Abstract
Rationale
Immunoglobulins (Ig) protect against pathogens frequently implicated in COPD exacerbations. We previously demonstrated an association of low-normal serum IgA and IgG concentrations with prospective exacerbation risk, but responsible mechanisms are undefined. Here, we examined associations of lower respiratory tract bacterial diversity to Ig levels in serum and bronchoalveolar lavage (BAL) and to the memory phenotypes of blood and BAL B cells.
Methods
We analyzed data from phase I of SPIROMICS, an observational cohort study of smoking-related COPD. A subset of participants completed comprehensive research bronchoscopies, including analysis of BAL bacterial microbiota by 16 S rRNA gene (V4 region) sequencing and of blood and BAL B-cells by 12-color flow cytometry. In some participants, we also analyzed serum and BAL Ig levels by ELISA. We constructed linear regression models including either serum or BAL (albumin-corrected) Ig measurements as the independent variable and separate dependent variables, including B-cell subsets, BAL bacterial diversity metrics (Faith phylogenetic diversity, inverse Simpson, and richness indices), and clinical measures (FEV1% predicted, risk of prospective exacerbations), adjusted by age, sex, race, educational attainment, smoking status, and use of inhaled corticosteroids.
Results
Serum IgG and IgA (n = 66 participants) were 1,486.1 ± 510.6 mg/dL [mean ± standard deviation (SD)] and 237.7 ± 131.6 mg/dL, respectively. Albumin-corrected BAL IgG and IgA (n = 117) were 0.03 ± 0.02 mg/dL and 0.01 ± 0.01 mg/dL, respectively. B-cells (n = 82) comprised 3.5 ± 3.0% of blood leukocytes. Serum IgA was associated with higher blood switched memory (IgD- CD27+) B-cell percentages (β 6.06, p = 0.01) and inversely associated with blood double-negative (IgD-CD27-) B-cell percentages (β − 9.96, p = 0.02). Available BAL microbiome data (n = 107) showed that reduced lung bacterial diversity associated with lower serum IgG, but not with serum IgA, BAL IgA, or BAL IgG concentrations. Neither BAL IgG nor IgA were associated with lung function or exacerbations.
Conclusions
These results demonstrate an association of low serum IgG with reduced lung bacterial diversity, a feature of dysbiosis that may predispose to exacerbation. Defining the role of Ig in specific anatomic compartments is relevant to designing vaccine strategies.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12931-025-03310-w.
Background
Immunoglobulins crucially defend against respiratory pathogens implicated in chronic obstructive pulmonary disease (COPD) exacerbations. Recurrent respiratory infections with encapsulated bacteria and respiratory viruses, the same microorganisms implicated in COPD exacerbations, are common in individuals with common variable immunodeficiency, specific antibody deficiency, or selective IgA deficiency. However, in COPD even mildly reduced serum concentrations of total immunoglobulin A (IgA), immunoglobulin G (IgG), and IgG subclasses that do not fit established immunodeficiency syndromes are associated with greater exacerbation risk [1–5]. More recently, we identified associations of reduced pneumococcal-specific serum antibodies concentrations with increased exacerbations [6].
Immunoglobulins are produced by B-cells, which are themselves not only protective but also potentially pathogenic, being strongly implicated in emphysema [7] driven by B-cell activating factor [8, 9] and in small airway disease in asthma [10]. One cause of B-cell activation is reduced microbial diversity, which influences Ig levels in the gastrointestinal tract [11], and which has been implicated in asthma [12] and celiac disease [13]. Congruently, results from the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) showed significant relationships in COPD between microbial community structure in the lower respiratory tract (LRT), lung function, and symptom burden [14, 15] and LRT dysbiosis has been shown to be associated with increased lung inflammation [16]. However, the relationships of alveolar and serum Ig to LRT microbial community structure and to B-cell subpopulations accessible to monitoring remain incompletely defined [17, 18] and of considerable interest.
Our study aimed to assess the relationship of IgG and IgA in serum and BAL to: (a) B-cell phenotypes in blood and BAL and (b) microbiota composition in BAL, which we hypothesized are all interrelated.
Methods
SPIROMICS (ClinicalTrials.gov NCT01969344, registration date: October, 2013) is an ongoing prospective observational cohort study to identify new COPD subgroups and intermediate markers of disease progression [19]. SPIROMICS participants were 40–80 years of age at baseline, who were either healthy persons with a smoking history of ≤ 1 pack-year (“never smokers”) and no known current lung disease or current or former smokers (≥ 20 pack-years) with and without evidence of obstructive lung disease. Given our focus on those with or at risk of smoking-related COPD, never smokers were not included in this analysis. Informed written consent was obtained from all participants and the study was approved by the IRB and ethics committee of each site. A subset of participants completed a comprehensive research bronchoscopy substudy [20]. Spirometry before and after bronchodilator was performed according to standard guidelines. Serum immunoglobulins were measured as previously described [4, 5]. For this analysis, we limited exacerbation data to one-year post-bronchoscopy, which we assessed prospectively by quarterly phone calls. We defined exacerbation as worsening respiratory symptoms requiring antibiotics or oral steroids, and severe exacerbations as requiring an emergency department visit or hospitalization.
BAL Ig levels were measured by ELISA and were corrected for BAL albumin concentrations. We analyzed BAL bacterial microbiota by 16 S rRNA gene (V4 region) sequencing on an Illumina MiSeq [14], quantifying BAL bacterial diversity utilizing Faith phylogenetic diversity, inverse Simpson, and richness indices [10]. Bronchoscopy and blood samples were obtained on the same day. We analyzed BAL and blood B-cells by 12-color flow cytometry as previously described [21]. Among total (CD20+) B-cells in blood and BAL, we measured proportions of the following subsets: naïve (IgD+ CD27+); unswitched memory (IgD– CD27+); switched memory (IgD– CD27+) (denoting commitment to IgA or IgG production); double-negative (IgD– CD27–) (DN), a heterogenous population including exhausted memory cells; and the activated CD80 + switched memory subset.
We constructed linear regression models that included serum or albumin-corrected BAL Ig measurements as the independent variable and the following dependent variables: blood and BAL B-cell subsets, and BAL bacterial diversity metrics (Faith phylogenetic diversity, inverse Simpson, and richness indices). For BAL Ig, we also assessed FEV1% predicted using a linear regression model and risk of prospective exacerbations using a negative binomial regression as dependent variables. Models were adjusted by age, sex, race, educational attainment, smoking status, inhaled corticosteroid use, and, except when it was the outcome, with FEV1% predicted.
Results
Of the n = 213 bronchoscopy participants, 209 had a BAL samples and we then excluded never smokers (n = 27) and those with insufficient BAL samples for Ig determination (n = 65). Among the remaining 117 participants, 66 had serum Ig measurements, 107 had BAL microbiome assessments, and B-lymphocyte flow cytometry data were available on 82 and 85 in blood and BAL, respectively (Fig. 1). Those with immunoglobulin data did not differ significantly from all bronchoscopy participants (Table 1). Compared to all ever-smoker participants within SPIROMICS, those within this bronchoscopy immunoglobulin analysis generally had milder disease (Table 1).
Fig. 1.
Flow diagram for study cohort
Table 1.
Participant characteristics
| Research Bronchoscopy Cohort (N = 184) | Analytic Sample with BAL IgG/IgA (N = 117) | P-value | |
|---|---|---|---|
| Age, yrs | 60.11 ± 8.91 | 59.35 ± 8.93 | 0.47 |
| Sex, n (% female) | 82 (44.6%) | 51 (43.6%) | 0.87 |
| Race n (% white) | 128 (69.6%) | 73 (62.4%) | 0.20 |
| Education n (% ≥ HS grad.) | 112 (60.9%) | 65 (55.6%) | 0.36 |
| Body Mass Index | 28.50 ± 4.80 | 28.50 ± 4.79 | 0.99 |
| Medication n (% LABA/LAMA) | 27 (14.8%) | 17 (14.7%) | 0.97 |
| Pack-Years, yrs | 47.35 ± 28.79 | 47.45 ± 32.12 | 0.98 |
| COPD, n (% yes) | 93 (50.5%) | 54 (46.2%) | 0.46 |
| FEV1% predicted | 88.34 ± 19.21 | 90.83 ± 18.54 | 0.27 |
| Current Smoker, n (% yes) | 78 (43.3%) | 57 (49.6%) | 0.30 |
| Eosinophil count, cells/µL | 183 ± 117 | 171 ± 107 | 0.37 |
| ICS, n (% yes) | 37 (20.1%) | 22 (18.8%) | 0.78 |
| OCS, n (% yes) | 1 (0.6%) | 1 (0.9%) | 0.75 |
| CAT | 11.97 ± 8.57 | 11.44 ± 8.35 | 0.61 |
| mMRC | 0.69 ± 0.87 | 0.64 ± 0.80 | 0.62 |
| SGRQ | 25.01 ± 20.41 | 23.67 ± 19.65 | 0.60 |
| Any exacerbations in the prior 12 mo, n (% yes) | 31 (16.9%) | 17 (14.7%) | 0.60 |
| Severe exacerbations in the prior 12 mo, n (% yes) | 15 (8.2%) | 7 (6.0%) | 0.49 |
| CHD, n (% yes) | 21 (11.5%) | 8 (6.9%) | 0.19 |
| Diabetes, n (% yes) | 21 (11.5%) | 13 (11.2%) | 0.93 |
| CHF, n (% yes) | 2 (1.1%) | 0 (0%) | 0.26 |
| Stroke, n (% yes) | 5 (2.7%) | 1 (0.9%) | 0.26 |
| Hypertension, n (% yes) | 80 (44.0%) | 53 (45.7%) | 0.77 |
| Serum IgG (n = 66) | 1,486.1 ± 510.6 mg/dL | ||
| Serum IgA (n = 66) | 237.7 ± 131.6 mg/dL | ||
| BAL IgG, albumin-corrected (n = 117) | 0.03 ± 0.02 mg/dL | ||
| BAL IgA, albumin-corrected (n = 117) | 0.01 ± 0.01 mg/dL | ||
| Blood total (CD20+) B-cells (n = 82), (% of CD45+ blood cells) | 3.45 ± 3.02 | ||
| Blood switched Memory (IgD–CD27+) B cells (n = 82) a | 10.66 ± 15.27 | ||
| Blood unswitched Memory (IgD–CD27+) B-cells (n = 82) a | 4.77 ± 6.93 | ||
| Blood naïve (IgD + CD27+) B-cells (n = 82) a | 52.43 ± 25.60 | ||
| Blood double negative (IgD– CD27–) B-cells (n = 82) a | 32.17 ± 24.96 | ||
| CD80 positivity among Blood Switched Memory B-cells (n = 82) | 70.56 ± 32.41 | ||
| BAL Total (CD20+) B-cells, (% of CD45+ BAL cells) | 0.30 ± 0.57 | ||
| BAL switched Memory (IgD-CD27+) B-cells b | 14.45 ± 14.00 | ||
| BAL unswitched Memory (IgD-CD27+) B-cells b | 20.86 ± 21.11 | ||
| BAL naïve (IgD + CD27+) B-cells b | 27.35 ± 21.04 | ||
| BAL double negative (IgD-CD27) B-cells b | 37.35 ± 28.96 |
a, as a percentage of all blood B cells; b, as a percentage of all BAL B cells
Greater serum IgA was significantly associated with higher switched memory (IgD– CD27+) and lower double-negative (IgD– CD27-) (DN) B-cell percentages in peripheral blood. BAL IgA was associated with higher total BAL B-cells and with a greater proportion of switched memory (IgD– CD27+) B-cells in blood that expressed CD80+, an activation marker (Table 2). However, there were no associations of IgA in serum or BAL with other blood or BAL B-cell subsets; nor did IgG in serum or BAL associate with any of the blood or BAL B-cell subsets.
Table 2.
Levels and associations of serum and BAL Immunoglobulins with BAL microbiome, blood B-lymphocytes, and clinical outcomes
| Serum IgG | Serum IgA | BAL IgG | BAL IgA | |
|---|---|---|---|---|
| (albumin-corrected) | ||||
| Concentrations (mean ± SD) | 1,486.1 ± 510.6 mg/dL | 237.7 ± 131.6 mg/dL | 0.029 ± 0.022 mg/dL | 0.006 ± 0.005 mg/dL |
| β per 1 SD increase (95% CI) with p-value | ||||
| Blood B-cell subsets | ||||
| Total (CD20+) B-cells | 0.39 (-0.97, 1.74) p = 0.57 | 0.86 (-0.47, 2.19) p = 0.20 | -0.06 (-0.89, 0.78) p = 0.89 | 0.05 (-0.73, 0.83) p = 0.87 |
| Switched Memory (IgD–CD27+) B-cells | 1.63 (-3.47, 6.72) p = 0.52 | 6.06 (1.37,> 10.76)p = 0.01 | -0.81 (-4.51, 2.90) p = 0.67 | -0.19 (-3.67, 3.28) p = 0.91 |
| Unswitched Memory (IgD–CD27+) B-cells | -0.12 (-3.10, 2.86) p = 0.93 | 0.22 (-2.77, 3.21) p = 0.88 | -0.83 (-2.76, 1.11) p = 0.40 | -0.52 (-2.34, 1.29) p = 0.57 |
| Naïve (IgD+ CD27+) B-cells | -3.40 (-11.83, 5.03) p = 0.42 | 3.67 (-4.78, 12.13) p = 0.38 | -1.20 (-7.84, 5.44) p = 0.72 | 2.74 (-3.46, 8.93) p = 0.38 |
| Double negative (IgD– CD27–) B-cells | 1.89 (-6.72, 10.51) p = 0.66 | -9.96 (-17.91, -2.00)p = 0.02 | 2.85 (-4.05, 9.76) p = 0.41 | -2.03 (-8.51, 4.46) p = 0.54 |
| CD80 + Switched Memory B-cells | -0.17 (-10.81, 10.46) p = 0.97 | 8.25 (-1.98, 18.47) p = 0.11 | 2.85 (-6.38, 12.09) p = 0.54 | 5.81 (-2.72, 14.34) p = 0.18 |
| β per 1 SD increase (95% CI) with p-value | ||||
| BAL B-cell subsets | ||||
| Total (CD20+) B-cells | 0.09 (-0.17, 0.35) p = 0.49 | 0.05 (-0.22, 0.32) p = 0.73 | 0.05 (-0.12, 0.22) p = 0.59 | 0.16 (0.01, 0.30)p = 0.04 |
| Switched Memory (IgD-CD27+) B-cells | 0.35 (-7.93, 8.64) p = 0.93 | 3.49 (-3.99, 10.97) p = 0.37 | -1.91 (-6.52, 2.71) p = 0.42 | -1.45 (-5.22, 2.32) p = 0.46 |
| Unswitched Memory (IgD-CD27+) B-cells | 1.96 (-9.72, 13.64) p = 0.75 | 7.91 (-2.32, 18.15) p = 0.15 | 5.18 (-2.50, 12.85) p = 0.19 | 1.01 (-5.37, 7.40) p = 0.76 |
| Naïve (IgD + CD27+) B-cells | -7.74 (-16.42, 0.94) p = 0.09 | 1.22 (-7.31, 9.75) p = 0.78 | 5.68 (-1.51, 12.86) p = 0.13 | 3.31 (-2.64, 9.25) p = 0.28 |
| Double negative (IgD-CD27) B-cells | 5.44 (-10.66, 21.53) p = 0.52 | -12.60 (-26.57, 1.36) p = 0.09 | -8.95 (-18.95, 1.05) p = 0.08 | -2.88 (-11.29, 5.53) p = 0.51 |
| BAL Bacterial Microbiota Metrics | ||||
| Faith phylogenetic diversity | 0.41 (0.12, 0.71)p = 0.01 | -0.05 (-0.37, 0.27) p = 0.76 | 0.11 (-0.09, 0.30) p = 0.29 | -0.07 (-0.26, 0.13) p = 0.49 |
| Inverse Simpson diversity | 1.72 (0.41, 3.03)p = 0.01 | 0.19 (-1.22, 1.61) p = 0.79 | 0.61 (-0.29, 1.50) p = 0.18 | -0.30 (-1.17, 0.57) p = 0.50 |
| Richness | 5.56 (1.30, 9.82)p = 0.01 | -1.03 (-5.62, 3.56) p = 0.65 | 1.73 (-1.08, 4.54) p = 0.22 | -0.78 (-3.51, 1.96) p = 0.57 |
| Clinical metrics | ||||
| FEV1% predicted | 3.80 (-1.23, 8.82) p = 0.14 | 2.73 (-2.29, 7.76) p = 0.28 | 0.03 (-3.18, 3.24) p = 0.99 | -1.21 (-4.46, 2.03) p = 0.46 |
| Severe exacerbations over 1 year of follow-up (IRR) | 2.05 (0.51, 8.28) p = 0.32 | 0.64 (0.13, 3.07) p = 0.58 | 1.33 (0.34, 5.29) p = 0.69 | 0.40 (0.06, 2.67) p = 0.34 |
Bolded values indicated p < 0.05. Linear regression was used for all outcomes except exacerbations for which negative binominal regression was used. All models were adjusted for age, sex, race, educational attainment, smoking status, inhaled corticosteroid use, and, except when it was the outcome, FEV1% predicted
Lower serum IgG was significantly associated with reduced alveolar bacterial diversity across all three measures (Fig. 2). By contrast, there was no association of serum IgA or of either BAL IgG or IgA with LRT bacterial diversity. Concentrations of BAL immunoglobulins were not associated with exacerbations or lung function, which is unsurprising given the small sample size and milder disease severity in the bronchoscopy sub-study.
Fig. 2.
Lower serum IgG associated with reduced BAL bacterial diversity as assessed by (a) Faith’s Phylogenetic Diversity, (b) Inverse Simpson Mean, and (c) Richness Mean
Discussion
This analysis of the largest bronchoscopy study to date of smokers without or with spirometrically-defined COPD identified a significant association of decreased LRT bacterial diversity with lower serum concentrations of total IgG. Because reduced LRT microbial diversity is a feature of dysbiosis, these results extend several previous studies that have shown associations between serum IgG concentrations and prospective risk of exacerbation [1–4]. Somewhat unexpectedly, LRT bacterial microbiota diversity associated neither with IgG in BAL, nor with IgA in serum or BAL. Collectively, these results heighten interest in determining why serum IgG are uniquely relevant in COPD exacerbations, while suggesting a possible lesser protective role for LRT Ig concentrations.
Greater relative importance of serum rather than airway immunoglobulins might seem paradoxical, as COPD exacerbations appear largely to be intrapulmonary processes associated with infections. However, our results are actually in line with a growing body of literature that implies that a large percentage of exacerbations are triggered by failure to contain an initial viral respiratory infection, in some cases with secondary bacterial infection resulting from host-induced disruption of détente between bacterial species [22, 23]. From this perspective, effective upper airway and systemic IgG-mediated viral clearance might be quite important to augment the demonstrated role of innate immune defenses by airway epithelial cells and macrophages [24–26].
Effects of BAL IgG and IgA on clinical outcomes may also be more relevant across different COPD phenotypes. For example, BAL IgA and IgG1 were elevated in eosinophilic-predominant COPD (> 250 cells/µL), relative to those with lower numbers of blood eosinophils [17], whereas eosinophils in our study were 171 ± 107 cells/µL (mean ± SD). The relationship of BAL immunoglobulins to clinical outcomes among individuals with eosinophilic versus non-eosinophilic COPD and the LRT microbiome community structure before versus after intravenous Ig therapy are important topics for future research. Furthermore, the participants included in our bronchoscopy study had milder disease (Table 1), and therefore the relevance of BAL IgG and IgA may be greater among those with more severe disease. This possibility is underscored by the absence of association with serum IgG and lung function or exacerbations among this milder disease bronchoscopy subgroup (Table 2).
Our finding of a strong inverse association, independent of aging, between serum IgA levels and blood DN B cells, to our knowledge previously unreported, might reflect overall immune system health. DN B cells are a complex grouping containing subsets linked to aging, autoimmune conditions, and granulomatous lung diseases [27, 28]. As our staining scheme lacked the markers (CD11c, CD21 and T-bet) needed to identify such age-associated B cells, further studies in COPD are clearly indicated. No such associations were observed with BAL IgA or IgG, which may again reflect the unique importance of serum immunoglobulins to decrease exacerbations. This is not to detract from the key importance of secretory IgA to prevent small airway disease [29]. Class-switched memory B-cells have been observed to be lower among COPD patients [30], hence their positive association with BAL IgA may also be beneficial in COPD.
Our study has the strength of assessing distinct anatomic compartments with complementary modalities in a large, well-phenotyped, multicenter cohort. It is limited by its associative approach and the single cross-sectional measurements of immunoglobulins, precluding determination of causality. Additionally, the immunoglobulins were measured using baseline samples and thus were not simultaneously obtained on the day of BAL. Our flow cytometry panels, designed between 2009 and 11 based on specific contemporary hypotheses, lacks the sophistication that could now be achieved by single-cell RNA sequencing.
In conclusion, we identified two potential mechanisms by which reduced serum IgG and IgA may increase COPD exacerbation risk: reduced LRT microbiome diversity for IgG and fewer memory B cells and more DN B cells for IgA. These findings invite further mechanistic studies to define causality, crucial to determine if therapies to augment even minimally reduced Ig levels would reduce exacerbation risk.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
The authors thank the SPIROMICS participants and participating physicians, investigators, study coordinators, and staff for making this research possible. More information about the study and how to access SPIROMICS data is available at www.spiromics.org. The authors would like to acknowledge the University of North Carolina at Chapel Hill BioSpecimen Processing Facility (http://bsp.web.unc.edu/) and Alexis Lab (https://www.med.unc.edu/cemalb/facultyresearch/alexislab/) for sample processing, storage, and sample disbursements.We would like to acknowledge the following current and former investigators of the SPIROMICS sites and reading centers: Neil E Alexis, MD; Wayne H Anderson, PhD; Mehrdad Arjomandi, MD; Igor Barjaktarevic, MD, PhD; R Graham Barr, MD, DrPH; Patricia Basta, PhD; Lori A Bateman, MS; Christina Bellinger, MD; Surya P Bhatt, MD; Eugene R Bleecker, MD; Richard C Boucher, MD; Russell P Bowler, MD, PhD; Russell G Buhr, MD, PhD; Stephanie A Christenson, MD; Alejandro P Comellas, MD; Christopher B Cooper, MD, PhD; David J Couper, PhD; Gerard J Criner, MD; Ronald G Crystal, MD; Jeffrey L Curtis, MD; Claire M Doerschuk, MD; Mark T Dransfield, MD; M Bradley Drummond, MD; Christine M Freeman, PhD; Craig Galban, PhD; Katherine Gershner, DO; MeiLan K Han, MD, MS; Nadia N Hansel, MD, MPH; Annette T Hastie, PhD; Eric A Hoffman, PhD; Yvonne J Huang, MD; Robert J Kaner, MD; Richard E Kanner, MD; Mehmet Kesimer, PhD; Eric C Kleerup, MD; Jerry A Krishnan, MD, PhD; Wassim W Labaki, MD; Lisa M LaVange, PhD; Stephen C Lazarus, MD; Fernando J Martinez, MD, MS; Merry-Lynn McDonald, PhD; Deborah A Meyers, PhD; Wendy C Moore, MD; John D Newell Jr, MD; Elizabeth C Oelsner, MD, MPH; Jill Ohar, MD; Wanda K O’Neal, PhD; Victor E Ortega, MD, PhD; Robert Paine, III, MD; Laura Paulin, MD, MHS; Stephen P Peters, MD, PhD; Cheryl Pirozzi, MD; Nirupama Putcha, MD, MHS; Sanjeev Raman, MBBS, MD; Stephen I Rennard, MD; Donald P Tashkin, MD; J Michael Wells, MD; Robert A Wise, MD; and Prescott G Woodruff, MD, MPH. The project officers from the Lung Division of the National Heart, Lung, and Blood Institute were Lisa Postow, PhD, and Lisa Viviano, BSN; SPIROMICS was supported by contracts from the NIH/NHLBI (HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHSN268200900020C), grants from the NIH/NHLBI (U01 HL137880, U24 HL141762, R01HL182622, and R01 HL144718), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from Amgen; AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc.; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; Genentech; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; MGC Diagnostics; Novartis Pharmaceuticals Corporation; Nycomed GmbH; Polarean; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; and Theravance Biopharma and Mylan/Viatris.
Author contributions
VT, NP, HW, CL, CMF, YJH, and JLC contributed to the conception and design of the study, interpretation of results, drafting and revising the manuscript. DL, NEA, AA, RGB, IB, RPB, AC, MTD, AF, MKH, NNH, RJK, JK, NM, FJM, JO, WO, VEO, RP, TDP, MS, LR, PGW, YJH, and JLC contributed to the acquisition of the data. VT, NP, DL, AA, TDP, MS, CMF, YJH, and JLC contributed to analysis of biospecimens. VT, NP, HW, CL, DL, DJC, CMF, YJH, and JLC contributed to interpretation of the results and revisions of the manuscript for critically important intellectual content. All of the authors approved this version of the manuscript to be published and agree to be accountable for all aspects of the work.
Funding
V.T. is supported by K23 HL173570. SPIROMICS was supported by contracts from the NIH/NHLBI(HHSN268200900013C, HHSN268200900014C, HHSN268200900015C, HHSN268200900016C, HHSN268200900017C, HHSN268200900018C, HHSN268200900019C, HHS N268200900020C), grants from the NIH/NHLBI(U01 HL1 37880 and U24 HL141762), and supplemented by contributions made through the Foundation for the NIH and the COPD Foundation from AstraZeneca/MedImmune; Bayer; Bellerophon Therapeutics; Boehringer-Ingelheim Pharmaceuticals, Inc.; Chiesi Farmaceutici S.p.A.; Forest Research Institute, Inc.; GlaxoSmithKline; Grifols Therapeutics, Inc.; Ikaria, Inc.; Novartis Pharmaceuticals Corporation; Nycomed GmbH; ProterixBio; Regeneron Pharmaceuticals, Inc.; Sanofi; Sunovion; Takeda Pharmaceutical Company; and Theravance Biopharma and Mylan. Analysis of specimens for this work was additionally supported by CSL Behring.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
All participants signed an informed consent. All studies were approved by ethics and review boards (Institutional Review Boards [IRB]) at all participating centers. Institution names (IRB names) are as follows: Columbia University Medical Center (Columbia University IRB 2), University of Iowa (University of Iowa IRB-01), Johns Hopkins University (Johns Hopkins IRB-5), University of California at Los Angeles (UCLA Medical IRB 1 (MIRB1)), University of Michigan (University of Michigan Medical School Institutional Review Board (IRBMED)), National Jewish Health (National Jewish Health IRB- BRANY IRB), University of California at San Francisco (UCSF IRB Parnassus Panel), Temple University (Temple University IRB A1), University of Alabama at Birmingham (University of Alabama at Birmingham IRB), University of Illinois (University of Illinois IRB), University of Utah (University of Utah IRB), Wake Forest Baptist Health (Wake Forest University Health Sciences IRB), University of North Carolina at Chapel Hill (UNC Non-Biomedical IRB).
Consent for publication
Not applicable.
Competing interests
VT declares no competing interests related to this work, grant support from NIH, Grifols and Fisher and Paykel Healthcare outside of this work, and compensation for consulting by Regeneron/Sanofi outside of this work. RP declares support from the NHLBI, and the COPD Foundation related to the current project, and from the Department of Veterans Affairs (research grant) and Partner Therapeutics (consultant) unrelated to the current work. NP declares no competing interests related to this work, grant support from NIH outside of this work, and compensation for advisory board participation by AstraZeneca and Verona Pharma outside of this work. JAK declares no competing interests related to this work, grant support from NIH outside of this work, and compensation for advisory board participation by Genentech, Verona Pharma, Dynamed, AstraZeneca BioVie, RespirAI, Inogen, and Regeneron outside of this work. No other authors reported any other competing interests relevant to this work.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Leitao Filho FS, Mattman A, Schellenberg R, et al. Serum IgG levels and risk of COPD hospitalization: A pooled Meta-analysis. Chest. 2020;158(4):1420–30. 10.1016/j.chest.2020.04.058. [DOI] [PubMed] [Google Scholar]
- 2.Leitao Filho FS, Won Ra S, Mattman A, et al. Serum IgG and risk of exacerbations and hospitalizations in chronic obstructive pulmonary disease. J Allergy Clin Immunol. 2017;140(4):1164–e11676. 10.1016/j.jaci.2017.01.046. [DOI] [PubMed] [Google Scholar]
- 3.Leitao Filho FS, Ra SW, Mattman A, et al. Serum IgG subclass levels and risk of exacerbations and hospitalizations in patients with COPD. Respir Res. 2018;19(1):30. 10.1186/s12931-018-0733-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Putcha N, Paul GG, Azar A, et al. Lower serum IgA is associated with COPD exacerbation risk in SPIROMICS. PLoS ONE. 2018;13(4):e0194924. 10.1371/journal.pone.0194924. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Burnim M, Putcha N, LaFon D et al. Serum Immunoglobulin G Levels Are Associated with Risk for Exacerbations: An Analysis of SPIROMICS. Am J Respir Crit Care Med. Published online October 23, 2024. 10.1164/rccm.202311-2184OC [DOI] [PMC free article] [PubMed]
- 6.LaFon DC, Woo H, Fedarko N, et al. Reduced quantity and function of Pneumococcal antibodies are associated with exacerbations of COPD in SPIROMICS. Clin Immunol. 2023;250:109324. 10.1016/j.clim.2023.109324. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Faner R, Cruz T, Casserras T, et al. Network analysis of lung transcriptomics reveals a distinct B-Cell signature in emphysema. Am J Respir Crit Care Med. 2016;193(11):1242–53. 10.1164/rccm.201507-1311OC. [DOI] [PubMed] [Google Scholar]
- 8.Polverino F, Cosio BG, Pons J, et al. B Cell-Activating factor. An orchestrator of lymphoid follicles in severe chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2015;192(6):695–705. 10.1164/rccm.201501-0107OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Seys LJM, Verhamme FM, Schinwald A, et al. Role of B Cell-Activating factor in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2015;192(6):706–18. 10.1164/rccm.201501-0103OC. [DOI] [PubMed] [Google Scholar]
- 10.Habener A, Grychtol R, Gaedcke S, et al. IgA + memory B-cells are significantly increased in patients with asthma and small airway dysfunction. Eur Respir J. 2022;60(5):2102130. 10.1183/13993003.02130-2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Janzon A, Goodrich JK, Koren O, Study Group TEDDY, Waters JL, Ley RE. Interactions between the gut Microbiome and mucosal Immunoglobulins A, M, and G in the developing infant gut. mSystems. 2019;4(6):e00612–19. 10.1128/mSystems.00612-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Dzidic M, Abrahamsson TR, Artacho A, et al. Aberrant IgA responses to the gut microbiota during infancy precede asthma and allergy development. J Allergy Clin Immunol. 2017;139(3):1017–e102514. 10.1016/j.jaci.2016.06.047. [DOI] [PubMed] [Google Scholar]
- 13.De Palma G, Nadal I, Medina M, et al. Intestinal dysbiosis and reduced immunoglobulin-coated bacteria associated with coeliac disease in children. BMC Microbiol. 2010;10:63. 10.1186/1471-2180-10-63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Opron K, Begley LA, Erb-Downward JR, et al. Lung microbiota associations with clinical features of COPD in the SPIROMICS cohort. NPJ Biofilms Microbiomes. 2021;7(1):14. 10.1038/s41522-021-00185-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Madapoosi SS, Cruickshank-Quinn C, Opron K, et al. Lung microbiota and metabolites collectively associate with clinical outcomes in milder stage chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2022;206(4):427–39. 10.1164/rccm.202110-2241OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Sulaiman I, Wu BG, Chung M, et al. Lower airway dysbiosis augments lung inflammatory injury in Mild-to-Moderate chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2023;208(10):1101–14. 10.1164/rccm.202210-1865OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Southworth T, Higham A, Kolsum U, et al. The relationship between airway Immunoglobulin activity and eosinophils in COPD. J Cell Mol Med. 2021;25(4):2203–12. 10.1111/jcmm.16206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Staples KJ, Taylor S, Thomas S, et al. Relationships between mucosal antibodies, Non-Typeable Haemophilus influenzae (NTHi) infection and airway inflammation in COPD. PLoS ONE. 2016;11(11):e0167250. 10.1371/journal.pone.0167250. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Couper D, LaVange LM, Han M, et al. Design of the subpopulations and intermediate outcomes in COPD study (SPIROMICS). Thorax. 2014;69(5):491–4. 10.1136/thoraxjnl-2013-203897. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wells JM, Arenberg DA, Barjaktarevic I, et al. Safety and tolerability of comprehensive research bronchoscopy in chronic obstructive pulmonary disease. Results from the SPIROMICS bronchoscopy substudy. Ann Am Thorac Soc. 2019;16(4):439–46. 10.1513/AnnalsATS.201807-441OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Freeman CM, Crudgington S, Stolberg VR, et al. Design of a multi-center immunophenotyping analysis of peripheral blood, sputum and Bronchoalveolar lavage fluid in the subpopulations and intermediate outcome measures in COPD study (SPIROMICS). J Transl Med. 2015;13:19. 10.1186/s12967-014-0374-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Zhu J, Mallia P, Footitt J, et al. Bronchial mucosal inflammation and illness severity in response to experimental rhinovirus infection in COPD. J Allergy Clin Immunol. 2020;146(4):840–e8507. 10.1016/j.jaci.2020.03.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Katsoulis O, Toussaint M, Jackson MM, et al. Neutrophil extracellular traps promote Immunopathogenesis of virus-induced COPD exacerbations. Nat Commun. 2024;15(1):5766. 10.1038/s41467-024-50197-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Singanayagam A, Loo SL, Calderazzo M, et al. Antiviral immunity is impaired in COPD patients with frequent exacerbations. Am J Physiol Lung Cell Mol Physiol. 2019;317(6):L893–903. 10.1152/ajplung.00253.2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Oliver BGG, Lim S, Wark P, et al. Rhinovirus exposure impairs immune responses to bacterial products in human alveolar macrophages. Thorax. 2008;63(6):519–25. 10.1136/thx.2007.081752. [DOI] [PubMed] [Google Scholar]
- 26.Finney LJ, Belchamber KBR, Fenwick PS, et al. Human rhinovirus impairs the innate immune response to Bacteria in alveolar macrophages in chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2019;199(12):1496–507. 10.1164/rccm.201806-1095OC. [DOI] [PubMed] [Google Scholar]
- 27.Beckers L, Somers V, Fraussen J. IgD-CD27- double negative (DN) B cells: origins and functions in health and disease. Immunol Lett. 2023;255:67–76. 10.1016/j.imlet.2023.03.003. [DOI] [PubMed] [Google Scholar]
- 28.Phalke S, Aviszus K, Rubtsova K, et al. Age-associated B cells appear in patients with granulomatous lung diseases. Am J Respir Crit Care Med. 2020;202(7):1013–23. 10.1164/rccm.201911-2151OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Polosukhin VV, Richmond BW, Du RH, et al. Secretory IgA deficiency in individual small airways is associated with persistent inflammation and remodeling. Am J Respir Crit Care Med. 2017;195(8):1010–21. 10.1164/rccm.201604-0759OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Brandsma CA, Hylkema MN, Geerlings M, et al. Increased levels of (class switched) memory B cells in peripheral blood of current smokers. Respir Res. 2009;10(1):108. 10.1186/1465-9921-10-108. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.