Biology subjects

McDonough, J. E.

Publications and source records attributed to McDonough, J. E..

7 recordsLinked to original sources

bioRxiv2025

Integration of Multiomic and Multi-phenotypic Data Identifies Biological Pathways Associated with Physical Fitness

Unraveling the complex associations between human phenotypes and molecular pathways can pave the way to improved health and performance, but faces a fundamental challenge: the measurable genes, proteins, and metabolites vastly outnumber the participants in even the largest studies, yielding spurious correlations. To address this imbalance, we have developed a bioinformatic framework and computational approach ("PhenoMol") to discover biological drivers of phenotypic characteristics that integrates all available phenotypic data predictive of outcomes and reduces multi-omic data dimensionality by generating "expression circuits" via graph theory constrained by prior biological knowledge of molecular interactions. We applied PhenoMol to analyze causal patterns and predict elite physical performance in a healthy cohort with deep physiological, physical, behavioral, cognitive, and molecular characterization. PhenoMol outperforms regression models based on equivalent analytic methodologies that do not employ network biology for dimensionality reduction. The PhenoMol software is provided for future studies.

bioinformatics↗

bioRxiv2025

Alveolar epithelial cell plasticity and injury memory in human pulmonary fibrosis

Acute and repetitive lung epithelial injury can lead to irreversible and even progressive pulmonary fibrosis; Idiopathic pulmonary fibrosis (IPF) is a fatal disease and quintessential example of this phenomenon. The composition of epithelial cells in human pulmonary fibrosis - irrespective of disease etiology - is marked by the presence of Aberrant Basaloid cells: an abnormal cell phenotype with pro-fibrotic and senescent features, localized to the surface of fibrotic lesions. Despite their relevance to human pulmonary fibrosis, the exotic molecular profile of Aberrant Basaloid cells has obscured their etiology, preventing insights into how or why these cells emerge with fibrosis. Here we identify cellular intermediaries between Aberrant Basaloid and normal alveolar epithelial cells in human IPF tissue. We track the emergence of Aberrant Basaloid cells from alveolar epithelial cells ex vivo and uncover a role for similar cells in epithelial regeneration under normal conditions. Lastly, we characterize the epigenetic changes that distinguish Aberrant Basaloid cells from their progenitors and identify hallmarks of AP-1 injury memory retention. This study elucidates the phenomenon of maladaptive epithelial plasticity and regeneration in pulmonary fibrosis and re-contextualizes therapeutic strategies for epithelial dysfunction.

cell biology↗

bioRxiv2025

The ferroptosis inhibitor NUPR1 coordinates the mitochondrial response to oxidative stress and cell metabolism during COPD pathogenesis in the lung

Chronic obstructive pulmonary disease (COPD) is characterized by chronic injury and oxidative stress leading to progressive lung tissue destruction. Emerging evidence suggests that regulated cell death pathways, particularly ferroptosis, contribute to COPD pathology. We previously identified the stress response protein and known ferroptosis inhibitor nuclear protein 1 (NUPR1) as markedly downregulated in lung tissue from COPD patients. Here, we demonstrate that NUPR1 inhibition exacerbates iron accumulation, enhances lipid peroxidation, impairs mitochondrial function, disrupts cellular metabolism, and increases oxidative stress in lung epithelial cells. Furthermore, Nupr1 knockout mice exhibit mitochondrial abnormalities, increased oxidative damage, and lung tissue changes consistent with COPD pathology. Collectively, our findings establish NUPR1 as a critical regulator of ferroptosis, stress responses, mitochondrial integrity, and metabolic balance in the lung, highlighting its potential contribution to COPD pathogenesis.

cell biology↗

bioRxiv2024

The role of GPR87 in Pulmonary Fibrosis

RationaleG- protein coupled receptor 87 (GPR87), an alternative lysophosphatidic acid (LPA) receptor previously implicated in cancer, is highly expressed in basal and aberrant basaloid cells in idiopathic pulmonary fibrosis (IPF). We sought to determine whether signaling through GPR87 is important to the development of pulmonary fibrosis. MethodsReanalysis of bulk and single cell RNA sequencing dataset was performed to confirm the increased expression of GPR87 in pulmonary fibrosis. The role of GPR87 in fibrosis in-vivo was assessed using global GPR87 knockout (GPR87-/-) and wildtype mice in the bleomycin model of pulmonary fibrosis, in-vitro in induced pluripotent stem cells (iPSCs) derived airway basal cells (iBC) using GPR87 siRNAs, and ex-vivo in human precision cut slices using disease free tissues in the fibrotic cocktail model as well as IPF tissues treated with GPR87 siRNA. ResultsGPR87 is highly expressed in IPF lungs, and its expression correlates with disease severity. Furthermore, It is highly expressed in basal and aberrant basaloid cells. GPR87-/- mice are protected against bleomycin induced pulmonary fibrosis. GPR87 knockdown is protective against fibrosis development in normal PCLS treated with fibrotic cocktail and leads to fibrosis regression in IPF PCLS. In iBC, GPR87 knockdown leads to decreased expression of fibrosis related genes, proteins and microRNAs. GPR87 stimulation with LPA leads to the opposite results. The main downstream pathways are PI3K, mTOR, and TNF/NFkB; stimulation or inhibition of PI3K pathway mimics GPR87 stimulation or inhibition responses, respectively. ConclusionGPR87 is highly expressed in basal and aberrant basaloid cells in IPF lungs and seems to mediate profibrotic effects based on in-vivo, ex-vivo and in-vitro models of disease, suggesting that it should be studied as a potential epithelial specific therapeutic target in pulmonary fibrosis.

cell biology↗

bioRxiv2023

A Multi-omic Analysis of the Human Lung Reveals Distinct Cell Specific Aging and Senescence Molecular Programs

Age is a major risk factor for lung disease. To understand the mechanisms underlying this association, we characterized the changing cellular, genomic, transcriptional, and epigenetic landscape of lung aging using bulk and single-cell RNAseq (scRNAseq) data. Our analysis revealed age-associated gene networks that reflected hallmarks of aging, including mitochondrial dysfunction, inflammation, and cellular senescence. Cell type deconvolution revealed age-associated changes in the cellular composition of the lung: decreased alveolar epithelial cells and increased fibroblasts and endothelial cells. In the alveolar microenvironment, aging is characterized by decreased AT2B cells and reduced surfactant production, a finding that was validated by scRNAseq and IHC. We showed that a previously reported senescence signature, SenMayo, captures cells expressing canonical senescence markers. SenMayo signature also identified cell-type specific senescence-associated co-expression modules that have distinct molecular functions, including ECM regulation, cell signaling, and damage response pathways. Analysis of somatic mutations showed that burden was highest in lymphocytes and endothelial cells and was associated with high expression of senescence signature. Finally, aging and senescence gene expression modules were associated with differentially methylated regions, with inflammatory markers such as IL1B, IL6R, and TNF being significantly regulated with age. Our findings provide new insights into the mechanisms underlying lung aging and may have implications for the development of interventions to prevent or treat age-related lung diseases.

genomics↗

bioRxiv2022

Spatiotemporal coordination of stem cell behavior following alveolar injury

Tissue repair requires a highly coordinated cellular response to injury. In the lung, alveolar type 2 (AT2) cells act as stem cells and can replace both themselves and alveolar type 1 cells (AT1); however, the complex orchestration of AT2 stem cell activity following lung injury is poorly understood owing to the inability of tracking individual stem cells and their dynamic behavior over time. Here, we apply live time lapse imaging to ex vivo mouse precision cut lung slice (PCLS) culture and in vivo mouse lung to track individual GFP-labeled AT2 cells following induction of alveolar injury by bleomycin. We observe highly dynamic movement of AT2 cells, including migration within and between alveoli. To map the dynamic evolution of AT2 cell behavior, we introduce Live Cell Encoder (LCE-PHATE), a novel method for converting static snapshots from time lapse imaging into single points representative of entire, dynamic cellular trajectories. Applying LCE-PHATE, we observe the emergence of at least three distinct morphokinetic AT2 cell states associated with AT2 stem cell injury response. Finally, small molecule-based inhibition of Rho-associated protein kinase (ROCK) pathway significantly reduced motility of AT2 stem cells following injury and reduced expression of Krt8, a marker of intermediate progenitor cells. Together, our results uncover motility of alveolar stem cells as a new injury response mechanism in the lung and reveal properties of stem cell motility at high cellular resolution.

cell biology↗

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↗