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1. Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance

   https://doi.org/10.3389/fgene.2021.652189  

   Gupta, Chirag; Ramegowda, Venkategowda; Basu, Supratim; Pereira, Andy (June 2021, Frontiers in Genetics)

   null (Ed.)

   Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice ( Oryza sativa ). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties. 

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2. The fire ant social chromosome exerts a major influence on genome regulation

   https://doi.org/10.1093/molbev/msaf112  

   Jones, Beryl M; Waugh, Alex H; Catto, Michael A; Kay, Sasha; Glastad, Karl M; Goodisman, Michael_A D; Kocher, Sarah D; Hunt, Brendan G (May 2025, Molecular Biology and Evolution)

   Parsch, John (Ed.)

   Abstract Supergenes underlying complex trait polymorphisms ensure sets of coadapted alleles remain genetically linked. Despite their prevalence in nature, the mechanisms of supergene effects on genome regulation are poorly understood. In the fire ant Solenopsis invicta, a supergene containing over 500 individual genes influences trait variation in multiple castes to collectively underpin a colony level social polymorphism. Here, we present results of an integrative investigation of supergene effects on gene regulation. We present analyses of ATAC-seq data to investigate variation in chromatin accessibility by supergene genotype and STARR-seq data to characterize enhancer activity by supergene haplotype. Integration with gene co-expression analyses, newly mapped intact transposable elements (TEs), and previously identified copy number variants (CNVs), collectively reveal widespread effects of the supergene on chromatin structure, gene transcription, and regulatory element activity, with a genome-wide bias for open chromatin and increased expression in the presence of the derived supergene haplotype, particularly in regions that harbor intact TEs. Integrated consideration of CNVs and regulatory element divergence suggests each evolved in concert to shape the expression of supergene encoded factors, including several transcription factors that may directly contribute to the trans-regulatory footprint of a heteromorphic social chromosome. Overall, we show how genome structure in the form of a supergene has wide-reaching effects on gene regulation and gene expression. 

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3. Diverse gene regulatory mechanisms alter rattlesnake venom gene expression at fine evolutionary scales

   https://doi.org/10.1093/gbe/evae110  

   Gopalan, Siddharth S; Perry, Blair W; Francioli, Yannick Z; Schield, Drew R; Guss, Hannah D; Bernstein, Justin M; Ballard, Kaas; Smith, Cara F; Saviola, Anthony J; Adams, Richard H; et al (May 2024, Genome Biology and Evolution)

   Gossmann, Toni (Ed.)

   Abstract Understanding and predicting the relationships between genotype and phenotype is often challenging, largely due to the complex nature of eukaryotic gene regulation. A step towards this goal is to map how phenotypic diversity evolves through genomic changes that modify gene regulatory interactions. Using the Prairie Rattlesnake (Crotalus viridis) and related species, we integrate mRNA-seq, proteomic, ATAC-seq and whole genome resequencing data to understand how specific evolutionary modifications to gene regulatory network components produce differences in venom gene expression. Through comparisons within and between species, we find a remarkably high degree of gene expression and regulatory network variation across even a shallow level of evolutionary divergence. We use these data to test hypotheses about the roles of specific trans-factors and cis-regulatory elements, how these roles may vary across venom genes and gene families, and how variation in regulatory systems drive diversity in venom phenotypes. Our results illustrate that differences in chromatin and genotype at regulatory elements play major roles in modulating expression. However, we also find that enhancer deletions, differences in transcription-factor expression, and variation in activity of the insulator protein CTCF also likely impact venom phenotypes. Our findings provide insight into the diversity and gene-specificity of gene regulatory features and highlight the value of comparative studies to link gene regulatory network variation to phenotypic variation. 

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4. Evolutionary flexibility in flooding response circuitry in angiosperms

   https://doi.org/10.1126/science.aax8862  

   Reynoso, Mauricio A.; Kajala, Kaisa; Bajic, Marko; West, Donnelly A.; Pauluzzi, Germain; Yao, Andrew I.; Hatch, Kathryn; Zumstein, Kristina; Woodhouse, Margaret; Rodriguez-Medina, Joel; et al (September 2019, Science)

   Flooding due to extreme weather threatens crops and ecosystems. To understand variation in gene regulatory networks activated by submergence, we conducted a high-resolution analysis of chromatin accessibility and gene expression at three scales of transcript control in four angiosperms, ranging from a dryland-adapted wild species to a wetland crop. The data define a cohort of conserved submergence-activated genes with signatures of overlapping cis regulation by four transcription factor families. Syntenic genes are more highly expressed than nonsyntenic genes, yet both can have the cis motifs and chromatin accessibility associated with submergence up-regulation. Whereas the flexible circuitry spans the eudicot-monocot divide, the frequency of specific cis motifs, extent of chromatin accessibility, and degree of submergence activation are more prevalent in the wetland crop and may have adaptive importance. 

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5. A Variational Inference Approach to Single-Cell Gene Regulatory Network Inference using Probabilistic Matrix Factorization

   https://doi.org/10.1101/2022.09.09.507305  

   Mahmood, Omar; Skok Gibbs, Claudia; Bonneau, Richard; Cho, Kyunghyun (March 2023, ICML 2023)

   Inferring gene regulatory networks (GRNs) from single-cell gene expression datasets is a challenging task. Existing methods are often designed heuristically for specific datasets and lack the flexibility to incorporate additional information or compare against other algorithms. Further, current GRN inference methods do not provide uncertainty estimates with respect to the interactions that they predict, making inferred networks challenging to interpret. To overcome these challenges, we introduce Probabilistic Matrix Factorization for Gene Regulatory Network inference (PMF-GRN). PMF-GRN uses single-cell gene expression data to learn latent factors representing transcription factor activity as well as regulatory relationships between transcription factors and their target genes. This approach incorporates available experimental evidence into prior distributions over latent factors and scales well to single-cell gene expression datasets. By utilizing variational inference, we facilitate hyperparameter search for principled model selection and direct comparison to other generative models. To assess the accuracy of our method, we evaluate PMF-GRN using the model organisms Saccharomyces cerevisiae and Bacillus subtilis, benchmarking against database-derived gold standard interactions. We discover that, on average, PMF-GRN infers GRNs more accurately than current state-of-the-art single-cell GRN inference methods. Moreover, our PMF-GRN approach offers well-calibrated uncertainty estimates, as it performs gene regulatory network (GRN) inference in a probabilistic setting. These estimates are valuable for validation purposes, particularly when validated interactions are limited or a gold standard is incomplete. 

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