Type I interferon transcriptional network regulates expression of coinhibitory receptors in human T cells.

Although inhibition of T cell coinhibitory receptors has revolutionized cancer therapy, the mechanisms governing their expression on human T cells have not been elucidated. In the present study, we show that type 1 interferon (IFN-I) regulates coinhibitory receptor expression on human T cells, inducing PD-1/TIM-3/LAG-3 while inhibiting TIGIT expression. High-temporal-resolution mRNA profiling of IFN-I responses established the dynamic regulatory networks uncovering three temporal transcriptional waves. Perturbation of key transcription factors (TFs) and TF footprint analysis revealed two regulator modules with different temporal kinetics that control expression of coinhibitory receptors and IFN-I response genes, with SP140 highlighted as one of the key regulators that differentiates LAG-3 and TIGIT expression. Finally, we found that the dynamic IFN-I response in vitro closely mirrored T cell features in acute SARS-CoV-2 infection. The identification of unique TFs controlling coinhibitory receptor expression under IFN-I response may provide targets for enhancement of immunotherapy in cancer, infectious diseases and autoimmunity.

Immune dysregulation and autoreactivity correlate with disease severity in SARS-CoV-2-associated multisystem inflammatory syndrome in children.

Multisystem inflammatory syndrome in children (MIS-C) is a life-threatening post-infectious complication occurring unpredictably weeks after mild or asymptomatic SARS-CoV-2 infection. We profiled MIS-C, adult COVID-19, and healthy pediatric and adult individuals using single-cell RNA sequencing, flow cytometry, antigen receptor repertoire analysis, and unbiased serum proteomics, which collectively identified a signature in MIS-C patients that correlated with disease severity. Despite having no evidence of active infection, MIS-C patients had elevated S100A-family alarmins and decreased antigen presentation signatures, indicative of myeloid dysfunction. MIS-C patients showed elevated expression of cytotoxicity genes in NK and CD8+ T cells and expansion of specific IgG-expressing plasmablasts. Clinically severe MIS-C patients displayed skewed memory T cell TCR repertoires and autoimmunity characterized by endothelium-reactive IgG. The alarmin, cytotoxicity, TCR repertoire, and plasmablast signatures we defined have potential for application in the clinic to better diagnose and potentially predict disease severity early in the course of MIS-C.

Distinguishing features of long COVID identified through immune profiling.

Post-acute infection syndromes may develop after acute viral disease1. Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions2-4. However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein-Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers.

Challenges and perspectives in computational deconvolution of genomics data.

Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.

Lung Cell Atlases in Health and Disease.

The human lung cellular portfolio, traditionally characterized by cellular morphology and individual markers, is highly diverse, with over 40 cell types and a complex branching structure highly adapted for agile airflow and gas exchange. While constant during adulthood, lung cellular content changes in response to exposure, injury, and infection. Some changes are temporary, but others are persistent, leading to structural changes and progressive lung disease. The recent advance of single-cell profiling technologies allows an unprecedented level of detail and scale to cellular measurements, leading to the rise of comprehensive cell atlas styles of reporting. In this review, we chronical the rise of cell atlases and explore their contributions to human lung biology in health and disease.

A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases.

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy.

Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.

Single-Cell Profiling Reveals Immune Aberrations in Progressive Idiopathic Pulmonary Fibrosis.

Rationale: Changes in peripheral blood cell populations have been observed, but not detailed, at single-cell resolution in idiopathic pulmonary fibrosis (IPF). Objectives: We sought to provide an atlas of the changes in the peripheral immune system in stable and progressive IPF. Methods: Peripheral blood mononuclear cells (PBMCs) from patients with IPF and control subjects were profiled using 10× chromium 5' single-cell RNA sequencing. Flow cytometry was used for validation. Protein concentrations of regulatory T cells (Tregs) and monocyte chemoattractants were measured in plasma and lung homogenates from patients with IPF and control subjects. Measurements and Main Results: Thirty-eight PBMC samples from 25 patients with IPF and 13 matched control subjects yielded 149,564 cells that segregated into 23 subpopulations. Classical monocytes were increased in patients with progressive and stable IPF compared with control subjects (32.1%, 25.2%, and 17.9%, respectively; P < 0.05). Total lymphocytes were decreased in patients with IPF versus control subjects and in progressive versus stable IPF (52.6% vs. 62.6%, P = 0.035). Tregs were increased in progressive versus stable IPF (1.8% vs. 1.1% of all PBMCs, P = 0.007), although not different than controls, and may be associated with decreased survival (P = 0.009 in Kaplan-Meier analysis; and P = 0.069 after adjusting for age, sex, and baseline FVC). Flow cytometry analysis confirmed this finding in an independent cohort of patients with IPF. The fraction of Tregs out of all T cells was also increased in two cohorts of lung single-cell RNA sequencing. CCL22 and CCL18, ligands for CCR4 and CCR8 Treg chemotaxis receptors, were increased in IPF. Conclusions: The single-cell atlas of the peripheral immune system in IPF reveals an outcome-predictive increase in classical monocytes and Tregs, as well as evidence for a lung-blood immune recruitment axis involving CCL7 (for classical monocytes) and CCL18/CCL22 (for Tregs).

Single Cell Analysis Reveals Novel Immune Perturbations in Fibrotic Hypersensitivity Pneumonitis.

Rationale: Fibrotic hypersensitivity pneumonitis is a debilitating interstitial lung disease driven by incompletely understood immune mechanisms. Objectives: To elucidate immune aberrations in fibrotic hypersensitivity pneumonitis in single-cell resolution. Methods: Single-cell 5' RNA sequencing was conducted on peripheral blood mononuclear cells and bronchoalveolar lavage cells obtained from 45 patients with fibrotic hypersensitivity pneumonitis, 63 idiopathic pulmonary fibrosis, 4 non-fibrotic hypersensitivity pneumonitis, and 36 healthy controls in the United States and Mexico. Analyses included differential gene expression (Seurat), transcription factor activity imputation (DoRothEA-VIPER), and trajectory analyses (Monocle3/Velocyto-scVelo-CellRank). Measurements and Main Results: Overall, 501,534 peripheral blood mononuclear cells from 110 patients and controls and 88,336 bronchoalveolar lavage cells from 19 patients were profiled. Compared to controls, fibrotic hypersensitivity pneumonitis has elevated classical monocytes (adjusted-p=2.5e-3) and are enriched in CCL3hi/CCL4hi and S100Ahi classical monocytes (adjusted-p<2.2e-16). Trajectory analyses demonstrate that S100Ahi classical monocytes differentiate into SPP1hi lung macrophages associated with fibrosis. Compared to both controls and idiopathic pulmonary fibrosis, fibrotic hypersensitivity pneumonitis patient cells are significantly enriched in GZMhi cytotoxic T cells. These cells exhibit transcription factor activities indicative of TGFß and TNFα/NFκB pathways. These results are publicly available at https://ildimmunecellatlas.org. Conclusions: Single-cell transcriptomics of fibrotic hypersensitivity pneumonitis patients uncovered novel immune perturbations, including previously undescribed increases in GZMhi cytotoxic CD4+ and CD8+ T cells - reflecting this disease's unique inflammatory T-cell driven nature - as well as increased S100Ahi and CCL3hi/CCL4hi classical monocytes also observed in idiopathic pulmonary fibrosis. Both cell populations may guide the development of new biomarkers and therapeutic interventions.

Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report.

Despite progress in elucidation of disease mechanisms, identification of risk factors, biomarker discovery, and the approval of two medications to slow lung function decline in idiopathic pulmonary fibrosis and one medication to slow lung function decline in progressive pulmonary fibrosis, pulmonary fibrosis remains a disease with a high morbidity and mortality. In recognition of the need to catalyze ongoing advances and collaboration in the field of pulmonary fibrosis, the NHLBI, the Three Lakes Foundation, and the Pulmonary Fibrosis Foundation hosted the Pulmonary Fibrosis Stakeholder Summit on November 8-9, 2022. This workshop was held virtually and was organized into three topic areas: 1) novel models and research tools to better study pulmonary fibrosis and uncover new therapies, 2) early disease risk factors and methods to improve diagnosis, and 3) innovative approaches toward clinical trial design for pulmonary fibrosis. In this workshop report, we summarize the content of the presentations and discussions, enumerating research opportunities for advancing our understanding of the pathogenesis, treatment, and outcomes of pulmonary fibrosis.

Microenvironmental sensing by fibroblasts controls macrophage population size.

Animal tissues comprise diverse cell types. However, the mechanisms controlling the number of each cell type within tissue compartments remain poorly understood. Here, we report that different cell types utilize distinct strategies to control population numbers. Proliferation of fibroblasts, stromal cells important for tissue integrity, is limited by space availability. In contrast, proliferation of macrophages, innate immune cells involved in defense, repair, and homeostasis, is constrained by growth factor availability. Examination of density-dependent gene expression in fibroblasts revealed that Hippo and TGF-ß target genes are both regulated by cell density. We found YAP1, the transcriptional coactivator of the Hippo signaling pathway, directly regulates expression of Csf1, the lineage-specific growth factor for macrophages, through an enhancer of Csf1 that is specifically active in fibroblasts. Activation of YAP1 in fibroblasts elevates Csf1 expression and is sufficient to increase the number of macrophages at steady state. Our data also suggest that expression programs in fibroblasts that change with density may result from sensing of mechanical force through actin-dependent mechanisms. Altogether, we demonstrate that two different modes of population control are connected and coordinated to regulate cell numbers of distinct cell types. Sensing of the tissue environment may serve as a general strategy to control tissue composition.

Mast-cell expressed membrane protein-1 is expressed in classical monocytes and alveolar macrophages in idiopathic pulmonary fibrosis and regulates cell chemotaxis, adhesion, and migration in a TGFß-dependent manner.

Mast-cell expressed membrane protein-1 (MCEMP1) is higher in patients with idiopathic pulmonary fibrosis (IPF) with an increased risk of death. Here we aimed to establish the mechanistic role of MCEMP1 in pulmonary fibrosis. We identified increased MCEMP1 expression in classical monocytes and alveolar macrophages in IPF compared with controls. MCEMP1 is upregulated by transforming growth factor beta (TGFß) at the mRNA and protein levels in monocytic leukemia THP-1 cells. TGFß-mediated MCEMP1 upregulation results from the cooperation of SMAD3 and SP1 via concomitant binding to SMAD3/SP1 cis-regulatory elements within the MCEMP1 promoter. We also found that MCEMP1 regulates TGFß-mediated monocyte chemotaxis, adhesion, and migration. Our results suggest that MCEMP1 may regulate the migration and transition of monocytes to monocyte-derived alveolar macrophages during pulmonary fibrosis development and progression.NEW & NOTEWORTHY MCEMP1 is highly expressed in circulating classical monocytes and alveolar macrophages in IPF, is regulated by TGFß, and participates in the chemotaxis, adhesion, and migration of circulating monocytes by modulating the effect of TGFß in RHO activity.

Anti-inflammatory roles of type I interferon signaling in the lung.

Excessive or persistent inflammation may have detrimental effects on lung structure and function. Currently, our understanding of conserved host mechanisms that control the inflammatory response remains incompletely understood. In this study, we investigated the role of type I interferon signaling in the inflammatory response against diverse clinically relevant stimuli. Using mice deficient in type I interferon signaling (IFNAR1-/-), we demonstrate that the absence of interferon signaling resulted in a robust and persistent inflammatory response against Pseudomonas aeruginosa, lipopolysaccharide, and chemotherapeutic agent bleomycin. The elevated inflammatory response in IFNAR1-/- mice was manifested as elevated myeloid cells, such as macrophages and neutrophils, in the bronchoalveolar lavage. The inflammatory cell response in the IFNAR1-/- mice persisted to 14 days and there is impaired recovery and fibrotic remodeling of the lung in IFNAR1-/- mice after bleomycin injury. In the Pseudomonas infection model, the elevated inflammatory cell response led to improved bacterial clearance in IFNAR1-/- mice, although there was similar lung injury and survival. We performed RNA sequencing of lung tissue in wild-type and IFNAR1-/- mice after LPS and bleomycin injury. Our unbiased analysis identified differentially expressed genes between IFNAR1-/- and wild-type mice, including previously unknown regulation of nucleotide-binding oligomerization domain (NOD)-like receptor signaling, retinoic acid-inducible gene-I (RIG-I) signaling, and necroptosis pathway by type I interferon signaling in both models. These data provide novel insights into the conserved anti-inflammatory mechanisms of the type I interferon signaling.NEW & NOTEWORTHY Type I interferons are known for their antiviral activities. In this study, we demonstrate a conserved anti-inflammatory role of type I interferon signaling against diverse stimuli in the lung. We show that exacerbated inflammatory response in the absence of type I interferon signaling has both acute and chronic consequences in the lung including structural changes.

Plasma collagen neoepitopes are associated with multiorgan disease in the ACCESS and GRADS sarcoidosis cohorts.

INTRODUCTION: The pathogenesis of sarcoidosis involves tissue remodelling mediated by the accumulation of abnormal extracellular matrix, which is partly the result of an imbalance in collagen synthesis, cross-linking and degradation. During this process, collagen fragments or neoepitopes, are released into the circulation. The significance of these circulating collagen neoepitopes in sarcoidosis remains unknown. METHODS: We employed plasma samples from patients with sarcoidosis enrolled in A Case Control Etiologic Study of Sarcoidosis (ACCESS) and Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS), and healthy control patients recruited from the Yale community. Plasma concentrations of type III and VI collagen degradation (C3M and C6M) and formation (PRO-C3 and PRO-C6) were quantified via neoepitope-specific competitive ELISA, and statistical associations were sought with clinical phenotypes. RESULTS: Relative to healthy controls, the plasma of both sarcoidosis cohorts was enriched for C3M and C6M, irrespective of corticosteroid use and disease duration. While circulating collagen neoepitopes were independent of Scadding stage, there was a significant association between multiorgan disease and PRO-C3, PRO-C6 and C3M in the ACCESS cohort; PRO-C3 and C6M displayed this property in GRADS. These findings were unrelated to plasma levels of interleukin-4 (IL-4), IL-5, IL-6, IL-9, IL-10 and IL-13. Moreover, PRO-C3 was associated with dermatological disease in both cohorts. DISCUSSION: In two well-characterised sarcoidosis cohorts, we discovered that the plasma is enriched for neoepitopes of collagen degradation (C3M and C6M). In multiorgan disease, there was an association with circulating neoepitopes of type III formation (PRO-C3), perhaps mediated by dermatological sarcoidosis. Further investigation in this arena has the potential to foster new insights into the pathogenic mechanisms of this complex disease.

Adaptive multi-interventional trial platform to improve patient care for fibrotic interstitial lung diseases.

BACKGROUND: Fibrotic interstitial lung diseases (fILDs) are a heterogeneous group of lung diseases associated with significant morbidity and mortality. Despite a large increase in the number of clinical trials in the last 10 years, current regulatory-approved management approaches are limited to two therapies that prevent the progression of fibrosis. The drug development pipeline is long and there is an urgent need to accelerate this process. This manuscript introduces the concept and design of an innovative research approach to drug development in fILD: a global Randomised Embedded Multifactorial Adaptive Platform in fILD (REMAP-ILD). METHODS: Description of the REMAP-ILD concept and design: the specific terminology, design characteristics (multifactorial, adaptive features, statistical approach), target population, interventions, outcomes, mission and values, and organisational structure. RESULTS: The target population will be adult patients with fILD, and the primary outcome will be a disease progression model incorporating forced vital capacity and mortality over 12 months. Responsive adaptive randomisation, prespecified thresholds for success and futility will be used to assess the effectiveness and safety of interventions. REMAP-ILD embraces the core values of diversity, equity, and inclusion for patients and researchers, and prioritises an open-science approach to data sharing and dissemination of results. CONCLUSION: By using an innovative and efficient adaptive multi-interventional trial platform design, we aim to accelerate and improve care for patients with fILD. Through worldwide collaboration, novel analytical methodology and pragmatic trial delivery, REMAP-ILD aims to overcome major limitations associated with conventional randomised controlled trial approaches to rapidly improve the care of people living with fILD.

Single-cell transcriptomic analysis of human pleura reveals stromal heterogeneity and informs in vitro models of mesothelioma.

The pleural lining of the thorax regulates local immunity, inflammation and repair. A variety of conditions, both benign and malignant, including pleural mesothelioma, can affect this tissue. A lack of knowledge concerning the mesothelial and stromal cells comprising the pleura has hampered the development of targeted therapies. Here, we present the first comprehensive single-cell transcriptomic atlas of the human parietal pleura and demonstrate its utility in elucidating pleural biology. We confirm the presence of known universal fibroblasts and describe novel, potentially pleural-specific, fibroblast subtypes. We also present transcriptomic characterisation of multiple in vitro models of benign and malignant mesothelial cells, and characterise these through comparison with in vivo transcriptomic data. While bulk pleural transcriptomes have been reported previously, this is the first study to provide resolution at the single-cell level. We expect our pleural cell atlas will prove invaluable to those studying pleural biology and disease. It has already enabled us to shed light on the transdifferentiation of mesothelial cells, allowing us to develop a simple method for prolonging mesothelial cell differentiation in vitro.

Increased expression of CXCL6 in secretory cells drives fibroblast collagen synthesis and is associated with increased mortality in idiopathic pulmonary fibrosis.

RATIONALE: Recent data suggest that the localisation of airway epithelial cells in the distal lung in idiopathic pulmonary fibrosis (IPF) may drive pathology. We set out to discover whether chemokines expressed in these ectopic airway epithelial cells may contribute to the pathogenesis of IPF. METHODS: We analysed whole lung and single-cell transcriptomic data obtained from patients with IPF. In addition, we measured chemokine levels in blood, bronchoalveolar lavage (BAL) of IPF patients and air-liquid interface cultures. We employed ex vivo donor and IPF lung fibroblasts and an animal model of pulmonary fibrosis to test the effects of chemokine signalling on fibroblast function. RESULTS: By analysis of whole-lung transcriptomics, protein and BAL, we discovered that CXCL6 (a member of the interleukin-8 family) was increased in patients with IPF. Elevated CXCL6 levels in the BAL of two cohorts of patients with IPF were associated with poor survival (hazard ratio of death or progression 1.89, 95% CI 1.16-3.08; n=179, p=0.01). By immunostaining and single-cell RNA sequencing, CXCL6 was detected in secretory cells. Administration of mCXCL5 (LIX, murine CXCL6 homologue) to mice increased collagen synthesis with and without bleomycin. CXCL6 increased collagen I levels in donor and IPF fibroblasts 4.4-fold and 1.7-fold, respectively. Both silencing of and chemical inhibition of CXCR1/2 blocked the effects of CXCL6 on collagen, while overexpression of CXCR2 increased collagen I levels 4.5-fold in IPF fibroblasts. CONCLUSIONS: CXCL6 is expressed in ectopic airway epithelial cells. Elevated levels of CXCL6 are associated with IPF mortality. CXCL6-driven collagen synthesis represents a functional consequence of ectopic localisation of airway epithelial cells in IPF.

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES.

MOTIVATION: Recent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. RESULTS: NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms, is compatible with existing single-cell packages, and allows rapid, flexible analysis of cell-cell signaling at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: NICHES is an open-source software implemented in R under academic free license v3.0 and it is available at http://github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

COVID-19 annual update: a narrative review.

Three and a half years after the pandemic outbreak, now that WHO has formally declared that the emergency is over, COVID-19 is still a significant global issue. Here, we focus on recent developments in genetic and genomic research on COVID-19, and we give an outlook on state-of-the-art therapeutical approaches, as the pandemic is gradually transitioning to an endemic situation. The sequencing and characterization of rare alleles in different populations has made it possible to identify numerous genes that affect either susceptibility to COVID-19 or the severity of the disease. These findings provide a beginning to new avenues and pan-ethnic therapeutic approaches, as well as to potential genetic screening protocols. The causative virus, SARS-CoV-2, is still in the spotlight, but novel threatening virus could appear anywhere at any time. Therefore, continued vigilance and further research is warranted. We also note emphatically that to prevent future pandemics and other world-wide health crises, it is imperative to capitalize on what we have learnt from COVID-19: specifically, regarding its origins, the world's response, and insufficient preparedness. This requires unprecedented international collaboration and timely data sharing for the coordination of effective response and the rapid implementation of containment measures.

An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.