Human Gut Microbes Associated with Systolic Blood Pressure.

Emerging studies have revealed a strong link between the gut microbiome and several human diseases. Since human gut microbiome mirrors variations in lifestyle and environment, whether associations between disease conditions and gut microbiome are consistent across populations-particularly in communities practicing traditional subsistence strategies whose microbiomes differ markedly from industrialists-remains unknown. Cardiovascular diseases are the leading cause of mortality in India affecting 55 million people, and high blood pressure is one of the primary risk factors for cardiovascular diseases. We examined associations between gut microbiome and blood pressure along with 14 other variables associated with lifestyle, dietary habits, disease conditions, and clinical blood markers in the three Assamese populations. Our analysis reveals a robust link between the gut microbiome diversity and composition and systolic blood pressure. Moreover, several genera previously associated with hypertension in non-Indian populations were also associated with systolic blood pressure in this cohort and these genera were predictors of elevated blood pressure in these populations. These findings confer opportunities to design personalized, preventative, and targeted interventions harnessing the gut microbiome to tackle the burden of cardiovascular diseases in India.

Taxonomy-aware feature engineering for microbiome classification.

BACKGROUND: What is a healthy microbiome? The pursuit of this and many related questions, especially in light of the recently recognized microbial component in a wide range of diseases has sparked a surge in metagenomic studies. They are often not simply attributable to a single pathogen but rather are the result of complex ecological processes. Relatedly, the increasing DNA sequencing depth and number of samples in metagenomic case-control studies enabled the applicability of powerful statistical methods, e.g. Machine Learning approaches. For the latter, the feature space is typically shaped by the relative abundances of operational taxonomic units, as determined by cost-effective phylogenetic marker gene profiles. While a substantial body of microbiome/microbiota research involves unsupervised and supervised Machine Learning, very little attention has been put on feature selection and engineering. RESULTS: We here propose the first algorithm to exploit phylogenetic hierarchy (i.e. an all-encompassing taxonomy) in feature engineering for microbiota classification. The rationale is to exploit the often mono- or oligophyletic distribution of relevant (but hidden) traits by virtue of taxonomic abstraction. The algorithm is embedded in a comprehensive microbiota classification pipeline, which we applied to a diverse range of datasets, distinguishing healthy from diseased microbiota samples. CONCLUSION: We demonstrate substantial improvements over the state-of-the-art microbiota classification tools in terms of classification accuracy, regardless of the actual Machine Learning technique while using drastically reduced feature spaces. Moreover, generalized features bear great explanatory value: they provide a concise description of conditions and thus help to provide pathophysiological insights. Indeed, the automatically and reproducibly derived features are consistent with previously published domain expert analyses.

Global transcriptome analysis of salt acclimated Prochlorococcus AS9601.

Molecular processes leading to salt stress acclimation in the model cyanobacterium Prochlorococcus are not known. To address this, we used RNA sequencing (RNAseq) to compare the global transcriptome of two exponential-phase populations of Prochlorococcus AS9601 cells - acclimated to high salt (5%, w/v) and normal seawater salt (3.8%, w/v). Experiments showed that salt acclimated cells exhibit slower growth rates with a doubling time almost twice as controls. Approximately 1/3 of the genome was found to be differentially expressed (p-value <0.05), but a considerably large number of these genes are "hypothetical proteins" with unknown function. Transcript abundance were higher for genes involved in respiratory electron flow, carbon fixation, osmolyte/compatible solute biosynthesis and inorganic ion transport. Many of the highly expressed genes are 'high light inducible proteins' believed to be part of the general Prochlorococcus stress response. Transcript abundance were lower for genes involved in photosynthetic electron transport and cell division. The relative reduction in transcript abundance for genes encoding proteins containing heme groups and iron transporters suggests cellular iron requirements in salt acclimated cells maybe lower. The results presented here provide the first glimpse into global gene expression changes in Prochlorococcus cells due to salt stress.