Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

Abstract

The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708 regulons and 1776 systems. All this brings together a large corpus of data that will surely inspire studies to generate hypothesis regarding the principles governing the evolution and organization of systems and the functional architectures controlling them.Database URL: http://abasy.ccg.unam.mx

Functional architecture and global properties of the Corynebacterium glutamicum regulatory network: Novel insights from a dataset with a high genomic coverage

“…The fraction of intermodular genes found in the new RN models are consistent with those found in the RN models of E. coli K-12 MG1655 andB. subtilis 168 (Freyre-Gonzalez et al, 2012;Ibarra-Arellano et al, 2016) (Table 1).…”

In silico model-guided identification of transcriptional regulator targets for efficient strain design

“…In this work, we also applied h-BeReTa to C. glutamicum, an industrially important gram-positive bacterium and a representative host lesser studied compared to E. coli , in order to test its wider applicability. The most comprehensive TRN of C. glutamicum available to date [ 29 ] was used to retrieve six levels of top–down TR hierarchy (Additional file 1 : Table S3). We specifically applied h-BeReTa in C. glutamicum to identify the TR manipulation targets for glutamate, an amino acid which it naturally produces under several conditions, and lycopene.…”

“…The TRN information of E. coli was downloaded from RegulonDB version 9.0 [ 28 ] including a total 4787 TR-gene interactions and 200 TRs. The TRN information of C. glutamicum was obtained from the Abasy Atlas database [ 29 ] which accounts for 3330 TR-gene interactions excluding self-regulators and 102 TRs. Here, it should be noted that the levels of TR regulation hierarchy were manually reconstructed from the RegulonDB TRN based on the TR–TR interaction relationships.…”

Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions

“…Interestingly, the most complete networks were the ones based mostly on manual curation (e.g., E. coli GRNs). [21][22][23]39 These networks showed a higher average clustering to coverage ratio than the ones based mostly on high-throughput 40 , computational predictions 24 or meta-curations 14…”