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.

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 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.

Activated sputum eosinophils associated with exacerbations in children on mepolizumab.

BACKGROUND: MUPPITS-2 was a randomized, placebo-controlled clinical trial that demonstrated mepolizumab (anti-IL-5) reduced exacerbations and blood and airway eosinophils in urban children with severe eosinophilic asthma. Despite this reduction in eosinophilia, exacerbation risk persisted in certain patients treated with mepolizumab. This raises the possibility that subpopulations of airway eosinophils exist that contribute to breakthrough exacerbations. OBJECTIVE: We aimed to determine the effect of mepolizumab on airway eosinophils in childhood asthma. METHODS: Sputum samples were obtained from 53 MUPPITS-2 participants. Airway eosinophils were characterized using mass cytometry and grouped into subpopulations using unsupervised clustering analyses of 38 surface and intracellular markers. Differences in frequency and immunophenotype of sputum eosinophil subpopulations were assessed based on treatment arm and frequency of exacerbations. RESULTS: Median sputum eosinophils were significantly lower among participants treated with mepolizumab compared with placebo (58% lower, 0.35% difference [95% CI 0.01, 0.74], P = .04). Clustering analysis identified 3 subpopulations of sputum eosinophils with varied expression of CD62L. CD62Lint and CD62Lhi eosinophils exhibited significantly elevated activation marker and eosinophil peroxidase expression, respectively. In mepolizumab-treated participants, CD62Lint and CD62Lhi eosinophils were more abundant in participants who experienced exacerbations than in those who did not (100% higher for CD62Lint, 0.04% difference [95% CI 0.0, 0.13], P = .04; 93% higher for CD62Lhi, 0.21% difference [95% CI 0.0, 0.77], P = .04). CONCLUSIONS: Children with eosinophilic asthma treated with mepolizumab had significantly lower sputum eosinophils. However, CD62Lint and CD62Lhi eosinophils were significantly elevated in children on mepolizumab who had exacerbations, suggesting that eosinophil subpopulations exist that contribute to exacerbations despite anti-IL-5 treatment.

iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. RESULTS: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. CONCLUSIONS: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

Signaling through the adaptor molecule MyD88 in CD4+ T cells is required to overcome suppression by regulatory T cells.

Innate immune recognition controls adaptive immune responses through multiple mechanisms. The MyD88 signaling adaptor operates in many cell types downstream of Toll-like receptors (TLRs) and interleukin-1 (IL-1) receptor family members. Cell-type-specific functions of MyD88 signaling remain poorly characterized. Here, we have shown that the T cell-specific ablation of MyD88 in mice impairs not only T helper 17 (Th17) cell responses, but also Th1 cell responses. MyD88 relayed signals of TLR-induced IL-1, which became dispensable for Th1 cell responses in the absence of T regulatory (Treg) cells. Treg cell-specific ablation of MyD88 had no effect, suggesting that IL-1 acts on naive CD4(+) T cells instead of Treg cells themselves. Together, these findings demonstrate that IL-1 renders naive CD4(+) T cells refractory to Treg cell-mediated suppression in order to allow their differentiation into Th1 cells. In addition, IL-1 was also important for the generation of functional CD4(+) memory T cells.

Lung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis.

Rationale: Fibrotic hypersensitivity pneumonitis (fHP) is an interstitial lung disease caused by sensitization to an inhaled allergen. Objectives: To identify the molecular determinants associated with progression of fibrosis. Methods: Nine fHP explant lungs and six unused donor lungs (as controls) were systematically sampled (4 samples/lung). According to microcomputed tomography measures, fHP cores were clustered into mild, moderate, and severe fibrosis groups. Gene expression profiles were assessed using weighted gene co-expression network analysis, xCell, gene ontology, and structure enrichment analysis. Gene expression of the prevailing molecular traits was also compared with idiopathic pulmonary fibrosis (IPF). The explant lung findings were evaluated in separate clinical fHP cohorts using tissue, BAL samples, and computed tomography scans. Measurements and Main Results: We found six molecular traits that associated with differential lung involvement. In fHP, extracellular matrix and antigen presentation/sensitization transcriptomic signatures characterized lung zones with only mild structural and histological changes, whereas signatures involved in honeycombing and B cells dominated the transcriptome in the most severely affected lung zones. With increasing disease severity, endothelial function was progressively lost, and progressive disruption in normal cellular homeostatic processes emerged. All six were also found in IPF, with largely similar associations with disease microenvironments. The molecular traits correlated with in vivo disease behavior in a separate clinical fHP cohort. Conclusions: We identified six molecular traits that characterize the morphological progression of fHP and associate with in vivo clinical behavior. Comparing IPF with fHP, the transcriptome landscape was determined considerably by local disease extent rather than by diagnosis alone.

Integrated Analysis of Tracheobronchial Fluid from Before and After Cardiopulmonary Bypass Reveals Activation of the Integrated Stress Response and Altered Pulmonary Microvascular Permeability.

Objective: We aim to comprehensively describe the transcriptional activity and signaling of pulmonary parenchymal and immune cells before and after cardiopulmonary bypass (CPB) by using a multi-omic approach coupled with functional cellular assays. We hypothesize that key signaling pathways from specific cells within the lung alter pulmonary endothelial cell function resulting in worsening or improving disease. Methods: We collected serial tracheobronchial lavage samples from intubated patients less than 2-years-old undergoing surgery with CPB. Samples were immediately processed for single cell RNA sequencing (10x Genomics). Cell clustering, cell-type annotation, and visualization were performed, and differentially expressed genes (DEG) between serial samples were identified. Metabolomic and proteomic analyses were performed on the supernatant using mass spectrometry and a multiplex assay (SomaScan) respectively. Functional assays were done using electric cell-substrate impedance sensing to measure resistance across human pulmonary microvascular endothelial cells (HPMECs). Results: Analysis of eight patients showed a heterogeneous mixture of pulmonary parenchymal and immune cells. Cell clustering demonstrated time-dependent changes in the transcriptomic signature indicating altered cellular phenotypes after CPB. DEG analysis was represented by genes involved in host defense, innate immunity, and the mitochondrial respiratory transport chain. Ingenuity pathway analysis showed upregulation of the integrated stress response across all cell types after CPB. Metabolomic analysis demonstrated upregulation of ascorbate and aldarate metabolism. Unbiased proteomic analysis revealed upregulation of proteins involved in cytokine and chemokine pathways. Post-CPB patient supernatant improved HMPEC barrier function, suggesting a protective cellular response to CPB. Conclusion: Children who undergo CPB for cardiac surgery have distinct cell populations, transcriptional activity, and metabolism that change over time. The response to ischemia-reperfusion injury in the lower airway of children appears to be protective, with the need to identify potential targets through future investigations.

Integrated Single-Cell Atlas of Endothelial Cells of the Human Lung.

BACKGROUND: Cellular diversity of the lung endothelium has not been systematically characterized in humans. We provide a reference atlas of human lung endothelial cells (ECs) to facilitate a better understanding of the phenotypic diversity and composition of cells comprising the lung endothelium. METHODS: We reprocessed human control single-cell RNA sequencing (scRNAseq) data from 6 datasets. EC populations were characterized through iterative clustering with subsequent differential expression analysis. Marker genes were validated by fluorescent microscopy and in situ hybridization. scRNAseq of primary lung ECs cultured in vitro was performed. The signaling network between different lung cell types was studied. For cross-species analysis or disease relevance, we applied the same methods to scRNAseq data obtained from mouse lungs or from human lungs with pulmonary hypertension. RESULTS: Six lung scRNAseq datasets were reanalyzed and annotated to identify >15 000 vascular EC cells from 73 individuals. Differential expression analysis of EC revealed signatures corresponding to endothelial lineage, including panendothelial, panvascular, and subpopulation-specific marker gene sets. Beyond the broad cellular categories of lymphatic, capillary, arterial, and venous ECs, we found previously indistinguishable subpopulations; among venous EC, we identified 2 previously indistinguishable populations: pulmonary-venous ECs (COL15A1neg) localized to the lung parenchyma and systemic-venous ECs (COL15A1pos) localized to the airways and the visceral pleura; among capillary ECs, we confirmed their subclassification into recently discovered aerocytes characterized by EDNRB, SOSTDC1, and TBX2 and general capillary EC. We confirmed that all 6 endothelial cell types, including the systemic-venous ECs and aerocytes, are present in mice and identified endothelial marker genes conserved in humans and mice. Ligand-receptor connectome analysis revealed important homeostatic crosstalk of EC with other lung resident cell types. scRNAseq of commercially available primary lung ECs demonstrated a loss of their native lung phenotype in culture. scRNAseq revealed that endothelial diversity is maintained in pulmonary hypertension. Our article is accompanied by an online data mining tool (www.LungEndothelialCellAtlas.com). CONCLUSIONS: Our integrated analysis provides a comprehensive and well-crafted reference atlas of ECs in the normal lung and confirms and describes in detail previously unrecognized endothelial populations across a large number of humans and mice.

Emerging insights in sarcoidosis: moving forward through reverse translational research.

Sarcoidosis is a chronic granulomatous disease of unknown etiology that primarily affects the lungs. The development of stage IV or fibrotic lung disease accounts for a significant proportion of the morbidity and mortality attributable to sarcoidosis. Further investigation into the active mechanisms of disease pathogenesis and fibrogenesis might illuminate fundamental mediators of injury and repair while providing new opportunities for clinical intervention. However, progress in sarcoidosis research has been hampered by the heterogeneity of clinical phenotypes and the lack of a consensus modeling system. Recently, reverse translational research, wherein observations made at the patient level catalyze hypothesis-driven research at the laboratory bench, has generated new discoveries regarding the immunopathogenic mechanisms of pulmonary granuloma formation, fibrogenesis, and disease model development. The purpose of this review is to highlight the promise and possibility of these novel investigative efforts.

Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.

MOTIVATION: Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. RESULTS: We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. AVAILABILITY AND IMPLEMENTATION: The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

G2S3: A gene graph-based imputation method for single-cell RNA sequencing data.

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

Gene coexpression networks reveal novel molecular endotypes in alpha-1 antitrypsin deficiency.

BACKGROUND: Alpha-1 antitrypsin deficiency (AATD) is a genetic condition that causes early onset pulmonary emphysema and airways obstruction. The complete mechanisms via which AATD causes lung disease are not fully understood. To improve our understanding of the pathogenesis of AATD, we investigated gene expression profiles of bronchoalveolar lavage (BAL) and peripheral blood mononuclear cells (PBMCs) in AATD individuals. METHODS: We performed RNA-Seq on RNA extracted from matched BAL and PBMC samples isolated from 89 subjects enrolled in the Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Subjects were stratified by genotype and augmentation therapy. Supervised and unsupervised differential gene expression analyses were performed using Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene profiles associated with subjects' clinical variables. The genes in the most significant WGCNA module were used to cluster AATD individuals. Gene validation was performed by NanoString nCounter Gene Expression Assay. RESULT: We observed modest effects of AATD genotype and augmentation therapy on gene expression. When WGCNA was applied to BAL transcriptome, one gene module, ME31 (2312 genes), correlated with the highest number of clinical variables and was functionally enriched with numerous immune T-lymphocyte related pathways. This gene module identified two distinct clusters of AATD individuals with different disease severity and distinct PBMC gene expression patterns. CONCLUSIONS: We successfully identified novel clusters of AATD individuals where severity correlated with increased immune response independent of individuals' genotype and augmentation therapy. These findings may suggest the presence of previously unrecognised disease endotypes in AATD that associate with T-lymphocyte immunity and disease severity.

Transcriptomics of bronchoalveolar lavage cells identifies new molecular endotypes of sarcoidosis.

BACKGROUND: Sarcoidosis is a multisystem granulomatous disease of unknown origin with a variable and often unpredictable course and pattern of organ involvement. In this study we sought to identify specific bronchoalveolar lavage (BAL) cell gene expression patterns indicative of distinct disease phenotypic traits. METHODS: RNA sequencing by Ion Torrent Proton was performed on BAL cells obtained from 215 well-characterised patients with pulmonary sarcoidosis enrolled in the multicentre Genomic Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) study. Weighted gene co-expression network analysis and nonparametric statistics were used to analyse genome-wide BAL transcriptome. Validation of results was performed using a microarray expression dataset of an independent sarcoidosis cohort (Freiburg, Germany; n=50). RESULTS: Our supervised analysis found associations between distinct transcriptional programmes and major pulmonary phenotypic manifestations of sarcoidosis including T-helper type 1 (Th1) and Th17 pathways associated with hilar lymphadenopathy, transforming growth factor-ß1 (TGFB1) and mechanistic target of rapamycin (MTOR) signalling with parenchymal involvement, and interleukin (IL)-7 and IL-2 with airway involvement. Our unsupervised analysis revealed gene modules that uncovered four potential sarcoidosis endotypes including hilar lymphadenopathy with increased acute T-cell immune response; extraocular organ involvement with PI3K activation pathways; chronic and multiorgan disease with increased immune response pathways; and multiorgan involvement, with increased IL-1 and IL-18 immune and inflammatory responses. We validated the occurrence of these endotypes using gene expression, pulmonary function tests and cell differentials from Freiburg. CONCLUSION: Taken together, our results identify BAL gene expression programmes that characterise major pulmonary sarcoidosis phenotypes and suggest the presence of distinct disease molecular endotypes.

Single-cell characterization of a model of poly I:C-stimulated peripheral blood mononuclear cells in severe asthma.

BACKGROUND: Asthma has been associated with impaired interferon response. Multiple cell types have been implicated in such response impairment and may be responsible for asthma immunopathology. However, existing models to study the immune response in asthma are limited by bulk profiling of cells. Our objective was to Characterize a model of peripheral blood mononuclear cells (PBMCs) of patients with severe asthma (SA) and its response to the TLR3 agonist Poly I:C using two single-cell methods. METHODS: Two complementary single-cell methods, DropSeq for single-cell RNA sequencing (scRNA-Seq) and mass cytometry (CyTOF), were used to profile PBMCs of SA patients and healthy controls (HC). Poly I:C-stimulated and unstimulated cells were analyzed in this study. RESULTS: PBMCs (n = 9414) from five SA (n = 6099) and three HC (n = 3315) were profiled using scRNA-Seq. Six main cell subsets, namely CD4 + T cells, CD8 + T cells, natural killer (NK) cells, B cells, dendritic cells (DCs), and monocytes, were identified. CD4 + T cells were the main cell type in SA and demonstrated a pro-inflammatory profile characterized by increased JAK1 expression. Following Poly I:C stimulation, PBMCs from SA had a robust induction of interferon pathways compared with HC. CyTOF profiling of Poly I:C stimulated and unstimulated PBMCs (n = 160,000) from the same individuals (SA = 5; HC = 3) demonstrated higher CD8 + and CD8 + effector T cells in SA at baseline, followed by a decrease of CD8 + effector T cells after poly I:C stimulation. CONCLUSIONS: Single-cell profiling of an in vitro model using PBMCs in patients with SA identified activation of pro-inflammatory pathways at baseline and strong response to Poly I:C, as well as quantitative changes in CD8 + effector cells. Thus, transcriptomic and cell quantitative changes are associated with immune cell heterogeneity in this model to evaluate interferon responses in severe asthma.

A Network of Sputum MicroRNAs Is Associated with Neutrophilic Airway Inflammation in Asthma.

Rationale: MicroRNAs are potent regulators of biologic systems that are critical to tissue homeostasis. Individual microRNAs have been identified in airway samples. However, a systems analysis of the microRNA-mRNA networks present in the sputum that contribute to airway inflammation in asthma has not been published.Objectives: Identify microRNA and mRNA networks in the sputum of patients with asthma.Methods: We conducted a genome-wide analysis of microRNA and mRNA in the sputum from patients with asthma and correlated expression with clinical phenotypes. Weighted gene correlation network analysis was implemented to identify microRNA networks (modules) that significantly correlate with clinical features of asthma and mRNA expression networks. MicroRNA expression in peripheral blood neutrophils and lymphocytes and in situ hybridization of the sputum were used to identify the cellular sources of microRNAs. MicroRNA expression obtained before and after ozone exposure was also used to identify changes associated with neutrophil counts in the airway.Measurements and Main Results: Six microRNA modules were associated with clinical features of asthma. A single module (nely) was associated with a history of hospitalizations, lung function impairment, and numbers of neutrophils and lymphocytes in the sputum. Of the 12 microRNAs in the nely module, hsa-miR-223-3p was the highest expressed microRNA in neutrophils and was associated with increased neutrophil counts in the sputum in response to ozone exposure. Multiple microRNAs in the nely module correlated with two mRNA modules enriched for TLR (Toll-like receptor) and T-helper cell type 17 (Th17) signaling and endoplasmic reticulum stress. hsa-miR-223-3p was a key regulator of the TLR and Th17 pathways in the sputum of subjects with asthma.Conclusions: This study of sputum microRNA and mRNA expression from patients with asthma demonstrates the existence of microRNA networks and genes that are associated with features of asthma severity. Among these, hsa-miR-223-3p, a neutrophil-derived microRNA, regulates TLR/Th17 signaling and endoplasmic reticulum stress.

Single-Cell Transcriptional Archetypes of Airway Inflammation in Cystic Fibrosis.

Rationale: Cystic fibrosis (CF) is a life-shortening, multisystem hereditary disease caused by abnormal chloride transport. CF lung disease is driven by innate immune dysfunction and exaggerated inflammatory responses that contribute to tissue injury. To define the transcriptional profile of this airway immune dysfunction, we performed the first single-cell transcriptome characterization of CF sputum.Objectives: To define the transcriptional profile of sputum cells and its implication in the pathogenesis of immune function and the development of CF lung disease.Methods: We performed single-cell RNA sequencing of sputum cells from nine subjects with CF and five healthy control subjects. We applied novel computational approaches to define expression-based cell function and maturity profiles, herein called transcriptional archetypes.Measurements and Main Results: The airway immune cell repertoire shifted from alveolar macrophages in healthy control subjects to a predominance of recruited monocytes and neutrophils in CF. Recruited lung mononuclear phagocytes were abundant in CF and were separated into the following three archetypes: activated monocytes, monocyte-derived macrophages, and heat shock-activated monocytes. Neutrophils were the most prevalent in CF, with a dominant immature proinflammatory archetype. Although CF monocytes exhibited proinflammatory features, both monocytes and neutrophils showed transcriptional evidence of abnormal phagocytic and cell-survival programs.Conclusions: Our findings offer an opportunity to understand subject-specific immune dysfunction and its contribution to divergent clinical courses in CF. As we progress toward personalized applications of therapeutic and genomic developments, we hope this inflammation-profiling approach will enable further discoveries that change the natural history of CF lung disease.

Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients.

BACKGROUND: The pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression data. RESULTS: Here, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine regression. CONCLUSION: We found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.

Severe respiratory viral infection induces procalcitonin in the absence of bacterial pneumonia.

INTRODUCTION: Procalcitonin expression is thought to be stimulated by bacteria and suppressed by viruses via interferon signalling. Consequently, during respiratory viral illness, clinicians often interpret elevated procalcitonin as evidence of bacterial coinfection, prompting antibiotic administration. We sought to evaluate the validity of this practice and the underlying assumption that viral infection inhibits procalcitonin synthesis. METHODS: We conducted a retrospective cohort study of patients hospitalised with pure viral infection (n=2075) versus bacterial coinfection (n=179). The ability of procalcitonin to distinguish these groups was assessed. In addition, procalcitonin and interferon gene expression were evaluated in murine and cellular models of influenza infection. RESULTS: Patients with bacterial coinfection had higher procalcitonin than those with pure viral infection, but also more severe disease and higher mortality (p<0.001). After matching for severity, the specificity of procalcitonin for bacterial coinfection dropped substantially, from 72% to 61%. In fact, receiver operating characteristic curve analysis showed that procalcitonin was a better indicator of multiple indices of severity (eg, organ failures and mortality) than of coinfection. Accordingly, patients with severe viral infection had elevated procalcitonin. In murine and cellular models of influenza infection, procalcitonin was also elevated despite bacteriologic sterility and correlated with markers of severity. Interferon signalling did not abrogate procalcitonin synthesis. DISCUSSION: These studies reveal that procalcitonin rises during pure viral infection in proportion to disease severity and is not suppressed by interferon signalling, in contrast to prior models of procalcitonin regulation. Applied clinically, our data suggest that procalcitonin represents a better indicator of disease severity than bacterial coinfection during viral respiratory infection.

Integrating multiomics longitudinal data to reconstruct networks underlying lung development.

A comprehensive understanding of the dynamic regulatory networks that govern postnatal alveolar lung development is still lacking. To construct such a model, we profiled mRNA, microRNA, DNA methylation, and proteomics of developing murine alveoli isolated by laser capture microdissection at 14 predetermined time points. We developed a detailed comprehensive and interactive model that provides information about the major expression trajectories, the regulators of specific key events, and the impact of epigenetic changes. Intersecting the model with single-cell RNA-Seq data led to the identification of active pathways in multiple or individual cell types. We then constructed a similar model for human lung development by profiling time-series human omics data sets. Several key pathways and regulators are shared between the reconstructed models. We experimentally validated the activity of a number of predicted regulators, leading to new insights about the regulation of innate immunity during lung development.