Nato, A. Q. | Search bioRxiv

bioRxiv2020

RNA-Seq data analysis of a rodent model of adolescent binge drinking reveals pathways and candidate genes involved in neuronal remodeling and neuroimmune activation

IntroductionBinge drinking is common among adolescents and young adults and is associated with an increased risk of developing alcohol use disorder (AUD) and long-term cognitive deficits. We analyzed RNA-seq data from male Sprague Dawley rats to identify candidate genes that may play a role in the acute and chronic changes in cognitive function during binge-like adolescent alcohol/EtOH exposure and after a period of abstinence. MethodsAt postnatal day (PND) 30, male rats received chronic intermittent EtOH across 16 days. RNA was extracted from hippocampal tissue and sequenced at two acute timepoints, PND 35 and PND 46, and after 24 days forced abstinence (PND 70). We processed RNA-seq data, compiled gene counts, and performed normalization and differential expression analysis (DESeq2). Gene set enrichment analysis was performed through the R package fgsea. Gene sets of the Molecular Signatures Database (MSigDB) collections were used to identify gene pathways that were dysregulated following EtOH exposure. We also evaluated overlapping gene pathways that were affected across all timepoints. ResultsMultiple gene pathway analyses revealed that EtOH has robust effects on neuroinflammation, cellular remodeling, sleep, and bioenergetics. Changes were heavily dependent on whether gene expression was assessed during acute EtOH exposure or after abstinence. Genes involved in sleep regulation were selectively impacted during the acute timepoints, whereas dysregulation of genes involved in bioenergetics were only impacted after abstinence. The most striking changes occurred in genes that regulate neuroinflammatory processes and cellular remodeling. ConclusionThese data reveal acute and chronic effects of EtOH on multiple gene pathways that persist across analytic approaches and identify genes that have increased sensitivity to EtOH. These findings contribute to our understanding of the temporal effects of adolescent EtOH exposure and how gene pathway dysregulation contributes to the protracted emergence of neuronal remodeling in the hippocampus during a critical period of brain maturation.

bioinformatics↗

bioRxiv2018

Quality Control and Integration of Genotypes from Two Calling Pipelines for Whole Genome Sequence Data in the Alzheimer’s Disease Sequencing Project

The Alzheimers Disease Sequencing Project (ADSP) performed whole genome sequencing (WGS) of 584 subjects from 111 multiplex families at three sequencing centers. Genotype calling of single nucleotide variants (SNVs) and insertion-deletion variants (indels) was performed centrally using GATK-HaplotypeCaller and Atlas V2. The ADSP Quality Control (QC) Working Group applied QC protocols to project-level variant call format files (VCFs) from each pipeline, and developed and implemented a novel protocol, termed \"consensus calling,\" to combine genotype calls from both pipelines into a single high-quality set. QC was applied to autosomal bi-allelic SNVs and indels, and included pipeline-recommended QC filters, variant-level QC, and sample-level QC. Low-quality variants or genotypes were excluded, and sample outliers were noted. Quality was assessed by examining Mendelian inconsistencies (MIs) among 67 parent-offspring pairs, and MIs were used to establish additional genotype-specific filters for GATK calls. After QC, 578 subjects remained. Pipeline-specific QC excluded ~12.0% of GATK and 14.5% of Atlas SNVs. Between pipelines, ~91% of SNV genotypes across all QCed variants were concordant; 4.23% and 4.56% of genotypes were exclusive to Atlas or GATK, respectively; the remaining ~0.01% of discordant genotypes were excluded. For indels, variant-level QC excluded ~36.8% of GATK and 35.3% of Atlas indels. Between pipelines, ~55.6% of indel genotypes were concordant; while 10.3% and 28.3% were exclusive to Atlas or GATK, respectively; and ~0.29% of discordant genotypes were. The final WGS consensus dataset contains 27,896,774 SNVs and 3,133,926 indels and is publicly available.\n\nAbbreviationsAD, Alzheimers disease; QC, Quality Control; LSSAC, Large-Scale Sequencing and Analysis Center; Broad, Broad Institute Genomics Service; Baylor, Baylor College of Medicine Human Genome Sequencing Center; WashU, Washington University-St. Louis McDonnell Genome Institute; WGS, whole genome sequencing; WES, whole exome sequencing; indel, insertion-deletion variants; VCF, variant control format; MI, Mendelian inconsistency; MC, Mendelian consistency; GWAS, genome-wide association study; VR, referent allele read depth; DP, overall read depth; MS, mapping score; GQ, genotype quality score; Ti/Tv, Transition/Transversion; CS, concordance code

genetics↗