Dataset Information

Protein modularity, cooperative binding, and hybrid regulatory states underlie transcriptional network diversification.

ABSTRACT: We examine how different transcriptional network structures can evolve from an ancestral network. By characterizing how the ancestral mode of gene regulation for genes specific to a-type cells in yeast species evolved from an activating paradigm to a repressing one, we show that regulatory protein modularity, conversion of one cis-regulatory sequence to another, distribution of binding energy among protein-protein and protein-DNA interactions, and exploitation of ancestral network features all contribute to the evolution of a novel regulatory mode. The formation of this derived mode of regulation did not disrupt the ancestral mode and thereby created a hybrid regulatory state where both means of transcription regulation (ancestral and derived) contribute to the conserved expression pattern of the network. Finally, we show how this hybrid regulatory state has resolved in different ways in different lineages to generate the diversity of regulatory network structures observed in modern species.

SUBMITTER: Baker CR

PROVIDER: S-EPMC3519278 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Protein modularity, cooperative binding, and hybrid regulatory states underlie transcriptional network diversification.

Baker Christopher R CR Booth Lauren N LN Sorrells Trevor R TR Johnson Alexander D AD

Cell 20120901 1

We examine how different transcriptional network structures can evolve from an ancestral network. By characterizing how the ancestral mode of gene regulation for genes specific to a-type cells in yeast species evolved from an activating paradigm to a repressing one, we show that regulatory protein modularity, conversion of one cis-regulatory sequence to another, distribution of binding energy among protein-protein and protein-DNA interactions, and exploitation of ancestral network features all c ...[more]

PMID: 23021217

Similar Datasets

Project description:Background & Aims: Tissue metaplasia is uncommon in adults because established cis-element programs resist rewiring. In Barrett’s esophagus, the distal esophageal mucosa acquires predominantly intestinal character, with notable gastric features, and is predisposed to develop invasive cancers. We sought to understand the chromatin underpinnings of Barrett’s metaplasia and why it commonly displays simultaneous gastric and intestinal properties. Methods: We profiled cis-regulatory elements with active histone modifications in primary human biopsy materials using chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq). Mutations in Barrett’s esophagus were examined in relation to tissue-specific enhancer landscapes using a random-forest machine learning algorithm. We also profiled open chromatin at single-cell resolution in primary Barrett’s biopsy specimens using the assay for transposase-accessible chromatin (ATAC-seq). We used one- and two-color immunohistochemistry to examine protein expression of tissue-restricted genes. Results: Barrett’s esophagus bears epigenome fingerprints of human stomach and intestinal columnar, but not esophageal squamous, epithelia. Mutational patterns were best explained as arising on the epigenome background of active gastric cis-elements, supporting the view that adjoining stomach epithelium is a likely tissue source. Individual cells in Barrett’s metaplasia co-express gastric and intestinal genes, reflecting concomitant chromatin access at enhancers ordinarily restricted to one or the other epithelium. Protein expression of stomach-specific mucins, CLDN18, and a novel gastric marker, ANXA10, revealed extensive tissue and sub-clonal heterogeneity of dual stomach-intestinal cell states. Conclusions: These findings reveal mixed and dynamic tissue-restricted chromatin states and phenotypic heterogeneity in Barrett’s esophagus. Pervasive intra-gland variation argues against stem-cell governance of this phenotype.

Project description:BACKGROUND: The structure of regulatory networks remains an open question in our understanding of complex biological systems. Interactions during complete viral life cycles present unique opportunities to understand how host-parasite network take shape and behave. The Anticarsia gemmatalis multiple nucleopolyhedrovirus (AgMNPV) is a large double-stranded DNA virus, whose genome may encode for 152 open reading frames (ORFs). Here we present the analysis of the ordered cascade of the AgMNPV gene expression. RESULTS: We observed an earlier onset of the expression than previously reported for other baculoviruses, especially for genes involved in DNA replication. Most ORFs were expressed at higher levels in a more permissive host cell line. Genes with more than one copy in the genome had distinct expression profiles, which could indicate the acquisition of new functionalities. The transcription gene regulatory network (GRN) for 149 ORFs had a modular topology comprising five communities of highly interconnected nodes that separated key genes that are functionally related on different communities, possibly maximizing redundancy and GRN robustness by compartmentalization of important functions. Core conserved functions showed expression synchronicity, distinct GRN features and significantly less genetic diversity, consistent with evolutionary constraints imposed in key elements of biological systems. This reduced genetic diversity also had a positive correlation with the importance of the gene in our estimated GRN, supporting a relationship between phylogenetic data of baculovirus genes and network features inferred from expression data. We also observed that gene arrangement in overlapping transcripts was conserved among related baculoviruses, suggesting a principle of genome organization. CONCLUSIONS: Albeit with a reduced number of nodes (149), the AgMNPV GRN had a topology and key characteristics similar to those observed in complex cellular organisms, which indicates that modularity may be a general feature of biological gene regulatory networks.

Dataset Information

Protein modularity, cooperative binding, and hybrid regulatory states underlie transcriptional network diversification.

Publications

Protein modularity, cooperative binding, and hybrid regulatory states underlie transcriptional network diversification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure