Biology subjects

Glatt, S. J.

Publications and source records attributed to Glatt, S. J..

5 recordsLinked to original sources

bioRxiv2024

Genetic and environmental contributions to eigengene expression

Multivariate network-based analytic methods such as weighted gene co-expression network analysis are being increasingly applied to human and animal gene-expression data to estimate module eigengenes (MEs). MEs represent multivariate summaries of correlated gene-expression patterns and network connectivity across genes within a module. Although this approach has the potential to elucidate the mechanisms by which molecular genomic variations contribute to individual differences in complex traits, the genetic etiology of MEs has never been empirically established. It is unclear if and to what degree individual differences in blood derived MEs reflect random variation versus familial aggregation arising from heritable or shared environmental influences. We used biometrical genetic analyses to estimate the contribution of genetic and environmental influences on MEs derived from blood lymphocytes collected on a sample of N=661 older male twins from the Vietnam Era Twin Study of Aging (VETSA) whose mean age at assessment was 67.7 years (SD=2.6 years, range=62-74 years). Of the 26 detected MEs, 14 (56%) had statistically significant additive genetic variation with an average heritability of 44% (SD=0.08, range=35-64%). Despite the relatively small sample size, this demonstration of significant family aggregation including estimates of heritability in 14 of the 26 MEs suggests that blood-based MEs are reliable and merit further exploration in terms of their associations with complex traits and diseases.

genetics↗

bioRxiv2020

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. We developed a novel method called the Brain Gene Expression and Network Imputation Engine (BrainGENIE) that uses peripheral-blood transcriptomes to predict brain-tissue-specific gene-expression levels. BrainGENIE reliably predicted brain-tissue-specific expression levels for 1,733 - 11,569 genes (false-discovery rate-adjusted p<0.05), including many transcripts that cannot be predicted reliably by a transcriptome imputation method such as PrediXcan. We tested the generalizability of BrainGENIE in external within-individual data from ex vivo peripheral blood and postmortem brain samples from the Religious Orders Study and Memory and Aging Project, wherein we validated 39% of predicted gene expression levels as concordant with observed expression levels in dorsolateral prefrontal cortex and 23% in caudate. BrainGENIE recapitulated diagnosis-related gene expression changes in brain better than direct correlations from blood and predictions from PrediXcan. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

bioinformatics↗

bioRxiv2019

Spatial organization of cells and variable expression of autophagy, apoptosis, and neurodevelopmental genes might underlie selective brain region vulnerability in Attention-Deficit/Hyperactivity Disorder

Genetically influenced changes in brain organization occur over the course of development. Large-scale brain imaging studies by the ENIGMA Consortium identified structural changes associated with attention-deficit/hyperactivity disorder (ADHD). It is not clear why some brain regions are impaired and others spared by the etiological risks for ADHD. We hypothesized that spatial variation in brain cell organization and/or pathway expression levels contribute to selective brain region vulnerability (SBRV) in ADHD. In this study, we used the largest available collection of imaging results from the ADHD ENIGMA Consortium along with high-resolution postmortem brain microarray data from Allen Brain Atlas from 22 sub-cortical and cortical brain regions to investigate our selective brain region vulnerability (SBRV) hypothesis. We performed deconvolution of the bulk transcriptomic data to determine abundances of neuronal and non-neuronal cells in the brain. We then assessed the relationships between gene set expression levels, cell abundance, and standardized effect sizes representing regional changes in brain sizes in cases of ADHD. Our analysis yielded significant correlations between apoptosis, autophagy, and neurodevelopment genes with reductions in regional brain sizes in ADHD, along with associations to regional abundances of dopaminergic neurons, astrocytes, oligodendrocytes, and neural progenitor cells. This works provides novel mechanistic insights into SBRV in ADHD.

neuroscience↗

bioRxiv2018

Solving for X: evidence for sex-specific autism biomarkers across multiple transcriptomic studies

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers (and characterise their function), we present an analysis of a single paired-end post-mortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both post-mortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

neuroscience↗

bioRxiv2017

PTSD Blood Transcriptome Mega-Analysis: Shared Inflammatory Pathways Across Genders And Modes Of Traumas

Transcriptome-wide screens of peripheral blood during the onset and development of posttraumatic stress disorder (PTSD) indicate widespread immune dysregulation. However, little is known as to whether biological sex and the type of traumatic event influence shared or distinct biological pathways in PTSD. We performed a combined analysis of five independent PTSD blood transcriptome studies covering seven types of trauma in 229 PTSD and 311 comparison individuals to synthesize the extant data. Analyses by trauma type revealed a clear pattern of PTSD gene expression signatures distinguishing interpersonal (IP)-related traumas from combat-related traumas. Co-expression network analyses integrated all data and identified distinct gene expression perturbations across sex and modes of trauma in PTSD, including one wound-healing module down-regulated in men exposed to combat traumas, one IL12-mediated signaling module up-regulated in men exposed to IP-related traumas, and two modules associated with lipid metabolism and MAPK-activity up-regulated in women exposed to IP-related traumas. Remarkably, a high degree of sharing of transcriptional dysregulation across sex and modes of trauma in PTSD was also observed converging on common signaling cascades, including cytokine, innate immune and type I interferon pathways. Collectively, these findings provide a broad view of immune dysregulation in PTSD and demonstrate inflammatory pathways of molecular convergence and specificity, which may inform mechanisms and diagnostic biomarkers for the disorder.

neuroscience↗