Biology subjects

Perumal, T.

Publications and source records attributed to Perumal, T..

5 recordsLinked to original sources

bioRxiv2025

Inflammatory Biomarkers of Asymptomatic and Symptomatic Tuberculosis

A large proportion of individuals with tuberculosis (TB) are asymptomatic. The biological and inflammatory underpinnings of asymptomatic TB are unknown and may differ from symptomatic TB. We characterised blood transcriptomic and proteomic profiles in South African community screening vs. health facility-based triage cohorts. Asymptomatic TB shared core transcriptomic and proteomic features with symptomatic TB, including upregulation of innate, interferon and inflammatory pathways and downregulation of T and B cell pathways. Integration of transcriptomic and proteomic data from asymptomatic TB individuals identified two distinct sub-clusters characterized by higher or lower bacterial burden, blood IFN-{gamma} responses, BMI, and chest radiographic abnormalities, suggesting different disease severity. We identified a new blood transcriptomic signature of asymptomatic TB. However, diagnostic performance of transcriptomic and proteomic markers was weaker for asymptomatic TB than symptomatic TB, suggesting that policy development for community-based, asymptomatic TB screening should not adopt biomarkers developed for symptomatic TB triage without further optimization.

immunology↗

bioRxiv2019

Functional annotation of rare structural variation in the human brain

Structural variants (SVs) contribute substantially to risk of many brain related disorders including autism and schizophrenia. However, annotating the potential contribution of SVs to disease remains a major challenge. Here, we integrated high resolution SV calling from genome-sequencing in 755 human post-mortem brains with dorsal lateral prefrontal cortex RNA-sequencing from a subset of 629 samples to quantify the dosage and regulatory effects of SVs. We show that genic (p = 5.44x10-9) and regulatory SVs (enhancer p = 3.22x10-23, CTCF p = 3.86x10-18) are present at significantly lower frequencies than intergenic SVs after correcting for SV length. Copy number variants (CNVs)--deletions and duplications--exhibit a significant quantitative and directional relationship between the proportion of genic and regulatory content altered and gene expression, and the size of the effect is inversely correlated with the loss-of-function intolerance of the gene. We trained a joint linear model that leverages genic and regulatory annotations to predict expression effects of rare CNVs in independent samples (R2 = 0.21-0.41). We further developed a regulatory disruption score for each CNV that aggregates the predicted expression across all affected genes weighted by the genes intolerance score and applied it to an independent set of SVs from 14,891 genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders by ClinGen had significantly more extreme regulatory disruption scores than the rest of the SVs. Rank ordering based on the most extreme regulatory disruption scores prioritized pathogenic deletions that would not have been prioritized by frequency or length alone. This work points to the deleteriousness of regulatory SVs, particularly those altering CTCF sites. We further provide a simple approach for functionally annotating the regulatory effects of SVs in the human brain that has potential to be useful in larger SV studies and should improve as more regulatory annotation data is generated.

genomics↗

bioRxiv2019

A novel systems biology approach to evaluate mouse models of late-onset Alzheimer’s disease

BackgroundLate-onset Alzheimers disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimers have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes.\n\nResultsThis resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter(R) Mouse AD panel was designed to correlate key human disease processes and pathways with mRNA from mouse brains. Analysis of three mouse models based on LOAD genetics, carrying APOE4 and TREM2*R47H alleles, demonstrated overlaps with distinct human AD modules that, in turn, are functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq shows strong correlation between gene expression changes independent of experimental platform.\n\nConclusionsTaken together, we show that the nCounter Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models.

neuroscience↗

bioRxiv2019

Identifying and ranking potential driver genes of Alzheimer's Disease using multi-view evidence aggregation

MotivationLate onset Alzheimers disease (LOAD) is currently a disease with no known effective treatment options. To address this, there have been a recent surge in the generation of multi-modality data (Hodes and Buckholtz, 2016; Mueller et al., 2005) to understand the biology of the disease and potential drivers that causally regulate it. However, most analytic studies using these data-sets focus on uni-modal analysis of the data. Here we propose a data-driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our paper are: i) A general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature-sets and identifying other potential driver genes which have similar feature representations, and ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study (GWAS) summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. ResultsWe demonstrate the utility of our machine learning algorithm on two benchmark multi-view datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimers. We show that our ranked genes show a significant enrichment for SNPs associated with Alzheimers, and are enriched in pathways that have been previously associated with the disease. AvailabilitySource code and link to all feature sets is availabile at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking. Contactben.logsdon@sagebionetworks.org

bioinformatics↗

bioRxiv2017

Co-localization of Conditional eQTL and GWAS Signatures in Schizophrenia

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which SNPs underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissecting this signal into multiple independent eQTL for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (N=467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context specific (i.e. tissue, cell type, or developmental time point specific) regulation of gene expression. Integrating the Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC conditional eQTL data reveals forty loci with strong evidence for co-localization (posterior probability >0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes and identify novel genes for schizophrenia risk, and provide specific hypotheses for their functional follow-up.

genetics↗