The role of Interleukin-32 in autoimmunity

Interleukin-32 (IL-32) is a pro-inflammatory cytokine that induces other cytokines involved in inflammation, including tumour necrosis factor (TNF)-alpha, IL-6 and IL-1 beta. Recent evidence suggests that IL-32 has a crucial role in host defence against pathogens, as well as in the pathogenesis of chronic inflammation. Abnormal IL-32 expression has been linked to several autoimmune diseases, such as rheumatoid arthritis and inflammatory bowel diseases, and a recent study suggested the importance of IL-32 in the pathogenesis of type 1 diabetes. However, despite accumulating evidence, many molecular characteristics of this cytokine, including the secretory route and the receptor for IL-32, remain largely unknown. In addition, the IL-32 gene is found in higher mammals but not in rodents. In this review, we outline the current knowledge of IL-32 biological functions, properties, and its role in autoimmune diseases. We particularly highlight the role of IL-32 in rheumatoid arthritis and type 1 diabetes

Early DNA methylation changes in children developing beta cell autoimmunity at a young age

Aims/hypothesis Type 1 diabetes is a chronic autoimmune disease of complex aetiology, including a potential role for epigenetic regulation. Previous epigenomic studies focused mainly on clinically diagnosed individuals. The aim of the study was to assess early DNA methylation changes associated with type 1 diabetes already before the diagnosis or even before the appearance of autoantibodies. Methods Reduced representation bisulphite sequencing (RRBS) was applied to study DNA methylation in purified CD4(+) T cell, CD8(+) T cell and CD4(-)CD8(-) cell fractions of 226 peripheral blood mononuclear cell samples longitudinally collected from seven type 1 diabetes-specific autoantibody-positive individuals and control individuals matched for age, sex, HLA risk and place of birth. We also explored correlations between DNA methylation and gene expression using RNA sequencing data from the same samples. Technical validation of RRBS results was performed using pyrosequencing. Results We identified 79, 56 and 45 differentially methylated regions in CD4(+) T cells, CD8(+) T cells and CD4-CD8- cell fractions, respectively, between type 1 diabetes-specific autoantibody-positive individuals and control participants. The analysis of pre-seroconversion samples identified DNA methylation signatures at the very early stage of disease, including differential methylation at the promoter of IRF5 in CD4(+) T cells. Further, we validated RRBS results using pyrosequencing at the following CpG sites: chr19:18118304 in the promoter of ARRDC2; chr21:47307815 in the intron of PCBP3; and chr14:81128398 in the intergenic region near TRAF3 in CD4(+) T cells. Conclusions/interpretation These preliminary results provide novel insights into cell type-specific differential epigenetic regulation of genes, which may contribute to type 1 diabetes pathogenesis at the very early stage of disease development. Should these findings be validated, they may serve as a potential signature useful for disease prediction and management.Peer reviewe

Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells

T helper 17 (Th17) cells and induced regulatory T (iTreg) cells are two subsets of T helper cells differentiated from naïve cells that play important roles in autoimmune diseases, immune homeostasis, and tumor immunity. The differentiation process is achieved by changes in numerous proteins, including transcription regulators, enzymes, membrane receptors, and cytokines, which are critical in lineage commitment. To profile protein expression changes in Th17 and iTreg cells, we polarized murine naïve CD4+ T (Thp) cells in vitro to Th17 and iTreg cells and performed quantitative proteomic analysis of these cells. More than 4,000 proteins, covering almost all subcellular compartments, were detected. Quantitative comparison of the protein expression profiles resulted in the identification of proteins specifically expressed in the Th17 and iTreg cells. Importantly, our combined analysis of proteome and gene expression data revealed protein expression changes that were not associated with changes at the transcriptional level. The present study serves as a valuable resource that may prove useful in developing treatment of autoimmune diseases and cancer.</p

Early DNA methylation changes in children developing beta cell autoimmunity at a young age

Aims/hypothesis Type 1 diabetes is a chronic autoimmune disease of complex aetiology, including a potential role for epigenetic regulation. Previous epigenomic studies focused mainly on clinically diagnosed individuals. The aim of the study was to assess early DNA methylation changes associated with type 1 diabetes already before the diagnosis or even before the appearance of autoantibodies.Methods Reduced representation bisulphite sequencing (RRBS) was applied to study DNA methylation in purified CD4(+) T cell, CD8(+) T cell and CD4(-)CD8(-) cell fractions of 226 peripheral blood mononuclear cell samples longitudinally collected from seven type 1 diabetes-specific autoantibody-positive individuals and control individuals matched for age, sex, HLA risk and place of birth. We also explored correlations between DNA methylation and gene expression using RNA sequencing data from the same samples. Technical validation of RRBS results was performed using pyrosequencing.Results We identified 79, 56 and 45 differentially methylated regions in CD4(+) T cells, CD8(+) T cells and CD4-CD8- cell fractions, respectively, between type 1 diabetes-specific autoantibody-positive individuals and control participants. The analysis of pre-seroconversion samples identified DNA methylation signatures at the very early stage of disease, including differential methylation at the promoter of IRF5 in CD4(+) T cells. Further, we validated RRBS results using pyrosequencing at the following CpG sites: chr19:18118304 in the promoter of ARRDC2; chr21:47307815 in the intron of PCBP3; and chr14:81128398 in the intergenic region near TRAF3 in CD4(+) T cells.Conclusions/interpretation These preliminary results provide novel insights into cell type-specific differential epigenetic regulation of genes, which may contribute to type 1 diabetes pathogenesis at the very early stage of disease development. Should these findings be validated, they may serve as a potential signature useful for disease prediction and management.</p

Gene expression signature predicts rate of type 1 diabetes progression

Background Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.Methods Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diag-nosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.Findings We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.Interpretation There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.Peer reviewe

Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells

The transcriptional network and protein regulators that govern T helper 17 (Th17) cell differentiation have been studied extensively using advanced genomic approaches. For a better understanding of these biological processes, we have moved a step forward, from gene- to protein-level characterization of Th17 cells. Mass spectrometry–based label-free quantitative (LFQ) proteomics analysis were made of in vitro differentiated murine Th17 and induced regulatory T (iTreg) cells. More than 4,000 proteins, covering almost all subcellular compartments, were detected. Quantitative comparison of the protein expression profiles resulted in the identification of proteins specifically expressed in the Th17 and iTreg cells. Importantly, our combined analysis of proteome and gene expression data revealed protein expression changes that were not associated with changes at the transcriptional level. Our dataset provides a valuable resource, with new insights into the proteomic characteristics of Th17 and iTreg cells, which may prove useful in developing treatment of autoimmune diseases and developing tumor immunotherapy.Peer reviewe

Validation of protein expression changes with different technologies.

<p>(a) Heatmap showing the log fold change values (in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004194#pbio.2004194.s010" target="_blank">S5 Data</a>) of proteins and mRNA DE in Th17 and iTreg cells in comparison with Th0 cells and Th17 compared with iTreg cells. (b) Flow cytometry analysis demonstrating the expression of surface molecules CD69 and CD101 in murine Th0, iTreg, and Th17 cells. One replicate is shown. (c) Immunoblot analysis of DE proteins in iTreg and Th17 cells compared to Th0 cells. Representative blots from 2–3 independent experiments are shown. DE, differentially expressed; ENO3, enolase 3; FOXO1, forkhead box O1; iTreg, induced regulatory T; NFATC2, nuclear factor of activated T cells 2; PSMB5, proteasome subunit beta 5; SMYD3, SET and MYND domain containing 3; Th0, T cell receptor–activated helper T; Th17, T helper 17; Thp, naïve CD4+ T; VIM, vimentin.</p

Correlation of protein and RNA expression changes during Th17 and iTreg cell differentiation.

<p>Venn diagram showing the comparison of DE proteins with corresponding transcripts and DE transcripts with encoded detected proteins in comparison of Th17 and Th0 cells (a) or iTreg versus Thp cells (b). Scatterplot of proteins that were observed in proteomic and transcriptomic comparison of Th17 versus Th0 cell (a) or iTreg versus Thp cells (b). The lists of detected proteins and transcripts in Th17 versus Th0 cells and in iTreg versus Thp cells are in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004194#pbio.2004194.s009" target="_blank">S4 Data</a>. Ahr, aryl hydrocarbon receptor; Cnot2, CCR4-NOT transcription complex subunit 2; Coa6, cytochrome c oxidase assembly factor 6; DE, differentially expressed; Eno3, enolase 3; Foxo1, forkhead box O1; Foxp3, forkhead box P3; Gimap5, GTPase IMAP family member 5; Il17f, interleukin 17F; Isg15, interferon-stimulated gene 15; iTreg, induced regulatory T; Psmb5, proteasome subunit beta 5; Rorc, retinoic acid receptor–related orphan receptor C; Th0, T cell receptor–activated helper T; Th17, T helper 17; Thp, naïve CD4+ T.</p

VIM is highly expressed in iTreg cells and influences TGFβ-induced Foxp3 expression.

<p>(a) mRNA expression of Vim from RNA-seq data generated in the present study. WT Thp cells were cultured under Th0, iTreg, and Th17 polarizing condition for 3 d. RNAs were isolated and processed for RNA-seq. Data shown are median FPKM values from 3 independent experiments (in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004194#pbio.2004194.s011" target="_blank">S6 Data</a>) with the SEM. Statistical analysis was performed by using paired Student <i>t</i> test. ***: <i>p</i> < 0.01. (b) Immunoblot analysis of Thp cells cultured to Th0 and iTreg with and without 1 μM LY2109761 for 3 d. VIM, Foxp3, and loading control <b>β</b>-actin were shown. Representative of 3 independent experiments is shown. (c) Thp cells cultured to Th0 and iTreg for 3 d. Vim, Foxp3, and loading control <b>β</b>-actin were shown. Representative blots of 3 independent experiments are shown. (d) Flow cytometry analysis of WT and Vim-deficient CD4+ T cells cultured with TCR activation (Th0) and with cytokines (IL2+ TGFβ1, TGFβ1, IL2) for 3 d. Representative intracellular cytokine staining for Foxp3 was shown on left panel, and percentage of Foxp3 expression cells detected from 4 independent experiments (in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004194#pbio.2004194.s011" target="_blank">S6 Data</a>) was shown on right panel. CTRL, control; Foxp3, forkhead box P3; FPKM, fragments per kilobase of transcript per million mapped reads; IL2, interleukin 2; iTreg, induced regulatory T; SEM, standard error of the mean; TCR, T cell receptor; TGFβ, transforming growth factor beta; Th0, T cell receptor–activated helper T cell; Th17, T helper 17; Thp, naïve CD4+ T; VIM, vimentin; WT, wild-type.</p

Th17 and iTreg cell proteome.

<p>(a) Illustration of experimental and proteomic workflow in the study. (b) Representative flow cytometry plots showing the expression of murine IL17 in Th0 and Th17 cells cultured for 3 d and Foxp3 in Th0 and iTreg cells cultured for 7 d followed by restimulation with anti-CD3/CD28 and polarizing cytokines for additional 3 d. Percentages of positive cells were indicated. (c) Pearson’s correlation plots showing the correlation value of biological triplicates for Th17, iTreg, Th0 paired with Th17, and Th0 paired with iTreg cells. (d) Cumulative protein abundances plotted against ranked proteins. The number of proteins in each quantile was shown on the lists and in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004194#pbio.2004194.s006" target="_blank">S1 Data</a>. (e) Pie chart with percentages of proteins identified across different cell compartments in Th17 and iTreg cells. The complete lists of proteins are in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004194#pbio.2004194.s006" target="_blank">S1 Data</a>. (f) Venn diagram with quantified proteins across Th0, Th17, and iTreg cells. The complete lists of proteins are in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2004194#pbio.2004194.s006" target="_blank">S1 Data</a>. Actb, β-actin; Actg1, γ-actin 1; Ahr, aryl hydrocarbon receptor; Aldoa, aldolase A; Atp5b, ATP synthase subunit beta; Chd4, chromodomain helicase DNA-binding protein 4; Coro1a, coronin 1A; Ddx5, DEAD-box helicase 5; Eif4a1, eukaryotic translation initiation factor 4a1; Eno1, enolase 1; Fasn, fatty acid synthase; Foxp3, forkhead box P3; Gapdh, glyceraldehyde-3-phosphate dehydrogenase; Gimap4, GTPase IMAP family member 4; Hist1h1e; histone cluster 1 H1 family member E; Hk1, hexokinase 1; Hprt, hypoxanthine phosphoribosyltransferase; Hspd1, heat shock protein family D1; IL2, interleukin 2; Il4r, interleukin 4 receptor; IL6, interleukin 6; Il17 A, interleukin 17 A; IL17f, interleukin 17f; iTreg, induced regulatory T; Lck, lymphocyte cell-specific protein tyrosine kinase; LC-MS/MS, liquid chromatography–tandem mass spectrometry; Mif, macrophage migration inhibitory faction; Myh9, myosin heavy chain 9; Ncl, nucleolin; Phb, prohibitin; Pkm, pyruvate kinase M; Ppia, peptidylprolyl isomerase A; Rac2, ras-related C3 botulinum toxin substrate 2; Ran, Ras-related nuclear protein; Rorc, retinoic acid receptor–related orphan receptor C; Runx3, runt-related transcription factor 3; Satb1, special AT-rich sequence-binding protein 1; Serpinb5, Serpin Family B Member 5; Slc25a2, solute carrier family 25 member 2; Stat1, signal transducer and activator of transcription 1; Stat3, signal transducer and activator of transcription 3; Stat5a, signal transducer and activator of transcription 5A; Stip1, stress-induced phosphoprotein 1; TGFβ, transforming growth factor β; Th0, T cell receptor–activated helper T; Th17, T helper 17; Uba1, ubiquitin 1; Uba52, ubiquitin 52; Vim, vimentin; Wdr1, WD repeat domain 1; Zap70, zeta chain of T cell receptor–associated protein kinase 70.</p