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1. 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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2. The Great Regulatory Dodge

   Strandburg, K; Viljoen, S; Nissenbaum, H (May 2024, Harvard journal of law technology)
3. Mapping Regulatory Determinants in Plants

   https://doi.org/10.3389/fgene.2020.591194  

   Galli, Mary; Feng, Fan; Gallavotti, Andrea (October 2020, Frontiers in Genetics)
4. Annotating the Insect Regulatory Genome

   https://doi.org/10.3390/insects12070591  

   Asma, Hasiba; Halfon, Marc S. (July 2021, Insects)

   null (Ed.)

   An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods. 

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5. Irreversibility in bacterial regulatory networks

   https://doi.org/10.1126/sciadv.ado3232  

   Zhao, Yi; Wytock, Thomas P; Reynolds, Kimberly A; Motter, Adilson E (August 2024, Science Advances)

   Irreversibility, in which a transient perturbation leaves a system in a new state, is an emergent property in systems of interacting entities. This property has well-established implications in statistical physics but remains underexplored in biological networks, especially for bacteria and other prokaryotes whose regulation of gene expression occurs predominantly at the transcriptional level. Focusing on the reconstructed regulatory network ofEscherichia coli, we examine network responses to transient single-gene perturbations. We predict irreversibility in numerous cases and find that the incidence of irreversibility increases with the proximity of the perturbed gene to positive circuits in the network. Comparison with experimental data suggests a connection between the predicted irreversibility to transient perturbations and the evolutionary response to permanent perturbations. 

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