Biology subjects

Chupp, G. L.

Publications and source records attributed to Chupp, G. L..

7 recordsLinked to original sources

bioRxiv2026

Asthma Exacerbations: Integrative Analysis of miRNA Activity Using Single-Cell Transcriptomics

BackgroundAsthma exacerbations are caused by dysregulated cellular interactions between airway and immune cell populations. Circulating microRNAs (miRNAs) are potential biomarkers for asthma exacerbations; however, their target airway cells remain poorly defined. ObjectiveTo identify the cell types that are regulated by the circulating microRNAs linked to asthma exacerbations and the extent to which the cells are regulated by miRNAs. MethodsWe integrated a curated panel of exacerbation-associated circulating miRNAs with single-cell RNA sequencing (scRNA-seq) profiles from induced sputum of 16 asthma patients and 8 healthy controls. Experimentally validated miRNA-target interactions were combined with cell-type-specific differential expression. Elastic Net regression and SHAP analysis quantified gene-level regulatory contributions, yielding a composite Regulation Strength metric. Findings were validated against four independent GEO datasets. ResultsImmune cells, including monocytes, dendritic cells, and macrophages, demonstrated the strongest statistically significant miRNA regulatory signals, in contrast to airway epithelial cells.hsa-miR-222-3p showed opposing regulatory effects in mature versus alveolar macrophages, indicating differentiation-state-dependent activity, while B_Plasma cells showed no detectable regulatory effect from any miRNA tested. Independent GEO validation confirmed higher expression of protective miRNAs (hsa-miR-126-3p, hsa-miR-146b-5p) in healthy individuals, consistent with prior CAMP cohort associations. ConclusionCirculating miRNAs show cell-type-specific regulatory activity, strongest in monocytes, dendritic cells, and macrophages. hsa-miR-222-3p showed opposing regulatory directions between macrophage subtypes, while B_Plasma cells showed no effect, validated across independent GEO cohorts.

bioinformatics↗

bioRxiv2026

Biologic therapy is associated with selective changes in airway eosinophil subpopulations in severe asthma

RationaleEosinophilic airway inflammation is common in severe asthma and strongly associated with symptoms, exacerbations, and impaired lung function. Although type 2 (T2)-targeted biologics improve outcomes and reduce eosinophils, many patients experience residual symptoms and exacerbations. Emerging evidence suggests that these biologics may differentially affect specific airway eosinophil subpopulations, representing a potential mechanism of suboptimal treatment response. ObjectiveDetermine the effect of biologic treatment on eosinophil subpopulations in adults with severe asthma using in-depth immune profiling with mass cytometry (CyTOF). MethodsFifty adults with severe asthma (28 biologic-naive, 22 on stable-dose biologic therapy for [≥]6 months) underwent clinical phenotyping, spirometry, blood sampling, and sputum induction. Twenty-nine sputum samples passed quality control thresholds and were profiled by CyTOF. Manually gated sputum eosinophils were clustered using FlowSOM to identify eosinophil subpopulations, and cluster abundances and marker expression were compared across treatment groups. Measurements and Main ResultsCyTOF revealed treatment-associated shifts in circulating immune cells (lower CD4+ T cells and B cells, higher monocytes) and lower sputum CD8+ T cells. Unsupervised clustering of sputum eosinophils identified eight distinct subpopulations, and selective depletion of Cluster 6 was noted in biologic-treated participants (biologic-naive vs anti-TSLP logFC -4.98, p=0.003; biologic-naive vs anti-IL5 logFC -6.89, p=0.01). Higher Cluster 6 proportion correlated with worse ACT scores (rho = -0.44, p = 0.02) and lung function (FEV1 % predicted: rho = -0.47, p < 0.01; FEV1/FVC: rho = -0.40, p = 0.03). Functionally, Cluster 6 displayed enriched trafficking/activation markers (CCR3/Eotaxin-1, CD69, CD80, CRTH2) and non-T2 inflammatory mediators (TNF, IL-8, TLR7). ConclusionBiologic therapy in severe asthma was associated with selective depletion of a highly activated sputum eosinophil subpopulation with capability to drive both T2 and non-T2 inflammatory pathways. This cluster correlated with worse asthma control and lung function, indicating it may be a biologically important driver of persistent disease and potential biomarker to more accurately predict treatment response.

immunology↗

bioRxiv2025

Single-cell RNA Sequencing Analysis of Sputum Cell Transcriptomes Reveals Pathways and Communication Networks That Contribute to the Pathogenesis of Asthma

BackgroundAsthma is driven by complex interactions amongst structural airway cells, cells of the immune system, and the environmental. While sputum cell characterization has been instrumental in studying asthma pathogenesis and refining treatment strategies, the nuances of cellular transcriptomes and intercellular communication in asthmatic sputum remain poorly understood. MethodsWe employed single-cell RNA sequencing to analyze cells isolated form the sputum from 16 asthma patients and 8 non-asthmatic controls. Cell identities were established using curated marker genes and SingleR annotation. We compared cell-specific gene expression and communication networks between asthmatic and control groups, correlating findings with distinct pathways that were dysregulated in asthma. Findings37,565 cellular transcriptomes were captured and analyzed. 15 distinct cell populations were identified, including various macrophages, monocytes, dendritic cells, and lymphocytes, along with rare cell types such as mast cells, innate lymphoid cells, bronchial epithelial cells, and eosinophils. Intercellular communication analysis indicated heightened signaling activity in asthma compared to controls, particularly in CD4+ T cells and dendritic cells which exhibited the most significant increases in RNA expression of outgoing signaling molecules. Notably, the ADAM12-SDC4 and CCL22-CCR4 ligand-receptor pathways demonstrated the strongest shifts between asthma and control subjects, particularly between dendritic cells and CD4 lymphocytes. InterpretationSC RNA seq profiling the asthma cellular transcriptome analysis of sputum highlights both innate and adaptive immune mechanisms that are significantly amplified in asthma. The elevated expression of ADAM12-SCD4 and CCL22-CC4 point to their critical role in asthma pathogenesis, suggesting potential avenues for targeted therapies and improved management of this chronic condition. Research in contextO_ST_ABSEvidence Before This StudyC_ST_ABSAsthma is a chronic inflammatory disease of the airways driven by intricate interactions between airway structural and immune cells. Previous transcriptomic studies have focused on bulk RNA samples from the airway, leaving significant gaps in our understanding of the cellular dynamics that characterize the disease. Added Value of This StudyThis study pioneers the use of single-cell RNA sequencing on sputum samples from patients with asthma, revealing a detailed landscape of cell phenotypes and dynamic communication patterns that distinguish asthmatic individuals from those without the disease. Notably, heightened intercellular communication was observed in asthma, particularly between CD4+ T cells and dendritic cells, confirming that there is a robust network of interactions between immune and structural cells. The notable increase of ADAM12-CCR4 communication from dendritic cells to other cell populations further emphasizes the dysregulation present in asthma. Implications of All Available EvidenceOur transcriptomic profiling illuminates distinct and amplified communication pathways involving CD4+ T cells and dendritic cells, aligning with established paradigms of both adaptive and innate immune responses in asthma pathogenesis. The identification of ADAM12 and CCR4 pathway dysregulation adds a critical layer to our understanding of the molecular mechanisms underpinning asthma, paving the way for potential therapeutic targets and personalized treatment strategies. Single cell profiling of the sputum has the capacity to characterize the breadth of cellular phenotypes, their functional status, and the communication in the airway at a level not previously attainable.

genomics↗

bioRxiv2022

A dual role for CRTH2 in acute lung injury

Acute respiratory distress syndrome (ARDS) is a life-threatening clinical condition defined by rapid-onset respiratory failure following acute lung injury (ALI). The high mortality rate and rising incidence of ARDS due to COVID-19 make it an important research priority. Here we sought to investigate the role of chemoattractant receptor-homologous molecule expressed on Th2 cells (CRTH2) in ARDS. CRTH2 is a G protein-coupled receptor best studied in the context of type 2 immunity, but it also exerts effects on neutrophilic inflammation. To evaluate its role in mouse models of ARDS, we first examined its expression pattern on murine neutrophils. We found it is expressed on neutrophils, but only after extravasation into the lung. Next, we showed that CRTH2 expression on extravasated lung neutrophils promotes cell survival, as genetic deletion of CRTH2 and pharmacologic inhibition of CRTH2 using fevipiprant both led to increased apoptosis in vitro. We then evaluated the role of CRTH2 in vivo using a murine model of LPS-induced ALI. In line with the pro-inflammatory effects of CRTH2 in vitro, we observed improvement of lung injury in CRTH2-deficient mice in terms of vascular leak, weight loss and survival after LPS administration. However, neutrophilic inflammation was elevated, not suppressed in the CRTH2 KO. This finding indicated a second mechanism offsetting the pro-survival effect of CRTH2 on neutrophils. Bulk RNAseq of lung tissue indicated impairments in type 2 immune signaling in the CRTH2 KO, and qPCR and ELISA confirmed downregulation of IL-4, which is known to suppress neutrophilic inflammation. Thus, CRTH2 may play a dual role in ALI, directly promoting neutrophil cell survival, but indirectly suppressing neutrophil effector function via IL-4.

immunology↗

bioRxiv2022

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

Single-cell RNA sequencing (scRNA-seq) enables assessment of transcriptome-wide changes at single-cell resolution. However, dominant subject effect in scRNA-seq datasets with multiple subjects severely confounds cell-type-specific differential expression (DE) analysis. We developed iDESC to separate subject effect from disease effect with consideration of dropouts to identify DE genes. iDESC was shown to have well-controlled type I error and high power compared to existing methods and obtained the best consistency between datasets and disease relevance in two scRNA-seq datasets from same disease, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects.

bioinformatics↗

bioRxiv2019

Approaches for integrating heterogeneous RNA-seq data reveals cross-talk between microbes and genes in asthmatic patients

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNAseq) can be used on sputum, but it can be challenging to interpret because sputum contains a complex and heterogeneous mixture of human cells and exogenous (microbial) material. In this study, we developed a methodology that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. We use this to relate bulk RNAseq data from 115 asthmatic patients with clinical information, microscope images, and single-cell profiles. First, we mapped sputum RNAseq to human and exogenous sources. Next, we decomposed the human reads into cell-expression signatures and fractions of these in each sample; we validated the decomposition using targeted single-cell RNAseq and microscopy. We observed enrichment of immune-system cells (neutrophils, eosinophils, and mast cells) in severe asthmatics. Second, we inferred microbial abundances from the exogenous reads and then associated these with clinical variables -- e.g., Haemophilus was associated with increased white blood cell count and Candida, with worse lung function. Third, we applied a generative model, Latent Dirichlet allocation (LDA), to identify patterns of gene expression and microbial abundances and relate them to clinical data. Based on this, we developed a method called LDA-link that connects microbes to genes using reduced-dimensionality LDA topics. We found a number of known connections, e.g. between Haemophilus and the gene IL1B, which is highly expressed by mast cells. In addition, we identified novel connections, including Candida and the calcium-signaling gene CACNA1E, which is highly expressed by eosinophils. These results speak to the mechanism by which gene-microbe interactions contribute to asthma and define a strategy for making inferences in heterogeneous and noisy RNAseq datasets.

genomics↗

bioRxiv2019

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

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. In this work, we developed a framework that incorporates a denoising autoencoder and a supervised learning approach to identify gene signatures related to asthma severity. The autoencoder embeds high-dimensional gene expression data into a lower-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from gene expression data. We found that the weights on hidden units in this latent space correlate well with previously defined and clinically relevant clusters of patients. Moreover, pathway analysis based on each genes contribution to the hidden units showed significant enrichment in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary supervised classifier (based on random forest) for directly predicting asthma severity. The random-forest importance metric from this classifier identified a signature based on 50 key genes, which can predict severity with an AUROC of 0.81 and thus have potential as diagnostic biomarkers. Furthermore, the key genes could also be used for successfully estimating, via support-vector-machine regression, the FEV1/FVC ratios across patients, achieving pre- and post-treatment correlations of 0.56 and 0.65, respectively (between predicted and observed values). The 50 biomarker candidate genes can be found in supplementary. The source codes are freely available upon request.

bioinformatics↗