Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa

Filho, Fernando Medeiros; Nascimento, Ana Paula Barbosa do; Santos, M. Tadeu dos; Carvalho-Assef, Ana Paula D’Alincourt; Silva, Fabrício

doi:10.1590/0074-02760190105

Cited by 7 publications

(38 citation statements)

References 33 publications

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“…The fleQ gene is also among the hubs and affects psl (polysaccharide synthesis locus) genes and the regulation of the efflux pump genes, mexA, mexE , and oprH , by brlR . (2,142) The psl cluster comprises 15 exopolysaccharide biosynthesis-related genes organized in tandem that are important for biofilm formation. (143) The mexT and soxR genes positively regulate an efflux pump system and several virulence factors, (144,145) and pmrA regulates efflux pumps and the polymyxin B and colistin resistance.…”

Section: Discussionmentioning

confidence: 99%

“…(37) Hubs are fundamental for determining therapeutic targets against an infectious agente. (2) Scale-free networks are heterogeneous, (49) so random node disruptions (the 70%) do not lead to a significant loss of connectivity. However, the loss of the hubs (the 30%) causes the breakdown of the network into isolated clusters.…”

Section: Methodsmentioning

confidence: 99%

“…The Supplementary Data, the codes for the structural analysis in R and for finding RBH in python, implemented by Medeiros et al . (2) , and the CCBH-2022 file in CSV format are available in our Github repository (https://github.com/FioSysBio/CCBH2022).…”

Section: Methodsmentioning

confidence: 99%

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

Chagas

Filho

Santos

et al. 2022

Preprint

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BACKGROUNDHealthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behavior and provide novel ways to identify potential therapeutic targets and the development of new drugs. Gene regulatory networks (GRN) are an example of interaction representation in silico between regulatory genes and their targets.OBJECTIVESIn this work, we update the reconstruction of the MDR P. aeruginosa CCBH4851 GRN, and analyze and discuss its structural properties.METHODSWe based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process.FINDINGSOur result is a GRN with a larger number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa.MAIN CONCLUSIONSHere, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Chagas

Filho

Santos

et al. 2022

Preprint

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“…Other approaches in the reconstruction of regulatory networks in particular for P. aeruginosa have focused on methodologies based on the gene orthology inference by the reciprocal best hit method as in P. aeruginosa strain CCBH4851 ( 23 ) and Abasy (Across-bacteria systems) Atlas that integrate regulatory information on different bacteria ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

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

Galán-Vásquez

Olivera²,

Ramírez-Ibáñez³

et al. 2020

Database

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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.

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“…Allagainst-all BLAST alignments were performed using the following parameters: ≥ 90% coverage, ≥ 90% similarity and E value cut-off of 1e-10. A Python algorithm was applied over BLAST results to identify the BBHs 49 . Core, accessory, and unique genomes were analyzed using a MySQL database created with the data generated in previous steps.…”

Section: Methodsmentioning

confidence: 99%

Characterization of a SPM-1 metallo-beta-lactamase-producing Pseudomonas aeruginosa by comparative genomics and phenotypic analysis

Nascimento

Filho

Pauer

et al. 2020

Sci Rep

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Pseudomonas aeruginosa is one of the most common pathogens related to healthcare-associated infections. The Brazilian isolate, named CCBH4851, is a multidrug-resistant clone belonging to the sequence type 277. The antimicrobial resistance mechanisms of the CCBH4851 strain are associated with the presence of the bla SPM-1 gene, encoding a metallo-beta-lactamase, in combination with other exogenously acquired genes. Whole-genome sequencing studies focusing on emerging pathogens are essential to identify key features of their physiology that may lead to the identification of new targets for therapy. Using both illumina and pacBio sequencing data, we obtained a single contig representing the CCBH4851 genome with annotated features that were consistent with data reported for the species. However, comparative analysis with other Pseudomonas aeruginosa strains revealed genomic differences regarding virulence factors and regulatory proteins. In addition, we performed phenotypic assays that revealed CCBH4851 is impaired in bacterial motilities and biofilm formation. On the other hand, CCBH4851 genome contained acquired genomic islands that carry transcriptional factors, virulence and antimicrobial resistance-related genes. presence of single nucleotide polymorphisms in the core genome, mainly those located in resistance-associated genes, suggests that these mutations may also influence the multidrug-resistant behavior of CCBH4851. Overall, characterization of Pseudomonas aeruginosa CCBH4851 complete genome revealed the presence of features that strongly relates to the virulence and antibiotic resistance profile of this important infectious agent. Pseudomonas aeruginosa is one of the most common pathogens related to healthcare-associated infections in hospitalized individuals worldwide. Multidrug-resistant (MDR) isolates, particularly those non-susceptible to carbapenems, have become a major concern of health institutions. In addition to mechanisms such as loss of porins or overexpression of efflux pumps, carbapenem resistance is produced by the acquisition of genes encoding carbapenem-hydrolyzing beta-lactamases 1. These enzymes are classified into classes A and D, the activesite serine beta-lactamases (SBLs), and class B, the zinc-dependent or metallo-beta-lactamases (MBLs). SBLs have a broad spectrum of activity against beta-lactams but are inhibited by common beta-lactamase inhibitors.

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Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa

Cited by 7 publications

References 33 publications

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

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

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

Characterization of a SPM-1 metallo-beta-lactamase-producing Pseudomonas aeruginosa by comparative genomics and phenotypic analysis

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