Biology subjects

Flynn, E.

Publications and source records attributed to Flynn, E..

16 recordsLinked to original sources

bioRxiv2026

Comparative molecular analysis of histopathologic subtypes reveals shared epithelial remodeling yet distinct fibroblast programs in scleroderma lung disease

Rationale: Interstitial lung disease (ILD) drives mortality in systemic sclerosis (SSc), with a subset developing progressive pulmonary fibrosis (PPF) resembling idiopathic pulmonary fibrosis (IPF). Despite recognition of two distinct histopathologic patterns, the cellular basis of nonspecific interstitial pneumonia (NSIP) and usual interstitial pneumonia (UIP) remains poorly understood. Objectives: To define shared and distinct cellular and molecular features of SSc-NSIP and SSc-UIP compared to IPF. Methods: Single-nucleus RNA sequencing and spatial transcriptomics were performed on explanted lungs from patients with SSc-NSIP (n=6), SSc-UIP (n=6), or IPF (n=6), and control donors (n=12) with immunohistochemistry and western blot validation. Main Results: Across 120,254 nuclei from 35 cell types, there was striking similarity across fibrotic subgroups in epithelial, endothelial, and immune compartments, including alveolar type 1 cell loss, pathologic alveolar type 2 (AT2) gene expression, and expansion of COL15A1 endothelial cells and profibrotic macrophages. Diseased AT2s showed activation of the p53/DNA damage pathway. Aberrant basaloid (AB) cells were present in both SSc-ILD subtypes and transcriptionally similar to IPF ABs. They consistently neighbored profibrotic CTHRC1 fibroblasts, even in SSc-NSIP, despite lacking fibroblast foci. Fibroblast heterogeneity differentiated the histopathologic subtypes. Inflammatory fibroblasts were enriched in SSc-NSIP and CTHRC1 fibroblasts in UIP. AB-to-pathologic fibroblast crosstalk was defined by profibrotic signaling, with amplified TGF-{beta} signaling in UIP. Conclusions: Convergent epithelial remodeling is a hallmark of PPF in SSc-ILD and IPF. Fibroblast heterogeneity characterized by altered TGF-beta signaling distinguishes SSc-NSIP from SSc-UIP and IPF. Accelerated aging, DNA damage, and p53-dependent senescence pathways may represent shared drivers of PPF.

genomics↗

bioRxiv2026

Dopamine Agonists in Parkinson's Disease Decouple Risk-Taking from Reward-Paired Cues by Enhancing Risky Choice in Their Absence

Background: Common side effects of dopamine replacement therapy (DRT) for Parkinson's Disease (PD) are addiction-like impulse control disorders, including compulsive gambling. Gambling products such as slot machines prominently feature reward-paired audiovisual stimuli, which can promote riskier choices on laboratory gambling tasks in both humans and rodents. In rats, risky decision making in the presence of reward-paired cues is dependent on dopamine D3 receptor activity and is increased by ropinirole, a D2/3 receptor agonist with a higher affinity for D3 receptors. In humans, it remains uncertain whether cue-induced risky choice is amplified by dopamine agonism. Methods: We tested effects of DRT on cue-induced risky choice in 62 patients with PD (34 on levodopa monotherapy, 27 on levodopa and dopamine agonists). Patients performed two versions of a risky decision-making task: one with and one without reward-paired sensory cues, both ON and OFF DRT. A group of 36 age-matched controls completed the same task versions without DRT. Results: Across all groups, participants made riskier choices when the task included reward-paired sensory cues. Levodopa monotherapy did not affect risky decision-making. However, a combination of levodopa and dopamine agonists increased risky choices specifically in the uncued version of the task, making performance indistinguishable from that on the cued task. Conclusions: Dopamine agonists combined with levodopa promote risk-taking even in the absence of reward-paired cues. This could translate into risky reward-seeking behaviors that are decoupled from contextual factors.

neuroscience↗

bioRxiv2026

Spatial Orchestration of Skin Fibrosis by a CD8+ T cell-Myofibroblast Axis

Fibrosing skin diseases are highly morbid conditions with diverse clinical and histopathologic features. Prior work, primarily in systemic sclerosis (SSc), has yielded mixed data regarding the immune drivers of fibrosis, as well as the identity and spatial localization of pro-fibrotic fibroblast cell subsets. Here, we focus on morphea and eosinophilic fasciitis (EF), which cause more acutely inflammatory skin fibrosis. Using multimodal single-nucleus and spatial transcriptomics, we find that effector CD8+ T cells are highly enriched in fibrotic skin. These cells are particularly abundant in inflammatory tissue domains bordering fibrotic stroma, which are marked by expression of interferon-{gamma} stimulated genes. Inflammatory domains feature a loss of local homeostatic fibroblast populations and replacement with ADAM12-expressing inflammatory fibroblasts and myofibroblasts, which co-localize closely with CD8+ T cells. All subtypes of morphea featured similar patterns of CD8+ T-cell-associated fibro-inflammatory zonation and fibroblast transformation, suggesting that shared mechanisms can drive fibrosis across stromal compartments of skin. We apply these findings to a large publicly available scleroderma dataset and find that similar processes occur in SSc. Mechanistically, ablation of CD8+ T cells in mice ameliorates bleomycin-driven inflammation and fibrosis, as does fibroblast-intrinsic abrogation of IFN-{gamma} signaling. These data establish CD8+ T cell-driven fibrogenesis as a key feature of fibrosing skin diseases and raise the prospect of targeting CD8+ T cells in autoimmune fibrosis more broadly. One sentence summarySpatial profiling of morphea-spectrum diseases reveals CD8 T cells as key drivers of fibrosis through fibroblast IFN-{gamma} signaling.

immunology↗

bioRxiv2025

Single-Cell Profiling Reveals Altered Endometrial Cellular Features Across the Menstrual Cycle in Endometriosis Patients

Endometriosis is a chronic, estrogen-dependent condition affecting over 190 million women globally, characterized by the ectopic presence of endometrial-like tissue that leads to inflammation, pain, and infertility. Despite its prevalence, the pathogenesis of endometriosis remains poorly understood. Here, we present a comprehensive single-cell transcriptomic atlas comprising 228,000 cells derived from 43 eutopic endometrial biopsies from patients with endometriosis, fibroid controls and healthy controls, sampled across the menstrual cycle. This analysis reveals previously uncharacterized subpopulations of endometrial fibroblasts and epithelial cells undergoing epithelial-mesenchymal transition, alongside disrupted immune cell communication networks. Comparative gene expression profiling implicates oxidative stress, aberrant cell migration, and dysregulated apoptosis as central features of the disease state. These findings suggest that endometriosis alters eutopic endometrial homeostasis, with potential consequences for fertility, regeneration, and disease progression. Our dataset provides a valuable resource for biomarker discovery and identifies candidate therapeutic targets aimed at restoring endometrial function and alleviating symptoms in affected individuals.

bioinformatics↗

bioRxiv2025

Differential Assembly of Mouse and Human Tumor Microenvironments

Mouse models are frequently used to develop treatments for human cancer. Yet, we lack a comprehensive understanding of the comparative organization of mouse and human tumor microenvironments (mu/huTMEs). Through immunoprofiling of commonly used mouse models, we found that the immune composition of most muTMEs resemble poorly infiltrated human tumors extensively biased toward high macrophages densities. Relatedly, we discover species-specific biases of chemokine expression networks, factors which drive TMEs assembly. Further, assessing coarse cellular networks, we find conserved correlations between some immune cell frequencies, while other relationships only appear conserved in the huTMEs matching muTME profiles. Despite this variable alignment, we define robust cell type-specific gene expression programs conserved in TMEs across species and cohorts and identify ones that are coordinated between cell populations in both species. Together, we isolate and offer methods to study the multiple areas of hazard and opportunities for using mice to model human cancer. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=192 HEIGHT=200 SRC="FIGDIR/small/661217v1_ufig1.gif" ALT="Figure 1"> View larger version (71K): org.highwire.dtl.DTLVardef@22bcf7org.highwire.dtl.DTLVardef@a410b7org.highwire.dtl.DTLVardef@1455eb5org.highwire.dtl.DTLVardef@14cc6f5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Single-cell RNA sequencing of peripheral blood defines two immunological subtypes of Sjögren's disease

Sjogrens disease (SjD) is a heterogeneous autoimmune disorder with significant clinical and molecular diversity. While anti-SSA antibodies serve as a hallmark serological biomarker, nearly half of patients lack them, raising fundamental questions about distinct pathogenic mechanisms. To address this, we performed single-cell RNA sequencing with surface protein profiling on 1.5 million PBMCs from 333 participants in the Sjogrens International Collaborative Clinical Alliance cohort, comparing those with and without SjD and stratifying them by anti-SSA status. Our analysis identified two immunologically distinct subtypes of SjD, with SSA-positive patients exhibiting a dominant and persistent IFN-I signature, profoundly impacting immune cell maturation. Notably, transitional B cells were disproportionately affected, displaying signs of premature maturation, reduced BCR diversity, and shorter CDR3 regions, reinforced interactions with proinflammatory cells, thereby fostering an environment conducive to autoreactivity. By contrast, SSA-negative SjD participants lacked an upregulated IFN-I signature, challenging prevailing pathogenic models and suggesting alternative immune dysregulation pathways. These findings support a two-disease model of SjD and highlight transitional B cells as both a key biomarker and a potential therapeutic target. Targeting IFN-I signaling and transitional B cell maturation may represent a novel therapeutic avenue to modulate immune dysregulation and prevent autoreactivity in SjD.

genomics↗

bioRxiv2023

Coordinated immune dysregulation in Juvenile Dermatomyositis revealed by single-cell genomics

Juvenile Dermatomyositis (JDM) is one of several childhood-onset autoimmune disorders characterized by a type I interferon response and autoantibodies. Treatment options are limited due to incomplete understanding of how the disease emerges from dysregulated cell states across the immune system. We therefore investigated the blood of JDM patients at different stages of disease activity using single-cell transcriptomics paired with surface protein expression. By immunophenotyping peripheral blood mononuclear cells, we observed skewing of the B cell compartment towards an immature naive state as a hallmark of JDM. Furthermore, we find that these changes in B cells are paralleled by signatures of Th2-mediated inflammation. Additionally, our work identified SIGLEC-1 expression in monocytes as a composite measure of heterogeneous type I interferon activity in disease. We applied network analysis to reveal that hyperactivation of the type I interferon response in all immune populations is coordinated with dysfunctional protein processing and regulation of cell death programming. This analysis separated the ubiquitously expressed type I interferon response into a central hub and revealed previously masked cell states. Together, these findings reveal the coordinated immune dysregulation underpinning JDM and provide novel insight into strategies for restoring balance in immune function.

immunology↗

bioRxiv2023

Deciphering Cell-types and Gene Signatures Associated with Disease Activity in Rheumatoid Arthritis using Single Cell RNA-sequencing

ObjectiveSingle cell profiling of synovial tissue has previously identified gene signatures associated with rheumatoid arthritis (RA) pathophysiology, but synovial tissue is difficult to obtain. This study leverages single cell sequencing of peripheral blood mononuclear cells (PBMCs) from patients with RA and matched healthy controls to identify disease relevant cell subsets and cell type specific signatures of disease. MethodsSingle-cell RNA sequencing (scRNAseq) was performed on peripheral blood mononuclear cells (PBMCs) from 18 RA patients and 18 matched controls, accounting for age, gender, race, and ethnicity). Samples were processed using standard CellRanger and Scanpy pipelines, pseudobulk differential gene expression analysis was performed using DESeq2, and cell-cell communication analysis using CellChat. ResultsWe identified 18 distinct PBMC subsets, including a novel IFITM3+ monocyte subset. CD4+ T effector memory cells were increased in patients with moderate to high disease activity (DAS28-CRP [≥] 3.2), while non-classical monocytes were decreased in patients with low disease activity or remission (DAS28-CRP < 3.2). Differential gene expression analysis identified RA-associated genes in IFITM3+ and non-classical monocyte subsets, and downregulation of pro-inflammatory genes in the V{delta} subset. Additionally, we identified gene signatures associated with disease activity, characterized by upregulation of pro-inflammatory genes TNF, JUN, EGR1, IFIT2, MAFB, G0S2, and downregulation of HLA-DQB1, HLA-DRB5, TNFSF13B. Notably, cell-cell communication analysis revealed upregulation of immune-associated signaling pathways, including VISTA, in patients with RA. ConclusionsWe provide a novel single-cell transcriptomics dataset of PBMCs from patients with RA, and identify insights into the systemic cellular and molecular mechanisms underlying RA disease activity.

immunology↗

bioRxiv2022

Decoding the Variant-to-Function Relationship for LIPA, a Risk Locus for CAD

Translating human genomic discoveries into mechanistic insights requires linking genetic variations to candidate genes and their causal functional phenotypes. Genome-wide association studies have consistently identified LIPA as a risk locus for coronary artery disease (CAD), with previous expression quantitative trait loci (eQTL) analyses prioritizing LIPA as a candidate causal gene. However, functional studies elucidating the causal variants, regulatory mechanisms, target cell types, and their causal impact on atherosclerosis have been lacking. To address this gap, we applied functional genomics and experimental mouse models to establish the variant-to-function relationship at the LIPA locus. Our findings show that CAD risk alleles in the LIPA locus increase LIPA expression and enzyme activity specifically in monocytes/macrophages by enhancing PU.1 binding to an intronic enhancer region that interacts with the LIPA promoter. In myeloid Lipa-overexpressing mice, we observed larger atherosclerotic lesions accompanied by altered macrophage function, including increased macrophage accumulation due to enhanced monocyte recruitment, reduced neutral lipid accumulation, and upregulation of integrin and extracellular matrix pathways. Our work establishes a direct causal link between LIPA risk alleles and increased monocyte/macrophage LIPA that exacerbates atherosclerosis, bridging human functional genomic evidence to the mechanistic understanding of CAD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/516293v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@11ce287org.highwire.dtl.DTLVardef@15c201aorg.highwire.dtl.DTLVardef@13f90c9org.highwire.dtl.DTLVardef@3aa3be_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2021

Comprehensive Assessment of Smoking and Sex Related Effects in Publicly Available Gene Expression Data

Smoking greatly reduces life expectancy in both men and women, but with different patterns of morbidity. After adjusting for smoking history, women have higher risk of respiratory effects and diabetes from smoking, while men show greater mortality from smoking-related cancers. While many smoking-related sex differences have been documented, the underlying molecular mechanisms are not well understood. To date, identification of sex differences in response to smoking has been limited to a small number of studies and the resulting smoking-related effects require further validation. Publicly available gene expression data present a unique opportunity to examine molecular-level sex and smoking effects across many tissues and studies. We performed a systematic search to identify smoking-related studies from healthy tissue samples and found 31 separate studies as well as an additional group of overlapping studies that in total span 2,177 samples and 12 tissues. These samples and studies were overall male-biased. In smoking, while effects appeared to be somewhat tissue-specific and largely autosomal, we identified a small number of genes that were consistently differentially expressed across tissues, including AHRR and GZMH. We also identified one gene, AKR1C3, encoding an aldo-keto reductase, which showed strong opposite direction, smoking-related effects in blood and airway epithelium, with higher expression in airway epithelium and lower expression in blood of smokers versus non-smokers. By contrast, at similar significance thresholds, sex-related effects were entirely sex chromosomal and consistent across tissues, providing evidence of stronger effects of smoking than sex on autosomal expression. Due to sample size limitations, we only examined interaction effects in the largest study, where we identified 30 genes with sex differential effects in response to smoking, only one of which, CAPN9, replicated in a held-out analysis. Overall these results present a comprehensive analysis of smoking-related effects across tissues and an initial examination of sex differential smoking effects in public gene expression data.

bioinformatics↗

bioRxiv2021

Transcription factor regulation of eQTL activity across individuals and tissues

Tens of thousands of genetic variants associated with gene expression (cis-eQTLs) have been discovered in the human population. These eQTLs are active in various tissues and contexts, but the molecular mechanisms of eQTL variability are poorly understood, hindering our understanding of genetic regulation across biological contexts. Since many eQTLs are believed to act by altering transcription factor (TF) binding affinity, we hypothesized that analyzing eQTL effect size as a function of TF level may allow discovery of mechanisms of eQTL variability. Using GTEx Consortium eQTL data from 49 tissues, we analyzed the interaction between eQTL effect size and TF level across tissues and across individuals within specific tissues and generated a list of 6,262 TF-eQTL interactions across 1,598 genes that are supported by at least two lines of evidence. These TF-eQTLs were enriched for various TF binding measures, supporting with orthogonal evidence that these eQTLs are regulated by the implicated TFs. We also found that our TF-eQTLs tend to overlap genes with gene-by-environment regulatory effects and to colocalize with GWAS loci, implying that our approach can help to elucidate mechanisms of context-specificity and trait associations. Finally, we highlight an interesting example of IKZF1 TF regulation of an APBB1IP gene eQTL that colocalizes with a GWAS signal for blood cell traits. Together, our findings provide candidate TF mechanisms for a large number of eQTLs and offer a generalizable approach for researchers to discover TF regulators of genetic variant effects in additional QTL datasets. Author SummaryGene expression is regulated by local genomic sequence and can be affected by genetic variants. In the human population, tens of thousands of cis-regulatory variants have been discovered that are associated with altered gene expression across tissues, cell types, or environmental conditions. Understanding the molecular mechanisms of how these small changes in the genome sequence affect genome function would offer insight to the genetic regulatory code and how gene expression is controlled across tissues and environments. Current research efforts suggest that many regulatory variants effects on gene expression are mediated by them altering the binding of transcription factors, which are proteins that bind to DNA to regulate gene expression. Here, we exploit the natural variation of TF activity among 49 tissues and between 838 individuals to elucidate which TFs regulate which regulatory variants. We find 6,262 TF-eQTL interactions across 1,598 genes that are supported by at least two lines of evidence. We validate these interactions using functional genomic and experimental approaches, and we find indication that they may pinpoint mechanisms of environment-specific genetic regulatory effects and genetic variants associated to diseases and traits.

genomics↗

bioRxiv2020

Large-Scale Labeling and Assessment of Sex Bias in Publicly Available Expression Data

Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we infer sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of single sex studies, split between female-only and male-only (33.3% vs 18.4% in human and 31.0% vs 30.4% in mouse respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies respectively. Our expression-based sex labels allow us to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%). We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

bioinformatics↗

bioRxiv2019

Sex-specific genetic effects across biomarkers

Sex differences have been shown in laboratory biomarkers; however, the extent to which this is due to genetics is unknown. In this study, we infer sex-specific genetic parameters (heritability and genetic correlation) across 33 quantitative biomarker traits in 181,064 females and 156,135 males from the UK Biobank study. We apply a Bayesian mixture model, Sex Effects Mixture Model, to Genome-wide Association Study summary statistics in order to (1) estimate the contributions of sex to the genetic variance of these biomarkers and (2) identify variants whose statistical association with these traits is sex-specific. We find that the genetics of most biomarker traits are shared between males and females, with the notable exception of testosterone, where we identify 119 female and 444 male-specific variants. These include protein-altering variants in steroid hormone production genes (POR, CYP3A43, UGT2B7). Using the sex-specific variants as genetic instruments for Mendelian Randomization, we find evidence for causal links between testosterone levels and height, body mass index, waist circumference, and type 2 diabetes. We also show that sex-specific polygenic risk score models for testosterone outperform a combined model. Overall, these results demonstrate that while sex has a limited role in the genetics of most biomarker traits, sex plays an important role in testosterone genetics.

genetics↗

bioRxiv2019

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

genetics↗

bioRxiv2019

Risks to pollinators from different land-use transitions: bee species’ responses to agricultural expansion show strong phylogenetic signal

Bee species worldwide are facing a future of further land-use change and intensification. Populations of closely-related species with similar ecological characteristics are likely to respond similarly to such pressures. Such phylogenetic signal in species responses could undermine the stability of pollination services in agricultural and natural systems. We use abundance data from a global compilation of bee assemblages in different land uses to assess the sensitivity of 573 bee species to agricultural expansion, intensification and urbanization; and combine the results with the Bee Tree of Life to assess phylogenetic signal. In addition, we assess whether variation in species sensitivity to land-use change is better explained by phylogenetic or available functional trait differences. Bee species show strong phylogenetic signal in sensitivity to agricultural land expansion but only a weak signal in sensitivity to agricultural intensification and urbanisation. Sensitivities were usually best explained by a combination of functional and phylogenetic distances. This finding suggests that the commonly-recorded traits, despite being meaningful as functional response traits, do not capture all important determinants of bee species vulnerability or resistance. However, it also suggests that model-based predictions of the sensitivity of poorly known species may be sufficient to help guide conservation efforts.

ecology↗

bioRxiv2019

GRep: Gene Set Representation via Gaussian Embedding

Molecular interaction networks are our basis for understanding functional interdependencies among genes. Network embedding approaches analyze these complicated networks by representing genes as low-dimensional vectors based on the network topology. These low-dimensional vectors have recently become the building blocks for a larger number of systems biology applications. Despite the success of embedding genes in this way, it remains unclear how to effectively represent gene sets, such as protein complexes and signaling pathways. The direct adaptation of existing gene embedding approaches to gene sets cannot model the diverse functions of genes in a set. Here, we propose GRep, a novel gene set embedding approach, which represents each gene set as a multivariate Gaussian distribution rather than a single point in the low-dimensional space. The diversity of genes in a set, or the uncertainty of their contribution to a particular function, is modeled by the covariance matrix of the multivariate Gaussian distribution. By doing so, GRep produces a highly informative and compact gene set representation. Using our representation, we analyze two major pharmacogenomics studies and observe substantial improvement in drug target identification from expression-derived gene sets. Overall, the GRep framework provides a novel representation of gene sets that can be used as input features to off-the-shelf machine learning classifiers for gene set analysis.

bioinformatics↗