Biology subjects

Meyers, J.

Publications and source records attributed to Meyers, J..

13 recordsLinked to original sources

bioRxiv2026

Leveraging Pretrained Vision Transformers for classifying Alcohol Use Disorder using Raw Resting-State EEG

Alcohol Use Disorder (AUD) is a prevalent and debilitating neuropsychiatric condition characterized by compulsive alcohol consumption, impaired control, and negative emotional states, affecting about 28 million adults in the United States. Despite its significant public health burden, there are few objective biomarkers and no reliable neurophysiological tools to assist in its clinical diagnosis. In this study, we investigated the potential of deep learning to classify individuals with AUD using raw resting-state electroencephalogram (EEG) data. EEG recordings were obtained from the Collaborative Study on the Genetics of Alcoholism (COGA), a large, longitudinal, multi-site dataset. The initial cohort included a total of 5,402 recordings from 2,710 participants (aged 12-83, mean age 24; 1,338 males and 1,372 females). To reduce confounding factors, we applied demographic matching, and to address class imbalance, we applied undersampling. Minimal preprocessing was applied to preserve the raw EEG features. We utilized EEGViT, a hybrid deep learning architecture that combines convolutional patch embedding with a Vision Transformer (ViT) pretrained on ImageNet, thereby enabling end-to-end learning directly from raw EEG input. The analysis was stratified by sex and age, and all groups were age-matched. To validate the generalization of the model, models were also trained for Cannabis Use Disorder (CUD) and Opioid Use Disorder (OUD). Results for the AUD model showed a classification accuracy of approximately 56% in the overall dataset, 54% for males, and 58% for females. The CUD model showed an accuracy of about 63% with 59% for females and 69% for males. The OUD model showed an accuracy of about 63% with 61% for females and 65% for males. Temporal analysis indicated that the models performance varied across time intervals, with higher accuracy observed in later minutes compared to earlier ones. While modest, these findings underscore the potential of transformer-based models in psychiatric classification using raw EEG data and provide a foundation for future development of EEG-based diagnostic tools for AUD.

neuroscience↗

bioRxiv2025

A transition zone enriched WIF1⁺ basal cell subtype is associated with benign prostatic hyperplasia

ABSTRACT/SUMMARYThe cellular composition and disease susceptibilities of the distinct zones of the human prostate remain incompletely understood. Through extensive single-cell RNA sequencing (scRNA-seq) of benign regions from prostatectomy specimens, we identified a basal cell population expressing WIF1, VCAN, and NRG1, among other genes, that was significantly enriched in the transition zone (TZ). Benign prostatic hyperplasia (BPH) is a common condition that causes widespread morbidity and is nearly exclusively localized to the TZ. Analysis of previously published scRNA-seq datasets further confirmed that WIF1+ basal cells were significantly enriched in BPH compared to normal prostate. Pathway and cell-cell communication analyses revealed that this basal subtype is associated with programs related to cell proliferation, epithelial-mesenchymal transition (EMT), angiogenesis, and hormone response. Together, the molecular signature, zonal distribution, and pathway enrichment suggest that TZ-enriched WIF1+ basal cells may contribute to BPH pathogenesis by promoting epithelial and stromal remodeling.

cell biology↗

bioRxiv2024

Disease-Linked Regulatory DNA Variants and Homeostatic Transcription Factors in Epidermis

Identifying noncoding single nucleotide variants (SNVs) in regulatory DNA linked to polygenic disease risk, the transcription factors (TFs) they bind, and the target genes they dysregulate is a goal in polygenic disease research. Massively parallel reporter gene analysis (MPRA) of 3,451 SNVs linked to risk for polygenic skin diseases characterized by disrupted epidermal homeostasis identified 355 differentially active SNVs (daSNVs). daSNV target gene analysis, combined with daSNV editing, underscored dysregulated epidermal differentiation as a pathomechanism shared across common polygenic skin diseases. CRISPR knockout screens of 1772 human TFs revealed 108 TFs essential for epidermal progenitor differentiation, uncovering novel roles for ZNF217, CXXC1, FOXJ2, IRX2 and NRF1. Population sampling CUT&RUN of 27 homeostatic TFs identified allele-specific DNA binding (ASB) differences at daSNVs enriched near epidermal homeostasis and monogenic skin disease genes, with notable representation of SP/KLF and AP-1/2 TFs. This resource implicates dysregulated differentiation in risk for diverse polygenic skin diseases.

genetics↗

bioRxiv2024

Polygenic Risk for Alcohol Use Disorder Affects Cellular Responses to Ethanol Exposure in a Human Microglial Cell Model

Polygenic risk scores (PRS) assess genetic susceptibility to Alcohol Use Disorder (AUD), yet their molecular implications remain underexplored. Neuroimmune interactions, particularly in microglia, are recognized as significant contributors to AUD pathophysiology. We investigated the interplay between AUD PRS and ethanol in human microglia derived from iPSCs from individuals with high- or low-PRS (HPRS or LPRS) of AUD. Ethanol exposure induced elevated CD68 expression and morphological changes in microglia, with differential responses between HPRS and LPRS microglial cells. Transcriptomic analysis revealed expression differences in MHCII complex and phagocytosis-related genes following ethanol exposure; HPRS microglial cells displayed enhanced phagocytosis and increased CLEC7A expression, unlike LPRS microglial cells. Synapse numbers in co-cultures of induced neurons with microglia after alcohol exposure were lower in HRPS co-cultures, suggesting possible excess synapse pruning. This study provides insights into the intricate relationship between AUD PRS, ethanol, and microglial function, potentially influencing neuronal functions in developing AUD.

neuroscience↗

bioRxiv2024

A hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data

BackgroundMethylation Quantitative Trait Loci (meQTLs) are chromosomal regions that harbor genetic variants affecting DNA methylation levels. The identification of meQTLs can be accomplished through quantifying the effects of single nucleotide polymorphisms (SNPs) on DNA methylation levels, and these inferred meQTLs can shed light on the complex interplay between the genome and methylome. However, most meQTL studies to date utilize bulk methylation datasets composed of different cell types that may have distinct methylation patterns in each cell type. Current technological challenges hinder the comprehensive collection of large-scale, cell-type-specific (CTS) methylation data, which limits our understanding of CTS methylation regulation. To address this challenge, we propose a hierarchical Bayesian interaction model (HBI) to infer CTS meQTLs from bulk methylation data. ResultsOur HBI method integrates bulk methylations data from a large number of samples and CTS methylation data from a small number of samples to estimate CTS meQTLs. Through simulations, we show that HBI improves the estimation (accuracy and power) of CTS genetic effects on DNA methylation. To systematically characterize genome-wide SNP-methylation level associations in multiple cell types, we apply HBI to bulk methylation data measured in peripheral blood mononuclear cells (PBMC) from a cohort of 431 individuals together with flow-sorted cell-derived methylation sequencing (MC-seq) data measured in isolated white blood cells (CD4+ T-cells, CD8+ T-cells, CD16+ monocytes) for 47 individuals. We demonstrate that HBI can identify CTS meQTLs and improve the functional annotation of SNPs. ConclusionsHBI can incorporate strong and robust signals from MC-seq data to improve the estimation of CTS meQTLs. Applying HBI to link the methylome and genome data helps to identify biologically relevant cell types for complex traits.

genetics↗

bioRxiv2023

Convergent alterations in the tumor microenvironment of MYC-driven human and murine prostate cancer

The tissue microenvironment in prostate cancer is profoundly altered. While such alterations have been implicated in driving prostate cancer initiation and progression to aggressive disease, how prostate cancer cells and their precursors mediate those changes is unclear, in part due to the inability to longitudinally study the disease evolution in human tissues. To overcome this limitation, we performed extensive single-cell RNA-sequencing (scRNA-seq) and rigorous molecular pathology of the comparative biology between human prostate cancer and key time points in the disease evolution of a genetically engineered mouse model (GEMM) of prostate cancer. Our studies of human tissues, with validation in a large external data set, revealed that cancer cell-intrinsic activation of MYC signaling was the top up-regulated pathway in human cancers, representing a common denominator across the well-known molecular and pathological heterogeneity of human prostate cancer. Likewise, numerous non-malignant cell states in the tumor microenvironment (TME), including non-cancerous epithelial, immune, and fibroblast cell compartments, were conserved across individuals, raising the possibility that these cell types may be a sequelae of the convergent MYC activation in the cancer cells. To test this hypothesis, we employed a GEMM of prostate epithelial cell-specific MYC activation in two mouse strains. Cell communication network and pathway analyses suggested that MYC oncogene-expressing neoplastic cells, directly and indirectly, reprogrammed the TME during carcinogenesis, leading to the emergence of cascading cell state alterations in neighboring epithelial, immune, and fibroblast cell types that paralleled key findings in human prostate cancer. Importantly, among these changes, the progression from a precursor-enriched to invasive-cancer-enriched state was accompanied by a cell-intrinsic switch from pro-immunogenic to immunosuppressive transcriptional programs with coinciding enrichment of immunosuppressive myeloid and Treg cells in the immune microenvironment. These findings implicate activation of MYC signaling in reshaping convergent aspects of the TME of prostate cancer as a common denominator across the otherwise well-documented molecular heterogeneity of human prostate cancer.

cancer biology↗

bioRxiv2022

Single-cell atlas of epithelial and stromal cell heterogeneity by lobe and strain in the mouse prostate

Evaluating the complex interplay of cell types in the tissue microenvironment is critical to understanding the origin and progression of diseases in the prostate and potential opportunities for intervention. Mouse models are an essential tool to investigate the molecular and cell-type-specific contributions of prostate disease at an organismal level. While there are well-documented differences in the extent, timing, and nature of disease development in various genetically engineered mouse models in different mouse strains and prostate lobes within each mouse strain, yet, the underlying molecular phenotypic differences in cell types across mouse strains and prostate lobes are incompletely understood. To address this, we examined the single-cell transcriptomes of individual mouse prostate lobes from two commonly used mouse strains, FVB/NJ and C57BL/6J. Data dimensionality reduction and clustering analysis revealed that basal and luminal cells possessed strain-specific transcriptomic differences, with luminal cells also displaying marked lobe-specific differences. Additionally, three rare populations of epithelial cells clustered independently of strain and lobe: one population of luminal cells expressing Foxi1 and components of the vacuolar ATPase proton pump (Atp6v0d2 and Atp6v1g3), another population expressing Psca and other stem cell-associated genes (Ly6a/Sca-1, Tacstd2/Trop-2), and a neuroendocrine population expressing Chga, Chgb, and Syp. In contrast, stromal cell clusters, including fibroblasts, smooth muscle cells, endothelial cells, pericytes, and immune cell types, were conserved across strain and lobe, clustering largely by cell type and not by strain or lobe. One notable exception to this was the identification of two distinct fibroblast populations that we term subglandular fibroblasts and interstitial fibroblasts based on their strikingly distinct spatial distribution in the mouse prostate. Altogether, these data provide a practical reference of the transcriptional profiles of mouse prostate from two commonly used mouse strains and across all four prostate lobes.

cell biology↗

bioRxiv2021

3-hydroxyanthranilic acid - a new metabolite for healthy lifespan extension

The metabolism of tryptophan to nicotinamide adenine dinucleotide (NAD+) through the kynurenine pathway is increasingly linked to aging and age-associated disease. Kynurenine pathway enzymes and metabolites influence a range of molecular processes critical to healthy aging, including regulation of inflammatory and immune responses, cellular redox homeostasis, and energy production. Aberrant kynurenine metabolism is observed during normal aging and has been implicated in a range of age-associated pathologies, including chronic inflammation, atherosclerosis, neurodegeneration, and cancer. In previous work, we and others identified three genes--kynu-1, tdo-2, and acsd-1--encoding kynurenine pathway enzymes for which decreasing expression extends lifespan in invertebrate models. Here we report that knockdown of haao-1, a fourth kynurenine pathway gene encoding the enzyme 3-hydroxyanthranilic acid dioxygenase (HAAO), extends lifespan by ~30% and delays age-associated decline in health in Caenorhabditis elegans. This lifespan extension is mediated by increased physiological levels of the HAAO substrate 3-hydroxyanthranilic acid (3HAA). 3HAA increases resistance to oxidative stress during aging by directly degrading hydrogen peroxide and activating the Nrf2/SKN-1 oxidative stress response. Aging mice fed a diet supplemented with 3HAA are similarly long-lived. Our results identify HAAO and 3HAA as novel therapeutic targets for age-associated disease. This works provides a foundation for more detailed examination of the molecular mechanisms underlying the benefits of 3HAA, and how these mechanisms interact with other interventions both within and beyond the kynurenine pathway. We anticipate that these findings will bolster growing interest in developing pharmacological strategies to target tryptophan metabolism to improve health aging.

molecular biology↗

bioRxiv2021

The proximal proteome of 17 SARS-CoV-2 proteins links to disrupted antiviral signaling and host translation

Viral proteins localize within subcellular compartments to subvert host machinery and promote pathogenesis. To study SARS-CoV-2 biology, we generated an atlas of 2422 human proteins vicinal to 17 SARS-CoV-2 viral proteins using proximity proteomics. This identified viral proteins at specific intracellular locations, such as association of accessary proteins with intracellular membranes, and projected SARS-CoV-2 impacts on innate immune signaling, ER-Golgi transport, and protein translation. It identified viral protein adjacency to specific host proteins whose regulatory variants are linked to COVID-19 severity, including the TRIM4 interferon signaling regulator which was found proximal to the SARS-CoV-2 M protein. Viral NSP1 protein adjacency to the EIF3 complex was associated with inhibited host protein translation whereas ORF6 localization with MAVS was associated with inhibited RIG-I 2CARD-mediated IFNB1 promoter activation. Quantitative proteomics identified candidate host targets for the NSP5 protease, with specific functional cleavage sequences in host proteins CWC22 and FANCD2. This data resource identifies host factors proximal to viral proteins in living human cells and nominates pathogenic mechanisms employed by SARS-CoV-2. Author SummarySARS-CoV-2 is the latest pathogenic coronavirus to emerge as a public health threat. We create a database of proximal host proteins to 17 SARS-CoV-2 viral proteins. We validate that NSP1 is proximal to the EIF3 translation initiation complex and is a potent inhibitor of translation. We also identify ORF6 antagonism of RNA-mediate innate immune signaling. We produce a database of potential host targets of the viral protease NSP5, and create a fluorescence-based assay to screen cleavage of peptide sequences. We believe that this data will be useful for identifying roles for many of the uncharacterized SARS-CoV-2 proteins and provide insights into the pathogenicity of new or emerging coronaviruses.

microbiology↗

bioRxiv2020

Multi-omics integration analysis identifies novel genes for alcoholism with potential link to neurodegenerative diseases

SignificanceIdentification of causal variants and genes underlying genome-wide association study (GWAS) loci is essential to understanding the biology of alcohol use disorder (AUD). MethodsIntegration of "multi-omics" data is often necessary to nominate candidate causal variants and genes and prioritize them for follow up studies. Here, we used Mendelian randomization to integrate AUD and drinks per week (DPW) GWAS summary statistics with the gene expression and methylation quantitative trait loci (eQTLs and mQTLs) in the largest brain and myeloid datasets. We also used AUD-related single cell epigenetic data to nominate candidate causal variants and genes associated with DPW and AUD. ResultsOur multi-omics integration analyses prioritized unique as well as shared genes and pathways among AUD and DPW. The GWAS variants associated with both AUD and DPW showed significant enrichment in the promoter regions of fetal and adult brains. The integration of GWAS SNPs with mQTLs from fetal brain prioritized variants on chromosome 11 in both AUD and DPW GWASs. The co-localized variants were found to be overlapping with promoter marks for SPI1, specifically in human microglia, the myeloid cells of the brain. The co-localized SNPs were also strongly associated with SPI1 mRNA expression in myeloid cells from peripheral blood. The prioritized variant at this locus is predicted to alter the binding site for a transcription factor, RXRA, a key player in the regulation of myeloid cell function. Our analysis also identified MAPT as a candidate causal gene specifically associated with DPW. mRNA expression of MAPT was also correlated with daily amounts of alcohol intake in post-mortem brains (frontal cortex) from alcoholics and controls (N = 92). Results may be queried and visualized in an online public resource of these integrative analysis (https://lcad.shinyapps.io/alc_multiomics/). These results highlight overlap between causal genes for neurodegenerative diseases, alcohol use disorder and alcohol consumption. In conclusionintegrating GWAS summary statistics with multi-omics datasets from multiple sources identified biological similarities and differences between typical alcohol intake and disordered drinking highlighting molecular heterogeneity that might inform future targeted functional and cross-species studies. Interestingly, overlap was also observed with causal genes for neurodegenerative diseases.

genomics↗

bioRxiv2020

Pervasive promoter hypermethylation of silenced TERT alleles in human cancers

In cancers, maintenance of telomeres often occurs through activation of the catalytic subunit of telomerase, encoded by TERT. Yet, most cancers show only modest levels of telomerase gene expression, even in the context of activating hotspot promoter mutations (C228T and C250T). The role of epigenetic mechanisms, including DNA methylation, in regulating telomerase gene expression in cancer cells is not fully understood. Here, we have carried out the most comprehensive characterization to date of TERT promoter methylation using ultra-deep bisulfite sequencing spanning the CpG island surrounding the core TERT promoter in 96 different human cell lines. In general, we observed that immortalized and cancer cell lines were hypermethylated in a region upstream of the recurrent C228T and C250T TERT promoter mutations, while non-malignant primary cells were comparatively hypomethylated in this region. However, at the allele-level, we generally observe hypermethylation of promoter sequences in cancer cells is associated with repressed expression, and the remaining unmethylated alleles marked with open chromatin are largely responsible for the observed TERT expression in cancer cells. Our findings suggest that hypermethylation of the TERT promoter alleles signals transcriptional repression of those alleles, leading to the attenuation of TERT activation in cancer cells. SIGNIFICANCEHypermethylation of the TERT promoter alleles to attenuate TERT activation in cancer cells may account for the modest activation of TERT expression in most cancers.

cancer biology↗

bioRxiv2019

DeeplyTough: Learning Structural Comparison of Protein Binding Sites

MotivationProtein binding site comparison (pocket matching) is of importance in drug discovery. Identification of similar binding sites can help guide efforts for hit finding, understanding polypharmacology and characterization of protein function. The design of pocket matching methods has traditionally involved much intuition, and has employed a broad variety of algorithms and representations of the input protein structures. We regard the high heterogeneity of past work and the recent availability of large-scale benchmarks as an indicator that a data-driven approach may provide a new perspective.\n\nResultsWe propose DeeplyTough, a convolutional neural network that encodes a three-dimensional representation of protein binding sites into descriptor vectors that may be compared efficiently in an alignment-free manner by computing pairwise Euclidean distances. The network is trained with supervision: (i) to provide similar pockets with similar descriptors, (ii) to separate the descriptors of dissimilar pockets by a minimum margin, and (iii) to achieve robustness to nuisance variations. We evaluate our method using three large-scale benchmark datasets, on which it demonstrates excellent performance for held-out data coming from the training distribution and competitive performance when the trained network is required to generalize to datasets constructed independently.\n\nAvailabilityhttps://github.com/BenevolentAI/DeeplyTough\n\nContactmartin.simonovsky@enpc.fr, joshua.meyers@benevolent.ai

bioinformatics↗

bioRxiv2017

Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity

Oscillations in neuronal activity are widely thought to play a crucial role in information processing and cortical communication 1-8. Brain oscillations have been widely investigated as biomarkers of psychiatric disorders and variation in normal human behavior 9, 10, including intelligence 11, 12, schizophrenia 13, 14, attentional deficits 15, 16, and substance use 17, 18. Beyond the biomarker, oscillatory activity may indeed cause variation in behavior, as it has been shown that disrupting oscillatory activity by blocking GABAergic fast-spiking interneurons in the frontal cortex of mice impairs behavioral flexibility8, consistent with ...

genetics↗