Early events marking lung fibroblast transition to profibrotic state in idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic Pulmonary Fibrosis (IPF) is an age-associated progressive lung disease with accumulation of scar tissue impairing gas exchange. Previous high-throughput studies elucidated the role of cellular heterogeneity and molecular pathways in advanced disease. However, critical pathogenic pathways occurring in the transition of fibroblasts from normal to profibrotic have been largely overlooked. METHODS: We used single cell transcriptomics (scRNA-seq) from lungs of healthy controls and IPF patients (lower and upper lobes). We identified fibroblast subclusters, genes and pathways associated with early disease. Immunofluorescence assays validated the role of MOXD1 early in fibrosis. RESULTS: We identified four distinct fibroblast subgroups, including one marking the normal-to-profibrotic state transition. Our results show for the first time that global downregulation of ribosomal proteins and significant upregulation of the majority of copper-binding proteins, including MOXD1, mark the IPF transition. We find no significant differences in gene expression in IPF upper and lower lobe samples, which were selected to have low and high degree of fibrosis, respectively. CONCLUSIONS: Early events during IPF onset in fibroblasts include dysregulation of ribosomal and copper-binding proteins. Fibroblasts in early stage IPF may have already acquired a profibrotic phenotype while hallmarks of advanced disease, including fibroblast foci and honeycomb formation, are still not evident. The new transitional fibroblasts we discover could prove very important for studying the role of fibroblast plasticity in disease progression and help develop early diagnosis tools and therapeutic interventions targeting earlier disease states.

Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling.

Background: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in patients with COPD can be important for disease management strategies. Although all-cause mortality predictors have been developed previously, limited research exists on factors directly affecting COPD-specific mortality. Methods: In a retrospective study, we used probabilistic graphs to analyse clinical cross-sectional data (COPDGene cohort), including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data. COPDGene recruited current and former smokers, aged 45-80 years with >10 pack-years smoking history, from across the USA (Phase 1, 11/2007-4/2011) and invited them for a follow-up visit (Phase 2, 7/2013-7/2017). ECLIPSE cohort recruited current and former smokers (COPD patients and controls from USA and Europe), aged 45-80 with smoking history >10 pack-years (12/2005-11/2007). We applied graphical models on multi-modal data COPDGene Phase 1 participants to identify factors directly affecting all-cause and COPD-specific mortality (primary outcomes); and on Phase 2 follow-up cohort to identify additional molecular and social factors affecting mortality. We used penalized Cox regression with features selected by the causal graph to build VAPORED, a mortality risk prediction model. VAPORED was compared to existing scores (BODE: BMI, airflow obstruction, dyspnoea, exercise capacity; ADO: age, dyspnoea, airflow obstruction) on the ability to rank individuals by mortality risk, using four evaluation metrics (concordance, concordance probability estimate (CPE), cumulative/dynamic (C/D) area under the receiver operating characteristic curve (AUC), and integrated C/D AUC). The results were validated in ECLIPSE. Findings: Graphical models, applied on the COPDGene Phase 1 samples (n = 8610), identified 11 and 7 variables directly linked to all-cause and COPD-specific mortality, respectively. Although many appear in both models, non-lung comorbidities appear only in the all-cause model, while forced vital capacity (FVC %predicted) appears in COPD-specific mortality model only. Additionally, the graph model of Phase 2 data (n = 3182) identified internet access, CD4 T cells and platelets to be linked to lower mortality risk. Furthermore, using the 7 variables linked to COPD-specific mortality (forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) ration, FVC %predicted, age, history of pneumonia, oxygen saturation, 6-min walk distance, dyspnoea) we developed VAPORED mortality risk score, which we validated on the ECLIPSE cohort (3-yr all-cause mortality data, n = 2312). VAPORED performed significantly better than ADO, BODE, and updated BODE indices in predicting all-cause mortality in ECLIPSE in terms of concordance (VAPORED [0.719] vs ADO [0.693; FDR p-value 0.014], BODE [0.695; FDR p-value 0.020], and updated BODE [0.694; FDR p-value 0.021]); CPE (VAPORED [0.714] vs ADO [0.673; FDR p-value <0.0001], BODE [0.662; FDR p-value <0.0001], and updated BODE [0.646; FDR p-value <0.0001]); 3-year C/D AUC (VAPORED [0.728] vs ADO [0.702; FDR p-value 0.017], BODE [0.704; FDR p-value 0.021], and updated BODE [0.703; FDR p-value 0.024]); integrated C/D AUC (VAPORED [0.723] vs ADO [0.698; FDR p-value 0.047], BODE [0.695; FDR p-value 0.024], and updated BODE [0.690; FDR p-value 0.021]). Finally, we developed a web tool to help clinicians calculate VAPORED mortality risk and compare it to ADO and BODE predictions. Interpretation: Our work is an important step towards improving our identification of high-risk patients and generating hypotheses of potential biological mechanisms and social factors driving mortality in patients with COPD at the population level. The main limitation of our study is the fact that the analysed datasets consist of older people with extensive smoking history and limited racial diversity. Thus, the results are relevant to high-risk individuals or those diagnosed with COPD and the VAPORED score is validated for them. Funding: This research was supported by NIH [NHLBI, NLM]. The COPDGene study is supported by the COPD Foundation, through grants from AstraZeneca, Bayer Pharmaceuticals, Boehringer Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion.

Disentangling Predictors of COPD Mortality with Probabilistic Graphical Models.

Background-Research question: Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in COPD patients can be important for disease management strategies. Although scores for all-cause mortality have been developed previously, there is limited research on factors that may directly affect COPD-specific mortality. Study design-Methods: used probabilistic (causal) graphs to analyze clinical baseline COPDGene data, including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data (from year-5). Results: We identified factors linked to all-cause and COPD-specific mortality. Although many were similar, there were differences in certain comorbidities (all-cause mortality model only) and forced vital capacity (COPD-specific mortality model only). Using our results, we developed VAPORED , a 7-variable COPD-specific mortality risk score, which we validated using the ECLIPSE 3-yr mortality data. We showed that the new model is more accurate than the existing ADO, BODE, and updated BODE indices. Additionally, we identified biological signatures linked to all-cause mortality, including a plasma cell mediated component. Finally, we developed a web page to help clinicians calculate mortality risk using VAPORED, ADO, and BODE indices. Interpretation: Given the importance of predicting COPD-specific and all-cause mortality risk in COPD patients, we showed that probabilistic graphs can identify the features most directly affecting them, and be used to build new, more accurate models of mortality risk. Novel biological features affecting mortality were also identified. This is an important step towards improving our identification of high-risk patients and potential biological mechanisms that drive COPD mortality.

The transcriptome of CD14 + CD163 - HLA-DR low monocytes predicts mortality in Idiopathic Pulmonary Fibrosis.

Rationale: The association between immune-cell-specific transcriptomic profiles and Idiopathic Pulmonary Fibrosis (IPF) mortality is unknown. Objectives: To determine immune-cell-specific transcriptomic profiles associated with IPF mortality. Methods: We profiled peripheral blood mononuclear cells (PBMC) in 18 participants [University of South Florida: IPF, COVID-19, post-COVID-19 Interstitial Lung Disease (Post-COVID-19 ILD), controls] by single-cell RNA sequencing (scRNA-seq) and identified 16 immune-cell-specific transcriptomic profiles. The Scoring Algorithm of Molecular Subphenotypes (SAMS) was used to calculate Up-scores based on these 16 gene profiles. Their association with outcomes was investigated in peripheral blood, Bronchoalveolar Lavage (BAL) and lung tissue of N=416 IPF patients from six cohorts. Findings were validated in an independent IPF, PBMC scRNA-seq dataset (N=38). Measurements and main results: Cox-regression models demonstrated that 230 genes from CD14 + CD163 - HLA-DR low circulating monocytes predicted IPF mortality [Pittsburgh (p=0.02), Chicago (p=0.003)]. PBMC proportions of CD14 + CD163 - HLA-DR low monocytes were higher in progressive versus stable IPF (Yale, 0.13±0.05 versus 0.09±0.05, p=0.034). Receiving operating characteristic identified a 230 gene, Up-score >41.84 (Pittsburgh) predictive of mortality in Chicago (HR: 6.58, 95%CI: 2.15-20.13, p=0.001) and in pooled analysis of BAL cohorts (HR: 2.20, 95%CI: 1.44-3.37, p=0.0003). High-risk patients had decreased expression of the T-cell co-stimulatory genes CD28 , ICOS , ITK and LCK (Pittsburgh and Chicago, p<0.01). 230 gene-up-scores negatively correlated with Forced Vital Capacity (FVC) in IPF lung tissues (LGRC, rho=-0.2, p=0.02). Results were replicated using a subset of 13 genes from the 230-gene signature (pooled PBMC cohorts - HR: 5.34, 95%CI: 2.83-10.06, p<0.0001). Conclusions: The transcriptome of CD14 + CD163 - HLA-DR low monocytes is associated with increased IPF mortality.

IL-4-induced SOX9 confers lineage plasticity to aged adult lung stem cells.

Wound healing in response to acute injury is mediated by the coordinated and transient activation of parenchymal, stromal, and immune cells that resolves to homeostasis. Environmental, genetic, and epigenetic factors associated with inflammation and aging can lead to persistent activation of the microenvironment and fibrosis. Here, we identify opposing roles of interleukin-4 (IL-4) cytokine signaling in interstitial macrophages and type II alveolar epithelial cells (ATIIs). We show that IL4Ra signaling in macrophages promotes regeneration of the alveolar epithelium after bleomycin-induced lung injury. Using organoids and mouse models, we show that IL-4 directly acts on a subset of ATIIs to induce the expression of the transcription factor SOX9 and reprograms them toward a progenitor-like state with both airway and alveolar lineage potential. In the contexts of aging and bleomycin-induced lung injury, this leads to aberrant epithelial cell differentiation and bronchiolization, consistent with cellular and histological changes observed in interstitial lung disease.

LEF1 isoforms regulate cellular senescence and aging.

Background: The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. Methods: With the focus on identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Results: We identified Lymphoid Enhancer Binding Factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with Idiopathic Pulmonary Fibrosis (IPF), an age-related disease with strong ties to cellular senescence, we demonstrated a stark dysregulation of LEF1. Conclusions: Collectively, our results suggest that the LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

Transcriptional changes of the aging lung.

Aging is a natural process associated with declined organ function and higher susceptibility to developing chronic diseases. A systemic single-cell type-based study provides a unique opportunity to understand the mechanisms behind age-related pathologies. Here, we use single-cell gene expression analysis comparing healthy young and aged human lungs from nonsmoker donors to investigate age-related transcriptional changes. Our data suggest that aging has a heterogenous effect on lung cells, as some populations are more transcriptionally dynamic while others remain stable in aged individuals. We found that monocytes and alveolar macrophages were the most transcriptionally affected populations. These changes were related to inflammation and regulation of the immune response. Additionally, we calculated the LungAge score, which reveals the diversity of lung cell types during aging. Changes in DNA damage repair, fatty acid metabolism, and inflammation are essential for age prediction. Finally, we quantified the senescence score in aged lungs and found that the more biased cells toward senescence are immune and progenitor cells. Our study provides a comprehensive and systemic analysis of the molecular signatures of lung aging. Our LungAge signature can be used to predict molecular signatures of physiological aging and to detect common signatures of age-related lung diseases.

LEF1 isoforms regulate cellular senescence and aging.

The study of aging and its mechanisms, such as cellular senescence, has provided valuable insights into age-related pathologies, thus contributing to their prevention and treatment. The current abundance of high-throughput data combined with the surge of robust analysis algorithms has facilitated novel ways of identifying underlying pathways that may drive these pathologies. For the purpose of identifying key regulators of lung aging, we performed comparative analyses of transcriptional profiles of aged versus young human subjects and mice, focusing on the common age-related changes in the transcriptional regulation in lung macrophages, T cells, and B immune cells. Importantly, we validated our findings in cell culture assays and human lung samples. Our analysis identified lymphoid enhancer binding factor 1 (LEF1) as an important age-associated regulator of gene expression in all three cell types across different tissues and species. Follow-up experiments showed that the differential expression of long and short LEF1 isoforms is a key regulatory mechanism of cellular senescence. Further examination of lung tissue from patients with idiopathic pulmonary fibrosis, an age-related disease with strong ties to cellular senescence, revealed a stark dysregulation of LEF1. Collectively, our results suggest that LEF1 is a key factor of aging, and its differential regulation is associated with human and murine cellular senescence.

Causal Discovery in High-dimensional, Multicollinear Datasets.

As the cost of high-throughput genomic sequencing technology declines, its application in clinical research becomes increasingly popular. The collected datasets often contain tens or hundreds of thousands of biological features that need to be mined to extract meaningful information. One area of particular interest is discovering underlying causal mechanisms of disease outcomes. Over the past few decades, causal discovery algorithms have been developed and expanded to infer such relationships. However, these algorithms suffer from the curse of dimensionality and multicollinearity. A recently introduced, non-orthogonal, general empirical Bayes approach to matrix factorization has been demonstrated to successfully infer latent factors with interpretable structures from observed variables. We hypothesize that applying this strategy to causal discovery algorithms can solve both the high dimensionality and collinearity problems, inherent to most biomedical datasets. We evaluate this strategy on simulated data and apply it to two real-world datasets. In a breast cancer dataset, we identified important survival-associated latent factors and biologically meaningful enriched pathways within factors related to important clinical features. In a SARS-CoV-2 dataset, we were able to predict whether a patient (1) had Covid-19 and (2) would enter the ICU. Furthermore, we were able to associate factors with known Covid-19 related biological pathways.

Myofibroblast transcriptome indicates SFRP2hi fibroblast progenitors in systemic sclerosis skin.

Skin and lung fibrosis in systemic sclerosis (SSc) is driven by myofibroblasts, alpha-smooth muscle actin expressing cells. The number of myofibroblasts in SSc skin correlates with the modified Rodnan skin score, the most widely used clinical measure of skin disease severity. Murine fibrosis models indicate that myofibroblasts can arise from a variety of different cell types, but their origin in SSc skin has remained uncertain. Utilizing single cell RNA-sequencing, we define different dermal fibroblast populations and transcriptome changes, comparing SSc to healthy dermal fibroblasts. Here, we show that SSc dermal myofibroblasts arise in two steps from an SFRP2hi/DPP4-expressing progenitor fibroblast population. In the first step, SSc fibroblasts show globally upregulated expression of transcriptome markers, such as PRSS23 and THBS1. A subset of these cells shows markers indicating that they are proliferating. Only a fraction of SFRP2hi SSc fibroblasts differentiate into myofibroblasts, as shown by expression of additional markers, SFRP4 and FNDC1. Bioinformatics analysis of the SSc fibroblast transcriptomes implicated upstream transcription factors, including FOSL2, RUNX1, STAT1, FOXP1, IRF7 and CREB3L1, as well as SMAD3, driving SSc myofibroblast differentiation.