Gut microbiome contributes to impairment of immunity in pulmonary tuberculosis patients by alteration of butyrate and propionate producers.

Tuberculosis (TB) is primarily associated with decline in immune health status. As gut microbiome (GM) is implicated in the regulation of host immunity and metabolism, here we investigate GM alteration in TB patients by 16S rRNA gene and whole-genome shotgun sequencing. The study group constituted of patients with pulmonary TB and their healthy household contacts as controls (HCs). Significant alteration of microbial taxonomic and functional capacity was observed in patients with active TB as compared to the HCs. We observed that Prevotella and Bifidobacterium abundance were associated with HCs, whereas butyrate and propionate-producing bacteria like Faecalibacterium, Roseburia, Eubacterium and Phascolarctobacterium were significantly enriched in TB patients. Functional analysis showed reduced biosynthesis of vitamins and amino acids in favour of enriched metabolism of butyrate and propionate in TB subjects. The TB subjects were also investigated during the course of treatment, to analyse the variation of GM. Although perturbation in microbial composition was still evident after a month's administration of anti-TB drugs, significant changes were observed in metagenome gene pool that pointed towards recovery in functional capacity. Therefore, the findings from this pilot study suggest that microbial dysbiosis may contribute to pathophysiology of TB by enhancing the anti-inflammatory milieu in the host.

CARDIO-PRED: an in silico tool for predicting cardiovascular-disorder associated proteins.

Interactions between proteins largely govern cellular processes and this has led to numerous efforts culminating in enormous information related to the proteins, their interactions and the function which is determined by their interactions. The main concern of the present study is to present interface analysis of cardiovascular-disorder (CVD) related proteins to shed lights on details of interactions and to emphasize the importance of using structures in network studies. This study combines the network-centred approach with three dimensional studies to comprehend the fundamentals of biology. Interface properties were used as descriptors to classify the CVD associated proteins and non-CVD associated proteins. Machine learning algorithm was used to generate a classifier based on the training set which was then used to predict potential CVD related proteins from a set of polymorphic proteins which are not known to be involved in any disease. Among several classifying algorithms applied to generate models, best performance was achieved using Random Forest with an accuracy of 69.5 %. The tool named CARDIO-PRED, based on the prediction model is present at http://www.genomeinformatics.dce.edu/CARDIO-PRED/. The predicted CVD related proteins may not be the causing factor of particular disease but can be involved in pathways and reactions yet unknown to us thus permitting a more rational analysis of disease mechanism. Study of their interactions with other proteins can significantly improve our understanding of the molecular mechanism of diseases.