Biology subjects

Whitfield, M. L.

Publications and source records attributed to Whitfield, M. L..

8 recordsLinked to original sources

bioRxiv2026

Compartmental Profiling of PDE4B in Systemic Sclerosis

ObjectivesThe preferential phosphodiesterase 4B (PDE4B) inhibitor nerandomilast was recently approved for treatment of idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis. Its proposed immunomodulatory, anti-fibrotic, and endothelial-stabilising actions target all three cardinal features of SSc, yet PDE4B expression has not been systematically characterised in SSc tissue. We aimed to define PDE4B expression across fibrotic organs and cellular compartments in SSc. MethodsPDE4B expression was profiled in SSc lung, peripheral blood mononuclear cells (PBMCs) and skin on the transcript level using single-cell RNA sequencing data and on the protein level using immunohistochemistry, immunofluorescence and multiplexed immunofluorescent stainings. ResultsPDE4B was consistently dysregulated in immune cells across SSc tissue and PBMCs, with compartment-specific direction and distribution. In SSc-ILD lung, expression was increased in CD8 and CD4 memory T-cells. In PBMCs, expression was increased in B cells, monocytes, and CD8 T-cells, and stratified patients into three endotypes (PDE4B//hi) not distinguishable by clinical variables. In skin, bulk RNA-seq showed a significant global increase, which localized to myeloid cells in scRNA-seq data. Approximately 90% of FAP activated fibroblasts co-expressed PDE4B at the protein level in SSc skin, identifying the activated fibroblast compartment as a candidate target for PDE4B inhibition. No PDE4B dysregulation was detected in vascular cell types. ConclusionsThis first cell-type-resolved characterisation of PDE4B in SSc demonstrates consistent immune-cell dysregulation across tissues and protein-level enrichment in activated fibroblasts. This provides a human-tissue rationale for the immunomodulatory and anti-fibrotic effects of PDE4B inhibition and supporting PDE4B as a disease-relevant therapeutic target in SSc. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSO_LINerandomilast (BI 1015550), a PDE4B-preferential inhibitor, was approved for idiopathic pulmonary fibrosis and progressive pulmonary fibrosis. C_LIO_LIPre-clinical studies indicate that PDE4B inhibition may act on all cardinal features of SSc. C_LI What this study addsO_LIFirst cell-type-resolved characterization of PDE4B expression across SSc-affected lung, PBMCs, and skin. C_LIO_LIPBMC PDE4B expression is heterogeneous, stratifying patients into PDE4B// endotypes independent of standard clinical variables. C_LIO_LIscRNA-seq shows increased myeloid PDE4B expression in SSc skin, while [~]90% of FAP activated fibroblasts in SSc skin express PDE4B protein. C_LI How this study might affect research, practice or policyO_LIThe study strengthens the human-level evidence underpinning the target rationale for PDE4B inhibition in SSc. C_LI

molecular biology↗

bioRxiv2025

Anti-CD206 CAR T Cell Treatment Restores Fibrosis-Induced Loss of Dermal White Adipose Tissue

Fibrosis drives pathology in the chronic autoimmune disease systemic sclerosis (SSc), which has the highest case fatality rate of any systemic autoimmune disease with no validated biomarkers or curative treatments. Our prior work has shown that CD206+ macrophages and dermal fibroblasts engage in cooperative mechanisms of inflammatory and fibrotic activation in SSc. Here, we designed a targeted immunotherapeutic approach to eliminate CD206+ macrophages using chimeric antigen receptor (CAR) T cells. We demonstrate that systemic delivery of a single dose of anti-CD206 CAR T cells restores dermal white adipose tissue (DWAT) in vivo. Notably, loss of subcutaneous fat is a well-recognized but poorly understood aspect of SSc pathogenesis that precedes the development of fibrosis and is driven by changes in lineage commitment of adipose-derived stem cells (ADSCs). Using snRNA-seq and a newly-developed in vitro co-culture model, we report that CD206high macrophages mediate ADSC shift from adipocytic to fibrotic activation in part through an IL-6-dependent mechanism. This report implicates a novel function for macrophages in the regulation of early SSc pathogenesis and is the first to establish the therapeutic efficacy of using CAR T cell immunotherapy to target macrophages in the treatment of SSc skin disease.

synthetic biology↗

bioRxiv2024

RUNX1 is Expressed in a Subpopulation of Dermal Fibroblasts and Higher RUNX1 Levels are Associated with the Severity of Systemic Sclerosis

The activation of Runt-related transcription factor 1 (RUNX1) in fibroblasts has been implicated in wound healing and fibrosis; however, the role of RUNX1 in the fibrotic progression of the autoimmune disease systemic sclerosis (SSc) is not known. Through gene expression analysis, we have demonstrated an association between the severity of dermal fibrosis and the expression levels of RUNX1 in the skin of patients with SSc. Additionally, we identified hypomethylated CpG sites proximal to the RUNX1 gene, implicating their potential role in the increased expression of RUNX1. Analysis of single-cell RNA-seq data from skin biopsies of individuals with SSc revealed that RUNX1 is higher in subpopulations of fibroblasts enriched in SSc, which are believed to contribute to fibrosis. Lastly, modulation of RUNX1 activity using an inhibitor caused a reduction in fibroblast contraction and proliferation rates. Altogether, this study is the first to demonstrate a potential role for RUNX1 in the pathogenesis of systemic sclerosis dermal fibrosis.

genomics↗

bioRxiv2024

Single-cell epigenomic dysregulation of Systemic Sclerosis fibroblasts via CREB1/EGR1 axis in self-assembled human skin equivalents

Systemic sclerosis (SSc) is an autoimmune disease characterized by skin fibrosis, internal organ involvement and vascular dropout. We previously developed and phenotypically characterized an in vitro 3D skin-like tissue model of SSc, and now analyze the transcriptomic (scRNA-seq) and epigenetic (scATAC-seq) characteristics of this model at single-cell resolution. SSc 3D skin-like tissues were fabricated using autologous fibroblasts, macrophages, and plasma from SSc patients or healthy control (HC) donors. SSc tissues displayed increased dermal thickness and contractility, as well as increased -SMA staining. Single-cell transcriptomic and epigenomic analyses identified keratinocytes, macrophages, and five populations of fibroblasts (labeled FB1 - 5). Notably, FB1 APOE-expressing fibroblasts were 12-fold enriched in SSc tissues and were characterized by high EGR1 motif accessibility. Pseudotime analysis suggests that FB1 fibroblasts differentiate from a TGF-{beta}1-responsive fibroblast population and ligand-receptor analysis indicates that the FB1 fibroblasts are active in macrophage crosstalk via soluble ligands including FGF2 and APP. These findings provide characterization of the 3D skin-like model at single cell resolution and establish that it recapitulates subsets of fibroblasts and macrophage phenotypes observed in skin biopsies.

genomics↗

bioRxiv2023

CDDO-Methyl Ester Inhibits BRAF Inhibitor Resistance and Remodels the Myeloid Compartment in BRAF-mutant Melanoma

Approximately 50% of advanced melanomas harbor activating BRAFV600E mutations that are sensitive to BRAF inhibition. However, the duration of the response to BRAF inhibitors (BRAFi) has been limited due to the development of acquired resistance, which is preceded by recruitment of immunosuppressive myeloid cells and regulatory T cells (Tregs). While the addition of MAPK/ERK kinase 1 inhibitors (MEKi) prolongs therapeutic response to BRAF inhibition, most patients still develop resistance. Using a BrafV600E/+/Pten-/- graft mouse model of melanoma, we now show that the addition of the methyl ester of the synthetic triterpenoid 2-cyano-3,12-dioxooleana-1,9(11)-dien-28-oic acid (C-Me) to the BRAFi vemurafenib analog PLX4720 at resistance significantly reduces tumor burden. Dual treatment remodels the BRAFi resistant-tumor microenvironment (TME), reducing infiltration of Tregs and tumor associated macrophages (TAMs), and attenuates immunosuppressive cytokine production. For the first time, we characterize myeloid populations using scRNA-seq in BRAFi-resistant tumors and demonstrate that restoration of therapeutic response is associated with significant changes in immune-activated myeloid subset representation. Collectively, these studies suggest that C-Me inhibits acquired resistance to BRAFi. Use of C-Me in combination with other therapies may both inhibit melanoma growth and enhance therapeutic responsiveness more broadly.

immunology↗

bioRxiv2018

RNA-seq analyses of molecular abundance (RoMA) for detecting differential gene expression

MotivationVarious methods have been proposed, each with its own limitations. Some naive normal-based tests have low testing power with invalid normal distribution assumptions for RNA-seq read counts, whereas count-based methods lack a biologically meaningful interpretation and have limited capability for integration with other analysis packages for mRNA abundance. In this study, we propose an improved method, RoMA, to accurately detect differential expression and unlock the integration with upstream and downstream analyses on mRNA abundance in RNA-seq studies.\n\nResultsRoMA incorporates information from both mRNA abundance and raw counts. Studies on simulated data and two real datasets showed that RoMA provides an accurate quantification of mRNA abundance and a data adjustment-tolerant DE analysis with high AUC, low FDR, and an efficient control of type I error rate. This study provides a valid strategy for mRNA abundance modeling and data analysis integration for RNA-seq studies, which will greatly facilitate the identification and interpretation of DE genes.\n\nAvailability and implementationRoMA is available at https://github.com/GuoshuaiCai/RoMA.\n\nContactGCAI@mailbox.sc.edu or Michael.L.Whitfield@Dartmouth.edu

bioinformatics↗

bioRxiv2017

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

BackgroundCardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups.\n\nMethodsWe used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters.\n\nResultsFour SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5-33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1-52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters.\n\nConclusionsMachine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.

genomics↗

bioRxiv2016

A functional genomic meta-analysis of clinical trials in systemic sclerosis: towards precision medicine and combination therapy

Systemic sclerosis (SSc) is an orphan, systemic autoimmune disease with no FDA-approved treatments. Its heterogeneity and rarity often result in underpowered clinical trials making the analysis and interpretation of associated molecular data challenging. We performed a meta-analysis of gene expression data from skin biopsies of SSc patients treated with five therapies: mycophenolate mofetil (MMF), rituximab, abatacept, nilotinib, and fresolimumab. A common clinical improvement criterion of -20% OR -5 modified Rodnan Skin Score was applied to each study. We developed a machine learning approach that captured features beyond differential expression that was better at identifying targets of therapies than the differential expression alone. Regardless of treatment mechanism, abrogation of inflammatory pathways accompanied clinical improvement in multiple studies suggesting that high expression of immune-related genes indicates active and targetable disease. Our framework allowed us to compare different trials and ask if patients who failed one therapy would likely improve on a different therapy, based on changes in gene expression. Genes with high expression at baseline in fresolimumab non-improvers were downregulated in MMF improvers, suggesting that immunomodulatory or combination therapy may have benefitted these patients. This approach can be broadly applied to increase tissue-specificity and sensitivity of differential expression results.

genomics↗