Biology subjects

Reed, E. F.

Publications and source records attributed to Reed, E. F..

6 recordsLinked to original sources

bioRxiv2025

Tensor-Based Integration of Time-Series Measurements Reveals Relationships Between Underlying Disease, Early Injury, and CD4+ Polarization in Liver Transplantation

For patients with end-stage liver failure, liver transplantation (LT) remains the standard-of-care, though five-year allograft survival rates remain below 80%. Liver ischemia reperfusion injuries (LIRI) arise during transplant and contribute to allograft dysfunction. While many drivers of LIRI have been well-characterized, the relationships between LIRI and later immunological signatures remain poorly understood, possibly because of limited integrated studies examining immunological signatures across the LT process. Furthermore, the role of underlying disease in LT remains poorly understood as it is unknown if later immunological signatures are universal or etiology dependent. To better examine etiology- and LIRI-driven immunological mechanisms across the LT lifetime, we collected cytokine and liver function test (LFT) measurements from LT donors and recipients at multiple pre- and post-operative timepoints. By developing a tensor-based factorization method to integrate these longitudinal measurements, we reduced the datasets to four immunological signatures that manifested across cytokines, LFTs, and time. Correlative analyses revealed associations between these integrated immunological signatures with etiology and LIRI. Later integration of allograft survival into this factorization method reduced these four signatures to two that were predictive of five-year allograft survival. Examination of these signatures prognostic of allograft survival found that CD4+ hyperpolarization towards Th1 or Th2 subtypes impaired growth factor expression and led to increased allograft loss risk. Clinical correlates revealed that donor and recipient age alongside etiology and donor liver health modulate this balance of CD4+ polarization in LT. Collectively, these results link immunological mechanisms to etiology and LIRI offering immediately actionable clinical insights alongside etiology- and donor-specific therapeutic targets for improving LT outcomes.

immunology↗

bioRxiv2025

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↗

bioRxiv2024

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↗

bioRxiv2023

Integrated longitudinal multi-omics study identifies immune programs associated with COVID-19 severity and mortality in 1152 hospitalized participants

Hospitalized COVID-19 patients exhibit diverse clinical outcomes, with some individuals diverging over time even though their initial disease severity appears similar. A systematic evaluation of molecular and cellular profiles over the full disease course can link immune programs and their coordination with progression heterogeneity. In this study, we carried out deep immunophenotyping and conducted longitudinal multi-omics modeling integrating ten distinct assays on a total of 1,152 IMPACC participants and identified several immune cascades that were significant drivers of differential clinical outcomes. Increasing disease severity was driven by a temporal pattern that began with the early upregulation of immunosuppressive metabolites and then elevated levels of inflammatory cytokines, signatures of coagulation, NETosis, and T-cell functional dysregulation. A second immune cascade, predictive of 28-day mortality among critically ill patients, was characterized by reduced total plasma immunoglobulins and B cells, as well as dysregulated IFN responsiveness. We demonstrated that the balance disruption between IFN-stimulated genes and IFN inhibitors is a crucial biomarker of COVID-19 mortality, potentially contributing to the failure of viral clearance in patients with fatal illness. Our longitudinal multi-omics profiling study revealed novel temporal coordination across diverse omics that potentially explain disease progression, providing insights that inform the targeted development of therapies for hospitalized COVID-19 patients, especially those critically ill.

immunology↗

bioRxiv2023

Spatial Multi-omics of Arterial Regions from Cardiac Allograft Vasculopathy Rejected Grafts

2. AbstractO_ST_ABSBackgroundC_ST_ABSCardiac allograft vasculopathy (CAV) is a major cause of late-graft failure and mortality following heart transplantation. The mechanisms underlying vascular remodeling are poorly understood. A major immune risk factor associated with the development of CAV is the presence of donor-specific antibodies (DSA) that induce chronic endothelial cell (EC) injury, leukocyte recruitment and inflammation resulting in thickening of arterial intima. Here, we molecularly characterized innate and adaptive immune cells present in the arteries of rejected cardiac allografts with DSA and identified protein and transcriptomic signatures distinguishing early and late CAV lesions. MethodsArterial areas of interest (AOIs) from CAV+DSA+ rejected cardiac allografts (N=3; 2 females, 1 male) were subjected to GeoMx digital spatial profiling (DSP). AOIs were scored on the level of CAV progression/neointimal thickening (22 AOIs total; 11 high and 11 low neointima) and were subjected to whole transcriptome and protein profiling. ResultsAOIs with low neointima significantly increased markers for activated inflammatory infiltrates, transcripts of EC activation and gene modules involved in activating metalloproteinases and TP53 regulation of caspases. Inflammatory and apoptotic protein markers significantly correlated with inflammatory modules in AOIs with low neointima. AOIs with high neointima increased TGF{beta}-regulated transcripts and modules enriched for platelet activation/aggregation. Proteins encoding SMCs and growth factors/survival correlated with modules enriched for proliferation/repair in AOIs with high neointima. Key transcripts in promoting proliferation, migration, and EndoMT were significantly associated with increasing neointima scores. ConclusionOur results reveal new protein and transcriptomic signatures associated with CAV progression. Lesions exhibiting inflammatory profiles appear to be early lesions that transition to later proliferative/pro-fibrotic phenotype CAV lesions. These findings should form the foundation for the identification of improved biomarkers to guide CAV treatment.

immunology↗

bioRxiv2022

Cytokine-expression patterns reveal coordinated immunological programs associated with persistent MRSA bacteremia

Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is a common, life-threatening infection that imposes up to 30% mortality even when appropriate therapy is used. Despite in vitro efficacy, antibiotics often fail to resolve the infection in vivo, resulting in persistent MRSA bacteremia. Recently, several genetic, epigenetic, and proteomic correlates of persistent outcomes have been identified. However, the extent to which single variables or composite patterns operate as independent predictors of outcome or reflect shared underlying mechanisms of persistence is unknown. To explore this question, we employed a tensor-based integration of host transcriptional and proteomic data across a well-characterized cohort of patients with persistent and resolving MRSA bacteremia outcomes. Tensor-based data integration yielded high correlative accuracy with persistence and revealed immunologic signatures shared across both the transcriptomic and proteomic datasets. We find that elevated proliferation of mature granulocytes associates with resolving bacteremia outcomes. In contrast, patients with persistent bacteremia heterogeneously exhibit correlates of granulocyte dysfunction or immature granulocyte proliferation. Collectively, these results suggest that transcriptional and proteomic correlates of persistent versus resolving bacteremia outcomes are complex and may not be disclosed by conventional modeling. However, a tensor-based integration approach can help to reveal consensus molecular mechanisms in an interpretable manner. Significance StatementWhile antibacterial therapies effectively resolve MRSA in vitro, these treatments often fail to clear MRSA bacteremia in vivo, suggesting that host-pathogen interactions are essential to persistent MRSA bacteremia. Recent studies have identified genetic, transcriptomic, and proteomic determinants of MRSA persistence. These determinants independently, however, provide insufficient mechanistic insight and it is unclear if they indicate unique or overlapping persistence mechanisms. Here, we use tensor-based decomposition to jointly analyze cytokine and transcriptomic measurements from patients with MRSA bacteremia. Results indicate that persistence mechanisms integrated across biological modalities reflect diverging mechanisms of persistent bacteremia. Ultimately, these results may help to identify future therapeutic targets for treating persistent MRSA bacteremia.

immunology↗