Gut metagenomic analysis of gastric cancer patients reveals Akkermansia, Gammaproteobacteria, and Veillonella microbiota as potential non-invasive biomarkers.

The goal of the study was to investigate the changes in the gut microbiota during the advancement of gastric cancer (GC) and identify pertinent taxa associated with the disease. We used a public fecal amplicon gastric cancer dataset from the Sequence Retrieval Archive (SRA), of patients with GC, gastritis, and healthy individuals. We did sequence pre-processing, including quality filtering of the sequences. Then, we performed a diversity analysis, evaluating α- and ß-diversity. Next, taxonomic composition analysis was performed and the relative abundances of different taxa at the phylum and genus levels were compared between GC, gastritis, and healthy controls. The obtained results were subsequently subjected to statistical validation. To conclude, metagenomic function prediction was carried out, followed by correlation analysis between the microbiota and KEGG pathways. α analysis revealed a significant difference between male and female categories, while ß analysis demonstrated significant distinctions between GC, gastritis, and healthy controls, as well as between sexes within the GC and gastritis groups. The statistically confirmed taxonomic composition analysis highlighted the presence of the microbes Bacteroides and Veillonella. Furthermore, through metagenomic prediction analysis and correlation analysis with pathways, three taxa, namely Akkermansia, Gammaproteobacteria, and Veillonella, were identified as potential biomarkers for GC. Additionally, this study reports, for the first time, the presence of two bacteria, Desulfobacteriota and Synergistota, in GC, necessitating further investigation. Overall, this research sheds light on the potential involvement of gut microbiota in GC pathophysiology; however, additional studies are warranted to explore its functional significance.

Molecular interaction and MD-simulations: investigation of Sizofiran as a promising anti-cancer agent targeting eIF4E in colorectal cancer.

CRC has a major global health impact due to high mortality rates. CRC shows high expression of eukaryotic translation initiation factor (eIF4E) protein, the rapid development of lung, bladder, colon, prostate, breast, head, and neck cancer is attributed to the dysregulation of eIF4E making an important target for treatment. Targeting eIF4E-mediated translation is a promising anti-cancer strategy. Many organic compounds that inhibit eIF4E are being studied clinically. The compound Sizofiran has emerged as a promising eIF4E inhibitor candidate, but its exact mechanism of action is unclear. In an effort to close this discrepancy by clarifying the mechanism of the interactions between phytochemical substances and eIF4E, molecular docking and dynamics studies were conducted. Molecular docking studies found Sizofiran (- 12.513 kcal/mol) has the most affinity eIF4E binding energy out of 93 phytochemicals, 5 current drugs, and 4 known inhibitors. This positions it as a top eIF4E inhibitor candidate. An alignment of eIF4E protein sequences from multiple pathogens revealed that the glutamate103 interacting residues are evolutionarily conserved across the different eIF4E proteins. Further insights from 100 ns of MD simulations supported Sizofiran having superior stability and eIF4E inhibition compared to reference compounds. Designed Sizofiran-related compounds showed better activity than the current drugs such as Camptosar, Sorafenib, Regorafenib, Doxorubicin, and Kenpaullone, indicating strong potential to suppress CRC progression by targeting eIF4E. This research aims to significantly aid development of improved eIF4E-targeting drugs for cancer treatment. Graphical abstract: Showing the Graphical abstract of the complete study. Supplementary Information: The online version contains supplementary material available at 10.1007/s40203-024-00206-3.

SPP1, a potential therapeutic target and biomarker for lung cancer: functional insights through computational studies.

NIH reported 128 different types of cancer of which lung cancer is the leading cause of mortality. Globally, it is estimated that on average one in every seventeen hospitalized patients was deceased. There are plenty of studies that have been reported on lung cancer draggability and therapeutics, but yet a protein that plays a central specific to cure the disease remains unclear. So, this study is designed to identify the possible therapeutic targets and biomarkers that can be used for the potential treatment of lung cancers. In order to identify differentially expressed genes, 39 microarray datasets of lung cancer patients were obtained from various demographic regions of the GEO database available at NCBI. After annotating statistically, 6229 up-regulated genes and 10324 down-regulated genes were found. Out of 17 up-regulated genes and significant genes, we selected SPP1 (osteopontin) through virtual screening studies. We found functional interactions with the other cancer-associated genes such as VEGF, FGA, JUN, EGFR, and TGFB1. For the virtual screening studies,198 biological compounds were retrieved from the ACNPD database and docked with SPP1 protein (PDBID: 3DSF). In the results, two highly potential compounds secoisolariciresinol diglucoside (-12.9 kcal/mol), and Hesperidin (-12.0 kcal/mol) showed the highest binding affinity. The stability of the complex was accessed by 100 ns simulation in an SPC water model. From the functional insights obtained through these computational studies, we report that SPP1 could be a potential biomarker and successive therapeutic protein target for lung cancer treatment.

A comprehensive review on human disease-causing bacterial proteases and their impeding agents.

Proteases are enzymes that catalyze the amide bond dissociation in polypeptide and protein peptide units. They are categorized into seven families and are responsible for a wide spectrum of human ailments, such as various types of cancers, skin infections, urinary tract infections etc. Specifically, the bacterial proteases cause a huge impact in the disease progression. Extracellular bacterial proteases break down the host defense proteins, while intracellular proteases are essential for pathogens virulence. Due to its involvement in disease pathogenesis and virulence, bacterial proteases are considered to be potential drug targets. Several studies have reported potential bacterial protease inhibitors in both Gram-positive and Gram-negative disease causing pathogens. In this study, we have comprehensively reviewed about the various human disease-causing cysteine, metallo, and serine bacterial proteases as well as their potential inhibitors.

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.

Comparative co-expression analysis of RNA-Seq transcriptome revealing key genes, miRNA and transcription factor in distinct metabolic pathways in diabetic nerve, eye, and kidney disease.

Diabetes and its related complications are associated with long term damage and failure of various organ systems. The microvascular complications of diabetes considered in this study are diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. The aim is to identify the weighted co-expressed and differentially expressed genes (DEGs), major pathways, and their miRNA, transcription factors (TFs) and drugs interacting in all the three conditions. The primary goal is to identify vital DEGs in all the three conditions. The overlapped five genes (AKT1, NFKB1, MAPK3, PDPK1, and TNF) from the DEGs and the co-expressed genes were defined as key genes, which differentially expressed in all the three cases. Then the protein-protein interaction network and gene set linkage analysis (GSLA) of key genes was performed. GSLA, gene ontology, and pathway enrichment analysis of the key genes elucidates nine major pathways in diabetes. Subsequently, we constructed the miRNA-gene and transcription factorgene regulatory network of the five gene of interest in the nine major pathways were studied. hsa-mir-34a-5p, a major miRNA that interacted with all the five genes. RELA, FOXO3, PDX1 and SREBF1 were the TFs interacting with the major five gene of interest. Finally, drug-gene interaction network elucidates five potential drugs to treat the genes of interest. This research reveals biomarker genes, miRNA, TFs, and therapeutic drugs in the key signaling pathways, which may help us, understand the processes of all three secondary microvascular problems and aid in disease detection and management.

Transcriptomic meta-analysis reveals biomarker pairs and key pathways in Tetralogy of Fallot.

Tetralogy of Fallot (TOF) is a cyanotic congenital condition contributed by genetic, epigenetic as well as environmental factors. We applied sparse machine learning algorithms to RNAseq and sRNAseq data to select the prospective biomarker candidates. Furthermore, we applied filtering techniques to identify a subset of biomarker pairs in TOF. Differential expression analysis disclosed 2757 genes and 214 miRNAs, which are dysregulated. Weighted gene co-expression network analysis on the differentially expressed genes extracted five significant modules that are enriched in GO terms, extracellular matrix, signaling and calcium ion binding. Also, voomNSC selected two genes and five miRNAs and transformed PLDA-predicted 72 genes and 38 miRNAs as prognostic biomarkers. Out of the selected biomarkers, miRNA target analysis revealed 14 miRNA-gene interactions. Also, 10 out of 14 pairs were oppositely expressed and four out of 10 oppositely expressed biomarker pairs shared common pathways of focal adhesion and P13K-Akt signaling. In conclusion, our study demonstrated the concept of biomarker pairs, which may be considered for clinical validation due to the high literature as well as experimental support.

A Text Mining Protocol for Mining Biological Pathways and Regulatory Networks from Biomedical Literature.

A biological pathway or regulatory network is a collection of molecular regulators which can activate the changes in cellular processes leading to an assembly of new molecules by series of actions among the molecules. There are three important pathways in system biology studies namely signaling pathways, metabolic pathways, and genetic pathways (or) gene regulatory networks. Recently, biological pathway construction from scientific literature is given much attention as the scientific literature contains a rich set of linguistic features to extract biological associations between genes and proteins. These associations can be united to construct biological networks. Here, we present a brief overview about various biological pathways, biomedical text resources/corpora for network construction and state-of-the-art existing methods for network construction followed by our hybrid text mining protocol for extracting pathways and regulatory networks from biomedical literature.

A review on structural genomics approach applied for drug discovery against three vector-borne viral diseases: Dengue, Chikungunya and Zika.

Structural genomics involves the advent of three-dimensional structures of the genome encoded proteins through various techniques available. Numerous structural genomics research groups have been developed across the globe and they contribute enormously to the identification of three-dimensional structures of various proteins. In this review, we have discussed the applications of the structural genomics approach towards the discovery of potential lead-like molecules against the genomic drug targets of three vector-borne diseases, namely, Dengue, Chikungunya and Zika. Currently, all these three diseases are associated with the most important global public health problems and significant economic burden in tropical countries. Structural genomics has accelerated the identification of novel drug targets and inhibitors for the treatment of these diseases. We start with the current development status of the drug targets and antiviral drugs against these three diseases and conclude by describing challenges that need to be addressed to overcome the shortcomings in the process of drug discovery.

The potential role of procyanidin as a therapeutic agent against SARS-CoV-2: a text mining, molecular docking and molecular dynamics simulation approach.

A novel coronavirus (SARS-CoV-2) has caused a major outbreak in human all over the world. There are several proteins interplay during the entry and replication of this virus in human. Here, we have used text mining and named entity recognition method to identify co-occurrence of the important COVID 19 genes/proteins in the interaction network based on the frequency of the interaction. Network analysis revealed a set of genes/proteins, highly dense genes/protein clusters and sub-networks of Angiotensin-converting enzyme 2 (ACE2), Helicase, spike (S) protein (trimeric), membrane (M) protein, envelop (E) protein, and the nucleocapsid (N) protein. The isolated proteins are screened against procyanidin-a flavonoid from plants using molecular docking. Further, molecular dynamics simulation of critical proteins such as ACE2, Mpro and spike proteins are performed to elucidate the inhibition mechanism. The strong network of hydrogen bonds and hydrophobic interactions along with van der Waals interactions inhibit receptors, which are essential to the entry and replication of the SARS-CoV-2. The binding energy which largely arises from van der Waals interactions is calculated (ACE2=-50.21 ± 6.3, Mpro=-89.50 ± 6.32 and spike=-23.06 ± 4.39) through molecular mechanics Poisson-Boltzmann surface area also confirm the affinity of procyanidin towards the critical receptors. Communicated by Ramaswamy H. Sarma.

Probing intermolecular interactions and binding stability of antimicrobial peptides with beta-lactamase of Klebsiella aerogenes by comparing FDA approved beta-lactam drugs: a docking and molecular dynamics approach.

Hospital pathogens, including Klebsiella aerogenes are becoming increasingly common, with the rise of Beta-lactam-resistant strains, especially in isolates recovered from intensive care rooms. Beta-lactamases participate in both the antibacterial activity and the mediation of the antibiotic resistance of Beta-lactams. The rapid spread of broad-spectrum Beta-lactam antibiotic resistance in pathogenic bacteria has recently become a major global health problem. As a result, new drugs that specifically target Beta-lactamases are urgently needed, and this enzyme has been identified to resolve the problem of bacterial resistance. In previous work, we de-novo developed, synthesized, and studied the in-vitro and in-silico behavior of four novel broad spectrum antimicrobial peptides, namely PEP01 to PEP04. All four peptides had significant antibacterial action against K. aerogenes. The literature evidence strongly suggests that Beta-lactamases are extremely important for bacteria, including K. aerogenes, and hence are therapeutically important and possible targets. Therefore, in this study we incorporated molecular modeling, docking, and simulation studies of the above four AMPs against the Beta-lactamase protein of K. aerogenes. The docking findings were also compared to eight FDA approved Beta-lactam antibiotics. According to our findings, all four peptides have strong binding affinity and interactions with Beta-lactamases and PEP02 has the highest docking score. In MD simulations, the protein-peptide complexes were more stable at 50 ns. We found that the new AMP-PEP02 is the most efficient and suitable drug candidate for inactivating Beta-lactamase protein, and that it is an alternative to or complements existing antibiotics for managing Beta-lactamase related resistance mechanisms based on this computational conclusion.Communicated by Ramaswamy H. Sarma.

GalNAc-siRNA conjugates: Prospective tools on the frontier of anti-viral therapeutics.

The growing use of short-interfering RNA (siRNA)-based therapeutics for viral diseases reflects the most recent innovations in anti-viral vaccines and drugs. These drugs play crucial roles in the fight against many hitherto incurable diseases, the causes, pathophysiologies, and molecular processes of which remain unknown. Targeted liver drug delivery systems are in clinical trials. The receptor-mediated endocytosis approach involving the abundant asialoglycoprotein receptors (ASGPRs) on the surfaces of liver cells show great promise. We here review N-acetylgalactosamine (GalNAc)-siRNA conjugates that treat viral diseases such as hepatitis B infection, but we also mention that novel, native conjugate-based, targeted siRNA anti-viral drugs may also cure several life-threatening diseases such as hemorrhagic cystitis, multifocal leukoencephalopathy, and severe acute respiratory syndrome caused by coronaviruses and human herpes virus.

Prioritization of potential drug targets and antigenic vaccine candidates against Klebsiella aerogenes using the computational subtractive proteome-driven approach.

Klebsiella aerogenes is a multidrug-resistant Gram-negative bacterium that causes nosocomial infections. The organism showed resistance to most of the conventional antibiotics available. Because of the high resistance of the species, the treatment of K. aerogenes is difficult. These species are resistant to third-generation cephalosporins due to the production of chromosomal beta-lactams with cephalosporin activity. The lack of better treatment and the development of therapeutic resistance in hospitals hinders better/new broad-spectrum-based treatment against this pathogen. This study identifies potential drug targets/vaccine candidates through a computational subtractive proteome-driven approach. This method is used to predict proteins that are not homologous to humans and human symbiotic intestinal flora. The resultant proteome of K. aerogenes was further searched for proteins, which are essential, virulent, and determinants of antibiotic/drug resistance. Subsequently, their druggability properties were also studied. The data set was reduced based on its presence in the pathogen-specific metabolic pathways. The subtractive proteome analysis predicted 13 proteins as potential drug targets for K. aerogenes. Furthermore, these target proteins were annotated based on their spectrum of activity, cellular localization, and antigenicity properties, which ensured that they are potent candidates for broad-spectrum antibiotic and vaccine design. The results open up new opportunities for designing and manufacturing powerful antigenic vaccines against K. aerogenes and the detection and release of new and active drugs against K. aerogenes without altering the gut microbiome. Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-021-00068-9.

Screening of FDA-approved compound library identifies potential small-molecule inhibitors of SARS-CoV-2 non-structural proteins NSP1, NSP4, NSP6 and NSP13: molecular modeling and molecular dynamics studies.

COVID-19, the current global pandemic has caused immense damage to human lives and the global economy. It is instigated by the SARS-CoV-2 virus and there is an immediate need for the identification of effective drugs against this deadly virus. SARS-CoV-2 genome codes for four structural proteins, sixteen non-structural proteins (NSPs) and several accessory proteins for its survival inside the host cells. In the present study, through in silico approaches, we aim to identify compounds that are effective against the four NSPs namely, NSP1, NSP4, NSP6 and NSP13 of SARS-CoV-2. The selection criteria of these four NSP proteins are they are least explored and potential targets. First, we have modeled the 3D structures of these proteins using homology modeling methods. Further, through molecular docking studies, we have screened the FDA-approved compounds against these modeled proteins and reported their docking scores. To gain dynamic insights, molecular dynamics studies have also been carried out for the best scored ligand against the NSPs. This study can further pave way for exposing more number of compounds against these proteins and enhance COVID-19 treatment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42485-021-00067-w.

Discovering mycobacterial lectins as potential drug targets and vaccine candidates for tuberculosis treatment: a theoretical approach.

M. tuberculosis proliferates within the macrophages during infection and they are bounded by carbohydrates in the cell wall, called lectins. Despite their surface localization, the studies on exact functions of lectins are unexplored. Hence, in our study, using insilico approaches, 11 potential lectins of Mtb was explored as potential drug targets and vaccine candidates. Initially, a gene interaction network was constructed for the 11 potential lectins and identified its functional partners. A gene ontology analysis was also performed for the 11 mycobacterial lectins along with its functional partners and found most of the proteins are present in the extracellular region of the bacterium and belongs to the PE/PPE family of proteins. Further, molecular docking studies were performed for two of the potential lectins (Rv2075c and Rv1917c). A novel series of quinoxalinone and fucoidan derivatives have been made to dock against these selected lectins. Molecular docking study reveals that quinoxalinone derivatives showed better affinity against Rv2075c, whereas fucoidan derivatives have good binding affinity against Rv1917c. Moreover, the mycobacterial lectins can interact with the host and they are considered as potential vaccine candidates. Hence, immunoinformatics study was carried out for all the 11 potential lectins. B-cell and T-cell binding epitopes were predicted using insilico tools. Further, an immunodominant epitope 1062SIPAIPLSVEV1072 of Rv1917c was identified, which was predicted to bind B-cell and most of the MHC alleles. Thus, the study has explored that mycobacterial lectins could be potentially used as drug targets and vaccine candidates for tuberculosis treatment. Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-021-00065-y.

Integrated regulatory network based on lncRNA-miRNA-mRNA-TF reveals key genes and sub-networks associated with dilated cardiomyopathy.

Dilated Cardiomyopathy (DCM) is a multifactorial condition often leading to heart failure in many clinical cases. Due to the high number of DCMincidence reported as familial, a gene level network based study was conducted utilizing high throughput next generation sequencing data. We exploited the exome and transcriptome sequencing data in NCBI-SRA database to construct a high confidence scale-free regulatory network consisting of lncRNA, miRNA, mRNA and Transcription Factors (TFs). Analysis of RNA-Seq data revealed 477 differentially expressed coding transcripts and 77 lncRNAs. 268 miRNAs regulated either lncRNAs or mRNAs. Out of the 477 coding transcripts that are deregulated, 82 were TFs. We identified three major hub nodeslncRNA (XIST), miRNA (hsa-miR-195-5p) and mRNA (NOVA1) from the network. We also found putative disease associations of DCM with diabetes and DCM with hypoventillation syndrome. Five highly connected modules were also identified from the network. The hubs showed significant connectivity with the modules.Through this study we were able to gain insights into the underlying lncRNA-miRNA-mRNA-TF network. From a high throughput dataset we have isolated a handful of probable targets that may be utilized for studying the mechanisms of DCM development and progression to heart failure.

Integrated meta-analysis and machine learning approach identifies acyl-CoA thioesterase with other novel genes responsible for biofilm development in Staphylococcus aureus.

Biofilm forming Staphylococcus aureus is a major threat to the health-care industry. It is important to understand the differences between planktonic and biofilm growth forms in the pathogen since conventional treatments targeting the planktonic forms are not effective against biofilms. The current study conducts a meta-analysis of three public transcriptomic profiles to examine the differences in gene expression between the planktonic and biofilm states of S. aureus using random-effects modeling. Mean effect sizes were calculated for 2847 genes among which 726 differentially expressed genes were taken for further analysis. Major genes that are discriminatory between the two conditions were mined using supervised learning techniques and validated by high-accuracy classifiers. Ten different feature selection algorithms were applied and used to rank the most important genes in S. aureus biofilms. Finally, an optimal set of 36 genes are presented as candidate genes in biofilm formation or development while throwing light on the novel roles of an acyl-CoA thioesterase enzyme and 10 hypothetical proteins in biofilms. The relevance of the identified gene set was further validated by building five different classification models using SVM, RF, kNN, NB and DT algorithms that were compared with models built from other relevant gene sets and by reviewing the functional role of 25 previously known genes in biofilm development. The study combines meta-analysis of differential expression with supervised machine learning strategies and feature selection for the first time to identify and validate a discriminatory set of genes important in biofilms of S. aureus. The functional roles of the identified genes predicted to be important in biofilms are further scrutinized and can be considered as a signature target list to develop anti-biofilm therapeutics in S. aureus.

Two way network construction and analysis of mRNA, miRNA and lncRNA reveals critical regulators and regulatory modules in cardiovascular diseases.

BACKGROUND: Cardiovascular diseases contribute to the leading cause of deaths (31%) in the world population. OBJECTIVE: The objective of this study is to compile non-coding RNA-gene interaction into a core regulatory network whose dysregulation might play an important role in disease progression. METHOD: We applied a structured approach to reconstruct the interaction network of lncRNAs, miRNAs and genes involved in cardiovascular diseases. For network construction, we used 'diseasome to interactome' and 'interactome to diseasome' approaches and developed two regulatory networks in heart disorders. In diseasome to interactome approach, starting from a disease-centric network we, expanded the data into an interaction network. However in interactome to diseasome, we used a set of guide genes, miRNAs and lncRNAs to arrive at the common diseases. The disease-centric network in combination with the interaction network will shed light on the interconnected components in a huge diseasome network implicated in heart disorders and manifested through small sub-networks while progressing. Using the above networks we created a sub-networks consisting only of hub genes, miRNAs and lncRNAs on both approaches. The dysregulation of any one of the hubs can lead to a disease condition. RESULTS: The top ranking hubs common in both the sub-networks were found to be VEGFA, MALAT1, HOTAIR, H19 and hsa-miR-15a. Our network based study reveals an entanglement of regulatory sub-network of miRNAs, lncRNAs and genes in multiple conditions. CONCLUSION: The identification of hubs in the core triple node network of elements in disease development and progression demonstrates a promising role for network based approaches in targeting critical molecules for drug development.

Automated Extraction and Visualization of Protein-Protein Interaction Networks and Beyond: A Text-Mining Protocol.

Proteins perform their functions by interacting with other proteins. Protein-protein interaction (PPI) is critical for understanding the functions of individual proteins, the mechanisms of biological processes, and the disease mechanisms. High-throughput experiments accumulated a huge number of PPIs in PubMed articles, and their extraction is possible only through automated approaches. The standard text-mining protocol includes four major tasks, namely, recognizing protein mentions, normalizing protein names and aliases to unique identifiers such as gene symbol, extracting PPIs, and visualizing the PPI network using Cytoscape or other visualization tools. Each task is challenging and has been revised over several years to improve the performance. We present a protocol based on our hybrid approaches and show the possibility of presenting each task as an independent web-based tool, NAGGNER for protein name recognition, ProNormz for protein name normalization, PPInterFinder for PPI extraction, and HPIminer for PPI network visualization. The protocol is specific to human but can be generalized to other organisms. We include KinderMiner, our most recent text-mining tool that predicts PPIs by retrieving significant co-occurring protein pairs. The algorithm is simple, easy to implement, and generalizable to other biological challenges.

RNA-seq analysis reveals resistome genes and signalling pathway associated with vancomycin-intermediate Staphylococcus aureus.

Context: Vancomycin-intermediate Staphylococcus aureus remains one of the most prevalent multidrug-resistant pathogens causing healthcare infections that are difficult to treat. Aims: This study uses a comprehensive computational analysis to systematically investigate various gene expression profiles of resistant and sensitive S. aureus strains on exposure to antibiotics. Settings and Design: The transcriptional changes leading to the development of multiple antibiotic resistance were examined by an integrative analysis of nine differential expression experiments under selected conditions of vancomycin-intermediate and -sensitive strains for four different antibiotics using publicly available RNA-Seq datasets. Materials and Methods: For each antibiotic, three experimental conditions for expression analysis were selected to identify those genes that are particularly involved in the development of resistance. The results were further scrutinised to generate a resistome that can be analysed for their role in the development or adaptation to antibiotic resistance. Results: The 99 genes in the resistome are then compiled to create a multiple drug resistome of 25 known and novel genes identified to play a part in antibiotic resistance. The inclusion of agr genes and associated virulence factors in the identified resistome supports the role of agr quorum sensing system in multiple drug resistance. In addition, enrichment analysis also identified the kyoto encyclopedia of genes and genomes (KEGG) pathways - quorum sensing and two-component system pathways - in the resistome gene set. Conclusion: Further studies on understanding the role of the identified molecular targets such as SAA6008_00181, SAA6008_01127, agrA, agrC and coa in adapting to the pressure of antibiotics at sub-inhibitory concentrations can help in learning the molecular mechanisms causing resistance to the pathogens as well as finding other potential therapeutics.