In the present study, we have constructed an interaction network of 29 antibiotic resistant genes along with 777 interactions in E. coli O157:H7. Gene ontology analysis reveals that 94, 89 and 67 genes have roles in the cellular process, biological process and molecular function respectively. Gene complexes related to tripartite efflux pumps mdtEF-tolC and ABC family efflux pump macAB-tolC play key roles in multidrug efflux systems. It is noteworthy to mention that, 19 genes are involved in multi-efflux pumps and they play a significant role in multidrug resistance (MDR); while 18 genes are vital for fatty acid synthesis. Interestingly, we found that the four genes arnABCD are involved in both MDR and in fatty acid synthesis. Hence these genes could be targeted for new drug discovery. On the whole, our results provide a detailed understanding of the mode of MDR mechanisms in E.coli O157:H7.

Sudha Ramaiah

Department of Bio-Sciences

School of Bio Sciences and Technology

Vellore Campus

Miryala S.K

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Genomics

The antimicrobial resistance (AMR) exhibited against broad spectrum and new generation antibiotics used for Pseudomonas infections is a major threat and renders the treatment ineffective. In our present study, we have used a computational approach to understand various drug resistance mechanisms which contribute to Multi-Drug Resistance (MDR) in P. aeruginosa. The interaction network of 60 AMR genes along with the 337 functional interactions was analyzed. Functional enrichment analysis of AMR genes has shown that the genes in the network are mainly associated with efflux pump mechanisms, alginate biosynthesis, biofilm formation, and ampC beta-lactamase biosynthesis. Interestingly, the genes phoP, phoQ, and cat genes are observed to have roles in more than one drug-resistant mechanism. The genes phoP and phoQ apart from their role in two-component regulatory systems also play major roles in multidrug efflux pumps and alteration in drug target. The gene cat involves in alteration of drug target and enzymatic inactivation. The interaction network analysis has shown that the AMR genes oprJ, oprM, oprN, ampC, gyrA, mexA, oprD, mexB and nfxB have higher number of direct interactors and they are considered as the hub nodes in the network and these genes can be used as potential drug targets for developing new drugs. The results from our study will be helpful in better understanding of the antibiotic resistance mechanisms in P. aeruginosa. The gene targets reported, can be used for new drug discovery against Pseudomonas infections. © 2019 Elsevier Ltd

Microbial Pathogenesis

Systems biology studies in Pseudomonas aeruginosa PA01 to understand their role in biofilm formation and multidrug efflux pumps

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. © 2017

Gene

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

Staphylococcus aureus (S. aureus) is an emerging concern in hospital settings as it causes serious human infections. The multidrug resistance (MDR) in S. aureus is a complicated problem that is difficult to overcome due to the presence of numerous antibiotic resistance genes and it exhibit resistance to most of the currently available antibiotics. Presently, the resistance mechanisms of these genes/proteins are not completely understood. Therefore, identifying and understanding the functional relationship between the antibiotic resistant genes and their associated proteins might provide necessary information on resistance mechanisms and thereby help in designing successful drugs to combat the antibiotic resistance. In this study, we propose a model based on protein/gene network to identify genes/proteins associated with drug resistance in S. aureus. We filtered 50 functional partners in NorA, aacA-aphD (aac6ie), aad9ib (ant), aadd (knt), baca (uppP), bl2a_pc (blaZ), ble, ermA, SAV0052 (ermb), ermc, fosB, mecA (mecI), mecR (mecr1), mepA, msrA1, qacA, vraR (str), tet38 and tetM while 40 functional partners are identified in tet and aphA-3 (aph3iiia). The average shortest path length and betweenness centrality of functional partners in the clusters are calculated and they are functionally enriched with the Gene Ontology (GO) terms with a p-value cut-off ≤0.05. Interestingly, the constructed network reveals many associated antibiotic resistant genes and proteins and their role in resistance mechanisms. Thus, our results might provide a better understanding of the molecular mechanisms of action and their mode of drug resistance that will be useful for researchers exploring in the field of antibiotic resistance mechanisms.

Gene network analysis reveals the association of important functional partners involved in antibiotic resistance: A report on an important pathogenic bacterium Staphylococcus aureus

AmpC β-lactamase is a cephalosporinase, which exhibits resistance against all existing β-lactam antibiotics except carbapenems. Their occurrence in many bacterial pathogens poses a threat to public health and is a growing concern in the medical world. The ampC gene is highly inducible in the presence of β-lactam antibiotics and can be expressed in high levels due to mutation. This inducible expression is regulated by several functional genes. Several studies on functional relationship of these genes and its resistance mechanisms are carried out but it still lacks comprehensible evidences. Thus, in our current study, we used computational gene networks to analyze ampC gene. Based on its interaction type, co-expression, Gene Ontology, and text mining, a functional interaction network is constructed. Around 247 functional genes in 15 different bacterial genus have a functional association with ampC gene. It is predicted that 19.8% ampD, 13.3% frdD, 8.5% gcvA, 2.4% ampR, and 55.7% of other functional partners are associated with ampC gene. Our present study provides a glimpse about the functional gene network of ampC gene and also provides the integrated evidence for ampC gene in regulating the β-lactamase production and its role in antibiotic resistance.

Cell Biochemistry and Biophysics

Gene and Protein Network Analysis of AmpC β Lactamase

In the present study, we have analyzed functional gene interactions of adiponectin gene (adipoq). The key role of adipoq is in regulating energy homeostasis and it functions as a novel signaling molecule for adipose tissue. Modules of highly inter-connected genes in disease-specific adipoq network are derived by integrating gene function and protein interaction data. Among twenty genes in adipoq web, adipoq is effectively conjoined with two genes: Adiponectin receptor 2 (adipor2) and cadherin 13 (cdh13). The functional analysis is done via ontological briefing and candidate disease identification. We observed that the highly efficient-interlinked genes connected with adipoq are adipor2 and cdh13. Interestingly, the ontological aspect of adipor2 and cdh13 in the adipoq network reveal the fact that adipoq and adipor2 are involved mostly in glucose and lipid metabolic processes. The gene cdh13 indulge in cell adhesion process with adipoq and adipor2. Our computational gene web analysis also predicts potential candidate disease recognition, thus indicating the involvement of adipoq, adipor2, and cdh13 with not only with obesity but also with breast cancer, leukemia, renal cancer, lung cancer, and cervical cancer. The current study provides researchers a comprehensible layout of adipoq network, its functional strategies and candidate disease approach associated with adipoq network. © 2014, Springer Science+Business Media New York.

Revealing the Strong Functional Association of adipor2 and cdh13 with adipoq: A Gene Network Study

Network study on genes and proteins offers functional basics of the complexity of gene and protein, and its interacting partners. The gene fatty acid-binding protein 4 (fabp4) is found to be highly expressed in adipose tissue, and is one of the most abundant proteins in mature adipocytes. Our investigations on functional modules of fabp4 provide useful information on the functional genes interacting with fabp4, their biochemical properties and their regulatory functions. The present study shows that there are eight set of candidate genes: acp1, ext2, insr, lipe, ostf1, sncg, usp15, and vim that are strongly and functionally linked up with fabp4. Gene ontological analysis of network modules of fabp4 provides an explicit idea on the functional aspect of fabp4 and its interacting nodes. The hierarchal mapping on gene ontology indicates gene specific processes and functions as well as their compartmentalization in tissues. The fabp4 along with its interacting genes are involved in lipid metabolic activity and are integrated in multi-cellular processes of tissues and organs. They also have important protein/enzyme binding activity. Our study elucidated disease-associated nsSNP prediction for fabp4 and it is interesting to note that there are four rsID׳s (rs1051231, rs3204631, rs140925685 and rs141169989) with disease allelic variation (T104P, T126P, G27D and G90V respectively). On the whole, our gene network analysis presents a clear insight about the interactions and functions associated with fabp4 gene network.

Journal	Data powered by TypesetGenomics
Publisher	Data powered by TypesetElsevier BV
ISSN	0888-7543
Open Access	0