Biology subjects

Wingo, A. P.

Publications and source records attributed to Wingo, A. P..

10 recordsLinked to original sources

bioRxiv2024

The YTHDF Proteins Shape the Brain Gene Signatures of Alzheimer's Disease

The gene signatures of Alzheimers Disease (AD) brains reflect an output of a complex interplay of genetic, epigenetic, epi-transcriptomic, and post-transcriptional regulation., yet the dominant factor shaping these signatures remains unclear. To identify the most significant factor that shapes the AD brain signatures, we integrated cellular and molecular features with differential gene expression in in an explainable machine learning framework. Our result indicates that YTHDF proteins, the canonical readers of N6-methyladenosine RNA modification (m6A), are the most influential predictors of the AD brain signatures. We then show that protein modules containing YTHDFs are downregulated in human AD brains, and knocking down and pharmacologically inhibiting YTHDFs in iPSC-derived 2D and 3D neuronal models recapitulate the AD-associated transcriptional signatures. Furthermore, eCLIP-seq analysis revealed that YTHDF proteins influence AD signatures through both m6A-dependent and independent pathways. These results highlight the central role of YTHDF proteins in shaping the gene signatures of AD brains.

neuroscience↗

bioRxiv2024

SCAMPI: A scalable statistical framework for genome-wide interaction testing harnessing cross-trait correlations

Family-based heritability estimates of complex traits are often considerably larger than their single-nucleotide polymorphism (SNP) heritability estimates. This discrepancy may be due to non-additive effects of genetic variation, including variation that interacts with other genes or environmental factors to influence the trait. Variance-based procedures provide a computationally efficient strategy to screen for SNPs with potential interaction effects without requiring the specification of the interacting variable. While valuable, such variance-based tests consider only a single trait and ignore likely pleiotropy among related traits that, if present, could improve power to detect such interaction effects. To fill this gap, we propose SCAMPI (Scalable Cauchy Aggregate test using Multiple Phenotypes to test Interactions), which screens for variants with interaction effects across multiple traits. SCAMPI is motivated by the observation that SNPs with pleiotropic interaction effects induce genotypic differences in the patterns of correlation among traits. By studying such patterns across genotype categories among multiple traits, we show that SCAMPI has improved performance over traditional univariate variance-based methods. Like those traditional variance-based tests, SCAMPI permits the screening of interaction effects without requiring the specification of the interaction variable and is further computationally scalable to biobank data. We employed SCAMPI to screen for interacting SNPs associated with four lipid-related traits in the UK Biobank and identified multiple gene regions missed by existing univariate variance-based tests. SCAMPI is implemented in software for public use.

genetics↗

bioRxiv2024

Genetic regulation of microRNAs in the older adult brain and their contribution to neuropsychiatric conditions

MicroRNAs are essential post-transcriptional regulators of gene expression and involved in many biological processes; however, our understanding of their genetic regulation and role in brain illnesses is limited. Here, we mapped brain microRNA expression quantitative trait loci (miR-QTLs) using genome-wide small RNA sequencing profiles from dorsolateral prefrontal cortex (dlPFC) samples of 604 older adult donors of European ancestry. miR-QTLs were identified for 224 miRNAs (48% of 470 tested miRNAs) at false discovery rate < 1%. We found that miR-QTLs were enriched in brain promoters and enhancers, and that intragenic miRNAs often did not share QTLs with their host gene. Additionally, we integrated the brain miR- QTLs with results from 16 GWAS of psychiatric and neurodegenerative diseases using multiple independent integration approaches and identified four miRNAs that contribute to the pathogenesis of bipolar disorder, major depression, post-traumatic stress disorder, schizophrenia, and Parkinsons disease. This study provides novel insights into the contribution of miRNAs to the complex biological networks that link genetic variation to disease.

genetics↗

bioRxiv2022

APOE Expression and Secretion are Modulated by Copper-Dependent and -Independent Mitochondrial Dysfunction

Mitochondria influence cellular function through both cell-autonomous and non-cell autonomous mechanisms, such as production of paracrine and endocrine factors. Here, we demonstrate that mitochondrial regulation of the secretome is more extensive than previously appreciated, as both genetic and pharmacological disruption of the electron transport chain caused upregulation of the Alzheimers disease risk factor apolipoprotein E (APOE) and other secretome components. This upregulation of secretory proteins was of a similar extent as modifications to the mitochondrial annotated proteome. Indirect disruption of the electron transport chain by gene editing of SLC25A mitochondrial membrane transporters as well as direct genetic and pharmacological disruption of either complexes I, III, or the copper-containing complex IV of the electron transport chain, elicited upregulation of APOE transcript, protein, and secretion, up to 49-fold. These APOE phenotypes were robustly expressed in diverse cell types and iPSC-derived human astrocytes as part of an inflammatory gene expression program. Moreover, age- and genotype-dependent decline in brain levels of respiratory complex I preceded an increase in APOE in the 5xFAD mouse model. We propose that mitochondria act as novel upstream regulators of APOE-dependent cellular processes in health and disease.

cell biology↗

bioRxiv2021

Large-Scale Deep Multi-Layer Analysis of Alzheimer's Disease Brain Reveals Strong Proteomic Disease-Related Changes Not Observed at the RNA Level

The biological processes that are disrupted in the Alzheimers disease (AD) brain remain incompletely understood. We recently performed a proteomic analysis of >2000 brains to better understand these changes, which highlighted alterations in astrocytes and microglia as likely key drivers of disease. Here, we extend this analysis by analyzing >1000 brain tissues using a tandem mass tag mass spectrometry (TMT-MS) pipeline, which allowed us to nearly triple the number of quantified proteins across cases. A consensus protein co-expression network analysis of this deeper dataset revealed new co-expression modules that were highly preserved across cohorts and brain regions, and strongly altered in AD. Nearly half of the protein co-expression modules, including modules significantly altered in AD, were not observed in RNA networks from the same cohorts and brain regions, highlighting the proteopathic nature of AD. Two such AD-associated modules unique to the proteomic network included a module related to MAPK signaling and metabolism, and a module related to the matrisome. Analysis of paired genomic and proteomic data within subjects showed that expression level of the matrisome module was influenced by the APOE {varepsilon}4 allele, but was not related to the rate of cognitive decline after adjustment for neuropathology. In contrast, the MAPK/metabolism module was strongly associated with the rate of cognitive decline. Disease-associated modules unique to the proteome are sources of promising therapeutic targets and biomarkers for AD.

systems biology↗

bioRxiv2020

Multi-Tissue Neocortical Transcriptome-Wide Associations Study Implicates 8 Genes Across 6 Genomic Loci in Alzheimer's Disease

BackgroundAlzheimers disease (AD), an incurable neurodegenerative disease, currently affecting 1.75% of the United States population, with projected growth to 3.46% by 2050. Identifying common genetic variants driving differences in transcript expression that confer AD-risk is necessary to elucidate AD mechanism and develop therapeutic interventions. We modify the FUSION Transcriptome Wide Association Study (TWAS) pipeline to ingest expression from multiple neocortical regions, provide a set of 6780 gene weights which are abstracatable across the neocortex, and leverage these to find 8 genes from six loci with associated AD risk validated through summary mendelian randomization (SMR) utilizing IGAP summary statistics. MethodA combined dataset of 2003 genotypes clustered to Central European (CEU) ancestry was used to construct a training set of 790 genotypes paired to 888 RNASeq profiles across 6 Neo-cortical tissues (TCX=248, FP=50, IFG=41, STG=34, PHG=34, DLPFC=461). Following within-tissue normalization and covariate adjustment, predictive weights to impute expression components based on a genes surrounding cis-variants were trained. The FUSION pipeline was modified to support input of pre-scaled expression values and provide support for cross validation with a repeated measure design arising from the presence of multiple transcriptome samples from the same individual across different tissues. ResultsCis-variant architecture alone was informative to train weights and impute expression for 6780 (49.67%) autosomal genes, the majority of which significantly correlated with gene expression; FDR < 5%: N=6775 (99.92%), Bonferroni: N=6716 (99.06%). Validation of weights in 515 matched genotype to RNASeq profiles from the CommonMind Consortium (CMC) was (72.14%) in DLPFC profiles. Association of imputed expression components from all 2003 genotype profiles yielded 8 genes significantly associated with AD (FDR < 0.05); APOC1, EED, CD2AP, CEACAM19, CLPTM1, MTCH2, TREM2, KNOP1. ConclusionWe provide evidence of cis-genetic variation conferring AD risk through 8 genes across six distinct genomic loci. Moreover, we provide expression weights for 6780 genes as a valuable resource to the community, which can be abstracted across the neocortex and a wide range of neuronal phenotypes.

genomics↗

bioRxiv2019

Genetic control of the human brain proteome

Alteration of protein abundance and conformation are widely believed to be the hallmark of neurodegenerative diseases. Yet relatively little is known about the genetic variation that controls protein abundance in the healthy human brain. The genetic control of protein abundance is generally thought to parallel that of RNA expression, but there is little direct evidence to support this view. Here, we performed a large-scale protein quantitative trait locus (pQTL) analysis using single nucleotide variants (SNVs) from whole-genome sequencing and tandem mass spectrometry-based proteomic quantification of 12,691 unique proteins (7,901 after quality control) from the dorsolateral prefrontal cortex (dPFC) in 144 cognitively normal individuals. We identified 28,211 pQTLs that were significantly associated with the abundance of 864 proteins. These pQTLs were compared to dPFC expression quantitative trait loci (eQTL) in cognitive normal individuals (n=169; 81 had protein data) and a meta-analysis of dPFC eQTLs (n=1,433). We found that strong pQTLs are generally only weak eQTLs, and that the majority of strong eQTLs are not detectable pQTLs. These results suggest that the genetic control of mRNA and protein abundance may be substantially distinct and suggests inference concerning protein abundance made from mRNA in human brain should be treated with caution.

genetics↗

bioRxiv2019

Large-scale proteomic analysis of human prefrontal cortex identifies proteins associated with cognitive trajectory in advanced age

In advanced age, some individuals maintain a stable cognitive trajectory while others experience a rapid decline. Such variation in cognitive trajectory is only partially explained by traditional neurodegenerative pathologies. Hence, to identify new processes underlying variation in cognitive trajectory, we perform an unbiased proteome-wide association study of cognitive trajectory in a discovery (n=104) and replication cohort (n=39) of initially cognitively unimpaired, longitudinally assessed older-adult brain donors. We find 579 proteins associated with cognitive trajectory after meta-analysis. Notably, we present novel evidence for increased neuronal mitochondrial activities in cognitive stability regardless of the burden of traditional neuropathologies. Furthermore, we provide additional evidence for increased synaptic activities and decreased inflammation and apoptosis in cognitive stability. Importantly, we nominate proteins associated with cognitive trajectory, particularly the 38 proteins that act independently of neuropathologies and are also hub proteins of protein co-expression networks, as promising targets for future mechanistic studies of cognitive trajectory.

neuroscience↗

bioRxiv2018

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits

The transcriptome-wide association studies (TWAS) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWAS facilitates gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, we propose an improved Bayesian method that assumes a data-driven nonparametric prior to impute gene expression. Our method is general and flexible and includes both the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan. In real applications, our nonparametric Bayesian method fitted transcriptomic imputation models for 2X number of genes with 1.7X average regression R2 over PrediXcan, thus improving the power of follow-up TWAS. Hence, the nonparametric Bayesian model is preferred for modeling the complex genetic architecture of transcriptomes and is expected to enhance transcriptome-integrated genetic association studies. We implement our Bayesian approach in a convenient software tool \"TIGAR\" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWAS using individual-level or summary-level GWAS data.

genetics↗

bioRxiv2017

Gene expression in cord blood links genetic risk for neurodevelopmental disorders with maternal psychological distress and adverse childhood outcomes

Prenatal exposure to maternal stress and depression has been identified as a risk factor for adverse behavioral and neurodevelopmental outcomes in early childhood. However, the molecular mechanisms through which maternal psychopathology shapes offspring development remain poorly understood. We applied transcriptome-wide screens to 149 umbilical cord blood samples from neonates born to mothers with posttraumatic stress disorder (PTSD; n=20), depression (n=31) and PTSD with comorbid depression (n=13), compared to carefully matched trauma exposed controls (n=23) and healthy mothers (n=62). Analyses by maternal diagnoses revealed a clear pattern of gene expression signatures distinguishing neonates born to mothers with a history of psychopathology from those without. Co-expression network analysis identified distinct gene expression perturbations across maternal diagnoses, including two depression-related modules implicated in axon-guidance and mRNA stability, as well as two PTSD-related modules implicated in TNF signaling and cellular response to stress. Notably, these disease-related modules were enriched with brain-expressed genes and genetic risk loci for autism spectrum disorder and schizophrenia, which may imply a causal role for impaired developmental outcomes. These molecular alterations preceded changes in clinical measures at twenty-four months, including reductions in cognitive and socio-emotional outcomes in affected infants. Collectively, these findings indicate that prenatal exposure to maternal psychological distress induces neuronal, immunological and behavioral abnormalities in affected offspring and support the search for early biomarkers of exposures to adverse in utero environments and the classification of children at risk for impaired development.

developmental biology↗