Whole genome expression and biochemical correlates of extreme constitutional types defined in Ayurveda

<p>Abstract</p> <p>Background</p> <p>Ayurveda is an ancient system of personalized medicine documented and practiced in India since 1500 B.C. According to this system an individual's basic constitution to a large extent determines predisposition and prognosis to diseases as well as therapy and life-style regime. Ayurveda describes seven broad constitution types (<it>Prakriti</it>s) each with a varying degree of predisposition to different diseases. Amongst these, three most contrasting types, <it>Vata</it>, <it>Pitta</it>, <it>Kapha</it>, are the most vulnerable to diseases. In the realm of modern predictive medicine, efforts are being directed towards capturing disease phenotypes with greater precision for successful identification of markers for prospective disease conditions. In this study, we explore whether the different constitution types as described in Ayurveda has molecular correlates.</p> <p>Methods</p> <p>Normal individuals of the three most contrasting constitutional types were identified following phenotyping criteria described in Ayurveda in Indian population of Indo-European origin. The peripheral blood samples of these individuals were analysed for genome wide expression levels, biochemical and hematological parameters. Gene Ontology (GO) and pathway based analysis was carried out on differentially expressed genes to explore if there were significant enrichments of functional categories among <it>Prakriti </it>types.</p> <p>Results</p> <p>Individuals from the three most contrasting constitutional types exhibit striking differences with respect to biochemical and hematological parameters and at genome wide expression levels. Biochemical profiles like liver function tests, lipid profiles, and hematological parameters like haemoglobin exhibited differences between <it>Prakriti </it>types. Functional categories of genes showing differential expression among <it>Prakriti </it>types were significantly enriched in core biological processes like transport, regulation of cyclin dependent protein kinase activity, immune response and regulation of blood coagulation. A significant enrichment of housekeeping, disease related and hub genes were observed in these extreme constitution types.</p> <p>Conclusion</p> <p>Ayurveda based method of phenotypic classification of extreme constitutional types allows us to uncover genes that may contribute to system level differences in normal individuals which could lead to differential disease predisposition. This is a first attempt towards unraveling the clinical phenotyping principle of a traditional system of medicine in terms of modern biology. An integration of Ayurveda with genomics holds potential and promise for future predictive medicine.</p

NLRP3 Inflammasome is a Target for Development of Broad-Spectrum Anti-Infective Drugs

We describe the molecular mode of action and pharmacodynamics of a new molecular entity (NME) that induces the NLRP3 inflammasome-mediated innate immune response. This innate response reduces the pathogen load in an experimentally induced methicillin-resistant Staphylococcos aureus infection, enhances survival in an experimentally induced Gram-negative bacteremia, and overrides the escape mechanism of an obligate intracellular pathogen, viz. Chlamydia pneumoniae. Furthermore, the NME is more effective than standard-of-care antibiotic therapy in a clinically established multifactorial bacterial infection. Analysis of transcriptional regulation of inflammasome signaling genes and innate/adaptive immune genes revealed consistent and significant host changes responsible for the improved outcomes in these infections. These studies pave the way for the development of first-in-class drugs that enhance inflammasome-mediated pathogen clearance and identify the NLRP3 inflammasome as a drug target to address the global problem of emerging new infectious diseases and the reemergence of old diseases in an antibiotic-resistant form

The Stroke RiskometerTM App: Validation of a data collection tool and stroke risk predictor

Background: The greatest potential to reduce the burden of stroke is by primary prevention of first-ever stroke, which constitutes three quarters of all stroke. In addition to population-wide prevention strategies (the 'mass' approach), the 'high risk' approach aims to identify individuals at risk of stroke and to modify their risk factors, and risk, accordingly. Current methods of assessing and modifying stroke risk are difficult to access and implement by the general population, amongst whom most future strokes will arise. To help reduce the burden of stroke on individuals and the population a new app, the Stroke RiskometerTM, has been developed. We aim to explore the validity of the app for predicting the risk of stroke compared with current best methods. Methods: 752 stroke outcomes from a sample of 9501 individuals across three countries (New Zealand, Russia and the Netherlands) were utilized to investigate the performance of a novel stroke risk prediction tool algorithm (Stroke RiskometerTM) compared with two established stroke risk score prediction algorithms (Framingham Stroke Risk Score [FSRS] and QStroke). We calculated the receiver operating characteristics (ROC) curves and area under the ROC curve (AUROC) with 95% confidence intervals, Harrels C-statistic and D-statistics for measure of discrimination, R2 statistics to indicate level of variability accounted for by each prediction algorithm, the Hosmer-Lemeshow statistic for calibration, and the sensitivity and specificity of each algorithm. Results: The Stroke RiskometerTM performed well against the FSRS five-year AUROC for both males (FSRS=75·0% (95% CI 72·3%-77·6%), Stroke RiskometerTM=74·0(95% CI 71·3%-76·7%) and females [FSRS=70·3% (95% CI 67·9%-72·8%, Stroke RiskometerTM=71·5% (95% CI 69·0%-73·9%)], and better than QStroke [males - 59·7% (95% CI 57·3%-62·0%) and comparable to females=71·1% (95% CI 69·0%-73·1%)]. Discriminative ability of all algorithms was low (C-statistic ranging from 0·51-0·56, D-statistic ranging from 0·01-0·12). Hosmer-Lemeshow illustrated that all of the predicted risk scores were not well calibrated with the observed event data (P<0·006). Conclusions: The Stroke RiskometerTM is comparable in performance for stroke prediction with FSRS and QStroke. All three algorithms performed equally poorly in predicting stroke events. The Stroke RiskometerTM will be continually developed and validated to address the need to improve the current stroke risk scoring systems to more accurately predict stroke, particularly by identifying robust ethnic/race ethnicity group and country specific risk factors. International Journal of Strok