Glyphosate) Resistance and Tolerance in Agricultural Weeds Using Metabolomics Approach

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8-2017

Elucidating the Cellular Physiology Associated with the Herbicide (Glyphosate) Resistance and Tolerance in Agricultural Weeds Using Metabolomics Approach

Amith Sadananda Maroli Clemson University, [email protected]

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Recommended Citation Maroli, Amith Sadananda, "Elucidating the Cellular Physiology Associated with the Herbicide (Glyphosate) Resistance and Tolerance in Agricultural Weeds Using Metabolomics Approach" (2017). All Dissertations. 2016. https://tigerprints.clemson.edu/all_dissertations/2016

This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [email protected]. ELUCIDATING THE CELLULAR PHYSIOLOGY ASSOCIATED WITH HERBICIDE (GLYPHOSATE) RESISTANCE AND TOLERANCE IN AGRICULTURAL WEEDS USING METABOLOMICS APPROACH

A Dissertation Presented to the Graduate School of Clemson University

In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy Plant and Environmental Science.

by Amith Sadananda Maroli August 2017

Submitted to: Dr. Nishanth Tharayil, Committee Chair Dr. Paula Agudelo Prof. Patrick Gerard Prof. Hong Luo ABSTRACT

Current management practices overemphasizes on herbicides to manage weeds in crop production systems. However, indiscriminate use of herbicides to manage weeds has resulted in the development of resistance in several weed biotypes. Over-application on glyphosate to manage weeds in cropping system that uses RoundUp® Ready™ trait has resulted in the dominance of glyphosate resistant weeds across cropping systems.

Glyphosate resistance is an important, economically unviable and rapidly escalating problem across agricultural production systems. To combat herbicide resistance, current recommendations advocate for changes in chemical and cultural practices of weed control, including rotation of herbicide regimen with herbicides with alternate modes of action, and formulation of cultural practices that would penalize the expression of resistance. Some of the bottlenecks in practicing these approaches are the current lack of knowledge about the weed cellular physiology that ensues resistance expression, the potential metabolic cost associated with this resistance expression, and the occurrence of compensatory pathways that could defray the cost of resistance expression. Adopting an alternate herbicide regimen without an understanding of the cellular physiology of resistance expression would result in the development of herbicide cross resistance in weeds, which would further aggravate the problem. To bridge this knowledge-gap, in this studies, metabolomics approach and complementary biochemical analyses were used to track the changes in cellular metabolism in weed species and biotypes that are resistant and naturally tolerant to glyphosate.

In Ipomoea lacunosa, non-targeted metabolic profiling captured the differences in metabolic pool levels in two biotypes (WAS and QUI) with contrasting glyphosate

ii -1 -1 tolerance (GR50 = 151 g ae ha and 59 g ae ha ). Metabolic profiling followed by pathway topological analysis captured innate metabolic differences (22 significantly different metabolites) between WAS and QUI biotypes. Despite the glyphosate dose being half the

GR50 rate, shikimic acid accumulation was observed in both the biotypes. However, regardless of EPSPS inhibition, no changes in aromatic amino abundance was observed in the QUI biotype and WAS biotype, indicating their tolerance to the glyphosate. The results from this study implies that though I. lacunosa is tolerant to glyphosate, glyphosate exposure induces cellular metabolic perturbations. The varying tolerance to glyphosate could thus be due to physiological and metabolic adaptations between the different biotypes.

Following through, metabolite and biochemical profiling of a susceptible (S) and resistant (R) biotype of Amaranthus palmeri identified physiological perturbations induced by glyphosate in both the biotypes at 8 and 80 hours after treatment (HAT). Compared to the S-biotype, the R-biotype had a 17 fold resistance to the normal field recommended rate of glyphosate. At 8HAT, shikimic acid accumulation in both S- and R-biotypes in response to glyphosate application indicated that the R-biotype was equally susceptible to glyphosate toxicity. The metabolite pool of glyphosate-treated R-biotype was similar to that of the water-treated (control) S and R-biotype, indicating physiological recovery at 80

HAT. A key finding from this study is that despite being resistant to glyphosate, Palmer amaranth biotypes initially sustained metabolic perturbation from glyphosate. However, what differentiates them from the susceptible biotypes is their ability to recover from the glyphosate induced metabolic disruptions. In response to glyphosate, glyphosate-treated

iii R-biotype had lower reactive oxygen species (ROS) damage, higher ROS scavenging activity, and higher levels of secondary compounds of the shikimate pathway, leading to the finding that elevated anti-oxidant mechanisms in A. palmeri complements the resistance conferred due to increased EPSPS copy number.

Furthermore, metabolite dynamics in response to glyphosate application studied using stable isotope resolved metabolomics revealed that despite glyphosate toxicity induced decrease in soluble proteins, a proportional increase in both 14N and 15N amino acids was observed in the susceptible plants. This indicates that following glyphosate treatment, a potential increase in de novo amino acid synthesis, coupled with a lower protein synthesis, and higher protein catabolism is observed in the S-biotype. In contrast, the R-biotype, though affected by glyphosate initially, had higher de novo amino acid synthesis without significant disruptions. Moreover, it is to be noted that although the initial assimilation of inorganic nitrogen to organic forms is less affected in the S-biotype than the R-biotype by glyphosate, amino acid biosynthesis downstream of glutamine is disproportionately disrupted. It is thus concluded that the herbicide-induced amino acid abundance in the S-biotype is contributed to by both protein catabolism, and de novo synthesis of amino acids such as glutamine and asparagine.

Due to variability in the genetic makeup of populations, each biotype would exhibit different physiological manifestations when exposed to the same rate of glyphosate.

Biochemical and metabolic profiling of five different Palmer amaranth biotypes indicated that both the S- and R-biotypes had comparable innate phytochemical profile and similar abundance in flavonoids and phenolic. However, compared to the S-biotypes, the R-

iv biotypes had innately higher anti-oxidant capacity, and the antioxidant capacity was observed to correlate with the GR50 such that antioxidant capacity increased with increasing GR50. Upon treatment with glyphosate, there were significant alterations in the metabolic pool levels across all biotypes. After glyphosate treatment, the content of total phenolic and flavonoids decrease in S-biotypes, whereas the abundance of these metabolites either remained the same, or increased in the R-biotypes. These results indicate that antioxidant capacity is a complementary function aiding in conferring glyphosate resistance and the phytochemistry and the antioxidant capacity is partly induced after glyphosate application, rather than being constitutively expressed.

Overall, these study demonstrates that, across biotypes and species, irrespective of their degree of resistance/tolerance, glyphosate not only perturbs shikimate pathway, but also a multitude of other metabolic pathways that are independent of shikimate pathway

(secondary toxic effects) as early as eight hours after treatment. While in the susceptible biotypes these metabolic perturbations result in rapid cellular damage, these metabolic perturbations fail to translate to cellular damage in the resistant biotypes. The results indicate that the resistance of A. palmeri biotypes that were used in these studies partially stems from their ability to rapidly induce the production of phenylpropanoids soon after the glyphosate application. This induction of phytochemicals could quench the reactive molecules that are initially produced during the secondary metabolic perturbations, and would thus complement the glyphosate resistance in Amaranthus biotypes conferred by

EPSPS gene amplification.

v DEDICATION

Dedicated to my parents Roshni and Sadananda Maroli, my dear wife Thashi, and my Parents-in-law, V.K Prabha and (Late) Babu Bharadwaj. Without their unwavering love and support throughout the years, I would not be where I am today. Without their unconditional trust and belief in me, this journey would have been far more difficult.

To my cherished treasure, my loving daughter Aagneya (Aagu). Your arrival in our life gave us a whole new perspective (and lot more responsibility).

To my extended support system, Ashwinettan, Reshmechi, Dileepettan, Greeshmechi, Ajayettan, Priyechi and to all my teachers from kindergarten to PhD for holding my hand and guiding me along the right path.

vi ACKNOWLEDGMENTS

First and foremost, I would like to thank God.

I would like to sincerely thank my advisor Dr. Nishanth Tharayil for his guidance, encouragement, and most of all his immense patience in dealing with my slip-ups throughout my PhD program at Clemson. I cannot imagine a better person to have as my mentor. Without his trust in my capabilities and his encouraging words, I would not have been able to complete this degree. Though initially I got tired of the numerous corrections he made me do for each publication/presentation, I am now thankful for those sessions as it helped to develop my scientific skills to great extent.

I would like to thank my committee members Dr. Paula Agudelo, Prof. Hong Luo and Prof. Patrick Gerard for agree to be on my committee. I am grateful for the valuable suggestions and comments given during our meetings regarding the research and dissertation. I am also thankful to them for never denying me an appointment with them even if it was a last minute request.

I would like to thank Dr Vijay Nandula, USDA-ARS, Oxford, MS, for providing me with all the seeds I have used to carry out my research. Without his generous support and no-questions asked approach when providing the seeds, I would not have been able to get off the ground with my research. I would also like to Drs. Stephen Duke and Frank

Dayan, USDA-ARS, Oxford, MS, who read through every manuscript I wrote and gave me very valuble suggestions on improving the quality of the work. Their immense experience, knowledge and inputs greatly helped in the publication of my works.

vii I would also like to thank my former and current lab members, especially Dr Vidya

Suseela for her valuable suggestions in both professional and personal aspects, and

Nimisha Edayilam and Makhdoora Al-Muzhiny, for their constant support and encouranging words after every individual meetings I had with my advisor.

I would also like to extend my thanks Mrs. Tammy Morton, for her help with dealing with all the administrative paperwork and constantly reminding me about the various deadlines and also to Mr. Dan Hunnicutt and Mr. Taylor Martin, Clemson

University Greenhouse managers for helping me with any issues I faced with the greenhouse and spray chamber.

It would be a grave injustice if I fail to mention the help provided to me by Dr

Robert (Bob) Cross III. His generous donation of ‘RoundUp® ProMax” herbicide

(glyphosate) was used in all my experiments. On a similar note, I would also like to thank

Dr Frankie Felder, Senior Associate Dean, Graduate School, Clemson University and all other supporting members of the graduate school who helped me personally during a very complicated phase of life.

Last but not the least I would like to extend thanks to all my friends (especially Dr.

Pallavi Vedantam, Dr. Ramkrishna Podilla and Dr. Pooja Puneet, Dr. Kapil Madathil,

Sreenath, Nimisha, Sruthy and Anoop) at Clemson, my roommates (Girish, Ghanshyam,

Sujeeth, Naveen, Saptarshi, Nabarjun), my host family (The Campbell Family) and CAIF for making my stay at Clemson a very pleasant and enjoyable.

viii TABLE OF CONTENTS

Page

TITLE PAGE ...... i

ABSTRACT ...... ii

DEDICATION ...... vi

ACKNOWLEDGMENTS ...... vii

LIST OF TABLES ...... xi

LIST OF FIGURES ...... xii

CHAPTER

I. HINDSIGHT AND BACKGROUND INFORMATION ...... 1

Herbicide resistance and agricultural weeds ...... 1 Glyphosate and Glyphosate resistance...... 1 Amaranthus palmeri and Glyphosate resistance ...... 7 Ipomoea lacunosa and Glyphosate tolerance ...... 10 Metabolomics to elucidate cellular physiology ...... 12 Plant Metabolomics ...... 14 Metabolomics for exploring herbicide resistance in weeds ...... 18 Summary and Purpose ...... 21 Literature Cited ...... 23

II. COMPARATIVE METABOLOMIC ANALYSIS OF TWO IPOMOEA LACUNOSA BIOTYPES WITH CONTRASTING GLYPHOSATE TOLERANCE ELUCIDATES THE GLYPHOSATE INDUCED DIFFERENTIAL PERTURBATIONS IN CELLULAR PHYSIOLOGY ...... 48

Abstract ...... 48 Introduction ...... 49 Materials and Methods ...... 52 Results and Discussion ...... 56 Literature Cited ...... 66

ix Table of Contents (Continued)

...... Page

III. METABOLIC PROFILING AND ENZYME ANALYSES INDICATE A POTENTIAL ROLE OF ANTIOXIDANT SYSTEMS IN COMPLEMENTING GLYPHOSATE RESISTANCE IN AN AMARANTHUS PALMERI BIOTYPE ...... 89

Abstract ...... 89 Introduction ...... 90 Materials and Methods ...... 93 Results and Discussion ...... 100 Literature Cited ...... 111

IV. STABLE ISOTOPE RESOLVED METABOLOMICS REVEALS THE ROLE OF ANABOLIC AND CATABOLIC PROCESSES IN GLYPHOSATE-INDUCED AMINO ACID ACCUMULATION IN AMARANTHUS PALMERI BIOTYPES ...... 129

Abstract ...... 129 Introduction ...... 130 Materials and Methods ...... 132 Results and Discussion ...... 138 Literature Cited ...... 145

V. INVESTIGATION OF THE PERTURBATIONS IN PRIMARY AND SECONDARY METABOLIC PATHWAYS IN FIVE PALMER AMARANTH BIOTYPES WITH VARYING RESISTANCE TO GLYPHOSATE ...... 159

Abstract ...... 159 Introduction ...... 160 Materials and Methods ...... 163 Results and Discussion ...... 171 Literature Cited ...... 181

x Table of Contents (Continued)

...... Page

VI. CONCLUSION AND SYNTHESIS...... 201

Literature Cited ...... 206

APPENDICES ...... 209

xi LIST OF TABLES

Table Page

2.1 Significantly different metabolites identified in leaves of water treated WAS and QUI biotype of Ipomoea lacunosa harvested 80 HAT. Table A lists the 22 significant metabolites identified by an unpaired t-test analysis computed with equal group variance with a Pvalue threshold of 0.05. Table B lists the 11 significantly different metabolites having at least a 2-fold change between the two biotypes. Of the 11 metabolites, 7 were upregulated and 4 were downregulated (log2(FC)) in the WAS biotype with respect to the QUI biotype ...... 75

5.1 ANOVA of primary metabolites identified in water treated S- and R-biotypes of Amaranthus palmeri harvested 36HAT ...... 188

5.2 Partial list of secondary metabolites tentatively identified in water (W) and glyphosate (G) treated S- and R-biotypes of Palmer amaranth harvested 36HAT. The MS2 ions lists the top 3 most abundant fragment ions in their decreasing order. Calculated (cal) molecular weights were obtained from Chemspider database (www.chemspider.org)...... 197

xii LIST OF FIGURES

Figure Page

1.1 Molecular structure of glyphosate (N-(phosphonomethyl)glycine). The zwitterionic characteristics of glyphosate is due to combination of three acidic and one basic group enabling it move across the plant efficiently. Sourced image (Wikipedia) ...... 43

1.2 Leaves of Amaranthus palmeri plant. The leaves are broad with serrated edges and have the characteristic white chevron towards the middle of the leaves. Sourced image (Google images) ...... 44

1.3 Leaves of pitted morningglory (Ipomoea lacunosa). The leaves, with parallel venation, are broad at the base and tapers towards the top forming a heart shape. Sourced image (Google Images) ...... 45

1.4 Classical systems biology concept and omics organization. Omics techniques covers the progressive functionalization of the genotype to the phenotype. The various molecular entities (DNA, RNA, proteins, metabolites) that are tracked and captured encompasses the omics techniques ...... 46

1.5 General workflow for a metabolomics experiment. The first step for a metabolomics experiment is a relevant biological question and a valid experimental design to answer the question. Subsequent steps involve valid experimental design, selection of appropriate chromatographic separation methods, detection, statistical validation and functional and ontological interpretations ...... 47

2.1 Ontological classification of metabolites identified in leaves of water treated WAS and QUI biotypes of Ipomoea lacunosa harvested 80HAT. Panel A represent the general classification of the metabolites based on their functional role. Panel B represents the classification of metabolites based on their metabolic functions ...... 77

xiii List of Figures (Continued)

Figure Page

2.2 Classification of metabolites to biosynthetic pathways. Panel A represents the fold change of WAS biotype (Control) with respect to QUI biotype (Control). Panels B and C represents the metabolites involved in carbon and nitrogen metabolism respectively. The color corresponds to the fold change level as indicated in Panel A ...... 78

2.3 Differential grouping of water- and glyphosate-treated QUI and WAS biotypes of Ipomoea lacunosa based on metabolites identified at 80 HAT. Panel A represents the PCA score plots of metabolites in water- and glyphosate-treated QUI and WAS biotypes. The ellipses represent 95% confidence region. Panel B depicts the heatmap visualization of significant metabolite differences (ANOVA; Pvalue <0.05) in WAS and QUI biotypes in response to water and glyphosate treatments following hierarchical cluster analysis. The algorithm for clustering was based on Euclidean distance measure for similarity and Ward linkage method for biotype clustering. WW, WG, QW and QG correspond to WAS/Water, WAS/Glyphosate, QUI/Water and QUI/Glyphosate respectively ...... 80

2.4 Shikimic acid content in water and glyphosate treated WAS and QUI biotypes of I. lacunosa harvested at 80 HAT. The mean±SEM was tested by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%. Bars with the same letters are not significantly different at 0.05% confidence ...... 82

2.5 Influence of glyphosate on the amino acid and nitrogenous metabolites of water- and glyphosate-treated QUI and WAS biotypes of I. lacunosa leaves harvested at 80 HAT. Panel A represents comparative abundance of aromatic amino acids, while Panels B, represents comparative abundance of non-aromatic hydrophobic amino acids and Panel C represents polar amino acids and non-protein amino acids. The mean±SEM of all graphs were tested independently by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%...... 83

xiv List of Figures (Continued)

Figure Page

2.6 Influence of glyphosate on sugars, sugar alcohols and TCA cycle metabolites in water- and glyphosate-treated QUI and WAS biotypes of Ipomoea lacunosa leaves harvested at 80 HAT. Panel A represents the total sugars content and the most abundant sugars, Panel B represents the most abundant sugar alcohols (polyols) and Panel C represents the most abundant TCA cycle metabolites in water and glyphosate treated WAS and QUI biotypes respectively. The mean±SEM was tested by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%. Bars with the same letters are not significantly different at 0.05% confidence ...... 86

2.7 Metabolic pathways in control normalized glyphosate treated WAS and QUI biotypes of Ipomoea lacunosa harvested at 80 HAT ...... 88

3.1 PLS-DA Loading plots of water- and glyphosate-treated S- and R-biotypes at A) 8 HAT and B) 80 HAT and the corresponding score plots at C) 8 HAT and D) 80 HAT. The permutation cross validation of PLS-DA model for 8 and 80 HAT had P=0.0005 over 2000 iterations. The ellipse represent 95% confidence region ...... 119

3.2 Heatmap and two-way hierarchical clustering of metabolites at A) 8 HAT and B) 80 HAT. Algorithm for Heatmap clustering was based on Euclidean distance measure for similarity and Ward linkage method for biotype clustering. ANOVA results comparing means of each metabolite across treatment groups, and false discovery rate (FDR) for multiple testing corrections are given in Appendix B Table 3.3. Enlarged dendrogram is given in Appendix B, Figure 3.1...... 121

xv List of Figures (Continued)

Figure Page

3.3 Shikimic acid abundance. The data represent the mean±SD of shikimic acid abundance in glyphosate-treated plants normalized with respect to ribitol abundance and corrected with water-treated shikimic acid abundance. Zero represents water-control abundance. Bars with the same letters are not significantly different at 0.05% confidence ...... 122 3.4 Comparisons of aromatic amino acids across the biotypes and treatments at 8 and 80 HAT. The mean±SD was tested by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%. Tukey’s comparison between the treatment means are given in Appendix B, Table 3.4 ...... 123

3.5 TCA cycle metabolite pools in water- and glyphosate-treated S- and R-biotype at A) 8 HAT and B) 80 HAT. The data represent the mean±SD of the metabolites. Tukey’s comparison between the treatment means are given in Appendix B, Table 3.4 ...... 124

3.6 Total sugar metabolites at A) 8 HAT and B) at 80 HAT. The data represent the mean±SD of the total sugar abundance normalized with respect to ribitol abundance. Bars with the same letters are not significantly different at 0.05% confidence ...... 125

3.7 Lipid peroxidation damage quantitation and free radical quenching potential in water and glyphosate treated S- and R-biotypes of Amaranthus palmeri. A) TBARS assay quantifying malondialdehyde equivalents in S- and R- biotypes across the treatments at 80 HAT. B) Glutathione reductase assay comparing difference in NADPH levels in glyphosate-treated S- and R- biotypes across 8 and 80 HAT. The data for TBARS assay represent the mean±SD of the assay and the data for GR assay represents water-control normalized mean±SD data of fold changes in NADPH levels. Bars with the same letters are not significantly different at 0.05% confidence ...... 126

xvi List of Figures (Continued)

Figure Page

3.8 Phenylalanine ammonia lyase assay comparing difference in activities of the PAL enzyme in S- and R- biotypes across the treatments at A) 8 HAT and B) at 80 HAT. The data represent the mean±SD of the assays. Bars with the same letters are not significantly different at 0.05% confidence ...... 127

3.9 BCA assay for total soluble protein (TSP) quantification in plants harvested at 80 HAT. The data represent the mean±SD of the assay ...... 128

4.1 LC-MS chromatogram for the identification of 14N and 15N amino acids. Panel A represents the mass spectra of 14N-amino acids and the numbers correspond to the amino acids: 1) Lysine; 2) Histidine; 3) Arginine; 4) Serine; 5) Asparagine; 6) Alanine; 7) Threonine; 8) Proline; 9) Glutamine; 10) Glycine; 11) Glutamic acid; 12) Valine; 13) Methionine; 14) Isoleucine; 15) Leucine; 16) Tyrosine; 17) Phenylalanine; 18) Tryptophan; 19) Aspartic acid. Panel B represents the mass spectra of 15N-amino acids and the numbers correspond to the amino acids: 1) Lysine; 2) Histidine; 3) Arginine; 4) Serine; 5) N2-Asparagine; 6) Asparagine; 7) Alanine; 8) Threonine; 9) Glutamine; 10) Glutamic acid; 11) Proline; 12) Valine; 13) Methionine; 14) Isoleucine; 15) Leucine; 16) Tyrosine; 17) Phenylalanine; 18) Tryptophan; 19) N2-Glutamine; 20) Aspartic acid ...... 151

4.2 Relative proportion of 14N and 15N amino acids in S- and R- biotypes of Amaranthus palmeri grown in 15N supplemented MS solution. The data represents the relative proportion of 14N and 15N amino acids in water and glyphosate treated S- and R- biotypes of A. palmeri harvested at 36 HAT. The bars represent the mean ± 1 SD of total free amino acids and the mean ± 1 SD was tested by Tukey’s HSD. Means with different letters are significantly different at p < 0.05 (Two-Way ANOVA followed by Tukey's HSD test, P<0.05). Alphabets A and B represent significant differences between 15N amino acids while a and b represent significant differences between 14N amino acids ...... 153

xvii List of Figures (Continued)

Figure Page

4.3 Total soluble protein (TSP) concentration estimation by BCA assay. Concentration of TSPs in water and herbicide treated S- and R- biotypes of Amaranthus palmeri grown in 15N supplemented MS solution. The data represents the mean ± 1 SD of concentration of TSPs and the mean ± 1 SD was tested by Tukey’s HSD. Means with different letters are significantly different at p < 0.05 (Two-Way ANOVA followed by Tukey's HSD test, P<0.05) ...... 154

4.4 15N Isotopologue enrichment of amino acids in glyphosate treated S- and R-biotypes of Amaranthus palmeri harvested at 36 HAT. The data represent the mean ± 1 SD of each amino acid. Statistical significance of the mean was determined by paired t-test at a confidence level of 0.05. The dotted line (100%) correspond to natural 15N isotopologue enrichment of amino acids. The * indicates significant difference at p< 0.05 in the 15N isotopologue enrichment (Student’s t-test) ...... 155

4.5 Amino acid biosynthesis pathway. Concentration of 15N- and 14N- incorporated amino acid profile biosynthesis pathways in water- and herbicide-treated S- and R-biotypes of Amaranthus palmeri grown in 15N-supplemented solution and harvested at 36 HAT. The left half of the each graph represent the 14N and 15N amino acid levels in water treatment and the right half represent the 14N and 15N amino acid levels in glyphosate treatment The data represent the mean ± 1 SD of each amino acid ...... 156

4.6 Hierarchical cluster analysis of metabolites in S- and R-biotypes of A. palmeri. Heatmap depiction of two-way hierarchical clustering of total sugars, shikimate pathway and (TCA) cycle metabolites in leaves of water- and herbicide-treated S- and R- biotypes grown in 15N supplemented MS solution. Algorithm for heatmap clustering was based on Pearson distance measure for similarity and Ward linkage method for biotype clustering. Legend: SG, RG- Glyphosate treated S- and R-biotype respectively; SW, RW- Water treated S- and R-biotype respectively ...... 157

xviii List of Figures (Continued)

Figure Page

4.7 Regulation of GS-GOGAT cycle metabolites in response to glyphosate in S- and R- biotypes of A.palmeri grown in 15N supplemented MS solution and harvested at 36 HAT. A) Gln/Glu ratio in water-and herbicide-treated S and R biotypes B) Relative abundance of α-ketoglutarate in water- and herbicide treated S and R biotypes. The data represent the mean ± 1 SD of each amino acid. The mean ± 1 SD was tested by Tukey’s HSD comparing across all treatments at a confidence level of 0.05. Means with different letters are significantly different at p < 0.05 (Two-way ANOVA, Tukey’s HSD) ...... 158

5.1 Sparse-PLS-DA (sPLS-DA) score plot and hierarchical cluster analysis of metabolites identified by UHR-AM-MS in water and glyphosate treated S-and R-biotypes of A.palmeri. Panel A represents water treatment and Panel B represents glyphosate treatment. Red, blue, green, pink and cyan corresponds to C1B1, S-3, S-2, T4B2 and T2B4 respectively. The circles in the sPLS-DA score plot represents 95% confidence interval ...... 189

5.2 Biochemical assays for determining total phenolic acids, total flavonoids and % anti-oxidant capacity in water and glyphosate treated S- and R-biotypes of A. palmeri harvested 36HAT. Panels A, B and C represents the total phenolic content measured by F-C assay, the total flavonoid content measured by AlCl3 assay and the % anti-oxidant capacity measured by DPPH assay respectively in the water and glyphosate treated biotypes. Data are the means of responses with six replicates. * indicates significant differences between the treatment at P < 0.05 (paired t-test) ...... 190

5.3 Pathway representation of the significant primary metabolites identified in the water treated most glyphosate resistant C1B1 biotype and S-biotypes of A. palmeri. The colored circle represents identified metabolites. The top half of the circle represents the abundance ratio in C1B1 biotype over S-2 biotype and the bottom half represents the abundance ratio in C1B1 biotype over S-3 biotype. Uniform color indicates no significant difference between C1B1 and the two S-biotypes. Panel A depicts carbon metabolism including TCA cycle metabolites and Panel B represents amino acid metabolism ...... 191

xix List of Figures (Continued)

Figure Page

5.4 Pathway representation of the significant primary metabolites identified in the water treated glyphosate resistant T2B4 biotype and S-biotypes of A. palmeri. The colored circle represents identified metabolites. The top half of the circle represents the abundance ratio in T2B4 biotype over S-2 biotype and the bottom half represents the abundance ratio in T2B4 biotype over S-3 biotype. Uniform color indicates no significant difference between T2B4 and S-3 biotype. Panel A depicts TCA cycle metabolism and Panel B represents amino acid metabolism ...... 193

5.5 Differential abundance of secondary metabolites tentativley indentified in the most resistant (C1B1) and most susceptible (S-3) palmer amaranth biotypes after glyphosate exposure. Panel A represents the flavonols abundance between the S-3 and C1B1 biotype. * represents significant difference at P < 0.05. The glycosides are abbreviated as follows: Glucoside-gluc; Rhamnoside-rhamn; Neohesperidoside-neohesp; Rutinoside-rutino. Panel B represents the hierarchal cluster analysis of the top 50 significant metabolites identified between S-3 and C1B1 biotypes exposed to glyphosate. Panel C depicts the biosynthetic pathway of the flavonols. Colored circle (green) indicates flavonol abundance in glyphosate treated C1B1 over S-3 biotype ...... 195

xx CHAPTER ONE

HINDSIGHT AND BACKGROUND INFORMATION

(A part of this chapter has be submitted for publication in the journal “Weed Science”)

1.1 Herbicide resistance and agricultural weeds

Herbicide resistance is an important, economically unviable and rapidly escalating issue in agricultural production. Herbicide resistance in weeds has become more pronouced especially with the introduction of herbicide resistnant crops (e.g. RoundUp Ready crops) which resulted in growers relying more than ever on a single class of herbicides to control weeds. Loss of herbicide options to control weeds due to resistance development could have important economic and environmental consequences to agriculture.

1.1.1 Glyphosate and Glyphosate resistance

A major impact on the success of crop production is effective weed management strategies. One on the most routinely practiced strategy for controlling weed populations is the use of chemical herbicides. Of the 140 or so herbicides commercially available, the herbicide glyphosate is one of the most popular amongst agricultural producers (Vencil

2002; Duke and Powles 2008). First patented and sold as a commercial herbicide by

Monsanto in 1974 (Franz 1985; Dill et al. 2010) in UK for wheat, in Malaysia for rubber, and in the United States for industrial or non-crop use (Magin 2003) since then, glyphosate has been sold globally under several brand names such as Roundup®, Touchdown®,

Accord®, etc (Vencil 2002). Glyphosate is considered the most important herbicide ever developed (Baylis 2000; Duke and Powles 2008) and has been informally referred to as

‘once in a century herbicide’ due to its unique mechanism of action and wide applicability

(Duke and Powles 2008). It is the only herbicide commercially developed that can inhibit

5-enolpyruvylshikimate-3-phosphate synthase (EPSPS, EC 2.5.1.19), a key enzyme in shikimate pathway (Baird et al. 1971; Steinrucken and Amrhein, 1980; Rubin et al. 1984;

Bradshaw et al. 1997; Pline-Smic 2006). With the development and adoption of genetically modified crops having the glyphosate resistance trait (RoundUp crops, Liberty Link etc) it has become the principal post-emergence, systemic, nonselective, broad-spectrum herbicide for the control of annual and perennial weeds (Dekker and Duke 1995; Padgette et al. 1996; Magin 2003; Baylis 2000; Benbrook 2016). Glyphosate (N-(phosphonomethylglycine,) is a systemic, non-selective, foliar applied herbicide (Baird et al. 1971; Franz

1985; Vencil, 2002; Duke et al. 2003). When foliar applied, glyphosate is absorbed across the cuticle of the leaves (Sandberg et al. 1980; Amrhein et al. 1980) and subsequently translocated in the symplast to the roots, rhizomes, and apical tissues of treated plants

(Satchivi et al. 2000). Primarily, glyphosate migrates along the phloem from the site of application (source; mature leaves) to the site of action (sink; meristematic leaves) following sucrose movement (Gougler and Geiger 1981; Gougler and Geiger 1984;

McAllister and Haderlie 1985). The ease of phloem mobility of glyphosate is greatly dependent on its structure (Figure 1.1). The zwitterionic characteristics achieved due to combination of three acidic and one basic functions, assists in its ability to move across the plant (Bromilow and Chamberlain 2000). Once glyphosate enters the sieve element, it is trapped because of its hydrophilic properties and is transported to sink tissues (Shaner

2009).

The EPSPS enzyme catalyzes the conversion of shikimate-3-phosphate and phosphoenolpyruvate in to EPSP and inorganic phosphate in the shikimic acid pathway

(Herrmann 1995; Weaver and Herrmann 1997; Herrmann and Weaver 1999; Tzin and

Galili 2010; Maeda and Dudareva 2012). Disruption in shikimate pathway primarily affects the chorismate pathway and subsequent aromatic amino acid biosynthesis (Steinrücken and

Amrhein 1980; Boocock and Coggins 1983; Kishore and Shah 1988; Geiger and Fuchs

2002). Furthermore, inhibition of EPSPS enzyme results in shikimic acid accumulation and in reduction of biosynthetic processes, such as biosynthesis of essential secondary metabolites such as vitamins (K and E), proteins, alkaloids, lignin, flavonoids, coumarins, indole acetic acid (IAA), chlorophyll, carotenoids, benzoates and quinates (Amrhein et al.

1980; Anderson and Johnson 1990; Devine et al. 1993; Herrmann and Weaver 1999). The success of glyphosate stems from the fact that plants are not capable of metabolizing glyphosate when applied at a phytotoxic rate (Malik et al. 1989; Franz et al. 1997; Vencil

2002) and in the environment, it is tightly bound to soil particles, thereby does not leach into ground water (Vencil 2002). Furthermore, it also has a low environmental persistence with typical half-life of less than 47 days (Vencil 2002) and is easily degraded by soil microorganisms to glycine or aminomethylphosphonic acid (AMPA) as intermediate products (Sprankle et al. 1975; Rueppel et al. 1977; Dick and Quinn 1995). Though plant mediated metabolization of glyphosate is rare, possible metabolism of glyphosate into aminomethylphosphonic acid (AMPA) was reported in some weed species (Sandberg et al. 1980) such as field bindweed, Canada thistle, and tall momingglory (Sandberg et al.

1980) and some crop species, such as soybean, maize, and cotton (Rueppel et al. 1987).

Herbicide resistance in general terms, is defined as the capacity of a weed population to withstand and survive the herbicide when the herbicide is applied at its normal rate (Heap 1997). Herbicide resistance can be of two types- cross resistance and multiple resistance. If weed populations survive several herbicides that have the same mechanism of action, this is known as cross-resistance (Beckie and Tardif 2012); and if weeds survive many herbicides with different mode of actions, this is defined as a multiple resistance (Heap 1997). Evolution of herbicide resistant weeds is not due to a mutation caused by herbicide applications, rather, due to natural selection pressure on a susceptible weed population or small preexisting populations of resistant plants (Holt 1992; Jasieniuk et al. 1996; Powles et al. 1996; Heap et al. 1997; Heap 2014; Evans et al. 2016; Duke and

Heap 2017). With the adoption of transgenic herbicide-resistant (HR) crops an unprecedented change in agricultural practice have been brought about. Since most of the currently commercialized HR crops are resistant to a single herbicide, over application of the respective herbicide to control weeds. This over-simplification of weed control tactics and, consequently the change of weed communities (Owen and Zelaya 2005; Owen 2008) has resulted in an increased selection pressure exerted on the weed communities (Powles and Preston 2006; Powles 2008). Weeds have evolved resistance to herbicides in several chemical families including triazines, aryloxyphenoxypropionates, cyclohexanedinones, bipyridiliums, imidazolinones, dinitroanilines, triazoles, nitriles, substitute ureas, phenoxys, sulfonyl ureas within a short span of their introduction as commercial herbicides

(Bradshaw et al. 1997; Heap 2014;). Despite the use of glyphosate for over 30 years, contrary to the classic theory of herbicide resistance evolution which states that prolonged

and extensive use of a herbicide will lead to a rapid development of weeds resistant to the herbicide (Bradshaw et al. 1997; Shaner et al. 2012), no reports of evolved glyphosate resistance in weed species (Bradshaw et al. 1997) were reported till the last decade. The first glyphosate resistant weed was with rigid ryegrass (Lolium rigidum Gaudin) in

Australia (Powles et al. 1998; Pratley et al. 1999), followed by reports in other weed species such as goosegrass [Eleusine indica (L.) Gaertn] in Malaysia (Tran et al. 1999; Lee and

Ngim 2000), horseweed [Conyza canadensis (L.) Cronquist], common ragweed (Ambrosia artemisiifolia L.), giant ragweed (Ambrosia trifida L.), Palmer amaranth (Amaranthus palmeri S. Watson), common waterhemp [Amaranthus tuberculatus (Moq.) JD Sauer (syn.

A. rudis)], kochia [Bassia scoparia (L.) A. J. Scott (syn. Kochia scoparia)], annual bluegrass (Poa annua L.) and spiny amaranth (Amaranthus spinosus L.) in the United

States of America (VanGessel 2001; Koger et al. 2004a; Perez-Jones et al. 2005; Zelaya and Owen 2005; Nandula et al. 2005; Culpepper et al. 2006; Heap 2015), Italian ryegrass

[Lolium perenne L. ssp. multiflorum (Lam.)] in Chile (Perez and Kogan 2003), hairy fleabane [Conyza bonariensis (L.) Cronquist] and buckhorn plantain (Plantago lanceolata

L.) in South Africa (Urbano et al. 2005; Heap 2014), Johnsongrass [Sorghum halepense

(L.) Pers.], perennial ryegrass (Lolium perenne L.) and gramilla mansa (Cynodon hirsutus

Stent) in Argentina (Heap 2014), Ragweed parthenium (Parthenium hysterophorus L.),

Sourgrass [Digitaria insularis (L.) Mez ex Ekman], Sumatran fleabane [Conyza sumatrensis (Retz.) E. Walker] and tropical sprangletop [Leptochloa virgata (L.) P.

Beauv.] in Colombia, Paraguay, Spain and Mexico respectively (Heap 2014), Euphorbia heterophylla in Brazil (Vila‐Aiub et al. 2008), junglerice [Echinochloa colona (L.) Link],

liverseedgrass (Urochloa panicoides P. Beauv.), Australian fingergrass (Chloris truncata

R. Br.) and ripgut brome (Bromus diandrus Roth) in Australia (Heap 2014).

The mechanism of glyphosate resistance is crops is well characterized as it has resulted in the development of Roundup Ready® crops. Glyphosate resistance in these transgenic crops is conferred by the introduction of the EPSPS gene from bacteria (Kishore et al. 1992, Bradshaw et al. 1997; Dill 2005). The mechanisms by which resistance is conferred include (i) overproduction of EPSPS (target-site amplification mechanism)

(Shah et al. 1986); (ii) introduction of EPSPS with decreased affinity for glyphosate (target- site modification mechanism) (Padgette et al. 1996); and (iii) introduction of a glyphosate degradation gene (Barry et al. 1992). However, evolution of glyphosate resistance in the wild populations of weeds is not fully understood. In natural populations, very few herbicide-resistant plants are found unless repeated applications of the herbicide were made continually in past years (Perez-Jones et al. 2007). Naturally, a weed’s insensitivity to herbicide is thought to be conferred by different mechanisms including reduced herbicide absorption (morphological adaptations), reduced translocation of herbicide from the site of absorption to the target-site, enhanced metabolic detoxification of the herbicide, sequestration or compartmentalization of the herbicides away from the target site, target- site mutations, and gene amplification/overexpression (Devine and Eberlein 1997; Gaines et al. 2010; Koger and Reddy 2005; Preston and Wakelin 2008; Nandula 2010; Perez-Jones and Mallory-Smith 2010). Most of the weed species that are found to be resistant to glyphosate have evolved mechanisms that involve reduced translocation of herbicide from the site of absorption to the target-site or target-site mutation that prevents binding of

glyphosate to EPSPS enzyme (Baerson et al. 2002; Feng et al. 2004; Michitte et al. 2005;

Wakelin and Preston 2006; Preston and Wakelin 2008; Nandula et al. 2008, 2012; Perez-

Jones and Mallory-Smith 2010). Recently, Gaines et al. (2010, 2011) proposed a unique heritable glyphosate resistance mechanism in A. palmeri populations in Georgia involving gene amplification leading to multiple copy numbers of EPSPS and increased production of EPSPS protein.

1.1.2 Amaranthus palmeri and glyphosate resistance

Amaranthus palmeri or Palmer amaranth (Figure 1.2) is an annual non-graminoid, flowering plant native to North-western Mexico, Southern California, New Mexico and

Texas. Palmer amaranth (Amaranthus palmeri S. Wats.) is one of 10 dioecious pigweed species native to North America (Steckel 2007; Ward et al. 2013), originating from the desert southwest (Culpepper et al. 2010) and has established itself as a problematic weed in many regions of the mid-south and southeastern United States (Steckel 2007). First reported occurrence outside its native habitat was in 1915 in Virginia followed by

Oklahoma in 1926, and South Carolina in 1957 (Culpepper et al. 2010). By 2009, it was ranked as the most troublesome weed in the southern U.S. affecting cotton, corn and soybean productivity (Ward et al., 2013). Success of Palmer amaranth in row-crop fields is due to prolific seed production (up to 600,000 seeds per female plant), high water-use efficiency, aggressive growth at elevated temperatures, and C4 photosynthetic mechanism

(Keeley et al. 1987; Guo and Al-Khatib 2003; Massinga et al. 2003; Jha and Norsworthy

2008; Jha and Norsworthy 2009; Jha et al. 2010). The number of seeds produced by female plants is large in comparison to many other weed species. A single Palmer amaranth plant

can produce approximately 600,000 seeds (Keeley et al. 1987). At maturity, Palmer amaranth can reach 1.8 to 2.4 m tall and competes with the crop for water, nutrients, and light (Guo and Al-Khatib 2003; Ward et al. 2013). Unlike closely related species such as common waterhemp (Amaranthus rudis Sauer) and tall waterhemp [Amaranthus tuberculatus (Moq.) Sauer], Palmer amaranth has wider leaves which gives it a comparative better light interception advantage its counterparts (Ward et al. 2013). Palmer amaranth leaves are rhombiclanceolate, with petioles generally longer than the leaf blades.

The inflorescence of Palmer amaranth is a terminal spike that may reach 0.5 m in length

(Horak and Peterson 1995; Ward et al., 2013). Male and female inflorescences are distinguishable from one another by touch. While the female inflorescence has stiff, pointy bracts that make it prickly to touch, male inflorescence are soft and delicate (Ward et al,

2013). Coupled with aggressive growth and season-long emergence, the growing plants dominate competition with crops for light, water, nutrients, and space thus giving farmers a narrow window to control Palmer amaranth (Keeley et al. 1987; Jha and Norsworthy

2008). In addition to above ground advantages, Palmer amaranth roots have been reported to penetrate compact soils and access nitrogen better and faster than competing crops (Place et al. 2008). These and other characteristics make Palmer amaranth historically a difficult weed to control especially in cotton and soybean production (Smith et al. 2000; Morgan et al. 2001).

Palmer amaranth is one of the most resistance prone dicots, with confirmed resistance to five different herbicide mechanisms of action (MOAs) namely, ALS- inhibiting herbicides, dinitroanilines, triazines, glyphosate, and 4- hydroxyphenylpyruvate

dioxygenase (HPPD) inhibitors (Heap 2015). Some populations have evolved multiple herbicide resistance within the same biotypes. In 2010, a population in Georgia was confirmed to be multiple-resistant with resistance to glyphosate, ALS-inhibitors, and PSII- inhibitors (Sosnoskie et al. 2011), while a population of Palmer amaranth was confirmed to be resistant to ALS-, PSII-, and HPPD-inhibiting herbicides (Neve et al. 2011). The ability of A. palmeri for evolving multiple herbicide resistance has been suggested due to its obligate outcrossing reproductive biology (Ward et al. 2013; Teaster and Hoagland,

2014). The ability of a glyphosate-resistant male Palmer amaranth plant to transfer resistance to an herbicide-susceptible female plant through pollen flow amplifies the potential for glyphosate resistance in this weed. GR-Palmer amaranth has been reported to reduce yield or interfere with harvesting mostly in soybean (Glycine max L.) and cotton

(Gossypium hirsutum L.). At densities of 0.32 and 10 plants m-1 of row, Palmer amaranth reduced cotton and soybean yields by 28 and 68%, respectively (Klingaman and Oliver

1994; Smith et al. 2000). Yield loss has also been reported in grain sorghum (Sorghum bicolor L.), sweet potatoes [Ipomoea batatas (L.) Lam] and corn. (Moore et al. 2004).

According to the recent chemical usage data, herbicides constituted 95% of all pesticides being used on weight basis in the US (Grube et al. 2011). The dramatic rise in pesticide use is mainly because of increased adoption of herbicide-tolerant crops. Since the commercialization and success of glyphosate-resistant (GR) crops such as GR soybean

(Glycine max L.), maize (Zea mays L.), cotton (Gossypium hirsutum L.), canola (Brassica napus L. and B. rapa L.), alfalfa (Medicago sativa L.), and sugarbeet (Beta vulgaris L.) in mid-1990s, growers have used glyphosate more than any other herbicide to manage weeds

(Benbrooke 2016). The unprecedented extensive use of glyphosate alone over space and time exerted intense selection pressure, consequently, GR weed biotypes including Palmer amaranth have evolved (Culpepper et al. 2006; Heap 2015). Due to the repeated use of glyphosate in glyphosate-resistant crops, there are currently 24 weed species resistant to glyphosate worldwide (Heap 2015). First case of glyphosate-resistant Palmer amaranth was confirmed in Georgia in 2005, with the resistant biotype requiring eight times more glyphosate than the susceptible biotype to achieve 50% control (Culpeper et al. 2006). The continued use of glyphosate resulted in the evolution of glyphosate-resistant Palmer amaranth in several states in the US including Alabama, Arkansas, Georgia, Missouri,

North Carolina, South Carolina, Tennessee, Mississippi, Florida, Illinois, Michigan,

Louisiana, New Mexico, and Virginia. (Culpepper et al. 2006; Nichols et al. 2009; Davis et al. 2015; Heap 2015) and has become one of the most economically damaging glyphosate-resistant weed species in the U.S. (Beckie, 2006).

1.1.3 Ipomoea lacunosa and glyphosate tolerance

The genus Ipomoea contains the largest number of species (~700 species) within the flowering plant family Convolvulaceae (Austin et al. 2015). Among Ipomoea spp.,

Ipomoea lacunosa L. (pitted morningglory) (Figure 1.3) is one of the most common and troublesome weed species in southern U.S. row crop production systems (Webster and

Nichols, 2012). Over the years this species has risen to be one of the most common and difficult to control weeds in cotton (Gossypium hirsutum L.) and soybean (Glycine max

(L.) Merr.), especially in 11 southern states (Webster and Coble 1997; Webster and

MacDonald 2001). I. lacunosa competes aggressively with crops and is capable of reducing

crop yield up to 81% (Higgins et al. 1988; Norsworthy and Oliver, 2002). Lack of effective control of I. lacunosa has been described due to several reasons. Chachalis et al. (2001) observed that I. lacunosa control in the greenhouse with glyphosate was extremely dependent on plant size. While three- to four-leaf plants treated with 1.12 kg ha-1 glyphosate was completely controlled, only 38% of the more mature plants could be controlled. Though the authors did not report the reason for their observations, it could be hypothesized that the limited glyphosate efficacy could be potentially due to limited absorption into treated tissues. Contrastingly, Norsworthy et al. (2001) reported that when control of three to four-leaf plants were treated using 0.84 and 1.26 kg ha-1 glyphosate, only 59 and 69% of the plants were controlled respectively. The study argues that the potential reason for this because only 6% of the glyphosate applied to the leaves was absorbed, suggesting that increased tolerance to glyphosate may be attributed to limited herbicide absorption. Similar control results were reported by Shaw and Arnold (2002) who also reasoned that the reduced susceptibility of I. lacunosa may be due to a combination of limited foliar absorption through the plant cuticle and reduced translocation from the treated area to target site. The rapid prevalence of I. lacunosa in agricultural fields could be partly attributed to the dramatic increase in acreage of glyphosate-tolerant crops, coupled with the natural tolerance exhibited by some of the I. lacunosa biotypes to this herbicide (Koger et al., 2004b). Efficacy of glyphosate to control I. lacunosa is often variable, and several studies have reported an inadequate control of this species with glyphosate at the field-recommended rates of 0.84 to 1.26 kg ae ha-1 (Jordan et al. 1997;

Norsworthy et al., 2001; Norsworthy and Oliver 2002; Shaw and Arnold, 2002). Reduced

susceptibility of I. lacunosa to glyphosate is generally attributed to limited foliar absorption

(Norsworthy et al. 2001) or reduced translocation of the herbicide from the treated leaves to the target sites (Ribierio et al., 2015). However, other studies have reported a minimal role of differential absorption and translocation in conferring glyphosate tolerance in I. lacunosa (Koger et al., 2004b). Moreover, glyphosate tolerance due to the ability of I. lacunosa to metabolize the herbicide to the less toxic aminomethylphosphonic acid

(AMPA) has also been previously reported (Sandberg et al., 1980), but this metabolism mediated detoxification does not explain the tolerance of all biotypes (Ribierio et al. 2015).

1.2 Metabolomics to elucidate cellular physiology

Over the years, application of omics technologies have become an indispensable part for the progress in weed science research (Anderson 2008). However, the availability of an integrated omics platform has become the need of the hour due to the fact that there has been no major breakthroughs in the past two decades in new herbicide development to combat the ever increasing problem of herbicide resistance manifestations in weeds.

Evolution of resistance in weeds to commonly used herbicides, extended dormancy in seeds and vegetative buds that help them to circumvent the management practices, host- recognition and infestation of crops by parasitic weeds, and lack of new herbicide chemistries are some of the key contemporary challenges faced by the weed science community. As per recent reports, weeds have evolved resistance to almost all of the commercially used herbicides and alarmingly, few of them have resistance to more than one type of herbicide (Heap 2016). This distressing trend is further heightened by the fact that identifying a new herbicide mode of action (with existing techniques and knowledge)

or determining new target sites is a time consuming and laborious and no new herbicide mode of action has been recently developed (Duke and Dayan 2015). For developing new herbicides, it is essential to fully understand weed physiology under stressed conditions, in particular how weeds respond to herbicide application. Application of omics techniques would help the weed science community to elucidate the molecular physiology and chemical phenotype of weedy plants. By identifying the cues and markers of stress responses in weeds, agri-chemists would be able to strategize novel, targeted weed management practices that are more efficient, economic and environmental friendly. Post genomic sequencing era has seen a rapid rise in advocating the use of an integrated approach to answer some of the relevant biological observations (Oksman-Caldentey et al.

2004; Sweetlove and Fernie 2005; Yuan et al. 2008; Fukushima et al. 2009). Several early studies involving integrated omics approaches were able to identify correlations between functionally important metabolites and genes (ter Kuile and Westerhoff 2001; Hall et al.

2002; Urbanczyk-Wochniak et al. 2003; Hoefgen and Nikiforova 2008). Presently, the integrated omics approaches are used in several areas including crop improvement (Parry and Hawkesford, 2012), engineering plant metabolic pathways (Oksman-Caldentey and

Saito, 2005), elucidating plant stress responses (Hirayama and Shinozaki, 2010) etc. In weed science, the use of omics approaches such as transcriptomics and proteomics have been limited to elucidating mechanism of resistance development in weeds (Stewart et al.

2010; Shaner and Beckie 2014; Maroli et al. 2015). Despite the existence of omics techniques for over two decades, these techniques have not been exploited to their full potential to elucidate the finer physiological regulations that ultimately leads to resistance

evolution. However, there has been recent reviews which raises the suggestion of using omics to help advance the progresses in weed science (Aliferis and Jabaji 2011; Duke 2012,

Duke et al. 2013). With the advancements made in the field of systems biology, integration of omics data obtained from various platforms is not cumbersome and so we would have a consolidated information source that would help in understanding and addressing issues at a broader systemic level than studying them as independent discreet entities. Systems biology and integrated omics approach can serve as a powerful tool in developing new herbicides and understanding development of weed resistance and consequently employing more robust, economical and ecofriendly weed management practices.

1.2.1 Plant Metabolomics

Traditional functional analyses have focused on the central dogma of molecular biology and have primarily been reliant on the omics trilogy of genomics (genome profiling), transcriptomics (gene expression analyses) and proteomics (protein translation studies) (Goodcare 2005). Though application of these traditional ‘omics’ approaches provides a robust understanding of the genotypic regulation of the phenotype, a true physiological picture of the phenotypic manifestations of the system is not adequately elucidated (Ryan and Robards 2006). This limitation can be attributed to the susceptibility of the molecular markers to functional alteration either by epigenetic, post-transcriptional and/or post-translational modifications, resulting in altered phenotypes (Patti et al. 2012).

The realization that obtaining the genome sequence of a species does not in itself explain the fundamental nature of many physiological responses has triggered a marked increase in interest in approaches that relate gene expression to the final metabolic outcome. It is

essential to have information that would give a more definite and real-time representation of gene-environment interactions thereby providing a better understanding of the functional phenotypic characteristics of an organism in response to external abiotic or biotic stimuli. Metabolomics is a rapidly emerging field that is aimed at a comprehensive identification and quantitation of low-molecular weight metabolites (metabolome) present in any living system capturing the metabolic state of a cell that closely influences the phenotype (Fiehn 2002). It is unique in the fact that it provides information about the biological processes that forms the end results of the bio-cascade, beginning with gene expression and delivers an unaltered snapshot of the “current” physiological state.

(Raamsdonk et al. 2001) By directly measuring the final products of gene expression, protein expression and enzymatic activity as affected by the environment, it offers a powerful approach for molecular phenotyping (Anderson, 2008). Hence while traditional omics techniques such as genomics and proteomics provide information about the processes that could potentially occur, recent ‘omics’ approaches like metabolomics and phenomics, would complement them bridging the gap between the potential phenotype predicted by the genotype and the actual phenotype resulting from genotype-environment interactions (Figure 1.4). Thus metabolomics provides a clear picture of what is happening in real-time within a living organism exposed to a given environment. As the biological pathways proceeds in a regulated manner downstream from its genome to the metabolome via its transcriptome and proteome, integration of information obtained from relatively newer “omics” technologies (metabolomics) with that of the established “omics” technologies (genomics, transcriptomics and proteomics) would augment the existing

knowledge and help understand the networks regulating gene expression (Fukushima et al.

2009). The major challenge for developing an effective metabolomics technology platform similar to that of genomics, transcriptomics and proteomics is primarily due to the chemical complexity, metabolic heterogeneity, and dynamic range of the metabolites that need to be analyzed. This problem is further aggravated by the challenges in developing a single extraction procedure for all metabolites (Goodacre et al. 2004). Despite the lack of a single extraction and detection technique, adaptation to multi-parallel technologies have enabled us to gain the desired broad metabolic picture (Hall 2006). In order to achieve a robust complementary biochemical data, careful selection of appropriate combinations of extraction, separation and detection protocols needs to be optimized, particularly for plant metabolomics, where in addition to the classical polar/semi-polar (e.g. methanol

(MeOH)/water) and lipophilic (e.g. chloroform) extractions, analysis of the volatile components (hormones, ITCs) via solvent extraction or through headspace extraction (e.g. solid phase micro-extraction, SPME) is often desired (Tikunov et al. 2005; Hall 2006).

Plant extracts generally have a more complicated biochemical composition and requires extensive extraction and separation procedures to achieve reproducible results. However, with the advancement of analytical separation and detection techniques, the time duration of carrying out the extractions, separations and detection has been considerably brought down without compromising the integrity of the analysis. All metabolic platforms are based on the common workflow of metabolite extraction from sample tissues, chromatographic separation, analyte detection, pre-processing of raw data, metabolite identification and statistical validation of experimental results (Figure 1.5). With respect to plant

metabolomics, almost all analysis involves GC (gas chromatography) or LC (Liquid

Chromatography) chromatographic separations of analytes followed by mass spectrometry detection. Mass spectrometry (MS) is often selected as the primary detection method for plant metabolomics due to its sensitivity, speed and broad application. However, other separation and detection techniques such as capillary electrophoresis separation-MS and

Fourier transform-ICR-MS (FTMS) and Nuclear magnetic resonance based plant metabolomics is rapidly gaining interest (Sumner et al., 2003; Sato et al, 2004; Brown et al. 2005). For global metabolome profiling, gas chromatography–mass spectrometry (GC-

MS) is currently proving to be the most popular approach primarily due to the robustness of both the separation and the electron impact spectrometry technique. This further aided by the availability of curated metabolomic libraries and software for data deconvolution and metabolite identification. GC-MS based metabolomics is amenable to analyze metabolic groups that are naturally volatile at temperatures up to c. 250°C (e.g. alcohols, monoterpenes and esters), as well as nonvolatile, polar (mainly primary) metabolites, such as amino acids, sugars and organic acids, by means of chemical derivatization, which transforms them into volatile and thermostable compounds. Therefore, almost all of the key primary plant metabolites can be detected in a single chromatographic run (Desbrosses et al. 2005). Unlike GC-MS, Liquid chromatography-MS is a primarily used to analyze plant secondary metabolites (Verhoeven et al. 2006). However, this technique is restricted to detect only those metabolites that can be easily ionized either as positively or negatively charged ions. However with the advances in chromatographic technologies coupled with advances in column chemistry and chemical transformation to achieve ionizable

metabolites, LC-MS based metabolic profiling are yielding significantly improved separation potentials. The high analytical precision of modern LC techniques combined with the high sensitivity and mass accuracy and resolution of MS systems is proving very useful in the analysis of complex metabolite mixtures typified by plant extracts. Despite the technological advancements availability of few mass spectral libraries, unlike the GC-

MS, proves cumbersome for metabolite identification and validation for data obtained via

LC-MS (Verhoeven et al. 2006).

1.2.2 Metabolomics for exploring herbicide resistance in weeds

Though several plant species, both monocots and dicots such as maize (Zea mays L.), wild oat (Avena sterilis L.) and Arabidopsis (Arabidopsis thaliana L.) have been used for the study of the mechanisms of action of synthetic and natural herbicidal compounds by applying metabolomics (Aranibar et al. 2001; Oikawa et al. 2006), limited studies have employed metabolomics to characterize the physiology of herbicide resistance in weeds (Aliferis and Jabaji 2011). A metabolomics approach has been recently adopted to understand effect of chemical stresses on Lolium perenne upon exposure to a non-lethal dose of glyphosate (Serra et al 2015) as well as to identify a complementary mechanism of glyphosate resistance in Amaranthus palmeri (Maroli et al. 2015, 2016). Since many crop production systems employ glyphosate (N-(phosphonomethyl) glycine) to control annual and perennial grasses and broad-leaf weeds globally (Duke and Powles 2008; Dill 2005), glyphosate has been a target candidate for several scientific investigations for several decades. As such, application of genomics and transcriptomics technologies have helped to identity the common causes/mechanisms of evolved glyphosate resistance in some of

the weeds (Délye 2013; Délye et al. 2013) including a higher abundance of the EPSPS enzyme resulting from an increased EPSPS gene copy number (Gaines et al. 2010) and reduced translocation of glyphosate to the target site tissues (especially meristems) in A. palmeri (Nandula et al. 2012). However, it is well known that under stressful conditions, accumulation and depletion of several primary metabolites are commonly observed in plants (Molinari et al. 2007; Suseela et al. 2014). Therefore, monitoring the changes in metabolic levels can provide clues to their roles in stress responses and regulation. With respect to the herbicide glyphosate, its main target site is the shikimate pathway, a key pathway in plants that links carbon and nitrogen metabolism (Tzin and Galili 2010). Apart from its role in aromatic amino acid synthesis, it serves as a major sink for intermediate metabolites from the central carbon metabolism pathways (glycolytic and pentose phosphate pathways). Thus any perturbations in the shikimate pathway will lead to the disruption in aromatic amino acids synthesis, as well as alter the metabolic stoichiometry of other carbon intermediates resulting in system-wide perturbations. Thus it is hard to not envision that metabolomics would therefore be an ideal approach not only to map the glyphosate-induced disruption of plant metabolism, but also to illuminate the physiology that confers resistance to some biotypes. Such an approach has been able to demonstrate the application of metabolomics as a complementary tool for further elucidating glyphosate resistance at the phenotypic level in A. palmeri, which helped in identifying physiological differences between a glyphosate sensitive (S-) and resistant (R-) as well as demonstrate that the resistance to glyphosate in the R-biotype, though primarily conferred by the multiple copies of EPSPS gene in this resistant A. palmeri biotype, may be complemented

by the anti-oxidative protective mechanisms (Maroli et al. 2015). Findings from this study complements the recent genetic study finding that EPSPS gene expression is not equal in all tissue types within a single individual of glyphosate resistant A. palmeri biotypes

(Jugulam and Dillon 2016; Godar et al. 2015), and thus the enhanced antioxidant activity could help to mitigate the secondary toxicity of glyphosate generated through generation of free radicals in tissues with lower EPSPS copies.

Despite its advantages, metabolomics too is not a panacea for understanding the complexities of biological process. Unlike the central dogma, metabolic pathways do not follow a linear sequential flow, rather it is a complex network with multiple feedback loops and neighborhood interactions (Sumner et al. 2003). This complexity makes the deciphering of metabolomics data cumbersome. Moreover, unlike DNA and RNA, which are primarily made up of four nucleotides, metabolites are much larger, complex and short- lived compounds with varying physio-chemical properties and therefore simultaneously studying the cost-benefit analysis of all the metabolites would be challenging. However, metabolic data collected through strict statistical and experimental conditions could generate interesting information that can be used as a central milestone from which one can either trace-back to decipher genetic level changes or look forward to predict and construct metabolic pathway networks to simulate the metabolic perturbations and to have a partial understanding of the ﬂuxes occurring through them. Currently, much of the knowledge regarding cellular metabolism is obtained from information adapted from individual “omics” approaches (Verpoorte and Memelink 2002; Park et al. 2005; Urano et al. 2010; Maroli et al. 2015). However, by studying these biological entities in their

individual capacity, a comprehensive understanding of the complexities of weed genotype- phenotype interactions will not be achieved. Simply put, while transcriptomics, provides information about only those genes that are being actively expressed at a given point of time and due to the post-transcriptional regulatory control processes, not all of the expressed transcripts produce the final products (Brink-Jensen et al. 2013). Similarly, metabolomics data provides information about the functional state of the organism, but cannot fully explain the genetic changes inherent in the phenotype.

1.3 Summary and Purpose

Resistance and tolerance to glyphosate in weed species is a major challenge for the sustainability of glyphosate use in crop and non-crop systems. Glyphosate-resistant weeds have become an increasing economic hazard to producers, creating an urgency to understand the basis of resistance (Marshall 2001; Basu et al. 2004; Yuan et al. 2007). The judicious use of herbicides in agriculture, therefore, should be adapted with the advancing scientific knowledge to manage the resistance and tolerance to herbicides. Management of multiple herbicide resistant Palmer amaranth and variability in glyphosate tolerance levels in Pitted morningglory merits investigation into the identification and rapid discrimination of different biotypes having varying resistance/tolerance to glyphosate. This requires more research on the best herbicide options and programs to achieve optimal control while mitigating selection pressure for the development of additional herbicide resistances.

Despite the existing thorough knowledge of weedy traits and the molecular understanding of weed genomics, we are largely ignorant about the functional aspects of these underlying traits. Using the technique of metabolomics, we hope to improve our understanding of

weed resistance by finding and characterizing metabolites that might play a role in fitness, competitiveness and adaptations of weeds in the herbicide-applied agroecosystems and thus help to predict weed shifts and the herbicide resistance evolution rapidly.

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Figures

Figure 1.1: Molecular structure of glyphosate (N-(phosphonomethyl)glycine). The zwitterionic characteristics of glyphosate is due to combination of three acidic and one basic group which assists in its ability to move across the plant efficiently. Sourced image (Wikipedia).

Figure 1.2: Leaves of Amaranthus palmeri plant. The leaves are broad with serrated edges and have the characteristic white chevron towards the middle of the leaves. Sourced image (Google Images)

44 Figure 1.3: Leaves of pitted morningglory (Ipomoea lacunosa). The leaves, with parallel venation, are broad at the base and tapers towards the tip forming a heart shape. Sourced image (Google Images).

45 Figure 1.4: Classical systems biology concept and omics organization. Omics techniques covers the progressive functionalization of the genotype to the phenotype. The various molecular entities (DNA, mRNA, proteins, metabolites) that are tracked and captured encompasses the omics techniques.

Figure 1.5: General workflow for a metabolomics experiment. The first step for a metabolomics experiment is a relevant biological question and a valid experimental design to answer the question. Subsequent steps involve appropriate selection of chromatography separation, detection, statistical validation and functional and ontological interpretation

CHAPTER TWO

COMPARATIVE METABOLOMIC ANALYSIS OF TWO IPOMOEA LACUNOSA

BIOTYPES WITH CONTRASTING GLYPHOSATE TOLERANCE ELUCIDATES

THE GLYPHOSATE INDUCED DIFFERENTIAL PERTURBATIONS IN CELLULAR

PHYSIOLOGY

(This work has been submitted for publication in the Journal of Agriculture and Food Chemistry)

Abstract

Metabolome profiling is a reliable technique to identify innate physiological differences between plant biotypes as well as charting stress mitigation strategies. In this study, we used non-targeted metabolic profiling to capture differences in metabolic abundances in two biotypes of pitted morning glory with varying tolerance to glyphosate

[WAS (GR50 = 151 g ae/ha) and QUI (GR50 = 59 g ae/ha)]. Metabolic profiling followed by pathway topological analysis captured innate metabolic differences (22 significantly different metabolites) between WAS and QUI biotypes. These metabolic differences significantly influenced 16 metabolic pathways in the WAS biotype. Moreover, when exposed to a sub lethal glyphosate rate of 80 g ae/ha, both biotypes exhibited metabolic perturbations at 80 h after treatment. Shikimic acid accumulation in both the biotypes indicated 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) susceptibility. Despite

EPSPS inhibition, no changes in aromatic amino abundance was observed in the QUI biotype while a 133% increase in Tyr abundance was observed in the WAS biotype, indicating its tolerance to the glyphosate. Compared to the respective water control, a 112%

increase in the proline pool coupled with a 57% decrease in total sugar content was observed in the QUI biotype. In contrast, the WAS biotype had 69% increase in proline pool and 72% decrease in total sugar content. The reduced import of sugars from source to sink in these biotypes could be incidental to restricted glyphosate movement towards meristematic tissues. The results from this study imply that despite tolerance to glyphosate, the cellular metabolism of both biotypes is perturbed when exposed to sub lethal rates of glyphosate.

Introduction

Glyphosate tolerance is defined as a natural, inheritable mechanism that allows a plant species to survive and reproduce after exposure to normal glyphosate application rates.1 In contrast, glyphosate-resistant weed populations evolve due to repeated selection pressure through herbicide applications which selects for and promotes the expansion of resistant populations naturally present at low densities.2 Tolerance differs from resistance in the fact that tolerant populations have never been previously susceptible to the herbicide.3,4 Glyphosate (N-phosphonomethyl glycine) has been the most widely used broad-spectrum herbicide,5 and its continuous use has led to the evolution of 37 glyphosate- resistant weed species.6 However, several other species have been reported to have some degree of natural tolerance to glyphosate.7 Some of the reported glyphosate tolerant plant species include Canavalia ensiformis (L.) DC.,1 Ipomoea lacunosa L.,8-10 Ipomoea wrightii

Gray,8 Ipomoea purpurea (L.) Roth,11,12 Clitoria ternatea L.,13 Neonotonia wightii (Wight

& Arn.) J.A. Lackey,13 Mucuna pruriens (L.) DC.,14 Cologania broussonetii (Balb.)

DC.,2 and Synedrellopsis grisebachii Hieron. & Kuntze ex O.Hoffm.15 Adequate control

of these tolerant weeds (e.g., Ipomoea spp.) by glyphosate often requires higher and more frequent application rates than many other common weeds.7,8,16

The genus Ipomoea contains the largest number of species within the flowering plant family Convolvulaceae.17,18 Among Ipomoea spp., I. lacunosa (pitted morning glory) is one of the most common and troublesome weed species in southern U.S. row crop production systems.19,20 Over the years, this species has risen to be one of the most common and difficult to control weeds in cotton (Gossypium hirsutum L.) and soybean (Glycine max

(L.) Merr.), especially in 11 southern states.20,21 The rapid spread of I. lacunosa in agricultural fields could be partly attributed to the dramatic increase in acreage of glyphosate-resistant crops, coupled with its innate tolerance to glyphosate.9,22 Efficacy of glyphosate to control I. lacunosa is often variable with several studies reporting inadequate control at the field-recommended rates (0.84 to 1.26 kg ae ha-1).7,8,23,24 Reduced susceptibility of this species to glyphosate is generally attributed to limited foliar absorption7 or reduced translocation of the herbicide from the treated leaves to the target sites.10 However, other studies have reported a minimal role of differential absorption and translocation in conferring glyphosate tolerance in I. lacunosa.9 Metabolism of glyphosate to the less toxic aminomethylphosphonic acid (AMPA) has also been previously reported as a tolerance mechanism in several other weeds and crops,25 but this metabolism-mediated detoxification has not been reported in I. lacunosa biotypes.10 Thus, there is a critical knowledge gap in relating the differential glyphosate tolerance of I. lacunosa biotypes with their respective metabolic processes.

Traditional high-throughput functional analyses have been reliant on the omics trilogy of genomics, transcriptomics, and proteomics in exploring physiological systems.26 Though these techniques can depict the genetic regulation of the potential functioning of the organism in detail,27 a true physiological portrait of the phenotypic manifestations may not be adequately elucidated. This could be partly attributed to the susceptibility of the respective molecular markers to biological alterations and functional modifications.28

Metabolomics is an emerging field which provides a finer understanding of functioning of the physiological system by quantifying small intermediary molecules (metabolites) within the dynamic framework of the metabolome.29,30 Thus, deciphering the metabolome of an organism is vital to providing a direct and unbiased reflection of the physiological status of an organism.28 Rapid advances in plant metabolic profiling using mass spectrometry

(MS) and nuclear magnetic resonance (NMR) have empowered researchers to simultaneously detect a wide range of metabolites.29,31 Metabolomics could provide complementary data to explain, not only the physiology of herbicide-induced toxicity in weeds, but could also help understand the physiological mechanisms that confer resistance

(or tolerance) to commonly used herbicides. Despite this advantage, very few studies have employed metabolomics to better understand the physiological basis for herbicide resistance (or tolerance) in weeds.32,33 Understanding the cellular physiology of herbicide tolerance (or resistance) might enable knowledge-based adoption of alternate herbicides and cultural practices that may specifically target the metabolic vulnerabilities of resistant biotypes. Having a robust knowledge of weed physiology could potentially predict the

propensity of evolution of cross-resistance and therefore could help to control the spread of herbicide resistant weeds.34

The objective of the current study is to characterize and map glyphosate-induced physiological perturbations in two I. lacunosa biotypes having different levels of tolerance to glyphosate using non-targeted metabolite profiling. Deducing the dynamics of the metabolite pools could help unravel the role of the metabolites in conferring the differential glyphosate tolerance observed in these biotypes.

MATERIALS AND METHODS

Plants Biotypes. Glyphosate-tolerant I. lacunosa seeds were obtained from

Stoneville, Mississippi (Crop Production Systems Research Unit, USDA-ARS, Stoneville,

MS). Seeds from biotypes previously characterized as most tolerant (MS-WAS-8 (WAS);

-1 -1 GR50 151.4 g ae ha ) and least tolerant (MS-QUI-1 (QUI); GR50 59.1 g ae ha ) were used in this study.10 Seeds were planted in individual pots (10 cm diameter x 9 cm deep) containing commercial germination mixture (Sun-Gro Redi-Earth Plug and Seedling Mix,

Sun-Gro Horticulture, Bellevue, WA 98008), and following the emergence of two true leaves, the plants were fertilized with 50 ml of 4 g l-1 fertilizer (MiracleGrow®, 24%-8%-

16%, Scotts Miracle-Gro Products, Inc., Marysville, OH, USA). The plants were grown in a greenhouse maintained at day/night temperatures of 30°C/20°C respectively with a 14-h photoperiod and sub-irrigated every alternate day until harvest.

Experimental Design and Glyphosate Application. Plants of uniform growth

(height and leaf numbers) from each biotype were selected and randomly assigned to two treatment groups: water (control) and glyphosate. Two weeks after planting, the respective

treatments (water or glyphosate) were applied to five plants per biotype using an enclosed spray chamber (DeVries Manufacturing, Hollandale, MN) calibrated to deliver 187 L ha-1 through an 8001E ﬂat fan nozzle (TeeJet Spraying Systems Co., Wheaton, IL). As, the

-1 GR50 of WAS and QUI biotypes were previously estimated as 151 and 59 g ae ha respectively,10 glyphosate was applied at a median rate of 80 g ae ha-1 (0.1X field rate). At the time of treatment application, the experimental units (plants) had similar morphological growth (height, leaf number, no vining). At 80 h after treatment (HAT), the three top-most young leaves, along with the apical meristem, from five replicates per treatment were destructively harvested and immediately frozen using dry-ice blocks and stored at -80°C until further analysis. To minimize the variations due to circadian rhythm, the plants of both the treatments were harvested 8 h after sunrise. The harvested tissues were ground to a fine powder with dry ice prior to metabolite profiling.

Metabolite Profiling by GC-MS. Low-molecular weight polar metabolites in leaf tissues were identified and quantified using gas chromatography/mass-spectrometry (GC-

MS) as described by Maroli et al.33 with slight modifications. Briefly, about 100 mg of finely powdered leaf tissue was weighed into 5 ml methanol and homogenized by sonicating in an ice-bath followed by centrifugation at 671  g for 5 min and rapid cooling on ice. The supernatant was transferred to pre-chilled glass tubes and equal volume of ice cold chloroform was added. Metabolites were fractioned into polar and non-polar phases with addition of half volume of cold water and then centrifuged at 671  g for 1 min. About

1.5 ml of the aqueous-methanol phase was transferred to microfuge tubes. A subsample

(150 µl) of this extract was used for chemical derivatization. Prior to derivatization, the

samples were spiked with 5 µl of 5 mg ml-1 ribitol (internal standard) and 5 µl of 5 mg ml-

1 of d27-myristic acid (retention time lock). The mixture was methoxylaminated with methoxylamine-HCl and then silylated with N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) and 1% trimethylchlorosilane (TMCS). The derivatized metabolites were separated by gas chromatography (Agilent 7980; Agilent Technologies,

Santa Clara, CA) on a J&W DB-5ms column (30 m x 0.25 mm x 0.25 µm, Agilent

Technologies), and analyzed using a transmission quadrupole mass detector (Agilent 5975

C series) with an electron ionization interface. The initial oven temperature was maintained at 60°C for 1 min, followed by temperature ramp at 10°C per min to 300°C, with a 7 min hold at 300°C. Carrier gas (He) flow was maintained at a constant pressure of 76.53 kPa and the injection port and the MS interphase were maintained at a constant temperature of

270°C; the MS quad temperature was maintained at 150°C; and the MS source temperature was set at 260°C. The electron multiplier was operated at a constant gain of 10 (EMV =

1478 V), and the scanning range was set at 50–600 amu, achieving 2.66 scans sec-1. Peaks were identified with that of the in-house metabolomics library supplemented with Fiehn

Library (Agilent Technologies, Wilmington, DE, USA, G1676AA) and spectral deconvolution was performed using Automatic Mass Spectral Deconvolution and

Identification System (AMDIS v2.71, NIST) using the criteria described by before.33

Statistical analysis. The relative concentration of metabolites across the two biotypes and treatments were examined by multivariate and univariate statistical analyses.

Prior to univariate and multivariate statistical analyses, the ribitol normalized data was curated based on the following criteria, wherein, absence of a metabolite (possibly due to

low abundance) in not more than two out of five replicates was treated as missing and replaced with a value computed using the Bayesian PCA (BPCA) method.35,36 In contrast, absence of an observation in three out of five replicates was considered as an oddity and was replaced by 0 for all the replicates for the specific treatment. Any metabolite with more than 50% missing in all treatments pooled was removed from analysis. Following data curation, the processed data were statistically normalized by averaging the values of each variable and dividing it by its standard deviation (auto scaling option) to meet the assumptions of normality and equal variance.

Univariate statistical analyses were performed to determine the statistical significance of metabolites among the different treatment groups (biotypes and herbicide).

One-way analysis of variance (ANOVA) was performed to identify significant metabolites across the biotypes and treatments, while two-way ANOVA followed by Tukey’s Honest

Significant Differences (HSD) post-hoc test was employed to determine the statistical significance of the effect of biotype and treatment on the individual metabolites.

Differences among individual means were tested using Tukey’s HSD multiple comparison tests with Pvalue < 0.05 considered statistically significant. Univariate statistical analyses were performed using SAS (v 9.2, SAS Institute, Cary, NC), SigmaPlot for Windows v12.5

(Systat Software, San Jose, CA) and MetaboAnalyst.35 Graphs were constructed using

GraphPad Prism v6.01 (GraphPad Software, La Jolla, CA).

For multi-variate analyses, unsupervised principal component analysis (PCA) models were constructed using Metaboanalyst35 to examine the significance of the effects of treatments on the metabolite profiles of the two biotypes and were cross-validated using

permutational testing.37 Multivariate projection approaches such as principal component analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal

PLS-DA (OPLS-DA) are often used for analyzing metabolomics data because of their ability to cope with highly multivariate, noisy, collinear and most often incomplete datasets. Hierarchical cluster analyses, using Euclidean distance as a similarity measure, were performed to determine the clustering between the metabolites in response to treatments.

Pathway topological analysis comparing the differences in the metabolite abundance was performed using MetaboAnalyst while the metabolic pathway maps were generated through KaPPA-View.38 The global test algorithm was used for pathway enrichment analysis while the relative betweenness centrality algorithm was employed for pathway topological analysis. The Arabidopsis thaliana pathway library was used for pathway mapping. For pathway representation, the signal intensity of each metabolite was transformed to a log2 value and the ratio between the WAS biotype over the QUI-biotype were presented on the maps according to the documentation provided on their web site

(http://kpv.kazusa.or.jp/kpv4-kegg-1402).38

RESULTS AND DISCUSSION

Metabolite Profiling Reveals Inherent Differences Between the High

Glyphosate-Tolerant (WAS) and Low Glyphosate-Tolerant (QUI) I. lacunosa

Biotypes.

The innate physiological differences between the two biotypes were determined by comparing the metabolic profiles of high tolerant (WAS) with respect to the low tolerant

(QUI) water-treated (control) biotypes. An unpaired t-test analysis computed with equal group variance with a Pvalue threshold of 0.05 identified 22 significantly different metabolites between the two biotypes (Table 1A). Subsequently, fold change (FC) analysis between the two group means (WAS/QUI), calculated at a threshold ratio of 2-fold change, identified 11 significantly different metabolites of which 7 were upregulated and four were downregulated (Table 1B). Due to the high dimensionality of metabolomics data, significance analysis of metabolites/microarray (SAM) was performed at a delta of 0.6, to address the issue of false discovery rate (FDR).39 The 22 metabolites were found to be the most discriminant and significant with an FDR value of 0.167. In terms of metabolic pool abundance, the high tolerant WAS biotype was abundant in sugars (glucose, ribose, tagatose, altrose, allose) and organic acids (malate, succinate and fumarate) while the low tolerant QUI biotype was rich in sugar alcohols (arabitol, threitol, mannitol and myo- inositol) and nitrogenous metabolites including amino acids. Ontological analysis of the identified metabolites classified them into four broad functional groups (Figure 2.1a) with

90% of the metabolites having a functional role in plant metabolism (Figure 2.1b).

Metabolic Pathway and Functional Analysis. Pathway topological analysis comparing the differences in the metabolite abundance in the WAS biotype with respect to

QUI biotype classified the identified metabolites into 50 metabolic pathways (Appendix

A, Table 2.1). Pathway mapping indicated that the WAS biotype is metabolically more active and innately abundant (~1.5 fold) in most metabolites compared to the QUI biotype

(Figure 2.2a). With respect to carbon metabolism, all the identified metabolites, except for

2-oxoglutarate, were higher in the control WAS biotype compared to QUI biotype (Figure

2.2b). Of this, pyruvate was the most abundant metabolite (~ 3 fold). In plants, phosphoenol pyruvate (PEP) is a key metabolite that serves as a link between nitrogen and carbon metabolism. In addition to its role in glycolysis and gluconeogenesis, it is also involved in carbon fixation as well as in the biosynthesis of various aromatic compounds. Compared to the QUI biotype, the WAS biotype had higher abundance of pyruvate, potentially resulting in a proportionally higher availability of free PEP. As glyphosate is a competitive analog of PEP, higher abundance of PEP would result in glyphosate displacement from the

5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) enzyme which could potentially contribute to higher tolerance capacity of the WAS biotype over QUI biotype.40,41,42 With respect to non-aromatic amino acids, except for lysine and histidine, all other amino acids are more abundant in the WAS biotype in comparison with the QUI biotype (Figure 2.2c).

However, no significant differences were observed in the aromatic amino acid profiles between the two biotypes. Similar observations were also reported in a study comparing untreated plants of glyphosate- susceptible and -resistant Amaranthus palmeri S. Wats.33,43

Abundance of primary metabolites such as organic acids and amino acids indicates active metabolism and potentially increased reserve levels. Thus, in an event of abiotic stress exposure, the WAS biotype could have better capability to overcome and sustain the physiological activities by utilizing the reserve pools until the stress is mitigated.

Differential Physiological Adaptations in Response to Glyphosate Application

Contribute to Varying Glyphosate Tolerance.

Based on mass-spectral fingerprints and retention-indices matches, about 65 metabolites were identified across all samples (biotype and treatments). Following Pearson pairwise

correlation analysis, the dataset was curated to 56 metabolites having significant interaction between the biotypes and treatment. As shikimic acid is known to be highly correlated with glyphosate treatment, it was excluded from subsequent multivariate statistical analyses.

PCA analysis showed that the metabolic profiles of the control and treated biotypes significantly changed as a result of glyphosate application. The two component PCA model was able to explain about 61% of the total variance. Significance analysis of metabolites

(SAM) indicated 53 metabolites were highly significant (P<0.05) with a FDR value of

0.064 (controlled at a delta of 0.6). Furthermore, PCA score plots reveal a distinct clustering within the treatments and biotypes. Accordingly, the Component 1 (PC-1) axis differentiated between the two treatments (water and glyphosate), while the Component 1

(PC-2) axis delineated the two treatments (QUI and WAS) (Figure 2.3A). Hierarchical cluster analysis was performed to identify the metabolites responsible for the discrimination between the water and glyphosate treatment (Figure 2.3B). The identified metabolites were therefore considered as potentially responsible in conferring differential tolerance to glyphosate between the WAS and QUI biotypes.

Effects of Glyphosate on Metabolite Abundance.

A. Shikimic acid

Shikimic acid accumulation is a highly sensitive biomarker for glyphosate activity in plants.44 Consistent with a previously reported study,10 at 80h after glyphosate application, a 20-fold increase in shikimic acid accumulation was measured in both biotypes (Figure

2.4). Shikimic acid accumulation suggests that EPSPS in both the biotypes is equally susceptible to inhibition by glyphosate. However, despite EPSPS inhibition, both biotypes

survived this dose of glyphosate (visual observations, data not shown). Ribeiro et al.

(2015)10 reported that though both WAS and QUI had similar absorption of glyphosate, the lower tolerance of QUI was partly due to a greater translocation of the herbicide. This could partly explain the lower accumulation of shikimate in the QUI biotype compared to the

WAS biotype despite being treated with a glyphosate dose higher than its GR50.

B. Influence of glyphosate on nitrogen metabolism.

Based on physiological concentrations, amino acids can be classified as major and minor amino acids. Major amino acids such as Glu, Gln, Asp, Asn, Gly, Ser, Ala and Thr are normally present in high concentrations while the generally less abundant amino acids such as His, Arg, Tyr, Trp, Met, Val, Phe, Ile, Leu and Lys are considered minor amino acids.45

Furthermore, the biosynthetic pathways of most nitrogenous compounds are linked with either Gln or Asn or their respective acidic counterparts, Glu and Asp.46 Though glyphosate primarily inhibits aromatic amino acid biosynthesis, deregulation of the shikimate pathway results in the accumulation of amino acids synthesized through shikimate-independent pathways,33,43,47,48 suggesting that glyphosate triggers a nitrogen rich amino acid profile, wherein nitrogen rich compounds are accumulated at the expense of their precursors.48

The literature on glyphosate effects on free amino acid pools is conflicting. Several studies report increases in free amino acid pools,33,48-51 while another study reported a general decrease following glyphosate treatment,52 and yet another study reported no change in the amino acid profile.47 The variations could be in part or entirely due to differences in plant species, glyphosate dose, and/or time after treatment. In the current study, the Trp pool remained unchanged in both biotypes following glyphosate application.

In contrast, glyphosate treatment resulted in a 133% increase in Tyr abundance in the WAS biotype while no significant change was observed in the QUI biotype (Figure 2.5A).

Increases in Tyr pools following glyphosate application have also been previously reported in other species.32,50,53 Surprisingly, Phe could not be detected in either of the two biotypes.

Accumulation of hydrophobic amino acids such as Pro, Ala, Leu and Ile in response to abiotic stress have been well documented.54,55 Consistent with those studies, glyphosate application resulted in a significant increase in pool abundance of Gly, Ala, Val, Ile and

Pro in the WAS biotype, while only Pro and Gly increased in the QUI biotype (Figure

2.5B). Pro accumulation is a common physiological response to a wide range of biotic and abiotic stresses. Under glyphosate stress, a 69% and 112% increase in the pro pool was seen in the WAS and QUI biotypes, respectively. The higher proline accumulation in the

QUI biotype relative to the WAS biotype could be due to greater stress experienced by the

QUI biotype.

Glyphosate application significantly increased the pool levels of polar amino acids such as Asn, Gln, Lys, Ser, and Thr in both the biotypes. While Asn and Gln, the primary nitrogen donors for amino acid biosynthesis, increased by 77% and 129% respectively, a

32% reduction in Glu was observed only in the glyphosate-treated WAS biotype (Figure

2.5C). A similar increase of 81% and 158% in Asn and Gln, respectively, was observed in the QUI biotype. However, no significant change in the Glu abundance was observed. The increase in Asn and Gln in response to glyphosate application could be either due to a potential increase in their de novo synthesis or due to proteolysis.48 Other polar amino acids such as Ser, Thr and Lys increased by 46 and 27%, 149 and 235% and 255 and 180%,

respectively, in the WAS and QUI biotypes (Figure 2.5C). Of non-protein amino acids detected, ornithine had a 58 and 44% increase in WAS and QUI biotypes, respectively, compared to its water control (Figure 2.5C). A similar trend of increase in polar amino acids and non-protein amino acids was reported in glyphosate-treated glyphosate-sensitive and -resistant soybean.47 Consistent with our findings, the study also reported that only the sensitive genotype had an increase in Lys and Thr, accompanied by a decrease in Glu or by an increase in Gln and Ala. It can thus be inferred that, though the I. lacunosa biotypes are tolerant to glyphosate, they do not have the same physiological adaptations of glyphosate-resistant plants.

C. Influence of glyphosate on carbon metabolism

Reduced translocation is a common mechanism for herbicide resistance,56-58 Efficacy of glyphosate depends of the extent of its uptake by leaves and in vivo translocation.5,59,60

After application, glyphosate permeates the cuticle and leaf plasma membranes and is then translocated to the meristematic tissues, where the EPSPS genes are highly expressed,61,62 through the phloem along with photosynthate assimilates.59,63,64 Glyphosate resistance mechanisms involving active translocation of glyphosate along the sugar/carbohydrate concentration gradient away from the target sites have been previously reported.10,63,65

Consistent with these studies, the glyphosate-treated QUI biotype had a 57% decrease in total sugar concentration, whereas the WAS biotype had about a 72% decrease compared to their respective water-treated controls (Figure 2.6A). While glucose, tagatose and ribose constituted the bulk of the sugars in the WAS biotype, the QUI biotype sugars were composed primarily of sucrose, glucose and tagatose. Glucose and tagatose decreased by

more than 75% in both biotypes after glyphosate treatment. Primarily, glyphosate migrates along the phloem from the source leaves following sucrose movement.66,67 Inhibition of

EPSPS results in an unregulated flow of carbon into the shikimate pathway leading to the depletion of carbon cycle metabolites and carbon export from the source leaf.68 As the export of photosynthates from the source leaf to the metabolic sinks decreases, the movement of glyphosate to the sinks is also reduced, thereby reducing its herbicidal effects.56,68 Since EPSPS is found to be inhibited in both the biotypes (Figure 2.4), the decreased sugar abundance in the glyphosate-treated biotypes could potentially result in reduced translocation of sugars and glyphosate to the meristematic tissues, thus facilitating tolerance of the plants to glyphosate.

Polyols (sugar alcohols) are osmotically active carbon metabolites that accumulate at high levels in response to abiotic stress.69,70 They are generally formed under reducing conditions from their analog sugars.71 Following glyphosate application, polyols such as mannitol, myo-inositol and glycerol significantly decreased in both the biotypes (Figure

2.6B), which could be linked to the decreased sugar levels. As the levels of polyols and sugars decreases in the meristematic tissues, a negative osmotic gradient would be generated which might reduce translocation of glyphosate to the meristematic tissues.

Reduced translocation of glyphosate has been reported in these I. lacunosa biotypes.10

Glyphosate competing with phosphoenolpyruvate (PEP) for binding to EPSPS results in disruption of glycolysis in glyphosate-sensitive plants by feedback inhibition.33 However, in I. lacunosa biotypes where glyphosate is translocated away from the site of action, such perturbations in the glycolysis and TCA cycle pathway would be minimal. This is

supported by the observations that, except for succinate, fumarate and malate, all the other major metabolites of TCA cycle (pyruvate, citrate, α-keto glutarate, and oxalate) did not respond to glyphosate application in both the biotypes (Figure 2.6C). At 80 HAT, succinate and malate decreased by 63% and 68%, respectively, while fumarate was decreased 94% in the glyphosate-treated WAS biotype. Similarly, in the glyphosate-treated QUI biotype, both succinate and malate had ~49% reduction in their pool levels while fumarate decreased by 89%.

Metabolic profiling by GC-MS and multivariate data analysis showed significant metabolic alterations in both the I. lacunosa biotypes treated at doses insufficient to cause plant death. Importantly, metabolic profiling also captured the innate metabolic differences between the two biotypes which could help explain their varying tolerance to glyphosate.

Furthermore, as most of the primary metabolites are derived from precursors of carbon metabolism (pyruvate, phosphoenolpyruvate, oxaloacetate and α-ketoglutarate), perturbations in the carbon–nitrogen homeostasis would lead to disruptions in the global trans-regulation of primary metabolites in plants less tolerant to herbicides inhibiting amino acid biosynthesis such as glyphosate (Figure 2.7). The approaches of this study illustrate the usefulness of metabolomics as a tool in understanding weed physiology and herbicide tolerance. However, further studies are needed to establish metabolomics as a robust method in evaluating and diagnosing herbicide tolerance (or resistance). Elucidating physiological dynamics based on metabolic pathway intermediate pool sizes is challenging because the observed pool sizes do not reflect pool fluxes. However, metabolomics, in conjunction with other established biochemical and omics techniques, could help unravel

the complexities of herbicide-induced physiological perturbations reverberating across various metabolic pathways.

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Tables and Figures

Table 2.1. Significantly different metabolites identified in leaves of water treated WAS and QUI biotype of Ipomoea lacunosa harvested 80 HAT. Table A lists the 22 significant metabolites identified by an unpaired t-test analysis computed with equal group variance with a Pvalue threshold of 0.05. Table B lists the 11 significantly different metabolites having at least a 2-fold change between the two biotypes. Of the 11 metabolites, 7 were up-regulated and 4 were down-regulated (log2(FC)) in the WAS biotype with respect to the QUI biotype.

A) Metabolites P<0.05 FDR Metabolites P<0.05 FDR

Pyroglutamic acid 3.20e-12 1.79e-10 Altrose 0.00408 0.019039

α-ketoglutaric acid 2.58e-11 7.22e-10 Lysine 0.00479 0.020634

Glycerol 1-phosphate 0.012496 0.043737 4-GABA 0.00832 0.033279

Adenosine 7.43e-11 9.73e-10 Proline 0.009556 0.035676

Malonic acid 8.68e-11 9.73e-10 Sucrose 4.77e-11 8.90e-10

Histidine 1.36e-9 1.16e-8 Arabitol 0.025035 0.082467

Pyruvic acid 1.45e-9 1.16e-8 Valine 0.031023 0.096515

Palmitic acid 7.93e-7 5.55e-6 Tagatose 0.038129 0.11072

Glycerol 1.30e-6 8.07e-6 Ribose 0.039545 0.11072

Shikimic acid 0.000627 0.003513 Fumaric acid 0.043812 0.11277

Glucose 0.002057 0.010474 Allose 0.044301 0.11277

Upregulated Downregulated Fold Metabolites Fold Change log2(FC) Metabolites log2(FC) Change Pyroglutamic acid 1348.2 10.397 Sucrose 0.00019 -12.359 α- Adenosine 91.683 6.5186 ketoglutaric 0.024558 -5.3477 acid Histidine 24.887 4.6373 Malonic acid 0.15162 -2.7215

Palmitic acid 5.0239 2.3288 Pyruvic acid 0.30443 -1.7158

Glycerol 3.2491 1.7

Glycerol 1-phosphate 3.1632 1.6614

Altrose 2.1442 1.1005

Figure 2.1: Ontological classification of metabolites identified in leaves of water treated WAS and QUI biotypes of Ipomoea lacunosa harvested 80HAT. Panel A represent the general classification of the metabolites based on their functional role. Panel B represents the classification of metabolites based on their metabolic functions

Figure 2.2: Classification of metabolites to biosynthetic pathways. Panel A represents the fold change of WAS biotype (Control) with respect to QUI biotype (Control). Panel B and C represents the metabolites involved in carbon and nitrogen metabolism respectively. The color corresponds to the fold change level as indicated in Panel A.

Figure 2.3: Differential grouping of water- and glyphosate-treated QUI and WAS biotypes of Ipomoea lacunosa based on metabolites identified at 80 HAT. Panel A represents the PCA score plot of metabolites in water- and glyphosate-treated QUI and WAS biotypes. The ellipses represent 95% confidence region. Panel B depicts the heatmap visualization of significant metabolite differences (ANOVA; Pvalue <0.05) in WAS and QUI biotypes in response to water and glyphosate treatments following hierarchical cluster analysis. The algorithm for clustering was based on Euclidean distance measure for similarity and Ward linkage method for biotype clustering. ANOVA results comparing means of each metabolite across treatment groups, and false discovery rate (FDR) for multiple testing corrections are given in Appendix A, Table 2.2. WW, WG, QW and QG correspond to WAS/Water, WAS/Glyphosate, QUI/Water and, QUI/Glyphosate respectively.

t C o n tro l

n e

t G lyp h o s a te n

) 3

F P T re a tm e n t < 0 .0 0 1

i -

c P = 0 .3 0 9 9

g 2 B io ty p e

P = 0 .3 6 8 8

c In te ra c tio n



m i

( 1

h S B B 0 A A W AS Q U I

Figure 2.4: Shikimic acid content in water and glyphosate treated WAS and QUI biotypes of I. lacunosa harvested at 80 HAT. The mean±SEM was tested by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%. Bars with the same letters are not significantly different at 0.05% confidence.

0 .8 C o n tro l 3

t C o n tro l n

G lyp h o s a te t e

t 0 .6 G lyp h o s a te

t o

P = 0 .0 3 8 n o C T re a tm e n t 2 P = 0 .0 2 5 0 C T re a tm e n t

0 .4 P B io ty p e = 0 .5 6 8 0 e d P = 0 .0 0 2 0

z B io ty p e

e i

l P = 0 .5 7 1 7

In te ra c tio n z

i a

l P In te ra c tio n = 0 .0 0 7 5 a

m 1

r 0 .2

o N N 0 .0 A B A A 0 W AS Q U I W AS Q U I T ry p to p h a n T y r o s in e

B) P < 0 .0 0 1 6 2 0 0 T re a tm e n t

t C o n tro l P = 0 .1 1 0 9 C o n tro l

n B io ty p e

e n G lyp h o s a te t P = 0 .1 6 0 4 G lyp h o s a te

e In te ra c tio n n

t 1 5 0 o

n 4 o P < 0 .0 0 1 C

T re a tm e n t

d d

P B io ty p e = 0 .1 1 9 6 e 1 0 0

i z

l i P = 0 .7 6 2 5

l In te ra c tio n a

a 2

m m

r 5 0

r o

N N A B C A B C A A A B 0 0 W AS Q U I W AS Q U I G ly c in e P r o lin e

2 0 0 6

C o n tro l C o n tro l

n e

G lyp h o s a te e G lyp h o s a te t

1 5 0 t

n o o 4

C P < 0 .0 0 1

T re a tm e n t C

P = 0 .1 5 2

T re a tm e n t d P = 0 .0 1 1 2 d e 1 0 0 B io ty p e P < 0 .0 0 1

e B io ty p e

i i l P = 0 .0 0 7 In te ra c tio n l P = 0 .0 0 6 a In te ra c tio n

a 2

m m

r 5 0

N N A B A A A B A 0 0 A W AS Q U I W AS Q U I A la n in e V a lin e

1 .0

C o n tro l t

n 0 .8 G lyp h o s a te

e t

n o P T re a tm e n t < 0 .0 0 1

C 0 .6 P = 0 .0 0 2 9 d B io ty p e e z P = 0 .0 2 i In te ra c tio n

l 0 .4

a m

o 0 .2 N A B A 0 .0 A W AS Q U I Is o le u c in e

8 0 0 6 0 0 C o n tro l

C o n tro l

t t

n G lyp h o s a te n

e G lyp h o s a te t

6 0 0 e t

n o

o 4 0 0

C P < 0 .0 0 1

T re a tm e n t P < 0 .0 0 1

C T re a tm e n t

d 4 0 0 P = 0 .0 2 1 8 d e B io ty p e P B io ty p e = 0 .4 5 0 6

z l

P = 0 .4 1 1 7 i

In te ra c tio n l P = 0 .9 9 7 2 a In te ra c tio n

a 2 0 0 m

r 2 0 0

m r

N N A B C A B C 0 A B A B 0 W AS Q U I W AS Q U I A s p a ra g in e G lu ta m in e

2 0 1 0 0 C o n tro l C o n tro l

t n

n G lyp h o s a te 8 0 G lyp h o s a te

e t

t 1 5

n o o P T re a tm e n t < 0 .0 0 1

C 6 0 C P < 0 .0 0 1 T re a tm e n t

d P = 0 .1 1 8 0 d B io ty p e

1 0 e

e P = 0 .0 1 3 1

B io ty p e z

z i

i P = 0 .1 8 4 0 l 4 0 In te ra c tio n l P = 0 .0 0 8 a

a In te ra c tio n

m r

r 5

o 2 0

o N N A B A C A A A B 0 0 W AS Q U I W AS Q U I T h re o n in e S e rin e

1 5 2 .0 C o n tro l

C o n tro l t

t n

n G lyp h o s a te e

G lyp h o s a te t

e t

1 .5 n n

o 1 0 o P < 0 .0 0 1 C T re a tm e n t

P = 0 .0 0 5 4

C T re a tm e n t

d P = 0 .1 3 2 5

1 .0 P B io ty p e < 0 .0 0 1 e B io ty p e

z l i P = 0 .0 2 3 0

l P = 0 .1 4 0 0 In te ra c tio n

In te ra c tio n a

a 5

m r

r 0 .5

N N A C B A B A A B 0 .0 0 W AS Q U I W AS Q U I L y s in e H is tid in e

1 0 0 C o n tro l t 1 5 0

n C o n tro l t

e 8 0 G lyp h o s a te t

n n

e G lyp h o s a te

o n C 6 0 P = 0 .1 0 5 8

T re a tm e n t o 1 0 0

d C

P = 0 .0 2 6 3 e

P B io ty p e = 0 .0 5 6 4 T re a tm e n t

i l

4 0 e

P = 0 .1 4 7 8 P B io ty p e = 0 .0 1 2 1 a

In te ra c tio n z

i l

m P = 0 .0 3 2 6

a In te ra c tio n r 5 0

o 2 0

N o

0 N A B A A W AS Q U I 0 A s p a rtic a c id W AS Q U I G lu ta m ic a c id

2 5

C o n tro l t

n 2 0 G lyp h o s a te

n o P T re a tm e n t = 0 .0 0 3 1

C 1 5

d P B io ty p e = 0 .2 8 2 8

e z

i P = 0 .5 5 0 8 l 1 0 In te ra c tio n

m r

o 5 N A B A A 0 W AS Q U I O rn ith in e

Figure 2.5: Influence of glyphosate on the amino acid and nitrogenous metabolites of water- and glyphosate-treated QUI and WAS biotypes of Ipomoea lacunosa leaves harvested at 80 HAT. Panel A represents comparative abundance of aromatic amino acids, while Panels B, represents comparative abundance of non-aromatic hydrophobic amino acids and Panel C represents polar amino acids and non-protein amino acids. The mean±SEM of all graphs were tested independently by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%. Bars with the same letters are not significantly different at 0.05% confidence.

5 0 0 1 5 0

C o n tro l C o n tro l

t n

4 0 0 G lyp h o s a te G lyp h o s a te

t n

n 1 0 0 o

o P < 0 .0 0 1 P T re a tm e n t < 0 .0 0 1 C

C 3 0 0 T re a tm e n t

d P = 0 .0 0 1 1 d P = 0 .0 0 3 1 B io ty p e

B io ty p e e

z i

z P = 0 .0 1 6 6 l i In te ra c tio n

l 2 0 0 P = 0 .5 1 5 6

In te ra c tio n a 5 0 a

r o

o 1 0 0

N N A B C B A B C A B 0 0 W AS Q U I W AS Q U I G lu c o s e T o ta l S u g a rs

6 1 0 0

C o n tro l C o n tro l

t n

n G lyp h o s a te

G lyp h o s a te e 8 0

t n

n o

o 4 P < 0 .0 0 1

P = 0 .0 0 3 C 6 0 T re a tm e n t

C T re a tm e n t

d P = 0 .0 1 4 2 d B io ty p e

P B io ty p e = 0 .0 0 7 8 e

i z l 4 0 P = 0 .0 6 5 1

i In te ra c tio n

l P = 0 .1 2 5 4 In te ra c tio n a

a 2

m r

o 2 0 o N N A B B B A B B B 0 0 W AS Q U I W AS Q U I T a g a to s e R ib o s e

2 5 C o n tro l 5

t C o n tro l n

2 0 G lyp h o s a te t

e n

t 4 G lyp h o s a te

t o P T re a tm e n t < 0 .0 0 1 n

C 1 5 o

P < 0 .0 0 1 C d P < 0 .0 0 1 3 T re a tm e n t

B io ty p e

e d

z P = 0 .1 4 3 8

i B io ty p e P = 0 .0 0 3 e

l 1 0 In te ra c tio n

a i

l 2 P In te ra c tio n = 0 .1 5 6 7

r m

o 5

r N

o 1

A B C C N 0 A B C C A B W AS Q U I 0 W AS Q U I G ly c e ro l M a n n ito l

5 0

C o n tro l

t n

e 4 0 G lyp h o s a te

n o

C P T re a tm e n t < 0 .0 0 1 3 0

d P = 0 .4 0 9 0

e B io ty p e

z i

l 2 0 P In te ra c tio n = 0 .8 4 4 6

m r

o 1 0 N A B A B 0 W AS Q U I M y o -in o s ito l

5 C o n tro l 1 0

t C o n tro l t

n G lyp h o s a te n e 4 G lyp h o s a te

t 8

n o

P < 0 .0 0 1 o

C 3 T re a tm e n t P < 0 .0 0 1

C 6 T re a tm e n t

d P B io ty p e = 0 .5 1 8 3 d P = 0 .0 4 5 2

e B io ty p e

i i l 2 P = 0 .0 2 6 2

In te ra c tio n l 4 P In te ra c tio n = 0 .0 2 6 4

r o

1 o 2 N N B B A B A B A C 0 0 W AS Q U I W AS Q U I S u c c in ic a c id F u m a ric a c id

8 0

C o n tro l t

n G lyp h o s a te e

t 6 0 n

o P < 0 .0 0 1

C T re a tm e n t

d 4 0 P B io ty p e = 0 .5 9 6 3

z i

l P In te ra c tio n = 0 .0 2 3 0

a m

r 2 0

o N A B A B 0 W AS Q U I M a lic a c id

Figure 2.6: Influence of glyphosate on sugars, sugar alcohols and TCA cycle metabolites in water- and glyphosate-treated QUI and WAS biotypes of Ipomoea lacunosa leaves harvested at 80 HAT. Panel A represents the total sugars content and the most abundant sugars, Panel B represents the most abundant sugar alcohols (polyols) and Panel C represents the most abundant TCA cycle metabolites in water and glyphosate treated WAS and QUI biotypes respectively. The mean±SEM was tested by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%. Bars with the same letters are not significantly different at 0.05% confidence.

Figure 2.7: Metabolic pathways in control normalized glyphosate treated WAS and QUI biotypes of Ipomoea lacunosa harvested at 80 HAT

CHAPTER THREE

METABOLIC PROFILING AND ENZYME ANALYSES INDICATE A POTENTIAL

ROLE OF ANTIOXIDANT SYSTEMS IN COMPLEMENTING GLYPHOSATE

RESISTANCE IN AN AMARANTHUS PALMERI BIOTYPE

(This work has been published and should be cited as Maroli, Amith S., et al. "Metabolic profiling and enzyme analyses indicate a potential role of antioxidant systems in complementing glyphosate resistance in an Amaranthus palmeri biotype." J. Agric. Food Chem. (2015), 63 (41), pp 9199-9209)

Abstract

Metabolomics and biochemical assays were employed to identify physiological perturbations induced by glyphosate in a susceptible (S) and resistant (R) biotype of

Amaranthus palmeri. At 8 h after treatment (HAT), shikimic acid accumulated in both R- and S-biotypes in response to glyphosate application, which was accompanied by an increase in organic acids and aromatic amino acids in the R-biotype and an increase in sugars and branched-chain amino acids in the S-biotype. However, by 80 HAT the metabolite pool of glyphosate-treated R-biotype was similar to that of the water-treated

(control) S and R-biotype, indicating physiological recovery. Furthermore, glyphosate- treated R-biotype had lower reactive oxygen species (ROS) damage, higher ROS scavenging activity, and higher levels of secondary compounds of the shikimate pathway.

Thus metabolomics, in conjunction with biochemical assays, indicate that glyphosate induced metabolic perturbations are not limited to shikimate pathway, and the oxidant quenching efficiency could potentially complement the glyphosate resistance in this R- biotype.

Introduction

Globally, many crop production systems employ glyphosate (N-

(phosphonomethyl)glycine) to control annual and perennial grasses and broad-leaf weeds.1,2 Glyphosate works by specifically inhibiting the enzyme 5-enolpyruvylshikimate-

3-phosphate synthase (EPSPS) that catalyzes the penultimate step of shikimate pathway, the conversion of shikimic acid to chorismate, the precursor for aromatic amino acids

(tyrosine, phenylalanine and tryptophan) and other secondary plant metabolites.3

Glyphosate competes with phosphoenolpyruvate (PEP), a substrate for EPSPS enzyme, to form a very stable enzyme-herbicide complex that inhibits the product-formation reaction.4

The broad spectrum of herbicidal activity of glyphosate is optimally employed in modern agriculture by utilizing crops engineered for glyphosate resistance. However, due to the over-reliance of glyphosate to combat weeds, at least 32 weed species have evolved resistance to glyphosate.5 Of these, glyphosate-resistant Amaranthus palmeri is the most economically damaging weed, mainly in US cotton, corn and soybean production systems, causing yield losses ranging from 50 to 95% depending on several factors, including the competitive ability of crops.6,7

Application of genomics and transcriptomics technologies have helped to identity the common causes/mechanisms of evolved glyphosate resistance in some of the weeds,8,9 including a higher abundance of the EPSPS enzyme resulting from an increased EPSPS gene copy number,10 and reduced translocation of glyphosate to the target site tissues

(especially meristems) in A. palmeri.11 Though application of these approaches provide a robust understanding of the genetic regulation on the potential functioning of the organism,

a complete picture of the phenotypic manifestations of evolved glyphosate resistance is still lacking. This is partly because the molecular markers that are tracked using genomics, transcriptomics and proteomics (DNA, RNA and proteins, respectively) are susceptible to functional alteration either by epigenetic modifications or post-transcriptional and post- translational modifications, resulting in altered phenotypes.12 Metabolomics, which focuses on comprehensive identification and quantitation of low-molecular weight metabolites (metabolome) broadens the understanding of the functioning of a physiological system. Since metabolomics measures the final products of gene expression, protein expression and enzymatic activity as affected by the environment, the metabolites, it offers a powerful approach for molecular phenotyping.13 Thus, while genomics and proteomics provide information about the processes that are genetically programmed to happen, metabolomics gives a more definite and real-time representation of gene-environment interactions.

In plants, under stressful conditions, accumulation and depletion of several primary metabolites are commonly observed.14,15 Therefore, monitoring the changes in metabolic levels can provide clues to their roles in stress responses and regulation. A key pathway in plants is the shikimate pathway that links carbon and nitrogen metabolism.16 Apart from its role in aromatic amino acid synthesis, it serves as a major sink for intermediate metabolites from the central carbon metabolism pathways (glycolytic and pentose phosphate pathways). Any perturbations in the shikimate pathway will lead to the disruption in aromatic amino acids synthesis, as well as alter the metabolic stoichiometry of other carbon intermediates resulting in system-wide perturbations. Metabolomics would

therefore be an ideal approach not only to map the glyphosate-induced disruption of plant metabolism, but also to illuminate the physiology that confers resistance to some biotypes.

The glyphosate-mediated disruption of the shikimate pathway could have potential adverse effects on other cellular processes resulting in secondary effects such as creation of reactive radicals. Earlier studies reported that in addition to site-specific action, glyphosate also disrupts the photosynthetic machinery by reducing ribulose-1,5- bisphosphate carboxylase/oxygenase (RuBisCO) activity and 3-phosphoglyceric acid (3-

PGA) levels17, which can result in the generation of reactive oxygen species (ROS) causing several fold increase in lipid peroxidation.18,19 This ROS-mediated glyphosate damage is further supported by the proteomic analysis that shows glyphosate-treated rice plants have a compositionally similar, but quantitatively lower, proteomic profile as that of rice plants treated with paraquat,20 a free radical-producing herbicide. Thus, disruption of EPSPS could result in secondary toxic effects mediated through the production of ROS. Such an effect might be enhanced in glyphosate-resistant A.palmeri if some tissues have inadequate protection by EPSPS gene amplification as there is evidence that EPSPS gene expression is not equal in all tissue types in these plants.21

Previous application of metabolomics in understanding the effect of glyphosate on the physiology of plants was limited to the model plant species Arabidopsis thaliana and transgenic crop plants.22-24 Also metabolomics approach has been recently adopted to understand effect of chemical stresses on Lolium perenne upon exposure to a non-lethal dose of glyphosate.25 However, to date no studies have employed metabolomics to characterize the physiology of herbicide resistance in weeds. The objective of the present

study is to use non-targeted metabolite profiling to elucidate the differential metabolite- level responses to glyphosate in a resistant and a susceptible biotype of A. palmeri, and to assess the potential role of anti-oxidant machinery in complementing glyphosate resistance.

MATERIALS AND METHODS

Plants Biotypes

Glyphosate-susceptible (S-biotype) and glyphosate-resistant (T4B1, R-biotype)

Amaranthus palmeri seeds were obtained from Stoneville, Mississippi (Crop Production

Systems Research Unit, USDA-ARS, Stoneville, MS). These biotypes have been well characterized and GR50 values for the R-biotype and S-biotype were previously calculated as 1.3 kg ha-1 and 0.09 kg ha-1, respectively (~15-fold resistance).11 The seeds obtained were F-2 generation seeds (Nandula et al. 2012). F-2 generated seeds were planted in germination trays containing a commercial germination mixture and then were transplanted to individual pots (10 cm diameter x 9 cm deep) containing the germination mixture upon germination. The plants were grown in a greenhouse at 30°C/20°C day/night temp with a

14 h photoperiod. The pots were fertilized five days after transplanting with 50 ml of 4 g l-

1 fertilizer (MiracleGrow®, 24%-8%-16%, Scotts Miracle-Gro Products, Inc., Marysville,

OH, USA) and sub-irrigated every alternate day until harvest.

Experimental Design and Glyphosate Application

Eight days after transplanting, plants were randomly assigned to two treatment groups:- control (sprayed with water) and glyphosate (Roundup ProMAX®, Monsanto Co,

St. Louis, MO) sprayed at a rate of 0.4 kg ae ha-1 which corresponds to approximately half

the recommended field application rate of glyphosate (Though Roundup ProMAX® has about 49% of glyphosate-phosphate salt, the remaining inert proprietary ingredients may influence plant physiological responses moderately). Two weeks after transplanting, the respective treatments (water or glyphosate) were applied to 12 plants per biotype using an enclosed spray chamber (DeVries Manufacturing, Hollandale, MN) calibrated to deliver

374 L ha-1 through an 8001E ﬂat fan nozzle (Tee Jet Spraying Systems Co., Wheaton, IL).

Both R- and S- biotypes were morphologically similar (plant height and number of leaves) at the time of treatment application. Treatments were made on plants that were about 20- cm tall and at four-leaf stage. Three apical leaves along with the meristem were harvested individually from six plants per glyphosate and control treatments at 8 h after treatment

(HAT) and 80 HAT and were immediately frozen in dry-ice and stored at -80°C. Six independent treatment replicates were maintained for all subsequent analyses. At 8 HAT, no visible injuries were observed on either glyphosate treated S- or R-biotypes. At 80 HAT, the glyphosate treated S-biotype displayed visible leaf necrosis while the glyphosate treated

R-biotype and the water treated S- and R-biotypes showed no visible injuries. To minimize the variations due to circadian rhythm, the plants were harvested at the same time of the day (~8 h after sunrise) for both the sampling time. The harvested leaves were finely ground with dry ice using a mortar and pestle and stored at -80°C and the finely powdered leaves were used for all subsequent analyses.

Metabolite Profiling by GC/MS

Low-molecular weight polar metabolites in the tissues were identified and quantified using gas chromatography/mass-spectrometry. Polar metabolites were extracted

from the ground tissues as described by Lisec et al26 and Suseela et al,15 with slight modifications. Briefly, about 100 mg of finely powdered leaf tissues was weighed into 1 ml methanol and homogenized by sonicating in an ice-bath for 20 sec at 50% amplitude, centrifuged at 12,000  g and rapidly cooled on ice. The supernatant was transferred to pre-cooled glass tubes, and equal volume of cold chloroform was added and cooled at 4°C.

Metabolites were fractioned into polar and non-polar phases with addition of half volume of cold water and then centrifuged at 671  g for 1 min. About 1.5 ml of the aqueous- methanol phase was transferred to microfuge tubes. A subsample (150 µl) of this extract was transferred into vials with glass inserts and 5 µl of 5 mg ml-1 ribitol (internal standard) in hexane and 5 µl of 5 mg ml-1 of d27-myristic acid (retention time lock) in hexane were added to the vials and then completely dried under a nitrogen stream. Dried samples were methoxylaminated at 60°C with 20 µl of methoxylamine hydrochloride (20 mg ml-1) in pyridine for 90 min followed by silylation of the metabolites with 90 µl of N-methyl-N-

(trimethylsilyl) trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS) for

30 min at 40°C. The derivatized metabolites were separated by gas chromatography

(Agilent 7980; Agilent Technologies, Santa Clara, CA) on a J&W DB-5MS column (30 m x 0.25 mm x 0.25 µm, Agilent Technologies), and analyzed using a transmission quadrupole mass detector (Agilent 5975 C series) with an electron ionization interface. The initial oven temperature was maintained at 60°C for 1 min, followed by temperature ramp at 10°C per min to 300°C, with a 7 min hold at 300°C. Carrier gas (He) flow was maintained at a constant pressure of 76.53 kPa and the injection port and the MS interphase were maintained at a constant temperature of 270°C; the MS quad temperature was

maintained at 150°C; and the MS source temperature was set at 260°C. The electron multiplier was operated at a constant gain of 10 (EMV = 1478 V), and the scanning range was set at 50–600 amu, achieving 2.66 scans sec-1. Metabolite peaks were identified by comparing the absolute retention time, retention-time index of the sample with that of the in-house metabolomics library supplemented with Fiehn Library (Agilent Technologies,

Wilmington, DE, USA, G1676AA).27 Data deconvolution was performed using Automatic

Mass Spectral Deconvolution and Identification System (AMDIS v2.71, NIST) using the following parameters: Peak identification was limited to a minimum match factor of 70 relative to the Retention Index (RI) library and having a RI threshold window of 10 x 0.01

RI. A maximum match factor penalty of 30 was deducted from the final calculated net value in case of RI mismatch. The peak abundance (integrated signal) was normalized with that of the ribitol (internal standard) abundance and expressed as percent of ribitol

((metabolite intensity/ribitol intensity)x100).

Phenylalanine Ammonia Lyase (PAL) Assay

The PAL assay was carried according to Shang et al.28 with slight modifications.

Briefly, 0.15 g of the ground leaves were homogenized in 1 ml of ice-cold sodium borate buffer (pH 8.3) by sonication and the homogenates were immediately centrifuged (12,000

 g, 5 min). The reaction mixtures consisting of the buffer and homogenates were pre- incubated at 40°C for 5 min. The reaction was started by the addition of 50 mM l- phenylalanine. Controls (without l-phenylalanine) were prepared to determine plant endogenous trans-cinnamic acid (t-CA) content. The reaction was terminated after 1 h with

5 N HCl and analyzed on HPLC-UV (LC 20-AT UFLC and Prominence SPD M20-A

photodiode array, Shimadzu Scientific, Columbia, MD) by monitoring the abundance of t-

CA at 290 nm. HPLC separation of t-CA was performed on Kinetex® XB-C18 column

(100 mm x 4.60 mm x 2.6 µm, 100 Å, Phenomenex, Torrance, CA) at an isocratic flow rate of 0.7 ml min-1 using a solvent composition of 50:50 0.05% formic acid in water and methanol. The t-CA levels in the samples were calculated with respect to the calibration curve of the t-CA standard solution prepared in the same sodium-borate buffer as that of the samples.

Glutathione Reductase and Thiobarbituric Acid Reactive Substances (TBARS) Assays

Glutathione reductase (GR) activity was determined based on the oxidation of

NADPH at 340 nm in presence of oxidized glutathione disulfide (GSSG).29 GR enzyme was extracted from the leaves by sonication with extraction buffer [0.1 M potassium phosphate (pH 7.6) and 2 mM EDTA] and then transferred into the assay mixture composed of 100 mM potassium phosphate buffer (pH 7.8), 0.5 M GSSG and 0.2 M DTT.

The assay was initiated with the addition of 0.2 M NADPH and incubated at 25°C for 30 min. Endogenous levels of NADPH was determined with a blank (No NADPH addition).

The GR activity was determined spectrophotometrically based on the extinction coefficient of NADPH measured at 340 nm using a JASCO V-550 UV-Vis spectrophotometer

(Appendix B, Equation 3.1a).

Malondialdehyde (MDA) was quantified by a modified thiobarbituric acid reactive substances (TBARS) assay as reported by Hodges et al.30 Briefly, about 0.1 g of ground tissue was weighed into 1 ml extraction buffer (80:20, methanol:water solution) and sonicated thrice for 20 sec. A 250 µl volume of the extract was added to a final volume of

1 ml of assay mixture (50 mM NaOH, 25% (vol/vol) HCl and 7.5 mM thiobarbituric acid

(TBA)) and the reaction was started by incubating the assay mixture at 95°C. After 30 min, the reaction was stopped by rapidly cooling in ice for about 10 min. To account for interfering compounds, the absorbance was read at wavelengths of 440 nm, 532 nm and

600 nm. False positive absorbance (absorbance by non MDA compounds) was corrected by incubating the extracts in a TBA free assay solution. MDA equivalents were calculated as described by Hodges et al30 (Appendix B, Equation 3.1b).

Total Soluble Protein Quantification

Proteins were quantified with the bicinchoninic acid (BCA)-protein assay kit according to the manufacture’s recommended protocol (BCA Protein Assay kit; Pierce

Chemical Co., Rockford, Ill.). Proteins were extracted from ground tissue into extraction buffer (20 mM phosphate buffer, pH 7.6) by sonication. The extract was diluted 10X with water and added to 400 µl of BCA working reagent (BCA-WR, 50:1, BCA Reagent A:

BCA Reagent B) and made up to 1 ml using deionized (DI) water. The assay mix was incubated for 30 min at 37°C and then rapidly cooled in ice for 5 min. The absorbance of all the samples was measured at 562 nm using a UV-Vis spectrophotometer within 10 min of cooling. The concentrations of total soluble proteins (TSPs) were quantified using a protein concentration curve with bovine serum albumin as the standard protein.

Statistical Analysis

The relative concentration of metabolites across the biotypes and treatments were analyzed by multivariate and univariate statistical analyses. To evaluate the main and interactive effects of biotypes and herbicide treatments we first analyzed the metabolomics

data with permutational multivariate analysis of variance (PERMANOVA; Primer 7

PERMANOVA+, version 7.0.5, Primer-E Ltd, Plymouth, UK). Due to the non-normal distribution nature of metabolic data, PERMANOVA analysis is a more robust test than

ANOVA/MANNOVA to identify statistical significance of the treatments.31,32 The metabolomics data were auto-scaled to satisfy the assumptions of normality and equal variance before multivariate analyses. Supervised Partial Least Squares-Discriminant

Analysis (PLS-DA) models were constructed using Metaboanalyst33 and the Microsoft®

Excel® add-in Multibase package (Numerical Dynamics, Japan) to test the significance of the effects of biotype and herbicide treatments on the metabolite profiles, and the PLS-DA model was validated using permutation testing.34 Hierarchical cluster analysis, using

Euclidean distance as similarity measure, of the metabolite responses with respect to the treatments were visualized using heatmaps. Univariate statistical analysis (ANOVA) was also performed to determine the statistical significance of metabolite among the different treatment groups (biotypes and herbicide) followed by Tukey’s HSD post-hoc test.

Statistical significance of the biochemical assay responses (concentrations of t-CA,

NADPH and malondialdehyde (MDA)) were tested by two-way analysis of variance.

Differences among individual means were tested using Tukey’s HSD multiple comparison tests with Pvalue < 0.05 considered statistically significant. Statistical analyses were done using SAS (v 9.2, SAS Institute, Cary, NC).

RESULTS AND DISCUSSION

GC-MS Metabolite Profiling

Based on the pre-defined deconvolution parameters (see methods section), GC-MS analysis positively identified more than 60 metabolites across all samples based on their mass-spectral fingerprints and retention-index matches (Appendix B, Table 3.1). The pairwise correlations between these metabolites were quantified using the Pearson correlation coefficient as similarity measure with a threshold window of 0.5, and the resulting 51 metabolites that responded to treatments were used for further statistical analyses. PERMANOVA analysis confirmed that there was a significant interaction effect between biotype and herbicide treatment for both the time points of harvest (Appendix B,

Table 3.2). From the total identified metabolite pool at 8 and 80 HAT, 49 and 52 compounds were selected that had a false discovery rate (FDR; percent of false positive that are predicted to be significant) of less than 0.05 (delta = 0.6; Appendix B, Table 3.3).

The robustness of the class discrimination was verified through permutation testing of separation distance based on the ratio of the between group sum of the squares and the within group sum of squares. The permutation cross validation of PLS-DA model for 8 and

80 HAT had P =0.0005 over 2000 iterations and the models goodness of fit was > 0.85.

PLS-DA revealed a clustering of the treatments for both the harvest times (Figure 3.1). At

8HAT, component 1 axis differentiated between the two biotypes (S- and R-biotype) while the PC-2 axis delineated the two treatments (water and glyphosate) (Figure 3.1c).

Interestingly, at 80HAT, along PC-1 axis, the glyphosate-treated R-biotype had a metabolic pool similar to that of control R- and S- biotypes, indicating a potential recovery of R-

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biotype from glyphosate toxicity (Figure 3.1d). The distinction of the glyphosate treated S- biotype at 80 HAT from the rest of the treatments, could be due to the continued glyphosate induced disruption of the shikimate pathway resulting in the differential abundance of several key metabolites such as shikimate, 3-dehydroshikimate, tyrosine, tryptophan, asparagine etc. (Figure 3.1b)

Univariate analysis of the relative abundance of the metabolites in response to the treatments indicated significant differences in key metabolites (Appendix B, Table 3.3). At

8HAT, most of the organic acids (such as succinic, glyceric, malonic, citric, etc.) and aromatic amino acids were higher in the glyphosate-treated R-biotype, while the S-biotype had a higher abundance of sugar metabolites (glucose, sucrose, fructose, talose) and branched chain amino acids (Figure 3.2a). However, at 80 HAT, the glyphosate-treated S- biotype had the highest concentration of metabolites involved in primary carbon and nitrogen metabolism, possibly due to growth retardation and subsequent non-utilization of these primary metabolites due to glyphosate toxicity (Figure 3.2b). At 8 HAT, hierarchical clustering grouped the water-control and glyphosate treatment into major clusters indicating that, irrespective of their resistance level, the metabolism of both biotypes were similarly influenced by glyphosate (Appendix B, Figure 3.1a). Within the herbicide treatment-clusters the metabolic profile of the S-biotype was different from that of the R- biotype. Also, the metabolite pool of non-treated S- and R-biotypes were more similar to each other compared to the metabolic responses between glyphosate-treated S- and R- biotypes (Figure 3.2a). At 80 HAT, hierarchical clustering analysis grouped the glyphosate treated S-biotype into a distinct cluster while the glyphosate treated R-biotype was more

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closely associated with the water treated (control) S- and R-biotypes (Appendix B, Figure

3.1b). The metabolite pool of glyphosate treated R-biotype resembled that of the water treated S-biotype rather than the water treated R-biotype (Figure 3.2b).

Shikimic acid accumulated in both the glyphosate-treated S- and R-biotypes in 8

HAT, indicating an initial inhibition of EPSPS enzyme in both biotypes. Compared to the

8 HAT, at 80 HAT the shikimate accumulation did not differ significantly in the R-biotype, while the shikimate concentration increased by 3-fold in the S-biotype (Figure 3.3). This continued accumulation of shikimic acid in the S-biotype could be due to the continued inhibition of EPSPS by glyphosate, and/or due to a loss of feedback control of the shikimate pathway. In plants, about 20% of the fixed carbon that flows through the shikimate pathway is regulated by first enzyme 3-deoxy-d-arabinoheptulosonate 7-phosphate

(DAHP) synthase.35a Though no known inhibitor of plant DAHP synthase has been identified, arogenate is considered as a potential candidate for the allosteric inhibition of

DAHP synthase.35b As arogenate is synthesized downstream of shikimic acid, inhibition of

EPSPS by glyphosate could result in decreased levels of arogenate and therefore impairing the allosteric regulation of DAHP synthase in the S-biotype, leading to continued accumulation of shikimic acid. Studies have reported an initial increase, followed by subsequent decrease, in shikimate levels following exposure to sub-lethal doses of glyphosate in both glyphosate susceptible and resistant plants.36-38 The stable levels of shikimic acid at 80 HAT in the R-biotype could be possibly due to its conversion into downstream metabolites such as simple phenolic acid derivatives and/or loss of shikimic

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acid pathway inhibition due to the induction of expression of the multiple EPSPS gene copies, thereby diluting glyphosate effects on EPSPS.39,40

The inhibition of the shikimate pathway by glyphosate results in the disruption of aromatic amino acid biosynthesis. The aromatic amino acid concentrations in the control

S- and R- biotypes were similar at both 8 and 80 HAT indicating no inherent differences between the biotypes in the absence of herbicide-induced stress. However, after 80 HAT, there were significant changes in the concentrations of the aromatic amino acids in both the biotypes. The levels of phenylalanine in the S-biotype did not differ between 8 and 80

HAT but there was a 3-fold increase in the levels of tryptophan and tyrosine. A similar observation of tyrosine accumulation after glyphosate application was reported in glyphosate susceptible purple nutsedge.41 In the R-biotype the concentration of phenylalanine increased by more than 10-fold while the levels of tyrosine and tryptophan decreased at 80 HAT (Figure 3.4). However, phenylalanine increased similarly in the untreated S biotype between 8 and 80 HAT. A few studies have reported the effect of these aromatic amino acids in reversing glyphosate toxicity,42-44 although other studies have reported little or no effects of exogenous application of phenylalanine and tryptophan in reducing the toxicity of glyphosate.45a,45b The several fold larger phenylalanine pool in the glyphosate treated R-biotype than in the S-biotype at 80 HAT could serve as a significant pool for antioxidant phenylpropanoid metabolite synthesis in the R-biotype, which could partially mitigate the toxicity of glyphosate. Although, the non-aromatic amino acids did not differ in the control S- and R-biotypes, the glyphosate-treated S-biotype had a higher pool of non-aromatic amino acids, possibly due to reduced protein synthesis or other amino

103

acid utilization because of metabolic disruption caused by inhibition of the shikimate pathway. However, it should be noted that the interpretation of the physiology of plants based on pool sizes of pathway intermediates is difficult because pool size does not reflect pool flux.

Glyphosate has only one enzyme binding target in the plant, EPSPS. Blockage of the shikimate pathway at this site leads to many adverse secondary and tertiary effects on other pathways and processes. Most of the measured metabolite changes occur in the linked pathways of glycolysis, oxidative pentose pathway and the tricarboxylic acid (TCA) cycle

(Figure 3.5a, 3.5b). In plants, the energy producing stage of glycolysis is regulated by at least six known mechanisms.46 The rate limiting step of glycolysis, conversion of fructose-

6-phosphate to fructose-1,6-bis-phosphate by pyrophosphate dependent phosphofructokinase (PPi-PFK), is activated by inorganic phosphates (Pi) and inhibited by

PEP.47 The ratio of Pi:PEP regulates the activity of PPPi-PFK. As glyphosate competes with PEP for binding to EPSPS, the glyphosate sensitive biotype will have higher levels of under-utilized PEP which could then inhibit the conversion of fructose-6-phosphate to fructose-1, 6-bis-phosphate. This block in the rate limiting step of glycolysis could then lead to an overall accumulation of sugars due to impaired carbon metabolism.48,49 This influence of glyphosate on carbohydrate metabolism is reflected in the accumulation of sugars in both the S- and R- biotypes within 8 HAT (Figure 3.6a). Sugar moves from sources (photosynthetically active tissues) to metabolically active sinks (e.g., meristems and young leaves), so inhibition of growth of the sinks results in sugar accumulation.

Interestingly, at 80 HAT, the sugar levels in the glyphosate-treated S-biotype continued to

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accumulate, while that of the glyphosate-treated R-biotype was similar to that of the control biotypes (Figure 3.6b), indicating a recovery of the R-biotype.

The blockage of the shikimate pathway by glyphosate reverberated across other biochemical pathways and processes as evident by the fluxes of intermediates of the TCA cycle wherein all the major metabolites of TCA cycle (pyruvic, t-aconitic, citric, α-keto glutaric, succinic and malic acid) responded to glyphosate application (Appendix B, Table

3.4). At 8 HAT, a differential abundance in the concentrations of organic acids was observed in the glyphosate treated S- and R-biotypes compared to the respective water control, with the glyphosate-treated R-biotype exhibiting a substantial increase in concentrations (Figure 3.5a). However, at 80 HAT, in the glyphosate-treated S-biotype, except for succinate and α-ketoglutarate, all other metabolites of the TCA cycle responded to glyphosate application with a drastic reduction in citrate. In contrast, no significant perturbations of the TCA cycle were observed in the glyphosate-treated R-biotype except for the reduction in the levels of citric acid, similar to that of the glyphosate-treated S- biotype (Figure 3.5b). Overall, our results not only highlight the robustness of metabolomics approach in identifying differential physiological responses of S- and R- biotypes to glyphosate, but also identify metabolic-level differences between the R- and S- biotypes in the absence of herbicidal stress.

Biochemical Assays

The observed disruption of the oxidative pentose pathway and the energy- producing tricarboxylic acid cycle, following glyphosate application, could result in generation of reactive free radicals, which could initiate lipid peroxidation resulting in the

105

formation of the reactive compound malondialdehyde (MDA). Compared to the glyphosate-treated R-biotype, the glyphosate-treated S-biotype accumulated 49% higher

MDA after 80 HAT (Figure 3.7a), indicating that higher levels of free radicals are generated in the glyphosate-treated S-biotype. A similar observation of deleterious effect of glyphosate on maize plants due to increased lipid peroxidation via MDA production was reported by Sergiev et al.19 Thus, the death of the glyphosate-treated S-biotype could be partially accelerated by the complementary action of free radicals that result in observed lipid peroxidation.

Reduced levels of MDA accumulation in the R-biotype following glyphosate application (Figure 3.7a) could indicate an apparent increase in the activities of enzymes and metabolites which can effectively quench the free radicals. Plants have a well-defined and extensive enzymatic and non-enzymatic anti-oxidant machinery to protect them from the free radicals generated during normal physiological metabolism.50 In plants, the glutathione reductase (GR) enzyme system serves as a major anti-oxidant defense. GR catalyzes the reduction of oxidized glutathione disulfide (GSSG) to glutathione (GSH), its anti-oxidant compound which neutralizes highly reactive ROS molecules. Increased GR activity results in increased production of GSH, enhancing the antioxidant potential. The reduction of GSSG to GSH is energy dependent and utilizes NADPH as the energy supplier. Therefore the GR activity is captured by monitoring the levels of NADPH29 wherein a decrease/increase in NADPH levels indicate a proportional increase/decrease in

GR activity. At 8 HAT, compared to the water-treated S-biotype, the glyphosate-treated S- biotype had a 26% decrease in NADPH levels thereby indicating higher GR activity than

106

in the non-treated control. At 8 HAT, in the glyphosate treated R-biotype, there was an

85% increase in NADPH levels indicating lower GR activity in the glyphosate-treated R- biotype compared to its non-treated control (Figure 3.7b). However, at 80 HAT, compared to the water control R-biotype, there was a 51% reduction in the NADPH levels in the GR activity of glyphosate-treated R-biotype which potentially indicates higher GR activity in the glyphosate-treated R-biotype. Contrastingly, at 80 HAT, compared to the water treated

S-biotype, the NADPH levels increased 37% in the glyphosate-treated S-biotype, indicating a decrease in NADPH consumption. Lower GR activity (as indicated by higher

NADPH levels) in glyphosate-treated S-biotype at 80 HAT could indicate a decrease in synthesis of glutathione by GR, resulting in lower anti-oxidant potential despite higher levels of oxidative damage. Although it is possible that the lower utilization of NADPH in the S-biotype could also be due to the cessation of carbon fixation induced by the glyphosate toxicity, the concomitant higher accumulation of MDA in the glyphosate- treated S-biotype at 80 HAT (Figure 3.7a) indicates a lower GR activity (Figure 7b). Thus, the increased GR activity in glyphosate-treated R-biotype at 80 HAT indicates a responsive but delayed GR-dependent antioxidant activity. Similar observations of the induction of plant stress response genes following glyphosate application was shown in Festuca arundinacea by Unver et al.51 following miRNA and transcriptome analysis. Furthermore, transcriptome analysis of glyphosate-treated susceptible and resistant soybean plants

(Glycine max (L.) Merr.) by Zhu et al.52 had similar conclusions. Thus, the observed higher

GR activity combined with the lower accumulation of MDA in glyphosate-treated R- biotypes indicates the existence of a robust free radical scavenging system in this biotype

107

that could potentially complement the glyphosate resistance conferred by EPSPS gene copy amplification.

The phenylpropanoid pathway is a major secondary metabolic pathway that synthesizes a wide array of phenolic compounds (such as flavonoids and betalains) with radical-scavenging capacity to reduce oxidative stress. As the rate limiting step of this pathway is the conversion of phenylalanine to t-CA catalyzed by the phenylalanine ammonia-lyase (PAL) enzyme, the rate of formation of t-CA could partially reflect the antioxidant capacity of the plant. At 8 HAT extracted PAL activity was the same in the control groups for both biotypes and in the treated R biotype (Figure 8a). The activity from the treated S biotype was much higher at this time. However, at 80 HAT the PAL activity was same in all treatments except for reduced activity in the treated S biotype (Figure 3.8b).

This is in accordance to previous studies which observed rapid increases in PAL activity in glyphosate-treated plants that were transient, often peaking at about 24 HAT.53,54 In this previous work, extracted PAL activity was temporarily enhanced by glyphosate treatment, while phenylpropanoid levels were decreased, suggesting that the PAL gene(s) is upregulated by blockage of the shikimate pathway.

Based on the metabolite profiles of the S- and R-biotype under both water and glyphosate treatment, two phenylpropanoid secondary metabolites, ferulic and caffeic acid, were identified. While ferulic acid was detected in the R-biotype in both the water control and glyphosate treatments at 8 HAT and only in the glyphosate treatment at 80 HAT, no ferulic acid was detected in the S biotype in either water- or glyphosate-treated plants at both 8 and 80 HAT (Appendix B, Figure 3.2a). Similarly, at 8 HAT, caffeic acid was

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detected in both the control and glyphosate-treated S- and R-biotypes, but at 80 HAT, it was not detected in either control or glyphosate-treated S-biotype, but was still present in the R biotype in all treatments at all times. Interestingly, while the caffeic acid levels were elevated in the glyphosate treatment over the control at 8 HAT and not at 80 HAT, the level of caffeic acid in the control was much higher at 80 than 8 HAT (Appendix B, Figure 3.2b).

This suggests that due to the extra copies of EPSPS gene in the R biotype, there could be an increase in the levels of metabolites downstream of the shikimate pathway, including metabolites of the phenylpropanoid pathway. In addition to phenolic acids, LC-MS profiling of the S- and R-biotypes (Appendix B, Method 3.1, Figure 3.3) identified significantly higher abundance of phenylpropanoid pathway derived hydroxycinnamic acid esters of isocitric acid,55 particularly feruloylisocitric acid (FIA) and caffeoylisocitric acid

(CIA), in the R-biotype (Appendix B, Figure 3.4). This further supports our hypothesis that the R-biotype could possess an enhanced anti-oxidant capability which may play a complementary role in conferring resistance to glyphosate. The enhanced levels of certain phenylpropanoid derived compounds in the R biotype may play a role in the different spectral reflectivities of the two A. palmeri biotypes.56

At 80 HAT, the control R-biotype contained 60% higher total soluble proteins

(TSP) compared to the S-biotype, and the TSP in both biotypes decreased 80 HAT following glyphosate application. The R-biotype had about 35% reduction in protein levels as compared to the S- biotype (Figure 3.9). This decrease in protein levels in the R-biotype could be attributed to the initial inhibition of shikimate pathway as evident in the accumulation of shikimic acid at 8 HAT. The potential transient inhibition of the shikimate

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pathway could lead to protein catabolism to sustain growth, which might partly explain this observation. The reduced protein levels in the S-biotype could be attributed to the combined effect of catabolism and termination of protein synthesis.

Our results demonstrate the application of metabolomics as a complementary tool for further elucidating glyphosate resistance at the phenotypic level in A. palmeri.

Metabolite profiling differentiated physiological differences between S- and R-biotypes, both in the presence and absence of glyphosate treatment. Though both the S- and R- biotypes were exposed to identical environmental conditions under the two treatment conditions (water or glyphosate), the metabolite profile patterns of water-treated (control), plants within each biotype were not similar between the two harvest times. This could be attributed to the rapid fluxes of the measured metabolites that were altered in the intervening period between the two sampling time-point. Importantly, the metabolic changes observed after glyphosate application captures the differences in response to glyphosate application by the two biotypes. The resistance to glyphosate, though primarily conferred by the multiple copies of EPSPS gene in this resistant A. palmeri biotype, may be complemented by the anti-oxidative protective mechanisms. Considering the recent findings that EPSPS gene expression is not equal in all tissue types of glyphosate resistant

A. palmeri biotypes21, the enhanced antioxidant potential might help to mitigate the secondary toxicity of glyphosate generated through generation of free radicals in tissues with lower EPSPS copies.

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2D Graph 1 72HAT_Loadings

0.10 0.2 A 3-dehydroshikimate B 0.05 0.1 Dehydroascorbic acid 0.00 Glycine Palmitate Citrate Glucose Ferulic acid Porphine Tyrosine Sucrose 0.0 Malonate -0.05 Lysine Itaconate Succinate Glycerate Aspartate Lactate Benzoate Pyruvate Benzoate Succinate Fumarate Shikimate Malate Citrate Stearate -0.10 Glycolate alanine Lactate Shikimate Glycine ketoglutarate -0.1 Caffeic acid Asparagine Sucrose Glutamine Glycolate Malate Xylitol Valine Trans-aconitate Oxalate Porphine Dihydroxyacetone Isoleucine Threonine Asparagine Nicotinate Urea Serine Phosphoric acid Adenine Methionine Tryptophan Tagatose Lysine

Component 2 Component

-0.15 Component 2 Phosphoric acid Leucine Glycerol ketoglutarate Dehydroglutamate Dihydroxyacetone Fructose Allantoin Fructose -0.2 Tagatose 3-dehydroshikimate Ferulic acid Pyruvate Stearate alanine Phenylalanine Lyxoslamine Tryptophan Serine Proline Malonate Glycerol Leucine Urea Glutamine -0.20 Palmitate t-aconitate Threonine Alanine Tyrosine Guanidinobutyrate Xylitol Valine Proline Gluconate Allantoin Caffeic acid Glucose Adenine Gluconate Itaconate Alanine Lyxosylamine Guanidinobutyrate -0.3 Oxalate -0.25 Isoleucine Glycerate Nicotinate Dehydroascorbate Aspartate

-0.30 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 Component 1 Component 1

SG RC SG 6.321 C 4.634 D

-5.67 6.580

RC SC SC RG RG -7.45 9.240 RG RC SC

PC2 (16%) PC2 SG (19.5%) PC2 -3.54 -6.91 PC1 (36.3%) PC1 (38.7%)

119

Figure 3.1: PLS-DA Loading plots of water- and glyphosate-treated S- and R-biotypes at A) 8 HAT and B) 80 HAT and the corresponding score plots at C) 8 HAT and D) 80 HAT. The permutation cross validation of PLS-DA model for 8 and 80 HAT had P=0.0005 over 2000 iterations. The ellipse represent 95% confidence region.

120

A B

Figure 3.2: Heatmap and two-way hierarchical clustering of metabolites at A) 8 HAT and B) 80 HAT. Algorithm for Heatmap clustering was based on Euclidean distance measure for similarity and Ward linkage method for biotype clustering. ANOVA results comparing means of each metabolite across treatment groups, and false discovery rate (FDR) for multiple testing corrections are given in Appendix B, Table 3.3. Enlarged dendrograms are given in Appendix B, Figure 3.1.

121

)

1 3 0 -

g P T im e < 0 .0 0 1 8 H A T

)

m d P < 0 .0 0 1

e B io ty p e 8 0 H A T

u l

( P In te ra c tio n < 0 .0 0 1

t 2 0

r 1 0

( c i B A B

m B

i 0 k

i S -B io typ e R -B io typ e

h S

Figure 3.3: Shikimic acid abundance. The data represent the mean±SD of shikimic acid abundance in glyphosate-treated plants normalized with respect to ribitol abundance and corrected with water-treated shikimic acid abundance. Zero represents water-control abundance. Bars with the same letters are not significantly different at 0.05% confidence.

122

1 .5 4 P P

T re a tm e n t = 0 .0 3 4 S C T re a tm e n t < 0 .0 0 1 S C t P t n P

T im e < 0 .0 0 1 S G n T im e = 0 .0 0 7 S G

e t P t 3 P

n In te r a c tio n = 0 .1 0

R C n In te r a c tio n < 0 .0 0 1 R C o

1 .0 o

RG RG d

d 2

l a

0 .5 a

m r

r 1

N N

0 .0 0 8 H AT 8 0 H AT 8 H AT 8 0 H AT P h e n y la la n in e T ry p to p h a n

1 .0 P

T re a tm e n t = 0 .2 6 9 S C t P T im e = 0 .1 8 6 n S G

e 0 .8 t P In te r a c tio n = 0 .0 0 2

n R C o

C 0 .6

z i

l 0 .4

m r

o 0 .2 N

0 .0 8 H AT 8 0 H AT T y ro s in e Figure 3.4: Comparisons of aromatic amino acids across the biotypes and treatments at 8 and 80 HAT. The mean±SD was tested by Two-Way ANOVA with Tukey’s HSD comparing across all treatments and biotypes at a confidence level of 0.05%. Tukey’s comparison between the treatment means are given in Appendix B, Table 3.4.

123

Figure 3.5: TCA cycle metabolite pools in water- and glyphosate-treated S- and R-biotype at A) 8 HAT and B) 80 HAT. The data represent the mean±SD of the metabolites. Tukey’s comparison between the treatment means are given in Appendix B, Table 3.4.

124

P = 0 .0 1 6 2 0 0 0 8 0 H A T T re a tm e n t 8 0 0 8 H A T W a te r P B io ty p e = 0 .1 1 5

W a te r t t n G lyp h o s a te n P = 0 .0 0 5

G lyp h o s a te e ** In te ra c tio n t

e * * 1 5 0 0 t

6 0 0 n

P T re a tm e n t < 0 .0 0 1 d d 1 0 0 0

4 0 0 e e P = 0 .4 8 1 0

B io ty p e z

i l

l P = 0 .9 0 1 a

a In te ra c tio n

m m

r 5 0 0

2 0 0 r

N N B A B A B A B B 0 0 S -B io typ e R -B io typ e S -B io typ e R -B io typ e

Figure 3.6: Total sugar metabolites at A) 8 HAT and B) at 80 HAT. The data represent the mean±SD of the total sugar abundance normalized with respect to ribitol abundance. Bars with the same letters are not significantly different at 0.05% confidence.

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)

l 1

- 0 .4 m P < 0 .0 0 1 W a te r

l T re a tm e n t o P B io ty p e < 0 .0 0 1 G lyp h o s a te

m 0 .3 *** n

( P In te ra c tio n = 0 .0 0 2

t n

e 0 .2

i u

q 0 .1 E

D A B C C

0 .0 M S -B io typ e R -B io typ e

1 .5

) 8 H A T

e 1 .0 8 0 H A T

z e

i * l

n a m 0 .5 * P = 0 .0 1 4

r T im e h

o B C

P = 0 .3 9 8

N B io ty p e

B B

d 0 .0 r l P < 0 .0 0 1

e In te ra c tio n o A

t A

F a -0 .5

W ( A A B A -1 .0 S -B io typ e R -B io typ e

Figure 3.7: Lipid peroxidation damage quantitation and free radical quenching potential in water and glyphosate treated S- and R-biotypes of Amaranthus palmeri. A) TBARS assay quantifying malondialdehyde equivalents in S- and R- biotypes across the treatments at 80 HAT. B) Glutathione reductase assay comparing difference in NADPH levels in glyphosate-treated S- and R- biotypes across 8 and 80 HAT. The data for TBARS assay represent the mean±SD of the assay and the data for GR assay represents water-control normalized mean±SD data of fold changes in NADPH levels. Bars with the same letters are not significantly different at 0.05% confidence.

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A) 8 h

B) 80 h

Figure 3.8: Phenylalanine ammonia lyase assay comparing difference in activities of the PAL enzyme in S- and R- biotypes across the treatments at A) 8 HAT and B) at 80 HAT. The data represent the mean±SD of the assays. Bars with the same letters are not significantly different at 0.05% confidence.

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Figure 3.9: BCA assay for total soluble protein (TSP) quantification in plants harvested at 80 HAT. The data represent the mean±SD of the assay. Bars with the same letters are not significantly different at 0.05% confidence.

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CHAPTER FOUR

STABLE ISOTOPE RESOLVED METABOLOMICS REVEALS THE ROLE OF

ANABOLIC AND CATABOLIC PROCESSES IN GLYPHOSATE-INDUCED AMINO

ACID ACCUMULATION IN AMARANTHUS PALMERI BIOTYPES

(This work has been published (as ACS Editor’s Choice article) and should be cited as Maroli, Amith S., et al. "Stable Isotope Resolved Metabolomics Reveals the Role of Anabolic and Catabolic Processes in Glyphosate-Induced Amino Acid Accumulation in Amaranthus palmeri Biotypes." J. Agric. Food Chem. (2016), 64 (37), pp 7040-7048)

Abstract

Biotic and abiotic stressors often result in the build-up of amino acid pools in plants, which serves as stress mitigators. However, the role of anabolic (de novo amino acid synthesis) versus catabolic (proteolytic) processes in contributing to free amino acid pools is less understood. Using stable isotope resolved metabolomics (SIRM), we measured the de novo amino acid synthesis in glyphosate susceptible (S-) and resistant (R-) Amaranthus palmeri biotypes. In the S-biotype, glyphosate treatment at 0.4 kg ae/ha resulted in an increase in total amino acids, a proportional increase in both 14N and 15N amino acids, and a decrease in soluble proteins. This indicates a potential increase in de novo amino acid synthesis, coupled with a lower protein synthesis, and higher protein catabolism following glyphosate treatment in S-biotype. Furthermore, efficiency of the GS/GOGAT cycle in glyphosate-treated S- and R- biotypes, evaluated as a function of the glutamine/glutamic acid (Gln/Glu) ratio, indicated that although the initial assimilation of inorganic nitrogen to organic forms is less affected in the S-biotype than the R-biotype by glyphosate, amino acid biosynthesis downstream of glutamine is disproportionately disrupted. It is thus

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concluded that the herbicide-induced amino acid abundance in the S-biotype is contributed to by both protein catabolism, and de novo synthesis of amino acids such as glutamine and asparagine.

Introduction

Amino acids are the building blocks of proteins, and they also serve as precursors of nitrogen containing metabolites, including nucleic acids, polyamines, quaternary ammonium compounds, and some hormones. In addition to their primary role in growth and nitrogen transportation, amino acids play a critical role as regulatory and signaling compounds, as well as facilitate homeostasis when plants are subjected to biotic and abiotic stresses.1 In plants under environmental stress, de novo protein synthesis is generally inhibited, and protein turnover and proteolytic activity are increased, resulting in an increased level of total free amino acids.2-4 Also, environmental stress, by affecting plant metabolic pathways, can influence the de novo synthesis of amino acids. This higher cellular concentration of free amino acids is thought to contribute to the overall stress mitigation strategy in plants through their role as osmoregulants, ion uptake modulators, and hormone biosynthesis regulators.5-7 Though the catabolic versus anabolic pathways that contribute to the greater physiological concentration of amino acids could differentially influence the overall performance of plants under stressful environments, we currently lack a robust understanding of the relative contribution of these pathways to the buildup of amino acid pools.

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Present day agriculture relies heavily on herbicides for management of weeds. Of the several classes of herbicides that target various vital plant metabolic processes, N-

(phosphonomethyl)glycine (glyphosate), the compound that competitively inhibits the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) and thus disrupts aromatic amino acid synthesis, is the most widely used herbicide worldwide.8 Inhibition of EPSPS results in the blockage of the shikimate pathway, resulting in accumulation of shikimic acid and depletion of aromatic amino acid pools.9 Furthermore, inhibition of the shikimate pathway not only reduces aromatic amino acid biosynthesis, but also disrupts other physiological processes such as photosystem II quantum efficiency and photosynthetic carbon fixation, thereby, interfering with movement of assimilated carbon.10-11 Although several studies have confirmed the non-targeted effects following treatment with glyphosate, the secondary effects of EPSPS inhibition are poorly understood.2,12,13 Since the carbon backbones of amino acids are derived from carbon metabolic pathways, the inhibition of EPSPS has the potential to interfere with the synthesis of non-aromatic amino acids as well.14 A few studies have demonstrated that an exogenous supply of amino acids

(branched chain and aromatic) following the application of amino acid synthesis-inhibiting herbicides can not only prevent growth inhibition, but can also reverse herbicidal toxicity.15,16 However, total amino acid pools in weed and crop biotypes that are tolerant, susceptible, or resistant to glyphosate increase following glyphosate application.14,17,18

Considering the contrasting degree of EPSPS inhibition by glyphosate in susceptible (S-) and resistant (R-) biotypes, the observed initial increase in free amino acid concentration in both of these biotypes following glyphosate application challenges our understanding of

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the early effects of EPSPS inhibitors on cellular metabolism. The similar buildup of amino acids in R and S biotypes following glyphosate application could potentially be influenced by the differences in anabolic and catabolic processes that contribute to the amino acid pools.

In order to characterize the differential contribution of anabolic (de novo synthesis) versus catabolic (proteolysis) processes to the total amino acid pools, the current study examined the fluxes in amino acid pools in Amaranthus palmeri biotypes susceptible and resistant to glyphosate. We hypothesized that the elevated amino acid pool observed in R- biotype following glyphosate application would be primarily due to increased de novo synthesis, whereas the observed increase in the amino acid pool of the S-biotype would mainly be contributed by protein catabolism.

Materials and Methods

Plant biotypes and experimental design

Seeds of Amaranthus palmeri that are susceptible (S-) and resistant (R-) to the recommended field application rate of glyphosate (0.84 kg ae/ha) were obtained from the

Crop Production Systems Research Unit (USDA-ARS, Stoneville, MS). The GR50 values of S- and R-biotypes were 1.3 and 0.09 kg ae/ha of glyphosate, respectively (~15-fold resistance in the R-biotype).19 Seeds were germinated in a commercial germination mixture in a greenhouse maintained at 30 °C/20 °C day/night temperature with a 14 h photoperiod.

A week after germination individual seedlings were transplanted to 250 mL glass

Erlenmeyer flasks containing 0.5X Murashige and Skoog modified basal salt mixture without nitrogen (PhytoTechnology Laboratories, Shawnee Mission, KS) supplemented

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with 3 mM 14N ammonium nitrate solution. The hydroponic systems were constantly aerated with the solution changed at 5-day intervals, and were maintained under similar greenhouse conditions as above. A week after transplanting, both the biotypes were randomly assigned to two treatment groups - glyphosate treated and water treated.

Glyphosate (Roundup ProMax; Monsanto Co, St. Louis, MO) was sprayed at a rate of 0.4 kg ae/ha, which corresponds to approximately half the recommended field application rate of glyphosate. While Roundup ProMax formulation has about 49% glyphosate potassium salt and 51% other proprietary ingredients, we were not able to obtain the information of the proprietary ingredients that could be used to treat the control plants; hence, the control plants were treated with water. The results presented here reflect the overall metabolic perturbations induced in the S- and R-biotype plants in response to treatment with a commercial formulation of glyphosate (RoundUp ProMax). Though it could be argued that the inert proprietary ingredients in RoundUp ProMax may influence plant physiological responses, ammoniacal and nitrate nitrogen analysis of the commercial formulation showed <0.01 ppm of nitrate nitrogen and no detectable levels of ammoniacal nitrogen which, if present, could have influenced the experimental outcome. Furthermore, no adverse effects of the formulated product are found on Roundup Ready crops with a glyphosate-resistant EPSPS, indicating that there are no stress effects of the formulation materials. Thus, this approach was followed as it would mirror the mode of action of the glyphosate formulation that is routinely used in the field.

Before application of either water or herbicide, the roots of plants were carefully rinsed multiple times with de-ionized water and the plants were held in nitrogen-free

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Hoagland’s solution for 8 h in darkness to reduce the endogenous 14N mineral nitrogen pool. Respective treatments (water or glyphosate) were applied to 12 plants/biotype using an enclosed spray chamber (DeVries Manufacturing, Hollandale, MN) calibrated to deliver

374 L/ha through an 8001E ﬂat fan nozzle (Tee Jet Spraying Systems Co., Wheaton, IL).

At the time of treatment application, both S- and R- biotypes were morphologically similar and were ~20 cm tall with similar numbers of leaves. Following treatment application, six plants from each treatment per biotype were transferred to a modified Murashige and

Skoog (MS) solution containing 3 mM 14N-ammonium nitrate, while the remaining 6 plants

15 15 15 were transferred to modified MS solution containing 3mM NH4 NO3 (>98 atom % N2)

(Cambridge Isotope Laboratories, Tewksbury, MA). All plants were held under greenhouse conditions as specified above. Three young apical leaves along with the meristem were harvested individually from all six plants per glyphosate and control treatments at 36 h after treatment (HAT), stored at -80 °C and ground to a fine powder with dry-ice before analyses.

Analysis of amino acids and proteins

Total soluble protein (TSP) and amino acids in the ground leaf tissues were extracted as described by Maroli et al. (2015),20 and the TSP content was estimated by bicinchoninic acid (BCA) assay according to the manufacturer’s recommended protocol

(BCA Protein Assay kit; Pierce Chemical Co., Rockford, Ill.). Amino acid isotopes were chromatographically separated and analyzed using a Shimadzu Ultra-Fast Liquid

Chromatograph, equipped with a degasser and auto sampler connected in tandem to triple quadrupole mass spectrometer through an electrospray ionization interface (UFLC-ESI-

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MS/MS; Shimadzu 8030). The chromatographic separations of amino acids were achieved using a 100 mm x 3 mm i.d x 2.6µm, Kinetex XB-C18 column with a 4 mm x 2 mm i.d. guard column of the same material (Phenomenex, Torrance, CA) held at 22 °C using 0.05% formic acid (Solvent A) and methanol (Solvent B). The solvent flow rate was 0.3 mL/min with a gradient program where solvent B was initially held at 4% for 2 minutes, increased at a rate of 2% solvent B per min for next 7 minutes, followed by an increase in solvent B at a rate of 6% per min for the next 5 minutes, and re-equilibrated at 4% B for 7 minutes.

Tandem mass spectrometry experiments were performed on a triple quadrupole tandem mass spectrometer. Single ion transitions (m/z) for each of the 14N- and 15N-amino acids were optimized using pure 14N- and 15N-labeled amino acid standards (cell free amino acid mixture-15N [98 atom% 15N]) (Aldrich Chemistry, Sigma-Aldrich Co, St. Louis, MO).

Fragmentation of parent ion peaks were carried out at different collision energies ranging from -15 to -40 v at 5 v intervals to select the ideal transitions that provided the highest signal intensity with a dominant fragment ion. The optimized multiple reaction monitoring

(MRM) was used to analyze the amino acid composition of the samples. The mass spectrometer parameters were: Desolvation Line (DL) temperature maintained at 250 °C, heat block at 400 °C, capillary voltage at 22kV, nebulizing gas of nitrogen at 3 L/min and curtain gas nitrogen at a rate of 10 L/min.

Analysis of polar metabolites

Metabolite profiling of the leaves were carried out on a GC-MS as described previously.20 Briefly, 100 mg of finely powdered leaf tissues was extracted with 1 mL methanol by sonication in an ice-bath. The supernatant was transferred in to glass tubes

135 and the polar metabolites were separated by adding an equal volume of chloroform, followed by water. A subsample (150 µL) of the top aqueous-methanol phase were transferred into glass inserts and 5 µL of 5 mg/mL ribitol (internal standard) in hexane and

5 µL of 5 mg/mL of D27-myristic acid (retention time lock) in hexane were added, and then completely dried under nitrogen. Dried samples were methoxylaminated at 60 °C with 20

µL of methoxylamine hydrochloride (20 mg/mL) in pyridine for 90 min, and further silylated with 90 µL of N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane (TMCS) for 30 min at 40 °C. The metabolites were separated on a

30 m x 0.25 mm i.d x 0.25 µm, J&W DB-5ms column (Agilent Technologies, Santa Clara,

CA) and analyzed using a model 7980 gas chromatograph (Agilent Technologies) coupled to a 5975 C series quadrupole mass detector (Agilent Technologies). The initial oven temperature was maintained at 60 °C for 1 min, followed by temperature ramp at 10 °C per min to 300 °C, with a 7 min hold at 300 °C. Metabolite peaks were identified by comparing the absolute retention time, retention-time index of the sample with that of the in-house metabolomics library supplemented with Fiehn Library (Agilent Technologies,

Wilmington, DE).

15N-amino acid isotopologue enrichment calculation

Peak areas for the monoisotopic peak (m) and first isotope (m + 1) of all observed amino acids, as well as the second isotope (m + 2) for Gln and Asn, were determined using the Shimadzu LabSolutions v2.04 software (Shimadzu, Kyoto, Japan). Theoretical values of the isotopic ratios 13C isotope contribution (%) for each amino acid were calculated using MS-Isotope tool in Protein Prospector and these values were used to correct

136 the m + 1 peak area for all amino acids except Gln and Asn for which the m + 1 correction was applied to the m + 2 peak area for both the m peak and the isotopically labelled portion of the m + 1 peak as given in Appendix C, Table 4.1. 21,22 The m peak area corresponds to the 14N amino acid population whereas the corrected m + 1 peak area, or in the case of Gln and Asn, the sum of corrected peak areas for m + 1 and m + 2, represent the 15N amino acid population. These corrected peak areas were used to calculate the 15N amino acid enrichment according to the equation reported by Gaudin et al.23 Briefly, the 15N isotoplogue enrichment of each individual amino acid was calculated taking into account the endogenous amino acid as well as newly synthesized amino acid. The equation for estimating the isotopologue enrichment is given as:

15 [ 0푁퐴퐴푆푎푚푝푙푒] 14+15 15 [ 0푁퐴퐴푆푎푚푝푙푒] 0푁AA labeling = 15 [ 0푁 퐴퐴퐶표푛푡푟표푙] 14+15 [ 0푁퐴퐴퐶표푛푡푟표푙]

15 푎1 ∗ 0퐴퐴푆푎푚푝푙푒Area MRM 15 14 푎1 ∗ 0퐴퐴푆푎푚푝푙푒Area MRM + 푎2 ∗ 0퐴퐴푆푎푚푝푙푒Area MRM = 15 푎1 ∗ 0퐴퐴퐶표푛푡푟표푙Area MRM 15 14 푎1 ∗ 0퐴퐴퐶표푛푡푟표푙Area MRM + 푎2 ∗ 0퐴퐴퐶표푛푡푟표푙Area MRM

Where, a1 is the slope of the corresponding to labeled (15N) amino acid standards calibration curve and a2 is the slope of the corresponding to unlabeled (14N) amino acid standards calibration curve; control represents plants supplied with 14N ammonium nitrate

15 15 and sample represents plants supplied with NH4 NO3, and AreaMRM represents the area under the curve for the amino acid multiple reaction monitoring (MRM).

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Statistical analyses

Univariate statistical analysis (Student’s t-test, ANOVA) was performed to determine the statistical significance of amino acid pools among the different treatment groups (biotypes and herbicide) followed by Tukey’s HSD post-hoc test. Statistical significance of the BCA assay was tested by two-way analysis of variance. Differences among individual means were tested using Tukey’s HSD multiple comparison tests with

Pvalue < 0.05 considered statistically significant. Statistical analyses were performed using

SAS v 9.2 (SAS Institute, Cary, NC). For the metabolic profiling analysis, metabolomics data were auto-scaled to satisfy the assumptions of normality and equal variance before multivariate analyses. Supervised Partial Least Squares-Discriminant Analysis (PLS-DA) models were constructed using MetaboAnalyst to test the significance of the effects of biotype and herbicide treatments on the metabolite profiles.24 Hierarchical cluster analysis, using Pearson distance as the similarity measure, of the metabolite responses with respect to the treatments were visualized using a heatmap generated from MetaboAnalyst software.

RESULTS AND DISCUSSION

In this study, we report the effect of the EPSPS targeting herbicide, glyphosate, on plant nitrogen metabolism using stable isotope-resolved metabolomic (SIRM) analysis.

The metabolism of two biotypes of Amaranthus palmeri that are susceptible (S-) and resistant (R-) to glyphosate was evaluated by quantitative analysis of 15N amino acid- enriched isotopologues after supplying 15N-ammonium nitrate to plants immediately after the exposure to glyphosate. Of the 20 physiological amino acids, nineteen 14N amino acids, with the exception of cysteine, and eighteen 15N amino acids with the exception of cysteine

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and glycine were identified and resolved using mass spectrometry (Figure 1). Consistent with previously reported observations in A. palmeri populations, 20,25 a significant accumulation of amino acids was observed in the S-biotype following glyphosate application. The total amino acid pool doubled in the S-biotype 36 h after glyphosate application, whereas the observed increase in total amino acid pool of R- biotype was statistically non-significant (Figure 2). Similar observations of amino acid accumulation in glyphosate-sensitive plants following exposure to glyphosate has been previously reported.12,26

Since the S-biotype is highly susceptible to glyphosate (GR50 0.09 kg ae/ha), the observed increase of the amino acid pool following glyphosate application is thought to be primarily caused by accelerated protein catabolism and decreased downstream utilization of amino acids for protein synthesis. The argument favoring lower protein synthesis and/or higher protein catabolism in glyphosate-treated S-biotype is supported by the observation of lower concentration of total soluble protein (TSP) (Figure 3). While the total content of

TSP decreased by 43% in the glyphosate treated S-biotype compared to its water treated control (Figure 3), there was no statistically significant difference in the TSP content between the water treated control and glyphosate treated R-biotype. Increased protein catabolism is further supported by the concomitant doubling of the 14N amino acid pools in the glyphosate treated S-biotype compared to that of the water treated S-biotype (Figure

2). However, the proportional abundance of 15N-labeled amino acids also doubled in glyphosate treated S-biotype compared to that of water treated control. This increased 15N amino acid pool indicates an increase in the de novo amino acid synthesis, coupled with a

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lower protein synthesis rate in this biotype. Thus, the SIRM data suggests that the increase in amino acid pool in the S-biotype following glyphosate application is not only contributed by protein catabolism, but also through an increase in the de novo amino acid synthesis coupled with a potentially lower protein synthesis. Compared to the herbicide-treated S biotype, the relatively lower abundance of 15N amino acid in the herbicide-treated R biotype could be potentially attributed to a lower perturbation in protein synthesis, which is supported by its similar relative abundance of 14N and 15N amino acids (Figure 2) and

TSP (Figure 3) in both herbicide treated and water treated R- biotype.

Examination of the 15N amino acid enrichment profile of the glyphosate-treated S- biotype showed a significant decrease in the abundance of the aromatic amino acids, which is consistent with the inhibition of the shikimate pathway as shown in Figure 4.1 of

Appendix C. The glyphosate-treated S-biotype also revealed that the elevated isotopologue abundance of total 15N amino acid pool was primarily contributed to by the abundance of

15N-asparagine (Asn), 15N-glutamine (Gln), 15N-alanine (Ala) and 15N-serine (Ser) (Figure

4). These four amino acids, synthesized through shikimate-independent pathways, together constituted about 53% of the total amino acid abundance in the glyphosate-treated S- biotype. These observations are in agreement with reports of similar abundance of these four amino acids in plants exposed to various abiotic stresses.27-29 However, our results that document a relative abundance of 15N isotopologue of these amino acids, indicate that the increase in their concentration (Figure 4) could partly be attributed to de novo synthesis rather than from protein catabolism alone. The significantly lower isotopologue enrichment of aromatic amino acids in glyphosate-treated S- compared to the R-biotype was expected,

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however, this study also indicated that synthesis of serine, proline (a stress-related amino acid), glutamic acid, and methionine was significantly reduced as an indirect result of inhibition of EPSPS enzyme.

Physiologically, biosynthetic pathways of all nitrogenous compounds are linked with either glutamine or its acidic counterpart, glutamate.30 Glutamine, a primary amino- group donor for most N compounds, is derived from the processes of primary nitrogen assimilation (Figure 5) involving glutamate and ammonia and is regulated by several enzymes including the glutamine synthetase/glutamate synthase (GS/GOGAT) enzyme complex.31,32 The increase in 15N-enriched Gln in response to herbicide application in both

S- and R-biotypes indicates a potential increase in its biosynthesis potentially driven by downstream sink strength. However, despite the assimilation of inorganic nitrogen into Gln in the glyphosate-treated S-biotype, disruption of the carbon metabolism, as evidenced by higher sugar accumulation (Figure 6), could possibly interrupt the transport of amino acids to various organellar sites.32,33 The GS/GOGAT pathway is of crucial importance in plants, since the Gln and Glu produced are donors for the biosynthesis of major N-containing compounds, including amino acids, nucleotides and polyamines.34 The glutamine/glutamate (Gln/Glu) ratio is a good indicator of the balance between the capacity for C and N assimilation and N availability for biosynthesis.35,36 A lower Gln/Glu ratio indicates a higher de novo amino acid synthesis.37-39 While the herbicide-treated R-biotype had a Gln/Glu ratio of about 1.4, similar to that in the control S- and R-biotypes, the herbicide treated S-biotype had a 10-fold increase in the Gln/Glu ratio (Figure 7A). This indicates an accumulation of glutamine synthesized during the early process of nitrogen

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assimilation, coupled with decreased synthesis of glutamate.26 As the synthesis of glutamate is primarily dependent on the levels of 2-oxoglutarate (2-OG, α-ketoglutarate), decreased availability of 2-OG would hamper the synthesis of Glu. The decreased synthesis of glutamate in the glyphosate treated S-biotype, compared to the R-biotype, can thus be attributed to the lower availability of α-ketoglutarate (Figure 7B). Furthermore, inorganic nitrogen in plants is assimilated initially to Asn and Gln and these amino acids serve as important nitrogen carriers.12,40 The relatively higher abundance of these amino acids in the glyphosate-treated S- and R-biotypes, compared to other amino acids, could indicate that the initial assimilation of NH3 to amino acids is enhanced by glyphosate treatment, even though protein synthesis is hampered by the herbicide in the S-biotype (Figure 3).

Significant accumulation of Asn has been reported in plants subjected to various abiotic stresses, wherein the plant is unable to maintain protein synthesis.41,42

As most amino acids are primarily derived from precursors of carbon metabolism

(pyruvate, phosphoenolpyruvate, oxaloacetate and α-ketoglutarate), perturbations in the carbon–nitrogen homeostasis (Figure 6) would lead to disruptions in the global trans- regulation of amino acid metabolism in the sensitive biotypes (Figures 4 and 5). In response to herbicide application, a general increase in total free amino acid content with a transient decrease in the proportion of the amino acids whose pathways are specifically inhibited is commonly observed.12 The outcome of EPSPS inhibition is aromatic amino acid pool depletion. Consistent with this, the abundance of aromatic amino acids in the S-biotype decreased by 82% in comparison to the R-biotype which had only a 25% decrease, as shown in Figure 4.1 of Appendix C. This decrease in the R-biotype can be attributed to the

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initial transient inhibitory effect of glyphosate on shikimate pathway.20 Furthermore, as the

EPSPS-targeting herbicide is structurally analogous to phosphoenolpyruvate (PEP) and competes with PEP for the EPSPS enzyme, this could result in the potential accumulation of PEP. Thus, the enhanced abundance of 15N-Ala and 15N-Ser in the glyphosate treated S- biotype can be attributed to the transamination of glycolytic intermediates of pyruvate and

3-phosphoglycerate, respectively, via the carboxylation of freely available PEP.43

The observations from this study suggest that chemical stress-induced increase in amino acid pool in the S-biotype is partly caused by the de novo synthesis of amino acids involved in early transamination reactions. Furthermore, the effect of inhibiting EPSPS is not restricted to inhibition of aromatic amino acid biosynthesis alone, but it also disrupts the de novo biosynthesis of most non-aromatic acids as a consequence of deregulation of the shikimate pathway. Thus, the observed elevated amino acid pool in the S-biotype could be an amalgamation of both anabolic and catabolic processes. In contrast, in the R-biotype, the amino acid pool is enriched primarily by anabolic processes, and this elevated synthesis of amino acids in the R-biotype potentially complements the resistance mechanisms occurring in this biotype conferred via increased EPSPS gene copy number and elevated enzymatic and non-enzymatic anti-oxidant mechanisms.19,20 Our results potentially highlight the finding that stress induced amino acid accumulation in plants is not solely due to proteolysis but also contributed by de novo synthesis following nitrogen assimilation. However, diminished physiological activities results in decreased protein synthesis, and, hence, the newly synthesized amino acids accumulate. Furthermore it indicates that although glyphosate is a pathway specific inhibitor of a shikimate pathway

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enzyme, interactions between the various amino acid synthesis pathways may ensure resource allocation for de novo synthesis of amino acids as a means of stress mitigation, in addition to amino acid salvage following proteolysis. Conversely, the disruption of nitrogen metabolism outside the shikimate pathway after inhibition of EPSPS, may point the way to the processes that ultimately lead to cell death in glyphosate-treated, susceptible plants. Sustenance of the amino acid biosynthesis pathways in both S- and R- biotypes despite glyphosate stress indicates that there is a need for agricultural chemists to develop herbicides that could potentially target multiple pathways rather than relying on a single site of action in order to curtail resistance development in weeds.

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Figures

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Figure 4.1: LC-MS chromatogram for the identification of 14N and 15N amino acids. Panel A represents the mass spectra of 14N-amino acids and the numbers correspond to the amino acids: 1) Lysine; 2) Histidine; 3) Arginine; 4) Serine; 5) Asparagine; 6) Alanine; 7) Threonine; 8) Proline; 9) Glutamine; 10) Glycine; 11) Glutamic acid; 12) Valine; 13) Methionine; 14) Isoleucine; 15) Leucine; 16) Tyrosine; 17) Phenylalanine; 18) Tryptophan; 19) Aspartic acid. Panel B represents the mass spectra of 15N-amino acids and the numbers correspond to the amino acids: 1) Lysine; 2) Histidine; 3) Arginine; 4) Serine; 5) N2-Asparagine; 6) Asparagine; 7) Alanine; 8) Threonine; 9) Glutamine; 10) Glutamic acid; 11) Proline; 12) Valine; 13) Methionine; 14) Isoleucine; 15) Leucine; 16) Tyrosine; 17) Phenylalanine; 18) Tryptophan; 19) N2-Glutamine; 20) Aspartic acid.

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Figure 4.2: Relative proportion of 14N and 15N amino acids in S- and R- biotypes of Amaranthus palmeri grown in 15N supplemented MS solution. The data represents the relative proportion of 14N and 15N amino acids in water and glyphosate-treated S- and R- biotypes of A. palmeri harvested at 36 HAT. The bars represent the mean ± 1 SD of total free amino acids and the mean ± 1 SD was tested by Tukey’s HSD. Means with different letters are significantly different at p < 0.05 (Two-Way ANOVA followed by Tukey's HSD test, P<0.05). Alphabets A and B represent significant differences between 15N amino acids while a and b represent significant differences between 14N amino acids.

153

Figure 4.3: Total soluble protein (TSP) concentration estimation by BCA assay. Concentration of TSPs in water and herbicide-treated S- and R- biotypes of Amaranthus palmeri grown in 15N supplemented MS solution. The data represents the mean ± 1 SD of concentration of TSPs and the mean ± 1 SD was tested by Tukey’s HSD. Means with different letters are significantly different at p < 0.05 (Two-Way ANOVA followed by Tukey's HSD test, P<0.05).

154

t P < 0 .0 0 1

0 .0 5 S -B io ty p e

n e

3 0 0 0 % * R -B io ty p e

m h

c *

i 2 5 0 0 % r

n *

E 2 0 0 0 %

u 1 5 0 0 % *

g o

l 1 0 0 0 % * o

p * o *

t 5 0 0 %

s I

0 %

G P

Figure 4.4: 15N Isotopologue enrichment of amino acids in glyphosate-treated S- and R- biotypes of Amaranthus palmeri harvested at 36 HAT. The data represent the mean ± 1 SD of each amino acid. Statistical significance of the mean was determined by paired t-test at a confidence level of 0.05. The dotted line (100%) correspond to natural 15N isotopologue enrichment of amino acids. The * indicates significant difference at p< 0.05 in the 15N isotopologue enrichment (Student’s t-test).

155

Figure 4.5: Amino acid biosynthesis pathway. Concentration of 15N- and 14N- incorporated amino acid profile biosynthesis pathways in water- and herbicide-treated S- and R-biotypes of Amaranthus palmeri grown in 15N-supplemented solution and harvested at 36 HAT. The left half of the each graph represent the 14N and 15N amino acid levels in water treatment and the right half represent the 14N and 15N amino acid levels in glyphosate treatment The data represent the mean ± 1 SD of each amino acid.

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Figure 4.6: Hierarchical cluster analysis of metabolites in S- and R-biotypes of Amaranthus palmeri. Heatmap depiction of two-way hierarchical clustering of total sugars, shikimate pathway and tricarboxylic acid (TCA) cycle metabolites in leaves of water- and herbicide- treated S- and R-biotypes of A. palmeri grown in 15N supplemented MS solution and harvested at 36 HAT. Algorithm for heatmap clustering was based on Pearson distance measure for similarity and Ward linkage method for biotype clustering. Legend: SG, RG- Glyphosate treated S- and R-biotype respectively; SW, RW- Water treated S- and R- biotype respectively.

157

2 0

P T r e a tm e n t = 0 .0 0 3 5

o 1 5 i

t P B io ty p e = 0 .0 0 2 6

a R

P In te r a c tio n = 0 .0 0 3 u

l 1 0

n l

G 5

0 a b a a W a te r G ly p h W a te r G ly p h

S -B io typ e R -B io typ e

1 5  -k e to g lu ta r a te

a d

n 1 0

u b

P T r e a tm e n t = 0 .0 2 0

d P B io ty p e = 0 .1 9 5

z i

l 5 P In te r a c tio n = 0 .0 1 4

r o

N A B A A 0 W a te r G ly p h W a te r G ly p h

S -B io typ e R -B io typ e

Figure 4.7: Regulation of GS-GOGAT cycle metabolites in response to glyphosate application in S- and R- biotypes of Amaranthus palmeri grown in 15N supplemented MS solution and harvested at 36 HAT. A) Gln/Glu ratio in water- and herbicide-treated S and R biotypes B) Relative abundance of α-ketoglutarate in water- and herbicide-treated S and R biotypes. The data represent the mean ± 1 SD of each amino acid. The mean ± 1 SD was tested by Tukey’s HSD comparing across all treatments at a confidence level of 0.05. Means with different letters are significantly different at p < 0.05 (Two-way ANOVA, Tukey’s HSD).

158

CHAPTER FIVE

INVESTIGATION OF THE PERTURBATIONS IN PRIMARY AND SECONDARY

METABOLIC PATHWAYS IN FIVE PALMER AMARANTH BIOTYPES WITH

VARYING RESISTANCE TO GLYPHOSATE

Abstract

Palmer amaranth (Amaranthus palmeri) is a major threat to the sustainability of

Roundup-Ready cropping systems in southern US. With the evolution of resistance in several Palmer amaranth biotypes, control of these weeds with glyphosate has been rendered inadequate. Moreover, the variability in the genetic makeup of the different biotypes of Palmer amaranth populations makes generalization of their management strategies more challenging. In the current study, the metabolic perturbations following glyphosate application was compared across two susceptible (S-) and three resistant (R-) biotypes of Palmer amaranth. Compared to the S-biotypes, the R-biotypes had innately higher anti-oxidant capacity, and the anti-oxidant capacity was observed to correlate with the GR50 such that antioxidant capacity increased with increasing GR50. Metabolic profiling further indicated that the most resistant biotype (C1B1) was innately abundant in several metabolites derived from phenylpropanoid pathway. Upon treatment with glyphosate, the metabolic pool dynamics of all biotypes correlated with the respective GR50 levels, with the most resistant biotype having a higher pool of metabolites known to have anti-oxidant potential. Compared to the most resistant biotype, the S-biotypes had relatively low levels of both primary and secondary metabolites, indicating glyphosate induced metabolic inhibition. After glyphosate treatment, the content of total phenolic and

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flavonoids decrease in S-biotypes, whereas the abundance of these metabolites either remained the same, or increased in the R-biotypes. These results indicate that the phytochemistry and the antioxidant capacity that might play a complementary role in glyphosate resistance is partly induced after glyphosate application, rather than being constitutively expressed.

Introduction

Within the large Amaranthaceae family, there are nearly 85 species in the genus Amaranthus (Ward et al. 2013; Talebi et al. 2016). Of the 85 Amaranthus species,

10 of them are dioecious, native to North America and all of them are classified as weeds in agricultural production (Steckel 2007). In recent years, Palmer amaranth (Amaranthus palmeri S. Wats.), one of the 10 dioecious Amaranthus spp has become a very troublesome weed to cotton (Gossypium hirsutum L.), corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] production systems in large parts of the southern United States (Sosnoskie and Culpepper 2014; Duzy et al. 2016; Soltani et al. 2017; Alms et al. 2016; Webster and

Gray 2015). Being a dioecious species, Palmer amaranth has a high promiscuity for interspecific hybridization and cross-pollination (Hoagland et al., 2013), a herbicide resistant biotype can cross pollinate a biotype that is susceptible to herbicide, to produce a herbicide resistant offspring thus increasing the density of the resistant biotypes in the wild

(Sprague et al. 1997; Wetzel et al. 1999; Tranel et al. 2002; Steckel 2007; Ward et al.

2013). Due to the extensive use of glyphosate to control Palmer amaranth in row crops, the most commonly reported herbicide resistant biotype of Palmer amaranth are the glyphosate-resistant biotypes (Beckie, 2011; Duke, 2012). However, biotypes of Palmer

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amaranth have evolved resistance to most of the other commonly used herbicides

(pyrithiobac dinitroanilines and acetolactate synthase inhibitors etc.) that once effectively controlled this weed in row crop production systems (Heap 2016). Biotypes of a same weed species, although appear similar outwardly, tend to be genetically and physiologically dissimilar. Variability in their genetic makeup, and thus in their physiological manifestations would be higher in individuals from a localized area or among populations from different locations (Hoagland et al. 2013). This innate variability among the different biotypes of the same weed species population makes it difficult for the generalization of their development, competitiveness, spread, and control.

Detection of resistance beyond the recommended field application dose by a dose– response experiment indicates evolution of herbicide resistance, which would then call for a scientifically evaluated weed management strategies. Comparison between biotypes suspected to have varying resistance to glyphosate is most commonly done by determining herbicide dose that causes 50% inhibition of growth (GR50) noted by biomass reduction

(Seefeldt et al. 1995; Burgos et al. 2013). In glyphosate-resistant Palmer amaranth, the commonly reported resistance mechanism is elevated copy number of the EPSPS gene

(Gaines et al., 2010; 2011). Other mechanisms of glyphosate resistance which complements the resistance offered by increased EPSPS copy number includes target site mutations (Nandula et al., 2012), altered translocation (Shaner 2009; Shaner et al. 2012) and increased anti-oxidant machinery (Maroli et al. 2015). Studies by Ahsan et al. (2008) and Maroli et al. (2015) reported that in addition to inhibition of EPSPS enzyme, glyphosate also induces free radical generation (as a consequent effect of metabolic

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perturbations). Further studies by Maroli et al. (2015) determined significant differences in the anti-oxidant capacity between a sensitive and a resistant biotype of A. palmeri indicating that elevated anti-oxidant machinery complemented the glyphosate resistance conferred by increased EPSPS copy number (n = 45) in a Palmer amaranth biotype. The elevated anti-oxidant capacity in the R-biotype was attributed primarily to increased enzymatic and non-enzymatic anti-oxidants (such as low molecular weight phenolics including phenylpropanoid derivatives and flavonoids). As glyphosate also induces free radical generation, increased enzymatic and non-enzymatic anti-oxidants would be necessary to minimize the secondary toxic effects. Anti-oxidants function by either preventing free radical formation or more commonly by inhibiting free radical chain- propagation reactions (e.g., by reacting with peroxyl radical to form stable free species)

(Simic and Jovanovic, 1994; Hagerman et al. 1998). Metabolites such as vitamins, citric acid, flavonoids and other plant phytochemicals, such as phenolic acids and tannins, function as free-radical quenchers (Pratt 1992). Of the diverse secondary metabolites produced by plants, flavonoids are one the most abundant class having well characterized anti-oxidant potential.

The identification of existence of two independent glyphosate resistance mechanisms within the same Palmer amaranth biotype (Nandula et al. 2012; Maroli et al.

2015) merited the need to study multiple biotypes of Palmer amaranth having varying resistance to glyphosate. Therefore the objectives of the current study were to determine if the phytochemical profiles influence the varying resistance to glyphosate using multiple

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Palmer amaranth biotypes and to examine if anti-oxidant potential could be a function of increasing resistance rate (GR50).

Materials and Methods

Plants Biotypes

The seeds of the A. palmeri biotypes (NC-R and NC-S) were kindly provided by

Dr. Nandula (Crop Production Systems Research Unit, USDA-ARS, Stoneville, MS, USA) and were originally collected from Mississippi and Georgia, USA. Seeds of the two glyphosate-susceptible, S-2 and S-3, were field collected from MS and GA respectively and three glyphosate-resistant (T4B2, T2B4 and C1B1) were obtained from Stoneville,

-1 Mississippi. The GR50 of the five biotypes were previously estimated as 0.04 kg ae ha for the S-3 biotype (Gaines et al. 2011) and 0.09 kg ae ha-1 S-3 biotype. (Nandula et al. 2012).

-1 The GR50 of the R-biotypes were estimated to be 0.7, 1.03 and 1.54 kg ae ha for the T4B2,

T2B4 and C1B1 biotypes respectively (Nandula et al., 2012; Nandula, Personal communication). For bioassay and metabolic characterization, the seeds were germinated in a commercial germination mix (Sun-Gro Redi-Earth Plug and Seedling Mix, Sun-Gro

Horticulture, Bellevue, WA 98008) and the seedlings were transplanted to individual pots

(10 cm diameter x 9 cm deep) containing the germination mixture. The plants were grown in a greenhouse maintained at 30°C/20°C day/night temp with a 14 h photoperiod. The pots were fertilized five days after transplanting with 50 ml of 4 g l-1 fertilizer (MiracleGrow®,

24%-8%-16%, Scotts Miracle-Gro Products, Inc., Marysville, OH, USA) and sub-irrigated every alternate day until harvest.

163 Experimental Design and Glyphosate Application

Eight days after transplanting, plants from each biotype were randomly assigned to two treatment groups: control (sprayed with water) and glyphosate. Two weeks after transplanting, the respective treatments (water or glyphosate) were applied to 10 plants per biotype per treatment using an enclosed spray chamber (DeVries Manufacturing,

Hollandale, MN) and delivered through an 8001E ﬂat fan nozzle (Tee Jet Spraying Systems

Co., Wheaton, IL). Glyphosate (Roundup ProMAX®, Monsanto Co, St. Louis, MO) was sprayed at a rate of 0.4 kg ae ha-1. At the time of treatment application, all the experimental plants of both S- and R- biotypes were morphologically similar (plant height and number of leaves) to one another. Treatments were applied on plants that were about 15-cm tall and at four-leaf stage. Three apical leaves along with the meristem were harvested individually from six plants per glyphosate and control treatments at 36 h after treatment

(HAT) and were immediately frozen in dry-ice and stored at -80°C. To minimize the variations due to circadian rhythm, the plants from all biotypes were exposed to 24 h and

15 min of sunlight and 10 h and 15 min of dark period. The harvested leaves were finely ground with dry ice using a mortar and pestle and stored at -80°C. Six treatment replicates from each biotype were maintained for all subsequent analyses.

Metabolite Profiling by GC/MS

Polar metabolites in the tissues were characterized using gas chromatography/mass-spectrometry (GC-MS) as described by Maroli et al (2015). Briefly, about 100 mg of finely powdered leaf tissues was weighed into ice-cold methanol and homogenized by sonicating in an ice-bath for 20 sec at 50% amplitude and centrifuged at

164

12,000  g. The supernatant was transferred to pre-cooled glass tubes, and equal volume of ice-cold chloroform was added and cooled at 4°C. Polar metabolites were fractionated into the aqueous phase with the addition of half volume of cold water and then centrifuged at 671  g for 1 min. About 1.5 ml of the methanolic water was transferred to microfuge tubes. A subsample (150 µl) of this extract was used for derivatization (silylation) with N- methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) with 1% trimethylchlorosilane

(TMCS). Prior to silylation, the samples were spiked with 5 µl of 5 mg ml-1 ribitol (internal standard) in hexane and 5 µl of 5 mg ml-1 of d27-myristic acid (retention time lock) and methoxylaminated with methoxylamine hydrochloride. The derivatized metabolites were separated by gas chromatography (Agilent 7980; Agilent Technologies, Santa Clara, CA) on a J&W DB-5ms column (30 m x 0.25 mm x 0.25 µm, Agilent Technologies), and analyzed using a transmission quadrupole mass detector (Agilent 5975 C series) with an electron ionization interface. The initial oven temperature was maintained at 60°C for 1 min, followed by temperature ramp at 10°C per min to 300°C, with a 7 min hold at 300°C.

Carrier gas (He) flow was maintained at a constant pressure of 76.53 kPa and the injection port and the MS interphase were maintained at a constant temperature of 270°C; the MS quad temperature was maintained at 150°C; and the MS source temperature was set at

260°C. The electron multiplier was operated at a constant gain of 10 (EMV = 1478 V), and the scanning range was set at 50–600 amu, achieving 2.66 scans s-1. Metabolite peaks were identified by comparing the absolute retention time, retention-time index of the sample with that of the in-house metabolomics library supplemented with Fiehn Library (Agilent

Technologies, Wilmington, DE, USA, G1676AA). Data deconvolution was performed

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using Automatic Mass Spectral Deconvolution and Identification System (AMDIS v2.71,

NIST) using the following parameters: Peak identification was limited to a minimum match factor of 70 relative to the Retention Index (RI) library and having a RI threshold window of 10 x 0.01 RI. A maximum match factor penalty of 30 was deducted from the final calculated net value in case of RI mismatch. The peak abundance (integrated signal) was normalized with that of the ribitol (internal standard) abundance and expressed as percent of ribitol.

Metabolic profiling by UHPLC-UHRAM MS/MS analysis

Plant metabolites extracted with methanol and portioned against chloroform were also profiled via ultra-high-resolution accurate mass (UHR-AM) analysis using a quadrupole-orbitrap-iontrap mass spectrometer (Orbitrap Fusion™ Tribrid™ Mass

Spectrometer; Thermo Fisher Scientific, Waltham, MA, USA). Metabolites were separated by UHPLC (Dionex Ultimate 3000) on a reversed-phase column (ACQUITY UPLC HSS

T3, 150 × 2.1 mm, 1.8 μm; Waters Corporation, Milford, MA, USA) maintained at 35 °C, with gradient elution employing 0.1% formic acid and 100% acetonitrile. The solvent flow rate was maintained at constant rate of 0.22 ml/min with the gradient of acetonitrile increasing from the initial 5% to 90% over 24 min. Samples were introduced into the

Orbitrap Fusion mass spectrometer through a heated electrospray ionization (H-ESI) interface. The optimized parameters were set as follows: sheath gas 35, auxiliary gas 10, sweep gas 1, spray voltage 3.6 kV, probe temperature 350 °C, and transfer capillary temperature 300 °C. The Orbitrap Fusion was operated in full-scan mode (200–800 m/z) at a resolution of 120,000 (FWHM) using negative ion polarity mode, followed by data-

166

dependent MS2full wherein the most intense ions from pooled samples were selected for the data-dependent tandem (MS/MS) analysis (60–800 m/z), with the fragments analyzed in the orbitrap set at a resolution of 15,000. For the MS/MS scans, fragmentation was achieved via high-energy collisional dissociation (HCD) and collision-induced dissociation (CID) with the collision energy set at 25–35%. Metabolites were identified based on theoretical exact masses of [M-H]− ion with 4 significant figures and a scan width of ±5.0 ppm and with the fragmentation pattern by matching them with previously reported literature values.

Determination of total anti-oxidant capacity

Total anti-oxidant capacity was measured using DPPH assay as described by Maroli et al. (2015). This method depends on the reduction of purple DPPH• to a yellow colored diphenyl picrylhydrazine and the remaining DPPH• which showed maximum absorption at 517 nm was measured. Briefly, Stock solution of 0.5 mM 2,2-diphenyl-1- picrylhydrazyl (DPPH) was prepared in methanol and kept in dark at room temperature for two hours before using for assay. Prior to the assay, the absorbance of the working solution of DPPH (0.15 mM) was optimized at 517 nm to 1.0 AU in the absence of samples. For the assay, 200 µL of sample (methanolic plant extracts) and 400 µL of 0.1 M Tris-HCl (pH

7.3) buffer was added to microfuge tubes, followed by the addition of 500 µL of the absorbance optimized DPPH reagent. The tubes were immediately mixed for 10 s and kept in dark at room temperature for 30 minutes. Exactly after 30 minutes, the absorbance of the mixture was recorded at 517 nm. A mixed solution of 600 µL methanol and 400 µL 0.1

167 M Tris-HCl (ph 7.3) buffer was used as the blank (control). The anti-oxidant capacity of the methanolic plant extracts was determined as follows:

퐴 % 퐴푂퐶 = (1 − [ 푆푎푚푝푙푒 ]) × 100; 퐴퐶표푛푡푟표푙

where Asample and Acontrol are the absorbance of sample and control respectively.

Determination of total phenolic content

Total phenolics were determined using the Folin–Ciocalteau reagent as described by Abdel-Hameed ES (2009) with slight modifications. Briefly, about 1 g of the finely powdered leaf samples were homogenized in 100% ice cold methanol and centrifuged at

3500 rpm for 15 min. The resulting supernatant was used for estimating the total phenolic contents. A sub-sample (50 µL) of this extract was diluted to 1 mL with water and 0.5 mL of 10 % Folin–Ciocalteau reagent was added. After 2 min, 1.5 mL of 700mM of sodium carbonate was added and the contents were mixed thoroughly and incubated for 60 min at

45°C. Following this, the absorbance was measured at 765 nm in a UV spectrometer using gallic acid as the equivalent standard. The results were expressed as mg gallic acid equivalents/ mg of fresh weight material.

Determination of total flavonoid content

The flavonoids content was determined by aluminum trichloride (AlCl3) method as described by Abdel-Hameed ES (2009) with slight modifications. This method based on the formation of a complex flavonoid-aluminum having the absorptivity maximum at

415 nm. About 100 μl of plant extracts in methanol was mixed with 100 μl of 10% AlCl3 in methanol and a drop of acetic acid, and then diluted to 5 ml. The blank samples were prepared similarly with 100 μl of plant extracts, mixed with 100µL of deionized water and

168 a drop of acetic acid, and then diluted to 5 ml with methanol. All the tubes were incubated for 40 min at room temperature and then the absorption was read at 415 nm. Rutin was used as the reference compound and the absorption of standard rutin solution (0.1 mg/ml) in methanol was measured under the same conditions. The amount of flavonoids in plant extracts expressed as rutin equivalents (RE) was calculated by the following formula:

(퐴 × 푚 ) 푋 = [ 0 ] 퐴0 × 푚

where X is the flavonoid content expressed as mg rutin equivalents (RE)/mg plant extract, A is the absorption of plant extract solution, Ao is the absorption of standard rutin solution, m is the mg weight of plant extract and mo is the mg weight of rutin in solution.

Ultra-High Resolution-Accurate Mass (UHR-AM) Mass Spectrometry data analysis

MZmine was used for the pre-processing of the raw data obtained from Orbitrap

Fusion in terms of peak detection and peak alignment (Pluskal et al. 2010). Peak detection in MZmine was performed in a three-step manner wherein firstly the mass values were detected within each spectrum followed by constructing a chromatogram for each of the mass values and finally the chromatograms were, deconvoluted to recognize the actual chromatographic peaks. Following the peak detection step, further processing of peak detection results, including deisotoping, filtering, gap filling and alignment were carried out. Metabolites were tentatively identified based on the accurate mass of the precursor and fragment ions with a mass error tolerance of 2 ppm. For differential analysis, the collected raw data were processed by Compound Discoverer (Thermo Fisher Scientific) and analyzed as per the predefined workflow (Appendix D, Method 5.1).

169 Statistical analysis

The data for GR50 calculation were fitted to a log-logistic regression model given as

푈−퐿 푦 = 퐿 + 푥 1+푒푥푝[푏∗푙푛( )] 퐺푅50

where y represents shoot length or fresh weight (% of control), L is the mean response at very high herbicide rates (lower limit), U is the mean response when the herbicide rate is zero (upper limit), GR50 is the herbicide rate at the point of inflection halfway between L and U, b is the slope of the line at the GR50 and x is the herbicide dose.

To estimate the parameters of the log-logistic response curve, a non-linear regression procedure was used with the SigmaPlot software (v12.5 for Windows, Systat Software

Inc.).

The biochemical assay data are reported as means±SEM of the six replicates and analyzed by SigmaPlot. Correlations were obtained by Pearson correlation coefficient in bivariate correlations. Differences between means at 5% (P<0.05) level were considered significant. Significance of slope was tested by linear regressions between the respective independent variables and the GR50 of the biotypes. The graphs were constructed using

GraphPad Prism v6.01 (GraphPad Software, La Jolla, CA).

As a large number of highly correlated variables (metabolites) were generated for a small number of phenotypes by UHR-AM analyses sparse PLS-DA (sPLS-DA) function in MetaboAnalyst (Xia et al. 2015) was employed to effectively reduce the number of variables (metabolites) in high-dimensional metabolomics data by controlling the number of components in the model and the number of variables in each component to produce a

170 robust and easy-to-interpret model. Prior to sPLS-DA, missing values in the data, if any, were replaced with a value computed using the Bayesian PCA (BPCA) method (Xia et al.

2015) and then the processed data were normalized by dividing the mean of each variable by its standard deviation (auto scaling option) to satisfy the assumptions of normality and equal variance.

Pathway topological analysis comparing the differences in the metabolite abundance was performed using MetaboAnalyst while the metabolic pathway maps were generated through KaPPA-View (Tokimatsu et al. 2005; Sakurai et al. 2011). The global test algorithm was used for pathway enrichment analysis while the relative betweenness centrality algorithm was employed for pathway topological analysis. The Arabidopsis thaliana pathway library was used for pathway mapping. For pathway representation, the signal intensity of each metabolite was transformed to a log2 value and the ratio between the R-biotypes over the S-biotypes were presented on the maps according to the documentation provided on their web site (http://kpv.kazusa.or.jp/kpv4-kegg-1402).

RESULTS AND DISCUSSION

In the current study, phytochemical profiling of five Palmer amaranth biotypes (one glyphosate-sensitive and three resistant biotypes from MS, USA and one glyphosate- sensitive biotype from GA, USA) with varying sensitivity (or resistance) to glyphosate were undertaken to assess their innate metabolic differences as well as to understand the influence of glyphosate on their metabolite activity.

171 Metabolite analysis

Innate differences in the metabolic pools between the biotypes were determined by

GC-MS and UHR-AM-MS analyses of the different biotypes sprayed with water (Control).

One-way analysis of variance (ANOVA) followed by Tukey’s HSD identified 32 metabolites from GC-MS analysis that were significant (P < 0.05) in discriminating between the biotypes (Table 5.1). Most of the metabolites identified were found to be elevated in the R- biotypes (C1B1, T2B4, T4B2) compared to the S- biotypes (S2, S3).

-1 Within the R-biotypes, the most resistant biotype (C1B1, GR50 1.56 kg ae ha ) had a comparatively higher abundance of metabolites involved in energy production and carbon fixation (glycolysis and the tricarboxylic acid cycle metabolites). About 20% of the carbon fixed by plants flows through the shikimate pathway, of which most of it used for the synthesis of the various secondary metabolites (Herrmann 1995). The higher abundance of photosynthates in the C1B1 biotype potentially indicates that the most resistant biotype could be innately adapted to withstand environmental stress efficiently compared to the relatively less resistant and susceptible biotypes. Similarly, from UHR-AM analysis, a large number of mass features (> 2000 potential metabolites) were identified. Using one way ANOVA, 250 potential metabolites were identified to be significantly different between the water treated biotypes. A sPLS-DA analysis carried out using a 2 component model with 250 significant metabolites (determined by ANOVA) in each component supplemented with hierarchical cluster analysis delineated the biotypes based on the differences between their GR50 (Figure 5.1A). The two most resistant biotypes (C1B1 and

T2B4) formed one group while the two susceptible biotypes (S-2 and S-3) formed another

172 group. The moderately resistant biotype (T4B2) grouped independently. The principal components 1 (PC1) and 2 (PC2) accounted for 30% of the variation. Although 250 potential metabolites were identified to be significantly different between the water treated biotypes, a partial list of the metabolites were tentatively identified based on their accurate parent mass and fragment mass (Table 5.2).

Our previous study reported that elevated anti-oxidant capacity was a complementary resistance mechanism in a glyphosate resistant biotype (Maroli et al. 2015).

Hence, to determine if elevated anti-oxidant capacity is a widespread complementary resistance mechanism in Palmer amaranth biotypes, we investigated the total phenolic acid and flavonoid content in the different S- and R-biotypes before and after exposure to glyphosate stress. No significant differences was observed between the biotypes in their innate (pre stress) flavonoid and phenolic acid content (Figure 5.2A, B). However, despite having no differences in the innate flavonoid and phenolic acid content, it was observed that there were significant differences in the innate total anti-oxidant capacity between the

S- and R-biotypes. The anti-oxidant capacity was found to increase with increasing GR50

(Figure 5.2C). It was observed that while the S- biotypes had only 17% (S-3) and 37% (S-

2) innate anti-oxidant capacity (as measured by % anti-oxidant capacity), the R-biotypes had innately higher anti-oxidant capacity with the anti-oxidant capacity increasing with increasing GR50. The most resistant biotype (C1B1) had about 69% innate free radical scavenging potential while the relatively less resistant T2B4 and T4B2 biotypes had about

43% and 47% respectively. This indicates that the R-biotypes could possibly have higher

173

innate enzymatic anti-oxidant compounds and non-flavonoid anti-oxidants that drives its innate anti-oxidant capacity.

Compared to their respective water treated biotypes, the total phenolic acid content in T2B4 and T4B2 biotypes (R-biotypes) significantly increased by 53.38% and 102.65% respectively in response to glyphosate treatment (Figure 5.2A). The increase in the phenolic content is related to their GR50 such that the most resistant biotype (C1B1) which had innately higher phenolic content had a smaller increase (insignificant) in the content following glyphosate treatment while the relatively lesser resistant biotype (T4B2) had a greater increase in the phenolic content. In contrast, glyphosate application did not elicit any change in the total phenolic acid content in the S-biotypes (Figure 5.2A). About 20% of the primary carbon metabolites synthesized upstream is diverted through the shikimate pathway and subsequently used for the synthesis of the various secondary metabolites

(Herrmann 1995). The R-biotypes, having innate higher amounts of phenolic acids compared to the S-biotypes could therefore potentially have higher amounts of precursors for secondary metabolite biosynthesis. Secondary metabolites include alkaloids, phenolics, terpenoids, lignins, tannins etc. With respect to the flavonoid content, compared to the respective water treated biotypes, in the R-biotypes the total flavonoid content significantly increased with increasing GR50 (Figure 5.2B). Furthermore, the flavonoid content increase contributed significantly to the elevated anti-oxidant capacity of the R-biotypes. While

T4B2 had a 15% increase in flavonoid content (corresponding to a AOC of 47%), the T2B4 and C1B1 had a 25% and 65% increase in the flavonoid content following glyphosate application (corresponding to a AOC of 67% and 80% respectively) (Figure 5.2C). In

174 contrast, the total flavonoid content decreased significantly in the S-biotypes (a 65% and

67% decrease in S-2 and S-3 biotypes respectively) following glyphosate treatment which could be presumably due to decreased availability of precursor substrates. Similarly, no significant change in the anti-oxidant capacity was observed in the S-biotypes following glyphosate application. Both the S-biotypes (S-2 and S-3) had about 35% anti-oxidant capacity. In contrast, a significant increase was observed in the resistant biotypes, T4B2 and T2B4 (Figure 5.2F) with the anti-oxidant capacity in the T4B2 increasing from 33% in the water treated biotypes to 56% in the glyphosate treated biotypes and the anti-oxidant capacity of T2B4 increasing from 44% in the water treated biotypes to 67% in the glyphosate treated biotypes. However, in the most resistant C1B1 biotype (GR50 1.54 kg ae ha-1), there was no significant change in the anti-oxidant capacity between the respective water and glyphosate treated biotypes.

In addition to their primary role of synthesizing and releasing high energy compounds such as NADH, FADH2 and GTP (Voet and Voet, 2004; Dashty, 2013), recent studies have indicated that TCA cycle intermediates possess antioxidant properties (Puntel et al. 2005; 2007; Mailloux et al. 2007). The antioxidant properties of citrate, malate and oxaloacetate were attributed to their ability to chelate iron by forming complexes which diminishes the iron redox activity (Puntel et al. 2007). Comparing between the C1B1 and

S2 biotypes, TCA cycle metabolites had a small but significant increase in abundance in the C1B1 biotype. While 2-Oxoglutaric acid, succinate and fumarate had 0.24 fold, 0.33 fold and 0.23 fold abundance respectively, malate and citrate had significantly higher increase of 7 fold and 1.2 fold with respect to the S-2 biotype (Figure 5.3A). Similar pattern

175 was observed between C1B1 and S3 biotypes with the exception of malic acid which was higher in the S3 biotype compared to the S2 biotype. Comparing the S-biotypes with that

-1 of T2B4, the second most resistant biotype (GR50 1.03 kg ae ha ) a similar trend as that of

C1B1 was observed. Compared to S-2 biotype, in T2B4 biotype, the TCA cycle metabolites such as 2-Oxoglutaric acid, succinate and fumarate had a 0.59, 0.34 and 0.22 fold increase respectively, while malate and citrate had a 1.66 and 0.66 fold increase respectively (Figure 5.4A). However, with respect to the S-3 biotype, no significant fold change in the TCA cycle metabolites was observed. In contrast, the differential abundance of TCA cycle metabolites in the T4B2 biotype with that of the two S-biotypes were not very pronounced as that of the other two R-biotypes. This could be attributed to the low

-1 GR50 (0.7 kg ae ha ) of the T4B2 biotype. Thus the higher abundance of TCA cycle metabolites, in particular malate and citrate, in both the T2B4 and C1B1 biotypes, could correlate with the higher anti-oxidant capacity observed in these biotypes compared to the

S-biotypes and the T4B2 biotype.

Another group of primary metabolites that have a central role in growth and reproduction in plants are the amino acids. In a plant, nitrogen metabolism begins with uptake of ammonium derived from nitrate or directly from ammonium uptake by ammonium transporters (AMTs) and is then assimilated into amino acids via the glutamine synthase/glutamine-2-oxoglutarate aminotransferase (GS/GOGAT) cycle to synthesize

Gln and Glu and via asparagine synthetase (AS) which catalyzes the formation of asparagine (Asn) from glutamine (Gln) and aspartate (Xu et al. 2012). In addition, the carbon backbones produced by photosynthesis are required to assimilate inorganic N into

176 amino acids (Lam et al. 1996). Comparing the innate free amino acid pool levels between the C1B1 and S-biotypes, we observed that similar to the TCA cycle metabolites, the free amino acid levels too are relatively higher in the C1B1 biotype than the two S-biotypes

(Figure 5.3B). Comparing the non-aromatic amino acids synthesized via shikimate- independent pathway, Glu, Asn, Gln, Asn and Asp had 1.76, 1.29, 1.00 and 0.91 fold increase respectively while other amino acids had fold increases ranging from 0.25 to 0.75 across both the S-biotypes. With respect to aromatic amino acids in the C1B1 biotype compared to the S-2 and S-3 biotypes, tryptophan had a 3.68 and 1.33 fold increase respectively while tyrosine had a much higher abundance with a 4.22 fold and 3 fold increase respectively. Similar trend was observed in the T2B4 biotype also (Figure 5.4B).

As it was observed that across the two most resistant biotypes (C1B1 and T2B4), the innate abundance of primarily synthesized amino acids (Glu, Gln, Asn and Asp) were at a much higher levels than those of the derived amino acids, this indicates that the nitrogen assimilation reactions are much more efficient in the relatively more resistant biotypes than the S-biotypes. Innately higher abundance of carbon metabolites coupled with efficient nitrogen assimilation reactions would therefore provide the R-biotypes to better withstand and mitigate the toxicity when exposed to normally toxic doses of glyphosate.

As it has been previously reported that though glyphosate uniquely targets the shikimate pathway, it can cause global physiological perturbations (Vivancos et al. 2011;

Gomes et al. 2014; Maroli et al. 2015; Fernández Escalada et al. 2015), we further investigated the influence of glyphosate on primary and secondary metabolism in across the different S- and R-biotypes. Analysis of the metabolites detected by UHR-AM analysis

177 by one way ANOVA identified 230 potential metabolites to be significantly different between the glyphosate treated biotypes. A sPLS-DA analysis carried out using a 2 component model with 230 significant metabolites (determined by ANOVA) in each component supplemented with by hierarchical cluster analysis delineated the biotypes based on the differences between their GR50 (Figure 5.1B). The principal components 1

(PC1) and 2 (PC2) accounted for 30% of the variation. Accordingly, the two most resistant biotypes (C1B1 and T2B4) formed one group while the two susceptible biotypes (S-2 and

S-3) formed another group. The moderately resistant biotype (T4B2) grouped independently. Though the predominant mechanism of glyphosate resistance in Palmer amaranth is increased EPSPS copy number (Gaines et al. 2010, 2011; Nandula et al. 2012), the EPSPS enzymes subsequently synthesized in glyphosate resistant pseudo-F2 plants are equally sensitive to glyphosate inhibition as those from the susceptible plants (Gaines et al. 2010; Maroli at al. 2015). This indicates that the glyphosate resistant biotypes are capable of recovering itself from the initial stress which could partly be attributed to elevated anti-oxidant potential (Maroli at al. 2015) in addition to higher EPSPS copy number (Gaines et al. 2010, 2011; Nandula et al. 2012) and increased amino acid synthesis

(Maroli et al. 2016).

As with most other plant species including pseudocereal Amaranth spp, weedy

Palmer amaranth biotypes too had significant levels of the flavonoids present as glycosides

(Kühnau 1976). In plants, most commonly, ﬂavonoids exist in the form of the glycoside, in which one or more hydroxyl groups are joined by a hemiacetal link to a sugar (Kühnau

1976). Comparing the most resistant and most susceptible biotype, it was observed that

178 following glyphosate application, a contrasting response was observed in both the biotypes

(Figure 5.2B). Between the glyphosate treated, C1B1 and S-3 biotype, flavonol glycosides of quercetin, isorhamnetin, vitexin, kaempferide and rhamnetin were elevated in resistant biotype (Figure 5.5A). As flavonols are well known anti-oxidants, higher abundance of these metabolites in C1B1 biotype could protect it from the secondary oxidative stress triggered by glyphosate. In addition to the flavonols and the flavones, a chalcone glycoside

(Chalconaringenin-O-glucoside) was also found to be significantly different between the

S- and R-biotypes of Palmer amaranth. Chalconaringenin-O-glucoside, previously described from Sorghum (Gujer et al., 1986) and flavanone glycosides have been reported to possess some antioxidant activities, though not as potent as those other flavonoids

(Slimestad 2003). Furthermore, it can be observed that following glyphosate application, the S-3 biotype primarily accumulated amino acids. This accumulation could be attributed to the breakdown of proteins and trivial de novo synthesis (Maroli et al. 2016). In contrast,

C1B1 was abundant in secondary metabolites such as phenylpropanoids, hydroxycinnamic acid esters, flavonols, chalcones, terpenoids, as well as primary metabolites such as sugars,

TCA cycle metabolites etc. (Figure 5.5B). Abundance of these metabolites having regulatory and anti-oxidant potential would therefore contribute to conferring increased resistance to glyphosate.

It can thus be concluded that although the phytochemical profiles are comparable between the S- and R-biotypes in the absence of stress, the R-biotypes were observed to have innately higher anti-oxidant potential. However, when exposed to glyphosate stress, significant alterations occur in the metabolic profiles. After glyphosate treatment, the

179 content of total phenolic and flavonoids decrease in S-biotypes, whereas the abundance of these metabolites either remained the same, or increased in the R-biotypes depending on their GR50. Thus these results indicate that the phytochemistry and the antioxidant capacity that might play a complementary role in glyphosate resistance is partly induced after glyphosate application, rather than being constitutively expressed and also indicates that elevated anti-oxidant machinery is a prevalent complementary resistance mechanism and the resistance rate is a potential function of their anti-oxidant capacity.

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187 Figures and Tables

Table 5.1: ANOVA of primary metabolites identified in water treated S- and R-biotypes of Amaranthus palmeri harvested 36HAT.

Metabolite PValue Metabolite PValue Acetol 2.94E-27 Malic acid 0.000312 Glucose 6.90E-23 Benzoic acid 0.000562 Allantoin 1.46E-19 Fumaric acid 0.000768 Gluconic acid 4.76E-19 Tyramine 0.001049 Palmitic acid 7.81E-18 Valine 0.001317 Ribose 1.40E-17 Aspartic acid 0.001484 Glutamine 5.50E-17 Lactic acid 0.003453 Lyxosylamine 1.00E-16 Oxalic acid 0.003932 Stearic acid 4.51E-16 Dehydroascorbic acid 0.010262 trans-aconitic acid 1.26E-15 Shikimic acid 0.012757 Lysine 4.46E-15 Lyxose 0.025004 Asparagine 4.44E-10 Fructose 0.025851 Threonine 1.34E-05 Citric acid 0.036454 Porphine 5.13E-05 Glycolic acid 0.038423 Arabitol 5.85E-05 Glycerol 0.040143 Alanine 0.000136 Cellobiose 0.044266

188

Figure 5.1: Sparse-PLS-DA (sPLS-DA) score plot and hierachial cluster analysis of metabolites identified by UHR-AM-MS in water and glyphosate treated S-and R-biotypes of A.palmeri. Panel A represents water treatement and Panel B represents glyphosate treatment. Red, blue, green, pink and cyan corresponds to C1B1, S-3, S-2, T4B2 and T2B4 respectively. The circles in the sPLS-DA score plot represents 95% confidence interval.

189 A) 0 .8 P T 2 B 4 = 0 .0 1 T4 B 2 * C1 B 1 * T2 B 4 P T 4 B 2 < 0 .0 1 0 .6 W a te r S - 2 G lyp h o s a te 0 .4

P W - S lo p e = 0 .1 9 0 .2 S - 3 P G - S lo p e < 0 .0 1

mM Gallic0 .0acid Equivalents 0.0 0.5 1.0 1.5 2.0

-1 GR 5 0 (k g a e h a ) B)

1 .5 P C 1 B 1 < 0 .0 1

P S - 2 < 0 .0 1 C1 B 1 * P < 0 .0 1 1 .0 S - 3 W a te r T2 B 4 T4 B 2 S - 3* G lyp h o s a te S - 2 *

0 .5 P W - S lo p e = 0 .5 2 g R E /m g F W 

P G - S lo p e < 0 .0 1

0 .0 0.0 0.5 1.0 1.5 2.0 C) -1 GR 5 0 (k g a e h a ) 1 0 0 P < 0 .0 1 T 2 B 4 C1 B 1 P < 0 .0 1 * 8 0 S - 3 T2 B 4 T4 B 2*

6 0 W a te r

S - 3* G lyp h o s a te 4 0 % A O C

P W - S lo p e < 0 .0 1 2 0 S - 2

P G - S lo p e < 0 .0 1

0 0.0 0.5 1.0 1.5 2.0

-1 GR 5 0 (k g a e h a ) Figure 5.2: Biochemical assays for determining total phenolic acids, total flavonoids and % anti-oxidant capacity in water and glyphosate treated S- and R-biotypes of A. palmeri harvested 36HAT. Panels A, B and C represents the total phenolic content measured by F- C assay, the total flavonoid content measured by AlCl3 assay and the % anti-oxidant capacity measured by DPPH assay respectively in the water and glyphosate treated biotypes. Data are the means of responses with six replicates. * indicates significant at P < 0.05 (paired t-test).

190

191 B)

Figure 5.3: Pathway representation of the significant primary metabolites identified in the water treated most glyphosate resistant C1B1 biotype and S-biotypes of A. palmeri. The circle represents identified metabolites. The top half of the circle represents the abundance ratio in C1B1 biotype over S-2 biotype and the bottom half represents the abundance ratio in C1B1 biotype over S-3 biotype. Uniform color indicates no significant difference between C1B1 and the two S-biotypes. Panel A depicts carbon metabolism including TCA cycle metabolites and Panel B represents amino acid metabolism.

192

193 B)

Figure 5.4: Pathway representation of the significant primary metabolites identified in the water treated glyphosate resistant T2B4 biotype and S-biotypes of A. palmeri. The circle represents identified metabolites. The top half of the circle represents the abundance ratio in T2B4 biotype over S-2 biotype and the bottom half represents the abundance ratio in T2B4 biotype over S-3 biotype. Uniform color indicates no significant difference between T2B4 and S-3 biotype. Panel A depicts TCA cycle metabolism and Panel B represents amino acid metabolism.

194 A) B)

4 0 0

) SG 3 * CG 3 0 0 * * * * 2 0 0 * * 1 0 0 * Area abundance (x10 0 o c c lu lu c n in lu c m n b i R u tin n g lu o -g o h e s p r h a R o in in -O -g n r u t in -n e m n e ti e ti r c e tin -O -g in - V ite x e t a m n s o r h a Q u e a m n e t I R h a m n r h Is o r h Is o

195 C)

Figure 5.5: Differential abundance of secondary metabolites tentativley indentified in the most resistant (C1B1) and most susceptible (S-3) palmer amaranth biotypes after glyphosate exposure. Panel A represents the flavonols abundance between the S-3 and C1B1 biotype. * represents significant difference at P < 0.05. The glycosides are abbreviated as follows: Glucoside- gluc; Rhamnoside-rhamn; Neohesperidoside-neohesp; Rutinoside-rutino. Panel B represents the hierarchal cluster analysis of the top 50 significant metabolites identified between S-3 and C1B1 biotypes exposed to glyphosate. Panel C depicts the biosynthetic pathway of the flavonols. Colored circle (green) indicates flavonol abundance in glyphosate treated C1B1 over S- 3 biotype.

196

MW MW Error Metabolite Formula MS2 Treatment (cal) (obs) (ppm)

Eugenol C10H12O2 164.0837 164.0835 -1.21 147.0525 (100); 119.0340; 87.0448 W

Vanillic acid C8H8O4 168.0423 168.0420 -1.78 123.0448 (100); 152.0111; 108.0214 W

Hippuric acid C9H9O3 179.0582 179.0580 -1.11 134.0608 (100); 136.0400 W

Citric acid C6H8O7 192.0270 192.0267 -1.56 111.0083 (100); 102.9484; 146.9383 W

Tryptophan C11H12N2O2 204.0899 204.0898 -0.48 159.0924 (100); 116.0502; 142.0658 W

N-Acetyl-L-phenylalanine C11H13NO3 207.0895 207.0897 0.96 164.0713 (100); 147.0448; 165.0747 W

Panthothenic acid C9H17NO5 219.1107 219.1105 -0.91 88.0401 (100); 146.0819 W

N-Malonylanthranilate C10H9NO5 223.0481 223.0479 -0.896 178.0500 (100); 136.0400; 92.0502 W

Tuberonic acid C12H18O4 226.1205 226.1204 -0.44 165.0916 (100); 163.1125; 147.0808 W p-Hydroxy benzoic acid-β- C H O 300.0845 300.0844 0.33 137.0240 (100); 93.0342; 138.0273 W D-glucopyranoside 13 16 8 p-Coumaroyl-β-D-glucose C15H18O8 326.1002 326.1001 0.30 163.0396 (100); 119.0497; 145.0292 W Indole-acetyl-beta-D- C H NO 337.1162 337.1173 -3.26 173.0089 (100); 111.0084; 132.0451 W glucoside 16 19 7 Caffeoylisocitrate C15H14O10 354.0586 354.0588 0.56 173.0087 (100); 191.0195; 111.0085 W

Ferulic acid-O-glucoside C16H20O9 356.1107 356.1111 1.12 193.0502 (100); 175.0395; 217.0502 W

Caffeoylglucarate C15H16O11 372.0693 372.0689 -1.07 209.0297 (100); 191.0185; 173.0185 W

Feruloylgalactarate C16H18O11 386.0849 386.0847 -0.51 191.0192 (100); 173.0183; 367.0141 W

Tuberonic acid glucoside C18H28O9 388.1733 388.1739 1.54 207.1023 (100); 163.1124; 208.1055 W

Chalcone-O-glucoside C21H22O10 434.1213 434.1214 0.23 271.0607 (100); 313.0557; 151.0029 W

Paeonolide C20H28O12 460.1581 460.1588 1.52 193.0502 (100); 175.0396; 235.0607 W

Quercetin-O-glucoside C21H20O12 464.0954 464.0957 0.64 301.0344 (100); 300.0269; 343.0453 W

Isorhamnetin-O-β-glucoside C22H22O12 478.1111 478.1112 0.20 314.0425 (100); 315.0489: 357.0614 W

197 MW MW Error Metabolite Formula MS2 Treatment (cal) (obs) (ppm) Flavonol-O-D- C H O 532.1581 532.1580 -0.18 337.0923 (100); 217.0502; 175.0394 W xylosylgalctoside 26 28 12 Naringenin-O- C H O 580.1792 580.1799 1.20 193.0503 (100); 385.1136; 417.1183 W neohesperidoside 27 32 14 Vitexin-O-β-D-glucoside C27H30O15 594.1584 594.1577 -1.17 285.0394 (100); 193.0502; 257.0446 W Kaempferide-rhamnoside- C H O 608.1741 608.1744 0.49 413.1083 (100); 193.0502; 431.1185 W glucoside 28 32 15 Quercetin-O-rutinoside C27H30O16 610.1533 610.1529 -0.65 300.0270 (100); 301.0348; 302.0331 W Isorhamnetin C H O 624.1690 624.1691 0.16 315.0495 (100); 316.0542; 300.0266 W neohesperidoside 28 32 16 Quercetin-β-D-sophoroside C27H30O17 626.1483 626.1485 0.31 301.0351 (100); 343.0464; 299.0207 W Kaempferol-O-robinoside- C33H40O19 740.2163 740.2168 0.67 593.1507 (100); 431.0977; 285.0400 W O-rhamnoside Pfaffoside A C40H60O13 748.4034 748.4044 1.33 439.3217 (100); 467.3167, 535.3445 W Quercetin-O-rutinoside-O- C H O 756.2113 756.2108 0.92 609.1462 (100); 447.0927, 301.0351 W rhamnoside 33 40 20 Quercetin-O-alpha-L- rhamnopyranosyl-beta-D- C H O 756.2113 756.2126 1.71 609.1450 (100); 446.0853; 299.0183 W glucopyranoside-O-alpha-L- 33 40 20 rhamnopyranoside Spinacoside D C40H60O14 764.3983 764.3996 1.70 455.3158 (100); 437.3077; 551.3375 W

Rhamnetin-O-rhamnoside C34H42O20 770.2270 770.2275 0.64 623.1614 (100); 461.1078 W Quercetin-glucosyl- C H O 772.2062 772.2070 1.03 301.0349 (100); 300.0258; 343.0448 W glucosyl-rhamnoside 33 40 21 O-β-glucopyranosyl-β- glucopyranosyl-oleanolic C42H66O14 794.4452 794.4462 1.25 455.3525 (100); 631.3837; 337.0741 W acid

198 MW MW Error Metabolite Formula MS2 Treatment (cal) (obs) (ppm)

Caryophyllic acid C10H12O2 164.0837 164.0835 -1.21 148.0525 (100); 149.0559 G

Shikimic acid C7H10O5 174.0528 174.0527 -0.57 93.0342 (100); 111.0448; 155.0346 G

Hippuric acid C9H9O3 179.0582 179.0580 -1.11 134.0608 (100); 136.0400 G

Ferulic acid C10H10O4 194.0579 194.0583 2.06 149.0597; 134.0371; 155.4506 G

Oxodecanoic acid C12H22O3 214.1569 214.1570 0.46 195.1387 (100); 183.1386 G

Panthothenic acid C9H17NO5 219.1107 219.1105 -0.91 88.0401 (100); 146.0819 G

N-Malonylanthranilate C10H9NO5 223.0481 223.0479 -0.896 178.0500 (100); 136.0400; 92.0502 G

Coumaroyltyrosine C18H17NO5 327.1106 327.1109 0.91 206.0458 (100); G

Galloyl-β-D-glucose C13H16O10 332.0743 332.0746 0.90 169.0139 (100); 125.0240; 209.0297 G

Ferulic acid-O-glucoside C16H20O9 356.1107 356.1103 -1.12 193.0506 (100);191.0195; 111.0085 G

Feruloylisocitrate C16H16O10 368.0743 368.0748 1.35 173.0091 (100); 111.0085; 154.9983 G

Caffeoylglucarate C15H16O11 372.0693 372.0689 -1.07 209.0297 (100); 191.0185; 173.0185 G

Feruloylgalactarate C16H18O11 386.0849 386.0847 -0.51 191.0192 (100); 173.0183; 367.0141 G

Tuberonic acid glucoside C18H28O9 388.1733 388.1739 1.54 207.1023 (100); 163.1124; 208.1055 G

Chalcone-O-glucoside C21H22O10 434.1213 434.1214 0.23 271.0607 (100); 313.0557; 151.0029 G

Luteolin glucoside C21H20O11 448.1006 448.1008 0.44 284.0346 (100); 285.0419; 401.0900 G

Paeonolide C20H28O12 460.1581 460.1588 1.52 193.0502 (100); 175.0396; 235.0607 G

Quercetin-O-glucoside C21H20O12 464.0954 464.0957 0.64 301.0344 (100); 300.0269; 343.0453 G

Isorhamnetin glucoside C22H22O12 478.1111 478.1114 -1.12 209.0297 (100); 300.0273; 173.0185 G

Vitexin-O-β-D-glucoside C27H30O15 594.1584 594.1577 -1.17 285.0394 (100); 193.0502; 257.0446 G

Quercetin-O-rutinoside C27H30O16 610.1533 610.1529 -0.65 300.0270 (100); 301.0348; 302.0331 G

199

MW MW Error Metabolite Formula MS2 Treatment (cal) (obs) (ppm) Table 5.2: Isorhamnetin C28H32O16 624.1690 624.1691 0.16 315.0495 (100); 316.0542; 300.0266 G Partial list neohesperidoside of Kaempferol-O-robinoside- C H O 740.2163 740.2168 0.67 593.1507 (100); 431.0977; 285.0400 G secondary O-rhamnoside 33 40 19 Pfaffoside A C40H60O13 748.4034 748.4044 1.33 439.3217 (100); 467.3167, 535.3445 G

Quercetin-O-rhamnoside C33H40O20 756.2113 756.2112 -0.13 300.0269 (100); 489.1025, 591.1354 G

Rhamnetin-O-rhamninoside C34H42O20 770.2270 770.2275 0.64 623.1614 (100); 461.1078 G Isorhamnetin 3-rutinoside-7- C H O 786.2219 786.2223 0.50 623.1611 (100); 624.1639 G glucoside 34 42 21 metabolites tentatively identified in water (W) and glyphosate (G) treated S- and R-biotypes of Palmer amaranth harvested 36HAT. The MS2 ions lists the top 3 most abundant fragment ions in their decreasing order. Calculated (cal) molecular weights were obtained from Chemspider database (www.chemspider.org)

200 CHAPTR SIX

CONCLUSION AND SYNTHESIS

The dissertation can be surmised as a pioneering study demonstrating the potential strengths and applicability of metabolomics in deciphering the physiological and metabolic regulation of herbicide resistance in weeds. The current study primarily focused on the cellular physiology of glyphosate resistance mechanisms in Palmer amaranth and Ipomoea lacunosa. The study covered glyphosate tolerance because of the emerging trend of the weed shifts. It is well documented that continuous use of a same herbicide or herbicide class results in a change in the relative frequency of weed species (Coffman and Frank

1991; Webster and Cobble 1997; Owen 2008). For example, continuous use of glyphosate caused an increase in the infestation of Ipomoea spp over a three-year period compared to other herbicide programs (Cobble and Warren 1997; Shaner 2000). Repeated sprayings of glyphosate in glyphosate resistant (GR) cropping systems (RoundUp® Ready trait) would eventually result in the shifts in weed flora and cause glyphosate tolerant weeds to occupy the ecological niches vacated by other glyphosate sensitive weed species. In South

Carolina, top three agricultural crops grown are soybean, corn (for grains) and cotton, of which 95% comprise the RoundUp® Ready varieties. In GR corn, Ipomoea spp, Panicum texanum, Amaranthus spp are the major weeds affecting crop yield while in GR cotton,

Amaranthus, Ipomoea, and Cyperus species as well as annual grasses are more problematic than any other weeds. Similar to cotton, Ipomoea and Amaranthus species are the major weeds affecting GR soybean production. Of the Amaranthus spp, GR palmer A. palmeri is the most problematic weed affecting row crops in SC. GR palmer amaranth has been

201

reported to be found in 33 of the 46 counties infecting over 650,000 hectares of agricultural land resulting in revenue losses to the tune of $427 million annually (Mike Marshall;

Personal communication). Also, Palmer amaranth and Pitted morningglory were identified as the two most troublesome weed to control across cropping systems in U.S. (Wychen

2016). It is for these reasons our study focused on the glyphosate tolerant pitted morningglory and the glyphosate resistant Palmer amaranth as our study species to investigate the metabolic changes in response to glyphosate exposure, which could provide cues to glyphosate resistance manifestation. With respect to Palmer amaranth, our study identified elevated enzymatic and non-enzymatic anti-oxidant capacity in a glyphosate resistant biotype which potentially acts as a glyphosate resistance mechanism, complementing the resistance conferred due to increased EPSPS copy number (Maroli et al. 2015). Furthermore, we have also reported that the accumulation of amino acids in the

S-biotype following glyphosate application is not solely due to protein breakdown but is a result of synergistic process between de novo synthesis of amino acids (anabolism) involved in nitrogen assimilation (Gln and Asn) and proteolysis (catabolism). However, the proportion of de novo synthesis in contributing to the total amino acid pool is much smaller compared to proteolysis (Maroli et al. 2016). The final study showed that elevated anti-oxidant potential is a prevailing complementary glyphosate resistance mechanism across multiple biotypes of Palmer amaranth and also showed that the phytochemical changes and increasing antioxidant capacity is partly induced after glyphosate application.

The key findings from each study has be succinctly described below.

202 1. From Study 1, using a high glyphosate tolerant and a low glyphosate tolerant

biotypes of I. lacunosa, we observed that despite having a 3 fold increase in

tolerance to glyphosate, the cellular metabolism of both the low tolerant and

high tolerant biotypes are perturbed when exposed to sub lethal rates of

glyphosate but they undergo metabolic adaptations to mitigate the toxicity.

2. From study 2, using a glyphosate susceptible and a resistant palmer amaranth

biotype, we reported that despite the R-biotype having high EPSPS gene copies

than the S-biotype, glyphosate affects both the S- and R-biotypes initially

(8HAT). But what distinguishes the S- biotype from the R- biotype is the ability

of the R-biotype to recoup and recover from the initial glyphosate inhibition.

This recovery of the R-biotype after 80HAT is potentially attributed to its

elevated anti-oxidant capacity compared to the S-biotype.

3. From study 3, we inferred that though glyphosate specifically targets EPSPS

enzyme, its perturbations are not confined to the shikimate pathway alone, but

reverberates across other pathways upstream and downstream of the shikimate

pathway. Using stable isotope resolved metabolomics, we showed that despite

glyphosate toxicity, the S-biotype maintains it nitrogen assimilation activity but

is not able to sustain other biosynthetic activities. This results in a build of the

amino acids synthesized initially such as Asn and Gln. Thus it was concluded

that the amino acid accumulation commonly observed accompanying herbicide

application is an amalgamation of both anabolic and catabolic processes.

203 4. From study 4, we showed the elevated anti-oxidant capacity observed in a

glyphosate resistant Palmer amaranth biotype is not an isolated observation but

a prevalent complementary resistance mechanism that correlates with

increasing GR50. We observed in the absence of glyphosate stress, the S- and

R- biotypes had comparable phytochemical profile. Interestingly, the R-

biotypes had innately higher anti-oxidant potential. However, when exposed to

glyphosate, the resistant biotypes had an increase in abundance of metabolites

with known anti-oxidant potential (flavonols) which correlated with their

resistance levels (GR50). This indicates that rather than being constitutively

expressed, phytochemical changes and elevated antioxidant capacity is partly

induced in the resistant biotypes after glyphosate application.

With the rise in herbicide resistance weeds, the common practice of spraying a single herbicide to control them has become ineffective. Current weed management practices advocates the re-adoption of cultural practices such as scouting the fields for weeds regularly and at all stages of crop production cycle (Beckie and Harker 2017).

Changing cropping and herbicide spraying patterns to penalize expression of resistance would help manage weed infestation. But without a priori knowledge of the metabolic pathways that defrays the cost of expression of resistance, herbicide selection for controlling weed infestation is a risky gamble. Currently the herbicide of choice for control of glyphosate resistant Palmer amaranth is application of PPO inhibitors (Barkley et al.

2016; Sperry et al. 2017). This recommendation was based on the fact that Palmer amaranth has developed resistance to both glyphosate and ALS-targeting herbicides (Heap 2016).

204 However, the drawback of such an approach of spraying herbicides with alternate modes of action without knowledge of physiology of the weeds would contribute to the problem of unintentional selection pressure thereby increasing the odds of developing cross resistance to new herbicides. (Salas et al., 2016). While traditional omics such as genomics and transcriptomics can help to identify the evolution of herbicide resistance and precisely identify the genetic basis of herbicide resistance such as increased EPSPS gene copies as a mechanism of glyphosate resistance in Palmer amaranth (Gaines et al. 2010, 2011), emerging omics such as metabolomics and phenomics would help us to determine the fitness costs and compensatory physiology of weeds as a result of the evolution of herbicide resistance. Furthermore, metabolomics would help to robustly identify potential biochemical and physiological process that would help in defraying some of the metabolic costs incurred due to the compensatory physiology. Thus a robust understanding of cellular physiology would promote the judicious selection of herbicides with alternate mode of action to control herbicide resistant weeds based on a robust and scientific knowledgebase of weed resistance physiology.

205 Literature Cited

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D.W., 2016. Fomesafen programs for Palmer amaranth (Amaranthus palmeri)

control in sweetpotato. Weed Technology, 30(2), pp.506-515.

Beckie, H.J. and Harker, K.N., 2017. Our top 10 herbicide‐resistant weed management

practices. Pest Manag. Sci. 73, 1045–1052.

Coble, H.D. and Warren, L.S., 1997. Weed control investigations in corn, cotton, crop

rotations, soybean, small grain. Annu Rep Dept Crop Sci NCSU, 28, 103-113.

Coffman, C.B. and Frank, J.R., 1991. Weed-crop responses to weed management systems

in conservation tillage corn (Zea mays). Weed Technol. 5, 76-81.

Gaines, T.A., Zhang, W., Wang, D., Bukun, B., Chisholm, S.T., Shaner, D.L., Nissen, S.J.,

Patzoldt, W.L., Tranel, P.J., Culpepper, A.S. and Grey, T.L., 2010. Gene

amplification confers glyphosate resistance in Amaranthus palmeri. Proc. Natl.

Acad. Sci. U.S.A. 107, 1029-1034.

Gaines, T.A., Shaner, D.L., Ward, S.M., Leach, J.E., Preston, C. and Westra, P., 2011.

Mechanism of resistance of evolved glyphosate-resistant Palmer amaranth

(Amaranthus palmeri). J. Agric. Food Chem. 59, 5886-5889.

Heap, I. 2016. The international survey of herbicide resistant weeds. Online. URL: http://

www.weedscience.org. (Accessed: Monday, July 10th 2017)

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Maroli, A.S., Nandula, V.K., Dayan, F.E., Duke, S.O., Gerard, P. and Tharayil, N., 2015.

Metabolic profiling and enzyme analyses indicate a potential role of antioxidant

systems in complementing glyphosate resistance in an Amaranthus palmeri

biotype. J. Agric. Food Chem. 63, 9199-9209.

Maroli, A., Nandula, V., Duke, S. and Tharayil, N., 2016. Stable isotope resolved

metabolomics reveals the role of anabolic and catabolic processes in glyphosate-

induced amino acid accumulation in Amaranthus palmeri biotypes. J. Agric. Food

Chem. 64, 7040-7048.

Owen, M.D., 2008. Weed species shifts in glyphosate‐resistant crops. Pest Manag. Sci. 64,

377-387.

Salas, R.A., Burgos, N.R., Tranel, P.J., Singh, S., Glasgow, L., Scott, R.C. and Nichols,

R.L., 2016. Resistance to PPO‐inhibiting herbicide in Palmer amaranth from

Arkansas. Pest Manag. Sci. 72, 864-869.

Shaner, D.L., 2000. The impact of glyphosate‐tolerant crops on the use of other herbicides

and on resistance management. Pest Manag. Sci. 54, 320-326.

Sperry, B.P., Ferrell, J.A., Smith, H.C., Fernandez, V.J., Leon, R.G. and Smith, C.A., 2017.

Effect of Sequential Applications of Protoporphyrinogen Oxidase-Inhibiting

Herbicides on Palmer Amaranth (Amaranthus palmeri) Control and Peanut

Response. Weed Technol. 31, 46-52.

207

Wychen L.V. 2016. 2015 Baseline Survey of the Most Common and Troublesome Weeds

in the United States and Canada. Weed Science Society of America National Weed

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southern United States: 1974 to 1995. Weed Technol. 11, 308-317.

208 APPENDICES

209

Appendix A, Table 2.1: Pathway topolpgical analysis of metabolites identified in water treated WAS and QUI biotypes harvested 80HAT. Metabolites were mapped onto 50 metabolic pathways using Metaboanalyst

KEGG Metabolic Pathways Metabolite ID Pathways Hits FDR Impact Alanine C00041 Ala, Asp and Glu metabolism 8 3.30E-02 0.78 Valine C00183 Gly, Ser and Thr metabolism 7 2.42E-01 0.61 DL-Isoleucine C00407 Isoquinoline alkaloid biosynthesis 1 2.42E-01 0.5 Proline C00148 Arg and Proline metabolism 7 4.23E-02 0.37 Glycine C00037 Tyr metabolism 3 6.49E-02 0.27 Serine C00065 Glyoxylate and dicarboxylate metabolism 3 3.06E-01 0.27 Threonine C00188 Inositol phosphate metabolism 1 6.96E-01 0.25 Homoserine C00263 Citric acid cycle (TCA cycle) 5 1.60E-02 0.21 Aspartic acid C00049 Pyruvate metabolism 3 3.34E-02 0.2 Pyroglutamic acid C01879 Methane metabolism 2 8.37E-01 0.17 Glutamic acid C00025 Trp metabolism 1 8.86E-01 0.17 Asparagine C00152 Glycolysis or Gluconeogenesis 2 3.67E-03 0.11 Histidine C00135 Glycerophospholipid metabolism 1 4.27E-02 0.1 Lysine C00047 Aromatic amino acids biosynthesis 3 4.27E-02 0.1 Tyrosine C00082 Galactose metabolism 5 3.08E-03 0.09 Tryptophan C00078 Starch and sucrose metabolism 2 3.56E-03 0.09 β-alanine C00099 Glutathione metabolism 3 1.44E-02 0.09 β-cyano-L-alanine C00157 Aminoacyl-tRNA biosynthesis 14 5.97E-02 0.09 Allothreonine C05519 Glycerolipid metabolism 3 3.56E-03 0.08 Glutamine C00064 Lys biosynthesis 3 9.50E-02 0.07 Pyruvic acid C00022 His metabolism 1 3.56E-03 0.06 Lactic acid C00186 Carbon fixation 4 1.44E-02 0.03 Oxalic acid C00209 Val, Leu and Ile biosynthesis 3 3.88E-03 0.02

210

KEGG Metabolic Pathways Metabolite ID Pathways Hits FDR Impact Malonic acid C00383 Purine metabolism 3 1.60E-02 0.01 Succinic acid C00042 C5-Branched dibasic acid metabolism 1 3.51E-04 <0.01 Glyceric acid C00258 Terpenoid backbone biosynthesis 1 3.51E-04 <0.01 Fumaric acid C00122 Butanoate metabolism 3 3.56E-03 <0.01 Malic acid C00149 Pantothenate and CoA biosynthesis 2 3.56E-03 <0.01 Altrose C00392 Cys and Met metabolism 4 1.44E-02 <0.01 Shikimic acid C00493 Lys degradation 1 2.88E-02 <0.01 Citric acid C00158 Fatty acid elongation in mitochondria 1 3.34E-02 <0.01 Citruline C00327 Fatty acid metabolism 1 3.34E-02 <0.01 Tagatose C00795 Pentose phosphate pathway 1 6.87E-02 <0.01 α-ketoglutaric acid C00026 Fatty acid biosynthesis 2 1.08E-01 <0.01 Glycerol C00116 Biosynthesis of unsaturated fatty acids 2 1.08E-01 <0.01 Threitol C16884 Val, Leu and Ile degradation 1 1.14E-01 <0.01 Arabitol C01904 Propanoate metabolism 1 1.50E-01 <0.01 Galactinol C01235 Pyrimidine metabolism 1 4.33E-01 <0.01 Myo-inositol C00137 Glucosinolate biosynthesis 2 3.00E-01 <0.01 Ribose C00121 Selenoamino acid metabolism 1 6.96E-01 <0.01 Glycerol-1-phosphate C00093 Ascorbate and aldarate metabolism 1 6.96E-01 <0.01 Dehydroascorbic acid C00425 Porphyrin and chlorophyll metabolism 1 6.96E-01 <0.01 Allose C01487 β-Alanine metabolism 1 6.96E-01 <0.01 Glucose C00031 Nicotinate and nicotinamide metabolism 1 6.96E-01 <0.01 Altrose C19962 Sphingolipid metabolism 1 6.96E-01 <0.01 Sucrose C00089 Sulfur metabolism 1 6.96E-01 <0.01 Cellobiose C06422 Nitrogen metabolism 3 7.99E-01 <0.01 Urea C00086 Cyanoamino acid metabolism 3 8.86E-01 <0.01 Ornithine C00077 Indole alkaloid biosynthesis 1 8.86E-01 <0.01

211 Appendix A, Table 2.2: ANOVA results comparing means of each metabolite across treatment groups, and false discovery rate (FDR) for multiple testing corrections

Metabolite Pvalue < 0.05 FDR Metabolite Pvalue < 0.05 FDR 4-guanidinobutyric acid 0.007529 0.010352 Glycine 0.001668 0.002414 Adenosine 9.13E-07 4.33E-06 Histidine 0.001096 0.001675 Alanine 3.54E-06 1.28E-05 Lysine 4.60E-05 0.000121 Allantoin 0.025225 0.033033 Malic acid 1.83E-07 1.43E-06 Allose 0.011004 0.014762 Malonic acid 0.000112 0.000247 Allothreonine 3.22E-11 8.85E-10 Myo-inositol 2.89E-05 8.30E-05 α-ketoglutaric acid 7.97E-08 7.31E-07 Ornithine 0.005582 0.007872 Altrose 3.73E-06 1.28E-05 Oxalic acid 0.001461 0.002171 Arabitol 1.31E-06 5.54E-06 Palmitic acid 0.001062 0.001668 Asparagine 0.00016 0.000304 Proline 9.98E-05 0.000229 Pyroglutamic Beta- alanine 0.000126 0.000257 0.000392 0.000695 acid Cellobiose 9.61E-05 0.000229 Pyruvic acid 9.45E-07 4.33E-06 L-Isoleucine 1.48E-05 4.77E-05 Quinic acid 4.75E-07 2.72E-06 Fumaric acid 4.94E-07 2.72E-06 Ribose 0.000573 0.000955 Galactinol 8.60E-09 1.58E-07 Serine 0.000956 0.001547 Glucose 4.10E-07 2.72E-06 Succinic acid 1.90E-08 2.10E-07

212 Metabolite Pvalue < 0.05 FDR Metabolite Pvalue < 0.05 FDR Glutamic acid 1.79E-05 5.46E-05 Sucrose 6.20E-05 0.000155 Glutamine 0.000118 0.000251 Tagatose 0.000187 0.000342 Glyceric acid 3.02E-05 8.30E-05 Threonine 3.07E-11 8.85E-10 Glycerol 1.91E-08 2.10E-07 Tyrosine 0.000151 0.000297 Glycerol-1-phosphate 0.000438 0.000754 Valine 1.82E-06 7.14E-06

213

Appendix B, Table 3.1: Metabolites identified in water- and glyphosate-treated S- and R-biotypes of Amaranthus palmeri along with their respective fragment ions (m/z).

Fragment Fragment Metabolites Metabolites Ions (m/z) Ions (m/z) Amino acids Organic acids Alanine 116, 117, 190 Pyruvic acid 174, 115, 99 Valine 144, 73, 218 trans-Aconitic acid 229, 211, 285 Glycine 174, 248, 86 Citric acid 273, 347, 75 Serine 204, 218, 100 α-Ketoglutaric acid 198, 156, 186 DL-Isoleucine 158, 218, 147 Succinic acid 75, 247, 129 Threonine 117, 218, 291 Fumaric acid 245, 246, 143 Aspartic acid 232, 100, 218 D-Malic acid 147, 133,245 Phenylalanine 120, 130, 147 Oxalic acid 148, 149, 131 Glutamic acid 246, 128, 156 Glycolic acid 66, 177, 205 Glutamine 246, 128, 247 Dehydroascorbic acid 205, 244, 157 Tryptophan 202, 203, 291 Glyceric acid 189, 292, 133 Proline 70, 75, 103 Gluconic acid 333, 292, 205 Asparagine 188, 216 Lactobionic acid 204, 217, 191 Tyrosine 218, 219, 280 3-dehydroshikimic acid 296, 208, 224 Lysine 200, 156, 174 Shikimic acid 204, 205, 357 Leucine 158, 159, 232 Lactic acid 117, 190, 191 Methionine 176, 104 Malonic acid 66, 233, 133 β-Alanine 174, 248, 290 Benzoic acid 179, 77, 135 Nicotinic acid 180, 136, 106 Itaconic acid 259, 215, 74

214

Fragment Fragment Metabolites Metabolites Ions (m/z) Ions (m/z) Polyols Fatty acids Xylitol 217, 103, 205 Palmitic acid 117, 313, 278 Myo-Inositol 318, 217, 305 Stearic acid 117, 341, 145 Glycerol 205, 117, 103

Phenolic acids Sugars Caffeic acid 219, 396, 397 Ribose 217, 307, 189 Ferulic acid 338, 323, 308 Fructose 103, 217, 307 Tagatose 103, 217, 307 Nitrogen and Phosphate D-Galactose 205, 319, 217 Compounds 8-aminocaprylic acid 174, 175, 360 D-Glucose 205, 319, 217 D-sphingosine 204, 205, 412 Talose 319, 205, 217 Urea 186, 66, 98 Lactose 204, 205, 361 Adenine 264, 265, 279 Sucrose 361, 362, 363 D-Lyxosylamine 103, 217, 307 Cellobiose 204, 205, 361 Methyl-β- 4-Guanidinobutyric acid 174, 304, 75 204, 217, 133 Galactopyranoside Phosphoric acid 299, 314, 211 Porphine 184, 134, 285 Note: All metabolites produce fragments of m/z = 73 (base peak) that corresponds to [(CH3)3 SiOH], and m/z = 147 that corresponds to [(CH3)3 SiOSi(CH3)2] which are characteristics of MSTFA derivatization.

215 Appendix B, Table 3.2: Results of PERMANOVA analysis of the responses of metabolites in relation to treatments at 8 HAT and 80 HAT. df= degrees of freedom; SS = sum of squares; MS = mean sum of squares; Pseudo-F = F value by permutation. P- values are based on 9999 permutations [P(perm)]

SS 8HAT Type df MS Pseudo-F P(perm) Source (Type III) Biotype Fixed 1 132.51 132.51 4.2455 0.01 Herbicide Fixed 1 432.15 432.15 13.846 0.001 Biotype x Herbicide 1 53.097 53.097 1.702 0.022

216

Appendix B, Table 3.3: ANOVA on individual metabolites, false discovery rate (FDR; percent of false positive that are predicted to be significant), and post-hoc Tukey’s HSD comparison of metabolites at 8 and 80 HAT.

8HAT 80HAT Metabolites Tukey's Tukey's Pvalue FDR Pvalue FDR HSD HSD RG-RC; SG- Fructose 1.12E-08 5.43E-07 RC; SC-RG; 0.047423 0.06323 RG-RC SG-SC RG-RC; SG- RG-RC; SC- Tagatose 2.19E-08 5.43E-07 RC; SC-RG; 0.0063444 0.011376 RC; SG-RC SG-SC RG-RC; SG- RC; SC-RG; Aspartic acid 3.02E-08 5.43E-07 0.01495 0.025077 SC-RG SG-RG; SG- SC RG-RC; SC- SG-RC; SG- Itaconic acid 4.56E-07 6.16E-06 RC; SC-RG; 0.0051057 0.01062 SC SG-SC RG-RC; SG- Threonine 1.23E-06 1.11E-05 RC; SC-RG; 0.016994 0.027403 SG-RC SG-SC RG-RC; SG- Glucose 1.94E-05 8.99E-05 RC; SC-RG; 0.03099 0.04629 RG-RC SG-SC

217 8HAT 80HAT Metabolites Tukey's Tukey's Pvalue FDR Pvalue FDR HSD HSD RG-RC; SG- Xylitol 6.30E-06 4.54E-05 RC; SC-RG; Not Significant SG-SC RG-RC; SG- Lyxosylamine 1.09E-06 1.11E-05 RC; SC-RG; Not Significant SG-SC RG-RC; SG- RG-RC; SC- Glycerol 6.72E-06 4.54E-05 RC; SC-RG; 0.0010089 0.003279 RC; SG-RC SG-SC RG-RC; SG- RG-RC; SG- RC; SC-RG; Adenine 1.25E-05 7.52E-05 RC; SC-RG; 2.30E-06 1.99E-05 SG-RG; SG- SG-SC SC

218

8HAT 80HAT Metabolites Tukey's Tukey's Pvalue FDR Pvalue FDR HSD HSD SC-RC; SG- RG-RC; SC- Glyceric acid 1.96E-05 8.99E-05 1.81E-05 0.0001177 RC; SG-RG; RG; SG-SC SG-SC RG-RC; SG- SG-RC; SG- RC; SC-RG; Porphine 2.02E-05 8.99E-05 1.50E-09 3.89E-08 RG; SG-SC SG-RG; SG- SC SC-RC; SG- RG-RC; SC- Oxalic acid 2.17E-05 8.99E-05 0.003642 0.007891 RC; SC-RG; RG; SG-RG SG-RG RG-RC; SG- RG-RC; SG- Shikimic acid 5.72E-05 0.00022056 RC; SC-RG; 1.02E-09 3.89E-08 RC; SG-RG; SG-SC SG-SC 3- SG-RC; SG- SG-RC; SG- Dehydoshikimic 0.0019983 0.0041503 0.0013021 0.0035636 RG; SG-SC RG; SG-SC acid SC-RC; SG- RG-RC; SC- Dihydroxyacetone 7.80E-05 0.00028066 RC; SC-RG; 1.37E-08 1.79E-07 RC; SG-RC SG-RG RG-RC; SG- Stearic acid 0.00010805 0.00036467 RC; SC-RG; 0.039434 0.053962 SG-SC

219

8HAT 80HAT Metabolites Tukey's Tukey's Pvalue FDR Pvalue FDR HSD HSD RG-RC; SC- Alanine 0.00011966 0.00038009 Not Significant RG; SG-SC RG-RC; SC- Isoleucine 0.00017856 0.00053567 Not Significant RG; SG-SC RG-RC; SC- SC-RC; SG- Caffeic acid 0.00026984 0.00076691 0.013279 0.023017 RG; SG-RG RC RG-RC; SC- SG-RC; SG- Allantoin 0.00053695 0.0014498 6.52E-05 0.00033926 RG; SG-RG RG; SG-SC SC-RC; SG- Trans-aconitic RG-RC; SC- 0.00056831 0.0014614 0.00043224 0.001729 RC; SC-RG; acid RG SG-RG RG-RC; SG- RG-RC; SG- Malonic acid 0.00061232 0.001503 RC; SC-RG; 0.00030758 0.001454 RC; SG-SC SG-SC SG-RC; SG- SG-RC; SG- Lysine 0.00064144 0.001506 0.0012832 0.0035636 SC RG; SG-SC RG-RC; SC- RG-RC; SC- Nicotinic acid 0.001389 0.0031251 8.52E-09 1.48E-07 RG; SG-RG RC; SG-RC SC-RG; SG- Phenylalanine 0.0014927 0.0032242 Not Significant RG SC-RG; SG- Gluconic acid 0.0021567 0.0042406 Not Significant RG

220

8HAT 80HAT Metabolites Tukey's Tukey's Pvalue FDR Pvalue FDR HSD HSD RG-RC; SC- Palmitic acid 0.0021988 0.0042406 0.034045 0.049177 SG-RG RG Dehydroascorbic SC-RG; SG- SG-RC; SG- 0.003964 0.0071351 0.0029975 0.007085 acid RG SC Guanidinobutyric SC-RG; SG- 0.0049149 0.0085614 Not Significant acid RG SC-RG; SG- Phosphoric acid 0.0054041 0.0091194 Not Significant SC RG-RC; SC- SG-RC; SG- Pyruvic acid 0.0059896 0.0098011 0.0060561 0.011247 RG SC RG-RC; SG- Benzoic acid 0.0068796 0.010926 SG-RC 0.00034723 0.0015047 RC SC-RC; SG- SC-RG; SG- Serine 0.011727 0.01759 1.68E-05 0.0001177 RC; SC-RG; RG SG-RG SC-RG; SG- SG-RC; SG- Tyrosine 0.014291 0.020858 6.02E-05 0.00033926 RG RG; SG-SC SC-RC; SG- Leucine 0.023241 0.033026 SC-RG 0.0056437 0.01119 SC Lactic acid 0.025673 0.035547 SG-SC Not Significant Glycolic acid 0.027391 0.036977 SG-SC 0.01739 0.027403 SG-RC SG-RC; SG- RG-RC; SC- Ferulic acid 0.043028 0.056671 0.0008424 0.0029203 SC RG; SG-RG

221

8HAT 80HAT Metabolites Tukey's Tukey's Pvalue FDR Pvalue FDR HSD HSD SG-RC; SG- SG-RC; SG- β- alanine 0.010067 0.015532 0.0019036 0.0049493 SC RG; SG-SC SG-RC; SG- Methionine 0.044488 0.057199 Not Significant SC RG-RC; SG- Sucrose Not Significant 5.54E-07 5.77E-06 RC; SG-RG; SG-SC SC-RC; SG- Malic acid Not Significant 0.00060044 0.0022302 RC RG-RC; SC- Citric acid Not Significant 0.0011435 0.0034977 RC; SG-RC SG-RC; SG- Tryptophan Not Significant 0.0022594 0.0055946 RG; SG-SC Dehyro-glutamic SG-RC; SG- Not Significant 0.0035891 0.007891 acid RG; SG-SC SG-RC; SG- Valine Not Significant 0.0058103 0.01119 SC Glutamine Not Significant 0.031157 0.04629 SG-SC Proline Not Significant 0.03651 0.051312 SG-SC

222 Appendix B, Table 3.4: Tukey’s classification for TCA cycle metabolites and aromatic amino acids at 8 and 80 HAT

Biotype/Treatments/Time Pvalue < 0.05

Pyruvate S-Biotype:Water vs. S-Biotype:Glyphosate 0.0168

t-aconitate Susceptible:Water vs. Susceptible:Sprayed 0.0213

Citrate S-Biotype:Water vs. S-Biotype:Glyphosate 0.0087

α-ketoglutarate S-Biotype:Water vs. S-Biotype:Glyphosate 0.9694

Succinate S-Biotype:Water vs. S-Biotype:Glyphosate 0.9875

Fumarate S-Biotype:Water vs. S-Biotype:Glyphosate 0.0744

Malate S-Biotype:Water vs. S-Biotype:Glyphosate 0.0402

Phenylalanine 8HAT:SC vs. 80HAT:RG 0.0420 8HAT:SG vs. 80HAT:RG 0.0119 8HAT:RG vs. 80HAT:RG 0.0223 80HAT:SG vs. 80HAT:RG 0.0156

Tryptophan 8HAT:SC vs. 80HAT:SG <0.001 8HAT:SG vs. 80HAT:SG <0.001 8HAT:RC vs. 80HAT:SG <0.001 8HAT:RG vs. 80HAT:SG 0.0030 80HAT:SC vs. 80HAT:SG <0.001 80HAT:SG vs. 80HAT:RC <0.001 80HAT:SG vs. 80HAT:RG <0.001

Tyrosine Interaction 0.0203

223 Appendix B, Equation 3.1a:

Glutathione Reductase Activity can be determined from the reduction in NADPH. Since one molecule of NADPH is consumed when one molecule of GSSG reduced, the reduction of NADPH directly correlates with GSSG reduction. Therefore one nmol NADPH/ mL = one mU/mL Glutathione Reductase

M GR Activity (mU/mL) = ((ΔA340/min)/E ) x Ad x Sd

M -3 Where E = 6.22 x 10 mL/nmol; Ad = 1000 µL/sample volume (in µL) and Sd = Sample dilution prior to assay.

Appendix B, Equation 3.1b:

MDA Equivalents from TBARS assay according to Hodges et al.

Formula A: [(Abs532+TBA) - (Abs600+TBA)] – [(Abs532-TBA) – (Abs600-TBA)]

Formula B: [(Abs440+TBA) – (Abs600+TBA)] x 0.0571]

MDA equivalents (nmol/ml) = {[(Formula A) – (Formula B)]/157 000)} x 106

224 A B

Appendix B, Figure 3.1: A) Ward algorithm based hierarchical clustering analysis of the water- and glyphosate-treated S- and R-biotypes at 8 HAT and B) at 80 HAT.

225 Appendix B, Figure 3.2: A) Ferulic acid abundance and B) Caffeic acid abundance in S- and R- biotype at 8 and 80 HAT.

226

Appendix B, Method 3.1: Metabolomics using LC-MS/MS

Because of the wider diversity of compounds present in plant extracts, high pressure liquid chromatography (HPLC) was used to supplement the GC-MS metabolomic analysis.

For example, GC provides consistent, well resolved, separation of compounds that is advantageous for the analysis of complex matrix. Also, GC-MS based metabolomics could utilize custom reference libraries for the correct identification of unknown compounds.

However, due to the partial-derivatization, limited volatility and high thermo-liability of its silylated derivatives, flavonoids and phenolic esters including glycosides are less amenable to GC analysis. Hence the plant extracts were also analyzed using HPLC tandem mass spectrometry.

A sub-sample of the methanol extract was transferred into a low-volume insert and analyzed using a Shimadzu Ultra-Fast Liquid Chromatograph, equipped with a degasser and auto sampler connected in tandem to triple quadrupole mass spectrometer through an electrospray ionization interface (UFLC-ESI-MS/MS; Shimadzu 8030). The chromatographic separations of the compounds were achieved using a Kinetex ® XB-C18 column (100mm x3mm x 2.6µm; Phenomenex, Torrance, CA, USA) operated at 22°C using 0.1% formic acid (Solvent A) and methanol (Solvent B). Solvent flow rate was 0.4 ml min-1 with a gradient program where solvent B was initially held at 10% for 2 minutes, increased at a rate of 3% B per minute for next 10 minutes, increased at a rate of 5% B per minute for next 10 minutes, held at 90% B for next 5 minutes, and re-equilibrated to 10%

B for 5 minutes.

227 Tandem mass spectrometry experiments were performed on a triple quadrupole tandem mass spectrometer. The samples were first scanned from 200 to 900 amu in both positive and negative ionization mode. Considering the decrease in sensitivity with increase in scan range on quadrupole mass analyzer, the scans were performed in batches of 250 amu per scout-run, resulting in an acquisition of 40 spectral scans per second (scan speed of 15,000 amu per second), thus increasing the overall sensitivity of the analysis.

The MS parameters were: DL temperature maintained at 230°C, heat block at 400°C, capillary voltage at 22kV, nebulizing gas of nitrogen at 3 L min-1 and curtain (drying) gas nitrogen at a rate of 14 L min-1. From the scans, the chromatographic peaks with signal-to- noise ratio of >10:1 were selected for MS/MS experiments. Fragmentation of these identified peaks were carried out at different collision energies ranging from 15 to 40v at

5v intervals. The collision energy that yielded complete fragmentation of the parent molecule and a higher signal of product fragments were selected for multiple reaction monitoring. Through this approach the following reaction monitoring was developed for the high sensitive quantitation of the compounds in the negative ionization mode. Among the unique m/z that showed treatment responses, 4-O-Feruloylquinic acid (4-FQA) and 4-

O-Caffeoylquinic acid (4-CQA) were positively identified by comparing the observed mass spectral fragmentation pattern (Figure S3) with that of the literature reported values.1,2

228 Appendix B, Table 3.5: Multiple reaction monitoring optimized for the quantification of dominant compounds identified during the HPLC-MS analysis that showed treatment response.

Collision Energy Parent m/z (M-H)- Reaction Transitions (V) 323 323>119.00 25 387 387>163.00; 387>119.00; 387>207.00 28 336 336>131.00 25 351 351>111.00 28 353 353>191.00;173.00;179.00;111.00 15 367 367>111.00; 367>173.00; 367>163.00 20 479 479>433; 479>161; 479>119 28 455 455>409; 455>277; 455>161 28 463 463>417; 463>161 28 357 357>241; 357>139 40

229 Inten. 110

100 173.00

70 191.00 60 111.05 50

20 154.95

0 100.0 125.0 150.0 175.0 200.0 225.0 250.0 275.0 300.0 325.0 m/z

Appendix B, Figure 3.3: Mass spectra showing the fragmentation pattern for A) Feruloylisocitric acid (FIA; m/z 367; CV=20V) and B) Caffeoylisocitric acid (CQA; m/z 353; CV=15V) from A. palmeri biotypes in negative ionization mode.

230 Appendix B, Figure 3.4: Relative abundance of A) FIA and B) CIA in S- and R- biotypes of A. palmeri treated with water and glyphosate at 80 HAT.

References:

1. Kuhnert, N.; Jaiswal, R.; Matei, M.F.; Sovdat, T.; Deshpande, S. How to distinguish between feruloyl quinic acids and isoferuloyl quinic acids by liquid chromatography/tandem mass spectrometry. Rapid Commun. Mass Spectrom. 2010, 24, 1575–1582.

2. Clifford, M. N.; Knight, S.; and Kuhnert, N. Discriminating between the six isomers of dicaffeoylquinic acid by LC-MS n. J. Agric. Food Chem. 2005, 53, 3821-3832.

3. Shimamura, T.; Sumikura, Y.; Yamazaki, T.; Tada, A.; Kashiwagi, T.; Ishikawa, H.; Ukeda, H. Applicability of the DPPH Assay for Evaluating the Antioxidant Capacity of Food Additives–Inter-laboratory Evaluation Study. Analytical Sciences, 2014, 30, 717-721.

231 M)  M)  5 0 1 5

4 0

1 0 3 0

2 0 5 1 0

0 0 S -B io typ e R -B io typ e S -B io typ e R -B io typ e

T y ro s in e T ry p to p h a n Concentration of Total Amino acids ( Concentration of Total Amino acids ( M) 

1 5 1 5 N-Glyphosate

1 4 N-Glyphosate

1 0 1 5 N -W a te r

1 4 N -W a te r

0 S -B io typ e R -B io typ e

Phenylalanine Concentration of Total Amino acids (

Appendix C, Figure 4.1: Concentration of 15N- and 14N- aromatic amino acids in S- and R- biotypes of Amaranthus palmeri. Concentration of Tyr, Trp and Phe in water and herbicide treated S- and R- biotypes of A. palmeri harvested at 36 HAT. The data represent the mean ± 1 SD of each amino acid.

232

Appendix C, Table 4.1: Fragment ions of amino acids generated in LC-MS|MS (Triple quadrupole) analysis and theoretical Isotpoic mass abundance (%) used for correction amino acids abundance in 15N-amino acid isotopologue enrichment analysis.

Isotope contributions (%

total) 14N-amino Fragment Fragment i0 i1 i2 i3 acid ion Formula

Ala 89 C3H7NO2 95.8 3.74 0.45 0.02

Asn 132 C4H8N2O3 94.15 5.13 0.69 0.03

Asp 133 C4H7NO4 94.28 4.81 0.87 0.04

Arg 174 C6H14N4O2 91.54 7.76 0.67 0.04

Gln 146 C5H10N2O3 93.08 6.14 0.74 0.04

Glu 147 C5H9NO4 93.21 5.83 0.91 0.05

Gly 75 C2H5NO2 96.9 2.67 0.42 0.01

His 155 C6H9N3O2 91.95 7.38 0.64 0.04

Ile 131 C6H13NO2 92.57 6.81 0.59 0.03

Leu 131 C6H13NO2 92.57 6.81 0.59 0.03

Lys 146 C6H14N2O2 92.21 7.13 0.62 0.03

Met 149 C5H11NO2S 88.97 6.22 4.49 0.27

Phe 165 C9H11NO2 89.55 9.57 0.83 0.05

Pro 115 C5H9NO2 93.66 5.78 0.53 0.03

Ser 105 C3H7NO3 95.57 3.77 0.64 0.02

Thr 119 C4H9NO3 94.48 4.81 0.68 0.03

Tyr 181 C9H11NO3 89.34 9.58 1.01 0.07

Trp 204 C11H12N2O2 87.25 11.61 1.07 0.07

Val 117 C5H11NO2 93.63 5.81 0.53 0.03

233 Appendix D, Method 5.1: Data analysis using Compound Discoverer

The pre-defined metabolomics data analysis workflow template (Figure 5.1) was selected for analysis. Briefly, the raw files (sample and blank) were selected for input. The blank file is imported to mark background compounds. The software analyzes the individual raw files and selects the spectra based on the parameters viz, ion selection type of MS (n-1), looking for precursor selection, within a mass range of 100 Da - 5000 Da. Following this, for retention time alignment the adaptive curve model with mass tolerance of 2 ppm was selected. Unknown compounds were detected within a mass tolerance thereshold of 2ppm tolerance, 30% intensity tolerance for isotope search and signal t noise (s/n) ratio threshold of 3. Only peaks with a minimum peak intrensity of 2x 10^6 were considered as true metabolite peaks. Unkown compound grouping and gap filling were carried out at retention time (Rt) tolerance of 0.05 sec, 2 ppm mass tolerance and s/n threshold of 1.5 for centroid filtering.

Figure 5.1: Compound Discoverer workflow template for metabolomics data analysis.

234