RegulomePA: a database of transcriptional regulatory interactions in Pseudomonas aeruginosa PAO1

Abstract

We present RegulomePA, a database that contains biological information on regulatory interactions between transcription factors (TFs), sigma factor (SFs) and target genes in Pseudomonas aeruginosa PAO1. RegulomePA consists of 4827 regulatory interactions between 2831 nodes, which represent the interactions of TFs and SFs with their target genes, from the total of predicted RegulomePA including 27.27% of the TFs, 54.16% of SFs and 50.8% of the total genes. Each entry in the database corresponds to one node in the network and provides comprehensive details about the gene and its regulatory interactions such as gene description, nucleotide sequence, genome-strand position and links to other databases as well as the type of regulation it exerts or to which it is being subject (repression or activation), the associated experimental evidence and references, and topological information. Additionally, RegulomePA provides a way to recover information on the regulatory circuits of the network to which a gene pertains and also makes available the source codes to analyze the topology of any other regulatory network. The database will be updated yearly, by our team, with the contributions from ourselves and users, since the users are provided with an interactive platform where they can add interactions to the regulatory network feeding it with their respective references. Database URL: www.regulome.pcyt.unam.mx.

System Principles Governing the Organization, Architecture, Dynamics, and Evolution of Gene Regulatory Networks

“…There is no straightforward nor standard way to infer a global regulatory network. A few precomputed inferences based on sequence or transcriptomics are scattered across the literature and organism-specific databases ( Galan-Vasquez et al, 2020 ; Parise et al, 2020 ). Most of these inferences come from different approaches making it difficult to assess them.…”

An Updated Gene Regulatory Network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

“…(38,39) Studies have shown a scale-free structure in cellular metabolic networks, (32,40) protein interaction networks, including in câncer, (41,42) transcription regulatory networks, and GRN. (20,(43)(44)(45) Following the literature, (36,37,(46)(47)(48) there is some qualitative and quantitative characteristics to ensure that a network is scale-free: the power-law distribution appears as a straight line on a logarithmic plot; the γ value usually is in the range 2<γ<3; and the presence of high-degree nodes, called hubs, dominating the network, with most nodes clustered around them.…”

“…(65) Scales, dplyr, tibble, readr and igraph packages were used for data manipulation and plotting of the structural analyses. (2,20,66) The igraph library was used to compute most of the properties described above: the in and out degrees, centrality, clustering coefficients, feed-forward loop motifs, connectivity, cycles, paths, and hierarchical levels analyses. (67) The illustrations of the GRN, the hub's network and the connectivity analysis were made in Cytoscape.…”

“…Studies have shown a scale-free structure in cellular metabolic networks, (32,40) protein interaction networks, including in câncer, (41,42) transcription regulatory networks, and GRN. (20,43-45)…”

“…In 2019, Medeiros et al (2) described a GRN reconstruction of the CCBH4851 strain (CCBH-2019). Finally, in 2020, Galán-Vasquez et al (20) published the updated GRN of P. aeruginosa with the PAO1 strain (PAO1-2020), which was much larger than the previous ones, containing new interactions. All works analyzed the GRNs main structural properties and regulatory interactions.…”