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Atkinson, M. A.

Publications and source records attributed to Atkinson, M. A..

At least 19 recordsLinked to original sources

Mitochondrial and protein homeostasis pathways are transcriptionally impaired in islets during type 1 diabetes pathogenesis

The decline in first-phase insulin response (FPIR) during the presymptomatic period of type 1 diabetes (T1D) is well established. In-situ functional studies with pancreas tissue slices showed that {beta}-cell loss of glucose-responsiveness was independent of T-cell infiltration into islets in recent-onset T1D cases. However, the mechanisms driving {beta}-cell dysfunction before the onset of T1D remain unclear. In pancreas tissue from donors across the natural history of T1D, we utilized an in-situ, whole-islet phenotypical and transcriptomic approach to unravel novel targets in the glucose-stimulus coupled secretion pathway that are similarly impaired in T-cell infiltrated and non-infiltrated islets. Specifically, we observed that islets from autoantibody positive (single(s) or multiple(m) AAb+) donors exhibited activation of post-transcriptional gene regulation along with reduced protein translation, processing in the endoplasmic reticulum (ER), and ER stress. Disrupted mitochondrial metabolism and bioenergetics were prominent in islets from multiple AAb+ and T1D donors with disease durations [≤]7 years. In addition, T1D islets presented reduced mitochondrial protein import, quality control, and dynamics, together with downregulated genes in insulin secretory pathways. During infiltration, these pathways remain dysregulated while immune/inflammatory transcripts were increased. These studies identified novel mechanisms of {beta}-cell dysregulation before symptomatic onset and independent of T-cell infiltration in T1D pathogenesis.

physiology↗

Virtual multiplex staining of the pancreatic islets across type 1 diabetes progression using a Schroedinger bridge

Classical hematoxylin and eosin (H&E) staining enables review of tissue morphology but lacks information regarding the molecular state of cells. Immunohistochemical (IHC) techniques label specific proteins in tissue, allowing differentiation of relevant structures that may go undetectable in H&E. However, the IHC process is complex, expensive, and time-consuming, especially for multiplex IHC (mIHC) limiting its use in large cohorts. Stain conversion of H&E to IHC using generative artificial intelligence models such as generative adversarial networks (GANs) represent one solution to this problem. However, GANs are unstable during out of distribution sampling and are prone to hallucinations or mode collapse, limiting their accuracy in challenging image conversion tasks. To address this, the field has recently turned to diffusion models. Here, we introduce Schrodinger-bridge for Multiplex ImmunoLabel Estimation (SMILE). Unlike conventional diffusion models that map from source to target through an intermediate Gaussian noise, Schrodinger-bridge diffusion models skip this step and have been shown to better preserve structures during image translation. To test the performance of SMILE, we generated a large cohort of high-fidelity H&E-mIHC image pairs from pancreatic organ donors, targeting insulin, glucagon, and CD3. Our dataset well-sampled across type-1 diabetes status, pancreas anatomical location, age, and sex. Using this cohort, we demonstrate the superiority of SMILE compared to GANs via a comprehensive evaluation framework incorporating texture, distribution, and antibody-specific metrics, as well as blinded pathologist reviews. We further confirmed the ability of SMILE to generate accurate mIHC images from H&Es generated at an external site, to perform whole slide image conversion, and to generate realistic three-dimensional maps of the pancreatic islets in non-diabetic, auto-antibody positive, and type-1 diabetic donor tissue. Finally, we performed stain conversion of paired H&E to HER2 and Ki67 images in breast cancer, confirming the superiority of SMILE in diverse stain conversion applications. Collectively, this framework provides a scalable pipeline for high-throughput proteomic inference from archival H&Es, providing transformative potential for pancreatic research and digital pathology.

bioinformatics↗

Single-Islet Proteomics Maps Pseudo-Temporal Islet Immune Responses and Dysfunction in Stage 1 Type 1 Diabetes

Progressive {beta}-cell dysfunction precedes the onset of type 1 diabetes (T1D), yet the molecular mechanisms driving early T1D development remain poorly understood. Although single-cell RNA-sequencing has uncovered transcript-level changes in human islet cells, it offers limited insight into the heterogeneity of distinct islet microenvironments. Here, we applied a single-islet proteomics workflow to profile intra-donor islet heterogeneity in three stage 1 T1D cases with matched non-diabetic controls and define in situ protein signatures of pseudo-temporal islet dysfunction. Intra-donor analyses of [~]100 individual islets per donor revealed highly consistent proteomic patterns reflecting pseudo-time progression of islet immune responses and {beta}-cell dysfunction. Several pathways, including extracellular matrix remodeling and mRNA processing, were identified as closely associated with progressive islet immune activation and loss of {beta}-cell function. These findings provide robust proteome-wide evidence of the progression of islet dysfunction, offer a valuable resource for investigating early mechanisms of T1D pathogenesis-- including novel candidates for functional studies--and underscore the utility of single-islet spatial proteomics for examining islet heterogeneity in T1D.

systems biology↗

Inhibition of cell-mediated immunity in type 1 diabetes by beta cell-targeted PD-1 agonists in pancreas tissue slices

Tissue-targeted immunotherapies for type 1 diabetes (T1D) hold potential to protect pancreatic beta cells while minimizing systemic immunosuppression. We used a bispecific agonist called Immune Modulating Monoclonal-TCR Against Autoimmune Disease (ImmTAAI), consisting of a T cell receptor (TCR) targeting domain fused with a PD-1 agonist to specifically bind beta cells and suppress autoreactive T cells. We used live pancreas slices to demonstrate targeting of ImmTAAI molecules to pre-proinsulin peptide-HLA-A2 complexes on human beta cells. ImmTAAI protected beta cells from T cell killing by increasing T cell motility and inhibiting cytokine secretion. ImmTAAI treatment also increased the motility of islet-infiltrating T cells in slices from a donor with recent-onset T1D and preserved insulin secretion in slices co-cultured with T cell avatars transduced with diabetogenic TCRs. These data demonstrate that ImmTAAI molecules have the potential to limit T cell activity locally, making this an attractive platform to elicit targeted immunoregulation in T1D. One Sentence SummaryWe demonstrate inhibition of cellular immunity in human type 1 diabetes using a beta cell-targeting, affinity-enhanced TCR fused to a PD-1 agonist.

bioengineering↗

3D multi-omic mapping of whole nondiseased human fallopian tubes at cellular resolution reveals a large incidence of ovarian cancer precursors

Uncovering the spatial and molecular landscape of precancerous lesions is essential for developing meaningful cancer prevention and early detection strategies. High-Grade Serous Carcinoma (HGSC), the most lethal gynecologic malignancy, often originates from Serous Tubal Intraepithelial Carcinomas (STICs) in the fallopian tubes, yet their minute size and our historical reliance on standard 2D histology contribute to their underreporting. Here, we present a spatially resolved, multi-omics framework that integrates whole-organ 3D imaging at cellular resolution with targeted proteomic, metabolomic, and transcriptomic profiling to detect and characterize microscopic tubal lesions. Using this platform, we identified a total of 99 STICs and their presumed precursors that harbor TP53 mutations in morphologically unremarkable tubal epithelium in all five specimens obtained from cancer-free organ donors with average-risk of developing ovarian cancer. Although these lesions comprised only 0.2% of the epithelial compartment, they displayed geographic diversity, immune exclusion, metabolic rewiring, and DNA copy number changes among lesions and normal fallopian tube epithelium discovered alterations in STIC-associated genes and the pathways they control. In sum, this platform provides a comprehensive 3D atlas of early neoplastic transformation, yielding mechanistic insights into tumor initiation and informing clinical screening strategies for detecting cancer precursors in whole organs at cellular resolution.

bioinformatics↗

Whole tissue spatial cellular analysis reveals increased macrophage infiltration in pancreata of autoantibody positive donors and patients with type 1 diabetes

While extensive efforts have characterized lymphoid populations that contribute to pancreatic insulitis in type 1 diabetes, significant gaps remain regarding key properties of macrophages in this inflammatory lesion. Hence, we quantified the spatial distribution of macrophages infiltrating human pancreatic tissue sections across disease states. Whole slide images of immunofluoresent stainings were analyzed with a semiautomated machine learning approach to study the distribution of macrophages throughout pancreatic sections from non-diabetic, auto-antibody positive (Aab+), and type 1 diabetes organ donors. Across the entire tissue section, macrophage infiltration was highest in donors with type 1 diabetes, and appeared contingent on the prescence of insulin-containing islets. Aab+ donors had higher macrophage densities in islet and peri-islet regions than non-diabetic individuals, but there was large case-by-case variation. Additionally, a greater proportion of macrophages highly expressed HLA class II (HLA-II) near and within islet regions of the type 1 diabetes group, which correlated with the proportion of stressed beta cells expressing HLA-II. The higher density of macrophages in the Aab+ and type 1 diabetes donors, taken together with higher expression of HLA-II in the type 1 diabetes pancreas, suggests that macrophages play a pivotal role in human type 1 diabetes initiation and progression. Article HighlightsO_LIMacrophage infiltration is increased in all pancreatic regions of patients with type 1 diabetes, and this infiltration appears contingent on the presence of insulin-containing islets. C_LIO_LIAab+ donors have higher macrophage densities in islet and peri-islet regions than non-diabetic individuals, but there is large case-by-case variation within the Aab+ group. C_LIO_LIThe proportion of macrophages highly expressing HLA-II is increased near and within islets only in patients with type 1 diabetes, and this correlates with the proportion of stressed beta cells expressing HLA-II within the islet. C_LIO_LIMacrophages are linked to human type 1 diabetes initiation and progression, hence forming a rationale to examine therapeutic strategies involving this cellular population. C_LI

pathology↗

Aberrant immune regulation and enrichment of stem-like CD8+ T cells in the pancreatic lymph node during type 1 diabetes development

Effector CD8+ T cells are key cellular drivers of type 1 diabetes (T1D) pathogenesis, yet questions remain regarding the molecular defects leading to altered cytotoxicity, their signature in peripheral tissues, and their receptor specificity. Thus, we analyzed human pancreatic lymph nodes (pLN) using mass cytometry and single cell RNA sequencing (scRNAseq) with combined proteomic and T cell receptor (TCR) profiling. Cytometric analysis revealed an enriched population of T stem-cell memory (TSCM)-like cells (CD8+CD45RA+CD27+CD28+CCR7+CXCR3+ T cells) in T1D pLNs. scRNAseq profiling indicated an elevated inflammatory cytokine gene signature (IFITM3, LTB) along with regulators of terminal differentiation (BCL6, BCL3), coupled with reduced expression of exhaustion-associated genes (DUSP2, NR4A2, TSC22D3) in CD8+ T cells in T1D pLN. Additionally, effector CD8+ T cells expressed features of progenitor exhausted cells (BCL2) in T1D pLN. Immune Response Enrichment Analysis (IREA) indicated IL-15 signaling as a significant driver of these phenotypes. Integrated TCR and transcriptomic analysis revealed a cluster of diverse naive-like CD8+ T cell clones in T1D pLN. When comparing pLN and pancreatic slice cellular isolates, we observed sharing of effector CD8+ T cells, with upregulation of terminal effector signatures detected within the pancreas relative to paired pLN samples. Multiplex imaging revealed differential localization of TCF1 and TOX expressing T cells in the pancreas, with TCF1+TOX+ cells located in closer proximity to the islets and displaying a mixture of activation and exhaustion-associated phenotypes. Thus, we provide multimodal cellular profiles enriched in T1D tissues for consideration in therapeutic targeting.

immunology↗

Spatial transcriptomics from pancreas and local draining lymph node tissue reveals a lymphotoxin-β signature in human type 1 diabetes

This study explores the inflammatory response observed in pancreata and pancreatic lymph node (pLN) samples obtained throughout the natural history of type 1 diabetes (T1D) including non-diabetic individuals and non-diabetic autoantibody positive individuals with high susceptibility using spatial transcriptomics (ST). Integration of ST with public single-cell RNA sequencing data enabled interrogation of transcriptional alterations in T1D pathogenesis across both tissues and cellular scales. In the T1D pancreas, we observed global upregulation of multiple inflammation-associated transcripts, including regenerating islet-derived (REG) family genes, complement factor 3 (C3), SOD2, and OLFM4, and highlighted cellular candidates potentially contributing to these signatures. Within the T1D pLN, we observed spatially restricted upregulation of lymphotoxin-{beta} (LTB) alongside follicular dendritic cell (FDC)-associated transcripts including FDCSP, CLU, and FCER2. Collectively, these findings highlight distinct inflammation signatures in the pancreas and regional pLN which can help inform the development of future therapeutic interventions.

immunology↗

3D imaging of human pancreas suggests islet size and endocrine composition influence their loss in type 1 diabetes

A high-definition description of pancreatic islets would prove beneficial for understanding the pathophysiology of type 1 diabetes (T1D), yet significant knowledge voids exist in terms of their size, endocrine cell composition, and number in both health and disease. Here, 3-dimensional (3D) analyses of pancreata from control persons without diabetes (ND) revealed heretofore underappreciated frequencies (approximately 50%) of insulin-positive (INS+) glucagon-negative (GCG-) islets. Non-diabetic individuals positive for a single Glutamic acid decarboxylase autoantibody (GADA+) yet at increased risk for disease consistently demonstrated endocrine features, including islet volume and cell composition, closely resembling the age-matched ND controls. In contrast, pancreata from individuals with short-duration T1D demonstrated significantly reduced islet density and a dramatic loss of INS+GCG- islets with preservation of large INS+GCG+ islets. The size and cellular composition of pancreatic islets may, therefore, represent influential factors that impact {beta}-cell loss during T1D disease progression. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/654045v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1d2fdeaorg.highwire.dtl.DTLVardef@11c43edorg.highwire.dtl.DTLVardef@af8a82org.highwire.dtl.DTLVardef@ae8c58_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LI3D imaging of non-diabetic (ND) pancreas suggests up to 50% of INS+ islets lack GCG C_LIO_LIINS+GCG- islets are typically small-sized in ND and preferentially lost in T1D C_LIO_LIIn T1D pancreas, INS+ beta cells are preserved in large INS+GCG+ islets C_LIO_LIIslets from GADA+ individuals appear similar to ND for size and {beta}-to- cell ratios C_LI

pathology↗

Integration of nuclear morphology and 3D imaging to profile cellular neighborhoods

Nuclear morphology is an indicator of cellular function and disease states, as changes in nuclear size, shape, and texture often reflect underlying disease-related genetic, epigenetic, and microenvironmental alterations. For disease diagnosis, nuclear segmentation performed in 2D hematoxylin and eosin (H&E)-stained tissue sections has long represented the gold standard. However, recent advances in three-dimensional (3D) histology, which provide a more biologically accurate representation of the spatial heterogeneity of human microanatomy, has led to improved understandings of disease pathology. Yet challenges remain in the development of scalable and computationally efficient pipelines for extracting and interpreting nuclear features in 3D space. 2D histology neglects crucial spatial information, such as 3D connectivity, morphology, and rare events missed by sparser sampling. Here, through extension of the CODA platform, we integrate 3D imaging with nuclear segmentation to analyze nuclear morphological features in human tissue. Analysis of 3D tissue microenvironments uncovered critical changes in 3D morphometric heterogeneity. Additionally, it enables the spatial characterization of immune cell distribution in relation to tissue structures, such as variations in leukocyte density near pancreatic ducts and blood vessels of different sizes. This approach provides a more comprehensive understanding of tissue and nuclear structures, revealing spatial patterns and interactions that are critical for disease progression.

bioinformatics↗

Integrated histopathology of the human pancreas throughout stages of type 1 diabetes progression

Type 1 diabetes (T1D) is a progressive autoimmune condition that culminates in the loss of insulin-producing beta cells. Pancreatic histopathology provides essential insights into disease initiation and progression yet an integrated perspective of in situ pathogenic processes is lacking due to limited sample availability, the dispersed nature of anatomical lesions, and often restricted analytical dimensionality. Here, we combined multiplexed immunostaining, high-magnification whole-slide imaging, digital pathology, and semi-automated image analysis strategies to interrogate pancreatic tail and head regions obtained from organ donors across T1D stages including at-risk and at-onset cases. Deconvolution of architectural features, endocrine cell composition, immune cell burden, and spatial relations of [~]25,000 islets revealed a series of novel histopathological correlates especially in the prodromal disease stage preceding clinical T1D. Altogether, our comprehensive "single-islet" analyses permit the reconstruction of a revised natural T1D history with implications for further histopathological investigations, considerations of pathogenetic modalities, and therapeutic interventions.

immunology↗

Imaging mass cytometry reveals early β-cell dysfunction and changes in immune signatures during type 1 diabetes progression in human pancreata

The natural history and pathogenesis of type 1 diabetes, particularly during the autoantibody- positive stages preceding clinical onset, are not well understood, in part, due to limited availability of human pancreatic samples. Here, we studied 88 organ donors, including 28 single autoantibody-positive and 10 multiple autoantibody-positive donors, by imaging mass cytometry. Approximately 10,000 islets and 16 million single-cells were spatially analyzed using 79 antibodies revealing both {beta}-cell states and the islet-immune interface. We identified IAPP loss from {beta}-cells as an indicator of pre-clinical disease. Alterations in Interferon signatures and downregulation across lineage and functional markers, including markers of endoplasmic reticulum stress, were characteristic of recent-onset disease. Further, in single autoantibody- positive donors, we identified pro-inflammatory myeloid cells and PD1+ memory CD4+ T cells, and in multiple autoantibody-positive samples, found islet-specific and exhausted-like ebector CD8+ T cells. Multiple immune cell subtypes were associated with young age, disease severity and insulitis. This dataset is a major step toward creation of a multi-modal type 1 diabetes disease atlas that will be useful for identifying potential drug targets and association of disease features with clinical co-variates and trial outcomes.

immunology↗

Single-cell multiome and spatial profiling reveals pancreas cell type-specific gene regulatory programs driving type 1 diabetes progression

Cell type-specific regulatory programs that drive type 1 diabetes (T1D) in the pancreas are poorly understood. Here we performed single nucleus multiomics and spatial transcriptomics in up to 32 non-diabetic (ND), autoantibody-positive (AAB+), and T1D pancreas donors. Genomic profiles from 853,005 cells mapped to 12 pancreatic cell types, including multiple exocrine sub- types. Beta, acinar, and other cell types, and related cellular niches, had altered abundance and gene activity in T1D progression, including distinct pathways altered in AAB+ compared to T1D. We identified epigenomic drivers of gene activity in T1D and AAB+ which, combined with genetic association, revealed causal pathways of T1D risk including antigen presentation in beta cells. Finally, single cell and spatial profiles together revealed widespread changes in cell-cell signaling in T1D including signals affecting beta cell regulation. Overall, these results revealed drivers of T1D progression in the pancreas, which form the basis for therapeutic targets for disease prevention.

genomics↗

Identification of a multi-omics factor predictive of long COVID in the IMPACC study

Following SARS-CoV-2 infection, [~]10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics "recovery factor". IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids in LC patients. The recovery factor was also associated with altered circulating immune cell frequencies. Notably, recovery factor scores were predictive of LC occurrence in patients as early as hospital admission, irrespective of acute disease severity. Thus, the recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.

systems biology↗

Beta cell dysfunction occurs independently of insulitis in type 1 diabetes pathogenesis

The loss of insulin secretory function associated with type 1 diabetes (T1D) is attributed to the immune-mediated destruction of beta cells. Yet, at onset of T1D, patients often have a significant beta cell mass remaining while T cell infiltration of pancreatic islets is sporadic. Thus, we investigated the hypothesis that the remaining beta cells in T1D are largely dysfunctional using live human pancreas tissue slices prepared from organ donors with recently diagnosed T1D. Beta cells in slices from donors with T1D had significantly diminished Ca2+ mobilization and insulin secretion responses to glucose. Beta cell function was equally impaired in T cell-infiltrated and non-infiltrated islets. Fixed tissue staining and gene expression profiling of laser-capture microdissected islets revealed significant decreases of proteins and genes in the glucose stimulus secretion coupling pathway. From these data, we posit that functional defects occur in the remaining mass of beta cells during human T1D pathogenesis.

physiology↗

Mapping histological and functional maturation of human endocrine pancreas across early postnatal periods

Human endocrine cell differentiation and islet morphogenesis play critical roles in determining islet cell mass and function, but the events and timeline of these processes are incompletely defined. To better understand early human islet cell development and maturation, we collected 115 pediatric pancreata and mapped morphological and spatiotemporal changes from birth through the first ten years of life. Using quantitative analyses and a combination of complementary tissue imaging approaches, including confocal microscopy and whole-slide imaging, we developed an integrated model for endocrine cell formation and islet architecture, including endocrine cell type heterogeneity and abundance, endocrine cell proliferation, and islet vascularization and innervation. We also assessed insulin and glucagon secretory profiles in isolated islet preparations from pediatric donors aged 2 months to 10 years and found a temporal difference in the maturation of insulin secretion compared to glucagon secretion. This comprehensive summary of postnatal and pediatric pancreatic islet development provides a framework for future studies and integration of emerging genetic and genomic data related to islet biology and diabetes risk.

developmental biology↗

Unraveling SARS-CoV-2 Host-Response Heterogeneity through Longitudinal Molecular Subtyping

Hospitalized COVID-19 patients exhibit diverse immune responses during acute infection, which are associated with a wide range of clinical outcomes. However, understanding these immune heterogeneities and their links to various clinical complications, especially long COVID, remains a challenge. In this study, we performed unsupervised subtyping of longitudinal multi-omics immunophenotyping in over 1,000 hospitalized patients, identifying two critical subtypes linked to mortality or mechanical ventilation with prolonged hospital stay and three severe subtypes associated with timely acute recovery. We confirmed that unresolved systemic inflammation and T-cell dysfunctions were hallmarks of increased severity and further distinguished patients with similar acute respiratory severity by their distinct immune profiles, which correlated with differences in demographic and clinical complications. Notably, one critical subtype (SubF) was uniquely characterized by early excessive inflammation, insufficient anticoagulation, and fatty acid dysregulation, alongside higher incidences of hematologic, cardiac, and renal complications, and an elevated risk of long COVID. Among the severe subtypes, significant differences in viral clearance and early antiviral responses were observed, with one subtype (SubC) showing strong early T-cell cytotoxicity but a poor humoral response, slower viral clearance, and greater risks of chronic organ dysfunction and long COVID. These findings provide crucial insights into the complex and context-dependent nature of COVID-19 immune responses, highlighting the importance of personalized therapeutic strategies to improve both acute and long-term outcomes.

immunology↗

Chronic Viral Reactivation and Associated Host Immune Response and Clinical Outcomes in Acute COVID-19 and Post-Acute Sequelae of COVID-19

Chronic viral infections are ubiquitous in humans, with individuals harboring multiple latent viruses that can reactivate during acute illnesses. Recent studies have suggested that SARS- CoV-2 infection can lead to reactivation of latent viruses such as Epstein-Barr Virus (EBV) and cytomegalovirus (CMV), yet, the extent and impact of viral reactivation in COVID-19 and its effect on the host immune system remain incompletely understood. Here we present a comprehensive multi-omic analysis of viral reactivation of all known chronically infecting viruses in 1,154 hospitalized COVID-19 patients, from the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study, who were followed prospectively for twelve months. We reveal significant reactivation of Herpesviridae, Enteroviridae, and Anelloviridae families during acute stage of COVID-19 (0-40 days post- hospitalization), each exhibiting distinct temporal dynamics. We also show that viral reactivation correlated with COVID-19 severity, demographic characteristics, and clinical outcomes, including mortality. Integration of cytokine profiling, cellular immunophenotyping, metabolomics, transcriptomics, and proteomics demonstrated virus-specific host responses, including elevated pro-inflammatory cytokines (e.g. IL-6, CXCL10, and TNF), increased activated CD4+ and CD8+ T-cells, and upregulation of cellular replication genes, independent of COVID-19 severity and SARS-CoV-2 viral load. Notably, persistent Anelloviridae reactivation during convalescence ([≥]3 months post-hospitalization) was associated with Post-Acute Sequelae of COVID-19 (PASC) symptoms, particularly physical function and fatigue. Our findings highlight a remarkable prevalence and potential impact of chronic viral reactivation on host responses and clinical outcomes during acute COVID-19 and long term PASC sequelae. Our data provide novel immune, transcriptomic, and metabolomic biomarkers of viral reactivation that may inform novel approaches to prognosticate, prevent, or treat acute COVID- 19 and PASC.

immunology↗