Search

Abstract The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration.

Scientific advances due to conceptual or technological innovations can be revealed by examining how research topics have evolved. But such topical evolution is difficult to uncover and quantify because of the large body of literature and the need for expert knowledge in a wide range of areas in a field. Using plant biology as an example, we used machine learning and language models to classify plant science citations into topics representing interconnected, evolving subfields. The changes in prevalence of topical records over the last 50 years reflect shifts in major research trends and recent radiation of new topics, as well as turnover of model species and vastly different plant science research trajectories among countries. Our approaches readily summarize the topical diversity and evolution of a scientific field with hundreds of thousands of relevant papers, and they can be applied broadly to other fields.

The Oomycete plant pathogen, Phytophthora capsici, causes root, crown, and fruit rot of winter squash (Cucurbita moschata) and limits production. Some C. moschata cultivars develop age-related resistance (ARR), whereby fruit develop resistance to P. capsici 14 to 21 days postpollination (DPP) because of thickened exocarp; however, wounding negates ARR. We uncovered the genetic mechanisms of ARR of two C. moschata cultivars, Chieftain and Dickenson Field, that exhibit ARR at 14 and 21 DPP, respectively, using RNA sequencing. The sequencing was conducted using RNA samples from ‘Chieftain’ and ‘Dickenson Field’ fruit at 7, 10, 14, and 21 DPP. A differential expression and subsequent gene set enrichment analysis revealed an overrepresentation of upregulated genes in functional categories relevant to cell wall structure biosynthesis, cell wall modification/organization, transcription regulation, and metabolic processes. A pathway enrichment analysis detected upregulated genes in cutin, suberin monomer, and phenylpropanoid biosynthetic pathways. A further analysis of the expression profile of genes in those pathways revealed upregulation of genes in monolignol biosynthesis and lignin polymerization in the resistant fruit peel. Our findings suggest a shift in gene expression toward the physical strengthening of the cell wall associated with ARR to P. capsici. These findings provide candidate genes for developing Cucurbita cultivars with resistance to P. capsici and improve fruit rot management in Cucurbita species.

Switchgrass low-land ecotypes have significantly higher biomass but lower cold tolerance compared to up-land ecotypes. Understanding the molecular mechanisms underlying cold response, including the ones at transcriptional level, can contribute to improving tolerance of high-yield switchgrass under chilling and freezing environmental conditions. Here, by analyzing an existing switchgrass transcriptome dataset, the temporal cis-regulatory basis of switchgrass transcriptional response to cold is dissected computationally. We found that the number of cold-responsive genes and enriched Gene Ontology terms increased as duration of cold treatment increased from 30 min to 24 hours, suggesting an amplified response/cascading effect in cold-responsive gene expression. To identify genomic sequences likely important for regulating cold response, machine learning models predictive of cold response were established using k-mer sequences enriched in the genic and flanking regions of cold-responsive genes but not non-responsive genes. These k-mers, referred to as putative cis-regulatory elements (pCREs) are likely regulatory sequences of cold response in switchgrass. There are in total 655 pCREs where 54 are important in all cold treatment time points. Consistent with this, eight of 35 known cold-responsive CREs were similar to top-ranked pCREs in the models and only these eight were important for predicting temporal cold response. More importantly, most of the top-ranked pCREs were novel sequences in cold regulation. Our findings suggest additional sequence elements important for cold-responsive regulation previously not known that warrant further studies.

Abstract Background Availability of plant genome sequences has led to significant advances. However, with few exceptions, the great majority of existing genome assemblies are derived from short read sequencing technologies with highly uneven read coverages indicative of sequencing and assembly issues that could significantly impact any downstream analysis of plant genomes. In tomato for example, 0.6% (5.1 Mb) and 9.7% (79.6 Mb) of short-read based assembly had significantly higher and lower coverage compared to background, respectively. Results To understand what the causes may be for such uneven coverage, we first established machine learning models capable of predicting genomic regions with variable coverages and found that high coverage regions tend to have higher simple sequence repeat and tandem gene densities compared to background regions. To determine if the high coverage regions were misassembled, we examined a recently available tomato long-read based assembly and found that 27.8% (1.41 Mb) of high coverage regions were potentially misassembled of duplicate sequences, compared to 1.4% in background regions. In addition, using a predictive model that can distinguish correctly and incorrectly assembled high coverage regions, we found that misassembled, high coverage regions tend to be flanked by simple sequence repeats, pseudogenes, and transposon elements. Conclusions Our study provides insights on the causes of variable coverage regions and a quantitative assessment of factors contributing to plant genome misassembly when using short reads and the generality of these causes and factors should be tested further in other species.

Abstract Background Gene expression is regulated by DNA-binding transcription factors (TFs). Together with their target genes, these factors and their interactions collectively form a gene regulatory network (GRN), which is responsible for producing patterns of transcription, including cyclical processes such as genome replication and cell division. However, identifying how this network regulates the timing of these patterns, including important interactions and regulatory motifs, remains a challenging task. Results We employed four in vivo and in vitro regulatory data sets to investigate the regulatory basis of expression timing and phase-specific patterns cell-cycle expression in Saccharomyces cerevisiae. Specifically, we considered interactions based on direct binding between TF and target gene, indirect effects of TF deletion on gene expression, and computational inference. We found that the source of regulatory information significantly impacts the accuracy and completeness of recovering known cell-cycle expressed genes. The best approach involved combining TF-target and TF-TF interactions features from multiple datasets in a single model. In addition, TFs important to multiple phases of cell-cycle expression also have the greatest impact on individual phases. Important TFs regulating a cell-cycle phase also tend to form modules in the GRN, including two sub-modules composed entirely of unannotated cell-cycle regulators (STE12-TEC1 and RAP1-HAP1-MSN4). Conclusion Our findings illustrate the importance of integrating both multiple omics data and regulatory motifs in order to understand the significance regulatory interactions involved in timing gene expression. This integrated approached allowed us to recover both known cell-cycles interactions and the overall pattern of phase-specific expression across the cell-cycle better than any single data set. Likewise, by looking at regulatory motifs in the form of TF-TF interactions, we identified sets of TFs whose co-regulation of target genes was important for cell-cycle expression, even when regulation by individual TFs was not. Overall, this demonstrates the power of integrating multiple data sets and models of interaction in order to understand the regulatory basis of established biological processes and their associated gene regulatory networks.

The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e., ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e., feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.

Abstract Extensive transcriptional activity occurring in intergenic regions of genomes has raised the question whether intergenic transcription represents the activity of novel genes or noisy expression. To address this, we evaluated cross-species and post-duplication sequence and expression conservation of intergenic transcribed regions (ITRs) in four Poaceae species. Among 43,301 ITRs across the four species, 34,460 (80%) are species-specific. ITRs found across species tend to be more divergent in expression and have more recent duplicates compared to annotated genes. To assess if ITRs are functional (under selection), machine learning models were established in Oryza sativa (rice) that could accurately distinguish between phenotype genes and pseudogenes (area under curve-receiver operating characteristic = 0.94). Based on the models, 584 (8%) and 4391 (61%) rice ITRs are classified as likely functional and nonfunctional with high confidence, respectively. ITRs with conserved expression and ancient retained duplicates, features that were not part of the model, are frequently classified as likely-functional, suggesting these characteristics could serve as pragmatic rules of thumb for identifying candidate sequences likely to be under selection. This study also provides a framework to identify novel genes using comparative transcriptomic data to improve genome annotation that is fundamental for connecting genotype to phenotype in crop and model systems.

Abstract Background Transcription factors (TFs) play a key role in regulating plant development and response to environmental stimuli. While most genes revert to single copy after whole genome duplication (WGD) event, transcription factors are retained at a significantly higher rate. Little is known about how TF duplicates have diverged in their expression and regulation, the answer to which may contribute to a better understanding of the elevated retention rate among TFs. Results Here we assessed what features may explain differences in the retention of TF duplicates and other genes using Arabidopsis thaliana as a model. We integrated 34 expression, sequence, and conservation features to build a linear model for predicting the extent of duplicate retention following WGD events among TFs and 19 groups of genes with other functions. We found that TFs was the least well predicted, demonstrating the features of TFs are substantially deviated from duplicate genes in other function groups. Consistent with this, the evolution of TF expression patterns and cis-regulatory cites favors the partitioning of ancestral states among the resulting duplicates: one “ancestral” TF duplicate retains most ancestral expression and cis-regulatory sites, while the “non-ancestral” duplicate is enriched for novel regulatory sites. By modeling the retention of ancestral expression and cis-regulatory states in duplicate pairs using a system of differential equations, we found that TF duplicate pairs in a partitioned state are preferentially maintained. Conclusions These TF duplicates with asymmetrically partitioned ancestral states are likely maintained because one copy retains ancestral functions while the other, at least in some cases, acquires novel cis-regulatory sites that may be important for novel, adaptive traits.

The study of complex biological systems necessitates computational modeling approaches that are currently underutilized in plant biology. Many plant biologists have trouble identifying or adopting modeling methods to their research, particularly mechanistic mathematical modeling. Here we address challenges that limit the use of computational modeling methods, particularly mechanistic mathematical modeling. We divide computational modeling techniques into either pattern models (e.g., bioinformatics, machine learning, or morphology) or mechanistic mathematical models (e.g., biochemical reactions, biophysics, or population models), which both contribute to plant biology research at different scales to answer different research questions. We present arguments and recommendations for the increased adoption of modeling by plant biologists interested in incorporating more modeling into their research programs. As some researchers find math and quantitative methods to be an obstacle to modeling, we provide suggestions for easy-to-use tools for non-specialists and for collaboration with specialists. This may especially be the case for mechanistic mathematical modeling, and we spend some extra time discussing this. Through a more thorough appreciation and awareness of the power of different kinds of modeling in plant biology, we hope to facilitate interdisciplinary, transformative research.

Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity.

[This corrects the article DOI: 10.1371/journal.pgen.1003064.].

Gene co-expression has been widely used to hypothesize gene function through guilt-by association. However, it is not clear to what degree co-expression is informative, whether it can be applied to genes involved in different biological processes, and how the type of dataset impacts inferences about gene functions. Here our goal is to assess the utility and limitations of using co-expression as a criterion to recover functional associations between genes. By determining the percentage of gene pairs in a metabolic pathway with significant expression correlation, we found that many genes in the same pathway do not have similar transcript profiles and the choice of dataset, annotation quality, gene function, expression similarity measure, and clustering approach significantly impacts the ability to recover functional associations between genes using Arabidopsis thaliana as an example. Some datasets are more informative in capturing coordinated expression profiles and larger data sets are not always better. In addition, to recover the maximum number of known pathways and identify candidate genes with similar functions, it is important to explore rather exhaustively multiple dataset combinations, similarity measures, clustering algorithms and parameters. Finally, we validated the biological relevance of co-expression cluster memberships with an independent phenomics dataset and found that genes that consistently cluster with leucine degradation genes tend to have similar leucine levels in mutants. This study provides a framework for obtaining gene functional associations by maximizing the information that can be obtained from gene expression datasets.

Transcription factor (TF) binding is determined by the presence of specific sequence motifs (SM) and chromatin accessibility, where the latter is influenced by both chromatin state (CS) and DNA structure (DS) properties. Although SM, CS, and DS have been used to predict TF binding sites, a predictive model that jointly considers CS and DS has not been developed to predict either TF-specific binding or general binding properties of TFs. Using budding yeast as model, we found that machine learning classifiers trained with either CS or DS features alone perform better in predicting TF-specific binding compared to SM-based classifiers. In addition, simultaneously considering CS and DS further improves the accuracy of the TF binding predictions, indicating the highly complementary nature of these two properties. The contributions of SM, CS, and DS features to binding site predictions differ greatly between TFs, allowing TF-specific predictions and potentially reflecting different TF binding mechanisms. In addition, a "TF-agnostic" predictive model based on three DNA "intrinsic properties" (in silico predicted nucleosome occupancy, major groove geometry, and dinucleotide free energy) that can be calculated from genomic sequences alone has performance that rivals the model incorporating experiment-derived data. This intrinsic property model allows prediction of binding regions not only across TFs, but also across DNA-binding domain families with distinct structural folds. Furthermore, these predicted binding regions can help identify TF binding sites that have a significant impact on target gene expression. Because the intrinsic property model allows prediction of binding regions across DNA-binding domain families, it is TF agnostic and likely describes general binding potential of TFs. Thus, our findings suggest that it is feasible to establish a TF agnostic model for identifying functional regulatory regions in potentially any sequenced genome.

Pseudoperonospora cubensis, an obligate oomycete pathogen, is the causal agent of cucurbit downy mildew, a foliar disease of global economic importance. Similar to other oomycete plant pathogens, Ps. cubensis has a suite of RXLR and RXLR-like effector proteins, which likely function as virulence or avirulence determinants during the course of host infection. Using in silico analyses, we identified 271 candidate effector proteins within the Ps. cubensis genome with variable RXLR motifs. In extending this analysis, we present the functional characterization of one Ps. cubensis effector protein, RXLR protein 1 (PscRXLR1), and its closest Phytophthora infestans ortholog, PITG_17484, a member of the Drug/Metabolite Transporter (DMT) superfamily. To assess if such effector-non-effector pairs are common among oomycete plant pathogens, we examined the relationship(s) among putative ortholog pairs in Ps. cubensis and P. infestans. Of 271 predicted Ps. cubensis effector proteins, only 109 (41%) had a putative ortholog in P. infestans and evolutionary rate analysis of these orthologs shows that they are evolving significantly faster than most other genes. We found that PscRXLR1 was up-regulated during the early stages of infection of plants, and, moreover, that heterologous expression of PscRXLR1 in Nicotiana benthamiana elicits a rapid necrosis. More interestingly, we also demonstrate that PscRXLR1 arises as a product of alternative splicing, making this the first example of an alternative splicing event in plant pathogenic oomycetes transforming a non-effector gene to a functional effector protein. Taken together, these data suggest a role for PscRXLR1 in pathogenicity, and, in total, our data provide a basis for comparative analysis of candidate effector proteins and their non-effector orthologs as a means of understanding function and evolutionary history of pathogen effectors.

Unicellular marine algae have promise for providing sustainable and scalable biofuel feedstocks, although no single species has emerged as a preferred organism. Moreover, adequate molecular and genetic resources prerequisite for the rational engineering of marine algal feedstocks are lacking for most candidate species. Heterokonts of the genus Nannochloropsis naturally have high cellular oil content and are already in use for industrial production of high-value lipid products. First success in applying reverse genetics by targeted gene replacement makes Nannochloropsis oceanica an attractive model to investigate the cell and molecular biology and biochemistry of this fascinating organism group. Here we present the assembly of the 28.7 Mb genome of N. oceanica CCMP1779. RNA sequencing data from nitrogen-replete and nitrogen-depleted growth conditions support a total of 11,973 genes, of which in addition to automatic annotation some were manually inspected to predict the biochemical repertoire for this organism. Among others, more than 100 genes putatively related to lipid metabolism, 114 predicted transcription factors, and 109 transcriptional regulators were annotated. Comparison of the N. oceanica CCMP1779 gene repertoire with the recently published N. gaditana genome identified 2,649 genes likely specific to N. oceanica CCMP1779. Many of these N. oceanica-specific genes have putative orthologs in other species or are supported by transcriptional evidence. However, because similarity-based annotations are limited, functions of most of these species-specific genes remain unknown. Aside from the genome sequence and its analysis, protocols for the transformation of N. oceanica CCMP1779 are provided. The availability of genomic and transcriptomic data for Nannochloropsis oceanica CCMP1779, along with efficient transformation protocols, provides a blueprint for future detailed gene functional analysis and genetic engineering of Nannochloropsis species by a growing academic community focused on this genus.

Understanding the molecular mechanisms of pathogen emergence is central to mitigating the impacts of novel infectious disease agents. The chytrid fungus Batrachochytrium dendrobatidis (Bd) is an emerging pathogen of amphibians that has been implicated in amphibian declines worldwide. Bd is the only member of its clade known to attack vertebrates. However, little is known about the molecular determinants of - or evolutionary transition to - pathogenicity in Bd. Here we sequence the genome of Bd's closest known relative - a non-pathogenic chytrid Homolaphlyctis polyrhiza (Hp). We first describe the genome of Hp, which is comparable to other chytrid genomes in size and number of predicted proteins. We then compare the genomes of Hp, Bd, and 19 additional fungal genomes to identify unique or recent evolutionary elements in the Bd genome. We identified 1,974 Bd-specific genes, a gene set that is enriched for protease, lipase, and microbial effector Gene Ontology terms. We describe significant lineage-specific expansions in three Bd protease families (metallo-, serine-type, and aspartyl proteases). We show that these protease gene family expansions occurred after the divergence of Bd and Hp from their common ancestor and thus are localized to the Bd branch. Finally, we demonstrate that the timing of the protease gene family expansions predates the emergence of Bd as a globally important amphibian pathogen.

The emergence of multigene families has been hypothesized as a major contributor to the evolution of complex traits and speciation. To help understand how such multigene families arose and diverged during plant evolution, we examined the phylogenetic relationships of F-Box (FBX) genes, one of the largest and most polymorphic superfamilies known in the plant kingdom. FBX proteins comprise the target recognition subunit of SCF-type ubiquitin-protein ligases, where they individually recruit specific substrates for ubiquitylation. Through the extensive analysis of 10,811 FBX loci from 18 plant species, ranging from the alga Chlamydomonas reinhardtii to numerous monocots and eudicots, we discovered strikingly diverse evolutionary histories. The number of FBX loci varies widely and appears independent of the growth habit and life cycle of land plants, with a little as 198 predicted for Carica papaya to as many as 1350 predicted for Arabidopsis lyrata. This number differs substantially even among closely related species, with evidence for extensive gains/losses. Despite this extraordinary inter-species variation, one subset of FBX genes was conserved among most species examined. Together with evidence of strong purifying selection and expression, the ligases synthesized from these conserved loci likely direct essential ubiquitylation events. Another subset was much more lineage specific, showed more relaxed purifying selection, and was enriched in loci with little or no evidence of expression, suggesting that they either control more limited, species-specific processes or arose from genomic drift and thus may provide reservoirs for evolutionary innovation. Numerous FBX loci were also predicted to be pseudogenes with their numbers tightly correlated with the total number of FBX genes in each species. Taken together, it appears that the FBX superfamily has independently undergone substantial birth/death in many plant lineages, with its size and rapid evolution potentially reflecting a central role for ubiquitylation in driving plant fitness.

Due to the selection pressure imposed by highly variable environmental conditions, stress sensing and regulatory response mechanisms in plants are expected to evolve rapidly. One potential source of innovation in plant stress response mechanisms is gene duplication. In this study, we examined the evolution of stress-regulated gene expression among duplicated genes in the model plant Arabidopsis thaliana. Key to this analysis was reconstructing the putative ancestral stress regulation pattern. By comparing the expression patterns of duplicated genes with the patterns of their ancestors, duplicated genes likely lost and gained stress responses at a rapid rate initially, but the rate is close to zero when the synonymous substitution rate (a proxy for time) is > approximately 0.8. When considering duplicated gene pairs, we found that partitioning of putative ancestral stress responses occurred more frequently compared to cases of parallel retention and loss. Furthermore, the pattern of stress response partitioning was extremely asymmetric. An analysis of putative cis-acting DNA regulatory elements in the promoters of the duplicated stress-regulated genes indicated that the asymmetric partitioning of ancestral stress responses are likely due, at least in part, to differential loss of DNA regulatory elements; the duplicated genes losing most of their stress responses were those that had lost more of the putative cis-acting elements. Finally, duplicate genes that lost most or all of the ancestral responses are more likely to have gained responses to other stresses. Therefore, the retention of duplicates that inherit few or no functions seems to be coupled to neofunctionalization. Taken together, our findings provide new insight into the patterns of evolutionary changes in gene stress responses after duplication and lay the foundation for testing the adaptive significance of stress regulatory changes under highly variable biotic and abiotic environments.

Whole genome tiling arrays provide a high resolution platform for profiling of genetic, epigenetic, and gene expression polymorphisms. In this study we surveyed natural genomic variation in cytosine methylation among Arabidopsis thaliana wild accessions Columbia (Col) and Vancouver (Van) by comparing hybridization intensity difference between genomic DNA digested with either methylation-sensitive (HpaII) or -insensitive (MspI) restriction enzyme. Single Feature Polymorphisms (SFPs) were assayed on a full set of 1,683,620 unique features of Arabidopsis Tiling Array 1.0F (Affymetrix), while constitutive and polymorphic CG methylation were assayed on a subset of 54,519 features, which contain a 5'CCGG3' restriction site. 138,552 SFPs (1% FDR) were identified across enzyme treatments, which preferentially accumulated in pericentromeric regions. Our study also demonstrates that at least 8% of all analyzed CCGG sites were constitutively methylated across the two strains, while about 10% of all analyzed CCGG sites were differentially methylated between the two strains. Within euchromatin arms, both constitutive and polymorphic CG methylation accumulated in central regions of genes but under-represented toward the 5' and 3' ends of the coding sequences. Nevertheless, polymorphic methylation occurred much more frequently in gene ends than constitutive methylation. Inheritance of methylation polymorphisms in reciprocal F1 hybrids was predominantly additive, with F1 plants generally showing levels of methylation intermediate between the parents. By comparing gene expression profiles, using matched tissue samples, we found that magnitude of methylation polymorphism immediately upstream or downstream of the gene was inversely correlated with the degree of expression variation for that gene. In contrast, methylation polymorphism within genic region showed weak positive correlation with expression variation. Our results demonstrated extensive genetic and epigenetic polymorphisms between Arabidopsis accessions and suggested a possible relationship between natural CG methylation variation and gene expression variation.