Document text
Principal Investigator: Lucy Luo
Organization: NORTHWESTERN UNIVERSITY AT CHICAGO
Fiscal Year: 2024
Award: $52,897
Funding agency: National Heart Lung and Blood Institute
PROJECT SUMMARY: Chronic lung allograft dysfunction (CLAD) is the primary driver of morbidity and
mortality in lung transplant recipients. Currently there is a need to identify clinical and molecular biomarkers of
CLAD, where the latter has the potential to inform targetable pathways for intervention. There is growing
evidence to hypothesize that a key component of CLAD pathobiology is the recruitment of profibrotic
monocyte-derived alveolar macrophages (MoAM) upon injury to the lung epithelium. Profibrotic MoAMs
stimulate the activation, differentiation, and proliferation of myofibroblasts. With sustained injury, MoAMs are
continually maintained through colony stimulating factor 1 (CSF1) signaling through its cognate receptor
(CSF1R), leading to progressive fibrosis. T-regulatory cells dampen ongoing injury and have been shown to
mitigate CLAD in preclinical models and are also associated with more favorable lung transplant outcomes in
humans. Our group demonstrated that recipient-derived MoAMs express profibrotic genes in mice and humans
and that administering a CSF1R antagonist improved fibrosis in a murine model of CLAD. Translating these
findings in humans requires analyzing longitudinal data collected before CLAD diagnosis. Where most studies
focus on measurements made at one or two points in time, often when CLAD has significantly progressed, the
proposed work will leverage a machine learning approach developed by our group to determine clinical states
and their sequences that develop after lung transplantation. This work will examine associations between
these clinical states with flow cytometry, single cell and spatial transcriptomic analysis of BAL fluid across time
to identify early, predictive indicators of CLAD. Specifically, the proposed work will address the hypothesis that
the emergence of pathogenic MoAM and loss of tissue-resident donor-derived T-regs in serially sampled BAL
predict CLAD and ACR respectively. Aim 1 will determine whether the CSF1-driven maintenance of profibrotic
MoAMs precedes the clinical diagnosis of CLAD. Aim 2 will determine whether the paucity of tissue-resident
donor-derived T-regs is associated with CLAD after ACR. Both aims consist of 1. Combining flow cytometry
and single-cell transcriptomics to quantify cell abundances and ligand receptor analyses relevant to either aim
unique to CLAD BAL and 2. Integrating these molecular features with clinical data in machine learning models
for CLAD (aim 1) and ACR (aim 2) prediction. In leveraging these data-driven and machine learning
approaches, the long term goal of the proposed work is to reveal therapeutic targets and elucidate early signs
of ACR and CLAD for timelier intervention and to ultimately reduce lung transplant failure. The candidate and
her mentors have designed a detailed training plan that utilizes the support of diverse mentors and resources
in immunology, single-cell genomics, machine learning, and translational research. Ultimately, this training plan
enables the candidate to develop the rigorous computational and scientific skills to become an independent
physician scientist in the realm of biomedical machine learning and translational immunology.
Terms: <Acute><Alveolar><Alveolar Macrophages><Apoptosis><Apoptosis Pathway><Autocrine Systems><Biological><Biological Markers><Biopsy><Blood monocyte><Body Tissues><CD115 Antigens><CSF-1><CSF-1 Receptor><CSF-1-R><CSF1R Gene Product><Causality><Cell Body><Cell Communication and Signaling><Cell Signaling><Cells><Chronic><Clinical><Clinical Data><Colony Stimulating Factor 1 Receptor><Colony-Stimulating Factor 1><Data><Development><Diagnosis><Disease><Disorder><Dysfunction><Early identification><Epithelium><Etiology><FEV1><FEV1%VC><Failure><Fibroblasts><Fibrosis><Flow Cytofluorometries><Flow Cytofluorometry><Flow Cytometry><Flow Microfluorimetry><Flow Microfluorometry><Forced Expiratory Volume 1 Test><Forced Expiratory Volume in 1 Second><Functional disorder><Gene Expression><Genes><Goals><Human><Immunology><Injury><Interruption><Intervention><Intervention Strategies><Intracellular Communication and Signaling><Ligands><Link><Liquid substance><Lung><Lung Alveolar Epithelia><Lung Diseases><Lung Grafting><Lung Respiratory System><Lung Tissue Fibrosis><Lung Transplantation><M-CSF><M-CSF Receptors><Machine Learning><Macrophage Activation><Macrophage Colony Stimulating Factor I Receptor><Macrophage Colony-Stimulating Factor><Macrophage Colony-Stimulating Factor Receptor><Maintenance><Marrow monocyte><Measurement><Mentors><Mice><Mice Mammals><Modeling><Modern Man><Molecular><Morbidity><Morbidity - disease rate><Murine><Mus><Myofibroblast><Outcome><PFT/FEV1><Paracrine Communication><Paracrine Signaling><Pathogenesis><Pathogenicity><Pathway interactions><Patients><Physicians><Physiopathology><Population><Pre-Clinical Model><Preclinical Models><Preventative measure><Preventive measure><Programmed Cell Death><Proliferating><Proto-Oncogene Protein fms><Pulmonary Diseases><Pulmonary Disorder><Pulmonary Fibrosis><Pulmonary Function Test/Forced Expiratory Volume 1><Pulmonary Graft><Pulmonary Macrophages><Pulmonary Transplant><Pulmonary Transplantation><Receptor Protein><Regulatory T-Lymphocyte><Research><Research Resources><Resolution><Resources><Respiratory Epithelium><Risk Factors><Sampling><Scientist><Series><Signal Transduction><Signal Transduction Systems><Signaling><Single Base Polymorphism><Single Nucleotide Polymorphism><Source><Structure of respiratory epithelium><Testing><Time><Tissues><Training><Translating><Translational Research><Translational Science><Transplant Recipients><Transplantation><Transplanted Lung Complication><Treg><Visit><Work><airway epithelium><alveolar epithelium><analyzing longitudinal><antagonism><antagonist><autocrine><bio-markers><biologic><biologic marker><biological signal transduction><biomarker><c-fms Protein><causation><clinical biomarkers><clinical diagnosis><clinically useful biomarkers><cytokine><design><designing><developmental><differential expression><differentially expressed><disease causation><disease of the lung><disorder of the lung><early biomarkers><early detection biomarkers><early detection markers><epithelial repair><experiment><experimental research><experimental study><experiments><fibrosis in the lung><flow cytophotometry><fluid><hazard><improved><improved outcome><injuries><insight><interventional strategy><liquid><longitudinal analysis><lung allograft><lung disorder><lung fibrosis><lung transplant><machine based learning><machine learned algorithm><machine learning algorithm><machine learning based algorithm><machine learning based model><machine learning model><machine translation><molecular biomarker><molecular marker><monocyte><mortality><mouse model><murine model><novel><paracrine><pathophysiology><pathway><prevent><preventing><pulmonary><receptor><recruit><regulatory T-cells><repair><repaired><resolutions><respiratory tract epithelium><response><single cell genomics><single nucleotide variant><skills><standard of care><targeted drug therapy><targeted drug treatments><targeted therapeutic><targeted therapeutic agents><targeted therapy><targeted treatment><therapeutic target><transcriptional differences><transcriptomics><translation research><translational immunology><translational investigation><transplant><transplant patient><ventilator-acquired pneumonia><ventilator-associated pneumonia>