Immunoglobulin A in serum: an old acquaintance as a new prognostic biomarker in idiopathic pulmonary fibrosis

Klooster, Liesbeth ten; Moorsel, Coline H.M. van; Erp, Johanna M. Kwakkel-van; Velzen‐Blad, Heleen van; Grutters, Jan C.

doi:10.1111/cei.12636

Cited by 34 publications

(15 citation statements)

References 29 publications

(32 reference statements)

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“…). However, it must be noted that a serum IgA level in which the majority exists in the form of mIgA was reported to be a prognostic marker for IPF . Although we did not detect any pathogenic role or mIgA on activation of NHLFs, the level of mIgA may be linked to the level of sIgA because both mIgA and dIgA, a component of sIgA, are produced by plasma cells.…”

Section: Discussionmentioning

confidence: 99%

“…A recent report showed that the level of serum IgA can be used as a prognostic marker for idiopathic pulmonary fibrosis (IPF): a high level of serum IgA was associated with poor survival of IPF patients . Because transforming growth factor (TGF)‐β, the most potent cytokine involved in fibrogenesis in IPF, is critical for mucosal IgA responses , it is conceivable that IgA might play an important role in the pathogenesis of IPF.…”

Section: Introductionmentioning

confidence: 99%

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Secretory immunoglobulin A induces human lung fibroblasts to produce inflammatory cytokines and undergo activation

Arakawa

Suzukawa

Watanabe

et al. 2019

Clinical &Amp; Experimental Immunology

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Summary Immunoglobulin (Ig)A is the most abundant immunoglobulin in humans, and in the airway mucosa secretory IgA (sIgA) plays a pivotal role in first‐line defense against invading pathogens and antigens. IgA has been reported to also have pathogenic effects, including possible worsening of the prognosis of idiopathic pulmonary fibrosis (IPF). However, the precise effects of IgA on lung fibroblasts remain unclear, and we aimed to elucidate how IgA activates human lung fibroblasts. We found that sIgA, but not monomeric IgA (mIgA), induced interleukin (IL)‐6, IL‐8, monocyte chemoattractant protein (MCP)‐1 and granulocyte–macrophage colony‐stimulating factor (GM‐CSF) production by normal human lung fibroblasts (NHLFs) at both the protein and mRNA levels. sIgA also promoted proliferation of NHLFs and collagen gel contraction comparable to with transforming growth factor (TGF)‐β, which is involved in fibrogenesis in IPF. Also, Western blot analysis and real‐time quantitative polymerase chain reaction (PCR) revealed that sIgA enhanced production of α‐smooth muscle actin (α‐SMA) and collagen type I (Col I) by NHLFs. Flow cytometry showed that NHLFs bound sIgA, and among the known IgA receptors, NHLFs significantly expressed CD71 (transferrin receptor). Transfection of siRNA targeting CD71 partially but significantly suppressed cytokine production by NHLFs co‐cultured with sIgA. Our findings suggest that sIgA may promote human lung inflammation and fibrosis by enhancing production of inflammatory or fibrogenic cytokines as well as extracellular matrix, inducing fibroblast differentiation into myofibroblasts and promoting human lung fibroblast proliferation. sIgA’s enhancement of cytokine production may be due partially to its binding to CD71 or the secretory component.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Secretory immunoglobulin A induces human lung fibroblasts to produce inflammatory cytokines and undergo activation

Arakawa

Suzukawa

Watanabe

et al. 2019

Clinical &Amp; Experimental Immunology

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“…The serum IgA concentration is reported to reflect the severity of liver fibrosis in non‐alcoholic fatty liver disease and also the prognosis of IPF . While IgA can be a biomarker of fibrosis based on these findings, it is not clear whether IgA itself up‐regulates profibrotic cytokine synthesis.…”

Section: Discussionmentioning

confidence: 99%

Secretory IgA accumulated in the airspaces of idiopathic pulmonary fibrosis and promoted VEGF, TGF-β and IL-8 production by A549 cells

Kobayashi

Suzukawa

Watanabe

et al. 2019

Clinical and Experimental Immunology

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Summary Secretory IgA (SIgA) is a well‐known mucosal‐surface molecule in first‐line defense against extrinsic pathogens and antigens. Its immunomodulatory and pathological roles have also been emphasized, but it is unclear whether it plays a pathological role in lung diseases. In the present study, we aimed to determine the distribution of IgA in idiopathic pulmonary fibrosis (IPF) lungs and whether IgA affects the functions of airway epithelial cells. We performed immunohistochemical analysis of lung sections from patients with IPF and found that mucus accumulated in the airspaces adjacent to the hyperplastic epithelia contained abundant SIgA. This was not true in the lungs of non‐IPF subjects. An in‐vitro assay revealed that SIgA bound to the surface of A549 cells and significantly promoted production of vascular endothelial growth factor (VEGF), transforming growth factor (TGF)‐β and interleukin (IL)‐8, important cytokines in the pathogenesis of IPF. Among the known receptors for IgA, A549 cells expressed high levels of transferrin receptor (TfR)/CD71. Transfection experiments with siRNA targeted against TfR/CD71 followed by stimulation with SIgA suggested that TfR/CD71 may be at least partially involved in the SIgA‐induced cytokine production by A549 cells. These phenomena were specific for SIgA, distinct from IgG. SIgA may modulate the progression of IPF by enhancing synthesis of VEGF, TGF‐β and IL‐8.

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“…For instance, we see that idiopathic pulmonary fibrosis (IPF), is grouped together with common variable immunodeficiency and immunoglobulin alpha (IgA) deficiency. Indeed, IgA in serum has recently been proposed as a prognostic biomarker for IPF 33 . While space limitations preclude us from discussing every disease class in detail, given the high cohesiveness of multiplex disease classes, readers can draw similar possibly interesting disease-disease interactions from any of the classes using Supplementary Table 1 as reference.…”

Section: Resultsmentioning

confidence: 99%

The multiplex network of human diseases

Halu

Domenico

Arenas

et al. 2017

Preprint

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Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer.We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease classes, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers. 1 peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/100370 doi: bioRxiv preprint first posted online Jan. 18, 2017; IntroductionThe advent of next generation sequencing (NGS) and genome-wide association studies (GWAS) has led to the accumulation of a vast amount of disease-gene associations 1 . In addition, high throughput experimental studies and proteomic technologies have resulted in extensive protein interactions maps. Connecting disease related phenotypes to their underlying molecular mechanisms and genetic constituents is crucial for a better understanding of complex human diseases.The emerging field of network medicine offers the tools of network science for distilling relevant insight from the growing sets of molecular disease omics data 2 . One of the earliest attempts at exploring the higher-level implication of disease-gene associations from the network perspective was the construction of genotype-based disease networks, useful to show the global organization of diseases around functional modules 3 and to infer comorbidity relations between diseases 4 . On the same basis, phenotypic-based disease networks were constructed by text-mining large-scale Medicare data, systematically classifying diseases based on phenotype similarity 5 , facilitating the identification of patterns of disease progression 6 . Since these pioneering works, many studies have focused on adding to the growing compendium of disease-disease associations. For example, Suratanee et al. identified disease-disease associations using a scoring method based on random walk prioritization in the protein-protein interaction network and identified novel disease-disease interactions 7 . Yang et al. measured disease similarity based on differential coexpression analysis to elucidate dysfunctional regulatory mechanisms and arrived at novel interactions between diseases, whose shared ...

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Immunoglobulin A in serum: an old acquaintance as a new prognostic biomarker in idiopathic pulmonary fibrosis

Cited by 34 publications

References 29 publications

Secretory immunoglobulin A induces human lung fibroblasts to produce inflammatory cytokines and undergo activation

Secretory immunoglobulin A induces human lung fibroblasts to produce inflammatory cytokines and undergo activation

Secretory IgA accumulated in the airspaces of idiopathic pulmonary fibrosis and promoted VEGF, TGF-β and IL-8 production by A549 cells

The multiplex network of human diseases

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