Elucidating the multi-drug resistance mechanism of Enterococcus faecalis V583: A gene interaction network analysis

Naha, Aniket; Miryala, Sravan Kumar; Debroy, Reetika; Ramaiah, Sudha; Anbarasu, Anand

doi:10.1016/j.gene.2020.144704

Cited by 36 publications

(12 citation statements)

References 64 publications

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“…The involvement of broad physiological functions suggested that the antibiotic resistance genes of efflux pumps may have more connections in the network, which was supported by our observation: the known resistance genes in efflux pumps were hub genes in the internal network (Table 1). This finding is consistent with several previous studies showing that the resistance factors were likely to be the hub nodes or the key connected nodes in the network (Miryala et al, 2019(Miryala et al, , 2020Naha et al, 2020). Thus, based on the structural similarity of a protein to efflux pump and its location as a hub gene, we would be able to identify potential resistance factors, which could help us to understand the drug resistance mechanisms in bacteria.…”

Section: Discussionsupporting

confidence: 91%

Integrated Co-functional Network Analysis on the Resistance and Virulence Features in Acinetobacter baumannii

Xie¹,

Shao²,

Zheng³

2020

Front. Microbiol.

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Acinetobacter baumannii is one of the most troublesome bacterial pathogens that pose major public health threats due to its rapidly increasing drug resistance property. It is not only derived from clinic setting but also emerges from aquaculture as a fish pathogen, which could pass the resistant genes in the food chain. Understanding the mechanism of antibiotic resistance development and pathogenesis will aid our battle with the infections caused by A. baumannii. In this study, we constructed a co-functional network by integrating multiple sources of data from A. baumannii and then used the k-shell decomposition to analyze the co-functional network. We found that genes involving in basic cellular physiological function, including genes for antibiotic resistance, tended to have high k-shell values and locate in the internal layer of our network. In contrast, the non-essential genes, such as genes associated with virulence, tended to have lower k-shell values and locate in the external layer. This finding allows us to fish out the potential antibiotic resistance factors and virulence factors. In addition, we constructed an online platform ABviresDB (https://acba.shinyapps.io/ABviresDB/) for visualization of the network and features of each gene in A. baumannii. The network analysis in this study will not only aid the study on A. baumannii but also could be referenced for the research of antibiotic resistance and pathogenesis in other bacteria.

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Section: Discussionsupporting

confidence: 91%

Integrated Co-functional Network Analysis on the Resistance and Virulence Features in Acinetobacter baumannii

Xie¹,

Shao²,

Zheng³

2020

Front. Microbiol.

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“…Recent studies demonstrated the potential antibacterial activity of the novel SUL-DUR (βL-βLI) inhibitor combination which is currently under phase-III clinical trials and might be helpful in treating infections caused by A. baumannii [McLeod et al, 2020;Yahav et al, 2020]. Our research group has worked extensively to understand the protein-ligand interaction and complex AMR mechanisms of pathogenic bacteria through genomic analyses [Jacob et al, 2019;Vasudevan et al, 2020;Vijayakumar et al, 2016Vijayakumar et al, , 2020, in-silico structural [Basu et al, 2020[Basu et al, , 2021Lavanya et al, 2015;Thillainayagam et al, 2020], and systems biology approaches [Debroy et al, 2020;Miryala et al, 2020Miryala et al, , 2021Naha et al, 2020]. The aim of the present study was to decipher the activity of this βL-βLI combination against A. baumannii by characterising the inter-molecular binding profiles of SUL with PBP3 and the potency of DUR to restore the efficacy of SUL by inhibiting the β-lactamases and PBP2 through in-silico and in-vitro analyses.…”

Section: Introductionmentioning

confidence: 99%

Genome sequencing and molecular characterisation of XDR Acinetobacter baumannii reveal complexities in resistance: Novel combination of Sulbactam-Durlobactam holds promise for therapeutic intervention

Naha

Vijayakumar

Lal

et al. 2021

Preprint

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Acinetobacter baumannii is an emerging nosocomial strain expressing extensive drug resistance (XDR). Whole-genome sequencing and molecular characterisation analysis revealed the presence of carbapenemase in 92.86% of studied Indian isolates having blaOXA-51, blaOXA-23, blaOXA-58, and blaNDM genes, with a few evidences of dual carbapenemase genes. As per the MLST scheme, IC2Oxf/CC2Pas was the predominant clone, with 57.14% isolates belonging to this lineage. The presence of β-lactamases has rendered sulbactam (SUL) resistance (MIC: 16-256µg/ml) in all the studied isolates. The efficacy of novel durlobactam (DUR) in inhibiting β-lactamases and PBP2 was assessed through in-silico inter-molecular interaction analysis. Several non-synonymous single nucleotide polymorphisms (nsSNPs) were identified in PBP2 (G264S, I108V, S259T) and PBP3 (A515V, T526S) sequences. Minimal variations were recorded in the protein-backbone dynamics in active-site motifs of wild-type (WT) and mutants (MT), which correlated with the negligible binding energy fluctuations for PBP3-SUL (−5.85±0.04Kcal/mol) and PBP2-DUR (−5.16±0.66Kcal/mol) complexes. Furthermore, stronger binding affinities and low inhibition constants were noted in DUR complexed with OXA23 (−7.36Kcal/mol; 4.01µM), OXA58 (−6.44Kcal/mol; 19.07µM) and NDM (−6.82Kcal/mol; 10.01µM) when compared with conventional drugs avibactam and aztreonam. Stable interaction profiles of DUR, can possibly restore SUL activity against both PBP3WT and PBP3MTs. The study establishes the efficacy of novel SUL-DUR combination as a successful treatment strategy to combat emerging XDR strains.

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“…In network analysis, centrality is the main concept of identifying gravity of each node in the network. Centrality measures can be used to find most important HPs in HPPI network (HPPIN) to identify new drug targets [9][10][11][12][13][14][15]. Each centrality measure defines nodes' weight from a different perspective.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive host-pathogen protein-protein interaction network analysis

2020

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Background Infectious diseases are a cruel assassin with millions of victims around the world each year. Understanding infectious mechanism of viruses is indispensable for their inhibition. One of the best ways of unveiling this mechanism is to investigate the host-pathogen protein-protein interaction network. In this paper we try to disclose many properties of this network. We focus on human as host and integrate experimentally 32,859 interaction between human proteins and virus proteins from several databases. We investigate different properties of human proteins targeted by virus proteins and find that most of them have a considerable high centrality scores in human intra protein-protein interaction network. Investigating human proteins network properties which are targeted by different virus proteins can help us to design multipurpose drugs. Results As host-pathogen protein-protein interaction network is a bipartite network and centrality measures for this type of networks are scarce, we proposed seven new centrality measures for analyzing bipartite networks. Applying them to different virus strains reveals unrandomness of attack strategies of virus proteins which could help us in drug design hence elevating the quality of life. They could also be used in detecting host essential proteins. Essential proteins are those whose functions are critical for survival of its host. One of the proposed centralities named diversity of predators, outperforms the other existing centralities in terms of detecting essential proteins and could be used as an optimal essential proteins’ marker. Conclusions Different centralities were applied to analyze human protein-protein interaction network and to detect characteristics of human proteins targeted by virus proteins. Moreover, seven new centralities were proposed to analyze host-pathogen protein-protein interaction network and to detect pathogens’ favorite host protein victims. Comparing different centralities in detecting essential proteins reveals that diversity of predator (one of the proposed centralities) is the best essential protein marker.

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Elucidating the multi-drug resistance mechanism of Enterococcus faecalis V583: A gene interaction network analysis

Cited by 36 publications

References 64 publications

Integrated Co-functional Network Analysis on the Resistance and Virulence Features in Acinetobacter baumannii

Integrated Co-functional Network Analysis on the Resistance and Virulence Features in Acinetobacter baumannii

Genome sequencing and molecular characterisation of XDR Acinetobacter baumannii reveal complexities in resistance: Novel combination of Sulbactam-Durlobactam holds promise for therapeutic intervention

Comprehensive host-pathogen protein-protein interaction network analysis

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