DETECTION AND PREVALENCE OF EFFLUX PUMP-MEDIATED DRUG RESISTANCE IN CLINICAL ISOLATES OF MULTIDRUG-RESISTANT GRAM-NEGATIVE BACTERIA FROM NORTH KERALA, INDIA

Objectives: The present study was carried out to detect the prevalence of efflux pump-mediated drug resistance in clinical isolates of multidrugresistant(MDR) Gram-negativebacteriaisolatedfromNorth Kerala.Methods: Clinical isolates (n = 123) of MDR Gram-negative bacteria were collected from various clinical laboratories in North Kerala, and their effluxmediateddrug resistancewasdetectedbytwosimple phenotypic assays-ethidiumbromide(EB)-agarcartwheelmethod andefflux pump inhibitor(EPI)-basedmicroplateassay,employingphenylalanine-arginineβ-naphthylamideas inhibitor.Results: The 123 Gram-negative MDR strains tested comprised Escherichia coli, Pseudomonas aeruginosa, Acinetobacter spp., and Klebsiella spp. TheEB-agar cartwheel method of screening revealed efflux activity in 20% (n=25) of the strains with representatives from all 4 genera. The efflux activitywas revealed at a minimum concentration of EB at 1 mg/l. P. aeruginosa strains showed the highest activity, many folds higher up to a concentrationof 2.5 mg/l. The confirmatory EPI-based microplate assay showed efflux activity only in 15% (n=18) strains with 6% (n=7) active against more thanone antibiotic. Efflux pump-mediated drug resistance was found to be most prevalent in P. aeruginosa (34.8%, n=8 out of 23), followed by that in E. coli(18.6%, n=8 out of 43), Acinetobacter spp. (9%, n=1out of 11), and Klebsiella spp. (2%, n=1 out of 46).Conclusion: This study reports on the emergence of efflux pump-based multidrug-resistance in North Kerala. Our results showed that 15% of drugresistance in Gram-negative MDR strains is attributable to efflux-related mechanisms, thereby emphasizing the need for inclusion of efflux-relatedtests in the diagnostic regimen for MDR clinical bacteria.Keywords: Gram-negative bacteria, Multidrug-resistance, Efflux pumps, Ethidium bromide, Efflux pump-inhibitor

Prediction of nuclear proteins using SVM and HMM models

<p>Abstract</p> <p>Background</p> <p>The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.</p> <p>Results</p> <p>All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM) based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC) of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC) and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM) based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in <it>Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster</it>, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins <url>http://www.imtech.res.in/raghava/nppred/</url>.</p> <p>Conclusion</p> <p>This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predicting nuclear proteins. The performance of the method improved further by combining both approaches together.</p

Identifying sources, pathways and risk drivers in ecosystems of Japanese Encephalitis in an epidemic-prone north Indian district

Japanese Encephalitis (JE) has caused repeated outbreaks in endemic pockets of India. This study was conducted in Kushinagar, a highly endemic district, to understand the human-animal-ecosystem interactions, and the drivers that influence disease transmission. Utilizing the ecosystems approach, a cross-sectional, descriptive study, employing mixed methods design was employed. Four villages (two with pig-rearing and two without) were randomly selected from a high, a medium and a low burden (based on case counts) block of Kushinagar. Children, pigs and vectors were sampled from these villages. A qualitative arm was incorporated to explain the findings from the quantitative surveys. All human serum samples were screened for JE-specific IgM using MAC ELISA and negative samples for JE RNA by rRT-PCR in peripheral blood mononuclear cells. In pigs, IgG ELISA and rRT-PCR for viral RNA were used. Of the 242 children tested, 24 tested positive by either rRT-PCR or MAC ELISA; in pigs, 38 out of the 51 pigs were positive. Of the known vectors, Culex vishnui was most commonly isolated across all biotopes. Analysis of 15 blood meals revealed human blood in 10 samples. Univariable analysis showed that gender, religion, lack of indoor residual spraying of insecticides in the past year, indoor vector density (all species), and not being vaccinated against JE in children were significantly associated with JE positivity. In multivariate analysis, only male gender remained as a significant risk factor. Based on previous estimates of symptomatic: asymptomatic cases of JE, we estimate that there should have been 618 cases from Kushinagar, although only 139 were reported. Vaccination of children and vector control measures emerged as major control activities; they had very poor coverage in the studied villages. In addition, lack of awareness about the cause of JE, lack of faith in the conventional medical healthcare system and multiple referral levels causing delay in diagnosis and treatment emerged as factors likely to result in adverse clinical outcomes

Sarcopenia and Cardiovascular Diseases

Sarcopenia is the loss of muscle strength, mass, and function, which is often exacerbated by chronic comorbidities including cardiovascular diseases, chronic kidney disease, and cancer. Sarcopenia is associated with faster progression of cardiovascular diseases and higher risk of mortality, falls, and reduced quality of life, particularly among older adults. Although the pathophysiologic mechanisms are complex, the broad underlying cause of sarcopenia includes an imbalance between anabolic and catabolic muscle homeostasis with or without neuronal degeneration. The intrinsic molecular mechanisms of aging, chronic illness, malnutrition, and immobility are associated with the development of sarcopenia. Screening and testing for sarcopenia may be particularly important among those with chronic disease states. Early recognition of sarcopenia is important because it can provide an opportunity for interventions to reverse or delay the progression of muscle disorder, which may ultimately impact cardiovascular outcomes. Relying on body mass index is not useful for screening because many patients will have sarcopenic obesity, a particularly important phenotype among older cardiac patients. In this review, we aimed to: (1) provide a definition of sarcopenia within the context of muscle wasting disorders; (2) summarize the associations between sarcopenia and different cardiovascular diseases; (3) highlight an approach for a diagnostic evaluation; (4) discuss management strategies for sarcopenia; and (5) outline key gaps in knowledge with implications for the future of the field

Light-driven chloride transport kinetics of halorhodopsin

Despite growing interest in light-driven ion pumps for use in optogenetics, current estimates of their transport rates span two orders of magnitude due to challenges in measuring slow transport processes and determining protein concentration and/or orientation in membranes in vitro. In this study, we report, to our knowledge, the first direct quantitative measurement of light-driven Cl transport rates of the anion pump halorohodopsin from Natronomonas pharaonis (NpHR). We used light-interfaced voltage clamp measurements on NpHR-expressing oocytes to obtain a transport rate of 219 (± 98) Cl /protein/s for a photon flux of 630 photons/protein/s. The measurement is consistent with the literature-reported quantum efficiency of ∼30% for NpHR, i.e., 0.3 isomerizations per photon absorbed. To reconcile our measurements with an earlier-reported 20 ms rate-limiting step, or 35 turnovers/protein/s, we conducted, to our knowledge, novel consecutive single-turnover flash experiments that demonstrate that under continuous illumination, NpHR bypasses this step in the photocycle

Policy of foreign direct investment liberalisation in India: implications for retail sector

This study has analysed the impact of liberalisation of Indian economy and FDI policy on the retail sector since its implementation in the 1990s. It also further analyses sub-categories by investigating its impact on the unorganised retail sector and the flow of FDI in single-brand retail and multi-brand retail sectors. A comprehensive and critical review of the existing evidence on the subject was carried out, and descriptive statistical analysis of data from 1991 to 2013 was performed which leads to conclude that the policy of FDI liberalisation has proved to provide diversification and sustainable development to the Indian economy and specifically retail sector which is considered to be one of the significant pillars of economy. Furthermore, for continuous growth of the economy, it seems vital to encourage more investment in other sectors by liberalising the restrictive policies

TNF-α promoter polymorphism: a factor contributing to the different immunological and clinical phenotypes in Japanese encephalitis

<p>Abstract</p> <p>Background</p> <p>More than three billion populations are living under the threat of Japanese encephalitis in South East Asian (SEA) countries including India. The pathogenesis of this disease is not clearly understood and is probably attributed to genomic variations in viral strains as well as the host genetic makeup. The present study is to determine the role of polymorphism of TNF-alpha promoter regions at positions -238G/A, -308G/A, -857C/T and -863C/A in the severity of Japanese encephalitis patients.</p> <p>Methods</p> <p>Total of 142 patients including 66 encephalitis case (IgM/RT-PCR positive), 16 fever cases (IgM positive) without encephalitis and 60 apparently healthy individuals (IgG positive) were included in the study. Polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) using site specific restriction enzymes were implemented for polymorphism study of TNF alpha promoter.</p> <p>Results</p> <p>Following the analysis of the digestion patterns of four polymorphic sites of the TNF- alpha promoter region, a significant association was observed between the allele -308A and -863C with the patients of Japanese encephalitis.</p> <p>Conclusions</p> <p>TNF- alpha 308 G/A has been shown to be associated with elevated TNF- alpha transcriptional activity. On the other hand, polymorphism at position -863C/A in the promoter region has been reported to be associated with reduced TNF- alpha promoter activity and lower plasma TNF levels. As per the literature search, this is the first study to identify the role of TNF- alpha promoter in JE infection. Our results show that subjects with - 308A and -863C alleles are more vulnerable to the severe form of JE infection.</p

CyclinPred: A SVM-Based Method for Predicting Cyclin Protein Sequences

Functional annotation of protein sequences with low similarity to well characterized protein sequences is a major challenge of computational biology in the post genomic era. The cyclin protein family is once such important family of proteins which consists of sequences with low sequence similarity making discovery of novel cyclins and establishing orthologous relationships amongst the cyclins, a difficult task. The currently identified cyclin motifs and cyclin associated domains do not represent all of the identified and characterized cyclin sequences. We describe a Support Vector Machine (SVM) based classifier, CyclinPred, which can predict cyclin sequences with high efficiency. The SVM classifier was trained with features of selected cyclin and non cyclin protein sequences. The training features of the protein sequences include amino acid composition, dipeptide composition, secondary structure composition and PSI-BLAST generated Position Specific Scoring Matrix (PSSM) profiles. Results obtained from Leave-One-Out cross validation or jackknife test, self consistency and holdout tests prove that the SVM classifier trained with features of PSSM profile was more accurate than the classifiers based on either of the other features alone or hybrids of these features. A cyclin prediction server- CyclinPred has been setup based on SVM model trained with PSSM profiles. CyclinPred prediction results prove that the method may be used as a cyclin prediction tool, complementing conventional cyclin prediction methods