Biology subjects

Wells, Q. S.

Publications and source records attributed to Wells, Q. S..

6 recordsLinked to original sources

bioRxiv2026

Aging under immunosuppression reshapes human immune compartments and lowers clinical alloreactivity after heart transplantation

Solid-organ transplantation in aging recipients represents a unique opportunity to study how age-related immunity in the context of non-specific immunosuppression strategies balances infection, malignancy, and rejection. Heart transplantation is an exemplar platform, as routine endomyocardial biopsy for rejection surveillance is the clinical "gold standard" regardless of clinical status. Here, we undertook the largest granular study to date to characterize the association between increasing recipient age at heart transplantation with acute allograft rejection and age-related cell-specific transcriptomic changes in circulating immune cells. This single-center retrospective cohort study evaluated individuals undergoing heart transplantation between July 2013 and December 2023 at Vanderbilt University Medical Center. Eligible participants were aged [≥]18 years. A subset of individuals underwent single-cell RNA-sequencing of circulating immune cells. Among 799 adults, each one standard deviation increase in recipient age was associated with a [~]17% lower odds of allograft rejection (adjusted OR 0.83, 95% CI 0.71-0.98). In 40 individuals who underwent single-cell RNA-sequencing of circulating immune cells, increasing recipient age was associated with increases in CD4+ and CD8+ memory T cell subsets, monocytes, and NK cells. Furthermore, genes upregulated with increasing recipient age were associated with enrichment for pathways involved in immunosenescence and chronic low-grade inflammation while downregulated genes suggested decreased protein synthesis. These findings have clinical implications for an aging transplant population and support a more personalized approach to immunosuppression.

systems biology↗

bioRxiv2025

Dynamic responses to rejection in the transplanted human heart revealed through spatial transcriptomics

Allograft rejection following solid-organ transplantation is a major cause of graft dysfunction and mortality. Current approaches to diagnosis rely on histology, which exhibits wide diagnostic variability and lacks access to molecular phenotypes that may stratify therapeutic response. Here, we leverage image-based spatial transcriptomics at sub-cellular resolution in longitudinal human cardiac biopsies to characterize transcriptional heterogeneity in 62 adult and pediatric heart transplant (HT) recipients during and following histologically-diagnosed rejection. Across 28 cell types, we identified significant differences in abundance in CD4+ and CD8+ T cells, fibroblasts, and endothelial cells across different biological classes of rejection (cellular, mixed, antibody-mediated). We observed a broad overlap in cellular transcriptional states across histologic rejection severity and biological class and significant heterogeneity within rejection severity grades that would qualify for immunomodulatory treatment. Individuals who had resolved rejection after therapy had a distinct transcriptomic profile relative to those with persistent rejection, including 216 genes across 6 cell types along pathways of inflammation, IL6-JAK-STAT3 signaling, IFN/IFN{gamma} response, and TNF signaling. Spatial transcriptomics also identified genes linked to long-term prognostic outcomes post-HT. These results underscore importance of subtyping immunologic states during rejection to stratify immune-cardiac interactions following HT that are therapeutically relevant to short- and long-term rejection-related outcomes.

genomics↗

bioRxiv2024

A non-muscle α-actinin is an intrinsic component of the cardiac Z-disc and regulates sarcomere turnover, contractility, and heart remodeling

Cardiac sarcomeres generate the fundamental forces behind each heartbeat and are thought to contain only muscle-specific cytoskeletal proteins. We show that a widely expressed actin cross-linking protein, -actinin 4 (ACTN4), is a sarcomere component of the human and zebrafish heart in vivo and in human iPSC-derived cardiac myocytes (CMs) in vitro. A confluence of biochemical experiments, immunofluorescence, and AI modeling suggest ACTN4 forms a heterodimeric complex with muscle-specific ACTN2 at the cardiac Z-disc, the cardiac sarcomere border. ACTN4 depletion from human iPSC-CMs stabilizes canonical sarcomere proteins and drives contractility-dependent cellular hypertrophy while ACTN4 overexpression destabilizes sarcomeres. ACTN4 depletion from zebrafish embryos specifically increases ventricular contractility which drives atrial enlargement, suggesting biomechanically driven atrial remodeling. ACTN4-associated phenotypes in both model systems lack hallmarks of cardiac disease models and an ACTN4 variant in humans is associated with reduced risk for disease. Our findings suggest a "non-muscle" actinin regulates heart contractility and influences clinical outcomes related to heart failure.

cell biology↗

bioRxiv2023

Development and implementation of an integrated preclinical atherosclerosis database

BackgroundBasic scientists have used preclinical animal models to explore mechanisms driving human diseases for decades, resulting in thousands of publications, each supporting causative inferences. Despite substantial advances in the mechanistic construct of disease, there has been limited translation from individual studies to advances in clinical care. An integrated approach to these individual studies has the potential to improve translational success. MethodsUsing atherosclerosis as a test case, we extracted data from the two most common mouse models of atherosclerosis (ApoE and LDLR knockout). We restricted analyses to manuscripts published in two well-established journals, Arteriosclerosis, Thrombosis, and Vascular Biology and Circulation, as of query in 2021. Predefined variables including experimental conditions, intervention and outcomes were extracted from each publication to produce a preclinical atherosclerosis database. ResultsExtracted data include animal sex, diet, intervention type and distinct plaque pathologies (size, inflammation, lipid content). Procedures are provided to standardize data extraction, attribute interventions to specific genes and transform the database for use with available transcriptomics software. The database integrates hundreds of genes, each directly tested in vivo for causation in a murine atherosclerosis model. The database is provided to allow the research community to perform integrated analyses that reflect the global impact of decades of atherosclerosis investigation. ConclusionsFuture database uses include interrogation of sub-datasets associated with distinct plaque pathologies, cell-type or sex. We provide the methods and software needed to apply this approach to the extensive repository of peer-reviewed data utilizing preclinical models to interrogate mechanisms of diverse human diseases.

systems biology↗

bioRxiv2019

Genetic risk for major depressive disorder and loneliness in gender-specific associations with coronary artery disease

ImportanceEpidemiological evidence indicates that major depressive disorder (MDD) and loneliness both reduce life expectancies, but mechanisms underlying the excess morbidity are unclear. Electronic health records (EHRs) linked to genetic data offer new opportunities to address this knowledge gap.\n\nObjectiveTo determine the medical morbidity pattern associated with genetic risk factors for MDD and loneliness, two common psychological traits with adverse health outcomes.\n\nDesignPhenome-wide association study using EHRs spanning 1990 to 2017 from the Vanderbilt University Medical Center biobank, BioVU. Top associations with coronary artery disease (CAD) were replicated in the Atherosclerosis Risk in Communities (ARIC) cohort.\n\nSettingHospital-based EHR study, with replication in a population-based cohort study.\n\nParticipants18,385 genotyped adult patients in BioVU. Replication in ARIC included 7,197 genotyped participants. All participants were of European ancestry.\n\nExposuresPolygenic scores for MDD and loneliness were developed for each individual using previously published meta-GWAS summary statistics.\n\nMain Outcomes and MeasuresThe phenome-wide association study included 882 clinical diagnoses ascertained via billing codes in the EHR. ARIC included 1598 incident CAD cases.\n\nResultsBioVU patients had a median EHR length of 9.91 years. In the phenome-wide association study, polygenic scores for MDD and loneliness were significantly associated with psychiatric and cardiac phenotypes. Targeted analyses of CAD in 3,893 cases and 4,197 controls in BioVU found odds ratios of 1.11 (95% CI, 1.04-1.18; P=8.43x10-4) and 1.13 (95% CI, 1.07-1.20; P=4.51x10-6) per 1-SD increase in the polygenic scores for MDD and loneliness, respectively. Comparable hazard ratios in ARIC were 1.07 (95% CI, 0.99-1.14; P=0.07) and 1.07 (1.01-1.15; P=0.03). Across both studies, the increased risk persisted in women after adjusting for multiple conventional risk factors, a polygenic score for CAD, and psychiatric symptoms (available in BioVU). Controlling for genetic risk factors shared between MDD and loneliness, the polygenic score for loneliness conditioned on MDD remained associated with CAD risk, but the polygenic score for MDD conditioned on loneliness did not.\n\nConclusions and RelevanceGenetic risk factors for MDD and loneliness act pleiotropically to increase CAD risk in women. Continued research into the biological and clinical connections between the heart and mind is warranted.

genetics↗

bioRxiv2018

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

BackgroundCurrent approaches to predicting Cardiovascular disease rely on conventional risk factors and cross-sectional data. In this study, we asked whether: i) machine learning and deep learning models with longitudinal EHR information can improve the prediction of 10-year CVD risk, and ii) incorporating genetic data can add values to predictability.\n\nMethodsWe conducted two experiments. In the first experiment, we modeled longitudinal EHR data with aggregated features and temporal features. We applied logistic regression (LR), random forests (RF) and gradient boosting trees (GBT) and Convolutional Neural Networks (CNN) and Recurrent Neural Networks, using Long Short-Term Memory (LSTM) units. In the second experiment, we proposed a late-fusion framework to incorporate genetic features.\n\nResultsOur study cohort included 109, 490 individuals (9,824 were cases and 99, 666 were controls) from Vanderbilt University Medical Centers (VUMC) de-identified EHRs. American College of Cardiology and the American Heart Association (ACC/AHA) Pooled Cohort Risk Equations had areas under receiver operating characteristic curves (AUROC) of 0.732 and areas under receiver under precision and recall curves (AUPRC) of 0.187. LSTM, CNN and GBT with temporal features achieved best results, which had AUROC of 0.789, 0.790, and 0.791, and AUPRC of 0.282, 0.280 and 0.285, respectively. The late fusion approach achieved a significant improvement for the prediction performance.\n\nConclusionsMachine learning and deep learning with longitudinal features improved the 10-year CVD risk prediction. Incorporating genetic features further enhanced 10-year CVD prediction performance, underscoring the importance of integrating relevant genetic data whenever available in the context of routine care.

epidemiology↗