Post-translational modifications of histone proteins and exchanges of histone variants of chromatin are central to the regulation of nearly all DNA-templated biological processes. However, the degree and variability of chromatin modifications in specific human immune cells remain largely unknown. Here, we employ a highly multiplexed mass cytometry analysis to profile the global levels of a broad array of chromatin modifications in primary human immune cells at the single-cell level. Our data reveal markedly different cell-type- and hematopoietic-lineage-specific chromatin modification patterns. Differential analysis between younger and older adults shows that aging is associated with increased heterogeneity between individuals and elevated cell-to-cell variability in chromatin modifications. Analysis of a twin cohort unveils heritability of chromatin modifications and demonstrates that aging-related chromatin alterations are predominantly driven by non-heritable influences. Together, we present a powerful platform for chromatin and immunology research. Our discoveries highlight the profound impacts of aging on chromatin modifications.

Summary Background There is an urgent need for biomarkers to better stratify patients with idiopathic pulmonary fibrosis by risk for lung transplantation allocation who have the same clinical presentation. We aimed to investigate whether a specific immune cell type from patients with idiopathic pulmonary fibrosis could identify those at higher risk of poor outcomes. We then sought to validate our findings using cytometry and electronic health records. Methods We first did a discovery analysis with transcriptome data from the Gene Expression Omnibus at the National Center for Biotechnology Information for 120 peripheral blood mononuclear cell (PBMC) samples of patients with idiopathic pulmonary fibrosis. We estimated percentages of 13 immune cell types using statistical deconvolution, and investigated the association of these cell types with transplant-free survival. We validated these results using PBMC samples from patients with idiopathic pulmonary fibrosis in two independent cohorts (COMET and Yale). COMET profiled monocyte counts in 45 patients with idiopathic pulmonary fibrosis from March 12, 2010, to March 10, 2011, using flow cytometry; we tested if increased monocyte count was associated with the primary outcome of disease progression. In the Yale cohort, 15 patients with idiopathic pulmonary fibrosis (with five healthy controls) were classed as high risk or low risk from April 28, 2014, to Aug 20, 2015, using a 52-gene signature, and we assessed whether monocyte percentage (measured by cytometry by time of flight) was higher in high-risk patients. We then examined complete blood count values in the electronic health records (EHR) of 45 068 patients with idiopathic pulmonary fibrosis, systemic sclerosis, hypertrophic cardiomyopathy, or myelofibrosis from Stanford (Jan 01, 2008, to Dec 31, 2015), Northwestern (Feb 15, 2001 to July 31, 2017), Vanderbilt (Jan 01, 2008, to Dec 31, 2016), and Optum Clinformatics DataMart (Jan 01, 2004, to Dec 31, 2016) cohorts, and examined whether absolute monocyte counts of 0·95 K/μL or greater were associated with all-cause mortality in these patients. Findings In the discovery analysis, estimated CD14+ classical monocyte percentages above the mean were associated with shorter transplant-free survival times (hazard ratio [HR] 1·82, 95% CI 1·05–3·14), whereas higher percentages of T cells and B cells were not (0·97, 0·59–1·66; and 0·78, 0·45–1·34 respectively). In two validation cohorts (COMET trial and the Yale cohort), patients with higher monocyte counts were at higher risk for poor outcomes (COMET Wilcoxon p=0·025; Yale Wilcoxon p=0·049). Monocyte counts of 0·95 K/μL or greater were associated with mortality after adjusting for forced vital capacity (HR 2·47, 95% CI 1·48–4·15; p=0·0063), and the gender, age, and physiology index (HR 2·06, 95% CI 1·22–3·47; p=0·0068) across the COMET, Stanford, and Northwestern datasets). Analysis of medical records of 7459 patients with idiopathic pul...

Background The World Health Organization (WHO) and Foundation for Innovative New Diagnostics (FIND) have published target product profiles (TPPs) calling for non-sputum-based diagnostic tests for the diagnosis of active tuberculosis (ATB) disease and for predicting the progression from latent tuberculosis infection (LTBI) to ATB. A large number of host-derived blood-based gene-expression biomarkers for diagnosis of patients with ATB have been proposed to date, but none have been implemented in clinical settings. The focus of this study is to directly compare published gene signatures for diagnosis of patients with ATB across a large, diverse list of publicly available gene expression datasets, and evaluate their performance against the WHO/FIND TPPs. Methods and findings We searched PubMed, Gene Expression Omnibus (GEO), and ArrayExpress in June 2018. We included all studies irrespective of study design and enrollment criteria. We found 16 gene signatures for the diagnosis of ATB compared to other clinical conditions in PubMed. For each signature, we implemented a classification model as described in the corresponding original publication of the signature. We identified 24 datasets containing 3,083 transcriptome profiles from whole blood or peripheral blood mononuclear cell samples of healthy controls or patients with ATB, LTBI, or other diseases from 14 countries in GEO. Using these datasets, we calculated weighted mean area under the receiver operating characteristic curve (AUROC), specificity at 90% sensitivity, and negative predictive value (NPV) for each gene signature across all datasets. We also compared the diagnostic odds ratio (DOR), heterogeneity in DOR, and false positive rate (FPR) for each signature using bivariate meta-analysis. Across 9 datasets of patients with culture-confirmed diagnosis of ATB, 11 signatures had weighted mean AUROC > 0.8, and 2 signatures had weighted mean AUROC ≤ 0.6. All but 2 signatures had high NPV (>98% at 2% prevalence). Two gene signatures achieved the minimal WHO TPP for a non-sputum-based triage test. When including datasets with clinical diagnosis of ATB, there was minimal reduction in the weighted mean AUROC and specificity of all but 3 signatures compared to when using only culture-confirmed ATB data. Only 4 signatures had homogeneous DOR and lower FPR when datasets with clinical diagnosis of ATB were included; other signatures either had heterogeneous DOR or higher FPR or both. Finally, 7 of 16 gene signatures predicted progression from LTBI to ATB 6 months prior to sputum conversion with positive predictive value > 6% at 2% prevalence. Our analyses may have under- or overestimated the performance of certain ATB diagnostic signatures because our implementation may be different from the published models for those signatures. We re-implemented published models because the exact models were not publicly available. Conclusions We found that host-response-based diagnostics could accurately iden...

Key Points Question How does a previously described blood-based 3-gene tuberculosis score perform as a screening test and a disease monitoring tool for all stages of tuberculosis? Findings In this nested case-contral study, the 3-gene tuberculosis score was associated with progression from latent Mycobacterium tuberculosis infection to active tuberculosis 6 months prior to sputum conversion with 86% sensitivity and 84% specificity, diagnosed patients with active tuberculosis with 90% sensitivity and 70% specificity, and correlated with treatment response and the severity of lung pathology. Meaning The 3-gene tuberculosis score can be implemented as a rapid, blood-based screening and triage test with the required World Health Organization target product profiles for the accurate detection and tracking of progressive, active, and treated tuberculosis disease.

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), is a pulmonary pathogen of major global concern. A key feature of Mtb infection in primates is the formation of granulomas, dense cellular structures surrounding infected lung tissue. These structures serve as the main site of host-pathogen interaction in TB, and thus to effectively treat TB we must clarify mechanisms of granuloma formation and their function in disease. Fibrotic granulomas are associated with both good and bad disease outcomes. Fibrosis can serve to isolate infected tissue from healthy tissue, but it can also cause difficulty breathing as it leaves scars. Little is known about fibrosis in TB, and data from non-human primates is just beginning to clarify the picture. This work focuses on constructing a hybrid multi-scale model of fibrotic granuloma formation, in order to identify mechanisms driving development of fibrosis in Mtb infected lungs. We combine dynamics of molecular, cellular, and tissue scale models from previously published studies to characterize the formation of two common sub-types of fibrotic granulomas: peripherally fibrotic, with a cuff of collagen surrounding granulomas, and centrally fibrotic, with collagen throughout granulomas. Uncertainty and sensitivity analysis, along with large simulation sets, enable us to identify mechanisms differentiating centrally vs. peripherally fibrotic granulomas. These findings suggest that heterogeneous cytokine environments exist within granulomas and may be responsible for driving tissue scale morphologies. Using this model we are primed to better understand the complex structure of granulomas, a necessity for developing successful treatments for TB.

Mycobacterium tuberculosis is the pathogenic bacterium that causes tuberculosis (TB), one of the most lethal infectious diseases in the world. The only vaccine against TB is minimally protective, and multi-drug resistant TB necessitates new therapeutics to treat infection. Developing new therapies requires a better understanding of the complex host immune response to infection, including dissecting the processes leading to formation of granulomas, the dense cellular lesions associated with TB. In this work, we pair experimental and computational modeling studies to explore cytokine regulation in the context of TB. We use our next-generation hybrid multi-scale model of granuloma formation (GranSim) to capture molecular, cellular, and tissue scale dynamics of granuloma formation. We identify TGF-β1 as a major inhibitor of cytotoxic T-cell effector function in granulomas. Deletion of TGF-β1 from the system results in improved bacterial clearance and lesion sterilization. We also identify a novel dichotomous regulation of cytotoxic T cells and macrophages by TGF-β1 and IL-10, respectively. These findings suggest that increasing cytotoxic T-cell effector functions may increase bacterial clearance in granulomas and highlight potential new therapeutic targets for treating TB.

Fibroblasts play an important role in the wound-healing process by generating extracellular matrix (ECM) and undergoing differentiation into myofibroblasts, but these cells can also be involved in pathologic remodeling of tissue. Nascent ECM provides a substrate for re-epithelialization to occur, restoring damaged tissue to a functional state. Dysregulation of this process can result in fibrosis—stiffening and scarring of the tissue. Current treatments cannot halt or reverse this process. The molecular mechanisms underlying fibrotic dysregulation are poorly understood, providing an untapped pool of potential therapeutic targets. Transforming growth factor-β (TGF- β) and adhesion signaling are involved in inducing fibroblast differentiation into α-smooth muscle actin (α SMA) expressing myofibroblasts, while prostaglandin E2 (PGE2) has been shown to antagonize TGF β signaling; however, the temporal and mechanistic details of this relationship have not yet been fully characterized. We measured α SMA, a marker of fibroblast to myofibroblast differentiation, as a function of: TGF-β1 receptor–ligand complex internalization, PGE2 binding, and adhesion signaling and developed a mathematical model capturing the molecular mechanisms of fibroblast differentiation. Using our model, we predict the following: Periodic dosing with PGE2 temporarily renders fibroblasts incapable of differentiation and refractory to additional TGF-β1 stimulation; conversely, periodic dosing with TGF-β1 in the presence of PGE2 induces a reduced signal response that can be further inhibited by the addition of more PGE2. Controlled fibroblast differentiation is necessary for effective wound healing; however, excessive accumulation of α SMA-expressing myofibroblasts can result in fibrosis. Homeostasis of α SMA in our model requires a balance of positive and negative regulatory signals. Sensitivity analysis predicts that PGE2 availability, TGF-β1 availability, and the rate of TGF-β1 receptor recycling each highly influence the rates of α SMA production. With this model, we are able to demonstrate that regulation of both TGF-β1 and PGE2 signaling levels is essential for preventing fibroblast dysregulation.

Pulmonary fibrosis is pathologic remodeling of lung tissue that can result in difficulty breathing, reduced quality of life, and a poor prognosis for patients. Fibrosis occurs as a result of insult to lung tissue, though mechanisms of this response are not well-characterized. The disease is driven in part by dysregulation of fibroblast proliferation and differentiation into myofibroblast cells, as well as pro-fibrotic mediator-driven epithelial cell apoptosis. The most well-characterized pro-fibrotic mediator associated with pulmonary fibrosis is TGF-β1. Excessive synthesis of, and sensitivity to, pro-fibrotic mediators as well as insufficient production of and sensitivity to anti-fibrotic mediators has been credited with enabling fibroblast accumulation. Available treatments neither halt nor reverse lung damage. In this study we have two aims: to identify molecular and cellular scale mechanisms driving fibroblast proliferation and differentiation as well as epithelial cell survival in the context of fibrosis, and to predict therapeutic targets and strategies. We combine in vitro studies with a multi-scale hybrid agent-based computational model that describes fibroblasts and epithelial cells in co-culture. Within this model TGF-β1 represents a pro-fibrotic mediator and we include detailed dynamics of TGF-β1 receptor ligand signaling in fibroblasts. PGE2 represents an anti-fibrotic mediator. Using uncertainty and sensitivity analysis we identify TGF-β1 synthesis, TGF-β1 activation, and PGE2 synthesis among the key mechanisms contributing to fibrotic outcomes. We further demonstrate that intervention strategies combining potential therapeutics targeting both fibroblast regulation and epithelial cell survival can promote healthy tissue repair better than individual strategies. Combinations of existing drugs and compounds may provide significant improvements to the current standard of care for pulmonary fibrosis. Thus, a two-hit therapeutic intervention strategy may prove necessary to halt and reverse disease dynamics.