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.

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.

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.

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.

Genomic analysis of RNA-Seq and sRNA-Seq data identifies potential regulatory sRNAs and their functional roles in Staphylococcus aureus.

sRNAs are important post-transcriptional regulators in bacteria. The current study exploits potential of next-generation technology with computational analyses to develop a whole-genome sRNA-gene network for drug-resistant S. aureus by subjecting public expression-profiles to a novel analysis pipeline. Clustering and examination of the resultant global-interactome indicated a coordinated-regulation of numerous processes by various sRNAs with 9 sRNAs and 10 genes as potential hubs. 10 major sRNA-modules were annotated with various functions, among which a major module including of Rsa sRNAs was predicted to be a central regulatory unit. In addition, sRNA95, a hub molecule associated with this unit was predicted to be a vulnerable target. Finally, novel associations between transcriptional-regulators and sRNAs have been mined resulting in some insights into the association between RNAIII and RsaA. To our knowledge, this is the first study in S. aureus throwing insights into global sRNA-gene interactions and identify potential sRNAs to explore sRNA-based applications for therapeutics.

Molecular Mechanism of T-2 Toxin-Induced Cerebral Edema by Aquaporin-4 Blocking and Permeation.

The present study aimed to reveal the molecular mechanism of T-2 toxin-induced cerebral edema by aquaporin-4 (AQP4) blocking and permeation. AQP4 is a class of aquaporin channels that is mainly expressed in the brain, and its structural changes lead to life-threatening complications such as cardio-respiratory arrest, nephritis, and irreversible brain damage. We employed molecular dynamics simulation, text mining, and in vitro and in vivo analysis to study the structural and functional changes induced by the T-2 toxin on AQP4. The action of the toxin leads to disrupted permeation of water and permeation coefficients are found to be affected, from the native (2.49 ± 0.02 × 10-14 cm3/s) to toxin-treated AQP4 (7.68 ± 0.15 × 10-14 cm3/s) channels. Furthermore, the T-2 toxin forms strong electrostatic interactions at the binding site and pushes the key residues (Ala210, Phe77, Arg216, and His201) outward at the selectivity filter. Also, the role of a histidine residue in the AQP4 channel was identified by alchemical transformation and umbrella sampling methods. Alchemical free-energy perturbation energy for H201A ↔ A201H, which was found to be 3.07 ± 0.18 kJ/mol, indicates the structural importance of the histidine residue at 201. In addition, histopathology and expression of AQP4 in the Mus musculus brain tissues show the damaged and altered expression of the protein. Text mining reveals the co-occurrence of genes/proteins associated with the AQP4 expression and T-2 toxin-induced cell apoptosis, which leads to cerebral edema.

Prediction of microRNAs involved in immune system diseases through network based features.

MicroRNAs are a class of small non-coding regulatory RNA molecules that modulate the expression of several genes at post-transcriptional level and play a vital role in disease pathogenesis. Recent research shows that a range of miRNAs are involved in the regulation of immunity and its deregulation results in immune mediated diseases such as cancer, inflammation and autoimmune diseases. Computational discovery of these immune miRNAs using a set of specific features is highly desirable. In the current investigation, we present a SVM based classification system which uses a set of novel network based topological and motif features in addition to the baseline sequential and structural features to predict immune specific miRNAs from other non-immune miRNAs. The classifier was trained and tested on a balanced set of equal number of positive and negative examples to show the discriminative power of our network features. Experimental results show that our approach achieves an accuracy of 90.2% and outperforms the classification accuracy of 63.2% reported using the traditional miRNA sequential and structural features. The proposed classifier was further validated with two immune disease sub-class datasets related to multiple sclerosis microarray data and psoriasis RNA-seq data with higher accuracy. These results indicate that our classifier which uses network and motif features along with sequential and structural features will lead to significant improvement in classifying immune miRNAs and hence can be applied to identify other specific classes of miRNAs as an extensible miRNA classification system.

Stacked ensemble combined with fuzzy matching for biomedical named entity recognition of diseases.

Biomedical Named Entity Recognition (Bio-NER) is the crucial initial step in the information extraction process and a majorly focused research area in biomedical text mining. In the past years, several models and methodologies have been proposed for the recognition of semantic types related to gene, protein, chemical, drug and other biological relevant named entities. In this paper, we implemented a stacked ensemble approach combined with fuzzy matching for biomedical named entity recognition of disease names. The underlying concept of stacked generalization is to combine the outputs of base-level classifiers using a second-level meta-classifier in an ensemble. We used Conditional Random Field (CRF) as the underlying classification method that makes use of a diverse set of features, mostly based on domain specific, and are orthographic and morphologically relevant. In addition, we used fuzzy string matching to tag rare disease names from our in-house disease dictionary. For fuzzy matching, we incorporated two best fuzzy search algorithms Rabin Karp and Tuned Boyer Moore. Our proposed approach shows promised result of 94.66%, 89.12%, 84.10%, and 76.71% of F-measure while on evaluating training and testing set of both NCBI disease and BioCreative V CDR Corpora.

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks.

ProNormz--an integrated approach for human proteins and protein kinases normalization.

The task of recognizing and normalizing protein name mentions in biomedical literature is a challenging task and important for text mining applications such as protein-protein interactions, pathway reconstruction and many more. In this paper, we present ProNormz, an integrated approach for human proteins (HPs) tagging and normalization. In Homo sapiens, a greater number of biological processes are regulated by a large human gene family called protein kinases by post translational phosphorylation. Recognition and normalization of human protein kinases (HPKs) is considered to be important for the extraction of the underlying information on its regulatory mechanism from biomedical literature. ProNormz distinguishes HPKs from other HPs besides tagging and normalization. To our knowledge, ProNormz is the first normalization system available to distinguish HPKs from other HPs in addition to gene normalization task. ProNormz incorporates a specialized synonyms dictionary for human proteins and protein kinases, a set of 15 string matching rules and a disambiguation module to achieve the normalization. Experimental results on benchmark BioCreative II training and test datasets show that our integrated approach achieve a fairly good performance and outperforms more sophisticated semantic similarity and disambiguation systems presented in BioCreative II GN task. As a freely available web tool, ProNormz is useful to developers as extensible gene normalization implementation, to researchers as a standard for comparing their innovative techniques, and to biologists for normalization and categorization of HPs and HPKs mentions in biomedical literature. URL: http://www.biominingbu.org/pronormz.

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.

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.

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 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.

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.

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.

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.

miRNA-mRNA network detects hub mRNAs and cancer specific miRNAs in lung cancer.

MicroRNA expression profiles can improve classification, diagnosis, and prognostic information of malignancies, including lung cancer. In this paper, we undertook to develop a miRNA-mRNA network and uncover unique growth suppressive miRNAs in lung cancer using microarray data. The miRNA-mRNA network was developed based on a bipartite graph theory approach, and a number of miRNA-mRNA modules have been identified to mine associations between miRNAs and mRNAs. From the network, we identified totally 29 protective miRNA-mRNA regulatory modules, since we restricted our search to protective miRNAs. Subsequently we analyzed the pathways for the target genes in the protective miRNA-mRNA modules using Pathway-Express. The miRNA-mRNA network efficiently detects hub mRNAs deregulated by the protective miRNAs and identifies cancer specific miRNAs in lung cancer. From the pathway analysis results, the ECM receptor pathway, Focal adhesion pathway and cell adhesion molecules pathway seem to be more interesting to investigate, since these pathways were related to all the ten protective miRNAs. Furthermore, protective miRNA target analysis revealed that genes VCAN, SIL, CD44 and MMP14 were found to have an important role in these pathways. Hence, it was inferred that these genes can be important putative targets for those protective miRNAs. A greater understanding of the mechanisms regulating VCAN, SIL, CD44 and MMP14 expression and activity will assist in the development of specific inhibitors of cancer cell metastasis. Thus these observations are expected to have an intense implication in cancer and may be useful for further research.

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.