, RIGHT CHANNEL 2, LEFT Row Genotype Row Genotype Row Genotype 34 PR16-17- 217 34 PR16-17- 204 34 PR16-17- 243 35 PR16-17- 209 35 PR16-17- 236 35 PR16-17- 253 36 PR16-17- 214 36 PR16-17- 210 36 PR16-17- 246 37 PR16-17- 260 37 PR16-17- 266 37 PR16-17- 265 38 PR16-17- 213 38 PR16-17- 249 38 PR16-17- 229 39 PR16-17- 239 39 PR16-17- 260 39 PR16-17- 208 40 PR16-17- 210 40 PR16-17- 247 40 PR16-17- 228 41 PR16-17- 233 41 PR16-17- 257 41 PR16-17- 250 42 PR16-17- 245 42 PR16-17- 240 42 PR16-17- 282 43 PR16-17- 286 43 PR16-17- 286 43 PR16-17- 231 44 PR16-17- 285 44 PR16-17- 261 44 PR16-17- 252 45 PR16-17- 207 45 PR16-17- 220 45 PR16-17- 280 46 PR16-17- 276 46 PR16-17- 285 46 PR16-17- 224 47 PR16-17- 225 47 PR16-17- 274 47 PR16-17- 216 48 PR16-17- 211 48 PR16-17- 278 48 PR16-17- 277 49 PR16-17- 281 49 PR16-17- 265 49 PR16-17- 258 50 PR16-17- 251 50 PR16-17- 221 50 IS 29314 51 PR16-17- 208 51 PR16-17- 267 51 IS 12883 52 PR16-17- 227 52 PR16-17- 284 52 PR16-17- 256 53 PR16-17- 246 53 PR16-17- 276 53 PR16-17- 211 54 PR16-17- 283 54 PR16-17- 248 54 PR16-17- 245 55 PR16-17- 268 55 PR16-17- 269 55 PR16-17- 251 56 PR16-17- 230 56 PR16-17- 229 56 PR16-17- 219 57 PR16-17- 280 57 PR16-17- 206 57 PR16-17- 242 58 PR16-17- 244 58 PR16-17- 223 58 PR16-17- 227 59 PR16-17- 275 59 PR16-17- 238 59 PR16-17- 244 60 PR16-17- 270 60 PR16-17- 213 60 PR16-17- 283 61 PR16-17- 200 61 PR16-17- 271 61 PR16-17- 207 62 PR16-17- 264 62 PR16-17- 234 62 PR16-17- 232 63 PR16-17- 258 63 PR16-17- 202 63 PR16-17- 200 64 PR16-17- 228 64 PR16-17- 209 64 PR16-17- 210 65 PR16-17- 236 65 PR16-17- 211 65 PR16-17- 263 66 PR16-17- 263 66 PR16-17- 235 66 PR16-17- 278 67 PR16-17- 212 67 PR16-17- 253 67 PR16-17- 220 68 PR16-17- 243 68 PR16-17- 258 68 PR16-17- 213

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Table 4-4. Continued. Treatment Waterlogging Rain-watered CHANNEL 1, LEFT CHANNEL 1, RIGHT CHANNEL 2, LEFT Row Genotype Row Genotype Row Genotype 69 PR16-17- 254 69 PR16-17- 217 69 PR16-17- 284 70 IS 29314 70 PR16-17- 282 70 PR16-17- 271 71 IS 12883 71 PR16-17- 222 71 PR16-17- 223 72 PR16-17- 206 72 PR16-17- 228 72 PR16-17- 239 73 PR16-17- 221 73 PR16-17- 237 73 PR16-17- 215 74 PR16-17- 277 74 PR16-17- 231 74 PR16-17- 269 75 PR16-17- 234 75 PR16-17- 244 75 PR16-17- 218 76 PR16-17- 237 76 PR16-17- 281 76 PR16-17- 209 77 PR16-17- 256 77 PR16-17- 208 77 PR16-17- 237 78 PR16-17- 204 78 PR16-17- 212 78 PR16-17- 279 79 PR16-17- 262 79 PR16-17- 203 79 PR16-17- 249 80 PR16-17- 232 80 PR16-17- 243 80 PR16-17- 261 81 PR16-17- 224 81 IS 29314 81 PR16-17- 201 82 PR16-17- 269 82 IS 12883 82 PR16-17- 286 83 PR16-17- 215 83 PR16-17- 241 83 PR16-17- 255 84 PR16-17- 253 84 PR16-17- 254 84 PR16-17- 260 85 PR16-17- 202 85 PR16-17- 242 85 PR16-17- 247 86 PR16-17- 223 86 PR16-17- 283 86 PR16-17- 285 87 PR16-17- 274 87 PR16-17- 275 87 PR16-17- 234 88 PR16-17- 252 88 PR16-17- 216 88 PR16-17- 206 89 PR16-17- 279 89 PR16-17- 219 89 PR16-17- 259 90 PR16-17- 265 90 PR16-17- 215 90 PR16-17- 238 91 PR16-17- 272 91 PR16-17- 201 91 PR16-17- 241 92 PR16-17- 203 92 PR16-17- 280 92 PR16-17- 225 93 PR16-17- 266 93 PR16-17- 264 93 PR16-17- 254 94 edge row 94 edge row 94 edge row 95 edge row 95 edge row 95 edge row

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CHAPTER 5 CONCLUSIONS

The fundamental goal of my research projects has been to enhance biomass production and biomass conversion efficiency in sorghum (Sorghum bicolor (L.) Moench) as an approach to contribute to the food and energy demands for the near future in a sustainable manner.

In this context, waterlogging-triggered responses in six sorghum genotypes were identified. It was observed that the same genotypes, when exposed to waterlogging treatments of different severity, in the field and the greenhouse, differed in their response, providing evidence that the responses are dependent on the severity of the stress to which the genotypes are subjected. It was also determined that different phenotypic traits can be used to measure responses to waterlogging. For example, the concentration of magnesium ions in the root tips was shown to be positively correlated with accumulation of biomass in the stems in response to water-saturated soils in the greenhouse. Sorghum genotypes have high phenotypic plasticity, since all genotypes were able to acclimate and outperform the well-watered control plants, regardless of whether they had been previously classified as sensitive or tolerant based on field observations. Phenotypic plasticity is a desired trait for developing resilient crops in a changing climate with greater variation in weather conditions.

As a result of this research two advanced-generation biparental populations (F5 and F7) are available that can be used for elucidating the genetic basis of waterlogging-triggered responses in sorghum by mapping quantitative trait loci (QTL) associated with tolerance to waterlogging. Based on the results presented in chapter 2, waterlogging-triggered responses vary depending on the severity of the stress. Identification of quantifiable phenotypes under different kinds of waterlogging stress is necessary to determine the correct set of QTLs that might be responsible for the observed responses. The delineation of such QTL and their introgression in

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lines with other attractive traits would enable the development of more resilient sorghum for cultivation in low-productivity lands that are prone to flooding.

Waterlogging-triggered responses such as the reduction of secondary cell wall and cuticle thickness in leaves, suggest that some mechanisms for waterlogging adaptation are related to cell wall biosynthesis and the production of lipids and waxes (Mommer et al., 2005). It has been reported that certain kinds of waterlogging stress repress the expression of the gene encoding cinnamate 4-hydroxylase (C4H) along with other genes involved in the phenylpropanoid pathway, and altering the lignification of cell walls by strengthening of the secondary wall with hemicellulose and lignin deposition. (Gall et al., 2015).

I purified soluble and active recombinant SbC4H1 and SbC4H2 from E. coli. C4H is a key enzyme in the phenylpropanoid metabolism in general, and monolignol biosynthesis in particular. As a membrane-bound enzyme, C4H activity has up until now only been measured in partially purified microsomal fractions that contained additional proteins (Urban et al., 1994).

The presence of these other proteins and the localization in the membrane have also prevented the formation of crystallized C4H for structural analyses, notably X-ray crystallography. Hence, the availability of a soluble form of C4H paves the way for investigating its structure and kinetic properties. The results of this future research will provide a more detailed understanding of the physical interaction of C4H with other enzymes, particularly cytochrome P450 reductase (CPR), the accessory enzyme required for C4H activity (as shown in Chapter 3), phenylalanine ammonia lyase (PAL), which is the enzyme generating the substrate for C4H (Achnine et al., 2004), and potentially 4-coumarate CoA ligase, which is the enzyme converting the product of C4H.

Formation of multi-protein complexes can be analyzed implementing techniques such as yeast two hybrid (Fields and Song, 1989; Tanowitz and Zastrow, 2011), bimolecular

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fluorescence complementation (BiFC), based on association of two non-fluorescent fragments of a fluorescent protein, such as GFP, when they are brought to enough proximity by the interaction of the protein of interest fused to the non-fluorescent fragments (Chiu et al., 1996; Kerppola,

2008). Also, using fluorescence resonance energy transfer (FRET), a technique based on the interaction of fluorophores used to accurately measure molecular distances (10–100 Å) hence potential molecule-molecule interactions inside living cells can be identified (Sekar and

Periasamy, 2003). In the same way, cross-linking and co-immunoprecipitation of recombinant proteins, from in vivo systems based on affinity purification and posterior identification by mass spectrometry are additional techniques that could be useful to determine close interaction between proteins of interest (Vasilescu et al., 2004).

Methods for heterologous expression of P450s has been perfected in yeast, where coexpression of multiple expression cassettes on a single plasmid have been developed and redox environment for P450s functioning has been optimized (Pompon et al., 1996). Using eukaryotic systems such as yeast to express C4H in its complete form along with other enzymes required for optimal activity might enhance the probability to successfully examine more closely the role of anchoring in the endoplasmic reticulum membrane on the activity of C4H.

A detailed structural model and a thorough understanding of the substrate preference and catalytic mechanism enables the rational design of C4H with altered properties (substrate preference, catalytic efficiency) in the context of future engineering of plant cell wall architecture. Such an approach is feasible once genome editing methods, such as CRISPR/Cas 9

(Barrangou et al., 2007; Jiang et al., 2013; Jinek et al., 2013; Nekrasov et al., 2013; Sternberg et al., 2014) have been adapted for reliable use in sorghum. The edited alleles can then be used in plant breeding programs to reduce biomass recalcitrance so that biofuels, building blocks for

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renewable polymers, and value-added products from lignin can be produced with greater efficiency and lower cost.

As part of my doctoral training, I had the opportunity to teach and train six undergraduate students who participated in my research in laboratory and field techniques. I also participated in several projects involving science communication, public outreach and scientific advice for the development of GMO-labeling policies.

Additionally, I participated in collaborative projects with different research groups nationally and internationally. My research related to waterlogging was executed between the

University of Florida and the University of Missouri as part of a project funded by the U.S.

Department of Energy. Similarly, the IFAS Dean of Research awarded a mid-career international travel award that enabled me to carry out part of my water-table response experiments at

Wageningen University in the Netherlands, in the laboratory of Dr. Luisa Trindade. The collaborations have provided perspective on different approaches for research in related research topics and, most importantly, they have provided me with collaboration experience and team work essential for a successful scientific carrier.

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APPENDIX FINAL AND MODIFIED CDNA SEQUENCES FOR SORGHUM C4H1 AND C4H2

mSbC4H2.GCTAGCTCCAAAATGCGTGGCCGCAAACTTCGCCTGCCACCAGGT

CCGGTGCCAGTGCCGATTTTTGGTAACTGGCTGCAAGTCGGCGATGACCTGAACCAC

CGTAACTTAGCGGCCTTGGCGCGCAAATTTGGCGACATCTTCTTACTGCGCATGGGC

CAGCGCAATCTGGTGGTCGTGTCAAGTCCACCGTTGGCGCGTGAAGTGCTGCACACG

CAGGGCGTGGAATTTGGTTCTCGTACCCGCAATGTTGTATTCGATATTTTCACAGGT

GAAGGTCAGGATATGGTTTTTACGGTCTATGGTGACCATTGGCGCAAAATGCGCCGC

ATTATGACGGTTCCGTTTTTTACGAATAAAGTTGTGCAGCAGTACCGTCACGGGTGG

GAAGCAGAAGCGGCGGCCGTGGTCGACGATGTTCGTGCGGATCCGGCCGCGGCTAC

AGAAGGCGTGGTTTTACGTCGCCGTCTGCAACTGATGATGTATAATAACATGTATCG

CATTATGTTTGATCGCCGGTTTGAATCAATGGACGATCCGTTATTCCTGCGTCTTCGG

GCGCTGAATGGCGAACGCAGCCGTCTGGCCCAATCTTTTGAATATAATTATGGTGAT

TTTATTCCGATTTTGCGCCCTTTCTTACGCGGTTATCTGCGGATTTGCAAGGAAGTCA

AAGAGACCCGTCTGAAGCTTTTTAAAGATTTCTTTCTGGAAGAACGTAAGAAGTTAG

CGTCTACCAAAGCAGTCGATAGTAACGGTCTGAAATGCGCGATCGATCACATTTTAG

AAGCGCAGCAGAAAGGCGAAATTAATGAGGATAACGTTTTGTATATCGTTGAAAAC

ATCAATGTGGCCGCTATTGAAACGACTTTATGGTCGATTGAATGGGCCATTGCCGAA

CTGGTGAATCATCCGGAAATTCAACAGAAACTGCGTCAGGAACTGGATACCGTGCT

GGCTCCGGGTCAGCAAATTACCGAACCAGATACGCACAATCTCCCTTATTTACAGGC

GGTGATTAAAGAAACCCTTCGGCTGCGCATGGCGATCCCTCTGTTGGTTCCGCATAT

GAATCTCCATGACGCAAAACTGGGCGGCTACGATATTCCGGCCGAATCTAAAATTCT

GGTCAACGCGTGGTATTTAGCAAATAATCCTGAATCCTGGAAACGCCCGGAAGAGT

TTCGCCCAGAACGCTTTCTGGAAGAGGAAAAACATGTGGAAGCTAACGGCAATGAT

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TTTCGCTATCTGCCGTTTGGTGTTGGCCGTCGCAGCTGCCCAGGCATTATTCTGGCCC

TGCCGATTCTGGGTATCACCATTGGTCGTTTAGTTCAGAACTTTGAACTGCTGCCGCC

TCCGGGCCAAGATAAATTAGATACGACGGAAAAGGGTGGTCAGTTTAGCTTACATA

TTTTAAAACATTCCAACATCGTATGCAAGCCGCGTACCTTCTAAGAATTC

mSbC4H1.CACCATCATCATCATCATTCTTCTGGTCTGGTGCCACGCGGTTCTG

GTATGAAAGAAACCGCTGCTGCTAAATTCGAACGCCAGCACATGGACAGCCCAGAT

CTGGGTACCGATGACGACGACAAGAAACGCTATCGCCTGCCGCCGGGCCCGGCGGG

CGCGCCGGTGGTGGGCAACTGGCTGCAGGTGGGCGATGATTTAAACCATCGCAACC

TGATGTCACTTGCGAAACGCTTTGGCGATATTTTTCTGCTGCGCATGGGCGTGCGCA

ACCTGGTGGTGGTGTCGACCCCGGAACTTGCGAAAGAAGTGTTACATACCCAGGGC

GTGGAATTTGGCTCGCGCACCCGCAACGTGGTGTTTGATATTTTTACCGGCAAAGGC

CAGGATATGGTGTTTACCGTGTATGGCGATCATTGGCGCAAAATGCGCCGCATTATG

ACTGTGCCGTTTTTTACCAACAAAGTGGTGGCGCAGAACCGCGTGGGCTGGGAAGA

AGAAGCGCGCCTGGTGGTGGAAGATGTGCGCAAAGATCCGCGCGCGGCGGCGGAA

GGCGTGGTGATTCGCCGCCGCCTGCAGCTGATGATGTATAACGATATGTTTCGCATT

ATGTTTGATACTCGCTTTGAATCGGAACAGGATCCGCTGTTTAACAAACTGAAAGCG

TTAAACGCGGAACGCTCGCGCCTGTCGCAGTCGTTTGAATATAACTATGGCGATTTT

ATTCCGGTGCTTCGCCCGTTTCTGCGCGGCTATCTGAACCGCTGCCATGATTTAAAA

ACCCGCCGCATGAAAGTGTTTGAAGATAACTTTGTGCAGGAACGCAAAAAAGTGAT

GGCGCAGACTGGCGAAATTCGCTGCGCGATGGATCATATTCTGGAAGCGGAACGCA

AAGGCGAAATTAACCATGATAACGTGCTGTATATTGTGGAAAACATTAACGTGGCG

GCGATTGAAACCACTCTTTGGAGCATTGAATGGGGCATTGCGGAACTGGTGAACCAT

CCGGCGATTCAGAGCAAACTTCGCGAAGAAATGGATTCAGTGCTTGGCGCGGGCGT

141

GCCGGTGACCGAACCGGATCTGGAACGCCTGCCGTATCTTCAGGCGATTGTGAAAG

AAACCCTGCGCCTTCGCATGGCGATTCCGCTTTTAGTGCCGCATATGAACCTTAACG

ATGGCAAACTTGCGGGCTATGATATTCCGGCGGAATCGAAAATTCTGGTGAACGCGT

GGTTTTTAGCGAACGATCCGAAACGCTGGGTGCGCCCGGATGAATTTCGCCCGGAAC

GCTTTTTAGAAGAAGAAAAAACTGTGGAAGCGCATGGCAACGATTTTCGCTTTGTGC

CGTTTGGCGTGGGCCGCCGCTCGTGCCCGGGCATTATTCTGGCGCTGCCGATTATTG

GCATTACTCTTGGCCGCCTGGTTCAGAACTTTCAGCTTCTGCCGCCGCCGGGCCAGG

ATAAAATTGATACCACTGAAAAACCGGGCCAGTTTTCGAACCAGATTGCGAAACAT

GCGACCATTGTGTGCAAACCGCTCGAGGCCTAA

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BIOGRAPHICAL SKETCH

Alejandra was born in Bogotá, Colombia in the year 1990. Her first ten years of education took place in a private catholic school. At the age of fifteen she entered to a high- school with emphasis in mathematics where she won multiple awards for her academic performance. At the age of seventeen she graduated from high-school and went to Universidad de Los Andes where she started her career as biologist. At the university, she did research in multiple different areas of biology as ecology, herpetology, animal behavior, botany and phytopathology. At the age of 22 she graduated from the university and went to the University of

Southern California in Los Angeles, to intensify her English studies and gain experience in molecular biology techiques at the The Single Molecule Biophotonics lab under the direction of

Dr. Fabian Pinaud. In the fall of 2013, she started her doctorate in plant molecular and cellular biology at the University of Florida with a graduate assistantship research position under the guidance of Dr. Wilfred Vermerris. During her Ph.D. she worked on improvement of bioenergy crops utilizing molecular and physiological approaches.

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