Influences of Gravitational Intensity on the Transcriptional Landscape of Arabidopsis

thaliana

A dissertation presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Alexander D. Meyers

May 2020

This dissertation titled

Influences of Gravitational Intensity on the Transcriptional Landscape of Arabidopsis

thaliana

ALEXANDER D. MEYERS

has been approved for

the Department of Molecular and Cellular Biology

and the College of Arts and Sciences by

Sarah E. Wyatt

Professor of Environmental and Plant Biology

Florenz Plassmann

Dean, College of Arts and Sciences 3

Abstract

MEYERS, ALEXANDER D, Ph.D., May 2020, Molecular and Cellular Biology

Influences of Gravitational Intensity on the Transcriptional Landscape of Arabidopsis thaliana

Director of Dissertation: Sarah E. Wyatt

Plants use a myriad of environmental cues to inform their growth and development. The force of gravity has been a consistent abiotic input throughout plant evolution, and plants utilize gravity sensing mechanisms to maintain proper orientation and architecture. Despite thorough study, the specific mechanics behind plant gravity perception remain largely undefined or unproven. At the center of plant gravitropism are dense, specialized organelles called starch statoliths that sediment in the direction of gravity. Herein I describe a series of experiments in Arabidopsis that leveraged RNA sequencing to probe gravity response mechanisms in plants, utilizing reorientation in

Earth’s 1g, fractional gravity environments aboard the International Space Station, and simulated fractional and hyper gravity environments within various specialized hardware.

Seedlings were examined at organ-level resolution, and the statolith-deficient pgm-1 mutant was subjected to all treatments alongside wildtype seedlings in an effort to resolve the impact of starch statoliths on gravity response. In all, 132 unique genotype/tissue/treatment datasets were collected to help further our understanding of the gravitropic mechanisms in plants.

Dedication

For Carl

Acknowledgments

The work presented here represents a collaborative effort, and I would like to acknowledge the many people who made it possible. Thank you.

Table of Contents

Page

Abstract ...... 3 Dedication ...... 4 Acknowledgments...... 5 List of Tables ...... 8 List of Figures ...... 9 Chapter 1: An Overview of Gravity Sensing and Response in Plants ...... 15 Introduction ...... 15 Background ...... 15 Timeline of Gravity Response ...... 17 Gravity Sensing: The Role of Plastids ...... 18 Gravity Response: The Role of Auxin ...... 20 Other Players in Gravity Response ...... 23 Conclusions ...... 24 Chapter 2: Starch Statoliths and Their Role in Plant Gravity Response...... 25 Introduction and History ...... 25 Sensing Mechanisms ...... 28 Evidence for Statoliths ...... 31 Evolution of Starch Statoliths – Root Perspectives ...... 32 Statolith Production, Regulation, and Turnover ...... 34 Starchless and Starch-Deficient Mutants ...... 35 Cytoskeletal Features of the Statocyte ...... 36 Distinctions Between Root and Shoot Statocytes ...... 37 Paradigms of Perception and Future Outlook ...... 38 Chapter 3: Plant Gravity Perception – Molecular Definition for Spaceflight ...... 40 Introduction ...... 40 Post-Flight Definition ...... 42 Plant Material, Sampling, and Replicates ...... 43 Dissection ...... 47 Extraction ...... 50 Sequencing ...... 52 7

Validation ...... 53 Conclusions ...... 56 Chapter 4: Transcriptional Effects of Gravitational Reorientation in Starchless Mutants of Arabidopsis ...... 57 Introduction ...... 57 Methods...... 58 Plant Growth ...... 58 Treatment ...... 58 Processing ...... 59 Bioinformatics and Data Analysis ...... 60 Results ...... 63 Contrast Analysis ...... 76 KEGG Pathway Enrichments - Reorientation ...... 84 KEGG Pathway Enrichments - WT vs pgm-1 ...... 96 Network Analysis...... 114 Discussion ...... 118 Root Tip Differential Expression ...... 118 Hypocotyl Differential Expression ...... 122 Cotyledon Differential Expression ...... 124 Mature Root Differential Expression ...... 124 Metabolism in pgm-1 ...... 126 Contrast Analyses ...... 127 Conclusions ...... 130 Chapter 5: Future directions...... 132 PGP Flight ...... 134 PGP-ESTEC ...... 135 Results, Conclusions, & Future Directions ...... 137 References ...... 143 Appendix: Methods for Plant Growth and RNA Extraction for Polyethersulfone (PES) Membranes ...... 152

List of Tables

Page

Table 1. Most differentially expressed genes (Log2 Fold Change >1.5 or < -1.5) from WT hypocotyl after 10min reorientation...... 66 Table 2. All differentially expressed genes in the pgm-1 root tip after 10min reorientation...... 71 Table 3. Most differentially expressed genes (LFC >1.5 or < -1.5) from pgm-1 mature root...... 73 Table 4. Genes identified through contrast analysis in the root tip...... 78 Table 5. Genes identified through contrast analysis for mature root...... 82 Table 6. Genes identified through contrast analysis in the for hypocotyl...... 82 Table 7. Genes from root tip contrast with mutant phenotypes with by fixed rotation .. 129

List of Figures

Page

Figure 1. Mechanism of differential growth. Upon reorientation, auxin carrier proteins (not shown) are redistributed in the organ, leading to a differential auxin concentration gradient and differential growth. Adapted from Laxmi et al., 2017...... 17 Figure 2. Statoliths in gravity response. Top: statoliths in the root tip and hypocotyl of Arabidopsis. Bottom: The sedimentation of statoliths signals a gravitropic response. .... 20 Figure 3. Auxin overview. Left: Indole acetic acid (IAA), the most common natural auxin, and its precursor Indole butyric acid (IBA) (Enders and Strader 2015). Right: In the absence of auxin, ARF protein-mediated transcription is repressed. In the presence of auxin, the TIR1 SCF complex ubiquitylates the AUX/IAA repressor and auxin- responsive genes are expressed...... 21 Figure 4. Locations and structures of statocytes in Arabidopsis. Above: location of the starch sheath in the stem of Arabidopsis and blow-up of typical stem statocyte. Below: location of statocytes in the columella cells of the root tip and blow-up of a typical root statocyte. Adapted from Moritaka 2019...... 27 Figure 5. Statolith position sensor hypothesis of gravitropism. Blue x symbols represent possible sensory mechanism locations...... 31 Figure 6. Evolution of starch statoliths in rapid root gravitropism. Root amyloplasts appear to have begun their role as statoliths after the seed plants diverged from ferns. Adapted from Zhang et al. 2019...... 34 Figure 7. EMCS Seed cassette. Approximately 13 seedlings were grown in each cassette under fractional gravity aboard the International Space Station...... 41 Figure 8. Molecular definition phase of PGP. Left panel depicts what was known at the outset of the experiment, and the right panel depicts the eventual pipeline for definition phase...... 43 Figure 9. g levels tested in the PGP spaceflight experiment. Twelve g levels (green circles) were chosen for sequencing based on representation across the gravitational gradient and germination rates of seedlings. To visualize the distribution of sampling, each vertical line on the x-axis represents 20% of the g levels tested. Lunar gravity is represented by the solid grey circle, Martian gravity is represented by the solid red circle, and Earth gravity is represented by the solid green circle...... 45 Figure 10. Scalpels for plant dissection. A #11 or #11c scalpel (top) worked better than #15 or #15k (bottom) for this application...... 49 Figure 11. Pipeline validation of RNA integrity. Pilot experiment RNA integrity numbers >8 were deemed acceptable for sequencing. RNA integrity was evaluated by Agilent Bioanalyzer 6000 Pico Chip. CO=cotyledon, HY= hypocotyl, MR = mature root, RT= root tip. Each box plots represent 16 extractions, boxes represent interquartile range of RIN, error bars represent max and min values...... 54 10

Figure 12. Pipeline validation for RNA yields. RNA yields were variable within and between tissues, but were within acceptable parameters for Takara SMART-Seq v4 Ultra Low Input RNA Library preparation kit. Yields were assessed using Agilent Bioanalyzer 6000 Pico Chip. Box plots represent 16 samples each, boxes represent the interquartile range, error bars represent the max and min values...... 55 Figure 13. Seedlings in growth chamber (left) and during reorientation treatments (right). Seedlings were grown 4 days vertically and then turned 90° in custom 3D printed petri dish hardware...... 59 Figure 14. Comparison pipeline for pairwise datasets. Data from gene sets (beige) were analyzed for overlap and piped to Venn diagrams (pink) for visualization. Abbreviations: Col is Columbia-0, p and pgm are pgm-1, RT is root tip, MR is mature root, HY is hypocotyl, CO is cotyledon, t is treatment, v is vertical (untreated control)...... 62 Figure 15. Representation of the 16 unique transcriptomes constructed in the PGP-Pilot experiment. Each of the 16 transcriptome represent 4 pooled biological replicates...... 64 Figure 16. Pairwise comparison matrix of the number of differentially expressed genes. Comparisons of treatments within genotypes are highlighted in green, comparisons of WT and pgm-1 tissues under the same treatment highlighted in yellow, and comparisons of WT and pgm-1 tissues under opposite treatments are highlighted in red. Numbers indicate number of differentially expressed genes for each pairwise comparison...... 65 Figure 17. Venn diagram showing the number of genes differentially expressed (FDR = 0.05) between vertical and reoriented in each tissue for wildtype Col-0 (left) and pgm-1 seedlings (right)...... 65 Figure 18. Venn diagram of the results of the contrast analysis calculated using the wildtype (Col-0) vertical, Col-0 reoriented, pgm-1 vertical, and pgm-1 reoriented expression for each of the four organs sequenced...... 77 Figure 19. Venn diagrams of the comparison between differential expression datasets and contrast analysis in the root tip (left) and hypocotyl (right). The contrast analysis provided a distinct gene set as compared to the differential expression analysis...... 77 Figure 20. Subcellular localization of genes from the hypocotyl contrast. Plastid-localized genes represent the largest subset...... 78 Figure 21. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the glycolysis/gluconeogenesis pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 85 Figure 22. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the glycine, serine and threonine metabolism pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 86 Figure 23. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the pyruvate metabolism pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 87 11

Figure 24. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the glyoxylate and dicarboxylate metabolism pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 88 Figure 25. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the carbon fixation pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 89 Figure 26. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the porphyrin and chlorophyll metabolism pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 90 Figure 27. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the aminoacyl t-RNA biosynthesis pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 91 Figure 28. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicate changes in carbon metabolism. Green lines indicate an alteration in the pathway as measured by gene expression...... 92 Figure 29. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicate changes in biosynthesis of amino acids. Green lines indicate an alteration in the pathway as measured by gene expression...... 93 Figure 30. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the spliceosome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 94 Figure 31. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of endocytic pathways were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 95 Figure 32. KEGG pathway analysis showed the pgm-1 mature root had a drastic downregulation of ribosomal components upon reorientation. Blue and purple indicate a down regulation upon treatment...... 96 Figure 33. Glycolysis/gluconeogenesis KEGG pathway analysis for the differential expression gene set between wildtype hypocotyl untreated and pgm-1 untreated hypocotyl. Genes in blue are upregulated in the pgm-1 plants, and orange means upregulated in the wildtype plants. The gene labelled 5.4.2.2 is phosphoglucomutase, the gene knocked out in pgm-1 mutants, and appears in orange here...... 98 Figure 34. KEGG pathway analysis for the differential expression gene set between wildtype hypocotyl untreated and pgm-1 untreated hypocotyl indicates that components of the citrate cycle were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 99 Figure 35. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components 12 of purine metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 100 Figure 36. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of glycine, serine, and threonine metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 101 Figure 37. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of glutathione metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 102 Figure 38. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of the pyruvate metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 103 Figure 39. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of glyoxylate and dicarboxylate metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 104 Figure 40. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of aminoacyl t-RNA biosynthesis were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 105 Figure 41. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl. Green lines represent aspects of carbon metabolism that were significantly altered between genotypes...... 106 Figure 42. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl. Green lines represent aspects of amino acid biosynthesis that were significantly altered between genotypes. 107 Figure 43. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of the spliceosome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 108 Figure 44. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of protein export pathways were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 109 Figure 45. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of protein processing in the ER were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 110 13

Figure 46. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of endocytosis were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 111 Figure 47. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of the phagosome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 112 Figure 48. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the peroxisome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend...... 113 Figure 49. Network analysis of the contrast root tip gene set. Halos indicate the most up (red) and down (blue) regulated genes in the dataset. Edges indicate known interactions (light blue and pink), predicted interactions (green, red, and dark blue), text mining co- occurrence (yellow), co-expression (black) and protein homology (purple)...... 115 Figure 50. Network analysis of the contrast hypocotyl gene set. Halos represent the most up (red) and down (blue) regulated genes in the dataset. Edges represent known interactions (light blue and pink), predicted interactions (green, red, and dark blue), text mining co-occurrence (yellow), co-expression (black) and protein homology (purple). 116 Figure 51. Venn diagram representing the 6 pairwise hypocotyl comparisons (top). A subset of genes from the hypocotyl pairwise analysis was overlaid to the hypocotyl contrast analysis (bottom left), and common genes are highlighted in the network (bottom right)...... 117 Figure 52. Possible physiological explanations for observed transcriptional landscapes. Venn diagrams showing, the tissue-specific differential expression between vertical and reoriented wildtype (WT) and pgm-1 plants (left), and the possible physiological responses that could explain the expression (right)...... 120 Figure 53. Representation of one of the two rotors that comprise the European Modular Cultivation System. Each rotor holds four experimental containers (large rectangles). Each EC holds five seed cassettes (small rectangles). Gravitational force increases with distance from the center...... 133 Figure 54. Gravity simulation equipment at the European Space Research and Technology Centre. A. Random positioning machines (small, blue) and 3D clinostat (white). B. Large diameter centrifuge...... 136 Figure 55. Experimental setup for PGP-ESTEC. Growing the seed arcs in two different orientations (A, C, D) allowed for the introduction of a 90° reorientation into the fractional gravity treatment. The 2 seed arcs (B) allowed for two different gravitational intensities per plate...... 137 Figure 56. Sankey diagram of all samples from PGP, excluding replicates. The hardware each sample was run on is at the left of the diagram. RPM is random positioning machine. EMCS is the European Modular Cultivation System. LDC is the large diameter 14 centrifuge. Samples that from the pilot experiment (Chapter 4) are represented in pink...... 139 Figure 57. Representative images of plant growth during the EMCS-PGP experiment. 140

Chapter 1: An Overview of Gravity Sensing and Response in Plants

Introduction

Despite their reputation as static organisms, plants have the innate ability to sense and respond to the many varying environmental conditions they must endure. Water, nutrient availability, temperature, and light can all influence how a plant grows and interacts with its environment. The force of gravity is the most consistent of these environmental cues, and thus, plants use the unchanging directional properties of Earth’s gravitational field to help inform many aspects of their growth and development. Plant response to gravity has been researched for decades; Charles Darwin (1896) himself wrote on the subject, and the primary gravity perception mechanism was first proposed over 100 years ago (reviewed in Masson et al., 2002). Despite the long history of scientific investigation into gravity response, gravitropic research remains an active and relevant field of science, and the biology behind gravity response still holds many mysteries.

Background

A plant’s ability to sense gravity has a profound impact on its physiology, and a plant utilizes this ability from its very first life stages. A seed buried beneath the soil uses gravity cues to send its roots down and its shoots up in order to emerge from the earth and begin photosynthesis. As a plant continues to grow, gravity sensing dictates much of a plant’s shape and architecture. Gravity set point angle (GSA) is the nonvertical antigravitropic growth offset of secondary branches. GSA is a gravity-dependent factor, and one of the primary aspects of plant form (Roychoudhry et al., 2013). In the event 16 that a plant falls over, the ability to sense gravity is what allows a plant to right itself.

Gravity perception therefore influences many stages of plant growth, from germination, to architecture, to reorientation within its environment.

Gravitropic response in plants is delineated into three phases: perception, signaling, and growth (Molas and Kiss, 2009). Generally, the perception phase is considered to begin with the sedimentation of specialized organelles called statoliths.

Upon reorientation, these organelles sink to the new bottom of the cell. The biophysical stimulus caused by the sedimentation begins a cascade of cellular and molecular responses. The most impactful of these responses is the redistribution of the phytohormone auxin. Reorientation causes auxin to accumulate along the lower side of the plant organ. In the shoot, auxin signals cellular elongation along the lower side, which causes the shoot to bend upward. In the root, auxin on the lower side inhibits cellular growth, and the faster expansion along the upper side of the root causes a downward curvature (Su et al., 2019) (Figure 1). In this way, a parallel response system exists in the roots and shoots that ultimately leads to opposite directional effects.

Timeline of Gravity Response

Within the framework of sensing, signaling, and response, what is the timeline for these phases? Due to the challenges in studying shoots, much of the data available is biased toward root responses. In the scenario of a 90° reorientation in the root, the statoliths are unmoved from their previous position along the former bottom of the cell after 10 seconds. By 3 minutes however, statoliths have sedimented to the new bottom and are evenly distributed by 5 minutes (Leitz et al., 2009, Sato et al., 2014).

Relocalization of auxin carriers PIN3 and PIN7 begins 2 minutes after reorientation, and the subsequent auxin gradient generated peaks around 2 hours post-reorientation (Friml et al., 2002). Within 3 minutes of reorientation, the extracellular pH drops on the upper side of the root and rises on the lower side of the root (Monshausen et al., 2010). The pH changes are auxin-dependent and regulated through calcium-dependent signaling pathways (Monshausen et al., 2010). A change in root tip angle can be observed within 18

15 minutes, and the root tip is generally fully realigned along the vector of gravity in 5-6 hours (Masson et al., 2013). The rapid nature of gravity response has led to the hypothesis that the earliest signaling events are non-genomic (Swarup et al., 2012), and the full relationship between genomic and non-genomic gravity responses remains unclear.

Gravity Sensing: The Role of Plastids

The specialized cells responsible for gravity sensing are called statocytes and are contained in the root tip and the endodermis of the stem. These cells have a specific type of dense, starch-filled amyloplast called a statolith (Morita et al., 2010. Blancaflor et al.,

2003). The statoliths sediment to the bottom of the cell, and by this mechanism plants establish “up or down” directionality (Figure 2). The starch-statolith hypothesis was first proposed in 1900, and amyloplast sedimentation has been observed in almost all gravity- responsive organs of higher plants (reviewed in Morita, 2010). Although the idea of gravisensing through amyloplast statolith sedimentation has been around for over 100 years, there is no consensus on the specific mechanism (Sato et al., 2015). For example,

Arabidopsis mutants in phosphoglucomutase (pgm) lack starch entirely. Although the statoliths in these mutants do not fully sediment, the plants still have a clear (though diminished) response to gravity. Experimental data show that the presence of the plastid itself is required for proper gravisensing, with the added mass of the starch necessary for a full response (Caspar and Pickard 1989, Kiss et al., 1997). Nevertheless, a specific molecular mechanism by which statoliths are able to trigger a gravity response remains elusive. The interaction of F-actin with statoliths is thought to play a role in triggering 19 downstream responses, but this mechanism, too, remains unknown. Disrupting F-actin with Latrunculin B increases gravitropic response rather than attenuating it, as was expected (Hou et al., 2004). SGR9 is an E3 ligase shown to facilitate detachment of the amyloplast from actin bundles allowing sedimentation with mutants showing only a weak gravity response (Nakamura et al., 2011). A mutation in the DIS1 subunit of the actin branching protein ARP3 showed altered sedimentation tendencies and gravity response, presumably through altering the perceived cytoplasmic viscosity (Zou et al., 2016).

Therefore, amyloplasts are crucial for gravity response and that they interact with the cytoskeleton, but relationships between these interactions and downstream gravitropic events remain largely unresolved. For a deeper analysis on plastid involvement in gravity response see Chapter 2.

Figure 2. Statoliths in gravity response. Top: statoliths in the root tip and hypocotyl of Arabidopsis. Bottom: The sedimentation of statoliths signals a gravitropic response.

Gravity Response: The Role of Auxin

Auxin is an omnipresent small-molecule phytohormone generally associated with cell elongation (Enders and Strader 2015). The effects of auxin were first observed in the early 1900’s, and the compound was named auxin for the Greek word “to grow”. Now accepted as one of the quintessential hormone response pathways in plants, auxin’s mechanism of action was not uncovered until 2005, when auxin was shown to act through the ubiquitin 26S proteasome system (Estelle et al., 2005, Weijers and Wagner

2016). Auxin Response Factors (ARFs) are a family of 23 transcription factors that are 21 constitutively bound to auxin responsive elements (Chandler 2016). In the absence of auxin, AUX/IAA repressor proteins are bound to the ARFs, preventing their transcriptional enhancement. In the presence of auxin, however, the F-Box protein TIR1 can form a functional SCF complex to target the AUX/IAA proteins for ubiquitylation and subsequent degradation. In this way, the presence of auxin initiates the transcription of a multitude of genes and is an integral part of the gravitropic response (Estelle et al.,

2005, Chandler et al., 2016, Powers and Strader, 2016, Fendrych et al., 2016) (Figure 3).

Figure 3. Auxin overview. Left: Indole acetic acid (IAA), the most common natural auxin, and its precursor Indole butyric acid (IBA) (Enders and Strader 2015). Right: In the absence of auxin, ARF protein-mediated transcription is repressed. In the presence of auxin, the TIR1 SCF complex ubiquitylates the AUX/IAA repressor and auxin- responsive genes are expressed. 22

Elongation of cells along one side of a plant organ causes bending. Cholodny and

Went (1928) proposed that auxin was responsible for this elongation. The Cholodny-

Went model is still accepted as the mechanism behind gravitropic bending (Muday

2008). In the model of perception→signaling→growth, auxin is at the interface of the latter two phases. How, then, does auxin become preferentially distributed along the under-side of a stem or root?

Auxin efflux and influx carrier proteins move auxin across the plasma membrane by active transport. There are three main families of auxin carriers, but most relevant in gravity response are the PIN-FORMED (PIN) proteins, a family of eight auxin efflux carriers (Armengot et al., 2016). In both the root and the shoot, upon reorientation, auxin is redistributed to the lower side of the gravity sensing organ. Due to technical limitations of visualization techniques, PIN redistribution is often best documented in the root.

Auxin inhibits cellular elongation in the root rather than inducing growth as in the shoot thus causing the opposite directionality of growth between roots and shoots using a primarily parallel signaling pathway. Upon reorientation in the root, PIN2 (and AUX1) moves auxin from the shoot to the root tip, which is then moved from the vascular tissues to the columella cells by PIN4. PIN3 and PIN7 establishing a “downward” auxin concentration gradient, followed by PIN2 (and AUX1) distribute auxin along the bottom side of the root to the endodermal cells in the zone of elongation (Sato et al., 2015). PIN relocation itself is tightly regulated, primarily by IP3 lipid-based signaling pathways

(Perera et al., 2013). While some of the events preceding the redistribution of PIN 23 proteins are well-understood, there is still a great deal to be learned about the earliest signaling events linking gravity perception to late signals and growth.

Other Players in Gravity Response

Auxin is clearly the main elicitor of gravitropic bending in plant organs. However, many other pathways play roles in the modulation of this response, and many of the mechanisms are still unclear (Laxmi et al., 2017). Ethylene is often considered a plant stress hormone. Exogenously applied ethylene strongly inhibits gravitropic curvature and elongation and also delays asymmetric auxin distribution (Buer et al., 2006). Gibberellins are plant growth regulators strongly implicated in cell elongation, and they contribute to the gravitropic response through their transcriptional regulation of IAA19 (Gallego-

Bartolome 2011) and by stabilizing PIN proteins (Lofke et al., 2013). Another phytohormone, abscisic acid, seems to be a negative regulator of gravitropism, but the mechanism behind abscisic acid’s influence of gravity response has remained unsolved

(Han et al., 2009). Cytokinins, a class of phytohormones associated with cell division and growth, are somehow involved in the inhibition of elongation along the lower side of the root during gravitropic response (Aloni et al., 2004). Brassinosteroids increase sensitivity to auxin in roots and augment the gravity response (Kim et al., 2000). Jasmonic acid has been implicated in control of the formation of auxin gradients by regulating auxin synthesis and auxin redistribution (Staswick et al., 2009). Nitric oxide, reactive oxygen species, and IP3 have also each been implicated in the early, possibly non-genomic phases of gravity response, but many of those molecular pathways are similarly 24 unresolved (Swarup et al., 2014, Perera et al., 2013). Taken together, it is clear that there is a complex network of signals regulating gravity response.

Conclusions

Gravity response represents an integral part of plant physiology, and resolution of this biological process will represent a giant leap forward in our understanding of fundamental plant biology. As we move further into the molecular age, new tools become available for studying gravitropism. Auxin visualization has always relied on transcription- or protein turnover-based platforms. However, a FRET-based direct- detection system was recently reported for auxin visualization, which has the potential to greatly improve the spatio-temporal resolution of the gravity response data (Jurgens et al.,

2020). Furthermore, few studies have examined gene expression in different plant organs upon reorientation. As RNA sequencing continues to drop in price, the transcriptional data that will become available will open new doors for gravitropic investigation. For an in-depth examination of reorientation-based transcription, see Chapter 3.

Chapter 2: Starch Statoliths and Their Role in Plant Gravity Response

Introduction and History

A plant’s ability to sense and respond to the directional stimulus of gravity is a central aspect of its growth and architecture and the study of gravitational biology in plants has a long history. From the earliest experiments to demonstrate plant gravitational sensitivity (Knight 1805) to The Power of Movement in Plants (Darwin & Darwin 1885) to modern experiments aimed at studying plants in the microgravity environment of

Earth’s orbit, gravitropism has been of interest to plant scientists for over 200 years.

Despite a rich history and thorough study, many central questions about plant gravity sensing remain unanswered. Perhaps the most salient question pertains to the exact nature of the perception mechanisms employed by plants to sense gravity. At the heart of this debate are organelles called starch statoliths. With a history of study nearly as long as the study of gravitropism itself, the precise role of statoliths in gravity sensing has not yet been fully determined.

In the late 1800s, two botanists, Berthold and Noll, outlined the statolith theory of gravity perception (Noll 1892, Shen-Miller et al., 1974). They posited that gravity sensing could conceivably take place by the sedimentation of inclusions within the cell.

In 1900, Nemec and Haberlandt independently identified the sedimenting inclusions as starch granules. The term “statolith” was coined, derived from the Greek for “stationary stone” (Nemec 1900). Since that time, the gravitational biology community has gathered a wealth of evidence regarding the role of statoliths in gravity response. 26

Starch statoliths are dense, starch-packed amyloplasts that are contained in specialized cells called statocytes. Amyloplasts are sister organelles to chloroplasts, but rather than being the site of photosynthesis, their primary role is starch storage. In the statocytes, amyloplasts sediment in the direction of gravity. The statocytes in the root are the columella cells of the root cap. In aerial organs, statocytes are comprised of a single endodermal cell layer that surrounds the vasculature, called the starch sheath (Figure 4).

Statoliths can be found in virtually every gravity-sensitive vascular plant (Shen-Miller

1974). Some basal plant lineages have non-amyloplast statoliths, like the Streptophyte alga Chara that has calcium oxalate crystals that sediment in its rhizoids (Leitz et al.,

1995). 27

Figure 4. Locations and structures of statocytes in Arabidopsis. Above: location of the starch sheath in the stem of Arabidopsis and blow-up of typical stem statocyte. Below: location of statocytes in the columella cells of the root tip and blow-up of a typical root statocyte. Adapted from Moritaka 2019.

Plant gravity response has three phases: gravity perception, signal transduction, and differential growth. While the details of the mechanism are still debated, most models agree that starch statoliths serve as key components of gravity perception.

Statoliths sediment within the statocytes in the direction of gravity. This movement elicits 28 a cellular signal(s) that is transduced to the necessary location, driving the relocation of the auxin transport PIN proteins (Hashiguchi et al., 2013). The Cholodny-Went theory governs the response phase of gravitropism. This theory states that auxin gradients across the “top” and “bottom” of reoriented plant organs directs differential cell growth and causes the organ to bend. In the stem, auxin across the bottom causes cell expansion, resulting in the stem bending upward (Macdonald et al., 1987). In the root, auxin along the bottom inhibits growth, which has the opposite effect and the root tip reorients downward along the vector of gravity. This model of sensing, signaling, and response serves as a framework for gravitropic study, and a comprehensive understanding of plant gravity response requires an understanding of how the process begins. Despite all that is known in these processes, the mechanism by which statoliths act as gravity sensors remains unproven.

Sensing Mechanisms

The starch-statolith model states that the dense, starch-filled organelles sedimenting to the bottom of the statocytes is the mechanism by which the vector of gravity is sensed by the plant (Sack 1997), but there are multiple proposed models for their mechanism of action. The actin-tether model (Baluska et al., 1997) proposes that distinct connections between the sedimenting amyloplasts and actin filaments elicit a gravity response. The tensegrity model suggests the statoliths interact with a mesh of actin filaments in the columella cells, triggering mechanosensitive ion channels in the cell membrane to elicit a gravity response (Yoder et al., 2001). There is a general consensus that it is the density of the amyloplast (conveyed by starch), that is important for 29 gravitropic response, and not the starch itself. While a wealth of evidence suggests the importance of starch statoliths in gravity sensing, other models of gravity perception exclusive of statoliths have been proposed. According to the protoplast pressure hypothesis (Wayne et al., 1990), the action of gravity on the entire protoplast causes a non-uniform compression and slack across the cell membrane, and this mechanism is somehow utilized for gravity sensing. Importantly, these models are not necessarily mutually exclusive. In fact, there is compelling evidence that a plant utilizes multiple gravitational sensors (Wolverton et al., 2002).

Most recently, an additional compelling mechanism has been proposed. The

“statolith position hypothesis” posits that statocytes are responsible for sensing the inclination of the cell, rather than gravitational force (Chauvet et al., 2016). Results from recent experiments suggest that shoot gravity response is independent of the gravitational intensity. If gravitational intensity does not influence gravity response, the response is independent of the weight of the statoliths. This model is partially based on evidence of the dynamics of statolith sedimentation. It has been proposed that statolith movement may not represent traditional sedimentation as it has been previously conceived (Leitz

2009, Berut 2018). Rather, the collection of statoliths in the bottom of the cell demonstrates the dynamics of a “grain pile.” When the cell is inclined, the pile avalanches to the lower position in the cell, in that way conveying positional information

(Figure 5). The other compelling piece of evidence supporting this hypothesis is the adherence to the “sine law” of gravity response under various gravitational intensities

(Pouliquen et al., 2017). The sine law states that shoot response varies linearly with sine 30 of its inclination angle from the vertical position, presumably from the increased force of the statoliths against the side of the cell (Galland 1992, Dumais 2013). Experiments supporting the position sensor hypothesis involved a growth chamber on a rotating stage that could induce various gravitational forces. The sine law held true for all g levels assessed, and thus, the authors claim that the sensor operates independently of gravitational intensity. The independence of gravitational intensity on the sensor has been shown at g levels from 0.1g up to 2.5 g (Chauvet et al., 2016). These data suggest that the system works as a clinometer (angle sensor) rather than a sensor of force (Pouliquen et al., 2017, Moulia et al., 2019). Unique among the proposed models of perception, this newest mechanism precludes the existence of other models. For example, the authors claim that the position sensor model negates the protoplast pressure hypothesis. While the authors systematically discuss the compatibility of this model with existing data, this model does not adequately explain gravity responses in starchless mutants of

Arabidopsis.

Figure 5. Statolith position sensor hypothesis of gravitropism. Blue x symbols represent possible sensory mechanism locations.

Evidence for Statoliths

Some of the earliest evidence of sedimenting statoliths as gravity sensors came from their ubiquity in gravisensing organs. Indeed, a quantitative correlation exists between their presence and gravitropic sensitivity within a plant organ (Shen-Miller

1974). The root statocytes are the columella cells of the root tip. Removal of the columella cells by laser ablation (Blancaflor et al., 1998) or decapping (Sack 1997) leads to a loss of gravitropic curvature in the root. In the shoot, the statocytes are in the starch sheath of the endodermis, a single cell layer that covers the outside of the vasculature

(Figure 4). Some of the evidence supporting the role of starch statoliths as purveyors of the gravity signal in the shoot comes from genetic mutants in transcription factors necessary for the development of the endodermal cell layer that normally houses the 32 shoot statocytes. Various mutants in the transcription factor SHOOT GRAVITROPISM 7

(SGR7) lack the endodermal layer in the shoot and are deficient in shoot gravitropic response (Laurenzio et al., 1996). Gravity response exists as expected in the roots of those mutants since the development of the root cap statocytes is unaffected by the mutation.

One classic experiment that supports the importance of starch statoliths as gravity sensors involves the use of high gradient magnetic fields (HGMF). The starch in statoliths makes them more diamagnetic than the rest of the cell, and as such they can be positionally manipulated through repulsion by HGMF. Amyloplast movement through

HGMF leads to a differential growth as would be predicted under gravitational reorientation (Kuznetsov and Hasenstein 1996, 1997). HGMF experiments also outline the spatial separation of gravity sensing and response in the root tip/root, and the lack of such a spatial separation between the locations of sensing and response in the inflorescent stem of Arabidopsis (Weisse et al., 2000).

Evolution of Starch Statoliths – Root Perspectives

The ability to use gravity as an indicator of directional growth holds clear evolutionary advantages, but when did the starch statolith-dependent mechanisms develop in plant lineages? A recent study from Zhang et al. (2019) sheds some light on this question. A representative species was chosen from several of the major plant lineages: Physcomitrella patens from the mosses, Selaginella moellendorffii from the lycophytes, Ceratopteris richardii from the ferns, Gossypium arboreum and Arabidopsis from the dicots, and Oryza sativa from the monocots (Figure 6). Each was reoriented 90° 33 with respect to gravity to track root gravitropic response. The moss, which has rhizoids rather than roots, showed a very slow gravity response. Lycophytes and ferns have true roots, but also showed a much slower root response than more recent lineages (slight change in root tip angle after 36 hours) (Zhang et al., 2019). However, the gymnosperm showed root gravitropic responses comparable to that of flowering plants, and the flowering plants examined showed the expected rapid response. These results suggest that there may be distinct mechanisms for root gravitropism between the basal lineages and seed plants. Since gymnosperms and angiosperms display rapid root gravitropism, this trait may have evolved in their most recent common ancestor. The rate of response to reorientation is reflected in the plants’ organization of root amyloplasts. Starch staining of moss rhizoids revealed that they are devoid of amyloplasts. Staining of the lycophyte roots showed the presence of amyloplasts, but they are located at the lateral sides of the root above the apex. The fern roots showed amyloplasts within and above the apex. In the gymnosperm, amyloplasts were localized in the apex, the same place they are observed in flowering plants (Figure 6) (Zhang et al., 2019). Reorientation of roots reveals that the amyloplasts of the fern and lycophyte were randomly distributed and did not sediment like statoliths in seed plants. This pattern suggests that apex-localized starch statoliths may have evolved after the divergence of the fern lineage in the common ancestor of seed plants and appears to be a key feature for rapid root gravitropic response (Zhang et al.,

2019). Importantly, this study examined only “fast root gravitropism”, the gravitropic activity in the root that takes place on a timescale of minutes. This study does not 34 represent the evolution of gravitropic response as a whole and there are likely other conserved gravitropic mechanisms across land plant lineages (Volkmann et al., 2006).

Figure 6. Evolution of starch statoliths in rapid root gravitropism. Root amyloplasts appear to have begun their role as statoliths after the seed plants diverged from ferns. Adapted from Zhang et al. 2019.

Statolith Production, Regulation, and Turnover

Representing an artifact of their endosymbiotic lineage, all plastids are propagated from pre-existing plastids rather than de novo synthesis. In vascular plants, plastids are derived from proplastids in meristematic cells (Sakamoto et al., 2008). Amyloplasts, the type of plastids that represent statoliths, are best studied in their role as storage organs within potato tubers and wheat endosperm. However, as plastids can serve a wide range of metabolic purposes in the cell, it is unclear just how similar storage-type amyloplasts are to starch statoliths. Despite their importance, little is known about the molecular mechanisms that govern amyloplast development from proplastids. Furthermore, it 35 cannot be assumed that starch statoliths use the same differentiation mechanisms as storage amyloplasts from proplastid to functioning organelle (Jarvis et al., 2014). Starch accumulation in the statoliths of the root apex is regulated by local auxin concentrations

(Zhang et al., 2019). Auxin influences this starch grain accumulation through the

AXR/IAA17 transcriptional repressor that controls the expression of the starch grain synthesis genes PGM, ADG1, and SS4 (Zhang et al., 2019). When it is advantageous, plants will modulate gravitropic signaling through statolith turnover (Kim et al., 2010,

Nakayama 2012). In the hypocotyl of early seedlings, presence of red light inhibits negative gravitropism through a phytochrome pathway that converts endodermal amyloplasts to chloroplasts or etioplasts (Kim et al., 2010). This change encourages phototropic response over gravitropic response in seedlings. Conversely, Phytochrome

Interacting Factors (PIFs) inhibit the conversion of endodermal amyloplasts to etioplasts in the dark, encouraging gravitropic response pathways in dark-grown seedlings that lack a phototropic stimulus (Kim et al., 2010). When roots sense a hydrotropic stimulus, statoliths are rapidly degraded. Evidence suggests that this degradation is likely through an autophagy pathway (Nakayama et al., 2012). Thus, plants are able to modulate their response to gravitational stimuli in favor of water or light in various situations by fine- tuning the input from statolith sedimentation.

Starchless and Starch-Deficient Mutants

An invaluable tool in understanding the contributions of amyloplasts to gravity sensing are the starchless and starch-deficient mutants. Plants defective in phosphoglucomutase (PGM) are entirely devoid of starch, and thus the amyloplasts in the 36 statocytes of these plants do not sediment. Paradoxically, starchless mutants still display a mitigated but clear gravity response (Pickard et al., 1989). This finding represents a primary line of evidence for alternative gravity perception mechanisms in plants.

However, in general, the mass of the amyloplast is correlated to the magnitude of gravity response (Kiss et al., 1996), and hypergravity treatment between 2g and 10g restores gravity response in starch-deficient plants (Fitzelle and Kiss 2001). The starch over- accumulation mutant sex1 is deficient in starch mobilization having larger statocytes and more sensitive to gravity (Kiss et al., 2007). Analysis of starchless and starch-deficient mutant plants has provided some of the most valuable empirical data for delineation of a specific gravity perception mechanism.

Cytoskeletal Features of the Statocyte

Both microtubules and actin filaments exist in statocytes. A network of actin filaments surrounds the statoliths in the root columella cells, whereas endodermal statocytes have thick bundles of F-actin (Collings et al., 2001). Some gravitropic models posit that the interaction of statoliths with actin filaments could activate mechanosensitive channels (Sievers et al., 1991). Latrunculin B is a pharmacological tool that depletes F-actin. Treatment with Lat-B mitigates statolith sedimentation in hypocotyls and inflorescent stems, while enhancing amyloplast movement in root columella cells. Lat-B treatment causes an overshooting phenotype in the stem (Hou et al., 2004, Palmieri et al., 2005), and stops saltatory statolith movement in the columella cells. Pouliquen et al. (2017) suggested statolith interaction with the cytoskeleton may encourage sedimentation during small inclinations (<25°) by agitating the statoliths. 37

While the mechanism is unclear, the cytoskeleton seems to play some role in the statolith dynamics and possibly signal transduction.

Distinctions Between Root and Shoot Statocytes

Although both the root and shoot contain amyloplasts housed in statocytes, the similarities generally end there. First, there is the most obvious difference between the two organs, shoots grow against the vector of gravity while roots preferentially grow with the direction of gravity. A recent study identified a mutant phenotype in which roots grow upwards in the same direction as the shoot (Ge et al., 2016). The NGR gene

(Negative Gravitropic Response) identified by that study directs auxin efflux carrier localization and is expressed only in the root columella cells (Ge et al., 2019). NGR represents one of the differences in the molecular response between roots and shoots and helps to explain the difference in directional growth. Statocytes in the root and shoot also differ in their tissue type. Shoot endodermal cells are larger than the columella statocytes of the root tip, with far larger central vacuoles. In shoots, sedimentation through the vacuole may be necessary for proper gravitropic response in the shoot (Saito et al., 2005).

Several mutants have been isolated that implicate vacuolar function and membrane trafficking in gravitropic response (Morita et al., 2002, Yano et al., 2003). Another feature that is distinctly different between the root and shoot statocytes is the presence of cortical endoplasmic reticulum, as seen in the root tip (Morita et al., 2013). Perbal (2003) hypothesized that the sedimentation of the statoliths in the columella cells releases Ca2+ from the cortical ER. 38

The term amyloplast refers to any plastid that accumulates starch and is broad in definition. The amyloplasts that serve as statoliths are distinctly different between root and shoot statocytes. Root columella amyloplasts/statoliths do not contain organized thylakoid or photosynthetic pigments (Sack 1991). Amyloplasts/statoliths in dark-grown hypocotyls are similarly structured, with occasional prolamellar bodies that are typical of etioplasts. Statoliths in the endodermis of the shoot contain developed thylakoid membranes (Morita et al., 2002) and may be more accurately described as chloroplasts that are accumulating starch (Morita 2010). In endodermal cells, statoliths often undergo

F-actin-dependent saltatory movements. This movement is absent in Arabidopsis columella cells, but similar activity is observed in Maize columella cells (Sack et al.,

1986). Given the many structural differences in the statocytes, it is unclear how much of the gravity sensing mechanism(s) is conserved between the root and shoot.

Paradigms of Perception and Future Outlook

Some recent studies in plant gravity response have proposed the “gravity sensing” systems are actually sensing inclination angle rather than gravitational force (Chauvet et al., 2016). Other studies have brought the concept of proprioception and autotropic straightening into the model (Dumais 2013). Efforts have been made to disentangle sensing mechanisms from motor processes in plant posture control, and “gravity resistance” (mechanical resistance to gravitational force) has been delineated as a discrete physiological function with possibly its own sensing mechanism (Moulia et al., 2019,

Hoson et al., 1996, Soga et al., 2003, 2004). Thus, as the field advances, our understanding of plant gravitropism is becoming more integrated into systems biology. 39

However, a precise understanding of the specific cellular sensing mechanisms will still be key to complete our models of plant gravity response(s).

Chapter 3: Plant Gravity Perception – Molecular Definition for Spaceflight

Introduction

Experimental design is among the most important phases of any scientific experiment. An elegantly designed set of experiments can answer specific questions while addressing any doubts that could be raised by the results. In practice, however, the success and strength of an experimental design relies on the feasibility of the proposed pipeline of techniques. In a novel experimental pipeline, definition of these parameters can represent the bulk of an experimental timeline but is an indispensable step in the success of a project.

Spaceflight experiments differ from many traditional experiments in that they cannot be built on an iterative, trial and error-based execution. Spaceflight opportunities are uncommon and must be successfully executed in a single attempt. Experimental design must be outlined, tested, and retested many times and in many ways before an experiment can be sent to space. Additionally, the resulting samples are irreplaceable, and downstream analyses must be perfected before the samples are subject to those techniques. Definition phase is necessary to investigate every relevant experimental parameter, from broad feasibility to the minutiae of every procedural step.

Outlined here is the molecular definition of a spaceflight experiment aimed at examining the phenotypic and transcriptional responses of plants exposed to gravitational intensities between 0 and 1g. The Plant Gravity Perception (PGP) experiment flew aboard the International Space Station (ISS) in late 2017 into early 2018. In brief,

Arabidopsis seeds were adhered to polyethersulfone (PES) membranes with a guar gum 41 suspension. The membranes were placed into specialized seed cassette hardware compatible with a variable-g centrifuge on ISS (Figure 7). Seedlings were grown for 4 days and then subject to 30 sub-terrestrial gravitational intensities. Images were captured throughout the experiment to track gravitropic bending. At the end of the experiment, seedlings were frozen and returned to Earth for molecular analysis.

Figure 7. EMCS Seed cassette. Approximately 13 seedlings were grown in each cassette under fractional gravity aboard the International Space Station.

Definition for PGP had two distinct phases: pre-flight setup and post-flight analysis. The pre-flight definition phase of the experiment required the determination of optimal seed handling procedures prior to plant growth, including sterilization, planting, and hydration. The outcome of these efforts was a specific, reliable pipeline for plant growth on polyethersulfone membranes, which has been organized for community reference and the final procedure is provided in Appendix A. Post-flight analysis required determination of a sampling strategy, effective methods for seedling dissection and RNA extraction, and a viable sequencing pipeline. 42

Post-Flight Definition

The molecular segment of PGP was conceived after the finalization of flight design, and the rigidity of the experimental system required adapting the post-flight molecular pipeline to the existing experimental parameters. Thus, definition phase began only with knowledge of the input (plant material available) and desired output (RNA-seq data). The first step in the definition was to outline the parameters to be assessed (Figure

8). The post-flight pipeline can most simply be divided into 4 stages: sampling, dissection, extraction, and sequencing. However, these stages are entirely interdependent, which made a step-by-step definition an ineffective strategy. For example, replicate and sampling strategy impacted final read depth, which could not be determined until final decisions were made on sequencing platform. Library preparation for RNA sequencing depended on yield/tissue, which depended on sampling and replicate strategy. Feasibility of sampling and replicate strategy depended on the scalability and parallelization of dissection, which needed to dovetail with the extraction pipeline, which in turn needed to yield samples appropriate for the library preparation methods chosen. In this way, molecular definition of this project presented a non-modular and multi-variable obstacle.

The goal of the definition was not to find the best solution for each step individually, but rather to find the best solution for each step that was also compatible with the pipeline as a whole. For simplicity, the stages of molecular definition will be presented here in the order in which they were ultimately executed, rather than the order they were solved.

Figure 8. Molecular definition phase of PGP. Left panel depicts what was known at the outset of the experiment, and the right panel depicts the eventual pipeline for definition phase.

Plant Material, Sampling, and Replicates

The samples available were contained within 120 seed cassettes. Each seed cassette contained up to thirteen 5-day-old Arabidopsis seedlings representing one of two genotypes (wildtype Arabidopsis and pgm-1 mutants) and was exposed to one of 25 different gravitational intensities between 0.003g and 1g. Two cassettes were run per g level, per genotype. Malfunctions during the experiment led to the loss of 5 of the planned 30 g levels, which resulted in the 25 g levels from which samples were selected.

Considerations for sampling began with replicate requirement. If replicates were collected by cassette, only two biological replicates would be available per genotype per g level. At least 3 replicates are required for RNA-seq, so 1 cassette=1 replicate was an 44 inadequate solution. Alternatives included using cassettes from the closely spaced g levels as biologically equivalent. This alternative was not ideal, as it introduced experimental variability within replicate groups. More importantly, g level treatments were not evenly dispersed between 0.003g and 1g. In fact, 50% of the g levels assessed were below 0.1g (Figure 9). Therefore, the best alternative was to split each cassette into two replicates. The downside to this sampling strategy was that it meant a simultaneous doubling of any immediate downstream steps (dissection, extraction), as cassettes cannot be partially sampled. Cassettes must be thawed to be opened, thus plants not processed initially would undergo a freeze-thaw cycle, impacting RNA integrity. However, it was unclear if seedlings from half a cassette would yield adequate RNA for analysis. An added benefit to this strategy was that it would result in four replicates per sample. After testing yields and investigating RNA library input requirements, we ultimately decided that splitting a single cassette into two replicates was the best path forward, despite the parallelization required in dissection and extraction. 45

Figure 9. g levels tested in the PGP spaceflight experiment. Twelve g levels (green circles) were chosen for sequencing based on representation across the gravitational gradient and germination rates of seedlings. To visualize the distribution of sampling, each vertical line on the x-axis represents 20% of the g levels tested. Lunar gravity is represented by the solid grey circle, Martian gravity is represented by the solid red circle, and Earth gravity is represented by the solid green circle.

The next consideration in sampling was which plant tissues to use. In this step, there were both biological and practical experimental considerations. The easiest option was to use whole seedlings. However, RNA-seq from whole seedlings was not an appealing option, as the low organ/tissue resolution obfuscates any organ-specific insights provided by the data. Many spaceflight studies divide seedlings into roots and shoots, choosing to examine one or both. The obvious basis for this separation is the differences in gravity response between the root and shoot tissues: roots grow in the direction of gravity and shoots grow against gravity. Although providing better tissue specific resolution, differing root and shoot tissues are distinct in their responses to 46 gravity. In the root, only the root cap senses gravity. The bending response takes place further up the root, in the elongation zone (Morita et al., 2013. Masson et al., 2002.

Blancaflor et al., 2003).Thus, separating the root tip from the rest of the root for sequencing purposes provided higher resolution of the organ- and tissue-specific gravity sensing mechanisms. In the shoot, the hypocotyl is responsible for gravity sensing and response, while the cotyledons have neither gravity sensing nor response. Ideally then, the shoot should also be divided into two parts for sequencing. By this line of reasoning, each seedling would be divided into root tip, mature root, hypocotyl, and cotyledon fractions. Similar to the replicate considerations, this tissue collection strategy has considerable impacts downstream in the pipeline. First, four tissues per replicate and two replicates per seed cassette means the dissection and extraction pipeline must be capable of simultaneous parallelization up to eight samples. Additionally, the smallest of these tissues, the root tip, is defined in this study as the initial 3 mm of the growing root. The mass of the root tip is considerably lower than is generally used for traditional RNA sequencing, even with 6-7 root tips pooled into a single replicate. Nevertheless, the informational benefits of organ-level resolution were great enough to justify assessing the feasibility of this sampling and replicate strategy in the downstream methods, and the 4x4

(replicate x tissue) strategy was ultimately chosen to provide the most biologically relevant data and insights.

The implication of 4 tissues, 4 replicates, and 2 genotypes is that 32 samples were to be sequenced per g level. With 25 g levels to consider, two specific parameters needed to be resolved: the number of g levels to be sequenced and subsequently which g levels to 47 choose. With a fixed sequencing budget, the more g levels chosen, the fewer reads per sample. Ultimately, 12 g levels were chosen for sequencing, resulting in 384 samples representing 96 distinct tissue/genotype/g level combinations. The g levels chosen were based on several factors. First, we wanted g levels that represented the lowest g treatment, the highest g treatment, Lunar and Martian gravity. The other g levels were chosen based on the most uniform spread of sampling across the g gradient, with consideration given to germination rates at each g level, and the g levels above and below response thresholds observed in pgm-1 (Figure 9).

Dissection

With the vast number of seedlings to be dissected and the need to isolate quality

RNA for sequencing, the technique developed had to be both rapid and reproducible.

Seedlings were stored frozen at -80 °C within seed cassettes. To begin the dissection, a seed cassette was removed from the freezer and quickly disassembled to get to the seedlings. A layer of adhesive foil had to be removed from the cassette, then the cassette cover was carefully pried open, with care taken not to disturb the frozen and brittle seedlings. To ensure accuracy and precision, all dissections were performed using a dissecting microscope. To keep the samples cold, cassettes were kept on a cold block during dissection. Seedling tissue that was frozen to the membrane could not be removed effectively. To help release the tissue, the membranes were flooded in RNA extraction buffer RLT. Approximately 1mL was sufficient to saturate the membrane, but not enough to allow the seedlings to begin to “float, ” as during dissection the smaller tissues can quickly be lost to the eye. Some cassettes had as few as a single seedling per replicate, so 48 keeping track of tissue was vital. Additionally, RLT extraction buffer crystalizes near -80

°C. If the RLT crystalizes on the membrane, tissue recovery is nearly impossible.

Membranes were given a short period (<30 seconds) to warm slightly before flooding in

RLT. For the same reason, the cold block used for the dissections was kept at -20°C.

Before dissection, RLT extraction buffer was also added to eight 2-mL Omni homogenization microtubes with 2.8 mm ceramic beads. Tubes were kept on ice to prepare them for the tissue.

My dissection strategy defined the root tip as the last 3 mm of the root, and the

“mature root” was the rest of the root between the root tip and root/hypocotyl junction.

Importantly, cotyledons were excised at the petiole rather than taking the whole “shoot” of the plant. This was done to keep the apical meristem within the hypocotyl tissue. The apical meristem is the site of auxin synthesis and active growth, and it was thus important to keep that portion of the hypocotyl within the hypocotyl sample. Any remaining seed coat material was left behind. Sterile, RNase-free single use #11 scalpels (Cincinnati

Surgical) were used for dissection. I determined that the #15 scalpel works best for a

“chop” (cut by pressing down from the top), where the #11 works best for a slice

(dragging the scalpel across the membrane) (Figure 10). The chop and slice techniques each have trade-offs. Chopping keeps the tissue in place during the cut, but there is a higher chance the tissue comes away stuck to the scalpel. With the slice technique, the tissue is often moved away from where it was cut, but it rarely comes away with the blade. Ultimately slicing was the best alternative for tissue recovery. For this excision step, it was vital to ensure a complete cut was made. An incomplete cut necessitates an 49 additional switch between scalpel and forceps, which adds considerable time to the dissection and complicates the sample collection.

Figure 10. Scalpels for plant dissection. A #11 or #11c scalpel (top) worked better than #15 or #15k (bottom) for this application.

After much trial and error, the most effective dissection pipeline was determined to be cut, collect, repeat. The following procedure was performed for each cassette individually. After the membrane was flooded with RLT, all the root tips were excised at one time. Half the root tips were collected for each replicate. The forceps holding the tissue were thoroughly swished into the 2-mL bead tube with RLT. Forceps were inspected under the microscope to ensure all tissue was left in the tube, then the other half of the root tips were collected. Cuts were then made at the junction of the root and hypocotyl. At this time, if applicable, any seed coat tissue was excised from the other tissues. Half the mature roots were collected, then the other half. Excising the cotyledons from the hypocotyl required the most skillful cut. Dragging the hypocotyl down the membrane increased the likelihood that both cotyledons were positioned above the hypocotyl, so they could be removed by a single slice across the petiole. The cut to the petiole to release the cotyledons needed a bit more pressure than the others, as this tissue 50 seems more fibrous and often resisted single-cut excision. Half the hypocotyl tissue was collected into the Omni bead tubes, forceps inspected, then the other half collected.

Cotyledon tissue was then collected in the same manner. From freezer to bead tubes, a well-practiced, smoothly executed dissection takes around 12 minutes. After the entire pipeline is complete, forceps must be immediately rinsed. The RLT buffer corrodes the forceps, and I found that even a small amount of rust decimates the yields of the subsequent RNA extraction.

Extraction

The small amount of tissue and the number of parallel extractions precluded the use of mortar and pestle or micro-pestle for tissue lysis, so lysis by bead mill was assessed for compatibility in this pipeline. Qiagen RNeasy Plant kits were used for RNA extraction, and the first step in the extraction method is to place the lysed tissue into extraction buffer RLT. Therefore, I tested the efficacy of lysing the tissue on a bead mill while the tissue was submerged in RLT. The tissue could therefore move seamlessly from dissection, to lysis, to extraction, and the method proved to be effective in both maintaining RNA integrity and lysing the tissue.

The small quantity of the root tip tissue raised concerns in the extraction pipeline.

Specifically, the mass of the other three tissues was much greater than that of the root tip, and thus the entire extraction and sequencing pipeline needed to work over an order of magnitude. I found that the standard RNeasy Plant Mini Kit provided adequate results across tissue inputs and changing the pipeline to a Micro Kit was not necessary. After lysis on the bead mill, tubes were briefly centrifuged and put on ice. 51

RNA extraction was performed using the RNeasy kit (Qiagen) as per manufacture’s instructions with considerations given for 8 simultaneous extractions.

Since more time elapsed between steps than for a usual single sample extraction, samples were kept on ice between steps. Briefly, homogenate was aspirated from the bead tubes and placed into QiaShredder columns. QiaShredder columns are specific to Qiagen plant kits and help to separate cell wall particulate from the homogenate. These tubes were spun at full speed for two minutes, and flow-through was transferred to a 1.5-mL tube.

An aliquot of Ethanol (100%) was added to the solution, mixed by pipette, and transferred immediately to RNeasy columns. These columns were centrifuged briefly for

15 seconds, flow-through discarded, wash buffer RW1 added, and centrifuged again.

Flow-through was discarded, and wash buffer RPE was added and centrifuged. Flow- through was discarded, RPE wash added a second time, and this time centrifuged for two minutes to dry the column. RNase-free water (40 µL) was added to the column for elution, allowed to incubate for 30-60 seconds, and centrifuged. A sample of 3µL was sent for analysis by Bioanalyzer, 16 µL was aliquoted for sequencing, and the remaining

RNA was saved at -80 °C.

The chosen pipeline was tested for RNA yield and integrity. It became clear that the desired sampling methods would require use of a low-input library preparation kit

(see next section). The use of a low-input kit made total yield much less of a concern, as the low-input library kits are extremely sensitive. Frozen seedlings that were treated to emulate the PGP samples consistently yielded RNA integrity values between 8 and 10, the acceptable range for RNA-seq. 52

Sequencing

The major considerations for the RNA sequencing pipeline were library preparation method, sequencing platform, and sequencing depth. NASA GeneLab data standard for RNA sequencing require paired end reads, 150 bp chemistry sequencing. So that was the goal. With research into various options and upon the recommendations from the sequencing facility (Nationwide Children’s Hospital, Columbus, OH), we opted to use Takara SMART-Seq v4 Ultra Low Input RNA kits for library preparation. This kit requires only 10 pg-10 ng of total RNA per sample. This amount was ideal for our pipeline, as some of our smallest samples contained as little as a single root tip for input.

Indexing for the libraries was provided by Nextera DNA Flex Library kits. Nextera DNA

Flex allows up to 384 unique dual 10 base pair indexing sequences. Since several samples would be run per sequencing lane, indexing capabilities were an important factor in this decision.

With improved reference transcriptomes and alignment software, sequencing depth recommendations for Arabidopsis have been reduced in recent years. With the planned paired-end sequencing and 150 bp chemistry, the goal was to find a sequencing solution that would allow 40-50 million reads per sample (20-25 million paired reads).

With roughly 400 samples, approximately 25 billion reads were required. The NovaSeq

S2 flow cell provides 6.6-8.2 billion reads per lane. Running 3 S2 flow cells yields approximately 20-25 billion reads, which was an adequate range for total sequencing depth. 53

Validation

Preceding the 400 samples for PGP flight to be processed using these methods, the entire pipeline was tested with an initial 64 “pilot” samples (Chapter 4). Dissection, extraction, and sequencing was performed according to the methods outlined for the spaceflight experiment. RNA Integrity Numbers (RINs) were at or near the desired minimum value of 8 (Figure 11). The RINs that were lower than 8 were in photosynthetic tissues, which often have artificially low RINs due to the presence of chloroplast rRNA.

Yields were more variable than expected, but all yields were well above the 10pg minimum input of the Takara SMART-Seq v4 Ultra Low Input RNA (Figure 12). Despite the ground, reorientation ‘pilot’ experiment representing far fewer samples, the same library prep, indexing, and flow cell parameters were chosen in order to best validate the pipeline.

Figure 11. Pipeline validation of RNA integrity. Pilot experiment RNA integrity numbers >8 were deemed acceptable for sequencing. RNA integrity was evaluated by Agilent Bioanalyzer 6000 Pico Chip. CO=cotyledon, HY= hypocotyl, MR = mature root, RT= root tip. Each box plots represent 16 extractions, boxes represent interquartile range of RIN, error bars represent max and min values.

The facility responsible for library preparation (Ohio State College of Food,

Agriculture, and Environmental Sciences, Wooster, OH) reported that library preparation and indexing worked as anticipated based on MiSeq test runs. The S2 flowcell provided approximately 8 billion reads, or 130 million reads per sample. While that sequencing depth was more than necessary for the pilot project, the same flow cell yield numbers applied to the primary project would result in approximately 30 million reads per sample for the flight experiment, meeting our target sequencing coverage. These numbers 56 provided the final confirmation in the validation of the pipeline outlined by the definition phase.

Conclusions

Defining experimental parameters is an indispensable step in any project. While the process can be tedious, expensive, and time-consuming, a cleverly designed definition phase can yield additional data beyond that of the methodology. Additionally, properly tracking and sharing the methodological data can save others from repeating the efforts.

Data gathered in this study led to a robust pipeline for PGP spaceflight sample processing. Additionally, the “Pilot” phase has provided a wealth of insightful data of its own, detailed in Chapter 4. Lastly, the methods developed for plant growth on polyethersulfone membranes will be published as an invited chapter in Methods in Plant

Gravitropism (Springer Protocols 2020), and those methods can be found in Appendix A.

In these ways, the definition phase of PGP has accomplished its stated goals, while contributing additional experimental data and further contributing to the gravitational biology community.

Chapter 4: Transcriptional Effects of Gravitational Reorientation in Starchless

Mutants of Arabidopsis

Introduction

As molecular technologies have improved over the last 40 years, many methods have been employed to evaluate the importance of starch statoliths for proper gravity response in Arabidopsis, and there is a scientific consensus that statoliths are a requisite piece of the full gravity sensing machinery in plants (see Chapter 2). Paradoxically, the starchless pgm-1 mutant displays a delayed but clear root gravitropic response upon reorientation. This response may suggest the existence of a secondary, statolith- independent gravity sensor in Arabidopsis roots. However, an incomplete understanding of the statolith-dependent gravity response makes the delineation of a secondary response mechanism difficult. Molecular investigations have shown transcriptional changes within the gravity sensitive organs upon reorientation of the plant. Many of the changes in the transcriptional landscape are presumably the downstream signaling triggered by starch statolith sedimentation. Since gravitropic bending is mediated by auxin-induced differential growth, and auxin elicits growth responses through transcriptionally- controlled pathways, it is likely that many of these changes in gene expression are driven by auxin and are related to gravitropic reorientation. However, the existence of a statolith-independent gravity sensing mechanism raises the possibility of transcriptional changes that are independent of statolith sedimentation. By tracking gene expression in reoriented plants with and without functional starch statoliths, we can begin to tease apart which changes in the transcriptional landscape are sedimentation-dependent, and if there 58 are early reorientation responses that are maintained in starchless specimens of

Arabidopsis.

In the experiment outlined here, wildtype and pgm-1 mutant seedlings were reoriented 90° with respect to gravity, while controls were kept vertical. Tissue was collected 10 minutes post-reorientation, with the intention of capturing the earliest transcriptional events related to sedimentation and to mitigate noise from downstream signaling. This design allows investigation into the transcriptional changes that occur as a result of statolith sedimentation (WT seedlings) and the changes that occur upon reorientation that are independent of statolith sedimentation (pgm-1 seedlings).

Methods

Plant Growth

Arabidopsis ecotype Columbia-0 (Col-0) and pgm-1 mutant plants were plated on polyethersulfone (PES) membranes in 60 mm Petri dishes according to methods outlined

Chapter 3. Eight plates were planted for each of the two genotypes, with approximately

13 seedlings per plate. Plates were hydrated with ½ MS nutrient solution, sealed with parafilm, and grown vertically under constant light for 4 days.

Treatment

All plates were moved to a dark room to mitigate phototropic influences and allowed to acclimate for 30 minutes. Four plates each of the Col-0 and pgm-1 were reoriented 90° for 10 minutes (Figure 13). The control, vertical plates were also lifted briefly to replicate the mechanical stimulus. All seedlings were then flash frozen in liquid nitrogen and stored at -80 °C until RNA extraction. 59

Figure 13. Seedlings in growth chamber (left) and during reorientation treatments (right). Seedlings were grown 4 days vertically and then turned 90° in custom 3D printed petri dish hardware.

Processing

For RNA extraction, plates were flooded with RLT extraction buffer, the cell lysis and extraction buffer provided in Qiagen RNeasy Plant kits. A scalpel was used to dissect the seedlings into root tip, the remainder of the root (hereafter “mature root”), hypocotyl, and cotyledon fractions. Extractions were made from each organ separately. Individual organs collected from all seedlings from a single plate were pooled into a single extraction. This pooling resulted in 64 total samples (2 genotypes, 4 organs, treatment vs control, 4 replicates) representing 16 unique treatment groups (2 genotypes, 4 organs, treatment vs control). Tissue was placed in Omni 2 mL microtubes (2.8 mm ceramic beads) with RLT extraction buffer and processed on an OmniRuptor bead mill for (2 x 30 60 seconds) (Omni International, Kennesaw, GA). RNA was extracted from samples with

Qiagen RNeasy Plant Mini kit (Qiagen, Germantown, MD), and eluted with 40 µL H2O then stored at -80 °C. RNA Integrity was evaluated by Agilent BioAnalyzer, and all RIN values were above 8.0. RNA libraries were prepared using Nextera Low Input Library

Prep Kits (Illumina, San Diego, CA). Sequencing was performed via Illumina NovaSeq

S2 Flow cell, with paired-end reads and 150 base pair chemistry at the Institute for

Genomic Medicine at Nationwide Children’s Hospital (Columbus, OH). For additional details, see Chapter 2.

Bioinformatics and Data Analysis

Differential expression analysis was performed using the WySeq pipeline

(https://github.com/astauff/WyattSeq). Reads were aligned to the TAIR10 genome assembly and ARAPORT11 annotations (Cheng et al., 2017) with STAR version 2.6.0c

(Dobin 2013). Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:featureCount version 1.6.4 (Liao 2014). All gene counts were imported into the Bioconductor package EdgeR (Robinson 2010) and trimmed mean of M-values (TMM) normalization size factors were calculated to adjust for differences in library size. The TMM size factors and the matrix of counts were analyzed using the R package LIMMA (Ritchie 2015). Weighted likelihoods based on the observed mean-variance relationship were calculated for all samples and genes with voomWithQualityWeights (Law 2014). Blocking factors were specified to determine batch effects of samples from a specific plate. Batch effects of specific plates were subsequently normalized using the duplicateCorrelation function. Enrichment analyses 61 were performed using custom R scripts implemented with the BioConductor package

GAGE (Luo 2009). Genes were considered significant with adjusted P-value (FDR) ≤

0.05. Gene group enrichments were calculated with DAVID Gene Functional

Classification Tool (Huang et al., 2009). To most thoroughly analyze the data, a matrix was built comprising all possible pairwise comparisons. Additionally, a contrast analysis was employed using the R package LIMMA. The contrast analysis incorporated the expression values for all 16 samples (4 reps x 2 treatments x 2 genotypes) for each organ

(root tip, mature root, hypocotyl, and cotyledon) to calculate the response of each gene to reorientation. For example, the expression values for each gene in 1) WT vertical control root tip, 2) WT reorientation treatment root tip, 3) pgm-1 vertical control root tip, and 4) pgm-1 reorientation treatment root tip were placed in a comparison matrix and analyzed simultaneously. Network analysis was performed on gene sets using STRING

(Szklarczyk et al., 2013) run with default parameters. STRING also provided output of

GO enrichment and protein domain enrichment. Visual programming was performed using the Python user interface program Orange (Desmar et al., 2013). Hereafter

“pairwise dataset” refers to the gene lists from differential expression analysis between any two genotype-tissue-treatment combination. The Orange workflow for secondary comparisons of pairwise datasets is visualized in Figure 14. 62

Figure 14. Comparison pipeline for pairwise datasets. Data from gene sets (beige) were analyzed for overlap and piped to Venn diagrams (pink) for visualization. Abbreviations: Col is Columbia-0, p and pgm are pgm-1, RT is root tip, MR is mature root, HY is hypocotyl, CO is cotyledon, t is treatment, v is vertical (untreated control).

A subset of genes from the root tip contrast analysis list were chosen for mutant phenotype analysis. The genes were chosen based on availability of homozygous SALK insertion mutant lines. Analysis was performed in the laboratory of Chris Wolverton at 63

Ohio Wesleyan University (Delaware, OH). The assay performed is known as a fixed rotation analysis. Briefly, seedlings are grown vertically on a round petri dish. The seedlings are then reoriented 90° and slowly rotated at the typical wildtype gravity response rate. A wild type plant rotated at this speed will have no change in the angle of its root tip after a fixed amount of time. Mutant phenotypes here are defined by a root tip angle deviation from the expected angle, meaning the gravity response rate is behind (or ahead of) that anticipated for a wildtype root.

Results

In total, transcriptional profiles for 64 samples representing 16 unique tissue/genotype/treatment combinations were determined from this analysis (Figure 15).

A comparison matrix was generated, resulting in 120 comparative datasets (Figure 16). In the COL-0 wildtype (WT) plants, a 10-minute reorientation resulted in the differential expression of 1 gene in the root tip (AT5G51520), 0 genes in the root, 5041 in the hypocotyl (Table 1), and 0 in the cotyledon (Figure 17). In the pgm-1 mutant (pgm), the

10-minute reorientation resulted in 60 genes differentially expressed in the root tip (Table

2), 1705 in the mature root (Table 3), 0 in the hypocotyl, and 4 in the cotyledon (Figure

17). Tables 1 and 3 represent the most differentially expressed genes (Log2 Fold Change

>1.5 or < -1.5), and Table 2 represents the entire list of 60 genes from the root tip. 64

Figure 15. Representation of the 16 unique transcriptomes constructed in the PGP-Pilot experiment. Each of the 16 transcriptome represent 4 pooled biological replicates.

Figure 16. Pairwise comparison matrix of the number of differentially expressed genes. Comparisons of treatments within genotypes are highlighted in green, comparisons of WT and pgm-1 tissues under the same treatment highlighted in yellow, and comparisons of WT and pgm-1 tissues under opposite treatments are highlighted in red. Numbers indicate number of differentially expressed genes for each pairwise comparison.

Figure 17. Venn diagram showing the number of genes differentially expressed (FDR = 0.05) between vertical and reoriented in each tissue for wildtype Col-0 (left) and pgm-1 seedlings (right).

Table 1. Most differentially expressed genes (Log2 Fold Change >1.5 or < -1.5) from WT hypocotyl after 10min reorientation. log2 Gene ID Description Name FC AT5G64840 GCN5 ABCF5 2.18 AT2G27505 FBD-like domain family protein 2.09 AT3G22450 Expressed protein 2.05 AT5G16030 unknown protein 2.02 AT1G47390 Putative F-box protein At1g47390 2.00 AT4G17090 Beta-amylase 3 BAM3 1.91 AT5G02670 poly(A) polymerase 3 1.89 AT1G49970 Clp protease proteolytic subunit-related protein 1 CLPR1 1.84 Probable receptor-like serine/threonine-protein AT4G34500 kinase 1.83 AT2G18245 Alpha/beta-Hydrolases superfamily protein 1.78 AT3G29320 Alpha-glucan phosphorylase 1 PHS1 1.77 AT5G04810 Pentatricopeptide repeat-containing protein PPR4 1.77 AT4G32480 AT4g32480/F8B4_180 1.76 AT3G46990 DUF740 family protein, putative (DUF740) 1.75 AT1G42550 Protein PLASTID MOVEMENT IMPAIRED 1 PMI1 1.73 AT5G50920 Chaperone protein ClpC1 CLPC1 1.73 AT1G69780 Homeobox-leucine zipper protein ATHB-13 ATHB-13 1.72 AT1G55960 Polyketide cyclase/dehydrase and lipid transport 1.72 AT1G65230 Transmembrane protein, putative (DUF2358) 1.72 AT5G62520 Probable inactive poly polymerase SRO5 SRO5 1.72 AT5G14090 Protein LAZY 1 LA1 1.72 AT1G51110 Probable plastid-lipid-associated protein 12 PAP12 1.71 AT5G02830 Pentatricopeptide repeat-containing protein 1.70 AT1G12120 T28K15.14 protein 1.67 AT5G58003 CPL4 CPL4 1.66 AT4G28760 Methyl-coenzyme M reductase II subunit DUF3741 1.66 AT2G21560 Nucleolar-like protein 1.65 AT1G31800 Protein LUTEIN DEFICIENT 5 CYP97A3 1.64 AT2G35880 Protein WVD2-like 4 WDL4 1.62 AT1G08540 RNA polymerase sigma factor sigB SIGB 1.62 AT4G10180 Light-mediated development protein DET1 DET1 1.62 AT3G57710 Protein kinase superfamily protein 1.61

Table 1: continued AT2G20330 At2g20330/F11A3.12 1.61 AT1G54410 HIRD11 HIRD11 1.60 AT4G14590 Embryo defective 2739 emb2739 1.60 AT2G02160 Zinc finger CCCH domain-containing protein 17 1.60 AT5G03940 SRP54CP FFC 1.60 AT1G17850 Rhodanese-like domain-containing protein 8 STR8 1.58 AT5G42030 ABL interactor-like protein 4 ABIL4 1.58 AT5G13770 Pentatricopeptide repeat-containing protein 1.58 AT2G43680 IQD14 IQD14 1.57 AT5G48790 AT5g48790/K24G6_12 1.57 AT3G49730 Pentatricopeptide repeat-containing protein 1.56 AT1G58290 Glutamyl-tRNA reductase 1 HEMA1 1.56 AT1G30280 Chaperone DnaJ-domain superfamily protein 1.55 AT1G32060 Phosphoribulokinase PRK 1.55 AT3G14580 Pentatricopeptide repeat-containing protein 1.55 AT1G15040 Putative glutamine amidotransferase GAT1_2.1 GAT1_2.1 1.53 AT4G14560 Auxin-responsive protein IAA1 1.53 AT4G02405 SAM-dependent methyltransferases superfamily 1.52 AT5G58590 At5g58590 RANBP1C 1.52 AT1G03130 Photosystem I reaction center subunit II-2 PSAD2 1.52 AT5G40450 unknown protein 1.51 AT5G44650 Ycf3-interacting protein 1 Y3IP1 1.51 AT2G02780 Probable LRR receptor-like serine/threonine-kinase 1.51 AT3G18080 Beta-glucosidase 44 BGLU44 1.50 AT1G70760 NAD(P)H-quinone oxidoreductase subunit L ndhL 1.50 AT3G62140 NEFA-interacting nuclear protein 1.50 AT1G71080 At1g71080/F23N20_7 1.50 AT1G10385 Exocyst complex component EXO84A EXO84A -1.50 AT1G43810 unknown protein; Ha. -1.50 AT1G40083 unknown protein -1.50 AT3G22030 Receptor protein kinase-related -1.50 AT5G40220 AGAMOUS-like 43 AGL43 -1.51 AT5G44970 Protein with RNI-like/FBD-like domain -1.51 AT5G27780 SAUR-like auxin-responsive protein family -1.51 AT1G35210 Uncharacterized protein T32G9.25 -1.51

Table 1: continued AT5G55855 Putative small ubiquitin-related modifier 7 SUMO7 -1.52 AT1G80590 Probable WRKY transcription factor 66 WRKY66 -1.52 AT1G36272 unknown protein; Ha. -1.52 AT1G68845 unknown protein; Ha. -1.53 Disease resistance protein (TIR-NBS-LRR class) AT5G41540 family -1.53 AT1G10745 EMBRYO SURROUNDING FACTOR 1.2 ESF1.2 -1.53 AT1G72290 Cysteine protease inhibitor WSCP WSCP -1.53 AT1G32510 NAC011 ANAC011 -1.54 AT1G59500 Indole-3-acetic acid-amido synthetase GH3.4 GH3.4 -1.54 AT3G47090 Leucine-rich repeat protein kinase family protein -1.54 AT4G00232 Probable transcription factor At4g00232 -1.54 AT4G05240 Ubiquitin-like superfamily protein -1.55 AT5G67120 RING/U-box superfamily protein -1.56 AT1G24060 Uncharacterized protein At1g24060 -1.56 AT2G40440 BTB/POZ domain-containing protein -1.56 AT3G61117 unknown protein; Ha. -1.57 AT5G25290 F-box protein At5g25290 -1.57 AT3G49045 F-box/FBD/LRR protein -1.58 AT1G60880 AGAMOUS-like-56 AGL56 -1.58 AT5G41320 Stress response NST1-like protein -1.59 AT2G21455 SNF2 domain CLASSY-like protein -1.59 AT2G20465 Defensin-like protein 103 -1.60 AT5G40360 Transcription factor MYB115 MYB115 -1.60 AT5G37470 Family of unknown function (DUF577) -1.61 AT1G29005 Reverse transcriptase zinc-binding protein -1.61 AT5G46130 Putative uncharacterized protein -1.63 AT5G09750 Transcription factor HEC3 HEC3 -1.64 AT1G23810 F5O8.36 -1.65 AT2G17000 Mechanosensitive ion channel protein 7 MSL7 -1.65 AT2G41451 Glycosyltransferase-like protein (Fragment) -1.65 Encodes a Cysteine-rich peptide (CRP) family AT2G31005 protein -1.66 AT3G52320 Putative F-box protein At3g52320 -1.66 AT4G11485 Putative defensin-like protein 152 LCR11 -1.66

Table 1: continued AT5G55131 Putative defensin-like protein 84 -1.67 AT5G08141 Basic leucine-zipper 75 AtbZIP75 -1.67 AT4G04480 F-box protein with a domain protein -1.67 AT5G38740 AGAMOUS-like 77 AGL77 -1.68 AT5G41765 Probable transcription factor At5g41765 -1.68 AT2G24740 Histone-lysine N-methyltransferase SUVH8 -1.68 AT5G43185 At5g43185 -1.69 AT3G16580 F-box/kelch-repeat protein At3g16580 -1.69 AT1G19830 At1g19830 -1.70 AT2G23067 Putative membrane lipoprotein -1.70 AT1G22600 Late embryogenesis abundant protein (LEA) family -1.71 AT3G02125 Pinin-like protein -1.72 AT1G11620 Putative F-box/LRR-repeat/kelch-repeat -1.73 AT5G44590 SAM-dependent methyltransferases -1.76 AT5G09780 B3 domain-containing protein REM23 REM23 -1.76 AT4G18335 Putative uncharacterized protein -1.77 AT3G05300 Cytidine/deoxycytidylate deaminase family protein -1.77 AT3G43750 RBR-type E3 ubiquitin transferase -1.77 AT5G01440 Insulinase (Peptidase family M16) family protein -1.77 AT1G03670 Ankyrin repeat family protein -1.79 AT1G70960 Putative F-box protein At1g70960 -1.79 AT1G35353 -1.80 AT1G02320 unknown protein; Ha. -1.81 AT5G37220 RING/U-box superfamily protein -1.82 AT4G27565 Transmembrane protein -1.82 AT1G24650 Receptor-like kinase TMK2 TMK2 -1.85 AT1G10588 Gibberellin-regulated family protein -1.86 AT1G43666 Bifunctional inhibitor/lipid-transfer protein -1.88 AT5G06920 Fasciclin-like arabinogalactan protein 21 FLA21 -1.89 AT2G35270 AT-hook motif nuclear-localized protein 21 AHL21 -1.89 AT2G47770 TSPO TSPO -1.89 AT1G51250 At1g51250 -1.90 AT2G13500 Putative Ta11-like non-LTR retroelement protein -1.91 AT5G20460 unknown protein; Ha. -1.92 AT1G71150 Cyclin-D1-binding protein -1.92

Table 1: continued AT1G47300 Putative F-box protein At1g47300 -1.93 AT1G12615 Pentatricopeptide (PPR) repeat protein -1.98 AT4G03298 unknown protein -2.00 AT2G01560 Putative uncharacterized protein -2.04 AT4G02465 unknown protein; Ha. -2.05 AT3G05730 Defensin-like protein 205 -2.05 AT5G27200 Acyl carrier protein 5 ACP5 -2.06 AT5G53680 RNA-binding (RRM/RBD/RNP motifs) family protein -2.07 DNAJ heat shock N-terminal domain-containing AT2G21510 protein -2.13 AT1G42190 GAG/POL/ENV polyprotein -2.14 AT2G02690 Cysteine/Histidine-rich C1 domain family protein -2.14 AT1G26720 T24P13.10 -2.16 AT3G58280 MATH domain and coiled-coil domain-containing -2.18 AT1G09483 unknown protein NEAP4 -2.22 AT1G75870 T4O12.11 -2.23 AT3G17530 F-box/kelch-repeat protein At3g17530 -2.32 AT5G35715 cytochrome P450, family 71, subfamily B CYP71B8 -2.33 AT1G10715 EMBRYO SURROUNDING FACTOR 1-like protein 2 ESFL2 -2.34 AT4G19470 Leucine-rich repeat (LRR) family protein -2.36 AT3G12430 Polynucleotidyl transferase, ribonuclease H-like -2.37 AT4G35725 Transmembrane protein -2.55 AT3G45800 Plant protein 1589 of unknown function -2.70 AT2G05105 -2.71 AT5G51610 50S ribosomal protein L11-like -2.76 AT1G30930 Putative F-box protein At1g30930 -3.14 AT4G22600 Protein INAPERTURATE POLLEN1 INP1 -3.21 AT3G58415 -3.24 AT3G23250 Transcription factor MYB15 MYB15 -3.33 AT5G46000 Jacalin-related lectin 45 JAL45 -3.38

Table 2. All differentially expressed genes in the pgm-1 root tip after 10min reorientation. log2 Gene ID Description Name FC

AT5G28520 Jacalin-related lectin 40 JAL40 3.02 AT3G17530 F-box/kelch-repeat protein At3g17530 2.07 AT3G48208 Plant thionin family protein 2.02 AT2G41810 Uncharacterized protein At2g41810 1.81 AT1G75040 Pathogenesis-related protein 5 PR5 1.80 AT2G16005 MD-2-related lipid-recognition protein ROSY1 ROSY1 1.74 AT4G33550 At4g33550 1.54 AT4G11940 Protein ADMETOS ADM 1.50 Protein LIGHT-DEPENDENT SHORT AT5G28490 HYPOCOTYLS 1 LSH1 1.43 AT3G63093 1.41 AT5G09780 B3 domain-containing protein REM23 REM23 1.36 Heavy metal transport/detoxification AT5G05365 superfamily protein 1.34 AT4G28530 At4g28530 anac074 1.29 AT2G34490 Cytochrome P450 710A2 CYP710A2 1.26 AT3G26460 Major latex protein-like 1.20 AT5G42600 Marneral synthase MRN1 1.09 AT2G25297 1.07 AT4G08300 WAT1-related protein At4g08300 1.04 Pollen-specific leucine-rich repeat extensin-like AT3G19020 protein 1 PEX1 1.00 AT4G18550 alpha/beta-Hydrolases superfamily protein 0.77 Cytochrome P450, family 706, subfamily A, AT4G12330 polypeptide 7 CYP706A7 0.76 AT5G09990 Elicitor peptide 5 PEP5 0.72 AT1G04778 unknown protein 0.71 AT5G23030 Tetraspanin-12 TET12 0.69 AT5G19970 unknown protein 0.66 AT2G43890 Pectin lyase-like superfamily protein 0.65 AT2G29130 Laccase-2 LAC2 0.64 AT2G31560 AT2G31560 protein 0.60 AT2G22990 sinapoylglucose 1 SNG1 0.59

Table 2: continued AT2G38152 Alpha 1,4-glycosyltransferase family protein 0.56 AT2G16660 At2g16660/T24I21.7 0.53 AT3G51660 LS1-like protein 0.52 AT1G21360 Glycolipid transfer protein 2 GLTP2 0.51 AT5G19875 At5g19875 0.45 AT1G77850 auxin response factor 17 ARF17 0.36 AT3G23910 HAPp48,5 protein (Fragment) -0.27 AT3G57030 Protein STRICTOSIDINE SYNTHASE-LIKE SSL10 -0.30 AT5G17790 Zinc finger protein VAR3, chloroplastic VAR3 -0.34 Protein C-terminal S-isoprenylcysteine carboxyl AT5G59500 O-methyltransferase -0.34 AT1G01690 Putative recombination initiation defects 3 ATPRD3 -0.35 MMS19 nucleotide excision repair protein AT5G48120 homolog MMS19 -0.36 AT3G06910 Ubiquitin-like-specific protease 1A ULP1A -0.36 AT1G10520 DNA polymerase lambda POLL -0.39 AT1G24280 Glucose-6-phosphate 1-dehydrogenase G6PD3 -0.41 Anthranilate phosphoribosyltransferase, AT5G17990 chloroplastic PAT1 -0.42 Glyceraldehyde-3-phosphate dehydrogenase AT1G16300 GAPCP2, chloroplastic GAPCP2 -0.43 Pyrophosphate-energized membrane proton AT1G16780 pump 3 AVPL2 -0.46 AT5G51830 Probable fructokinase-7 -0.46 AT3G13180 NOL1/NOP2 antitermination NusB domain -0.48 AT2G35430 Zinc finger CCCH domain-containing protein 28 -0.50 AT5G13370 At5g13370 -0.51 AT2G42720 F-box/LRR-repeat protein At2g42720 -0.57 Glyceraldehyde-3-phosphate dehydrogenase AT3G26650 GAPA1, chloroplastic GAPA1 -0.80 AT3G21000 Gag-Pol-related retrotransposon family protein -0.93 AT2G46650 Cytochrome B5 isoform C CYTB5-C -0.96 AT5G07200 Gibberellin 20 oxidase 3 GA20OX3 -0.96 Class I glutamine amidotransferase-like AT5G38200 superfamily protein -1.03 AT5G44568 Transmembrane protein -1.19 AT5G43285 Protein LURE 1.1 LURE1.1 -1.97 AT4G17243 -2.27 73

Table 3. Most differentially expressed genes (LFC >1.5 or < -1.5) from pgm-1 mature root. log2 Gene ID Description Name FC AT5G66400 RAB18 RAB18 4.60 AT3G15670 Late embryogenesis abundant protein 29 LEA29 4.37 AT2G21490 Probable dehydrin LEA LEA 3.95 Late embryogenesis abundant protein (LEA) AT4G21020 family protein 3.62 Late embryogenesis abundant protein (LEA) AT3G17520 family protein 3.56 AT3G03341 Cold-regulated protein 3.18 AT4G36600 Late embryogenesis abundant (LEA) protein 3.16 AT5G06760 LEA4-5 LEA46 3.06 Adenine nucleotide alpha hydrolases-like AT3G58450 superfamily protein 3.05 AT1G71250 GDSL esterase/lipase At1g71250 2.98 AT3G02480 AT3g02480/F16B3_11 2.89 AT1G07645 DSI-1VOC ATDSI 2.86 AT1G52690 Late embryogenesis abundant protein 7 LEA7 2.80 Late embryogenesis abundant domain- AT1G72100 containing protein 2.73 AT3G01570 Oleosin 5 2.72 AT2G47780 REF/SRPP-like protein At2g47780 2.71 Late embryogenesis abundant protein (LEA) AT5G44310 family protein 2.61 AT2G47770 TSPO TSPO 2.44 Late embryogenesis abundant protein AT3G53040 At3g53040 2.39 AT2G31980 Cysteine proteinase inhibitor CYS2 2.37 AT5G40420 Oleosin 21.2 kDa OLEO2 2.36 AT5G52300 Low-temperature-induced 65 kDa protein LTI65 2.32 AT1G48130 1-Cys peroxiredoxin PER1 PER1 2.30 AT5G47450 Aquaporin TIP2-3 TIP2-3 2.23 AT2G38905 At2g38901/At2g38901 2.23 AT5G07330 At5g07330 2.23 AT2G23110 At2g23110 2.16 AT4G16160 ATOEP16-S OEP162 2.15

Table 3: continued AT1G32560 Late embryogenesis abundant protein 6 LEA6 2.13 AT1G17810 BETA-TIP TIP3-2 2.06 AT1G51620 Protein kinase superfamily protein 1.93 AT4G00780 TRAF-like family protein 1.80 AT2G41260 M17 M17 1.75 AT1G14930 Major latex homologue type2 1.72 NAD(P)-binding Rossmann-fold superfamily AT1G54870 protein 1.67 AT3G14880 DNA-binding protein-like protein 1.66 AT3G50980 Dehydrin Xero 1 XERO1 1.65 AT3G51810 Em-like protein GEA1 EM1 1.63 Late embryogenesis abundant domain- AT2G42560 containing protein 1.61 AT5G45340 Abscisic acid 8'-hydroxylase 3 CYP707A3 1.59 AT5G62490 HVA22-like protein b HVA22B 1.56 AT4G39675 1.52 p-loop containing nucleoside triphosphate AT1G64110 hydrolases superfamily protein 1.52 AT2G40610 Expansin-A8 EXPA8 -1.51 AT3G62570 AT3g62570/T12C14_270 -1.52 AT5G39320 UDP-glucose 6-dehydrogenase 4 UGD4 -1.52 AT3G14540 Terpenoid synthase 19 TPS19 -1.53 AT5G19520 Mechanosensitive ion channel protein 9 MSL9 -1.57 AT1G66470 RHD6 BHLH83 -1.57 AT5G48850 ATSDI1 SDI1 -1.59 Cytochrome P450, family 705, subfamily A, AT3G20940 polypeptide 30 CYP705A30 -1.61 AT1G74500 Transcription factor PRE3 PRE3 -1.62 AT2G41800 At2g41800/T11A7.10 -1.64 AT1G52070 Jacalin-related lectin 10 JAL10 -1.68 AT5G04120 Metal-independent phosphoserine phosphatase IPSP -1.69 AT2G04025 Root meristem growth factor 3 RGF3 -1.69 DNA-directed RNA polymerase subunit 5-like AT5G57980 protein 1 NRPB5L1 -1.70 AT2G20750 Expansin-B1 EXPB1 -1.70 AT1G12845 At1g12845 -1.71 AT2G17070 Putative uncharacterized protein -1.72

Table 3: continued AT1G17180 Glutathione S-transferase U25 GSTU25 -1.73 Cyclopropane-fatty-acyl-phospholipid AT3G23470 synthase -1.77 AT5G46950 At5g46950 -1.79 AT1G27140 GSTU14 GSTU14 -1.79 Pathogenesis-related thaumatin superfamily AT2G28790 protein -1.80 AT1G76240 At1g76240 -1.80 AT5G07040 Putative RING-H2 finger protein ATL69 ATL69 -1.80 AT2G23050 BTB/POZ domain-containing protein NPY4 NPY4 -1.81 AT4G22010 At4g22010 sks4 -1.81 AT5G66580 Uncharacterized protein At5g66580 -1.83 AT4G25260 Pectinesterase inhibitor 7 PMEI7 -1.84 AT1G03870 At1g03870 FLA9 -1.85 AT4G15160 Bifunctional inhibitor/lipid-transfer protein -1.86 AT5G15265 Transmembrane protein -1.88 AT1G71740 F14O23.12 -1.88 AT3G49580 LSU1 LSU1 -1.89 AT5G25460 At5g25460 -1.89 AT4G21850 MSRB9 MSRB9 -1.90 AT4G30290 Xyloglucan endotransglucosylase/hydrolase XTH19 -1.96 AT5G46960 Pectinesterase inhibitor 12 PMEI12 -1.96 AT2G20515 At2g20515 -1.99 AT3G12610 DRT100 DRT100 -2.10 AT1G22400 UDP-glycosyltransferase 85A1 UGT85A1 -2.13 AT1G75780 Tubulin beta-1 chain TUBB1 -2.19 AT5G46890 Bifunctional inhibitor/lipid-transfer protein -2.24 AT5G63660 Defensin-like protein 6 PDF2.5 -2.28 AT5G62340 At5g62340 -2.29 Endosomal targeting BRO1-like domain- AT5G14020 containing protein -2.33 AT5G10430 ATAGP4 AGP4 -2.34 Cysteine/Histidine-rich C1 domain family AT2G19660 protein -2.35 AT4G12510 At4g12520 -2.42 AT1G11545 Xyloglucan endotransglucosylase/hydrolase XTH8 -2.59

Table 3: continued AT5G12940 Leucine-rich repeat (LRR) family protein -2.72 AT1G14220 F7A19.32 protein -2.75 AT5G50175 Transmembrane protein -2.82 AT4G12520 At4g12520 -2.82 AT2G25810 Aquaporin TIP4-1 TIP4-1 -2.85 AT3G47380 Pectinesterase inhibitor 11 PMEI11 -2.91 AT5G66590 CAP superfamily protein -3.02 AT3G50640 At3g50640 -3.06 AT4G19030 NLM1 NIP1-1 -3.15

Contrast Analysis

In addition to standard pairwise analysis, we also performed a contrast analysis.

The contrast analysis exposed genes that were influenced differently between WT and pgm-1 upon reorientation. This analysis revealed 124 genes in the root tip (Table 4), 5 in the mature root (Table 5), 40 genes in the hypocotyl (Table 6), and 0 genes in the cotyledon (Figure 18). The gene lists generated in this fashion were fairly distinct from the differential expression analysis (Figure 19). Of the 124 genes in the contrast root list,

28 appeared in the list of differentially expressed genes in pgm-1 DE, and only 1 in common with the WT gene set. The list of 124 is enriched in transmembrane proteins (9 genes) and transcription factors (6 genes). A subset of the genes identified with the contrast analysis of the root tips were chosen for phenotypic analysis. 77

Figure 18. Venn diagram of the results of the contrast analysis calculated using the wildtype (Col-0) vertical, Col-0 reoriented, pgm-1 vertical, and pgm-1 reoriented expression for each of the four organs sequenced.

Figure 19. Venn diagrams of the comparison between differential expression datasets and contrast analysis in the root tip (left) and hypocotyl (right). The contrast analysis provided a distinct gene set as compared to the differential expression analysis.

The hypocotyl contrast revealed 40 genes. Using the Subcellular Localization Database for Arabidopsis Proteins (SUBA4, Hooper et al., 2017), 19/40 of the proteins for these genes were localized to the plastid (Figure 20)

Figure 20. Subcellular localization of genes from the hypocotyl contrast. Plastid-localized genes represent the largest subset.

Table 4. Genes identified through contrast analysis in the root tip. log2 Gene ID Description Name FC AT3G28510 AAA-ATPase At3g28510 3.52 AT5G43285 Protein LURE 1.1 LURE1.1 2.59 AT1G52770 At1g52770 2.27 AT4G36430 Peroxidase 49 PER49 2.11 Class I glutamine amidotransferase-like AT5G38200 superfamily protein 2.10 AT4G14690 Early light-induced protein 2, chloroplastic ELIP2 1.98 AT5G39050 Phenolic glucoside malonyltransferase 1 PMAT1 1.96 AT1G08430 Aluminum-activated malate transporter 1 ALMT1 1.89 AT5G64810 Probable WRKY transcription factor 51 WRKY51 1.79 AT4G08555 At4g08555 1.72 AT1G55600 Probable WRKY transcription factor 10 WRKY10 1.69 AT3G21690 Protein DETOXIFICATION 40 DTX40 1.59 AT5G44568 Transmembrane protein 1.55

Table 4: continued AT1G77760 Nitrate reductase NIA1 1.49 AT1G37130 Nitrate reductase 2 NIA2 1.48 AT5G07200 Gibberellin 20 oxidase 3 GA20OX3 1.37 AT3G56980 Transcription factor ORG3 ORG3 1.28 AT3G21000 Gag-Pol-related retrotransposon family protein 1.21 AT2G46650 Cytochrome B5 isoform C CYTB5-C 1.12 AT1G02850 Beta-glucosidase 11 BGLU11 1.06 NAD(P)-linked oxidoreductase superfamily AT1G60730 protein 1.04 AT1G77880 Putative F-box protein At1g77880 1.01 AT5G13370 At5g13370 0.93 AT4G16270 Peroxidase 40 PER40 0.91 AT1G33110 Protein DETOXIFICATION DTX21 0.90 AT3G59140 ABC transporter C family member 10 ABCC10 0.89 AT5G51830 Probable fructokinase-7 0.88 AT2G42720 F-box/LRR-repeat protein At2g42720 0.86 AT1G72490 unknown protein1)../I BLink). 0.80 Probable 3-hydroxyisobutyrate AT4G20930 dehydrogenase, mitochondrial 0.77 AT5G65750 2-oxoglutarate dehydrogenase, E1 component 0.75 AT3G44720 Arogenate dehydratase 4, chloroplastic ADT4 0.71 AT2G46420 At2g46420/F11C10.11 0.70 AT3G18290 Zinc finger protein BRUTUS BTS 0.69 DExH-box ATP-dependent RNA helicase AT2G47680 DExH8 0.66 AT5G54390 PAP-specific phosphatase HAL2-like AHL 0.66 Pyrophosphate-energized membrane proton AT1G16780 pump 3 AVPL2 0.65 AT2G47240 Long chain acyl-CoA synthetase 1 LACS1 0.64 AT3G53540 Afadin 0.62 AT3G13180 NOL1/NOP2/sun family protein 0.60 AT5G08380 Alpha-galactosidase 1 AGAL1 0.59 AT5G39040 ABC transporter B family member 27 ABCB27 0.59 Probable cyclic nucleotide-gated ion channel AT2G46450 12 CNGC12 0.59 AT3G10270 DNA gyrase subunit B GYRB1 0.58

Table 4: continued Pentatricopeptide repeat-containing protein AT1G26900 At1g26900, mitochondrial PCMP-E54 0.55 tRNA/rRNA methyltransferase (SpoU) family AT4G17610 protein 0.55 MMS19 nucleotide excision repair protein AT5G48120 homolog MMS19 0.53 AT1G58250 HYPERSENSITIVE TO PI STARVATION 4 SAB 0.51 Protein STRICTOSIDINE SYNTHASE-LIKE AT3G57030 10 SSL10 0.45 AT3G23910 HAPp48,5 protein (Fragment) 0.41 AT1G64690 branchless trichome BLT -0.57 AT5G44900 At5g44900 -0.64 AT1G11220 Cotton fiber, putative (DUF761) -0.66 AT3G06750 F3E22.11 protein -0.68 AT5G67210 IRX15-L IRX15-L -0.69 AT4G38210 Expansin-A20 EXPA20 -0.72 myb-like HTH transcriptional regulator family AT5G06800 protein -0.72 AT5G67620 At5g67620 -0.72 AT1G25450 3-ketoacyl-CoA synthase 5 KCS5 -0.76 AT2G22990 sinapoylglucose 1 SNG1 -0.77 AT2G16660 At2g16660/T24I21.7 -0.79 AT1G21360 Glycolipid transfer protein 2 GLTP2 -0.81 AT1G51500 WBC12 ABCG12 -0.82 AT2G27930 PLATZ transcription factor family protein -0.82 AT4G03460 Ankyrin repeat family protein -0.83 AT1G12550 Glyoxylate/hydroxypyruvate reductase HPR3 HPR3 -0.85 AT1G53680 Glutathione S-transferase U28 GSTU28 -0.86 AT1G52618 Putative uncharacterized protein -0.87 Histone acetyltransferase of the TAFII250 AT3G19040 family 2 TAF1 -0.88 AT3G47220 Phosphoinositide phospholipase C 9 PLC9 -0.89 AT1G13510 Uncharacterized protein At1g13510 -0.89 AT2G38152 Alpha 1,4-glycosyltransferase family protein -0.90 AT1G06923 Transcription repressor OFP17-like protein -0.92 AT3G51230 Chalcone-flavanone isomerase family protein -0.93 AT5G01520 AtAIRP2 AIRP2 -0.94

Table 4: continued AT4G06536 At4g06536 -0.95 Sequence-specific DNA binding transcription AT3G54390 factor -0.95 AT1G14240 Probable apyrase 3 APY3 -0.95 AT4G04450 WRKY transcription factor 42 WRKY42 -0.97 AT5G23030 Tetraspanin-12 TET12 -0.97 AT3G11260 WUSCHEL-related homeobox 5 WOX5 -0.98 AT5G42580 Cytochrome P450 705A12 CYP705A12 -0.99 AT1G52245 Dynein light chain -1.01 AT2G34810 Berberine bridge enzyme-like 16 -1.04 Polyketide cyclase/dehydrase and lipid AT5G28010 transport superfamily protein -1.06 AT5G47800 Phototropic-responsive NPH3 family protein -1.10 AT4G17030 Expansin-like protein EXLB1 -1.15 Cytochrome P450, family 706, subfamily A, AT4G12330 polypeptide 7 CYP706A7 -1.15 AT3G18450 Protein PLANT CADMIUM RESISTANCE 5 PCR5 -1.16 AT1G60787 Cysteine/histidine-rich C1 domain protein -1.17 AT4G34380 At4g34380 -1.18 AT1G79320 MCP2c AMC6 -1.21 AT1G80760 Aquaporin NIP6-1 NIP6-1 -1.22 AT5G51520 Invertase -1.22 AT1G80450 VQ motif-containing protein 11 VQ11 -1.24 AT3G48490 At3g48490 -1.27 AT3G48350 KDEL-tailed cysteine endopeptidase CEP3 CEP3 -1.28 Receptor-like serine/threonine-protein kinase AT1G78530 At1g78530 -1.32 AT3G20360 AT3g20360/MQC12_11 -1.35 AT1G10810 Probable aldo-keto reductase 1 -1.37 AT4G22230 Defensin-like protein 96 -1.41 AT1G29270 unknown protein1)../ BLink). -1.44 AT5G42600 Marneral synthase MRN1 -1.47 AT2G43890 Pectin lyase-like superfamily protein -1.50 AT1G79130 SAUR40 SAUR40 -1.51 AT4G08300 WAT1-related protein At4g08300 -1.52 AT2G19060 GDSL esterase/lipase At2g19060 -1.52

Table 4: continued AT4G23670 AT4G23670 protein -1.57 AT1G14550 Peroxidase 5 PER5 -1.62 Plant stearoyl-acyl-carrier-protein desaturase AT3G02620 family protein -1.69 AT5G16410 HXXXD-type acyl-transferase family protein -1.77 Plant intracellular Ras-group-related LRR AT4G26050 protein 8 PIRL8 -1.86 AT4G10530 Subtilase family protein -1.89 CBL-interacting serine/threonine-protein AT2G34180 kinase 13 CIPK13 -1.91 AT5G44130 Fasciclin-like arabinogalactan protein 13 FLA13 -1.92 AT5G62420 Aldo/keto reductase family protein -2.03 AT2G34490 Cytochrome P450 710A2 CYP710A2 -2.04 AT1G19540 At1g19540 -2.10 AT4G33550 At4g33550 -2.14 AT2G03821 Putative uncharacterized protein -2.44 Xyloglucan endotransglucosylase/hydrolase AT4G13090 protein 2 XTH2 -2.96 MD-2-related lipid-recognition protein AT2G16005 ROSY1 ROSY1 -3.01 AT2G41810 Uncharacterized protein At2g41810 -3.55 AT5G28520 Jacalin-related lectin 40 JAL40 -4.39

Table 5. Genes identified through contrast analysis for mature root. Log2 Gene ID Description Name FC AT5G25460 At5g25460 2.66 AT3G61890 HB-12 ATHB-12 -1.81 AT4G23670 AT4G23670 protein -2.24 AT5G52300 Low-temperature-induced protein LTI65 -2.83 AT3G01570 Oleosin 5 -4.77

Table 6. Genes identified through contrast analysis in the for hypocotyl.

Gene ID Description Name log2 FC AT5G16030 unknown protein 2.43 AT3G14210 GDSL esterase/lipase ESM1 ESM1 2.09

Table 6: continued AT4G17090 Beta-amylase 3, chloroplastic BAM3 2.06 Clp protease proteolytic subunit-related, AT1G49970 chloroplastic CLPR1 1.88 AT1G65230 Transmembrane protein, putative (DUF2358) 1.66 AT1G53230 TCP3 TCP3 1.58 AT5G48790 AT5g48790/K24G6_12 1.53 Clp protease adapter protein ClpF, AT2G03390 chloroplastic CLPF 1.52 AT1G62850 Class I peptide chain release factor 1.51 AT5G58003 CPL4 CPL4 1.50 AT3G51530 F-box/FBD/LRR-repeat protein At3g51530 1.49 Protein LUTEIN DEFICIENT 5, AT1G31800 chloroplastic CYP97A3 1.48 AT5G17230 PHYTOENE SYNTHASE PSY 1.47 Probable plastid-lipid-associated protein 12, AT1G51110 chloroplastic PAP12 1.46 AT4G02920 Uncharacterized protein At4g02920 1.45 AT4G15545 Uncharacterized protein At4g15545 1.42 Zinc finger CCCH domain-containing AT3G02830 protein 33 ZFN1 1.41 Transcription termination factor MTERF5, AT4G14605 chloroplastic MTERF5 1.38 AT5G16880 TOM1-like protein 1 TOL1 1.37 AT4G35090 catalase 2 CAT2 1.36 NAD(P)-linked oxidoreductase superfamily AT1G04420 protein 1.32 Protein TRIGALACTOSYLDIACYLGLYCEROL AT1G65410 3, chloroplastic TGD3 1.31 AT3G02220 Small acidic-like protein 1.29 AT1G16210 Coiled-coil protein 1.29 AT3G48420 CBBY-like protein CBBY 1.28 Acyl-lipid omega-3 desaturase (ferredoxin), AT5G05580 chloroplastic FAD8 1.28 AT1G53320 Tubby-like F-box protein TULP7 1.26 Large ribosomal RNA subunit accumulation AT3G19810 protein, chloroplastic 1.25 AT4G22890 PGR5-LIKE A PGRL1A 1.25

Table 6: continued AT4G09010 ascorbate peroxidase 4 APX4 1.25 Pyruvate, phosphate dikinase regulatory AT4G21210 protein 1, chloroplastic RP1 1.21 AT4G18740 Rho termination factor 1.21 AT5G23060 Calcium sensing receptor, chloroplastic CAS 1.16 AT1G45688 Transmembrane protein 1.12 ATP-dependent Clp protease proteolytic AT1G66670 subunit 3, chloroplastic CLPP3 1.11 Haloacid dehalogenase-like hydrolase AT4G39970 domain 1.10 Metallo-hydrolase/oxidoreductase AT1G29700 superfamily protein 1.08 AT5G08590 SRK2G SRK2G 1.07 AT4G35230 Serine/threonine-protein kinase BSK1 BSK1 1.03 AT5G47540 At5g47540 0.90

KEGG Pathway Enrichments - Reorientation

KEGG pathway enrichment analysis was performed to assess the effects of reorientation in each tissue. The dataset of 5,041 differentially expressed genes in the reoriented WT hypocotyl showed enrichment of several pathways. These pathways included glycolysis/gluconeogenesis (Figure 21), glycine, serine, and threonine metabolism (Figure 22), pyruvate metabolism (Figure 23), glyoxylate and dicarboxylate metabolism (Figure 24), carbon fixation (Figure 25), porphyrin and chlorophyll metabolism (Figure 26), aminoacyl-tRNA biosynthesis (Figure 27), carbon metabolism

(Figure 28), biosynthesis of amino acids (Figure 29), spliceosome (Figure 30), and endocytosis (Figure 31). The reoriented mature root in the pgm-1 mutant showed a change in ribosomal components (Figure 32).

Figure 21. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the glycolysis/gluconeogenesis pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 22. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the glycine, serine and threonine metabolism pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 23. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the pyruvate metabolism pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 25. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the carbon fixation pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 26. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the porphyrin and chlorophyll metabolism pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 27. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the aminoacyl t-RNA biosynthesis pathway were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 28. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicate changes in carbon metabolism. Green lines indicate an alteration in the pathway as measured by gene expression.

Figure 29. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicate changes in biosynthesis of amino acids. Green lines indicate an alteration in the pathway as measured by gene expression.

Figure 30. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the spliceosome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 31. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of endocytic pathways were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

Figure 32. KEGG pathway analysis showed the pgm-1 mature root had a drastic downregulation of ribosomal components upon reorientation. Blue and purple indicate a down regulation upon treatment.

KEGG Pathway Enrichments - WT vs pgm-1

KEGG pathway enrichment analysis was also performed between the untreated

(vertical) tissues of WT and pgm-1 to probe the effects of the pgm-1 mutation and lack of starch. In the hypocotyl, pathway enrichment was observed in glycolysis/gluconeogenesis

(Figure 33) the TCA cycle (Figure 34), purine metabolism (Figure 35), glycine, serine, and threonine metabolism (Figure 36), glutathione metabolism (Figure 37), pyruvate metabolism (Figure 38), glyoxylate and dicarboxylate metabolism (Figure 39), aminoacyl-tRNA biosynthesis (Figure 40), carbon metabolism (Figure 41), biosynthesis 97 of amino acids (Figure 42), spliceosome (Figure 43), protein export (Figure 44), protein processing (Figure 45), endocytosis (Figure 46), phagosome (Figure 47), and peroxisome biogenesis (Figure 48).

Figure 33. Glycolysis/gluconeogenesis KEGG pathway analysis for the differential expression gene set between wildtype hypocotyl untreated and pgm-1 untreated hypocotyl. Genes in blue are upregulated in the pgm-1 plants, and orange means upregulated in the wildtype plants. The gene labelled 5.4.2.2 is phosphoglucomutase, the gene knocked out in pgm-1 mutants, and appears in orange here.

Figure 34. KEGG pathway analysis for the differential expression gene set between wildtype hypocotyl untreated and pgm-1 untreated hypocotyl indicates that components of the citrate cycle were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

100

Figure 35. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of purine metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

101

Figure 36. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of glycine, serine, and threonine metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

102

Figure 37. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of glutathione metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

103

Figure 38. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of the pyruvate metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

104

Figure 39. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of glyoxylate and dicarboxylate metabolism were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

105

Figure 40. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of aminoacyl t-RNA biosynthesis were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

106

Figure 41. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl. Green lines represent aspects of carbon metabolism that were significantly altered between genotypes.

107

Figure 42. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl. Green lines represent aspects of amino acid biosynthesis that were significantly altered between genotypes.

108

Figure 43. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of the spliceosome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

109

Figure 44. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of protein export pathways were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

110

Figure 45. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of protein processing in the ER were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

111

112

Figure 47. KEGG pathway analysis for the differential expression gene set between wildtype untreated hypocotyl and pgm-1 untreated hypocotyl indicates that components of the phagosome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

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Figure 48. KEGG pathway analysis for vertical vs reoriented WT hypocotyls indicates that components of the peroxisome were significantly altered. Colors indicate Log2 fold change intensity according to the figure legend.

114

Network Analysis

Network analysis by STRING was used to assess connectivity within the contrast datasets. Connectivity represents a relationship between genes in the network, defined as experimentally proven protein interactions, predicted interactions, text mining co- occurrence, co-expression, and protein homology. The root tip (Figure 49) and hypocotyl

(Figure 50) each show an enrichment in connectivity when compared to what would be expected for a random sampling of genes. In the root tip, there were 22 edges between the

124 nodes, double the 11 edges expected from random gene sampling (enrichment p- value = 0.0024). In the hypocotyl, 83 edges were observed across 40 nodes, approximately a 6-fold enrichment from the 12 edges expected from random gene sampling (enrichment p-value < 1.0e-16). The network analyses were not run on the mature root or cotyledons because of the limited number of genes present in those lists.

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Figure 49. Network analysis of the contrast root tip gene set. Halos indicate the most up (red) and down (blue) regulated genes in the dataset. Edges indicate known interactions (light blue and pink), predicted interactions (green, red, and dark blue), text mining co- occurrence (yellow), co-expression (black) and protein homology (purple).

116

Figure 50. Network analysis of the contrast hypocotyl gene set. Halos represent the most up (red) and down (blue) regulated genes in the dataset. Edges represent known interactions (light blue and pink), predicted interactions (green, red, and dark blue), text mining co-occurrence (yellow), co-expression (black) and protein homology (purple).

Since there was a high number of differentially expressed genes in the wildtype hypocotyl dataset, a secondary comparison of the pairwise hypocotyl gene lists was performed. A gene list generated from that analysis was compared to the contrast analysis. Two genes overlap between the two gene sets, and each were highly connected in the contrast network (Figure 51). 117

Figure 51. Venn diagram representing the 6 pairwise hypocotyl comparisons (top). A subset of genes from the hypocotyl pairwise analysis was overlaid to the hypocotyl contrast analysis (bottom left), and common genes are highlighted in the network (bottom right). 118

Discussion

Plant gravity response is tightly integrated with many other aspects of plant physiology, which makes the delineation of gravity-specific molecular activity a challenge. While transcriptome data can never provide all of the answers to these questions, innovative comparative and statistical analyses can aid in the effort to map the components of early gravity sensing and response. In this study, RNA was extracted from the root tip, mature root, hypocotyl, and cotyledon tissues in WT and pgm-1 plants in an effort to track statolith-specific alterations upon gravitational reorientation.

The organ-level resolution chosen for plant dissection was informed by the unique relationship each organ has to gravity sensing and response. In the root, gravity is sensed in the root tip and the response takes place in the elongation zone of the root. Therefore, the root was dissected into root tip and mature root. The sensing and response to gravity are not spatially uncoupled in the hypocotyl, and so the whole organ was collected. No known gravity sensing or response exists in the cotyledons, and so they serve as an ideal control tissues for comparison with the others tissues and were separately collected. The lesion in the plastidic starch synthesis pathway in the pgm-1 mutant seedlings means the statoliths in the root tip and hypocotyl are non-functional in that they do not sediment.

Comparing the transcriptional response to reorientation across these various organs and in these two genotypes can shed light on unanswered questions.

Root Tip Differential Expression

Gravitropic response is largely reliant on sedimenting statoliths, and subsequent gravitational stimulus has downstream transcriptomic effects. Therefore, it stands to 119 reason that a gravitropic reorientation in a WT plant will have more of a transcriptional response than a plant that is deficient in statocyte-containing tissues. Furthermore, it would be expected that statocyte-free tissues respond differently. In the root tip, the opposite was observed. The WT revealed only a single gene differentially expressed,

AT5G51520, a member of the invertase/pectin methyltransferase protein superfamily.

Unexpectedly, 60 genes were differentially expressed in the pgm-1 reoriented root tip

(Table 2, Figure 17). The mechanism that led to more differential expression in the statolith-deficient root tip is unclear, but it may suggest a role for the statoliths in buffering environmental noise. A plant will sometimes dampen sedimentation-based signals by degrading its statoliths. For example, under a hydrotropic stimulus, statoliths in the root tip are degraded by autophagy so the root is free to move laterally to attain water (Nakayama et al., 2012) (Figure 52, bottom). In this way, a root tip without statoliths may be “primed” in some way to respond more readily to environmental stimuli. I hypothesize that the expression pattern observed from these data represents an augmented response to environmental stimulus due to lack of statoliths. 120

Figure 52. Possible physiological explanations for observed transcriptional landscapes. Venn diagrams showing, the tissue-specific differential expression between vertical and reoriented wildtype (WT) and pgm-1 plants (left), and the possible physiological responses that could explain the expression (right).

121

While the 60 genes in the root tip of pgm-1 represent a small list from which to pull meaningful conclusions, a handful of individual genes are may be good candidates for further investigation. AT5G17990 is a tryptophan biosynthetic enzyme that is downregulated in the root tip of reoriented pgm-1 seedlings. Tryptophan is synthesized in the plastid and is the precursor to auxin. Recently, Zhang et al. (2019) showed that auxin regulates starch accumulation in the statolith, and local auxin production may play a role.

Differential expression of a tryptophan synthesis gene upon a reorientation may suggest a feed-forward loop wherein local tryptophan production provides a pool of auxin precursor, which will facilitate the accumulation of starch in the plastid. Additionally, two glyceraldehyde-3-phosphatase dehydrogenases (GAPCP-2, AT1G16300 and GAPA,

AT3G26650), and a glucose-6-phosphate 1-dehydrogenase (G6PD3, AT1G24280) were all down regulated. GAPCP-2 is essential for the breakdown of starch, and G6PD3 is the rate-limiting step of the pentose phosphate pathway. This finding suggests the pgm-1 root tip is changing in carbohydrate utilization upon reorientation. It is unclear if this is due to cellular energy needs, a feedback loop connecting statolith sedimentation and plastidic starch deposition, an alteration in the production of secondary metabolites through the pentose phosphate pathway, or some combination of these three. Among the most downregulated genes in the dataset was LURE1.1 (AT5g43285). LURE1.1 is a cysteine- rich, diffusible peptide that has recently been shown to act as a signaling molecule to attract pollen tubes during fertilization (Zhong et al., 2019). The LURE1 peptides act as signals through their interaction with Pollen Receptor Kinases (PRKs), of which there are

6 in the Arabidopsis genome. While no PRKs are differentially expressed in the pgm-1 122 reoriented root tip, PRK2 and PRK3 are down regulated in the WT hypocotyl reorientation dataset. The LURE1 genes have never been implicated in gravity signaling, and the involvement of a protein known to act as a positional/location sensor within a cell-cell interaction system could represent an exciting candidate for further study.

Hypocotyl Differential Expression

In the hypocotyl, 5,041 genes were differentially expressed between the reoriented and vertical controls in the wild-type seedlings, whereas no genes were identified in the same comparison with pgm-1 (Figure 52). The hypocotyl has a more rapid gravity presentation time than the root, which may be reflected here by the higher number of genes differentially expressed during the short 10-minute treatment.

Physiological explanation for the differences in hypocotyl responses between WT and pgm-1 may be found in the biology of light response in hypocotyls. Under light stimulus, hypocotyl statoliths off-load their starch to prioritize phototropic response by mitigating statolith sedimentation (Kim et al., 2010). The starchless statoliths may be physiologically similar to the hypocotyl statoliths under this light response, and the transcriptomic profile observed in the pgm-1 seedlings may be a reflection of that similarity. Thus, knocking out pgm-1 may have triggered the native “gravity insensitive” state of the hypocotyl, and thus no transcriptional response was observed upon reorientation (Figure 52).

Many KEGG pathways showed significant enrichment in the WT hypocotyl reorientation dataset (Figures 21-31). Interestingly, there is no obvious connection between many of these pathways and known gravity responses. As the hypocotyl 123 responds quickly to gravitropic reorientation, these pathways may represent secondary responses related to necessary concomitant changes in physiology, rather than bona fide

“gravity response” (or, perhaps, a greater complexity in response than was previously thought). For example, enrichment in glycolysis, pyruvate metabolism, porphyrin metabolism, and carbon metabolism suggest the cells are shifting their energy utilization pathways. Gravitropic bending would require energy output from these cells, so it makes sense that there is a restructuring of those components under a reorientation scenario. It is important to remember that unlike in the root tip, not all the plastids in the hypocotyl are amyloplasts, and changes in plastid-localized genes may represent a shift in hypocotyl- localized chloroplasts rather than statoliths. Another interesting enrichment was observed in the spliceosome. The spliceosome has never been directly implicated in gravity reorientation response but may represent an appealing direction for future analysis, as these data suggest that alternative splicing may play a role. Lastly, endocytic components were enriched in this analysis. Auxin redistribution is regulated by the PIN proteins, which themselves are regulated on several levels. One of the primary means of PIN regulation is endocytic recycling. Under this regulation, the transmembrane PIN proteins are recycled into the cell to be degraded or used later (Krecek et al., 2009). As auxin is the primary driver of the downstream gravity response, a shift in the endocytic machinery might be expected upon a change in gravity stimulus.

An analysis of enrichments in PFAM protein domains revealed some further expression patterns. Six genes containing Jacalin-like lectin domains were observed as differentially expressed in this dataset, of the 48 of these genes in the genome. Lectin 124 domains are carbohydrate binding domains and are generally more involved signaling of environmental inputs than metabolism (Esch et al., 2017). Additionally, 18 F-box proteins were differentially expressed in this gene set, and all were down-regulated. F- box proteins act as the “adapters” that convey specificity to ubiquitin ligase complexes.

Stated differently, F-box proteins dictate which proteins are targeted for degradation, and thus represent a shift in the regulatory landscape of the cell. Though many F-box proteins are known to exist, the majority of their targets are unknown, including the 18 highlighted here. While the down-regulation of 18 F-box genes telegraphs a change in regulatory landscape, further conclusions about the nature of those changes remain elusive due to a lack in knowledge of their targets.

Cotyledon Differential Expression

As expected, the cotyledons showed little response to reorientation, with no genes differentially expressed in Columbia-0 and only 4 genes in pgm-1: AT2G39450 (a manganese transporter) AT3G07120 (a RING/U-box superfamily protein), AT3G58250

(a TRAF-like protein), and AT4G33290 (an F-box with no known target). While a lack of substantial changes in differential expression tells us little about gravity response, these data will aid in future experimental design by informing prioritization of tissues to be sequenced.

Mature Root Differential Expression

As was anticipated, the reorientation vs vertical control of the mature root in the wild type Col-0 resulted in no differentially expressed genes. However, perhaps the most confounding gene set in this analysis was the comparison between reorientation and 125 vertical control in the mature root of the starchless mutant with 1705 differentially expressed genes identified (Figure 52, middle). The mature root lacks starch statoliths and should be structurally comparable between the two genotypes examined. However, the region of gravity sensing and region of gravitropic response are spatially uncoupled in the root; the root tip senses and the tropic response is further up the root in the elongation zone. The differential expression observed in the reoriented pgm-1 mature root may be the result of signaling from the root tip. The relatively high number of differential gene expression in the pgm-1 root tip supports this hypothesis, as some of that expression may have signaled the expression patterns observed in the pgm-1 mature root. Very few genes were differentially expressed in the root tip and mature roots of the wildtype plants upon reorientation, which supports the hypothesis that differential expression in the root tip may be driving differential expression in the mature root.

KEGG analysis for the reoriented pgm-1 mature root revealed down regulation in ribosomal components (31 genes, mean log fold change -5.39, FDR 1.45x10-5) (Figure

32). Due to their immobility, plants use translational control as an adaptive measure. For example, translation in plants is reduced globally by 50-77% in response to heat stress or hypoxia (Zhao et al., 2019). The change in ribosomal constitution observed here upon reorientation may represent another layer of molecular regulation through overall translational activity.

Analysis for enrichment in molecular activity revealed an enrichment in water channel activity (9 genes of the represented of the 33 in the GO term). All nine of these were aquaporin or aquaporin-like proteins. Another nine genes in the Arabidopsis 126 genome are annotated as dehydrins. Interestingly, six of them were upregulated in this dataset. Generally, roots without statoliths are responding to hydrotropic stimulus, so alterations in genes related to water movement might be expected in the system created by a pgm-1 knockout. Eight genes with “invertase/pectin methyltransferase” domains were also differentially expressed, suggesting a change in cell wall architecture.

Furthermore, an enrichment was observed in the UniProt Keywords proteoglycan (8 genes of 52 in the pathway), GPI-anchor (13 genes of 115 in the pathway), and cell wall

(18 genes of the 258 in the pathway), which all suggest alterations to the cell wall.

Change in expression of cell wall genes suggests active preparation for a restructuring of the cell wall, which is a necessary step in tropic bending. Generally, the early cell wall changes are considered to be restricted to a “loosening” of the cell wall and mostly conveyed by changes in apoplastic pH. However, the transcriptional changes observed here may suggest a broader manifestation of cell wall remodeling relatively early upon reorientation.

Metabolism in pgm-1

In addition to examining the effects of reorientation, the datasets provide an excellent opportunity to study plant metabolism. For example, starch is an important molecule for energy storage in the plant, and the data gathered here can be used to track the transcriptional changes induced by the lack of starch in the pgm-1 mutants. The

KEGG pathway enrichment between the untreated (vertical) hypocotyl in WT and starchless plants shows the effects of the pgm-1 mutation on the biochemical pathways in glycolysis (Figure 33). As expected, PGM is expressed at a much higher rate in the WT 127 seedlings. Interestingly, many of the components of glycolysis were upregulated in the pgm-1 mutant. This suggests the pgm-1 seedlings activate pathways for energy production, likely in response to being starved of one of their principle energy storage molecules. It is unclear if the lack of starch as an energy source (rather than an inert purveyor of statolith density) has an influence on gravity response.

The most striking feature of the KEGG pathway analyses for the WT vs pgm-1 vertical hypocotyl is how closely the enrichment mirrors what was observed in the

KEGG analyses for the WT hypocotyl reorientation analysis. Many of the same pathways are observed as enriched for significant differential expression. Eight of the eleven enriched KEGG pathways in the reoriented WT hypocotyl were also enriched in the comparison between the WT and pgm-1 vertical hypocotyls. This suggests removing functional statoliths from the hypocotyl impacts the same pathways as reorienting a hypocotyl with functional statoliths.

Contrast Analyses

Contrast analyses are especially useful in teasing apart how multiple experimental groups respond differently to the same treatment. As with any statistical analysis, the most salient and first question is whether the contrast analysis is producing biologically relevant results. Figures 49 and 50 represent the network analyses performed on the contrast analysis for these gene sets. These networks, especially the hypocotyl, are highly connected. Network analyses combine protein interactions and co-expression with several other parameters, and an enrichment in network edges suggests an active biological unit as compared to a random sampling of genes. The connectivity in these two networks 128 suggests that contrast analysis is capable of highlighting genes that are interacting with each other in gravity response, rather than disparate, disjointed genes from the gravity response pathway. Therefore, contrast analyses may represent a valuable tool in beginning to model a molecular network for gravity response, rather than just pulling out gene candidates to further pursue.

Of the 124 genes in the contrast analysis, ~30 have been tested for phenotypes by fixed rotation tests. Of the genes tested, nine genes (30% of those tested) have shown a mutant phenotype in the fixed rotation assay (Table 7). Among the most intriguing genes from this list is CIPK13, a calcium-dependent protein kinase that is localized to the plastid (Schleibner et al., 2008). As calcium and plastids are both intimately involved in gravitropic response, and this gene is activated within 10 minutes of reorientation,

CIPK13 represents an exciting lead to follow for future investigations. The list also contains an F-box protein (AT2G42720) with no known target, which would also make a good candidate for future biochemical study. Successful mutant phenotype screening is further evidence that the contrast analysis is an effective means of gene candidate discovery.

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Table 7. Genes from root tip contrast with mutant phenotypes with by fixed rotation TAIR ID Description AT2G41810 Uncharacterized protein/imidazolonepropionase AT1G06923 Transcription repressor OFP17-like protein AT1G37130 Nitrate reductase [NADH] 2 AT4G23670 Protein of unknown function AT2G16660 Major facilitator superfamily protein AT2G42720 F-box/LRR-repeat protein AT2G34180 CIPK13- Calcium-dependent kinase AT4G17610 tRNA/rRNA methyltransferase family protein AT4G33550 Protein of unknown function

The large number of genes in the WT hypocotyl reorientation dataset prompted further comparisons among the hypocotyl datasets. A 6-way comparison of differential expression was performed between all the hypocotyl data generated (Figure 51). Within the 6-way comparison, 82 genes were differentially expressed in every comparison examined, and 2 of these genes were also observed in the contrast analysis. These two genes, AT3G48420 and AT4G09010, represent a haloacid dehalogenase-like hydrolase protein and ascorbate peroxidase 4. The two genes were also two of the most highly connected genes in the contrast analysis network analysis (Figure 51), and thus might make ideal candidates for future investigation, in addition to the other genes in the network. Furthermore, half of these are localized to the plastid (Figure 20).

Most interestingly, the reoriented WT hypocotyl had drastically fewer genes differentially expressed when compared with the pgm-1 hypocotyls as when compared to the WT vertical hypocotyl. The WT hypocotyl had over 5,000 genes differentially expressed when reoriented. However, less than 1/5 that number are differentially expressed between the WT reoriented hypocotyl and either of the pgm-1 hypocotyl 130 treatments. In other words, the reoriented WT hypocotyl is more transcriptionally similar to pgm-1 hypocotyls than to the vertical hypocotyl of its own genotype. As with the

KEGG pathway analyses, the results from this comparative analysis suggest that reorientation of the wildtype hypocotyl induces a response that brings it closer to the regulatory landscape of the pgm-1 hypocotyl.

Conclusions

One of the great benefits of a sequencing experiment with many tissues and treatments is that the data can be used to examine broad questions about the system while also providing smaller, individual avenues for further research. The latter was achieved by the contrast analysis, the utility of which has now been verified by mutant phenotyping and network analysis. The broad questions raised by this study concern the nature of the signal generated by sedimented plastids. First, we found that in the root tip the sedimented plastids seem to repress any immediate signal coming from reorientation.

The absence of sedimented plastids is necessary to overcome this repression, which emulates the natural hydrotropic response. In the hypocotyl, reorienting a starchless hypocotyl had no measurable effect. However, disturbing the wildtype plant’s sedimented plastids by reorientation appears to change the molecular landscape in such a way that it begins to converge with the pgm-1 transcriptional patterns. Perhaps this suggests that a fully sedimented, undisturbed set of plastids within a cell actively repress any molecular responses. Alternatively, this could be viewed as an activation of response upon statolith movement. Either way, this model would require a signal from whatever cellular component is responsible for sensing statolith sedimentation. As the statoliths move from 131 their initial position, a signal is rapidly released, which is only repressed again when the statoliths have fully sedimented again in their new orientation. This system would introduce a mechanism for the transient changes in expression that would be necessary for the temporary response that exists only until the organ has grown to proper orientation. Stated more simply, this model incorporates a signal not only upon sedimentation, but also a signal from the statolith’s former position. A cell without statoliths would not have a mechanism to repress transient signal. That would become the native molecular state of the cell and would appear similar to a recently reoriented cell with functional statoliths. Indeed, this is what was observed in the hypocotyl as measured by differential expression. Looking at early gravity responses through the lens of the possibility of this early perturbation signal may help to delineate further gravity sensing mechanisms.

The data generated here represent a valuable step forward in the field of plant gravitropic research and will continue to serve as an important framework in future studies.

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Chapter 5: Future directions

Bioregenerative life support will be an integral part of any viable plan for long term human space habitation. Beyond-Earth plant cultivation allows for the production of breathable oxygen while scrubbing CO2 from the environment and producing food, medicines, clothing, building materials, and providing psychological benefits. In order to bring plants with us into extraterrestrial environments, we need to know how plants will respond to the conditions they encounter. Principle among these considerations is how plants will respond outside of the 1g conditions under which they have evolved.

Plant Gravity Perception (PGP) was designed as a series of experiments aimed at understanding how plants respond to non-terrestrial gravitational intensities, and to further use those data to inform our understanding of plant gravity sensing mechanisms.

PGP was composed of three components: reorientation under Earth’s 1 g (Chapter 4), centrifugally induced gravitational treatments in low Earth orbit, and simulated partial- and hyper-gravity experiments using clinostats and centrifuges. When completed, each component has/will provide individual insights, but ultimately combining these datasets will provide for a deeper understanding of the fundamental landscape and idiosyncrasies of plant gravity response.

PGP 1 g reorientation is described in Chapter 4. PGP spaceflight and PGP simulated microgravity have each been completed and the RNA sequencing underway.

The set-up of those experiments is described here.

PGP spaceflight utilized the European Modular Cultivation System (EMCS) hardware aboard the International Space Station (ISS). The EMCS is comprised of a pair 133 of spinning rotors that confer a gravitational stimulus through centrifugation. Each of the rotors holds 4 experimental containers (ECs), each EC containing five seed cassettes at increasing radial distances. The rotational force induced upon a specimen simulates gravitational force that varies with the speed of the rotor and the position of the cassette

(Figure 53). The aims of this experiment were to 1) determine the gravitational thresholds required to induce a bending response in WT and pgm-1 seedlings and 2) determine the effects a gravitational gradient has on the transcriptional landscape in wildtype and starchless seedlings.

Figure 53. Representation of one of the two rotors that comprise the European Modular Cultivation System. Each rotor holds four experimental containers (large rectangles). Each EC holds five seed cassettes (small rectangles). Gravitational force increases with distance from the center. 134

PGP Flight

The PGP flight experiments were designed to assess the phenotypic effects of 30 g levels from 0.003g to 1g. Rotor run velocities were chosen by which velocities would give the best distribution of g levels, while obtaining representative data from Earth g

(1g), Martian g (0.38g) and lunar g (0.17g). Each of the two rotors (Rotor A, Rotor B) ran three times (Runs 1, 2, 3) while holding 20 cassettes representing 5 different gravitational intensities. Each radial position (g level) was occupied by 4 seed cassettes. Two of the seed cassettes contained Columbia-0 seeds and two contained pgm-1 seeds. Cassettes were seeded with 13 seeds. In total, over 1,500 seedlings were grown in 120 cassettes, representing 30 g levels and two genotypes. For RNA sequencing, 12 of the g levels were chosen. These were selected to best represent the gravitational gradient while also considering germination rates of the seed cassettes (Chapter 3, Figure 2).

Seeds were planted onto polyethersulfone membranes according to methods outlined in Appendix A and integrated into experimental hardware (seed cassettes) at

NASA Ames in Mountainview, CA then shipped to the launch site at Kennedy Space

Center in Cape Canaveral, FL. After several delays, Space-X Commercial Resupply

Service Mission 13 launched December 15th, 2017 and rendezvoused with the

International Space Station two days later. Experimental cassettes were loaded into the

EMCS rotors and run at 1g. Hydration of the cassettes was actuated remotely from

Kennedy Space Center. Seedlings were grown for four days at 1g, under white light coming in the same direction as the vector of simulated gravity. After four days, plants 135 were released from gravitational treatment (rotor stopped) and exposed to unilateral blue light perpendicular to their previous growth direction for 12 hours to orient the root tip outside the vector of gravity for the subsequent partial gravity treatment. EMCS contains cameras capable of capturing growth images, and infrared light was used to photograph the seedlings to avoid any phototropic influences. Images were collected at a rate of 6 per hour for 12 hours. At the end of treatment, seed cassettes were taken out of the EMCS and placed in the Minus Eighty-degree Laboratory Freezer for ISS (MELFI). Seedlings were returned to Earth aboard the Dragon capsule from the subsequent ISS resupply mission. The capsule splashed down off the coast of Baja, California and shipped immediately to NASA Ames. Seed cassettes were then shipped to Athens, Ohio on dry nitrogen, and stored at -80 °C until RNA was extracted.

PGP-ESTEC

After completion of the 1g ground experiments (Chapter 4) and the spaceflight experiment (Chapter 3, above) the third and final experimental phase of PGP was executed. The PGP-ESTEC (Plant Gravity Perception - European Space Research and

Technology Centre) experiments were designed to assess the hardware effects of simulated partial gravity environments using a 3D clinostat, a random positioning machine (RPM), and a large diameter centrifuge (LDC) (Figure 54). Plants were plated onto PES membranes according to methods outlined in Appendix A. Seeds were placed along an arc so that each seedling on a given plate would be along the same radial distance within the clinostat, RPM, or LDC (Figure 54, Figure 55). Two seed arcs were planted per plate, to allow for flexibility in experimental design if any problems were 136 encountered during the experiments. Two seed arcs resulted in 2 g treatments run on each plate, though ultimately only the outer arcs were used for sequencing. Blotter paper was soaked in ½ MS solution and dried. Blotter paper and membranes were placed in 100mm square Petri dishes. Petri Dishes were hydrated with 8 mL dH2O, sealed with Parafilm, and place in the growth chamber. Half of the plates were placed so the seed arc was perpendicular to the vector of gravity, and half were placed so the seed arc was parallel to the vector of gravity. This design ensured that half the seedlings would undergo a 90° reorientation when placed into their hardware treatments (Figure 55). Seedlings were grown at 24 °C under 24 hr 45-50 µmol m-1s-1 light for 4-5 days. Three plates each for

“vertical” growth and three plates for “90° reorientation” growth underwent one of simulated fractional- or hyper-gravity experimental treatment conditions. Unlike the 1g

Reorientation and Flight samples, only the root tip and hypocotyl were extracted from the

ESTEC samples.

Figure 54. Gravity simulation equipment at the European Space Research and Technology Centre. A. Random positioning machines (small, blue) and 3D clinostat (white). B. Large diameter centrifuge. 137

Figure 55. Experimental setup for PGP-ESTEC. Growing the seed arcs in two different orientations (A, C, D) allowed for the introduction of a 90° reorientation into the fractional gravity treatment. The 2 seed arcs (B) allowed for two different gravitational intensities per plate.

Results, Conclusions, & Future Directions

The Plant Gravity Perception project has resulted in nearly 600 samples representing replicates from 132 unique combinations of genotype, tissue, and gravitational treatment (Figure 56). While PGP-Pilot was primarily a proof of concept for the flight experiment, it has provided a wealth of data (Chapter 4). However, the bulk of 138 the data from PGP will come from the spaceflight experiment. The photographic data from flight has been analyzed by our collaborators and informed the selection of g levels for RNA extraction. Remarkably, a gravity response was observed in WT root tips at the lowest g level measured, 0.003g. In the pgm-1 mutant plants, consistent response was not observed until closer to 0.2g (Figure 57).

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Figure 56. Sankey diagram of all samples from PGP, excluding replicates. The hardware each sample was run on is at the left of the diagram. RPM is random positioning machine. EMCS is the European Modular Cultivation System. LDC is the large diameter centrifuge. Samples that from the pilot experiment (Chapter 4) are represented in pink.

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Figure 57. Representative images of plant growth during the EMCS-PGP experiment.

The transcriptomic comparisons may reveal important information about gravity response. First, I will search for any genes that vary directly or inversely with gravitational intensity. Next, I will run a weighted gene correlated network analysis

(WGCNA) to see if any gene groupings (gene modules) are moving together in response 141 to g treatment. I will then make direct pairwise comparisons between WT and pgm-1 expression for each tissue and g treatment. Of particular interest will be the transcriptional differences above and below the g levels that induced gravity response in the pgm-1 mutant. This analysis was originally going to be performed in the WT plants as well, but there is no “below response” g level to examine. To assess the expression patterns of specific genes across the gravity gradient, I plan to employ time-course gene expression tools and replace the dimension of time with that of gravitational intensity.

The ESTEC samples will also provide a unique group of datasets. NASA is particularly interested in the comparison of simulated fractional gravity and fractional gravity treatments from orbit. Additionally, to my knowledge, these experiments represent the first ever attempt to examine transcriptional effects of reorientation into a partial- or hyper-gravity treatment. Data collected here will have particular relevance to the discussion of inclination sensors vs gravitational force. Additionally, all the treatments were run on both Columbia-0 and pgm-1, so impacts of statoliths can also be examined.

While each of the three projects in PGP are standalone experiments, analyzing them together will provide valuable insight. Fractional gravity treatments from the

ESTEC samples will be compared to comparable g treatments from spaceflight. 1g Earth,

1g EMCS, and 1g clinostat samples can be compared to assess spaceflight- and hardware- specific transcription events. Additionally, the pilot experiments were run on 10-minute reorientation treatments, where the ESTEC samples were collected 20-minute after reorientation. The data for 1g samples can thus be treated as a time course, which may 142 reveal aspects of gravity response rates between WT and pgm-1 tissues. As always with omics data, this pipeline may evolve as the data are analyzed. While some analyses may not reveal the desired incite, other analyses will undoubtedly reveal themselves as relevant.

The PGP experiments have borne data that will advance the field of plant gravitropism. Specifically, the pilot experiment has revealed novel players in plant gravity response, the spaceflight samples will offer data on how plants respond to extraterrestrial gravitational intensities, and the ESTEC samples will allow examination of partial gravity analogs. Taken together, these experiments represent a comprehensive examination of gene expression as a function of gravitational intensity in plants.

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152

Appendix: Methods for Plant Growth and RNA Extraction for Polyethersulfone

(PES) Membranes

1. Introduction

When growing seedlings for phenotypic or transcriptomic analysis,

reproducibility in growth conditions is key. Polyethersulfone (PES) membranes

provide a simple platform for seedling growth and analysis and are especially

useful in gravitropic studies. The system’s merit lies in its simplicity, comprised

of only PES membranes, blotter paper to maintain hydration, guar gum to adhere

seeds to the membrane, Petri dishes of desired dimension, and nutrient solution.

Seeded plates maintain long-term viability, making this method ideal for high-

throughput phenotyping or spaceflight experiments. For spaceflight applications,

blotter paper can be soaked with nutrient media and dried, so only water is needed

for hydration during flight. This system has been reliably used in various

spaceflight experiments (Kiss 2019) but is also a useful method for ground-based

studies. Outlined here are the methods for application in 100 mm square Petri

dishes (see Note 1).

2. Materials

Sterile 100mm square petri dishes

Metricel PES membranes: cut to 90 x 90 mm and autoclaved

Blotter Paper: cut to 90 x 90 mm and autoclaved

Whatman blotter paper

Guar gum powder 153

Milli-Q water

70% (v/v) EtOH: 2 drops of Triton X-100 per 500mL

100% (v/v) EtOH

½ MS media: pH 5.8

Sterile scintillation vile

Sterile Pasteur pipette

Sterile fine-pointed forceps

Sterile microscope slide

Sterile 100mm glass Petri dish

Sterile DI water

Parafilm

Sterile scalpel

Bead beater

2 mL sterile ceramic bead tubes

RNA extraction buffer

3. Methods

3.1 1% (w/v) Guar gum preparation

1. Add 250 mL milli-Q water in a 500 mL beaker with a stir bar.

2. With stir bar spinning close to max speed, quickly add all 2.5 g guar

powder.

3. Allow to mix until stir bar slows or stops completely. 154

4. Pour guar suspension into 50 0mL media bottle and autoclave for 15 min.

at 120 °C.

5. Allow to cool. Sterile guar gum suspension can be stored at room

temperature.

3.2 Seed sterilization in laminar flow hood (see Note 2)

1. Add seeds to scintillation vial.

2. Add two Pasteur pipette volumes of 70% EtOH with Triton X-100 to the

vial and swirl for 5 minutes.

3. With a Pasteur pipette, aspirate off as much EtOH solution as possible and

discard.

4. With clean Pasteur Pipette, add two pipette volumes of 95% EtOH to the

scintillation vial and swirl for 1 minute.

5. Aspirate off as much EtOH as possible and discard.

6. Repeat step 4. After the minute rinse is complete, aspirate EtOH and seeds

onto a piece of sterile, dry blotter paper.

7. Allow seeds to dry in hood for 30-60 minutes.

3.3 Plating Seeds (performed in laminar flow hood) (See note 3)

1. Invert the bottom of the glass Petri dish so the bottom is facing up. This

will serve as a platform to work on.

2. Use forceps to transfer the sterile PES membrane to the top of the glass

Petri dish. 155

3. Use sterile DI water to lightly hydrate the membrane. This prevents the

membrane from sliding around while adhering seeds.

4. On a sterile microscope slide, pipette a streak of guar gum across the top.

5. Dip forceps into the streak of guar gum and use the sticky points to pick

up several Arabidopsis seeds.

6. Dip the seeded forceps back into the guar streak and allow the seeds to get

coated with guar gum, depositing the seeds into the streak.

7. Pick up one seed by dragging out of the streak towards the bottom of the

slide. Use a “flicking” motion with one of the points of the forceps to pick

up the seed. Excess guar may hinder germination; make sure there is not a

large amount of guar gum covering the seed.

8. Use the forceps to apply the seed to the membrane.

9. Repeat seed loading until the membrane is filled with 2 rows of about 10

seeds each.

10. Place the membrane in an open plastic petri dish to dry (see Note 4).

3.4 Hydration and Growth

1. Place a piece of blotter paper in a plastic 100mm Petri dish (see note 5).

2. With a Pasteur pipette, place a large dot of guar gum in the center of the

top piece of blotter paper and quickly place the center of the PES

membrane on top of the blotter paper.

3. Allow blotter/PES membrane to dry completely in laminar flow hood,

about 1 hour. 156

4. Pipette 5 mL of ½ MS solution onto the outside of the blotter paper and

allow it to soak into the membrane (see Notes 6, 7).

5. Wrap the dish in parafilm, and place vertically under desired growth

conditions.

6. After plant growth, if extracting RNA, use tongs to submerge the Petri

dish in liquid nitrogen for 10 seconds or until completely frozen, then

immediately store in -80 °C freezer.

3.5 Dissection under dissecting microscope (see Note 8).

1. Treat the dissection as part of your RNA extraction protocol, keeping your

area as RNase-free as possible.

2. Add RNA extraction buffer to bead beater tubes and keep on ice (see Note

9).

3. Remove Petri dish from freezer.

4. Remove lid from Petri dish. Flood the plants with RNA extraction buffer,

enough to thaw the tissue but not so much that tissues will float around the

plate during dissection (see Note 10).

5. With sterile scalpel, cut root tips from seedlings. Collect tissue with sterile

forceps and transfer to bead beater tubes. Thoroughly swish the forceps in

the tube to ensure all tissue has been transferred. Inspect the forceps after

to make sure no tissue remains on them, as you will use the same forceps

for the next tissue. 157

6. Repeat step 4 for the rest of the root, they hypocotyl, and the cotyledons

(see Notes 11, 12).

7. Transfer the tubes with tissue to bead beater. Tissue of this age is not

particularly lignified, so usually 2 rounds of 30 second homogenizations at

a speed of 2.5 m/s is sufficient to lyse cells (see Note 13).

8. After homogenization briefly spin down bead tubes, pipette the

homogenate, and continue through preferred RNA extraction protocol.

4. Notes

1. This system works best when the Petri dish is used “upside-down”; the blotter

paper is cut to the size of the lid (larger half) of the Petri dish. This makes the

bottom (smaller part) of the Petri dish hold the membrane in place. This also

protects against membrane dehydration, as condensation that drips back down

into the dish is wicked back up by the membrane.

2. This protocol is compatible with any seed sterilization method, but the

protocol outlined here has been reliably used in preparation for spaceflight

experiments with minimal loss in seed viability.

3. PES membrane is available gridded and non-gridded. The gridlines can help

in planting, phenotyping, and dissection. Pre-sterile PES membrane is sold in

66mm rounds. Larger sheets are available if using alternatively sized Petri

dish, but membrane must be cut to size. A rotary cutting tool works best when

cutting PES membrane. 158

4. Membranes can be stored long-term in the sterile Petri dishes they are to be

germinated in. Seeds stored in this manner have shown maintained viability

beyond 12 months of storage.

5. A 3-piece stack of Whatmann filter paper will work in place of blotter paper.

6. This volume works best for square 100mm Petri dishes with 60mm

membranes. For 100mm dishes with 100mm membranes, use 8mL ½ MS. For

60mm circle Petri dishes with 60mm membranes, 2-3mL is adequate for

hydration.

7. If desired, blotter paper can be soaked in ½ MS and allowed to dry, then

rehydrated later with DI H2O.

8. Our typical protocol is for dissecting a 3-5-day old seedlings into root tip,

root, hypocotyl, and cotyledons. Generally, we pool all like tissues from a

single plate, and doing the 4 subsequent RNA extractions simultaneously.

9. Volume will depend on RNA extraction protocol. For Qiagen RNeasy kits,

usually 450 µL RLT buffer is used.

10. Some extraction buffers will crystalize on the cold membrane. Crystalized

buffer makes the seedlings difficult to dissect, so test with a drop of buffer on

the membrane before flooding. If the drop freezes, wait a few seconds and try

again.

11. You may prefer to dissect the cotyledons at the petiole, so the apical meristem

is contained within the hypocotyl tissue. Grabbing the seedling by the bottom

with forceps and dragging it down the membrane usually brings the 159

cotyledons above the hypocotyl, and both cotyledons can often be dissected

from the seedling with a single cut.

12. Seedlings of this age often still have the seed coat attached to the tissue. This

tissue can be discarded or included in the root or hypocotyl extraction, but the

decision here should be consistent across extractions.

13. Higher speeds and homogenization times have been used without substantial

loss in RNA integrity, but this may require some optimization based on your

bead mill, tissue, and downstream application.

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