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The relation between crosstalk and gene regulation form revisited.

ABSTRACT: Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called "crosstalk", could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite 'idle' design, where the default unregulated state of genes is their frequently required activity state. We found, that 'idle' design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

SUBMITTER: Grah R

PROVIDER: S-EPMC7059967 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:PurposeThe pathogenesis of renal fibrosis (RF) involves intricate interactions between profibrotic processes and immune responses. This study aimed to explore the potential involvement of the pyroptosis signaling pathway in immune microenvironment regulation within the context of RF. Through comprehensive bioinformatics analysis and experimental validation, we investigated the influence of pyroptosis on the immune landscape in RF.MethodsWe obtained RNA-seq datasets from Gene Expression Omnibus (GEO) databases and identified Pyroptosis-Associated Regulators (PARs) through literature reviews. Systematic evaluation of alterations in 27 PARs was performed in RF and normal kidney samples, followed by relevant functional analyses. Unsupervised cluster analysis revealed distinct pyroptosis modification patterns. Using single-sample gene set enrichment analysis (ssGSEA), we examined the correlation between pyroptosis and immune infiltration. Hub regulators were identified via weighted gene coexpression network analysis (WGCNA) and further validated in a single-cell RNA-seq dataset. We also established a unilateral ureteral obstruction-induced RF mouse model to verify the expression of key regulators at the mRNA and protein levels.ResultsOur comprehensive analysis revealed altered expression of 19 PARs in RF samples compared to normal samples. Five hub regulators, namely PYCARD, CASP1, AIM2, NOD2, and CASP9, exhibited potential as biomarkers for RF. Based on these regulators, a classifier capable of distinguishing normal samples from RF samples was developed. Furthermore, we identified correlations between immune features and PARs expression, with PYCARD positively associated with regulatory T cells abundance in fibrotic tissues. Unsupervised clustering of RF samples yielded two distinct subtypes (Subtype A and Subtype B), with Subtype B characterized by active immune responses against RF. Subsequent WGCNA analysis identified PYCARD, CASP1, and NOD2 as hub PARs in the pyroptosis modification patterns. Single-cell level validation confirmed PYCARD expression in myofibroblasts, implicating its significance in the stress response of myofibroblasts to injury. In vivo experimental validation further demonstrated elevated PYCARD expression in RF, accompanied by infiltration of Foxp3+ regulatory T cells.ConclusionsOur findings suggest that pyroptosis plays a pivotal role in orchestrating the immune microenvironment of RF. This study provides valuable insights into the pathogenesis of RF and highlights potential targets for future therapeutic interventions.

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The relation between crosstalk and gene regulation form revisited.

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