Saturday, May 17, 2025
5/17/2025
Methods for Unraveling the Impact of Dietary Xenobiotics on COPD Exacerbations with Multi-Dimensional Networks - PROJECT SUMMARY/ABSTRACT Chronic obstructive pulmonary disease (COPD) is projected to become the third leading cause of death globally by 2030. While smoking is a primary risk factor, a growing body of epidemiological research points to the role of nutrition in modulating the risk for COPD and COPD-related manifestations, including acute COPD exacerbations (AE-COPD). However, the mechanisms underlying the impact of dietary components on COPD and AE-COPD are not well understood. The primary objective of this proposal is to develop and implement a new set of machine learning and network science-based methods specifically designed to explore the impact of dietary xenobiotics (DXs) on AE-COPD. DXs are small molecules directly derived from food digestion, absorption, and related metabolism, captured by untargeted metabolomics. This study ultimately aims to gain a precise molecular understanding of the mechanisms driving the systemic effects of diet on AE-COPD. The research design involves developing new methods that leverage the multi-omics data from COPDGene and SPIROMICS, including longitudinal clinical and molecular profiling of smokers with and without COPD. First, we will develop ensemble learning techniques using plasma metabolomic data to identify a DX signature predictive of AE-COPD. Multiple statistical strategies will be integrated to rank and group DXs based on their ability to predict AE- COPD frequency and severity (Aim 1). Second, we will combine metric learning algorithms and spectral theory to quantify the coordinated effect of DXs and circulating proteins on AE-COPD. The goal is to capture the interplay between proteins and DXs in discerning exacerbation-prone phenotypes (Aim 2). Third, we will utilize pre-trained deep learning models and network science to implicate potential mechanisms of action of DXs on AE-COPD. A pipeline will be designed to annotate and predict DXs’ protein targets and assess their proximity on the interactome to different subregions of the COPD disease module, to key inflammatory proteins and epigenetic modifiers (Aim 3). This K25 award will facilitate Dr. Menichetti’s comprehensive training in longitudinal analysis, pulmonology, inflammatory processes, and integrative omics methods. Addressing these training gaps will enhance her ability to develop reliable and translationally relevant methods establishing the foundation for personalized dietary interventions based on distinct COPD endotypes. Dr. Menichetti has crafted a detailed training plan and assembled a mentoring team with complementary expertise, ensuring she will receive the necessary guidance and support to successfully complete her proposal. This K25 will ultimately enable Dr. Menichetti’s independent career in the methodological foundation of Precision Nutrition for lung disease.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).