Expression pattern determines regulatory logic

Abstract

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

Early effects of gene duplication on the robustness and phenotypic variability of gene regulatory networks

“…Different kinds of models have allowed the study of GRNs, including thermodynamic models that allow assessing gene expression under equilibrium conditions [ 67 , 68 ], partial differential equation models [ 69 ], modifications of the Gillespie algorithm to consider stochastic reactions [ 70 ], or Boolean networks [ 71 ]. We used a slight variation of a model first proposed by A. Wagner to study the dynamics and evolution of gene regulatory networks [ 62 ].…”

Cell types as species: Exploring a metaphor

“… Xia and Yanai (2019) discussed the use of CoRC as a means of defining cell type, noting that the CoRC remains largely conceptual for most cell types, and that a proxy for the CoRC is to use expression profiles of TFs, among which are TSGs of the CoRC (see also Almeida et al, 2021 ). Monitoring transcription is difficult enough given that many if not most genes, including TFs, are expressed across cell types ( Figure 6 ; Xia and Yanai, 2019 ; Coate et al, 2020 ); regulatory regions differ by the strength with which they bind TFs rather than solely by which TFs they bind ( Mora-Martinez, 2021 ); and TF expression is quantitative, not qualitative (e.g., Fishell and Kepecs, 2020 ; Shojaee et al, 2021 ). The assembly of such complexes can also be spatially and temporally disjunct from where they function ( Charest et al, 2020 ).…”

Untitled

“…Motifs are employed by the viruses to mimic and hijack the host cell's essential process for its own survival [16]. Further detailed information on domain-motif interactions are available [17] that offers some valuable clue in conducting further bioinformatic studies related to drug discovery. In the current scenario, constructing a downstream network including all potential viral receptors, host cell proteases, and cofactors is necessary and should be used as an additional criterion for the validation of critical host machinery used for COVID-19 viral processing for therapeutic intervention.…”