Biology subjects

Lehti-Shiu, M. D.

Publications and source records attributed to Lehti-Shiu, M. D..

9 recordsLinked to original sources

bioRxiv2026

Spatial expression evolution of duplicated genes post-whole genome duplication in cultivated tomato

The mechanisms by which whole genome duplication contributes to expression divergence of duplicate genes, which can impact the evolution of form and function, has been a long-standing question in evolutionary biology. We compared expression patterns of paralogs derived from the most recent Solanaceae-specific genome triplication event across 23 tissues and inferred ancestral expression states to investigate patterns of expression evolution. While the partitioning of ancestral expression states is traditionally considered the second most common (after the gene loss) trajectory of duplicate expression evolution, we found that the ancestral expression states tended to be retained by most (~66%) duplicates, with ~6% of gene lineages experiencing expression gain events and ~20% experiencing expression losses. The genes that gained expression in fruit tissues are significantly enriched with functions associated with fruit quality. Notably, asymmetrical expression divergence is prevalent among duplicates, especially when ancestral genes were either not expressed or were highly expressed. Our study demonstrates the utility of integrating paralog expression comparisons with phylogenetic analyses to document the potential evolutionary history of spatial expression patterns and gain insight into how expression divergence contributes to the evolution of function across tissues.

plant biology↗

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↗

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↗

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

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↗

bioRxiv2018

Factors influencing gene family size variation among related species in a plant family

Gene duplication and loss contribute to gene content differences as well as phenotypic divergence across species. However, the extent to which gene content varies among closely related plant species and the factors responsible for such variation remain unclear. Here, we used the Solanaceae family as a model to investigate differences in gene family size and the likely factors contributing to these differences. We found that genes in highly variable families have high turnover rate and tend to be involved in processes that have diverged between Solanaceae species, whereas genes in low-variability families tend to have housekeeping roles. In addition, genes in high-and low-variability gene families tend to be duplicated by tandem and whole genome duplication, respectively. This finding together with the observation that genes duplicated by different mechanisms experience different selection pressures suggests that duplication mechanism impacts gene family turnover. We explored using pseudogene number as a proxy for gene loss but discovered that a substantial number of pseudogenes are actually products of pseudogene duplication, contrary to the expectation that most plant pseudogenes are remnants of once-functional duplicates. Our findings reveal complex relationships between variation in gene family size, gene functions, duplication mechanism, and evolutionary rate. The patterns of lineage-specific gene family expansion within the Solanaceae provide the foundation for a better understanding of the genetic basis underlying phenotypic diversity in this economically important family.

evolutionary biology↗