Convergence of cytokine dysregulation and antibody deficiency in common variable immunodeficiency with inflammatory complications

Abstract

Background: Noninfectious complications are the greatest cause of morbidity and mortality in common variable immunodeficiency (CVID), but their pathogenesis remains poorly defined.

Objective: Using high-throughput approaches, we aimed to identify, correlate, and determine the significance of immunologic features of CVID with noninfectious complications (CVIDc).

Methods: We simultaneously applied proteomics, RNA sequencing, and mass cytometry to a large cohort with primary antibody deficiency.

Results: CVIDc is differentiated from uncomplicated CVID, other forms of primary antibody deficiency, and healthy controls by a distinct plasma proteomic profile. In addition to confirming previously reported elevations of 4-1BB, IL-6, IL-18, and IFN-γ, we found elevations of colony-stimulating factor 1, IL-12p40, IL-18R, oncostatin M, TNF, and vascular endothelial growth factor A to differentiate CVIDc. This cytokine dysregulation correlated with deficiency of LPS-specific antibodies and increased soluble CD14, suggesting microbial translocation. Indicating potential significance of reduced LPS-specific antibodies and resultant microbial-induced inflammation, CVIDc had altered LPS-induced gene expression matching plasma proteomics and corresponding with increased CD14⁺CD16^- monocytes, memory T cells, and tissue inflammation ameliorated by T-cell-targeted therapy. Unsupervised machine learning accurately differentiated subjects with CVIDc and supported cytokine dysregulation, antibody deficit, and T-cell activation as defining and convergent features.

Conclusions: Our data expand understanding of CVIDc proteomics, establish its link with deficiency of IgA and LPS-specific antibodies, and implicate altered LPS-induced gene expression and elevated monocytes and T cells in this cytokine dysregulation. This work indicates that CVIDc results when insufficient antibody neutralization of pathogen-associated molecular patterns, like LPS, occurs in those with a heightened response to these inflammatory mediators, suggesting a 2-hit model of pathogenesis requiring further exploration.

Keywords: CVID; Common variable immunodeficiency; IFN-γ; IL-12; LPS; T cells; TNF; lipopolysaccharide; monocytes; noninfectious complications.

FIG 1.

Elevation of cytokines and molecules involved in T-cell function in CVIDc. A, Plasma cytokines by Olink in uncomplicated CVID, CVIDc, XLA, HIgM, IgAD, and HCs. B, Plasma IL-12, IFN-γ, and TNF by Luminex, IL-6 by ELISA. XLA (purple diamonds), HIgM (teal diamonds), IgAD (orange diamonds). HIgM, Hyper-IgM syndrome; IgAD, selective IgA deficiency; XLA, X-linked agammaglobulinemia. **P < .01, ***P < .001, **** P < .0001.

FIG 2.

Cytokine and sCD14 elevation corresponds with greater antibody defect in CVID. A, Plasma sCD14. XLA (purple diamonds), HIgM (teal diamonds), IgAD (orange diamonds). B, Serum IgA. C, IgA and sCD14 correlation. D, Plasma LPS-specific IgA. E, LPS-specific IgA and sCD14 correlation. F, Plasma LPS-specific IgM. G, LPS-specific IgM and sCD14 correlation. H, LPS-specific IgA and IL-12 correlation. I, LPS-specific IgM and IL-12 correlation. J, Isotype-switched memory B cells and IL-12 correlation. HIgM, Hyper-IgM syndrome; IgAD, selective IgA deficiency; XLA, X-linked agammaglobulinemia. Red triangles denote CVIDc, and blue triangles denote uncomplicated CVID. *P < .05, **P < .01, ***P < .001, ****P < .0001.

FIG 3.

Altered LPS-driven leukocyte gene expression mirrors plasma cytokine dysregulation in CVIDc. A, Global gene expression effect of LPS stimulation on PBMCs, showing CVIDc differs from the pattern shared between uncomplicated CVID and HCs. B, Gene set enrichment analysis comparing CVIDc against uncomplicated CVID and HCs grouped together. C, Heat maps of the hallmark inflammatory response and TNF signaling via NF-kB gene sets. *P < .05, **P < .01, ***P < .001.

FIG 4.

Cytokine dysregulation of CVIDc corresponds with expansion and activation of circulating monocytes. A, Innate immune-cell subsets in whole blood by mass cytometry. (B) CD14⁺CD16⁻ monocytes and (C) CD1c⁻ dendritic cells as percentages of whole blood. D, Plasma TNF and CD14⁺CD16⁻ monocyte correlation. E, CD86 and HLA-DR median intensity. *P < .05, **P < .01, ***P < .001, ****P < .0001.

FIG 5.

CVIDc cytokine dysregulation corresponds with T-cell activation and infiltration of tissues. A and B, CD4⁺ and CD8⁺ T-cell subsets as percentages of whole blood. C, Plasma IL-12 correlation with CD4⁺ T_CM cells. D, Representative CVIDc biopsies illustrating T-cell infiltration. E, Olink measurement of plasma chemokines. F, Luminex measurement of plasma CXCL10. G, Weight before and after immunomodulatory therapy (AZA 5 azathioprine, ABC 5 abatacept) in subjects with CVIDc. Data points are means with SD 3 weights measured before and after treatment. G, Ileum biopsies before and after abatacept in subject P22 with CVIDc. **P < .01, ***P < .001, ****P < .0001.

FIG 6.

Unsupervised machine learning reinforces link between cytokines, antibodies, and T cells in CVIDc. A, Partition around medoids (PAM) clustering of subjects with CVID and HC subjects. Subjects with CVID denoted by circles, HCs by triangles. Cluster x and y coordinates were determined by the Rtsne algorithm. Each dot is for 1 human subject, and its size indicates the number of subject’s noninfectious complications. B, Percentage of subjects with CVIDc assigned to cluster 1 and clusters 2 and 3. C, Percent contribution of parameters to the largest principle components (PC1) in which there is significant separation between subjects with CVID and HCs. Red dashed line indicates threshold of significant contribution.

FIG E1.

Definition of immune-cell subsets by mass cytometry. Figure displays the markers (top axis) and expression levels (darker green indicates higher expression) used to define leukocyte subsets (left axis) in mass cytometry analysis.

FIG E2.

Plasma IL-10 is increased in CVIDc, uncomplicated CVID, and other PAD compared with HCs. HIgM, Hyper-IgM syndrome. Plasma IL-10 measured by ELISA. Purple diamonds denote subjects with XLA, and teal diamonds denote subjects with HIgM. P value calculated by Kruskal-Wallis test. XLA, X-linked agammaglobulinemia. **P < .01, ****P < .0001.

FIG E3.

Relationship of plasma antibodies and cytokine levels. (A) Plasma IgM and (B) IgG. C, Plasma levels of LPS-specific IgG. (D) Correlation of sCD14 with IL-12 and (E) TNF. Plasma TNF levels were higher in those with lower LPS-specific IgM. Red dots denote subjects with CVIDc, and blue dots denote subjects with uncomplicated CVID. (F) No correlation between plasma sCD14 and IL-12 and (G) TNF in other PAD (purple denotes XLA, teal denotes HIgM, orange denotes IgAD). H, Correlation of plasma LPS-specific IgA with TNF. I, Correlation of plasma LPS-specific IgM with TNF. HIgM, Hyper-IgM syndrome; IgAD, selective IgA deficiency; r, Spearman rank correlation coefficient; XLA, X-linked agammaglobulinemia.

FIG E4.

TNF-induced cell death of PBMCs. Cell death measured by Cell Death Detection ELISA^PLUS (Roche, Basel, Switzerland) photometric detection of mono- and oligonucleosome fragments indicative of apoptosis after 18 hours of culture with 20 ng/mL TNF. P value calculated by Tukey multiple comparisons test. **P < .01, ***P < .001.

FIG E5.

Additional data supporting relationship of monocyte expansion and activation with cytokine dysregulation in CVIDc. A, Leukocyte subsets by cell-type deconvolution of RNAseq. **P < .01. B, Correlation of plasma IL-12 with circulating CD14⁺CD16⁻ monocytes. Red triangles denote subjects with CVIDc, blue triangles denote subjects with uncomplicated CVID, and green triangles denote HCs.

FIG E6.

Additional evidence of T-cell–mediated pathology in CVIDc. A, Hematoxylin and eosin stains of liver and kidney biopsies from patients with CVIDc. B, Percentage of subjects with CVIDc with interstitial lung disease, autoimmunity, liver disease, and inflammatory bowel disease. C, Jejunum biopsies before and after azathioprine in CVIDc. D, Platelet counts before and after immunomodulatory therapy (CSA 5 cyclosporine, MMF 5 mycophenolate, ABC 5 abatacept) in CVIDc. Data points are means with SD of 3 to 5 platelet count measurements before and after treatment. **P < .01, ***P < .001, ****P < .0001.

FIG E7.

MFA of data from subjects with CVID and HC subjects. The data consist of plasma cytokines and chemokines, total and LPS-specific antibody responses, peripheral blood leukocyte immunophenotyping, and medical complications. A, Projection of individual subjects on the first 2 principal components (Dim1 and Dim2) showing clear separation of subject groups. B, Projection of variable groups on the first 2 principal components, with antibodies, lymphocyte subset percentages, and cytokines in blood correlating closely with the disease phenotype. C, Projection of individual quantitative variables on the first 2 principal components. Plasma sCD14, antibodies, and CXCL10 contribute largely to PC1 (Dim1), and CD4⁺ and CD8⁺ T-cell subsets providing the major contribution to PC2 (Dim2). All other quantitative variables are also shown on the figure; however, they contribute very little to either PC1 or PC2 and are therefore located very closely around the origin.