In silico Identification of the Indispensable Quorum Sensing Proteins of Multidrug Resistant Proteus mirabilis

Abstract

Catheter-associated urinary tract infections (CAUTI) is an alarming hospital based disease with the increase of multidrug resistance (MDR) strains of Proteus mirabilis. Cases of long term hospitalized patients with multiple episodes of antibiotic treatments along with urinary tract obstruction and/or undergoing catheterization have been reported to be associated with CAUTI. The cases are complicated due to the opportunist approach of the pathogen having robust swimming and swarming capability. The latter giving rise to biofilms and probably inducible through autoinducers make the scenario quite complex. High prevalence of long-term hospital based CAUTI for patients along with moderate percentage of morbidity, cropping from ignorance about drug usage and failure to cure due to MDR, necessitates an immediate intervention strategy effective enough to combat the deadly disease. Several reports and reviews focus on revealing the important genes and proteins, essential to tackle CAUTI caused by P. mirabilis. Despite longitudinal countrywide studies and methodical strategies to circumvent the issues, effective means of unearthing the most indispensable proteins to target for therapeutic uses have been meager. Here, we report a strategic approach for identifying the most indispensable proteins from the genome of P. mirabilis strain HI4320, besides comparing the interactomes comprising the autoinducer-2 (AI-2) biosynthetic pathway along with other proteins involved in biofilm formation and responsible for virulence. Essentially, we have adopted a theoretical network model based approach to construct a set of small protein interaction networks (SPINs) along with the whole genome (GPIN) to computationally identify the crucial proteins involved in the phenomenon of quorum sensing (QS) and biofilm formation and thus, could be therapeutically targeted to fight out the MDR threats to antibiotics of P. mirabilis. Our approach utilizes the functional modularity coupled with k-core analysis and centrality scores of eigenvector as a measure to address the pressing issues.

Regulatory Mechanisms of the LuxS/AI-2 System and Bacterial Resistance

“…Further research in this area will reveal in depth the role of the LuxS/AI-2 system in controlling the effects of these efflux pumps on bacterial resistance (33). The ability of the QS system to regulate MDR efflux pumps represents a potential target for antibiotic resistance (36).…”

Techniques of Transcriptomic and Proteomic Data Integration

“…In this methodology, the regular utilitarian settings are separated through the investigation of both transcriptomic and proteomic datasets fair and square of protein communication organizations. This methodology was distributed in 2010 by Paul et al [56][57][58][59][60][61][62][63][64][65][66][67][68][69][70] which is examined in segment 5.2. Creators of this distribution likewise produced omicsNET for discovering reliance between highlights of proteomics and transcriptomics.…”

Transcriptomic data for analyzing global gene expression patterns in Methicillin-resistance Staphylococcus aureus in response to spermine and oxacillin stress

“…All the microarray data were analyzed using library ‘Affy’ package [5] on R platform [6] . Heat maps were generated using library ‘gplots’ [7] . Heat maps were developed on Z scores, which were calculated by heatmap.2 function of gplots [ Z score = (raw intensity − average)/standard deviation].…”