Genetic and multi-omic risk assessment of Alzheimer's disease implicates core associated biological domains.

INTRODUCTION: Alzheimer's disease (AD) is the predominant dementia globally, with heterogeneous presentation and penetrance of clinical symptoms, variable presence of mixed pathologies, potential disease subtypes, and numerous associated endophenotypes. Beyond the difficulty of designing treatments that address the core pathological characteristics of the disease, therapeutic development is challenged by the uncertainty of which endophenotypic areas and specific targets implicated by those endophenotypes to prioritize for further translational research. However, publicly funded consortia driving large-scale open science efforts have produced multiple omic analyses that address both disease risk relevance and biological process involvement of genes across the genome. METHODS: Here we report the development of an informatic pipeline that draws from genetic association studies, predicted variant impact, and linkage with dementia associated phenotypes to create a genetic risk score. This is paired with a multi-omic risk score utilizing extensive sets of both transcriptomic and proteomic studies to identify system-level changes in expression associated with AD. These two elements combined constitute our target risk score that ranks AD risk genome-wide. The ranked genes are organized into endophenotypic space through the development of 19 biological domains associated with AD in the described genetics and genomics studies and accompanying literature. The biological domains are constructed from exhaustive Gene Ontology (GO) term compilations, allowing automated assignment of genes into objectively defined disease-associated biology. This rank-and-organize approach, performed genome-wide, allows the characterization of aggregations of AD risk across biological domains. RESULTS: The top AD-risk-associated biological domains are Synapse, Immune Response, Lipid Metabolism, Mitochondrial Metabolism, Structural Stabilization, and Proteostasis, with slightly lower levels of risk enrichment present within the other 13 biological domains. DISCUSSION: This provides an objective methodology to localize risk within specific biological endophenotypes and drill down into the most significantly associated sets of GO terms and annotated genes for potential therapeutic targets.

Prevalence of differential microbiome in healthy, diseased and nipped colonies of corals, Porites lutea in the Gulf of Kachchh, north-west coast of India.

Coral reefs are constantly subjected to multiple stresses like diseases and fish predation, which can profoundly influence the coral microbiome. This study investigated the differences in bacterial community structure of healthy, white syndrome affected and blenny nipped coral colonies of Porites lutea, collected from the coral reefs of Gulf of Kachchh, north-west coast of India. Present study observed that the stressed coral colonies harbored more OTUs and contained higher diversity values compared to healthy corals colonies. Similarly, beta diversity analysis indicated the dissimilarities among the three coral samples analyzed. Though the taxonomy analysis indicated bacterial phyla like Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria among the entire coral samples studied, there was a variation in their relative abundances. Huge variations were observed in the relative dominance at the bacterial genera level. About 13phyla and 11 genera was identified in healthy coral. The PBN sample was found to contain Proteobacteria, Cyanobacteria, Verrucomicrobia, and Lentisphaerae as dominant phyla and Endozoicomonas, Dyella, Woeseia, and Winogradskyella as dominant genera. The PWS sample contained Proteobacteria, Lentisphaerae, Spirochaetes, and Tenericutes as dominant phyla and Endozoicomonas, Arcobacter, Sunxiuqinia, and Carboxylicivirgia as dominant genera. Among the healthy samples, sequences belonging to Uncultured Rhodospirillaceae were dominant, while Woeseia and sequences belonging to Uncultured Rhodovibrionaceae were dominant among the blenny nipped white syndrome infected corals. Although any previously established pathogen was not identified, present study revealed the presence of a potentially pathogenic bacterium, Arcobacter, among the diseased corals. It also demonstrated a dynamic microbiome among the Porites lutea colonies on subjecting to various stresses.

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data.

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics. Our iCOMIC toolkit pipeline featuring many independent workflows is embedded in the popular Snakemake workflow management system. It can analyze whole-genome and transcriptome data and is characterized by a user-friendly GUI that offers several advantages, including minimal execution steps and eliminating the need for complex command-line arguments. Notably, we have integrated algorithms developed in-house to predict pathogenicity among cancer-causing mutations and differentiate between tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM-GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r = 0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, significantly ameliorating complex data analysis pipelines.