Proteomic profiling of lung diffusion impairment in the recovery stage of SARS-CoV-2-induced ARDS.

Abstract

Clinical and Translational MedicineVolume 12, Issue 5 e838 LETTER TO EDITOROpen Access Proteomic profiling of lung diffusion impairment in the recovery stage of SARS-CoV-2–induced ARDS María C. García-Hidalgo, María C. García-Hidalgo Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorJessica González, Jessica González Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorIván D. Benítez, Iván D. Benítez Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorPaola Carmona, Paola Carmona Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorSally Santisteve, Sally Santisteve Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorAnna Moncusí-Moix, Anna Moncusí-Moix orcid.org/0000-0001-9092-2233 Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorClara Gort-Paniello, Clara Gort-Paniello orcid.org/0000-0003-3850-918X Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorFátima Rodríguez-Jara, Fátima Rodríguez-Jara Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorMarta Molinero, Marta Molinero Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorManel Perez-Pons, Manel Perez-Pons Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorGerard Torres, Gerard Torres Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorJesús Caballero, Jesús Caballero Intensive Care Department, University Hospital Arnau de Vilanova, IRBLleida, Lleida, SpainSearch for more papers by this authorCarme Barberà, Carme Barberà Intensive Care Department, University Hospital Santa María, IRBLleida, Lleida, SpainSearch for more papers by this authorAna P. Tedim, Ana P. Tedim Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, SpainSearch for more papers by this authorRaquel Almansa, Raquel Almansa CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, SpainSearch for more papers by this authorAdrián Ceccato, Adrián Ceccato CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorLaia Fernández-Barat, Laia Fernández-Barat CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Servei de Pneumologia, Hospital Clinic, Universitat de Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorRicard Ferrer, Ricard Ferrer CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Intensive Care Department, Vall d'Hebron Hospital Universitari, SODIR Research Group, Vall d'Hebron Institut de Recerca (VHIR), SpainSearch for more papers by this authorDario Garcia-Gasulla, Dario Garcia-Gasulla Barcelona Supercomputing Center (BSC), Barcelona, SpainSearch for more papers by this authorRosario Menéndez, Rosario Menéndez CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Pulmonology Service, University and Polytechnic Hospital La Fe, Valencia, SpainSearch for more papers by this authorAna Motos, Ana Motos CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Servei de Pneumologia, Hospital Clinic, Universitat de Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorOscar Peñuelas, Oscar Peñuelas CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Hospital Universitario de Getafe, Madrid, SpainSearch for more papers by this authorJordi Riera, Jordi Riera CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Intensive Care Department, Vall d'Hebron Hospital Universitari, SODIR Research Group, Vall d'Hebron Institut de Recerca (VHIR), SpainSearch for more papers by this authorJesús F. Bermejo-Martin, Jesús F. Bermejo-Martin CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, SpainSearch for more papers by this authorAntoni Torres, Antoni Torres CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Servei de Pneumologia, Hospital Clinic, Universitat de Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorFerran Barbé, Ferran Barbé Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorDavid de Gonzalo-Calvo, Corresponding Author David de Gonzalo-Calvo dgonzalo@irblleida.cat orcid.org/0000-0003-2240-3532 Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Correspondence David de Gonzalo-Calvo, PhD. Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Avda. Alcalde Rovira Roure 80 - 25198 Lleida, Spain. Email: dgonzalo@irblleida.catSearch for more papers by this authorCIBERESUCICOVID Project (COV20/00110, ISCIII), CIBERESUCICOVID Project (COV20/00110, ISCIII)Search for more papers by this author María C. García-Hidalgo, María C. García-Hidalgo Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorJessica González, Jessica González Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorIván D. Benítez, Iván D. Benítez Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorPaola Carmona, Paola Carmona Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorSally Santisteve, Sally Santisteve Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorAnna Moncusí-Moix, Anna Moncusí-Moix orcid.org/0000-0001-9092-2233 Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorClara Gort-Paniello, Clara Gort-Paniello orcid.org/0000-0003-3850-918X Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorFátima Rodríguez-Jara, Fátima Rodríguez-Jara Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorMarta Molinero, Marta Molinero Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, SpainSearch for more papers by this authorManel Perez-Pons, Manel Perez-Pons Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorGerard Torres, Gerard Torres Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorJesús Caballero, Jesús Caballero Intensive Care Department, University Hospital Arnau de Vilanova, IRBLleida, Lleida, SpainSearch for more papers by this authorCarme Barberà, Carme Barberà Intensive Care Department, University Hospital Santa María, IRBLleida, Lleida, SpainSearch for more papers by this authorAna P. Tedim, Ana P. Tedim Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, SpainSearch for more papers by this authorRaquel Almansa, Raquel Almansa CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, SpainSearch for more papers by this authorAdrián Ceccato, Adrián Ceccato CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorLaia Fernández-Barat, Laia Fernández-Barat CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Servei de Pneumologia, Hospital Clinic, Universitat de Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorRicard Ferrer, Ricard Ferrer CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Intensive Care Department, Vall d'Hebron Hospital Universitari, SODIR Research Group, Vall d'Hebron Institut de Recerca (VHIR), SpainSearch for more papers by this authorDario Garcia-Gasulla, Dario Garcia-Gasulla Barcelona Supercomputing Center (BSC), Barcelona, SpainSearch for more papers by this authorRosario Menéndez, Rosario Menéndez CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Pulmonology Service, University and Polytechnic Hospital La Fe, Valencia, SpainSearch for more papers by this authorAna Motos, Ana Motos CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Servei de Pneumologia, Hospital Clinic, Universitat de Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorOscar Peñuelas, Oscar Peñuelas CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Hospital Universitario de Getafe, Madrid, SpainSearch for more papers by this authorJordi Riera, Jordi Riera CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Intensive Care Department, Vall d'Hebron Hospital Universitari, SODIR Research Group, Vall d'Hebron Institut de Recerca (VHIR), SpainSearch for more papers by this authorJesús F. Bermejo-Martin, Jesús F. Bermejo-Martin CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, SpainSearch for more papers by this authorAntoni Torres, Antoni Torres CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Servei de Pneumologia, Hospital Clinic, Universitat de Barcelona, IDIBAPS, Barcelona, SpainSearch for more papers by this authorFerran Barbé, Ferran Barbé Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, SpainSearch for more papers by this authorDavid de Gonzalo-Calvo, Corresponding Author David de Gonzalo-Calvo dgonzalo@irblleida.cat orcid.org/0000-0003-2240-3532 Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain Correspondence David de Gonzalo-Calvo, PhD. Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Avda. Alcalde Rovira Roure 80 - 25198 Lleida, Spain. Email: dgonzalo@irblleida.catSearch for more papers by this authorCIBERESUCICOVID Project (COV20/00110, ISCIII), CIBERESUCICOVID Project (COV20/00110, ISCIII)Search for more papers by this author First published: 11 May 2022 https://doi.org/10.1002/ctm2.838Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Dear Editor, In survivors of acute respiratory distress syndrome (ARDS) secondary to SARS-CoV-2 infection, lung diffusion impairment is consistently associated with a characteristic plasma proteome. The mechanistic pathways linked to the proteomic pattern provide novel evidence on multiple biological domains relevant to the postacute pulmonary sequelae. Based on the increasing number of COVID-19 survivors affected by pulmonary abnormalities and the limited understanding of the pathophysiology of the sequelae,1 we analysed the systemic proteomic determinants of lung diffusion impairment in SARS-CoV-2–induced ARDS survivors. This is a substudy of a 3-month prospective cohort study including survivors of severe COVID-19 (n = 88).2 Patients admitted to the Hospital Universitari Arnau de Vilanova-Santa María (Lleida, Spain) between March and August 2020 were included if they fulfilled the following criteria: aged over 18, developed ARDS during hospital stay and attended a ‘post-COVID’ evaluation 3 months after hospital discharge. The study received approval from the medical ethics committee (CEIC/2273) and was performed in full compliance with the Declaration of Helsinki. The patients received written information about the study and signed an informed consent form. A complete pulmonary evaluation was performed as previously detailed.2 Blood samples were collected in EDTA tubes (BD, NJ, USA) and processed using standardised operating procedures with support by IRBLleida Biobank (B.0000682) and ‘Plataforma Biobancos PT20/00021’. Plasma proteomic profiling was performed using the PEA technology (Olink, Uppsala, Sweden). Four panels were analysed: organ damage, immune response, inflammation and metabolism. Additional details can be consulted at https://www.olink.com/resources-support/document-download-center/. A total of 364 proteins were measured. One hundred forty-five proteins were excluded from subsequent studies due to undetectable levels in more than 50% of the samples (Table S1). SARS-CoV-2 RNA was detected as previously described.3 STRING,4 Reactome,5 GTEX (https://www.gtexportal.org/home/) and Drug–Gene Interaction6 databases were used for bioinformatic analyses. All statistical analyses were performed using R software, version 4.0.2. The study flowchart is displayed in Figure S1. The most relevant demographic and clinical characteristics during the acute phase are shown in Table 1. The median (P25;P75) age was 60.0 years (53.0;65.5), and the prevalent sex was male (69.0%). At the 3-month follow-up, 30% of patients presented moderate-to-severe pulmonary diffusion impairment (DLCO < 60%) (Table 2). Using linear models for arrays, we found 15 differentially detected proteins (FDR < 0.05) in this study group (Figure 1A, Table S2). The 15 proteins separated the patients according to the grade of lung dysfunction (Figure 1B,C). All proteins showed higher concentrations in patients with DLCO < 60% (Figure 1D). Proteins showed a dose–response relationship with DLCO in unadjusted generalized additive models (GAM) models (Figure S2). Renal function at follow-up was associated with both diffusion impairment and several proteins (rho≥0.3) (Table 2, Figure S3). Therefore, glomerular filtration was considered a confounder, together with age, sex, previous chronic pulmonary disease, smoking history and the use of corticoids after hospital discharge. No impact of these confounding factors was observed (Figure 1E). Except for KIM1 (rho≥0.3 and rpb≥0.3), there was no correlation between protein levels and disease severity (Figure S4). KIM1, LAMP3 and PGF correlated with the presence of fibrotic lesions (rpb≥0.3) (Figure S5). Specific correlations were observed between protein levels and laboratory parameters (rho≥0.3) (Figure S3). TABLE 1. Characteristics of study sample All DLCO ≥ 60% predicted DLCO < 60% predicted N = 87 N = 61 N = 26 p-value N Sociodemographic characteristics Age (years) 60.0 [53.0;65.5] 56.0 [50.0;63.0] 63.5 [60.0;68.8] 0.009 87 Sex 0.193 87 Male 60 (69.0%) 39 (63.9%) 21 (80.8%) Female 27 (31.0%) 22 (36.1%) 5 (19.2%) BMI (kg/m2) 29.2 [25.6;33.1] 28.7 [25.8;33.2] 29.2 [25.1;31.8] 0.781 87 Smoking history 0.403 87 Former 45 (51.7%) 29 (47.5%) 16 (61.5%) Non-smoker 36 (41.4%) 28 (45.9%) 8 (30.8%) Current 6 (6.90%) 4 (6.56%) 2 (7.69%) Clinical characteristics Hypertension 37 (42.5%) 22 (36.1%) 15 (57.7%) 0.103 87 Type II Diabetes Mellitus 15 (17.2%) 11 (18.0%) 4 (15.4%) 1.000 87 Obesity 36 (41.4%) 25 (41.0%) 11 (42.3%) 1.000 87 Cardiovascular disease 7 (8.05%) 3 (4.92%) 4 (15.4%) 0.190 87 Chronic lung disease 7 (8.05%) 5 (8.20%) 2 (7.69%) 1.000 87 Asthma 7 (8.05%) 5 (8.20%) 2 (7.69%) 1.000 87 Chronic kidney disease 1 (1.15%) 1 (1.64%) 0 (0.00%) 1.000 87 Chronic liver disease 3 (3.45%) 2 (3.28%) 1 (3.85%) 1.000 87 Baseline characteristics in hospital admission Time since first symptoms to hospital admission (days) 7.00 [5.00;8.00] 7.00 [5.00;8.00] 7.00 [7.00;8.00] 0.022 87 Oxygen saturation (%) 92.0 [90.0;94.0] 92.0 [90.0;95.0] 92.0 [89.0;93.0] 0.133 79 FiO2 (%) 28.0 [21.0;44.5] 28.0 [21.0;44.0] 24.5 [21.0;47.5] 0.937 87 PaO2 (mmHg) 62.0 [50.8;73.2] 64.0 [51.0;74.5] 62.0 [49.0;72.0] 0.716 80 PaCO2 (mmHg) 34.0 [31.0;38.0] 33.5 [30.2;37.0] 35.0 [31.0;39.0] 0.200 79 PaO2/FiO2 (mmHg) 229 [155;285] 233 [156;271] 214 [155;286] 0.697 80 Glucose (mg/dL) 120 [108;146] 119 [107;144] 120 [109;147] 0.559 87 Creatinine (mg/dL) 0.82 [0.70;0.96] 0.81 [0.67;0.94] 0.86 [0.73;0.99] 0.393 87 C-reactive protein (mg/L) 107 [65.3;172] 107 [67.3;173] 114 [28.6;165] 0.445 84 Leukocyte count (x109/L) 6.24 [5.06;9.38] 6.24 [5.05;9.06] 6.26 [5.24;9.48] 0.857 87 Neutrophil count (x109/L) 5.08 [3.72;7.56] 5.08 [3.68;7.54] 4.98 [3.93;7.41] 0.893 87 Lymphocyte count (x109/L) 0.83 [0.64;1.08] 0.86 [0.68;1.09] 0.80 [0.57;1.03] 0.266 87 Monocyte count (x109/L) 0.32 [0.23;0.47] 0.31 [0.23;0.47] 0.35 [0.21;0.60] 0.806 87 Platelet count (x109/L) 193 [152;252] 197 [161;251] 178 [143;258] 0.704 87 Urea (mg/dL) 33.0 [28.0;44.5] 31.0 [26.0;41.0] 40.0 [31.8;57.2] 0.006 87 Hospital stay Worst PaO2/FiO2 (mmHg) 134 [94.5;188] 134 [95.0;181] 132 [95.2;189] 1.000 87 PaO2/FiO2 categories 0.790 87 PaO2/FiO2 201–300 mmHg 20 (23.0%) 15 (24.6%) 5 (19.2%) PaO2/FiO2 101–200 mmHg 39 (44.8%) 26 (42.6%) 13 (50.0%) PaO2/FiO2 ≤100 mmHg 28 (32.2%) 20 (32.8%) 8 (30.8%) Hospital stay (days) 21.0 [12.5;35.5] 18.0 [11.0;35.0] 26.5 [14.0;36.2] 0.288 87 ICU admission 75 (86.2%) 53 (86.9%) 22 (84.6%) 0.746 87 ICU stay (days) 13.0 [5.00;25.5] 13.0 [5.00;24.0] 14.0 [6.25;30.0] 0.474 75 High-flow nasal cannula 47 (54.0%) 34 (55.7%) 13 (50.0%) 0.798 87 Invasive mechanical ventilation 43 (49.4%) 29 (47.5%) 14 (53.8%) 0.761 87 Invasive mechanical ventilation duration (days) 17.0 [11.0;24.0] 16.0 [11.0;21.0] 18.0 [11.5;25.8] 0.467 43 Non-invasive mechanical ventilation 45 (51.7%) 30 (49.2%) 15 (57.7%) 0.622 87 Non-invasive mechanical ventilation duration (days) 2.50 [1.75;4.00] 2.00 [1.00;4.00] 3.00 [2.00;7.00] 0.216 44 Prone positioning 41 (47.1%) 26 (42.6%) 15 (57.7%) 0.292 87 Prone positioning duration (hours) 34.5 [19.2;62.8] 40.0 [24.0;72.0] 28.0 [8.50;47.0] 0.124 40 Antibiotics 80 (92.0%) 59 (96.7%) 21 (80.8%) 0.023 87 Hydroxychloroquine 66 (75.9%) 47 (77.0%) 19 (73.1%) 0.902 87 Tocilizumab 34 (39.1%) 25 (41.0%) 9 (34.6%) 0.751 87 Corticoids 66 (75.9%) 46 (75.4%) 20 (76.9%) 1.000 87 Remdesivir 13 (14.9%) 9 (14.8%) 4 (15.4%) 1.000 87 Lopinavir/ritonavir 64 (73.6%) 45 (73.8%) 19 (73.1%) 1.000 87 Corticoids at hospital discharge 12 (14.6%) 9 (15.5%) 3 (12.5%) 1.000 82 Abbreviations: BMI: body mass index; DLCO: carbon monoxide diffusing capacity; FiO2: fraction of inspired oxygen; ICU: intensive care unit; IMV: invasive mechanical ventilation; PaCO2: carbon dioxide partial pressure; PaO2: oxygen partial pressure; SaO2: arterial oxygen saturation. a Continuous variables are expressed as median [P25;P75]. b Categorical variables are expressed as n (%). TABLE 2. Pulmonary evaluation and laboratory tests at the 3-month follow-up All DLCO ≥ 60% predicted DLCO < 60% predicted N = 87 N = 61 N = 26 p-value N Pulmonary function DLCO (% predicted) 66.1 [57.1;73.9] 71.4 [65.6;77.0] 52.8 [47.1;55.9] <0.001 87 DLCO <0.001 87 < 60% predicted 26 (29.9%) 0 (0.00%) 26 (100%) < 80% predicted 50 (57.5%) 50 (82.0%) 0 (0.00%) ≥80% predicted 11 (12.6%) 11 (18.0%) 0 (0.00%) 6-min walking test Distance (m) 400 [365;430] 410 [390;448] 378 [305;404] 0.001 84 Oxygen saturation average (%) 96.0 [94.0;97.0] 96.0 [95.0;97.0] 95.0 [94.0;96.0] 0.009 85 Oxygen saturation initial (%) 97.0 [96.0;97.0] 97.0 [96.0;97.0] 96.0 [95.2;97.0] 0.005 85 Oxygen saturation final (%) 96.0 [95.0;97.0] 96.0 [95.0;97.0] 96.0 [94.0;96.0] 0.003 85 Oxygen saturation minimal (%) 95.0 [93.0;96.0] 95.0 [93.0;96.0] 94.0 [92.0;95.0] 0.017 85 Chest CT Lesions Reticular 42 (48.8%) 29 (48.3%) 13 (50.0%) 1.000 86 Fibrotic 20 (23.3%) 10 (16.7%) 10 (38.5%) 0.055 86 TSS score 5.00 [2.00;7.00] 2.50 [1.00;7.00] 8.00 [5.00;11.8] <0.001 86 Laboratory tests Creatinine (mg/dl) 0.82 [0.72;0.95] 0.81 [0.71;0.91] 0.90 [0.74;1.03] 0.126 87 Glomerular filtrate (ml/min/1.73m2) 90.0 [83.3;90.0] 90.0 [89.4;90.0] 88.2 [74.7;90.0] 0.011 87 C-reactive protein (mg/L) 2.40 [2.00;4.55] 2.50 [2.00;4.70] 2.20 [2.00;4.20] 0.602 87 Leucocyte count (x109/l) 6.49 [5.60;7.56] 6.44 [5.73;7.36] 7.11 [5.44;8.35] 0.756 87 Neutrophil count (x109/L) 3.40 [2.60;4.00] 3.53 [2.59;3.94] 3.22 [2.66;4.67] 0.770 87 Lymphocyte count (x109/L) 2.12 [1.79;2.79] 2.26 [1.81;2.76] 2.08 [1.71;2.88] 0.610 87 Monocyte count (x109/L) 0.54 [0.45;0.68] 0.51 [0.45;0.65] 0.59 [0.48;0.70] 0.161 87 Platelet count (x109/L) 241 [210;277] 243 [217;277] 240 [197;272] 0.433 87 Urea (mg/dL) 38.0 [33.0;45.0] 37.0 [33.0;44.0] 39.0 [35.0;49.5] 0.188 87 Abbreviations: DLCO: carbon monoxide diffusing capacity; TSS: total severity score. a Continuous variables are expressed as median [P25;P75]. b Categorical variables are expressed as n (%). FIGURE 1Open in figure viewerPowerPoint Differentially detected proteins according to the severity of lung diffusion impairment in survivors of ARDS secondary to SARS-CoV-2 infection. (A) Volcano plot showing the p-value versus the fold change for each detected protein. Blue dots indicate significantly detected proteins considering a p-value < 0.05. Green dots reflect significantly detected proteins with an FDR < 0.05. The FDR was obtained using the Benjamini–Hochberg method. (B) Heatmap representing unsupervised hierarchical clustering. Each column represents a survivor. Each row represents a differentially detected protein. The patient clustering tree is plotted on top. The protein clustering is shown on the left. Protein levels are represented through a colour scale, with green tones related to increasing levels and pink tones related to decreasing expression. (C) Principal component analysis using differentially detected proteins. Each point denotes a survivor and is represented with a specific colour depending on the presence or absence of severe diffusion impairment. (D) Violin plots of differentially detected proteins. Fold change (FC) and FDR are plotted for each protein. (E) Generalised additive model (GAM) with penalized cubic regression splines for DLCO (Y axis) and the levels of each of the differentially detected proteins (NPX) (X axis). The association was adjusted by age, sex, previous chronic pulmonary disease, smoking history, the use of corticoids after hospital discharge and glomerular filtration. All proteins included in the analysis showed an FDR < 0.05 The sparse partial least-squares discriminant analysis (sPLS-DA) generated a signature of 20 proteins that allowed optimal discrimination between study groups (AUC = 0.872) (Figure 2A–C). Based on the variable importance of component 1, the top five relevant contributors were PTN, KIM1, CALCA, CLEC7A and ENTPD6 (Figure 2A). The feature selection procedure based on random forest supported these results (Figure S6A). In addition, sPLS was used to determine the protein profile that best explained the DLCO levels (as a continuous variable) (Figures 2D,E,F). The analysis identified a signature of 35 proteins. PTN, PGF, NPDC1 and METRNL were the most weighted factors for defining component 1 (Figure 2D). The proteomic profile generated using random forest was in concordance with these findings (Figure S6B). IFN-γ, which participates in the response to infection,7 was associated with diffusion capacity. Therefore, we analysed viral load in plasma samples from a subset of 50 patients. Only one patient was positive for the presence of SARS-CoV-2 RNA. FIGURE 2Open in figure viewerPowerPoint Plasma proteomic signatures associated with moderate/severe diffusion impairment and DLCO levels in survivors of ARDS secondary to SARS-CoV-2 infection. (A) Proteins are ranked by their variable importance to component 1. (B) Supervised component analysis cluster through sparse partial least-squares discriminant analysis (sPLS-DA) discriminating between survivors with moderate/severe diffusion impairment (DLCO < 60%) and survivors with mild or an absence of alterations in diffusion capacity survivors (DLCO≥60%). Each point represents a patient. (C) Receiver operating characteristic (ROC) curve for the protein signature. The discriminative power of the signature is represented as the area under the ROC curve (AUC). (D) Proteins ranked by their variable importance for component 1. (E) Supervised component analysis cluster through sPLS according to the DLCO levels of the cohort. Each protein represents a survivor. (F) Generalized additive model (GAM) with penalized cubic regression splines for DLCO (Y axis) and the first component (X axis). The significance of the association is given by the coefficient of determination (R2) The signature including the higher number of proteins (n = 35) was used for bioinformatic analyses. An enrichment in pathways associated with cell proliferation and differentiation, tissue remodelling, inflammation and immune response, angiogenesis, coagulation and fibrosis was observed (Figure S7A, Tables S3 and S4). Three independent protein networks were identified (Figure S8). A generalized expression of the signature was observed in the lung but also in other tissues (Figure S7B). The proteomic pattern was enriched in lung epithelial, endothelial and immune cells (Figure S7C). The drug–gene interaction analysis identified several FDA-approved drugs that can target the proteins (Table S5). Postinfection long-term lung dysfunction has become clinically evident in a large percentage of SARS-CoV-2–induced-ARDS survivors. Systemic molecular profiling constitutes a promising strategy to decipher the underlying biological mechanisms linked to the pulmonary outcomes and, consequently, to identify candidates that may be amenable of therapeutic intervention.8-10 Here, we provide compelling evidence that (i) a set of plasma proteins are differentially detected in survivors with moderate-to-severe diffusion impairment; (ii) diffusion capacity is associated with alterations in the proteomic profile, even after adjustment for confounding factors; (iii) survivors with the most serious sequelae show higher disturbances in the protein levels; (iv) sPLS and random forest define protein signatures highly associated with pulmonary function; (v) the signatures are composed of heterogeneous factors implicating multiple biological pathways; (vi) the signatures constitute a source of targets for candidate drugs; (vii) plasma proteomic profiles accurately classify patients with respiratory sequelae; and (viii) no association was observed between blood viral load and diffusion impairment. CONCLUSION The plasma proteomic profile linked to lung diffusion impairment improves our understanding of the physiopathology of postacute pulmonary sequelae in COVID-19, and, consequently, constitutes a useful resource for the design of therapeutic strategies and the development of tools to improve medical decision-making in the “post-COVID” syndrome. Additional cohorts and functional analyses are needed to corroborate our findings. ACKNOWLEDGEMENTS This work was supported by IRBLleida Biobank (B.0000682) and “Plataforma Biobancos PT20/00021”. The human sample manipulation was performed in the Cell Culture Technical Scientific Service of the Universitat de Lleida (Lleida, Catalonia, Spain). The authors are indebted to María Arguimbau, Raquel Campo, Natalia Jarillo, Javier Muñoz and Manuel Sánchez for their extensive support with project management and article preparation. CONFLICT OF INTEREST The authors declare that they have no competing interests. FUNDING INFORMATION Financial support was provided by the Instituto de Salud Carlos III de Madrid (COV20/00110), co-funded by the European Development Regional Fund (A Way to Achieve Europe programme) and Centro de Investigación Biomedica En Red Enfermedades Respiratorias (CIBERES). CIBERES is an initiative of the Instituto de Salud Carlos III. Suported by: Programa de donaciones “estar preparados” UNESPA (Madrid, Spain); and Fundación Francisco Soria Melguizo (Madrid, Spain). Finançat per La Fundació La Marató de TV3, projecte amb codi 202108-30/-31. COVIDPONENT is funded by Institut Català de la Salut and Gestió de Serveis Sanitaris. APT was funded by the Sara Borrell Research Grant CD018/0123 funded by the Instituto de Salud Carlos III and co-financed by the European Development Regional Fund (A Way to Achieve Europe programme). MCGH is the recipient of a predoctoral fellowship from the “University of Lleida”. DdGC (Miguel Servet 2020: CP20/00041) and MM (PFIS: FI21/00187) have received financial support from the Instituto de Salud Carlos III, cofunded by the European Social Fund (ESF)/“Investing in your future”. Supporting Information Filename Description ctm2838-sup-0001-SuppMat.docx1.6 MB Supporting Material Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. REFERENCES 1Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med. 2021; 27(4): 601- 615. https://doi.org/10.1038/s41591-021-01283-zCrossrefCASPubMedWeb of Science®Google Scholar 2González J, Benítez ID, Carmona P, et al. Pulmonary function and radiologic features in survivors of critical COVID-19. 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