Dataset Information

Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution

ABSTRACT: We used deep RNA sequencing to obtain high coverage RNA-Seq data of five C. crescentus cell cycle stages, each with three biological replicates. We found that 1,586 genes (over a third of the genome) display significant differential expression between stages. This gene list, which contains many genes previously unknown for their cell cycle regulation, includes almost half of the genes involved in primary metabolism, suggesting that these "house-keeping" genes are not constitutively transcribed during the cell cycle, as often assumed. Gene and module co-expression clustering reveal co-regulated pathways and suggest functionally coupled genes. In addition, an evolutionary analysis of the cell cycle network shows a high correlation between co-expression and co-evolution. Most co-expression modules have strong phylogenetic signals, with broadly conserved genes and clade-specific genes predominating different substructures of the cell cycle co-expression network. We also found that conserved genes tend to determine the expression profile of their module. We describe the first phylogenetic and single-nucleotide-resolution transcriptomic analysis of a bacterial cell cycle network. In addition, the study suggests how evolution has shaped this network and provides direct biological network support that selective pressure is not on individual genes but rather on the relationship between genes, which highlights the importance of integrating phylogenetic analysis into biological network studies.

ORGANISM(S): Caulobacter vibrioides

PROVIDER: GSE46915 | GEO | 2013/05/15

SECONDARY ACCESSION(S): PRJNA203017

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Background: Multiple regulatory mechanisms have been identified employing conventional hypothesis-driven approaches as contributing to allergen-specific immunotherapy outcomes, but understanding of how these integrate to maintain immunological homeostasis is incomplete. Objective: To explore the potential for unbiased systems-level gene co-expression network analysis to advance understanding of immunotherapy mechanisms. Methods: We profiled genome-wide allergen-induced Th-cell responses prospectively during 24 months subcutaneous immunotherapy (SCIT) in 25 rhinitis, documenting changes in immunoinflammatory pathways and associated co-expression networks and their relationships to symptom scores out to 36 months. Results: Prior to immunotherapy, mite-induced Th-cell response networks involved multiple discrete co-expression modules including those related to Th2-, type1 IFN-, inflammation- and FOXP3/IL2-associated signalling. A signature comprising 109 genes correlated with symptom scores, and these mapped to cytokine signalling/T-cell activation-associated pathways, with upstream drivers including hallmark Th1/Th2- and inflammation-associated genes. Reanalysis after 3.5 months SCIT updosing detected minimal changes to pathway/upstream regulator profiles despite 32.5% symptom reduction; however, network analysis revealed underlying merging of FOXP3/IL2-with inflammation-and Th2-associated modules. By 12 months SCIT, symptoms had reduced by 41% without further significant changes to pathway/upstream regulator or network profiles. Continuing SCIT to 24 months stabilized symptoms at 47% of baseline, accompanied by upregulation of the type1 IFN-associated network module and its merging into the Th2/FOXP3/IL2/inflammation module. Conclusions: Subcutaneous immunotherapy stimulates progressive integration of mite-induced Th cell-associated Th2-, FOXP3/IL2-, inflammation- and finally type1 IFN-signalling subnetworks, forming a single highly integrated co-expression network module, maximizing potential for stable homeostatic control of allergen-induced Th2 responses via cross-regulation. Th2-antagonistic type1 IFN signalling may play a key role in stabilizing clinical effects of SCIT.

Dataset Information

Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure