T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection.

The clinical course of autoimmune and infectious disease varies greatly, even between individuals with the same condition. An understanding of the molecular basis for this heterogeneity could lead to significant improvements in both monitoring and treatment. During chronic infection the process of T-cell exhaustion inhibits the immune response, facilitating viral persistence. Here we show that a transcriptional signature reflecting CD8 T-cell exhaustion is associated with poor clearance of chronic viral infection, but conversely predicts better prognosis in multiple autoimmune diseases. The development of CD8 T-cell exhaustion during chronic infection is driven both by persistence of antigen and by a lack of accessory 'help' signals. In autoimmunity, we find that where evidence of CD4 T-cell co-stimulation is pronounced, that of CD8 T-cell exhaustion is reduced. We can reproduce the exhaustion signature by modifying the balance of persistent stimulation of T-cell antigen receptors and specific CD2-induced co-stimulation provided to human CD8 T cells in vitro, suggesting that each process plays a role in dictating outcome in autoimmune disease. The 'non-exhausted' T-cell state driven by CD2-induced co-stimulation is reduced by signals through the exhaustion-associated inhibitory receptor PD-1, suggesting that induction of exhaustion may be a therapeutic strategy in autoimmune and inflammatory disease. Using expression of optimal surrogate markers of co-stimulation/exhaustion signatures in independent data sets, we confirm an association with good clinical outcome or response to therapy in infection (hepatitis C virus) and vaccination (yellow fever, malaria, influenza), but poor outcome in autoimmune and inflammatory disease (type 1 diabetes, anti-neutrophil cytoplasmic antibody-associated vasculitis, systemic lupus erythematosus, idiopathic pulmonary fibrosis and dengue haemorrhagic fever). Thus, T-cell exhaustion plays a central role in determining outcome in autoimmune disease and targeted manipulation of this process could lead to new therapeutic opportunities

Regularized latent class model for joint analysis of high dimensional longitudinal biomarkers and a time-to-event outcome

Although many modeling approaches have been developed to jointly analyze longitudinal biomarkers and a time-to-event outcome, most of these methods can only handle one or a few biomarkers. In this article, we propose a novel joint latent class model to deal with high dimensional longitudinal biomarkers. Our model has three components: a class membership model, a survival submodel, and a longitudinal submodel. In our model, we assume that covariates can potentially affect biomarkers and class membership. We adopt a penalized likelihood approach to infer which covariates have random effects and/or fixed effects on biomarkers, and which covariates are informative for the latent classes. Through extensive simulation studies, we show that our proposed method has improved performance in prediction and assigning subjects to the correct classes over other joint modeling methods and that bootstrap can be used to do inference for our model. We then apply our method to a dataset of patients with idiopathic pulmonary fibrosis, for whom gene expression profiles were measured longitudinally.We are able to identify four interesting latent classes with one class being at much higher risk of death compared to the other classes. We also find that each of the latent classes has unique trajectories in some genes, yielding novel biological insights. This article is protected by copyright. All rights reserved

50-gene risk profiles in peripheral blood predict COVID-19 outcomes: A retrospective, multicenter cohort study

BACKGROUND: COVID-19 has been associated with Interstitial Lung Disease features. The immune transcriptomic overlap between Idiopathic Pulmonary Fibrosis (IPF) and COVID-19 has not been investigated. METHODS: we analyzed blood transcript levels of 50 genes known to predict IPF mortality in three COVID-19 and two IPF cohorts. The Scoring Algorithm of Molecular Subphenotypes (SAMS) was applied to distinguish high versus low-risk profiles in all cohorts. SAMS cutoffs derived from the COVID-19 Discovery cohort were used to predict intensive care unit (ICU) status, need for mechanical ventilation, and in-hospital mortality in the COVID-19 Validation cohort. A COVID-19 Single-cell RNA-sequencing cohort was used to identify the cellular sources of the 50-gene risk profiles. The same COVID-19 SAMS cutoffs were used to predict mortality in the IPF cohorts. FINDINGS: 50-gene risk profiles discriminated severe from mild COVID-19 in the Discovery cohort (P = 0·015) and predicted ICU admission, need for mechanical ventilation, and in-hospital mortality (AUC: 0·77, 0·75, and 0·74, respectively, P < 0·001) in the COVID-19 Validation cohort. In COVID-19, 50-gene expressing cells with a high-risk profile included monocytes, dendritic cells, and neutrophils, while low-risk profile-expressing cells included CD4+, CD8+ T lymphocytes, IgG producing plasmablasts, B cells, NK, and gamma/delta T cells. Same COVID-19 SAMS cutoffs were also predictive of mortality in the University of Chicago (HR:5·26, 95%CI:1·81-15·27, P = 0·0013) and Imperial College of London (HR:4·31, 95%CI:1·81-10·23, P = 0·0016) IPF cohorts. INTERPRETATION: 50-gene risk profiles in peripheral blood predict COVID-19 and IPF outcomes. The cellular sources of these gene expression changes suggest common innate and adaptive immune responses in both diseases

Blood transcriptomic predicts progression of pulmonary fibrosis and associates natural killer cells.

Rationale: Disease activity in idiopathic pulmonary fibrosis (IPF) remains highly variable, poorly understood, and difficult to predict. Objective: To identify a predictor using short-term longitudinal changes in gene-expression that forecasts future forced vital capacity (FVC) decline and to characterize involved pathways and cell types. Methods: Seventy-four patients from Correlating Outcomes with biochemical Markers to Estimate Time-progression in IPF (COMET) cohort were dichotomized as progressors (≥10% FVC decline) or stable. Blood gene-expression changes within individuals were calculated between baseline and 4 months, and regressed with future FVC status, allowing determination of expression variations, sample size, and statistical power. Pathway analyses were conducted to predict downstream effects and identify new targets. An FVC-predictor for progression was constructed in COMET and validated using independent cohorts. Peripheral blood mononuclear single-cell RNA-seq (PBMC scRNA-seq) data from healthy controls were used as references to characterize cell type compositions from bulk PBMC RNA-seq data that were associated with FVC decline. Results: The longitudinal model reduced gene-expression variations within stable and progressor groups, resulting in increased statistical power when compared to a cross-sectional model. The FVC-predictor for progression anticipated patients with future FVC decline with 78% sensitivity and 86% specificity across independent IPF cohorts. Pattern recognition receptor pathways and mTOR pathways were down- and up-regulated, respectively. Cellular deconvolution using scRNA-seq data identified natural killer (NK) cells as significantly correlated with progression. Conclusions: Serial transcriptomic change predicts future FVC decline. Analysis of cell types involved in the progressor signature supports the novel involvement of NK cells in IPF progression

Validation of a 52-gene risk profile for outcome prediction in patients with idiopathic pulmonary fibrosis: an international, multicentre, cohort study

BACKGROUND: The clinical course of idiopathic pulmonary fibrosis (IPF) is unpredictable. Clinical prediction tools are not accurate enough to predict disease outcomes. METHODS: We enrolled patients with IPF diagnosis in a six-cohort study at Yale University (New Haven, CT, USA), Imperial College London (London, UK), University of Chicago (Chicago, IL, USA), University of Pittsburgh (Pittsburgh, PA, USA), University of Freiburg (Freiburg im Breisgau, Germany), and Brigham and Women's Hospital-Harvard Medical School (Boston, MA, USA). Peripheral blood mononuclear cells or whole blood were collected at baseline from 425 participants and from 98 patients (23%) during 4-6 years' follow-up. A 52-gene signature was measured by the nCounter analysis system in four cohorts and extracted from microarray data (GeneChip) in the other two. We used the Scoring Algorithm for Molecular Subphenotypes (SAMS) to classify patients into low-risk or high-risk groups based on the 52-gene signature. We studied mortality with a competing risk model and transplant-free survival with a Cox proportional hazards model. We analysed timecourse data and response to antifibrotic drugs with linear mixed effect models. FINDINGS: The application of SAMS to the 52-gene signature identified two groups of patients with IPF (low-risk and high-risk), with significant differences in mortality or transplant-free survival in each of the six cohorts (hazard ratio [HR] range 2·03-4·37). Pooled data showed similar results for mortality (HR 2·18, 95% CI 1·53-3·09; p<0·0001) or transplant-free survival (2·04, 1·52-2·74; p<0·0001). Adding 52-gene risk profiles to the Gender, Age, and Physiology index significantly improved its mortality predictive accuracy. Temporal changes in SAMS scores were associated with changes in forced vital capacity (FVC) in two cohorts. Untreated patients did not shift their risk profile over time. A simultaneous increase in up score and decrease in down score was predictive of decreased transplant-free survival (3·18, 1·16-8·76; p=0·025) in the Pittsburgh cohort. A simultaneous decrease in up score and increase in down score after initiation of antifibrotic drugs was associated with a significant (p=0·0050) improvement in FVC in the Yale cohort. INTERPRETATION: The peripheral blood 52-gene expression signature is predictive of outcome in patients with IPF. The potential value of the 52-gene signature in predicting response to therapy should be determined in prospective studies. FUNDING: The Pulmonary Fibrosis Foundation, the Harold Amos Medical Faculty Development Program of the Robert Wood Johnson Foundation, and the National Heart, Lung, and Blood Institute of the US National Institutes of Health

PD-1 up-regulation on CD4+ T cells promotes pulmonary fibrosis through STAT3-mediated IL-17A and TGF-β1 production

Pulmonary fibrosis is a progressive inflammatory disease with high mortality and limited therapeutic options. Previous genetic and immunologic investigations suggest common intersections between idiopathic pulmonary fibrosis (IPF), sarcoidosis, and murine models of pulmonary fibrosis. To identify immune responses that precede collagen deposition, we conducted molecular, immunohistochemical, and flow cytometric analysis of human and murine specimens. Immunohistochemistry revealed programmed cell death-1 (PD-1) up-regulation on IPF lymphocytes. PD-1+CD4+ T cells with reduced proliferative capacity and increased transforming growth factor-β (TGF-β)/interleukin-17A (IL-17A) expression were detected in IPF, sarcoidosis, and bleomycin CD4+ T cells. PD-1+ T helper 17 cells are the predominant CD4+ T cell subset expressing TGF-β. Coculture of PD-1+CD4+ T cells with human lung fibroblasts induced collagen-1 production. Strikingly, ex vivo PD-1 pathway blockade resulted in reductions in TGF-β and IL-17A expression from CD4+ T cells, with concomitant declines in collagen-1 production from fibroblasts. Molecular analysis demonstrated PD-1 regulation of the transcription factor STAT3 (signal transducer and activator of transcription 3). Chemical blockade of STAT3, using the inhibitor STATTIC, inhibited collagen-1 production. Both bleomycin administration to PD-1 null mice or use of antibody against programmed cell death ligand 1 (PD-L1) demonstrated significantly reduced fibrosis compared to controls. This work identifies a critical, previously unrecognized role for PD-1+CD4+ T cells in pulmonary fibrosis, supporting the use of readily available therapeutics that directly address interstitial lung disease pathophysiology