A prion-like protein regulator of seed germination undergoes hydration-dependent phase separation.

Many organisms evolved strategies to survive desiccation. Plant seeds protect dehydrated embryos from various stressors and can lay dormant for millennia. Hydration is the key trigger to initiate germination, but the mechanism by which seeds sense water remains unresolved. We identified an uncharacterized Arabidopsis thaliana prion-like protein we named FLOE1, which phase separates upon hydration and allows the embryo to sense water stress. We demonstrate that biophysical states of FLOE1 condensates modulate its biological function in vivo in suppressing seed germination under unfavorable environments. We find intragenic, intraspecific, and interspecific natural variation in FLOE1 expression and phase separation and show that intragenic variation is associated with adaptive germination strategies in natural populations. This combination of molecular, organismal, and ecological studies uncovers FLOE1 as a tunable environmental sensor with direct implications for the design of drought-resistant crops, in the face of climate change.

A high-throughput behavioral screening platform for measuring chemotaxis by C. elegans.

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multiwell plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.

Plants use molecular mechanisms mediated by biomolecular condensates to integrate environmental cues with development.

This review highlights recent literature on biomolecular condensates in plant development and discusses challenges for fully dissecting their functional roles. Plant developmental biology has been inundated with descriptive examples of biomolecular condensate formation, but it is only recently that mechanistic understanding has been forthcoming. Here, we discuss recent examples of potential roles biomolecular condensates play at different stages of the plant life cycle. We group these examples based on putative molecular functions, including sequestering interacting components, enhancing dwell time, and interacting with cytoplasmic biophysical properties in response to environmental change. We explore how these mechanisms could modulate plant development in response to environmental inputs and discuss challenges and opportunities for further research into deciphering molecular mechanisms to better understand the diverse roles that biomolecular condensates exert on life.

CHIQUITA1 maintains the temporal transition between proliferation and differentiation in Arabidopsis thaliana.

Body size varies widely among species, populations and individuals, depending on the environment. Transitioning between proliferation and differentiation is a crucial determinant of final organ size, but how the timing of this transition is established and maintained remains unknown. Using cell proliferation markers and genetic analysis, we show that CHIQUITA1 (CHIQ1) is required to maintain the timing of the transition from proliferation to differentiation in Arabidopsis thaliana. Combining kinematic and cell lineage-tracking studies, we found that the number of actively dividing cells in chiquita1-1 plants decreases prematurely compared with wild-type plants, suggesting CHIQ1 maintains the proliferative capacity in dividing cells and ensures that cells divide a specific number of times. CHIQ1 belongs to a plant-specific gene family of unknown molecular function and genetically interacts with three close members of its family to control the timing of proliferation exit. Our work reveals the interdependency between cellular and organ-level processes underlying final organ size determination.

Uncoupling differential water usage from drought resistance in a dwarf Arabidopsis mutant.

Understanding the molecular and physiological mechanisms of how plants respond to drought is paramount to breeding more drought-resistant crops. Certain mutations or allelic variations result in plants with altered water-use requirements. To correctly identify genetic differences which confer a drought phenotype, plants with different genotypes must be subjected to equal levels of drought stress. Many reports of advantageous mutations conferring drought resistance do not control for soil water content (SWC) variations across genotypes and may therefore need to be re-examined. Here, we reassessed the drought phenotype of the Arabidopsis (Arabidopsis thaliana) dwarf mutant, chiquita1-1 (chiq1-1, also called constitutively stressed 1 (cost1)), by growing mutant seedlings together with the wild-type to ensure uniform soil water availability across genotypes. Our results demonstrate that the dwarf phenotype conferred by loss of CHIQ1 function results in constitutively lower water usage per plant, but not increased drought resistance. Our study provides an easily reproducible, low-cost method to measure and control for SWC and to compare drought-resistant genotypes more accurately.

Progress and challenges in sorghum biotechnology, a multipurpose feedstock for the bioeconomy.

Sorghum [Sorghum bicolor (L.) Moench] is the fifth most important cereal crop globally by harvested area and production. Its drought and heat tolerance allow high yields with minimal input. It is a promising biomass crop for the production of biofuels and bioproducts. In addition, as an annual diploid with a relatively small genome compared with other C4 grasses, and excellent germplasm diversity, sorghum is an excellent research species for other C4 crops such as maize. As a result, an increasing number of researchers are looking to test the transferability of findings from other organisms such as Arabidopsis thaliana and Brachypodium distachyon to sorghum, as well as to engineer new biomass sorghum varieties. Here, we provide an overview of sorghum as a multipurpose feedstock crop which can support the growing bioeconomy, and as a monocot research model system. We review what makes sorghum such a successful crop and identify some key traits for future improvement. We assess recent progress in sorghum transformation and highlight how transformation limitations still restrict its widespread adoption. Finally, we summarize available sorghum genetic, genomic, and bioinformatics resources. This review is intended for researchers new to sorghum research, as well as those wishing to include non-food and forage applications in their research.

Systems genomics approaches provide new insights into Arabidopsis thaliana root growth regulation under combinatorial mineral nutrient limitation.

The molecular mechanisms by which plants modulate their root growth rate (RGR) in response to nutrient deficiency are largely unknown. Using Arabidopsis thaliana accessions, we analyzed RGR variation under combinatorial mineral nutrient deficiencies involving phosphorus (P), iron (Fe), and zinc (Zn). While -P stimulated early RGR of most accessions, -Fe or -Zn reduced it. The combination of either -P-Fe or -P-Zn led to suppression of the growth inhibition exerted by -Fe or -Zn alone. Surprisingly, root growth responses of the reference accession Columbia (Col-0) were not representative of the species under -P nor -Zn. Using a systems approach that combines GWAS, network-based candidate identification, and reverse genetic screen, we identified new genes that regulate root growth in -P-Fe: VIM1, FH6, and VDAC3. Our findings provide a framework to systematically identifying favorable allelic variations to improve root growth, and to better understand how plants sense and respond to multiple environmental cues.

Plant Metabolic Network 15: A resource of genome-wide metabolism databases for 126 plants and algae.

To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.

Bioinformatics tools for the identification of gene clusters that biosynthesize specialized metabolites.

Specialized metabolites (also called natural products or secondary metabolites) derived from bacteria, fungi, marine organisms and plants constitute an important source of antibiotics, anti-cancer agents, insecticides, immunosuppressants and herbicides. Many specialized metabolites in bacteria and fungi are biosynthesized via metabolic pathways whose enzymes are encoded by clustered genes on a chromosome. Metabolic gene clusters comprise a group of physically co-localized genes that together encode enzymes for the biosynthesis of a specific metabolite. Although metabolic gene clusters are generally not known to occur outside of microbes, several plant metabolic gene clusters have been discovered in recent years. The discovery of novel metabolic pathways is being enabled by the increasing availability of high-quality genome sequencing coupled with the development of powerful computational toolkits to identify metabolic gene clusters. To provide a comprehensive overview of various bioinformatics methods for detecting gene clusters, we compare and contrast key aspects of algorithmic logic behind several computational tools, including 'NP.searcher', 'ClustScan', 'CLUSEAN', 'antiSMASH', 'SMURF', 'MIDDAS-M', 'ClusterFinder', 'CASSIS/SMIPS' and 'C-Hunter' among others. We also review additional tools such as 'NRPSpredictor' and 'SBSPKS' that can infer substrate specificity for previously identified gene clusters. The continual development of bioinformatics methods to predict gene clusters will help shed light on how organisms assemble multi-step metabolic pathways for adaptation to various ecological niches.

METACLUSTER-an R package for context-specific expression analysis of metabolic gene clusters.

SUMMARY: Plants and microbes produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have yet to be elucidated. Some biosynthetic pathways are encoded by enzymes collocated in the chromosome. To facilitate a more comprehensive condition and tissue-specific expression analysis of metabolic gene clusters, we developed METACLUSTER, a probabilistic framework for characterizing metabolic gene clusters using context-specific gene expression information. AVAILABILITY AND IMPLEMENTATION: METACLUSTER is freely available at https://github.com/mbanf/METACLUSTER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants.

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.

Systematic discovery of novel eukaryotic transcriptional regulators using sequence homology independent prediction.

BACKGROUND: The molecular function of a gene is most commonly inferred by sequence similarity. Therefore, genes that lack sufficient sequence similarity to characterized genes (such as certain classes of transcriptional regulators) are difficult to classify using most function prediction algorithms and have remained uncharacterized. RESULTS: To identify novel transcriptional regulators systematically, we used a feature-based pipeline to screen protein families of unknown function. This method predicted 43 transcriptional regulator families in Arabidopsis thaliana, 7 families in Drosophila melanogaster, and 9 families in Homo sapiens. Literature curation validated 12 of the predicted families to be involved in transcriptional regulation. We tested 33 out of the 195 Arabidopsis putative transcriptional regulators for their ability to activate transcription of a reporter gene in planta and found twelve coactivators, five of which had no prior literature support. To investigate mechanisms of action in which the predicted regulators might work, we looked for interactors of an Arabidopsis candidate that did not show transactivation activity in planta and found that it might work with other members of its own family and a subunit of the Polycomb Repressive Complex 2 to regulate transcription. CONCLUSIONS: Our results demonstrate the feasibility of assigning molecular function to proteins of unknown function without depending on sequence similarity. In particular, we identified novel transcriptional regulators using biological features enriched in transcription factors. The predictions reported here should accelerate the characterization of novel regulators.

Patterns of metabolite changes identified from large-scale gene perturbations in Arabidopsis using a genome-scale metabolic network.

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes.

Measuring semantic similarities by combining gene ontology annotations and gene co-function networks.

BACKGROUND: Gene Ontology (GO) has been used widely to study functional relationships between genes. The current semantic similarity measures rely only on GO annotations and GO structure. This limits the power of GO-based similarity because of the limited proportion of genes that are annotated to GO in most organisms. RESULTS: We introduce a novel approach called NETSIM (network-based similarity measure) that incorporates information from gene co-function networks in addition to using the GO structure and annotations. Using metabolic reaction maps of yeast, Arabidopsis, and human, we demonstrate that NETSIM can improve the accuracy of GO term similarities. We also demonstrate that NETSIM works well even for genomes with sparser gene annotation data. We applied NETSIM on large Arabidopsis gene families such as cytochrome P450 monooxygenases to group the members functionally and show that this grouping could facilitate functional characterization of genes in these families. CONCLUSIONS: Using NETSIM as an example, we demonstrated that the performance of a semantic similarity measure could be significantly improved after incorporating genome-specific information. NETSIM incorporates both GO annotations and gene co-function network data as a priori knowledge in the model. Therefore, functional similarities of GO terms that are not explicitly encoded in GO but are relevant in a taxon-specific manner become measurable when GO annotations are limited. Supplementary information and software are available at http://www.msu.edu/~jinchen/NETSIM .

Systems analysis of plant functional, transcriptional, physical interaction, and metabolic networks.

Physiological responses, developmental programs, and cellular functions rely on complex networks of interactions at different levels and scales. Systems biology brings together high-throughput biochemical, genetic, and molecular approaches to generate omics data that can be analyzed and used in mathematical and computational models toward uncovering these networks on a global scale. Various approaches, including transcriptomics, proteomics, interactomics, and metabolomics, have been employed to obtain these data on the cellular, tissue, organ, and whole-plant level. We summarize progress on gene regulatory, cofunction, protein interaction, and metabolic networks. We also illustrate the main approaches that have been used to obtain these networks, with specific examples from Arabidopsis thaliana, and describe the pros and cons of each approach.

Non-destructive, whole-plant phenotyping reveals dynamic changes in water use efficiency, photosynthesis, and rhizosphere acidification of sorghum accessions under osmotic stress.

Noninvasive phenotyping can quantify dynamic plant growth processes at higher temporal resolution than destructive phenotyping and can reveal phenomena that would be missed by end-point analysis alone. Additionally, whole-plant phenotyping can identify growth conditions that are optimal for both above- and below-ground tissues. However, noninvasive, whole-plant phenotyping approaches available today are generally expensive, complex, and non-modular. We developed a low-cost and versatile approach to noninvasively measure whole-plant physiology over time by growing plants in isolated hydroponic chambers. We demonstrate the versatility of our approach by measuring whole-plant biomass accumulation, water use, and water use efficiency every two days on unstressed and osmotically stressed sorghum accessions. We identified relationships between root zone acidification and photosynthesis on whole-plant water use efficiency over time. Our system can be implemented using cheap, basic components, requires no specific technical expertise, and should be suitable for any non-aquatic vascular plant species.

A reference-free algorithm discovers regulation in the plant transcriptome.

Most plant genomes and their regulation remain unknown. We used SPLASH - a new, reference-genome free sequence variation detection algorithm - to analyze transcriptional and post-transcriptional regulation from RNA-seq data. We discovered differential homolog expression during maize pollen development, and imbibition-dependent cryptic splicing in Arabidopsis seeds. SPLASH enables discovery of novel regulatory mechanisms, including differential regulation of genes from hybrid parental haplotypes, without the use of alignment to a reference genome.

Variability in drought gene expression datasets highlight the need for community standardization.

Physiologically relevant drought stress is difficult to apply consistently, and the heterogeneity in experimental design, growth conditions, and sampling schemes make it challenging to compare water deficit studies in plants. Here, we re-analyzed hundreds of drought gene expression experiments across diverse model and crop species and quantified the variability across studies. We found that drought studies are surprisingly uncomparable, even when accounting for differences in genotype, environment, drought severity, and method of drying. Many studies, including most Arabidopsis work, lack high-quality phenotypic and physiological datasets to accompany gene expression, making it impossible to assess the severity or in some cases the occurrence of water deficit stress events. From these datasets, we developed supervised learning classifiers that can accurately predict if RNA-seq samples have experienced a physiologically relevant drought stress, and suggest this can be used as a quality control for future studies. Together, our analyses highlight the need for more community standardization, and the importance of paired physiology data to quantify stress severity for reproducibility and future data analyses.

An efficient behavioral screening platform classifies natural products and other chemical cues according to their chemosensory valence in C. elegans.

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction, and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multi-well plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals, but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.