Gene Network Analysis of Metallo Beta Lactamase Family Proteins Indicates the Role of Gene Partners in Antibiotic Resistance and Reveals Important Drug Targets

Resistance and virulence in Staphylococcus aureus by whole-genome sequencing: a comparative approach in blaZ-positive isolates

Mastitis caused by Staphylococcus aureus is a worldwide problem in dairy farms, in part because of the pathogenicity of the bacteria, biofilm formation, and mechanisms of antimicrobial resistance that make the disease difficult to diagnose and treat, which is typically done with the use of beta-lactam antibiotics. The aim of the present study was to determine the virulence and resistance factors of S. aureus isolates from subclinical mastitis, blaZ + /mecA - /mecC - , resistant and sensitive to oxacillin. All isolates were classified as CC97 by MLST analysis, a clonal complex well adapted to the mammary gland and although STAU23 and STAU73 were resistant to oxacillin while STAU32 and STAU78 were sensitive, the genomic analysis identified only the blaZ operon corresponding to resistance to beta-lactams. However, the presence of the sdrC gene was revealed exclusively in resistant isolates, an important adhesin in the colonization process that potentiates pathogenicity in S. aureus. In addition, resistance islands (REIs) were identified in these isolates, suggesting more conserved REIs. In the analysis of SNPs throughout the genome, mutations were found in the trmB and smpB genes of the resistant isolates and in the murD and rimM genes of the sensitive isolates. This study highlights the potential benefit of genome-wide characterization tools to identify molecular mechanisms of S. aureus in bovine mastitis.

Interplay between the β-lactam side chain and an active-site mobile loop of NDM-1 in penicillin hydrolysis as a potential target for mechanism-based inhibitor design

Metallo-β-lactamases (MβLs) stand as significant resistant mechanism against β-lactam antibiotics in Gram-negative bacteria. The worldwide dissemination of New Delhi metallo-β-lactamases (NDMs) intensifies antimicrobial resistance, posing severe threats to human health due to the absence of inhibitors available in clinical therapy. L3, a flexible β-hairpin loop flanking the active site in MβLs, has been proven to wield influence over the reaction process by assuming a crucial role in substrate recognition and intermediate stabilization. In principle, it potentially retards product release from the enzyme, consequently reducing the overall turnover rate although the details regarding this aspect remain inadequately elucidated. In this study, we crystallized NDM-1 in complex with three penicillin substrates, conducted molecular dynamics simulations, and measured the steady-state kinetic parameters. These analyses consistently unveiled substantial disparities in their interactions with loop L3. We further synthesized a penicillin V derivative with increased hydrophobicity in the R1 side chain and co-crystallized it with NDM-1. Remarkably, this compound exhibited much stronger dynamic interplay with L3 during molecular dynamics simulation, showed much lower Km and kcat values, and demonstrated moderate inhibitory capacity to NDM-1 catalyzed meropenem hydrolysis. The data presented here may provide a strategic approach for designing mechanism-based MβL inhibitors focusing on structural elements external to the enzyme's active center.

Elucidating the mechanism of antimicrobial resistance in Mycobacterium tuberculosis using gene interaction networks

Antimicrobial resistance (AMR) in microorganisms is an urgent global health threat. AMR of Mycobacterium tuberculosis is associated with significant morbidity and mortality. It is of great importance to underpin the resistance pathways involved in the mechanisms of AMR and identify the genes that are directly involved in AMR. The focus of the current study was the bacteria M. tuberculosis, which carries AMR genes that give resistance that lead to multidrug resistance. We, therefore, built a network of 43 genes and examined for potential gene-gene interactions. Then we performed a clustering analysis and identified three closely related clusters that could be involved in multidrug resistance mechanisms. Through the bioinformatics pipeline, we consistently identified six-hub genes (dnaN, polA, ftsZ, alr, ftsQ, and murC) that demonstrated the highest number of interactions within the clustering analysis. This study sheds light on the multidrug resistance of MTB and provides a protocol for discovering genes that might be involved in multidrug resistance, which will improve the treatment of resistant strains of TB.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

Resistance elicited by sub-lethal concentrations of ampicillin is partially mediated by quorum sensing in Pseudomonas aeruginosa

The rapid increase of antibiotic resistance is a serious challenge around the world. Antibiotics are present in various environments at sub-lethal concentrations, but how resistance emerges under sub-lethal conditions is not fully clear. In this study, we evolved Pseudomonas aeruginosa PAO1 under sub-lethal conditions, in the presence of either 15-30 μg/mL or 150-300 μg/mL of ampicillin. We found a ~ 5-6 fold increase in the minimum inhibitory concentration (MIC) among evolved isolates exposed to 15-30 μg/mL of ampicillin, and more than a 19-fold of increase in 150-300 μg/mL of ampicillin exposure. DNA sequencing revealed that mpl and ampD were frequently mutated in these resistant strains. We performed a transcriptome analysis of deletion mutations of mpl or ampD, compared to PAO1. Both showed a two-fold increase in expression of quorum sensing (QS) genes including lasR and rhlI/R; the heightened expression was positively correlated with the expression of the ampicillin resistance gene ampC. We queried if quorum sensing contributes to the increase in the ampicillin MIC. After adding the quorum quencher acylase I, the growth yield both decreased by roughly 50% for Δmpl in 2000 μg/mL of ampicillin and ΔampD in 4000 μg/mL of ampicillin. Addition of the QS signals into synthase mutants restored the higher MIC, but only for the rhlI/R circuit. This study highlights the involvement of QS in antibiotic resistance evolution, and shows the multifactorial contributors to the observed phenotypes.

Transcriptional expression of secondary resistance genes ccdB and repA2 is enhanced in presence of cephalosporin and carbapenem in Escherichia coli

BACKGROUND

The issue of carbapenem resistance in E.coli is very concerning and it is speculated that cumulative effect of both primary resistance genes and secondary resistance genes that act as helper to the primary resistance genes are the reason behind their aggravation. Therefore, here we attempted to find the role of two secondary resistance genes (SRG) ccdB and repA2 in carbapenem resistance in E. coli (CRE). In this context influential genes belonging to secondary resistome that act as helper to the primary resistance genes like blaNDM and blaCTX-M in aggravating β-lactam resistance were selected from an earlier reported in silico study. Transcriptional expression of the selected genes in clinical isolates of E.coli that were discretely harboring blaNDM-1, blaNDM-4, blaNDM-5, blaNDM-7 and blaCTX-M-15 with and without carbapenem and cephalosporin stress (2 μg/ml) was determined by real time PCR. Cured mutants sets that were lacking (i) primary resistance genes, (ii) secondary resistance genes and (iii) both primary and secondary resistance genes were prepared by SDS treatment. These sets were then subjected to antibiotic susceptibility testing by Kirby Bauer disc diffusion method.

RESULTS

Out of the 21 genes reported in the in silico study, 2 genes viz. repA2 and ccdB were selected for transcriptional expression analysis. repA2, coding replication regulatory protein, was downregulated in response to carbapenems and cephalosporins. ccdB, coding for plasmid maintenance protein, was also downregulated in response to carbapenems except imipenem and cephalosporins. Following plasmid elimination assay increase in diameter of zone of inhibition under stress of both antibiotics was observed as compared to uncured control hinting at the reversion of antibiotic susceptibility by the-then resistant bacteria.

CONCLUSION

SRGs repA2 and ccdB help sustenance of blaNDM and blaCTX-M under carbapenem and cephalosporin stress.

Proteomic Analyses of Acinetobacter baumannii Clinical Isolates to Identify Drug Resistant Mechanism

Acinetobacter baumannii is one of the main causes of nosocomial infections. Increasing numbers of multidrug-resistant Acinetobacter baumannii cases have been reported in recent years, but its antibiotic resistance mechanism remains unclear. We studied 9 multidrug-resistant (MDR) and 10 drug-susceptible Acinetobacter baumannii clinical isolates using Label free, TMT labeling approach and glycoproteomics analysis to identify proteins related to drug resistance. Our results showed that 164 proteins exhibited different expressions between MDR and drug-susceptible isolates. These differential proteins can be classified into six groups: a. proteins related to antibiotic resistance, b. membrane proteins, membrane transporters and proteins related to membrane formation, c. Stress response-related proteins, d. proteins related to gene expression and protein translation, e. metabolism-related proteins, f. proteins with unknown function or other functions containing biofilm formation and virulence. In addition, we verified seven proteins at the transcription level in eight clinical isolates by using quantitative RT-PCR. Results showed that four of the selected proteins have positive correlations with the protein level. This study provided an insight into the mechanism of antibiotic resistance of multidrug-resistant Acinetobacter baumannii.

Unlocking Survival Mechanisms for Metal and Oxidative Stress in the Extremely Acidophilic, Halotolerant Acidihalobacter Genus

Microorganisms used for the biohydrometallurgical extraction of metals from minerals must be able to survive high levels of metal and oxidative stress found in bioleaching environments. The Acidihalobacter genus consists of four species of halotolerant, iron–sulfur-oxidizing acidophiles that are unique in their ability to tolerate chloride and acid stress while simultaneously bioleaching minerals. This paper uses bioinformatic tools to predict the genes and mechanisms used by Acidihalobacter members in their defense against a wide range of metals and oxidative stress. Analysis revealed the presence of multiple conserved mechanisms of metal tolerance. Ac. yilgarnensis F5^T, the only member of this genus that oxidizes the mineral chalcopyrite, contained a 39.9 Kb gene cluster consisting of 40 genes encoding mobile elements and an array of proteins with direct functions in copper resistance. The analysis also revealed multiple strategies that the Acidihalobacter members can use to tolerate high levels of oxidative stress. Three of the Acidihalobacter genomes were found to contain genes encoding catalases, which are not common to acidophilic microorganisms. Of particular interest was a rubrerythrin genomic cluster containing genes that have a polyphyletic origin of stress-related functions.

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.

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.

Gene interaction network studies to decipher the multi-drug resistance mechanism in Salmonella enterica serovar Typhi CT18 reveal potential drug targets

Salmonella enterica subsp. enterica serovar Typhi, a human enteric pathogen causing typhoid fever, developed resistance to multiple antibiotics over the years. The current study was dedicated to understand the multi-drug resistance (MDR) mechanism of S. enterica serovar Typhi CT18 and to identify potential drug targets that could be exploited for new drug discovery. We have employed gene interaction network analysis for 44 genes which had 275 interactions. Clustering analysis resulted in three highly interconnecting clusters (C1-C3). Functional enrichment analysis revealed the presence of drug target alteration and three different multi-drug efflux pumps in the bacteria that were associated with antibiotic resistance. We found seven genes (arnA,B,C,D,E,F,T) conferring resistance to Cationic Anti-Microbial Polypeptide (CAMP) molecules by membrane Lipopolysaccharide (LPS) modification, while macB was observed to be an essential controlling hub of the network and played a crucial role in MacAB-TolC efflux pump. Further, we identified five genes (mdtH, mdtM, mdtG, emrD and mdfA) which were involved in Major Facilitator Superfamily (MFS) efflux system and acrAB contributed towards AcrAB-TolC efflux pump. All three efflux pumps were seen to be highly dependent on tolC gene. The five genes, namely tolC, macB, acrA, acrB and mdfA which were involved in multiple resistance pathways, can act as potential drug targets for successful treatment strategies. Therefore, this study has provided profound insights into the MDR mechanism in S. Typhi CT18. Our results will be useful for experimental biologists to explore new leads for S. enterica.

Comparative Molecular Field Analysis and Molecular Docking Studies on Quinolinone Derivatives Indicate Potential Hepatitis C Virus Inhibitors

Presently, there are no effective vaccines and anti-virals for the prevention and treatment of Hepatitis C virus infections and hence there is an urgent need to develop potent HCV inhibitors. In this study, we have carried out molecular docking, molecular dynamics and 3D-QSAR on heteroaryl 3-(1,1-dioxo-2H-(1,2,4)-benzothiadizin-3-yl)-4-hydroxy-2(1H)-quinolinone series using NS5B protein. Total of 41 quinolinone derivatives is used for molecular modeling study. The binding conformation and hydrogen bond interaction of the docked complexes were analyzed to model the inhibitors. We identified the molecule XXXV that had a higher affinity with NS5B. The molecular dynamics study confirmed the stability of the compound XXXV-NS5B complex. The developed CoMFA descriptors parameters, which were calculated using a test set of 13 compounds, were statistically significant. Our results will provide useful insights and lead to design potent anti-Hepatitis C virus molecules.

Discerning molecular interactions: A comprehensive review on biomolecular interaction databases and network analysis tools

Computational analysis of biomolecular interaction networks is now gaining a lot of importance to understand the functions of novel genes/proteins. Gene interaction (GI) network analysis and protein-protein interaction (PPI) network analysis play a major role in predicting the functionality of interacting genes or proteins and gives an insight into the functional relationships and evolutionary conservation of interactions among the genes. An interaction network is a graphical representation of gene/protein interactome, where each gene/protein is a node, and interaction between gene/protein is an edge. In this review, we discuss the popular open source databases that serve as data repositories to search and collect protein/gene interaction data, and also tools available for the generation of interaction network, visualization and network analysis. Also, various network analysis approaches like topological approach and clustering approach to study the network properties and functional enrichment server which illustrates the functions and pathway of the genes and proteins has been discussed. Hence the distinctive attribute mentioned in this review is not only to provide an overview of tools and web servers for gene and protein-protein interaction (PPI) network analysis but also to extract useful and meaningful information from the interaction networks.

MurB as a target in an alternative approach to tackle the Vibrio cholerae resistance using molecular docking and simulation study

Cholera is a serious threat to a large population in the under developed countries. Though oral rehydration therapy is the preferred choice of treatment, the use of antibiotics could reduce the microbial load in the case of severity. The use of antibiotics is also sought in the scenarios where there is problem with access to clean water. However, Vibrio cholera (V. cholerae) strains have developed resistance to antibiotics such as amoxicillin, ampicillin, chloramphenicol, doxycycline, erythromycin, and tetracycline. In this work, we have addressed the resistance issue by targeting MurB protein which is essential for the cell wall biosynthesis in V. cholerae. 20 Phytochemical compounds were chosen to screen the potential inhibitors against V. cholerae to avoid the complications faced by synthesis of small molecules. The molecular docking and dynamics study indicates that quercetin is the most potential and stable inhibitor of Murb.

Natural Inhibitors of HMG-CoA Reductase-An Insilico Approach Through Molecular Docking and Simulation Studies

Plant products have always been considered for many important metabolic disorders due to its abundant medicinal properties. Alarming adverse effects of overuse of statins has been reported for patients with dyslipidemia. This study was aimed to identify compounds with potent anti-dyslipidemic property from selected plants and analyze them for their efficiency in binding with HMG-CoA reductase, a key enzyme in lipid metabolism. The docking studies indicate rutin as the best compound that can inhibit HMG-CoA reductase as it had strong binding affinity to the enzyme. The molecular dynamics simulation studies confirmed the stability of the HMG-CoA reductase-rutin complex. RMSD, RMSF, Rg, H-bond results indicated that the HMG-CoA reductase-rutin complex is highly stable. Presently, statins are not preferred for individuals with pre-existing liver disease. Our study identified rutin as a promising lead compound which could be further developed into an anti-dyslipidemic molecule. Our results will be a good starting point for future experimental and clinical studies and if the results from such studies match international standards plant derived rutin might emerge as a good alternative to statins. J. Cell. Biochem. 118: 52-57, 2017. © 2016 Wiley Periodicals, Inc.