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

Comparing genome-scale metabolic models of the non-resistant Enterococcus faecalis ATCC 19433 and the multi-resistant Enterococcus faecalis V583

Enterococcus faecalis is a versatile lactic acid bacterium with a large variety of implications for humans. While some strains of this species are pathobionts being resistant against most of the common antibiotics, other strains are regarded as biological protectants or even probiotics. Accordingly, E. faecalis strains largely differ in the size and content of their accessory genome. In this study, we describe the genome-scale metabolic network reconstruction of E. faecalis ATCC 19433, a non-resistant human-associated strain. A comparison of the genome-scale metabolic model (GSM) of E. faecalis ATCC 19433 with a previously published GSM of the multi-resistant pathobiontic E. faecalis V583 reveals high similarities in the central metabolic abilities of these two human associated strains. This is reflected, e.g., in the identical amino acid auxotrophies. The ATCC 19433 strain, however, has a 14.1 % smaller genome than V583 and lacks the multiple antibiotic resistance genes and genes involved in capsule formation. Based on the measured metabolic fluxes at different growth rates, the energy demand at zero growth was calculated to be about 40 % lower for the ATCC 19433 strain compared to V583. Furthermore, the ATCC 19433 strain seems less prone to the depletion of amino acids utilizable for energy metabolism. This might hint at a lower overall energy demand of the ATCC 19433 strain as compared to V583.

Unveiling the role of hub proteins in controlling quorum sensing regulated virulence through analogues in Pseudomonas aeruginosa PAO1: A functional protein-protein network biology approach

The protein-protein interaction (PPI) network analysis of specific genes identified for biofilm production and virulence/secretion system mediated by quorum sensing. The PPI depicted 13 hub proteins (namely rhlR, lasR, pscU, vfr, exsA, lasI, gacA, toxA, pilJ, pscC, fleQ, algR, and chpA) out of 160 nodes involving 627 edges. The PPI network analysis based on topographical features depicted pcrD with the highest degree value and vfr gene with the greatest betweenness centrality and closeness centrality (BC and CC) values. Based on in silico results, curcumin used as an Acyl homo-serine lactone (AHL) mimicker in P. aeruginosa, was also found effective in suppressing the quorum sensing regulated virulence factors such as elastase and pyocyanin. Based on in vitro experiment, curcumin suppressed biofilm formation at 62 μg/ml concentration. Host-pathogen interaction experiment showed that curcumin was also proved to be efficient in saving C. elegans from paralysis and killing effects of P. aeruginosa PAO1.

Gene network interaction analysis to elucidate the antimicrobial resistance mechanisms in the Clostridiumdifficile

Antimicrobial resistance has caused chaos worldwide due to the depiction of multidrug-resistant (MDR) infective microorganisms. A thorough examination of antimicrobial resistance (AMR) genes and associated resistant mechanisms is vital to solving this problem. Clostridium difficile (C. difficile) is an opportunistic nosocomial bacterial strain that has acquired exogenous AMR genes that confer resistance to antimicrobials such as erythromycin, azithromycin, clarithromycin, rifampicin, moxifloxacin, fluoroquinolones, vancomycin, and others. A network of interactions, including 20 AMR genes, was created and analyzed. In functional enrichment analysis, Cellular components (CC), Molecular Functions (MF), and Biological Processes (BP) were discovered to have substantial involvement. Mutations in the rpl genes, which encode ribosomal proteins, confer resistance in Gram-positive bacteria. Full erythromycin and azithromycin cross-resistance can be conferred if more than one of the abovementioned genes is present. In the enriched BP, rps genes related to transcriptional regulation and biosynthesis were found. The genes belong to the rpoB gene family, which has previously been related to rifampicin resistance. The genes rpoB, gyrA, gyrB, rpoS, rpl genes, rps genes, and Van genes are thought to be the hub genes implicated in resistance in C. difficile. As a result, new medications could be developed using these genes. Overall, our observations provide a thorough understanding of C. difficile AMR mechanisms.

Systems biology approach to understand the interplay between Bacillus anthracis and human host genes that leads to CVDs

Humans infected with invasive Bacillus anthracis (B. anthracis) have a very poor prognosis and are at high risk for developing cardiovascular diseases (CVDs) and shock. Several bacterial elements probably have significant pathogenic roles in this pathogenic process of anthrax. In our current work, we have analysed the molecular level interactions between B. anthracis and human genes to understand the interplay during anthrax that leads to the CVDs. Our results have shown dense interactions between the functional partners in both host and the B. anthracis Gene interaction network (GIN). The functional enrichment analysis indicated that the clusters in the host GIN had genes related to hypoxia and autophagy in response to the lethal toxin; and genes related to adherens junction and actin cytoskeleton in response to edema toxin play a significant role in multiple stages of the disease. The B. anthracis genes BA_0530, guaA, polA, rpoB, ribD, secDF, metS, dinG and human genes ACTB, EGFR, EP300, CTNNB1, ESR1 have shown more than 50 direct interactions with the functional partners and hence they can be considered as hub genes in the network and they are observed to have important roles in CVDs. The outcome of our study will help to understand the molecular pathogenesis of CVDs in anthrax. The hub genes reported in the study can be considered potential drug targets and they can be exploited for new drug discovery.

A polypeptide coating for preventing biofilm on implants by inhibiting antibiotic resistance genes

Aging population has been boosting the need for orthopedic implants. However, biofilm has been a major obstacle for orthopedic implants due to its insensitivity to antibiotics and tendency to drive antimicrobial resistance. Herein, an antibacterial polypeptide coating with excellent in vivo adhesive capacity was prepared to prevent implants from forming biofilms and inducing acquired antibiotic resistance. A peptide-based copolymer, poly[phenylalanine₁₀-stat-lysine₁₂]-block-3,4-dihydroxy-l-phenylalanine [Poly(Phe₁₀-stat-Lys₁₂)-DOPA] was modularly designed, where poly(Phe₁₀-stat-Lys₁₂) is antibacterial polypeptide with high antibacterial activity, and DOPA provides strong adhesion in both wet and dry microenvironments. Meanwhile, compared to traditional "graft-onto" methods, this antibacterial coating can be facilely achieved by immersing Titanium substrates into antibacterial polypeptide solution for 5 min at room temperature. The poly(Phe₁₀-stat-Lys₁₂)-DOPA polymer showed good antibacterial activity with minimum inhibitory concentrations against S. aureus and E. coli of 32 and 400 μg/mL, respectively. Compared to obvious antimicrobial resistance of S. aureus after continuous treatment with vancomycin, this antibacterial coating doesn't drive antimicrobial resistance upon long-term utilization. Transcriptome sequencing and qPCR tests further confirmed that the antibacterial coating was able to inhibit the expression of multiple peptide resistance factor (mprF) and lipoteichoic acid modification D-alanylation genes (dltB and dltC) that can increase the net positive charge of bacterial cell wall to induce the resistance to cationic antimicrobial peptides. In vivo experiments confirmed that this poly(Phe₁₀-stat-Lys₁₂)-DOPA coating can both effectively prevent biofilm formation through surface contact sterilization and avoid local and systemic infections. Overall, we proposed a facile method for preparing antibacterial orthopedic implants with longer indwelling time and without inducing antimicrobial resistance by coating a polypeptide-based polymer on the implants.

Deciphering the genetic network and programmed regulation of antimicrobial resistance in bacterial pathogens

Antimicrobial resistance (AMR) in bacteria is an important global health problem affecting humans, animals, and the environment. AMR is considered as one of the major components in the "global one health". Misuse/overuse of antibiotics in any one of the segments can impact the integrity of the others. In the presence of antibiotic selective pressure, bacteria tend to develop several defense mechanisms, which include structural changes of the bacterial outer membrane, enzymatic processes, gene upregulation, mutations, adaptive resistance, and biofilm formation. Several components of mobile genetic elements (MGEs) play an important role in the dissemination of AMR. Each one of these components has a specific function that lasts long, irrespective of any antibiotic pressure. Integrative and conjugative elements (ICEs), insertion sequence elements (ISs), and transposons carry the antimicrobial resistance genes (ARGs) on different genetic backbones. Successful transfer of ARGs depends on the class of plasmids, regulons, ISs proximity, and type of recombination systems. Additionally, phage-bacterial networks play a major role in the transmission of ARGs, especially in bacteria from the environment and foods of animal origin. Several other functional attributes of bacteria also get successfully modified to acquire ARGs. These include efflux pumps, toxin-antitoxin systems, regulatory small RNAs, guanosine pentaphosphate signaling, quorum sensing, two-component system, and clustered regularly interspaced short palindromic repeats (CRISPR) systems. The metabolic and virulence state of bacteria is also associated with a range of genetic and phenotypic resistance mechanisms. In spite of the availability of a considerable information on AMR, the network associations between selection pressures and several of the components mentioned above are poorly understood. Understanding how a pathogen resists and regulates the ARGs in response to antimicrobials can help in controlling the development of resistance. Here, we provide an overview of the importance of genetic network and regulation of AMR in bacterial pathogens.

Recent developments on UDP-N-acetylmuramoyl-L-alanine-D-gutamate ligase (Mur D) enzyme for antimicrobial drug development: An emphasis on in-silico approaches

Introduction

The rapid emergence of antibiotic resistance among various bacterial pathogens has been one of the major concerns of health organizations across the world. In this context, for the development of novel inhibitors against antibiotic-resistant bacterial pathogens, UDP-N-Acetylmuramoyl-L-Alanine-D-Glutamate Ligase (MurD) enzyme represents one of the most apposite targets.

Body

The present review focuses on updated advancements on MurD-targeted inhibitors in recent years along with genetic regulation, structural and functional characteristics of the MurD enzyme from various bacterial pathogens. A concise account of various crystal structures of MurD enzyme, submitted into Protein Data Bank is also discussed.

Discussion

MurD, an ATP dependent cytoplasmic enzyme is an important target for drug discovery. The genetic organization of MurD enzyme is well elucidated and many crystal structures of MurD enzyme are submitted into Protein Data bank. Various inhibitors against MurD enzyme have been developed so far with an increase in the use of in-silico methods in the recent past. But cell permeability barriers and conformational changes of MurD enzyme during catalytic reaction need to be addressed for effective drug development. So, a combination of in-silico methods along with experimental work is proposed to counter the catalytic machinery of MurD enzyme.

Structural chemistry and molecular-level interactome reveals histidine kinase EvgS to subvert both antimicrobial resistance and virulence in Shigella flexneri 2a str. 301

Multi-drug resistant (MDR) Shigella flexneri 2a, one of the leading bacterial agents of diarrhoeal mortality, has posed challenges in treatment strategies. The present study was conducted to identify potential therapeutic biomarkers using gene interaction network (GIN) in order to understand the cellular and molecular level interactions of both antimicrobial resistance (AMR) and virulence genes through topological and clustering metrics. Statistically significant differential gene expression (DGE), structural chemistry and dynamics were incorporated to elucidate biomarker for sustainable therapeutic regimen against MDR S. flexneri. Functional enrichments and topological metrics revealed evgS, ybjZ, tolC, gyrA, parC and their direct interactors to be associated with diverse AMR mechanisms. Histidine kinase EvgS was considered as the hub protein due to its highest prevalence in the molecular interactome profiles of both the AMR (71.6%) and virulence (45.8%) clusters interconnecting several genes concerning two-component system (TCS). DGE profiles of ΔPhoPQ (deleted regulatory PhoP and sensor PhoQ) led to the upregulation of TCS comprising EvgSA thereby validating EvgS as a promising therapeutic biomarker. Druggability and structural stability of EvgS was assessed through thermal shifts, backbone stability and coarse dynamics refinement. Structure-function relationship was established revealing the C-terminal extracellular domain as the drug-binding site which was further validated through molecular dynamics simulation. Structure elucidation of identified biomarker followed by secondary and tertiary structural validation would prove pivotal for future therapeutic interventions against subverting both AMR and virulence posed by this strain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03325-w.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

Functional insights of antibiotic resistance mechanism in Helicobacter pylori: Driven by gene interaction network and centrality-based nodes essentiality analysis

Increased antibiotic resistance in Helicobacter pylori (H. pylori), a major human pathogen, constitutes a substantial threat to human health. Understanding the pathophysiology and development of antibiotic resistance can aid our battle with the infections caused by H. pylori. The aim of this study is to discover the high-impact key regulatory mechanisms and genes involved in antimicrobial drug resistance (AMR). In this study, we constructed a functional gene interaction network by integrating multiple sources of data related to antibiotic resistant genes (number-77) from H. pylori. The gene interaction network was assortative, with a hierarchical, scale-free topology enriched in a variety of gene ontology (GO) categories and KEGG pathways. Using an iterative clustering methodology, we identified a number of communities in the AMR gene network that comprised nine genes (sodB, groEL, gyrA, recA, polA, tuf, infB, rpsJ, and gyrB) that were present at the deepest level and hence were key regulators of AMR. Further, an antibiotic-resistant gene network-based centrality analysis revealed superoxide dismutase (sodB) as a bottleneck node in the network. Our findings suggested that sodB is critically enriched in the cellular response to oxidative stress, removal of superoxide radicals, cellular oxidant detoxification processes, cellular component biogenesis, response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process and reactive oxygen species metabolic process. We demonstrated how the sodB, which are involved in the response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process, reactive oxygen species metabolic process, regulated by Fur gene/proteins, claim a major authority over regulation and signal propagation in the AMR.

FN1 encoding fibronectin as a pivotal signaling gene for therapeutic intervention against pancreatic cancer

The delayed diagnosis of pancreatic cancer has resulted in rising mortality rate and low survival rate that can be circumvented using potent theranostics biomarkers. The treatment gets complicated with delayed detection resulting in lowered 5-year relative survival rate. In our present study, we employed systems biology approach to identify central genes that play crucial roles in tumor progression. Pancreatic cancer genes collected from various databases were used to construct a statistically significant interactome with 812 genes that was further analysed thoroughly using topological parameters and functional enrichment analysis. The significant genes in the network were then identified based on the maximum degree parameter. The overall survival analysis indicated through hazard ratio [HR] and gene expression [log Fold Change] across pancreatic adenocarcinoma revealed the critical role of FN1 [HR 1.4; log2(FC) 5.748], FGA [HR 0.78; log2(FC) 1.639] FGG [HR 0.9; log2(FC) 1.597], C3 [HR 1.1; log2(FC) 2.637], and QSOX1 [HR 1.4; log2(FC) 2.371]. The functional significance of the identified hub genes signified the enrichment of integrin cell surface interactions and proteoglycan syndecan-mediated cell signaling. The differential expression, low overall survival and functional significance of FN1 gene implied its possible role in controlling metastasis in pancreatic cancer. Furthermore, alternate splice variants of FN1 gene showed 10 protein coding transcripts with conserved cell attachment site and functional domains indicating the variants' potential role in pancreatic cancer. The strong association of the identified hub-genes can be better directed to design potential theranostics biomarkers for metastasized pancreatic tumor.

Functional analysis of Escherichia coli K12 toxin-antitoxin systems as novel drug targets using a network biology approach

Bacterial resistance to various drugs and antibiotics has become a significant issue in the fight against infectious diseases. Due to the presence of diverse toxin-antitoxin (TA) systems, bacteria undergo adaptive metabolic alterations and can tolerate the effects of drugs and antibiotics. Bacterial TA systems are unique and can be therapeutic targets for developing new antimicrobial agents, owing to their ability to influence bacterial fate. With this background, our study aims to identify novel drug targets against Escherichia coli K12 MG1655 antitoxin using homology modelling approach. In this study, the protein-protein interaction network of 87 E. coli K12 MG1655 TA systems identified through literature mining was screened for the identification of hub proteins. The model evaluation, assessment, and homology modelling of the hub proteins were evaluated. Furthermore, computer-aided mathematical models of selected phytochemicals have been tested against the identified hub proteins. The TA system was functionally enriched in regulation of cell growth, negative regulation of cell growth, regulation of mRNA stability, mRNA catabolic process and RNA phosphodiester bond hydrolysis. RelE, RelB, MazE, MazF, MqsR, MqsA, and YoeB were identified as hub proteins. The robustness and superior quality of the RelB and MazE modelled structure were discovered by model evaluation, quality assessment criteria, and homology modelling of hub proteins. Clorobiocin was found to be a strong inhibitor by docking these modelled structures. Clorobiocin could be utilized as an antibacterial agent against multidrug resistant E. coli which may inactivate antitoxins and cause programmed cell death.

Organ-specific host differential gene expression analysis in systemic candidiasis: A systems biology approach

Patients admitted to the hospital with coronavirus disease (COVID-19) are at risk for acquiring mycotic infections in particular Candidemia. Candida albicans (C. albicans) constitutes an important component of the human mycobiome and the most common cause of invasive fungal infections. Invasive yeast infections are gaining interest among the scientific community as a consequence of complications associated with severe COVID-19 infections. Early identification and surveillance for Candida infections is critical for decreasing the COVID-19 mortality. Our current study attempted to understand the molecular-level interactions between the human genes in different organs during systematic candidiasis. Our research findings have shed light on the molecular events that occur during Candidiasis in organs such as the kidney, liver, and spleen. The differentially expressed genes (up and down-regulated) in each organ will aid in designing organ-specific therapeutic protocols for systemic candidiasis. We observed organ-specific immune responses such as the development of the acute phase response in the liver; TGF-pathway and genes involved in lymphocyte activation, and leukocyte proliferation in the kidney. We have also observed that in the kidney, filament production, up-regulation of iron acquisition mechanisms, and metabolic adaptability are aided by the late initiation of innate defense mechanisms, which is likely related to the low number of resident immune cells and the sluggish recruitment of new effector cells. Our findings point to major pathways that play essential roles in specific organs during systemic candidiasis. The hub genes discovered in the study can be used to develop novel drugs for clinical management of Candidiasis.

A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Designing Anti-Microbial Peptides Against Major β-Lactamase Enzymes in Clinically Important Gram-Negative Bacterial Pathogens: An In-Silico Study

Anti-microbial resistance (AMR) creating healthcare concerns worldwide requires ardent exploration of therapeutic alternatives. Although anti-microbial peptides (AMP) are popular for broad-spectrum activity, recent evidence of increasing resistance to membrane-acting AMPs by ESKAPE pathogens has compelled us to design novel AMPs as therapeutic candidates. A library of 60 AMPs comprising natural AMPs and their mutants was constructed through in-silico methods. After physico-chemical property evaluations, each peptide in the library was subjected to flexible molecular docking against four major β-lactamases in Gram-negative ESKAPE pathogens. Among the potent AMP mutants, a Lactoferricin B-Mutant (M4) possessed uniformly high affinity with SHV1, OXA48, NDM1, and AmpC having energies -842.0Kcal/mol, -774.8Kcal/mol, -1103.3Kcal/mol, and -858.8Kcal/mol respectively. Coarse-grained clustering and flexibility analysis further accounted for the residue-level stable configurations of the protein-peptide complexes with high affinity. Highest affinity of Lactoferricin B_M4 was found with NDM1 due to H-bonds, salt-bridges, and hydrophobic interactions with the metallo-β-lactamase domain including crucial active-site residue Asp124. Molecular dynamics simulation further confirmed the stability of Lactoferricin B_M4-NDM1 complex having low residue-level root-mean square deviations (RMSD), atomic-level fluctuations, and radius of gyration (Rg). The study encourages experimental validations and similar methods to identify potential AMPs against drug-resistant pathogens.

Network metrics, structural dynamics and density functional theory calculations identified a novel Ursodeoxycholic Acid derivative against therapeutic target Parkin for Parkinson's disease

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GIN analysis revealed PARK2, LRRK2, PARK7, PINK1 and SNCA as hub-genes.

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Topologically favoured Parkin was considered as a therapeutic target.

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ADMET screening identified a novel UDCA derivative as potential lead candidate.

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Chemical reactivity and ligand stability were analysed through DFT simulation.

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Docking and MDS established novel lead as potential Parkin inhibitor.

Parkinson's disease (PD) has been designated as one of the priority neurodegenerative disorders worldwide. Although diagnostic biomarkers have been identified, early onset detection and targeted therapy are still limited. An integrated systems and structural biology approach were adopted to identify therapeutic targets for PD. From a set of 49 PD associated genes, a densely connected interactome was constructed. Based on centrality indices, degree of interaction and functional enrichments, LRRK2, PARK2, PARK7, PINK1 and SNCA were identified as the hub-genes. PARK2 (Parkin) was finalized as a potent theranostic candidate marker due to its strong association (score > 0.99) with α-synuclein (SNCA), which directly regulates PD progression. Besides, modeling and validation of Parkin structure, an extensive virtual-screening revealed small (commercially available) inhibitors against Parkin. Molecule-258 (ZINC5022267) was selected as a potent candidate based on pharmacokinetic profiles, Density Functional Theory (DFT) energy calculations (ΔE = 6.93 eV) and high binding affinity (Binding energy = -6.57 ± 0.1 kcal/mol; Inhibition constant = 15.35 µM) against Parkin. Molecular dynamics simulation of protein-inhibitor complexes further strengthened the therapeutic propositions with stable trajectories (low structural fluctuations), hydrogen bonding patterns and interactive energies (>0kJ/mol). Our study encourages experimental validations of the novel drug candidate to prevent the auto-inhibition of Parkin mediated ubiquitination in PD.

Genome-scale protein interaction network construction and topology analysis of functional hypothetical proteins in Helicobacter pylori divulges novel therapeutic targets

The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.

Cellular and molecular level host-pathogen interactions in Francisella tularensis: A microbial gene network study

Due to the high infectivity and fatal effect on human population, Francisella tularensis (F. tularensis) is classified as a potential biological warfare agent. The interaction between host and pathogen behind the successful establishment of F. tularensis infection within the human host is largely unknown. In our present work, we have studied the molecular level interactions between the host cellular components and F. tularensis genes to understand the interplay between the host and pathogen. Interestingly, we have identified the pathways associated with the pathogen offensive strategies that help in invasion of host defensive systems. The F. tularensis genes purL, katG, proS, rpoB and fusA have displayed high number of interactions with the host genes and thus play a crucial role in vital pathogen pathways. The pathways identified were involved in adaptation to different stress conditions within the host and might be crucial for designing new therapeutic interventions against tularemia.

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

Emerging nosocomial strains of Acinetobacter baumannii are of recent concern as they are expressing extensive drug resistance (XDR). Using whole-genome sequencing and molecular characterisation analysis, the current study reveals the presence of carbapenemase genes in 92.86% of studied Indian isolates. These included bla_OXA-51 , bla_OXA-23 , bla_OXA-58 , and bla_NDM genes, with over a third expressing dual carbapenemase genes. As per the MLST scheme, IC2^Oxf /CC2^Pas was the predominant clone, with 57.14% isolates belonging to this lineage. The presence of these carbapenemase genes resulted in sulbactam (SUL) resistance (MIC: 16-256 µg/ml) in all of the studied isolates. The efficacy of durlobactam (DUR), a novel β-lactamase inhibitor that also inhibits PBP2 was assessed through in silico intermolecular interaction analysis. Several nonsynonymous single nucleotide polymorphisms 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 and mutants, which correlated with negligible binding energy fluctuations for the PBP3-SUL (-5.85 ± 0.04 kcal/mol) and PBP2-DUR (-5.16 ± 0.66 kcal/mol) complexes. Furthermore, higher binding affinities and low inhibition constants were noted in OXA23-DUR (-7.36 kcal/mol; 4.01 µM), OXA58-DUR (-6.44 kcal/mol; 19.07 µM), and NDM-DUR (-6.82 kcal/mol; 10.01 µM) complexes when compared with the conventional drugs avibactam and aztreonam. Stable interaction profiles of DUR with carbapenemases can possibly restore SUL activity against both PBP3_WT and PBP3_MTs . The study establishes the efficacy of the novel SUL-DUR combination as a successful treatment strategy in combating emerging XDR strains of A. baumannii.

In silico structure evaluation of BAG3 and elucidating its association with bacterial infections through protein-protein and host-pathogen interaction analysis

BAG3, a co-chaperone protein with a Bcl-2-associated athanogene (BAG) domain, has diverse functionalities in protein-folding, apoptosis, inflammation, and cell cycle regulatory cross-talks. It has been well characterised in cardiac diseases, cancers, and viral pathogenesis. The multiple roles of BAG3 are attributed to its functional regions like BAG, Tryptophan-rich (WW), isoleucine-proline-valine-rich (IPV), and proline-rich (PXXP) domains. However, to study its structural impact on various functions, the experimental 3D structure of BAG3 protein was not available. Hence, the structure was predicted through in silico modelling and validated through computational tools and molecular dynamics simulation studies. To the best of our knowledge, the role of BAG3 in bacterial infections is not explicitly reported. We attempted to study them through an in-silico protein-protein interaction network and host-pathogen interaction analysis. From structure-function relationships, it was identified that the WW and PXXP domains were associated with cellular cytoskeleton rearrangement and adhesion-mediated response, which might be involved in BAG3-related intracellular bacterial proliferation. From functional enrichment analysis, Gene Ontology terms and topological matrices, 18 host proteins and 29 pathogen proteins were identified in the BAG3 interactome pertaining to Legionellosis, Tuberculosis, Salmonellosis, Shigellosis, and Pertussis through differential phosphorylation events associated with serine metabolism. Furthermore, it was evident that direct (MAPK8, MAPK14) and associated (MAPK1, HSPD1, NFKBIA, TLR2, RHOA) interactors of BAG3 could be considered as therapeutic markers to curb down intracellular bacterial propagation in humans.

Ebolabase: Zaire ebolavirus-human protein interaction database for drug-repurposing

Ebola Virus (EBOV) is one of the deadliest pathogenic virus which causes hemorrhagic fever. Though many Ebola-human interaction studies and databases are already reported, the unavailability of an adequate model and lack of publically accessible resources requires a comprehensive study to curate the Ebola-Human-Drug interactions. In total, 270 human proteins interacted with EBOV are collected from published experimental evidence. Then the protein-protein interaction networks are generated as EBOV-human and EBOV-Human-Drugs interaction. These results can help the researcher to find the effective repurposed drug for EBOV treatment. Further, the illustration of gene enrichment and pathway analysis would provide knowledge and insight of EBOV-human interaction describes the importance of the study. Investigating the networks may help to identify a suitable human-based drug target for ebola research community. The inclusion of an emerging concept, a human-based drug targeted therapy plays a very significant role in drug repurposing which reduces the time and effort is the highlight of the current research. An integrated database namely, Ebolabase has been developed and linked with other repositories such as Epitopes, Structures, Literature, Genomics and Proteomics. All generated networks are made to be viewed in a customized manner and the required data can be downloaded freely. The Ebolabase is available at http://ebola.bicpu.edu.in.

Gene interaction network to unravel the role of gut bacterial species in cardiovascular diseases: E. coli O157:H7 host-bacterial interaction study

BACKGROUND

Cardiovascular Disease (CVD) is one of the most common causes of mortality in humans. Presently, the role of pathogens in the initiation and progression of the CVDs is not clearly understood. Hence, it is essential to understand the molecular-level interactions between the human proteins and the microbial proteins to deduce their functional roles in the CVDs.

METHOD

The host-pathogen interactions (HPI) related to CVDs in the case of E. coli str. O157:H7 colonization were curated, and also the protein-protein interactions (PPI) between humans and E. coli were collected. Gene interaction network (GIN) and functional enrichment analyses (FEA) were utilized for this.

RESULTS

The GIN revealed dense interactions between the functional partners. The FEA indicated that the essential pathways played a significant role in humans as well as in E. coli. The primary responses against most of the bacterial pathogens in humans are different from that of E. coli; Terpenoid biosynthesis and production of secondary metabolite pathways aid the survival of the E. coli inside the host. Interestingly, network analysis divulged that the E. coli genes ksgA, rpsT, ispE, rpsI, ispH, and the human genes TP53, CASP3, CYCS, EP300, RHOA communicated by significant numbers in direct interactions.

CONCLUSIONS

The results obtained from the present study will help researchers understand the molecular-level interactions in the CVDs between the human and the E. coli genes. The important genes with vital interactions can be considered as hub molecules and can be exploited for new drug discovery.

Low expression of FXYD5 reverses the cisplatin resistance of epithelial ovarian cancer cells

OBJECTIVE

To investigate the effect of the downregulation of FXYD domain-containing ion transport regulator 5 (FXYD5) on the cisplatin resistance (CisR) of epithelial ovarian cancer (EOC) cells.

METHODS

A2780-CisR and SKOV3-CisR cells were obtained through repeated administrations of different cisplatin concentrations, and the half-maximal inhibition concentration (IC50) was calculated by MTT assays. After transfection with FXYD5 siRNA-1 and FXYD5 siRNA-2, the IC50 values of the A2780-CisR and SKOV3-CisR cells were also detected by the MTT method. Cell proliferation, migration, invasion and apoptosis were evaluated through 5-ethynyl-2'-deoxyuridine (EdU) DNA synthesis, wound healing, Transwell invasion and Annexin-V-FITC/PI dual-staining assays, respectively. qRT-PCR and Western blotting were conducted to detect mRNA and protein expression.

RESULTS

Compared with the sensitive parental cells, the A2780-CisR and SKOV3-CisR cells had increased IC50 and FXYD5 expression. FXYD5 siRNA reduced the IC50 value of cisplatin in the A2780-CisR and SKOV3-CisR cells and decreased the expression of ABCG2 (BCRP) and ABCB1 (MDR1). In addition, FXYD5 inhibition reduced the invasion and migration of the A2780-CisR and SKOV3-CisR cells, with upregulation of E-cadherin and downregulation of Snail and Vimentin. Both FXYD5 siRNA-1 and FXYD5 siRNA-2 inhibited the proliferation and promoted the apoptosis of the A2780-CisR and SKOV3-CisR cells with reduced Ki-67 and increased caspase-3.

CONCLUSION

FXYD5 downregulation may reduce the invasion, migration and EMT formation of EOC cells to increase their sensitivity to cisplatin chemotherapy by inhibiting cell proliferation and promoting cell apoptosis.

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

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.

Amalgamation of 3D structure and sequence information for protein-protein interaction prediction

Protein is the primary building block of living organisms. It interacts with other proteins and is then involved in various biological processes. Protein-protein interactions (PPIs) help in predicting and hence help in understanding the functionality of the proteins, causes and growth of diseases, and designing new drugs. However, there is a vast gap between the available protein sequences and the identification of protein-protein interactions. To bridge this gap, researchers proposed several computational methods to reveal the interactions between proteins. These methods merely depend on sequence-based information of proteins. With the advancement of technology, different types of information related to proteins are available such as 3D structure information. Nowadays, deep learning techniques are adopted successfully in various domains, including bioinformatics. So, current work focuses on the utilization of different modalities, such as 3D structures and sequence-based information of proteins, and deep learning algorithms to predict PPIs. The proposed approach is divided into several phases. We first get several illustrations of proteins using their 3D coordinates information, and three attributes, such as hydropathy index, isoelectric point, and charge of amino acids. Amino acids are the building blocks of proteins. A pre-trained ResNet50 model, a subclass of a convolutional neural network, is utilized to extract features from these representations of proteins. Autocovariance and conjoint triad are two widely used sequence-based methods to encode proteins, which are used here as another modality of protein sequences. A stacked autoencoder is utilized to get the compact form of sequence-based information. Finally, the features obtained from different modalities are concatenated in pairs and fed into the classifier to predict labels for protein pairs. We have experimented on the human PPIs dataset and Saccharomyces cerevisiae PPIs dataset and compared our results with the state-of-the-art deep-learning-based classifiers. The results achieved by the proposed method are superior to those obtained by the existing methods. Extensive experimentations on different datasets indicate that our approach to learning and combining features from two different modalities is useful in PPI prediction.

Comprehensive host-pathogen protein-protein interaction network analysis

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.

Gene interaction network approach to elucidate the multidrug resistance mechanisms in the pathogenic bacterial strain Proteus mirabilis

Proteus mirabilis is one among the most frequently identified pathogen in patients with the urinary tract infection. The multidrug resistance exhibited by P. mirabilis renders the treatment ineffective, and new progressive strategies are needed to overcome the antibiotic resistance (AR). We have analyzed the evolutionary relationship of 29 P. mirabilis strains available in the National Center for Biotechnology Information-Genome database. The antimicrobial resistance genes of P. mirabilis along with the enriched pathways and the Gene Ontology terms are analyzed using gene networks to understand the molecular basis of AR. The genes rpoB, tufB, rpsl, fusA, and rpoA could be exploited as potential drug targets as they are involved in regulating the vital functions within the bacterium. The drug targets reported in the present study will aid researchers in developing new strategies to combat multidrug-resistant P. mirabilis.