Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein

Quorum sensing: An imperative longevity weapon in bacteria

Bacterial cells exhibit a complex pattern of co-operative behaviour as shown by their capacity to communicate amongst each other. Quorum sensing (QS) is a generic term used for bacterial cell-to-cell communication which secures survival of its species. Many QS bacteria produce and release autoinducers like acyl-homoserine lactone-signaling molecules to regulate cell population density. Different species of bacteria utilize different QS molecules to regulate its gene expression. A free-living marine bacterium, Vibrio harveyi, uses two QS system to control the density-dependent expression of bioluminescence (lux), commonly classified as sensor and autoinducer system. In Pseudomonas aeruginosa, QS not only controls virulence factor production but also biofilm formation. It is comprised two hierarchically organised systems, each consisting of an autoinducer synthetase (LasI/RhlI) and a corresponding regulator protein (LasR/RhlR). Biofilms produced by Pseudomonas, under control of QS, are ubiquitous in nature and contribute towards colonizations in patients of cystic fibrosis. Other organisms like Haemophilus influenzae and Streptococcus also utilize QS mechanism to control virulence in otitis and endocarditic decay. Overall, QS plays a major role in controlling bacterial economy. It is a simple, practical and effective mechanism of production and control. If the concentration of enzyme is critical, bacteria can sense it and perform a prompt activation or repression of certain target genes for controlling its environment. This review focuses on the QS mechanisms and their role in the survival of few important bacterial species

Computational Identification of Indispensable Virulence Proteins of Salmonella Typhi CT18

Typhoid infections have become an alarming concern with the increase of multidrug resistant strains of Salmonella serovars. The new pathogenic Gram-negative strains are resistant to most antibiotics such as chloramphenicol, ampicillin, trimethoprim, ciprofloxacin and even co-trimoxazole and their derivatives thereby causing numerous outbreaks in the Indian subcontinent, Southeast Asian and African countries. Conventional and modern methods of typing had been adopted to differentiate outbreak strains. However, identifying the most indispensable proteins from the complete set of proteins of the whole genome of Salmonella sp., comprising the Salmonella pathogenicity islands (SPI) responsible for virulence, has remained an ever challenging task. We have adopted a network-based method to figure out, albeit theoretically, the most significant proteins which might be involved in the resistance to antibiotics of the Salmonella sp. An understanding of the above will provide insight into conditions that are encountered by this pathogen during the course of infection, which will further contribute in identifying new targets for antimicrobial agents