Biology subjects

Woodfolk, J. A.

Publications and source records attributed to Woodfolk, J. A..

3 recordsLinked to original sources

bioRxiv2025

IgA antibodies against oxidation-specific epitopes are generated by double negative B cells and are associated with impaired lung function in fibrotic interstitial lung diseases.

BackgroundFibrotic interstitial lung diseases (fILD) are characterized by progressive scarring of the lung and high mortality. Oxidation-specific epitopes (OSEs) form on oxidized lipids and proteins, are abundant in lungs of patients with fILD and perpetuate lung injury and fibrosis in animal models. OSE-targeting antibodies bind to OSEs and modulate their downstream effects. The objective of this study was to determine the association of OSE-targeting antibodies with lung function and other clinical outcomes, and to characterize OSE-specific B cell phenotypes in fILD. MethodsTo determine the association of OSE-targeting antibodies and clinical outcomes in fILD, we measured plasma levels of OSE-specific antibodies against the malondialdehyde modified LDL mimotope P1 and anti-ApoB100 immune complex (anti-ApoB100 IC) in patients with fILD and age-matched controls. We then performed B cell phenotyping of patients with idiopathic pulmonary fibrosis (IPF) and healthy controls utilizing mass cytometry time of flight (CyTOF). To investigate effects of pro-fibrotic mediators on anti-OSE IgA antibody production, we measured B cell responses to OSEs in vitro. ResultsCohort of 109 patients with fILD (Hypersensitivity Pneumonitis, Idiopathic Pulmonary Fibrosis, Nonspecific Interstitial Pneumonia, Connective Tissue Disease-Associated Interstitial Lung Disease) and 55 healthy donors, and a second cohort of 20 patients with IPF and 9 healthy donors were included in the study. OSE-specific antibody levels anti-P1 and anti-ApoB100 IC IgA and IgG were significantly elevated in participants with fILD compared to healthy donors (p<0.0001). Higher anti-OSE IgA was associated with worse baseline lung function, including forced vital capacity (FVC) (p<0.001) and diffusing capacity for carbon monoxide (DLCO) (p<0.01). IgA+CD27-CD21-CD11c+ double negative type 2 (DN2) B cells correlated with anti-OSE IgA levels (p<0.05) and were enriched in OSE-specific cells. DN2 B cell precursors, CXCR5-CD11c+ naive B cells and IgA+CD27-CD21-CD11c- DN3 B cells, were significantly expanded in IPF compared to healthy donors. Both DN2 and DN3 B cells correlated with plasmablast pools in IPF patients and showed high baseline levels of IL-21 receptor, which was reduced in more differentiated cells. TGF-{beta} combined with OSEs, but not OSEs alone, induced OSE-specific IgA production in vitro. ConclusionsCirculating anti-OSE IgA antibodies are elevated in patients with fibrotic ILDs regardless of underlying pathology and negatively correlate with lung function. These antibodies correlated with the DN2 B cell subset which was enriched in OSE-specific cells. Phenotypic analysis of B cell subsets showed expression of markers consistent with DN to plasmablast differentiation. Finally, TGF-{beta} promoted OSE-IgA production in vitro. These findings show a novel link between oxidative tissue damage, anti-OSE antibodies and DN2 B cells in fILD which may provide opportunities for new targeted therapies.

immunology↗

bioRxiv2024

Rhinovirus as a Driver of Airway T-Cell Dynamics in Children with Severe Asthma

Severe asthma in children is notoriously difficult to treat, and its immunopathogenesis is complex. In particular, the contribution of T cells and relationships to anti-viral immunity, remain enigmatic. Here, we coupled deep phenotyping with machine learning methods to resolve the dynamics of T cells in the diseased lower airways, and examined rhinovirus (RV) as a driver. Our strategy revealed a T-cell landscape dominated by type 1 and type 17 CD8+ signatures. Interrogation of phenotypic relationships coupled with trajectory mapping identified T-cell migratory and differentiation pathways spanning the blood and airways that culminated in tissue residency, and included transitions between type 1 and type 17 tissue-resident types. These T-cell dynamics were reflected in cytokine polyfunctionality in situ. Use of machine learning to cross-compare T-cell populations that were enriched in the airways of RV-positive children with those induced in the blood after RV challenge in an experimental infection model, precisely pinpointed RV-responsive signatures that mapped to T-cell differentiation pathways. Despite their rarity, these signatures were detected in the airways of uninfected children. Together, our results underscore the aberrant nature of type 1 immunity in the airways of children with severe asthma, and implicate an important viral trigger as a driver.

immunology↗

bioRxiv2020

Unsupervised machine learning reveals key immune cell subsets in COVID-19, rhinovirus infection, and cancer therapy

For an emerging disease like COVID-19, systems immunology tools may quickly identify and quantitatively characterize cells associated with disease progression or clinical response. With repeated sampling, immune monitoring creates a real-time portrait of the cells reacting to a novel virus before disease specific knowledge and tools are established. However, single cell analysis tools can struggle to reveal rare cells that are under 0.1% of the population. Here, the machine learning workflow Tracking Responders Expanding (T-REX) was created to identify changes in both very rare and common cells in diverse human immune monitoring settings. T-REX identified cells that were highly similar in phenotype and localized to hotspots of significant change during rhinovirus and SARS-CoV-2 infections. Specialized reagents used to detect the rhinovirus-specific CD4+ cells, MHCII tetramers, were not used during unsupervised analysis and instead left out to serve as a test of whether T-REX identified biologically significant cells. In the rhinovirus challenge study, T-REX identified virus-specific CD4+ T cells based on these cells being a distinct phenotype that expanded by [≥]95% following infection. T-REX successfully identified hotspots containing virus-specific T cells using pairs of samples comparing Day 7 of infection to samples taken either prior to infection (Day 0) or after clearing the infection (Day 28). Mapping pairwise comparisons in samples according to both the direction and degree of change provided a framework to compare systems level immune changes during infectious disease or therapy response. This revealed that the magnitude and direction of systemic immune change in some COVID-19 patients was comparable to that of blast crisis acute myeloid leukemia patients undergoing induction chemotherapy and characterized the identity of the immune cells that changed the most. Other COVID-19 patients instead matched an immune trajectory like that of individuals with rhinovirus infection or melanoma patients receiving checkpoint inhibitor therapy. T-REX analysis of paired blood samples provides an approach to rapidly identify and characterize mechanistically significant cells and to place emerging diseases into a systems immunology context.

systems biology↗