Biology subjects

Shiu, S.-H.

Publications and source records attributed to Shiu, S.-H..

At least 19 recordsLinked to original sources

bioRxiv2026

Uncovering genetic mechanisms underlying trait variation in switchgrass using explainable artificial intelligence

Uncovering the genetic architecture of quantitative traits is challenging because polygenic control yields small individual gene effects and because gene-gene and genotype-by-environment interactions add further complexity. To understand the genetic basis of polygenic traits and their plasticity across environments, we integrated genome-wide SNPs and RNA-seq transcript data with interpretable statistical and machine learning models in a switchgrass (Panicum virgatum) diversity panel grown at contrasting field sites in Michigan and Texas. Notably, in addition to single environments, our trait prediction models were able to predict phenotypic differences, across environments i.e., plasticity. By interpreting trait prediction models with explainable machine learning methods, we identified genes that are the most predictive of flowering time and annual biomass production across environments, based on their associated gene expression levels and nearby SNPs. This approach recovered canonical flowering regulators and revealed novel, environment-specific candidate flowering genes. Further, transcriptome models consistently recovered more switchgrass genes homologous to experimentally validated genes in Arabidopsis and rice than SNP-based models. Feature interaction scores from the models also allow the identification of trait and environment-dependent gene-gene interactions, where flowering time showed stronger and more abundant interactions than biomass. While some of the interactions identified are consistent with the link between flowering time and yield, most are novel predictors that need to be further evaluated. These findings provide insight into the genetic basis of trait variation and plasticity across environments and help prioritize candidate genes for functional validation and cultivar improvement.

genetics↗

bioRxiv2025

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness traits and genotype-by-environment interactions hinder our ability to understand the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different types of genetic variants for a population of Saccharomyces cerevisiae isolates. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation further showed that different variant types identified distinct sets of genes associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to fitness predictions were both isolate and environment dependent, pointing to gene-by-gene and gene-by-environment interactions. Further interpretation of models uncovered experimentally validated and novel candidate genetic interactions for a well characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different types of genetic variants and offer novel targets for future functional analysis. Author SummaryOrganisms adapt to changing environments by acquiring beneficial traits, which are largely determined by genetic variation. However, predicting how genetic variation influences adaptation, and thus survival, remains a challenge. Here, we used machine learning to identify genes, gene-gene interactions, and gene-by-environment interactions underlying fitness. Specifically, we used machine learning to predict how different genetic variants--such as changes in single nucleotides, presence/absence of a sequence, and differences in copy number--affect fitness in yeast across 35 different environmental conditions. Our results show that prediction accuracy and our ability to interpret the underlying biology depend on the genetic variant type. For example, the best predictions were obtained using differences in copy number. We also found that the contributions of genetic variants to yeast fitness depend on the genetic background. Importantly, our models uncovered known and novel genes that were important across multiple and specific environments and revealed genetic interactions for a well characterized stress, offering insights into how organisms cope with environmental stress. These findings advance our understanding of the genetic basis of fitness and provide a framework for future functional studies and the design of stress-resilient yeast strains.

genomics↗

bioRxiv2024

Machine learning uncovers gene families impacting oxidative stress resistance across yeasts

Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterized the variation in resistance to ROS across the ancient yeast subphylum Saccharomycotina and used machine learning (ML) to identify gene families whose sizes were predictive of ROS resistance. The most predictive features were enriched in gene families related to cell wall organization and included two reductase gene families. We estimated the quantitative contributions of features to each species classification to guide experimental validation and showed that overexpression of the old yellow enzyme (OYE) reductase increased ROS resistance in Kluyveromyces lactis, while Saccharomyces cerevisiae mutants lacking multiple mannosyltransferase-encoding genes were hypersensitive to ROS. Altogether, this work provides a framework for how ML can uncover genetic mechanisms underlying trait variation across diverse species and inform trait manipulation for clinical and biotechnological applications.

microbiology↗

bioRxiv2024

Impact of genome assemblies, genotyping methods, variant types, ploidy levels and population structures on genomic prediction in switchgrass

Genomic prediction, the use of genetic information for predicting traits, has accelerated the breeding processes and provided mechanistic insights into the genetic bases of complex traits. While substantial efforts have been devoted to optimize genomic prediction, there remain areas that need to be further explored, including the impact of genome assemblies, genotyping approaches, variant types, allelic complexities, polyploidy levels, and population structures. Here, we assess the impact of these factors on the prediction of 20 complex traits in switchgrass (Panicum virgatum L.), a perennial biofuel feedstock. We found that short read-based genome assembly perform comparably to or even better than long read-based assembly in trait prediction; exome capture-based models have higher prediction accuracy than genotyping-by-sequencing-based models for 13 traits; bi-allelic insertion/deletions are as useful as bi-allelic single nucleotide polymorphisms in trait prediction, whereas multi-allelic variants outperform bi-allelic ones for 15 traits. Models built for tetraploids have higher prediction accuracy than those for octoploids for most traits. Traits of individuals with higher within-population genetic distances tend to have higher prediction accuracy. Finally, integrating different types of variants can improve the prediction accuracy. By exploring these factors, anthesis date prediction models built using multi-allelic insertion/deletions derived from exome capture led to the largest number of orthologs of benchmark flowering time genes compared to other models. Our study provides insights into the factors influencing genomic prediction outcomes that inform best practices for future studies and for improving agronomic traits in switchgrass and other species through selective breeding.

plant biology↗

bioRxiv2024

CLAVATA signalling shapes barley inflorescence architecture by controlling activity and determinacy of shoot apical and rachilla meristems

Grasses exhibit a large variety of diverse inflorescence architectures, from complex branched inflorescences in Oryzeae (rice) to simple spike-type inflorescences in Triticeae (e.g. barley, wheat). Inflorescence architecture depends on shape, longevity and determinacy of meristems that direct growth of the main rachis and lateral branches, but how individual meristem activities are determined and integrated within complex inflorescences is not yet understood. We found that activity of distinct meristems in the barley inflorescence is coordinated by a signalling pathway comprising the receptor like kinase Hordeum vulgare CLAVATA1 (HvCLV1) and the secreted CLAVATA3/ENDOSPERM SURROUNDING REGION (CLE)-family peptide FON2- LIKE CLE PROTEIN1 (HvFCP1). HvFCP1 interacts with HvCLV1 to promote spikelet formation but restricts inflorescence meristem and rachilla meristem proliferation. Hvfcp1 or Hvclv1 mutants generate branched inflorescences with additional rows of spikelets and supernumerary florets. Transcriptome analysis reveals that HvFCP1/HvCLV1 signalling controls inflorescence branching through the regulation of trehalose-6-phosphate synthesis and sugar transport. Our discoveries reveal the potential to engineer barley inflorescence architecture by manipulating regulation of distinct meristem activities.

plant biology↗

bioRxiv2023

Prediction of plant complex traits via integration of multi-omics data

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 investigated whether integrating different omics data could improve trait prediction. We built prediction models using genomic, transcriptomic, and methylomic data from the Arabidopsis 1001 Genomes Project for six Arabidopsis traits, and found that transcriptome- and methylome-based models had performances comparable to those of genome-based models. However, when comparing models for flowering time prediction, we found that models built using different omics data identified different benchmark genes. Nine novel genes identified as important for flowering time from our models were experimentally validated as regulating flowering. In addition, we found that gene contributions to flowering time prediction are accession-dependent and that distinct genes contribute to trait prediction in different genetic backgrounds. Models integrating multi-omics data performed best and revealed known and novel 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.

bioinformatics↗

bioRxiv2023

Evolution of research topics and paradigms in plant sciences

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 needs of expert knowledge from a wide range of areas in any field. Here 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 major research paradigm shifts and recent radiation of new topics, as well as turnovers 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. Significance statementChanges in scientific paradigms are foundational for the advancement of science, but such changes are difficult to summarize, quantify, and illustrate. These challenges are exacerbated by the rapid, exponential growth of literature. Applying a combination of machine learning and language modeling to hundreds of thousands of published abstracts, we demonstrate that a scientific field (i.e., plant science) can be summarized as interconnected subfields evolving from one another. We also reveal insights into major research trends and the rise and decline in the use of model organisms in different countries. Our study demonstrates how artificial intelligence and language models can be broadly applied to understand scientific advances that inform science policy and funding decisions.

plant biology↗

bioRxiv2022

Temporal Regulation of Cold Transcriptional Response in Switchgrass

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.

plant biology↗

bioRxiv2021

The SEEL Motif and Members of the MYB-related REVEILLE Transcription Factor Family are Important for the Expression of LORELEI in the Synergid Cells of the Arabidopsis Female Gametophyte

Synergid cells in the micropylar end of the female gametophyte are required for critical cell-cell signaling interactions between the pollen tube and the ovule that precede double fertilization and seed formation in flowering plants. LORELEI (LRE) encodes a GPI-anchored protein that is expressed primarily in the synergid cells, and together with FERONIA, a receptor-like kinase, it controls pollen tube reception by the receptive synergid cell. Still, how LRE expression is controlled in synergid cells remains poorly characterized. We identified candidate cis-regulatory elements enriched in LRE and other synergid cell-expressed genes. One of the candidate motifs ( TAATATCT) in the LRE promoter was an uncharacterized variant of the Evening Element motif that we named as the Short Evening Element-like (SEEL) motif. Deletion or point mutations in the SEEL motif of the LRE promoter resulted in decreased reporter expression in synergid cells, demonstrating that the SEEL motif is important for expression of LRE in synergid cells. Additionally, we found that LRE expression is decreased in the loss of function mutants of REVEILLE (RVE) transcription factors, which are clock genes known to bind the SEEL and other closely related motifs. We propose that RVE transcription factors regulate LRE expression in synergid cells by binding to the SEEL motif in the LRE promoter. Identification of a cis-regulatory element and transcription factors involved in the expression of LRE will serve as a foundation to characterize the gene regulatory networks in synergid cells and investigate the potential connection between circadian rhythm and fertilization. One sentence summaryA newly identified SEEL motif in the promoter of LORELEI and at least three members of the REVEILLE transcription factor family are important for LORELEI expression in synergid cells of the Arabidopsis female gametophyte.

plant biology↗

bioRxiv2021

High throughput measurement of Arabidopsis thaliana fitness traits using transfer learning

O_LIRevealing the contributions of genes to plant phenotype is frequently challenging because the effects of loss of gene function may be subtle or be masked by genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana. C_LIO_LIAn image segmentation-based (ImageJ) and a Faster Region Based Convolutional Neural Network (R-CNN) approach were used for measuring two Arabidopsis fitness traits: seed and fruit counts. C_LIO_LIAlthough straightforward to use, ImageJ was error-prone (correlation between true and predicted seed counts, r2=0.849) because seeds touching each other were undercounted. In contrast, Faster R-CNN yielded near perfect seed counts (r2=0.9996) and highly accurate fruit counts (r2=0.980). C_LIO_LIBy examining seed counts, we were able to reveal fitness effects for genes that were previously reported to have no or condition-specific loss-of-function phenotypes. C_LIO_LIOur study provides models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits in the study of gene functions. C_LI

bioinformatics↗

bioRxiv2020

Genome-wide predictions of genetic redundancy in Arabidopsis thaliana

Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features has not yet been established. In addition, the relative importance of these characteristics for genetic redundancy remains unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana. Benchmark gene pairs were classified based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including post-translational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used affected model performance significantly. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. Predictions were then tested using phenotype data withheld from model building and validated using well-characterized, redundant and nonredundant gene pairs. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs that are seemingly functionally redundant, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

evolutionary biology↗

bioRxiv2020

Optimizing the use of gene expression data to predict metabolic pathway memberships with unsupervised and supervised machine learning

Plant metabolites produced via diverse pathways are important for plant survival, human nutrition and medicine. However, the pathway memberships of most plant enzyme genes are unknown. While co-expression is useful for assigning genes to pathways, expression correlation may exist only under specific spatiotemporal and conditional contexts. Utilizing >600 expression values and similarity data combinations from tomato, three strategies for predicting membership in 85 pathways were explored: naive prediction (identifying pathways with the most similarly expressed genes), unsupervised and supervised learning. Optimal predictions for different pathways require distinct data combinations that, in some cases, are indicative of biological processes relevant to pathway functions. Naive prediction produced higher error rates compared with machine learning methods. In 52 pathways, unsupervised learning performed better than a supervised approach, which may be due to the limited availability of training data. Furthermore, using gene-to-pathway expression similarities led to prediction models that outperformed those based simply on gene expression levels. Our study highlights the need to extensively explore expression-based features and prediction strategies to maximize the accuracy of metabolic pathway membership assignment. We anticipate that the prediction framework outlined here can be applied to other species and also be used to improve plant pathway annotation.

plant biology↗

bioRxiv2020

Modeling gene regulation in response to wounding: temporal variations, hormonal variations, and specialized metabolism pathways induced by wounding

Plants respond to wounding stress by changing gene expression patterns and inducing jasmonic acid (JA), as well as other plant hormones. This includes activating some specialized metabolism pathways, including the glucosinolate pathways, in the case of Arabidopsis thaliana. We model how these responses are regulated by using machine learning to incorporate putative cis-regulatory elements (pCREs), known transcription factor binding sites from literature, in-vitro DNA affinity purification sequencing (DAP-seq) and DNase I hypersensitive sites to predict gene expression for genes clustered by their wound response using machine learning. We found temporal patterns where regulatory sites and regions of open chromatin differed between clusters of genes up-regulated at early and late wounding time points as well as clusters where JA response was induced relative to clusters where JA response was not induced. Overall, we identified pCREs that improved model predictions of expression clusters over known binding sites. We discovered 4,255 pCREs related to wound response at different time points and 2,569 pCREs related to differences between JA-induced and non-JA induced wound response. In addition, pCREs found to be important at different wounding time points were mapped to the promoters of genes in a glucosinolate biosynthesis pathway indicating regulation of this pathway under wounding stress. Finally, we experimentally validated a predicted cis-regulatory element, CCGCGT, showing that knock-out via CRISPR-Cas9 reduces gene expression in response to wounding.

plant biology↗

bioRxiv2020

Co-expression signatures of combinatorial gene regulation

Gene co-expression analyses provide a powerful tool to determine gene associations. The interaction of transcription factors (TFs) with their target genes is an essential step in gene regulation, yet to what extent TFs-target gene associations are recovered in co-expression studies remains unclear. Using the wealth of data available for Arabidopsis, we show here that protein-DNA interactions are overall poor indicators of TF-target co-expression, yet the inclusion of TF-TF interaction information significantly enhance co-expression signals. These results highlight the impact of combinatorial gene control on such gene association networks. We integrated this information to predict higher-order regulatory complexes, which are difficult to identify experimentally. We demonstrate that genes strongly co-expressed with a TF are also enriched in indirect targets. Our results have significant implications on the empirical understanding of complex gene regulatory networks and transcription factor function, and the significance of co-expression from the perspective of protein-protein and protein-DNA interactions.

systems biology↗

bioRxiv2020

Conserved Molecular Function and Regulatory Subfunctionalization of the LORELEI Gene Family in Brassicaceae

A signaling complex comprising members of the LORELEI (LRE)-LIKE GPI-anchored protein (LLG) and Catharanthus roseus RECEPTOR-LIKE KINASE 1-LIKE (CrRLK1L) families perceive RAPID ALKALINIZATION FACTOR (RALF) peptides and regulate growth, reproduction, immunity, and stress responses in Arabidopsis. Genes encoding these proteins are members of multi-gene families in most angiosperms and could generate thousands of signaling complex variants. However, the link(s) between expansion of these gene families and the functional diversification of this critical signaling complex as well as the evolutionary factors underlying the maintenance of gene duplicates remain unknown. Here, we investigated LLG gene family evolution, function, and expression in angiosperms. We found that LLGs in monocots and eudicots are descendants of a duplication early in angiosperm evolution and that both ancient and recent LLG duplicates are retained. Complementation and expression analysis showed that expression divergence of LLGs (regulatory subfunctionalization), rather than functional divergence, explains the retention of paralogs in Brassicales. All but one extant monocot and eudicot species examined maintained an LLG copy with preferential expression in male reproductive tissues, with the other duplicate copies showed highest levels of expression in female or vegetative tissues. Interestingly, the single LLG copy in Amborella (sister to all other angiosperms) is expressed vastly higher in male compared to female reproductive or vegetative tissues. Reconstruction of expression evolution showed that the highest inferred expression levels for the single copy ancestral angiosperm LLG was in male reproductive tissues. We propose that expression divergence played an important role in maintenance of LLG duplicates in angiosperms. One Sentence SummaryExpression divergence played an important role in maintenance of two sub-groups of LLG duplicates in angiosperms

plant biology↗

bioRxiv2020

Evolution of a plant gene cluster in Solanaceae and emergence of metabolic diversity

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, recruitment of primary metabolic enzymes and genomic clustering of pathway genes. Solanaceae plant glandular trichomes produce defensive acylsugars, with aliphatic 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 forming a supercluster, and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to emergence of this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals dynamics behind emergence of novel enzymes from primary metabolism, gene cluster evolution and cell-type specific metabolite diversity.

plant biology↗

bioRxiv2020

The cis-regulatory codes of response to combined heat and drought stress in Arabidopsis thaliana

Plants respond to their environment by dynamically modulating gene expression. A powerful approach for understanding how these responses are regulated is to integrate information about cis-regulatory elements (CREs) into models called cis-regulatory codes. Transcriptional response to combined stress is typically not the sum of the responses to the individual stresses. However, cis-regulatory codes underlying combined stress response have not been established. Here we modeled transcriptional response to single and combined heat and drought stress in Arabidopsis thaliana. We grouped genes by their pattern of response (independent, antagonistic, synergistic) and trained machine learning models to predict their response using putative CREs (pCREs) as features (median F-measure = 0.64). We then developed a deep learning approach to integrate additional omics information (sequence conservation, chromatin accessibility, histone modification) into our models, improving performance by 6.2%. While pCREs important for predicting independent and antagonistic responses tended to resemble binding motifs of transcription factors associated with heat and/or drought stress, important synergistic pCREs resembled binding motifs of transcription factors not known to be associated with stress. These findings demonstrate how in silico approaches can improve our understanding of the complex codes regulating response to combined stress and help us identify prime targets for future characterization.

genomics↗

bioRxiv2020

Within and cross species predictions of plant specialized metabolism genes using transfer learning

Plant specialized metabolites mediate interactions between plants and the environment and have significant agronomical/pharmaceutical value. Most genes involved in specialized metabolism (SM) are unknown because of the large number of metabolites and the challenge in differentiating SM genes from general metabolism (GM) genes. Plant models like Arabidopsis thaliana have extensive, experimentally derived annotations, whereas many non-model species do not. Here we employed a machine learning strategy, transfer learning, where knowledge from A. thaliana is transferred to predict gene functions in cultivated tomato with fewer experimentally annotated genes. The first tomato SM/GM prediction model using only tomato data performs well (F-measure=0.74, compared with 0.5 for random and 1.0 for perfect predictions), but from manually curating 88 SM/GM genes, we found many mis-predicted entries were likely mis-annotated. When the SM/GM prediction models built with A. thaliana data were used to filter out genes where the A. thaliana-based model predictions disagreed with tomato annotations, the new tomato model trained with filtered data improved significantly (F-measure=0.92). Our study demonstrates that SM/GM genes can be better predicted by leveraging cross-species information. Additionally, our findings provide an example for transfer learning in genomics where knowledge can be transferred from an information-rich species to an information-poor one.

plant biology↗