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Gene network analysis reveals the association of important functional partners involved in antibiotic resistance: A report on an important pathogenic bacterium Staphylococcus aureus
Published in Elsevier BV
2016
PMID: 26342962
Volume: 575
   
Issue: 2
Pages: 253 - 263
Abstract
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.
About the journal
JournalData powered by TypesetGene
PublisherData powered by TypesetElsevier BV
ISSN0378-1119
Open Access0