Chowdary, S. | Search bioRxiv

bioRxiv2024

The transition from a non-westernized to westernized gut microbiome in Indian-Immigrants and Indo-Canadians is associated with dietary acculturation

Indian immigration to westernized countries has recently surged, increasing their risk of Inflammatory Bowel Disease (IBD) post-migration. While crucial for understanding IBD risk, the gut microbiome remains understudied in Indians. This cross-sectional study examines the gut microbiomes of Indians residing in India, Indo-Immigrants, and Indo-Canadians in comparison to Euro-Canadian and Euro-Immigrant controls to understand the impact of westernization on their gut. Stool samples for 16S rRNA and shotgun sequencing assessed microbial taxa and functional profiles, complemented by dietary and demographic data to evaluate lifestyle patterns. Results revealed distinct microbiotas in Indians and Indo-Immigrants compared to control groups, with high abundances of Prevotella spp. and CAZymes reflecting their high complex carbohydrate diet. Indo-Canadians exhibited a transitional microbiome towards westernization, mirroring increasing dietary acculturation. Considering 44% of Canadians are first- and second-generation immigrants and the global adoption of westernized practices, future research should investigate the health implications of such microbiome transitions in immigrant populations and newly industrialized nations.

microbiology↗

bioRxiv2022

Phylovar: Towards scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing (scDNAseq) data, such as SCI{Phi} and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. Here we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCI{Phi} in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. We implemented Phylovar and made it publicly available at https://github.com/mae6/Phylovar.git.

cancer biology↗