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. 2025 Mar 3;11(2):00204-2024.
doi: 10.1183/23120541.00204-2024. eCollection 2025 Mar.

The lung proteome in HIV-associated obstructive lung disease

Sarah Samorodnitsky  1 , Danielle Weise  2 , Eric F Lock  1 , Ken M Kunisaki  2   3 , Alison Morris  4 , Janice M Leung  5 , Monica Kruk  2 , Laurie Parker  6 , Pratik Jagtap  6 , Timothy J Griffin  6 , Chris H Wendt  2   3
Affiliations

The lung proteome in HIV-associated obstructive lung disease

Sarah Samorodnitsky et al. ERJ Open Res. .

Abstract

Rationale: Obstructive lung disease is increasingly common among persons living with HIV (PLWH). There are currently no validated biomarkers that identify individuals at risk of developing obstructive lung disease (OLD), and specific mechanisms contributing to HIV-associated OLD remain elusive, independent of smoking. We sought to identify biomarkers and biological pathways associated with OLD using a broad proteomic approach.

Methods: We performed tandem mass tagging and mass spectrometry (MS) analysis on bronchoalveolar lavage fluid samples from persons living with HIV with OLD (n=26) and without OLD (n=26). We combined untargeted MS with a targeted SomaScan aptamer-based approach. We used Pearson correlation tests to identify associations between each protein and lung function (forced expiratory volume in 1 s (FEV1) % pred). We adjusted for multiple comparisons using a false discovery rate adjustment. Significant proteins were entered into a pathway over-representation analysis. Protein-driven endotypes were constructed using K-means clustering.

Measurements and main results: We identified over 3800 proteins by MS and identified 254 proteins that correlated with FEV1 % pred when we combined the MS and SomaScan proteomes when adjusting for smoking status. Pathway analysis revealed cell adhesion molecules as significant.

Conclusions: Protein expression differs in the lung of PLWH and decreased lung function (FEV1 % pred). Pathway analysis reveals cell adhesion molecules having potentially important roles in this process.

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Conflict of interest statement

Conflict of interest: S. Samorodnitsky reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1. Conflict of interest: D. Weise reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1; and support for attending meetings and/or travel from National Institutes of Health grant R01 HL140971-01A, outside the submitted work. Conflict of interest: E.F. Lock reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1. Conflict of interest: K.M. Kunisaki reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1; consulting fees from Allergan, outside the submitted work; participation in data safety monitoring board for Nuvaira and Organicell, outside the submitted work; and is a volunteer for the American Thoracic Society Proposal Review Committee, outside the submitted work. Conflict of interest: A. Morris reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1. Conflict of interest: J.M. Leung reports support for the present manuscript from the Canadian Institutes of Health Research and BC Lung Foundation; grants or contracts from the Canadian Institutes of Health Research and BC Lung Foundation, outside the submitted work; payment received from the BC Lung Foundation for patient forums, outside the submitted work; payment received from the University of British Columbia for education lectures, outside the submitted work; participation in the Enhance Quality Safety and Patient Experience in Chronic Obstructive Pulmonary Disorder Data and Safety Monitoring Board, outside the submitted work; and is a member of the steering committee for Canadian Respiratory Research Network and CanCOLD Study, outside the submitted work. Conflict of interest: M. Kruk reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1; and support for attending meetings and/or travel from National Institutes of Health grant R01 HL140971-01A, outside the submitted work. Conflict of interest: L. Parker reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1. Conflict of interest: P. Jagtap reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1. Conflict of interest: T.J. Griffin reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1. Conflict of interest: C.H. Wendt reports support for the present manuscript from National Institutes of Health grant R01 HL140971-01A1; support for attending meetings and/or travel from National Institutes of Health grant R01 HL140971-01A, outside the submitted work; and participation on a data and safety monitoring board for the Alpha1 Biomarkers Consortium and NIH, outside the submitted work.

Figures

FIGURE 1
FIGURE 1
a) Venn diagram of the overlap in proteins measured in each BALF component using either TMT (soluble component or insoluble component) or SomaScan (soluble component). b) Venn diagram of the overlap in proteins measured in each BALF component using either TMT (soluble component or insoluble component) or SomaScan (soluble component) that were also significantly correlated with FEV1 % pred prior to smoking adjustment and after multiple comparisons at an FDR 0.05 level. Correlation with FEV1 % pred was assessed using a permutation testing framework to use information across all three datasets. c) Venn diagram of the overlap in proteins measured in each BALF component using either TMT (soluble component or insoluble component) or SomaScan (soluble component) that were also significantly correlated with FEV1 % pred after smoking adjustment and after multiple comparisons at an FDR 0.05 level. Correlation with FEV1 % pred was assessed using a permutation testing framework to use information across all three datasets. TMT: tandem mass tagging; BALF: bronchoalveolar lavage fluid; FEV1: forced expiratory volume in 1 s; FDR: false discovery rate.
FIGURE 2
FIGURE 2
Heatmaps of protein abundances in BALF soluble component and insoluble component TMT datasets. Proteins are ordered using hierarchical clustering with average linkage. a) Protein abundances in the BALF soluble component TMT dataset with samples ordered from lowest to highest FEV1 % pred. b) Protein abundances in the BALF insoluble component TMT dataset with samples ordered from lowest to highest FEV1 % pred. c) Protein abundances in the BALF soluble component TMT dataset with samples grouped using K-means clustering. d) Protein abundances in the BALF insoluble component TMT dataset with samples grouped by K-means clustering. TMT: tandem mass tagging; BALF: bronchoalveolar lavage fluid; FEV1: forced expiratory volume in 1 s.
FIGURE 3
FIGURE 3
a) Histogram of the distribution of correlations between proteins measured in the BALF soluble component using TMT and SomaScan. b) Histogram of the distribution of correlations between proteins measured in the BALF insoluble component using TMT and in the soluble component using SomaScan. TMT: tandem mass tagging; BALF: bronchoalveolar lavage fluid.
See this image and copyright information in PMC

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