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1. Spatiotemporal analysis identifies ABF2 and ABF3 as key hubs of endodermal response to nitrate

   https://doi.org/10.1073/pnas.2107879119  

   Contreras-López, Orlando; Vidal, Elena A.; Riveras, Eleodoro; Alvarez, José M.; Moyano, Tomás C.; Sparks, Erin E.; Medina, Joaquín; Pasquino, Angelo; Benfey, Philip N.; Coruzzi, Gloria M.; et al (January 2022, Proceedings of the National Academy of Sciences)

   Nitrate is a nutrient and a potent signal that impacts global gene expression in plants. However, the regulatory factors controlling temporal and cell type–specific nitrate responses remain largely unknown. We assayed nitrate-responsive transcriptome changes in five major root cell types of the Arabidopsis thaliana root as a function of time. We found that gene-expression response to nitrate is dynamic and highly localized and predicted cell type–specific transcription factor (TF)–target interactions. Among cell types, the endodermis stands out as having the largest and most connected nitrate-regulatory gene network. ABF2 and ABF3 are major hubs for transcriptional responses in the endodermis cell layer. We experimentally validated TF–target interactions for ABF2 and ABF3 by chromatin immunoprecipitation followed by sequencing and a cell-based system to detect TF regulation genome-wide. Validated targets of ABF2 and ABF3 account for more than 50% of the nitrate-responsive transcriptome in the endodermis. Moreover, ABF2 and ABF3 are involved in nitrate-induced lateral root growth. Our approach offers an unprecedented spatiotemporal resolution of the root response to nitrate and identifies important components of cell-specific gene regulatory networks. 

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2. Meta Gene Regulatory Networks in Maize Highlight Functionally Relevant Regulatory Interactions

   https://doi.org/10.1105/tpc.20.00080  

   Zhou, Peng; Li, Zhi; Magnusson, Erika; Gomez Cano, Fabio; Crisp, Peter A.; Noshay, Jaclyn M.; Grotewold, Erich; Hirsch, Candice N.; Briggs, Steven P.; Springer, Nathan M. (March 2020, The Plant Cell)

   Abstract The regulation of gene expression is central to many biological processes. Gene regulatory networks (GRNs) link transcription factors (TFs) to their target genes and represent maps of potential transcriptional regulation. Here, we analyzed a large number of publically available maize (Zea mays) transcriptome data sets including >6000 RNA sequencing samples to generate 45 coexpression-based GRNs that represent potential regulatory relationships between TFs and other genes in different populations of samples (cross-tissue, cross-genotype, and tissue-and-genotype samples). While these networks are all enriched for biologically relevant interactions, different networks capture distinct TF-target associations and biological processes. By examining the power of our coexpression-based GRNs to accurately predict covarying TF-target relationships in natural variation data sets, we found that presence/absence changes rather than quantitative changes in TF gene expression are more likely associated with changes in target gene expression. Integrating information from our TF-target predictions and previous expression quantitative trait loci (eQTL) mapping results provided support for 68 TFs underlying 74 previously identified trans-eQTL hotspots spanning a variety of metabolic pathways. This study highlights the utility of developing multiple GRNs within a species to detect putative regulators of important plant pathways and provides potential targets for breeding or biotechnological applications. 

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3. Antibiotic tolerance is associated with a broad and complex transcriptional response in E. coli

   https://doi.org/10.1038/s41598-021-85509-7  

   Deter, Heather S.; Hossain, Tahmina; Butzin, Nicholas C. (March 2021, Scientific Reports)

   Abstract Antibiotic treatment kills a large portion of a population, while a small, tolerant subpopulation survives. Tolerant bacteria disrupt antibiotic efficacy and increase the likelihood that a population gains antibiotic resistance, a growing health concern. We examined howE. colitranscriptional networks changed in response to lethal ampicillin concentrations. We are the first to apply transcriptional regulatory network (TRN) analysis to antibiotic tolerance by leveraging existing knowledge and our transcriptional data. TRN analysis shows that gene expression changes specific to ampicillin treatment are likely caused by specific sigma and transcription factors typically regulated by proteolysis. These results demonstrate that to survive lethal concentration of ampicillin specific regulatory proteins change activity and cause a coordinated transcriptional response that leverages multiple gene systems. 

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4. 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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5. Multi-labeling live imaging to quantitate gene expression dynamics during Drosophila embryonic development

   Syed, Sahla; Lim, Bomyi (January 2022, Methods in molecular biology)

   Nelson, Celeste (Ed.)

   Transcription in developing metazoans is inherently stochastic, involving transient and dynamic interactions among transcriptional machinery. A fundamental challenge with traditional techniques, including fixed-tissue protein and RNA staining, is the lack of temporal resolution. Quantitating kinetic changes in transcription can elucidate underlying mechanisms of interaction among regulatory modules. In this protocol, we describe the successful implementation of a combination of MS2/MCP and PP7/PCP systems in living Drosophila embryos to further our understanding of transcriptional dynamics during development. Our technique can be extended to visualize transcriptional activities of multiple genes or alleles simultaneously, characterize allele-specific expression of a target gene, and quantitatively analyze RNA polymerase II activity in a single-cell resolution. 

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