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The extraction of genomic DNA is the crucial first step in large-scale epidemiological studies. Though there are many popular DNA isolation methods from human whole blood, only a few reports have compared their efficiencies using both end-point and real-time PCR assays. Genomic DNA was extracted from coronary artery disease patients using solution-based conventional protocols such as the phenol-chloroform/proteinase-K method and a non-phenolic non-enzymatic Rapid-Method, which were evaluated and compared vis-a-vis a commercially available silica column-based Blood DNA isolation kit. The appropriate method for efficiently extracting relatively pure DNA was assessed based on the total DNA yield, concentration, purity ratios (A/A and A/A), spectral profile and agarose gel electrophoresis analysis. The quality of the isolated DNA was further analysed for PCR inhibition using a murine specific ATP1A3 qPCR assay and mtDNA/Y-chromosome ratio determination assay. The suitability of the extracted DNA for downstream applications such as end-point SNP genotyping, was tested using PCR-RFLP analysis of the AGTR1-1166A>C variant, a mirSNP having pharmacogenetic relevance in cardiovascular diseases. Compared to the traditional phenol-chloroform/proteinase-K method, our results indicated the Rapid-Method to be a more suitable protocol for genomic DNA extraction from human whole blood in terms of DNA quantity, quality, safety, processing time and cost. The Rapid-Method, which is based on a simple salting-out procedure, is not only safe and cost-effective, but also has the added advantage of being scaled up to process variable sample volumes, thus enabling it to be applied in large-scale epidemiological studies.

Helicobacter pylori infection in stomach leads to gastric cancer, gastric ulcer, and duodenal ulcer. More than 1 million people die each year due to these diseases, but why most H. pylori-infected individuals remain asymptomatic while a certain proportion develops such severe gastric diseases remained an enigma. Several studies indicated that gastric and intestinal microbiota may play a critical role in the development of the H. pylori-associated diseases. However, no specific microbe in the gastric or intestinal microbiota has been clearly linked to H. pylori infection and related gastric diseases. Here, we studied H. pylori infection, its virulence genes, the intestinal microbiota, and the clinical status of Trivandrum residents (N = 375) in southwestern India by standard H. pylori culture, PCR genotype, Sanger sequencing, and microbiome analyses using Illumina Miseq and Nanopore GridION. Our analyses revealed that gastric colonization by virulent H. pylori strains (vacAs1i1m1cagA+) is necessary but not sufficient for developing these diseases. Conversely, distinct microbial pools exist in the lower gut of the H. pylori-infected vs. H. pylori-non-infected individuals. Bifidobacterium (belonging to the phylum Actinobacteria) and Bacteroides (belonging to the phylum Bacteroidetes) were present in lower relative abundance for the H. pylori+ group than the H. pylori- group (p < 0.05). On the contrary, for the H. pylori+ group, genus Dialister (bacteria belonging to the phylum Firmicutes) and genus Prevotella (bacteria belonging to the phylum Bacteroidetes) were present in higher abundance compared to the H. pylori- group (p < 0.05). Notably, those who carried H. pylori in the stomach and had developed aggressive gastric diseases also had extremely low relative abundance (p < 0.05) of several Bifidobacterium species (e.g., B. adolescentis, B. longum) in the lower gut suggesting a protective role of Bifidobacterium. Our results show the link between lower gastrointestinal microbes and upper gastrointestinal diseases. Moreover, the results are important for developing effective probiotic and early prognosis of severe gastric diseases.

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This study aims to find spatial clusters of diabetes and physical inactivity among a sample population in Kerala, India, and evaluate built environment characteristics within the high and low spatial clusters. Spatial clusters with a higher and lower likelihood of diabetes and physical inactivity were identified using spatial scan statistic at various radii. Built environment characteristics were captured at panchayat level and 1600 m buffer around participant location using Geographical Information Systems. Comparison of sociodemographic and built environment factors was carried out for participants within high and low spatial clusters using t tests. Ten high and 8 low spatial clusters of diabetes and 17 high and 23 low spatial clusters of physical inactivity were identified in urban and rural areas of Kerala. Significant differences in built environment characteristics were consistent for low spatial clusters of diabetes and physical inactivity in the urban scenario. Built environment characteristics were found to be relevant in both urban and rural areas of Kerala. There is an urgent call to explore spatial clustering of non-communicable diseases in Kerala and undo the one-size-fits-all approach for prevention and control of non-communicable diseases.