AUTOIMMUNITY

Aberrant immune responses to viral pathogens contribute to pathogenesis, but our understanding of pathological immune responses caused by viruses within the human virome, especially at a population scale, remains limited. We analyzed whole-genome sequencing datasets of 6,321 Japanese individuals, including patients with autoimmune diseases (psoriasis vulgaris, rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), pulmonary alveolar proteinosis (PAP) or multiple sclerosis) and coronavirus disease 2019 (COVID-19), or healthy controls. We systematically quantified two constituents of the blood DNA virome, endogenous HHV-6 (eHHV-6) and anellovirus. Participants with eHHV-6B had higher risks of SLE and PAP; the former was validated in All of Us. eHHV-6B-positivity and high SLE disease activity index scores had strong correlations.

 

Genome-wide association study and long-read sequencing mapped the integration of the HHV-6B genome to a locus on chromosome 22q. Epitope mapping and single-cell RNA sequencing revealed distinctive immune induction by eHHV-6B in patients with SLE. In addition, high anellovirus load correlated strongly with SLE, RA and COVID-19 status. Our analyses unveil relationships between the human virome and autoimmune and infectious diseases. Analysis of the blood DNA virome in patients with COVID-19 and autoimmune disease associates endogenous HHV-6 (eHHV-6) and high anellovirus load with increased disease risk, most notably for systemic lupus erythematosus. eHHV-6 carriers show a distinct immune response.

 

Published in NAt. Genetics (Jan. 3, 2025):

https://doi.org/10.1038/s41588-024-02022-z 

Via Juan Lama

Severe MIS-C patients exhibit hyperinflammation and cytokine storm. Our MIS-C cohort was composed of 7 patients with mild MIS-C and 20 patients with severe MIS-C (Table 1). MIS-C diagnosis was performed according to CDC guidelines, and patients who required treatment in the pediatric ICU were defined as severe MIS-C patients. We first compared circulating biomarkers of inflammation and heart failure, and cytokine profiles of severe and mild MIS-C patients. Both mild and severe MIS-C patients showed elevated levels of C-reactive protein (CRP), ferritin, fibrinogen, pro–B-type natriuretic peptide (proBNP), aspartate transaminase, alanine transaminase (ALT), D-dimers, and creatine compared with normal reference ranges (Supplemental Figure 1A; supplemental material available online with this article; https://doi.org/10.1172/JCI151520DS1). Severe MIS-C cases showed significantly higher levels of ferritin, proBNP, D-dimers, and creatine, and a trend toward increased levels of CRP, ALT, and fibrinogen compared with mild MIS-C patients (Supplemental Figure 1A). Table 1Patient demographics We next assessed and compared the circulating cytokine profiles of healthy controls, mild MIS-C, and severe MIS-C patients. MIS-C patients showed increased levels of IFN-γ, TNF-α, IL-6, IL-8, IL-10, and IL-1β, with severely ill patients showing stronger dysregulation than those with milder courses (Supplemental Figure 1B). Since some of the clinical symptoms of MIS-C, such as erythematous rashes, conjunctivitis, and inflammatory changes in the oral mucosa are suggestive of Kawasaki disease (KD), we also characterized the cytokine profiles of 7 KD patients who were recruited before the COVID-19 pandemic (Supplemental Figure 1B). KD diagnosis was performed according to the established American Heart Association guidelines (20). Except for IL-8, which appeared less dysregulated in KD, the overall cytokine pattern was similar in MIS-C and KD (Supplemental Figure 1B). Proteomic analysis identifies a highly inflammatory proteomics profile in MIS-C. To assist in the elucidation of the pathogenesis of MIS-C and to identify proteins associated with the severe form of the disease, we performed proteomics analysis of serum or plasma samples from our study cohort (Figure 1A). We collected serum from healthy children (n = 20), mild MIS-C patients (non-ICU, n = 5), and severe MIS-C patients who required ICU treatment (n = 20). We also included analysis of plasma samples from KD patients that were collected prior to the pandemic (n = 7; Table 1). Healthy adult serum (n = 4) was used for reference range quality control. To obtain a high resolution of protein expression we performed discovery proteomics analysis on native and depleted (top 14 most abundant proteins) serum or plasma samples (ref. 21, Figure 1B, and Supplemental Figure 2A). Both data sets were integrated at the transition, peptide, and protein level. As plasma was used for KD samples, clotting factors (Supplemental Figure 2B) were removed from the data set for any downstream analysis involving the KD group. Principal component analysis (PCA) (Figure 1B) and hierarchical clustering (Figure 1C) showed that the MIS-C and KD proteomes clustered separately from healthy controls. Similar to our cytokine analysis, MIS-C and KD showed similar protein profiles, indicating that shared pathological pathways likely exist between the diseases (Figure 1, B and C). The major proteins contributing to dimension 1 of the PCA plot, which separates disease samples (MIS-C and KD) from the healthy controls, included inflammatory markers and alarmins such as SERPINA3, CRP, haptoglobin (HP)/zonulin, LPS-binding protein (LBP), CD14, S100A8 and S100A9 (Figure 1B and Supplemental 2C). Figure 1Proteomic profiling of MIS-C cases. (A) Experimental design of native and depleted serum proteomics profiling of healthy controls (n = 20), mild MIS-C (n = 5), severe MIS-C (n = 20), and KD (n = 7) patients. (B) PCA of proteomics data and top proteins contributing to dimension 1 of the PCA plot. (C) Heatmap and hierarchical clustering of proteomics expression data revealed 3 protein sets (C1, C2, and C3) driving separation between 3 clades of MIS-C and KD patients (sample clusters S1, S2, and S3). (D) ClueGO ontology analysis via PINE (Protein Interaction Network Extractor) for visualization of pathways and functional categories significantly enriched within each of the 3 protein sets (C1, C2, and C3) revealed by hierarchical clustering analysis in panel C. The x axis denotes the negative decimal logarithm of the FDR of enrichment (term P value corrected with Bonferroni’s test). Size of the node denotes number of proteins within each term. Protein Interaction Network Extractor (PINE; ref. 22) analysis of differentially expressed proteins between the groups revealed an enrichment of protein networks involved in multiple inflammatory processes and pathways, including neutrophil degranulation, platelet activation, complement and coagulation cascades, phagocytosis, angiogenesis, acute-phase responses, oxidative stress, metabolism, and cell migration and adhesion (Supplemental Figure 2D). Hierarchical clustering analysis led to the identification of 3 main clusters of disease samples, sample clusters S1, S2, and S3 (Figure 1C). A large set of proteins (protein cluster 1, C1) were upregulated in both KD and MIS-C, most strikingly in sample clusters S1 and S2, compared with healthy controls (Figure 1C). Functional annotation analysis revealed that this cluster was enriched with proteins involved in leukocyte-mediated immunity, neutrophil-mediated immunity, humoral immune responses, and extracellular matrix (Figure 1D and Supplemental Figure 3A). Functional annotation analyses revealed that a second set of proteins (C2) enriched in sample cluster S2 included proteins associated with platelet activation and aggregation, myofibrils, and smooth muscle cell contraction (Figure 1D and Supplemental Figure 3B). Proteins in C3 were upregulated exclusively in sample cluster S1, mostly severe MIS-C patients, and included heavy and light chain immunoglobins (Igs), as well as components of the classical complement cascade, C1qA, C1qB, and C1qC (Figure 1D and Supplemental Figure 3C). Proteomic characterization reveals biomarkers that differentiate severe MIS-C from mild disease and from KD. We next sought to identify proteins and associated pathways upregulated or downregulated in severe MIS-C and mild MIS-C patients compared with healthy controls (Figure 2 and Supplemental Figure 4). We identified 244 proteins increased and 135 proteins decreased in quantity in severe MIS-C compared with healthy controls (top 25 modulated proteins; Figure 2, A and B). Network and pathway analysis of significantly increased (Figure 2, C and D) or decreased (Figure 2, E and F) proteins revealed that humoral immune responses and complement pathways were highly enriched in severe MIS-C, including various Igs and C1q proteins, C1qA, C1qB, and C1qC (Supplemental Figure 5, A and B). The expression of proteins associated with platelet activation and coagulation pathways, including von Willebrand factor (VWF), F5, F9, F11, fibrinogen α and β chains (FGA and FGB), and SERPINF2, was also increased in severe MIS-C compared with healthy controls (Supplemental Figure 5, A and B). Fc receptor signaling, neutrophil-mediated responses, and phagocytosis pathways were also enriched in severe MIS-C. These include FCGR3A (CD16a), IgGFc-binding protein (FCGBP), calprotectin (S100A8 and S100A9), tissue inhibitor of metalloproteinases 1 (TIMP1), SERPINA1, SERPINA3, and acute-phase-reactant leucine-rich α2-glycoprotein 1 (LRG1) (Supplemental Figure 6, A and B, and Supplemental Figure 2C). Proteins involved in VEGF signaling and smooth muscle cell contraction were increased in severe MIS-C, including ICAM1, tropomyosin 4 (TPM4), p21-activated kinase (PAK2), FGA, FGB, and filamin B (FLNB) (Supplemental Figure 6C and Supplemental Figure 5B). As these proteins are highly expressed in vascular smooth muscle cells, their presence in the serum may reflect release from damaged blood vessels. Overall, the proteins and pathways increased in severe MIS-C indicate Ig-mediated inflammatory responses and endothelial dysfunction. Figure 2Characterization of severe MIS-C. Protein expression was compared between severe MIS-C (n = 20) and healthy controls (n = 20). Proteins were considered significantly changed when FDR was less than 0.05, as determined by mapDIA statistical software for protein differential expression using MS/MS fragment-level quantitative data. (A) Top proteins enhanced in severe MIS-C, ranked by fold change. (B) Top proteins reduced in severe MIS-C, ranked by fold change. (C) ClueGO Ontology analysis via PINE visualized a network of pathways and functional annotation terms enriched in a set of proteins significantly increased in severe MIS-C population when compared with healthy controls. (D) Selected pathways and functional annotation terms from PINE analysis of proteins increased in severe MIS-C when compared with healthy controls. (E) Network plots visualized via PINE analysis of proteins reduced in severe MIS-C group when compared with healthy controls. (F) Selected pathways and functional annotation terms from protein functional enrichment analysis of proteins reduced in severe MIS-C group when compared with healthy controls. Networks of proteins reduced in severe MIS-C revealed pathways involved in regulation of lipid transport, lipid metabolic processes, and lipoprotein clearance, including APOA1, APOA2, APOA4, APOC1, and APOM (Figure 2, E and F, and Supplemental Figure 6D). Some components of clotting and coagulation pathways were also downregulated in severe MIS-C compared with healthy controls (Figure 2, E and F, and Supplemental Figure 5A). Furthermore, there was downregulation of proteins involved in the regulation of body fluids and relaxation of cardiac muscle, including CAMK2D, which aligns with the increase in proBNP and the cardiovascular manifestations and shock observed in MIS-C (Figure 2, E and F, and Supplemental Figure 6E). To determine factors contributing to severe disease, we compared severe MIS-C with mild MIS-C (Figure 3). We found that the expression of 75 proteins was significantly enhanced in severe MIS-C, while 61 proteins were significantly reduced when compared with mild MIS-C. Selected proteins differentially expressed between MIS-C severity groups are presented in Figure 3A. Compared with mild MIS-C, severe MIS-C patients had increased levels of proteins involved in pathways that included proteolysis, classical complement cascade, coagulation, acute-phase response, and inflammation (Figure 3, A and B, and Supplemental Figure 7). These included CRP, S100A9, SAA1, SAA2, STAT3, FCGR3A, LBP, CD163, ORM1, SERPINA1, SERPINA3, TIMP1, TLN-1, VWF, various Igs, and components of the C1 complex of the complement system (C1qA, C1qB, and C1qC) (Supplemental Figures 2C, 5B, 6B, and 7). In contrast, severe MIS-C patients had reduced expression of proteins in pathways, including negative regulators of peptidase activity, extracellular matrix proteoglycans, complement and coagulation cascades, and high-density lipid protein remodeling (Supplemental Figure 8, A and B). Figure 3Proteins distinguishing severe MIS-C from mild disease and KD. Protein differential expression analysis was performed between severe MIS-C (n = 20) and mild MIS-C (n = 5) groups. Proteins were considered significantly changed when FDR was less than 0.05 as calculated by mapDIA statistical software. (A) Bar graphs show top increased and top decreased proteins in severe MIS-C when compared with mild, ranked by fold change and excluding Igs. (B) Selected pathways and functional annotation terms from protein functional enrichment analysis facilitated by PINE software using proteins increased (top panel) and decreased (bottom panel) in severe MIS-C compared with mild MIS-C. (C) Venn diagram of proteins differentially regulated between severe MIS-C, mild MIS-C, and KD. (D) Heatmap of selected proteins distinguishing severe MIS-C from mild MIS-C and KD. (E) Box-and-whisker plots of selected proteins found increased in severe MIS-C compared with mild MIS-C and KD. For improved visualization purposes, box-and-whisker plots show scaled protein expression values. Scaling was performed by mean centering and division by SD of each protein variable. For box-and-whisker plots, the bounds of the boxes represent IQR (Q1 to Q3) and the whiskers represent the nonoutlier minimum and maximum values, 1.5 × IQR. The median values are marked with a horizontal line in the boxes, and outliers are marked with black centered points outside the whiskers. Statistical analysis was calculated by mapDIA statistical software for protein differential expression using MS/MS fragment-level quantitative data. **P < 0.01, ***P < 0.001. NS, not significant. Next, we aimed to determine which proteins distinguished severe MIS-C from mild MIS-C and KD (Figure 3, C–E), excluding clotting factors. Among proteins of interest, ferritin light chain (FTL) was highly expressed in severe MIS-C, as were proteins involved in Ig-mediated immune activation, including FCGR3A and components of the classical complement cascade, C1qA, C1qB, and C1qC (Figure 3E). Proteins that have been associated with heart failure were also identified as enhanced in severe MIS-C, including tenascin C (TNC; ref. 23) and QSOX1 (24) (Figure 3, D and E). Proteins with reduced expression in severe MIS-C were also identified and included histidine-rich glycoprotein (HRG), sex hormone–binding globulin (SHBG), and complement component 7 (C7) (Figure 3, D and E). Overall, the proteomic profiles of MIS-C and KD were similar, indicating shared pathogenic pathways. However, distinguishing proteins indicate MIS-C may be mediated more so by immune complexes, and have greater heart muscle involvement than KD. These proteins have potential to act as biomarkers to distinguish severe MIS-C from mild MIS-C or KD. RNA-seq reveals a subgroup of hyperinflammatory MIS-C patients with enhanced myeloid responses, TRBV11-2 expansion, and SARS-CoV-2–specific antibodies. We performed RNA-seq analysis using RNA isolated from whole blood of febrile controls (n = 13), mild MIS-C (n = 4), and severe MIS-C patients (n = 8; Figure 4A). Hierarchical clustering and PCA demonstrated 2 subsets of MIS-C patients (Figure 4B and Supplemental Figure 9A). The first subset of MIS-C patients clustered separately (cluster 1) from febrile controls, while the other overlapped with febrile controls (cluster 2; Figure 4B). Cluster 1 consisted predominantly of severe MIS-C patients (5 severe and 1 mild), while cluster 2 contained an equal number of severe and mild MIS-C patients (3 severe and 3 mild; Figure 4B). Analyses revealed a large set of genes differentially expressed between the 2 MIS-C clusters (2895 genes upregulated and 2921 genes downregulated in cluster 1, FDR < 0.05, fold change [FC] > 2). The top 20 genes up- and downregulated in cluster 1 are presented in Figure 4C. Functional annotation analysis revealed that genes with increased expression in cluster 1 were involved in macrophage activation, neutrophil chemotaxis, innate signaling pathways, T cell activation, cytokine signaling, complement pathways, response to wounding, and apoptosis (Figure 4D and Supplemental Figure 9B). Cell deconvolution analysis identified increased relative abundance of neutrophils in cluster 1 MIS-C samples (Figure 4E). Genes with reduced expression in cluster 1 were involved in adaptive immune responses, as well as ribonucleoprotein complexes and RNA processing (Figure 4, C and D, and Supplemental Figure 9C). In line with these findings, cell deconvolution analysis revealed a reduction in adaptive immune cells in cluster 1, most strikingly a reduction in naive B cells, which may reflect lymphopenia that is observed in MIS-C (Figure 4E). Figure 4RNA-seq analysis of MIS-C. RNA-seq was performed using whole-blood RNA isolated from febrile controls (n = 13), mild MIS-C (n = 4), and severe MIS-C (n = 8) patients. (A) Experimental design of RNA-seq analysis and patient groups. (B) PCA of RNA-seq profiles. (C) Genes up- or downregulated in cluster 1 vs. cluster 2 MIS-C patients (FDR < 0.05). (D) Selected pathways and functional annotation terms from gene functional enrichment analysis performed with PINE software using significantly up- and downregulated (FDR < 0.01, log2[FC] > 1.5 and < –1.25) genes in cluster 1 vs. cluster 2 MIS-C patients. (E) Cell deconvolution analysis of RNA-seq data by CIBERSORT. (F) Top proteins increased in cluster 1 vs. cluster 2, based on proteomics data. (G) Enriched pathways and functional annotation terms based on protein expression changes significantly (FDR < 0.05) upregulated in cluster 1 with respect to cluster 2. (H) TRBV11-2 expansion of RNA-seq samples (17). (I) IgG titers against Spike protein receptor binding domain (RBD). Data are presented as mean ± SEM. Statistical significance was determined by Mann-Whitney test (H and I). We next compared the proteomes of cluster 1 with cluster 2 patients (Figure 4, F and G, and Supplemental Figure 9, D and E). Cluster 1, which was primarily severe MIS-C cases, was characterized by significantly enhanced expression of inflammatory markers, including CRP, SAA1, and SAA2, as well as proteins associated with neutrophil activation, including myeloperoxidase (MPO), lipocalin 2 (LCN2), cathepsin B (CATB), ICAM1, granulin (GRN), and LBP (Figure 4F). In line with this, pathway analysis of protein expression in cluster 1 identified an enrichment of neutrophil-mediated responses (Figure 4G). Analysis of proteins and pathways downregulated in cluster 1 identified lipoprotein-particle proteins, including APOA1 and APOA4 (Supplemental Figure 9, D and E), which were also observed as downregulated in severe MIS-C compared with mild MIS-C or healthy controls (Supplemental Figure 6D). Interestingly, we found a reduction in complement and coagulation cascade protein pathways in cluster 1 compared with cluster 2 (Supplemental Figure 9, D and E). Since complement pathways were identified as increased in cluster 1 by transcriptomics (Figure 4D), we analyzed the correlation between the direction of protein expression and gene expression changes when comparing cluster 1 with cluster 2 (Supplemental Figure 10A). We did not find a significant correlation between protein and gene expression changes, likely because for a subset, the gene expression and protein expression changes occurred in opposite directions (increased by gene expression yet decreased by protein expression in cluster 1). Functional annotation analysis showed that these genes/proteins, including C5, C3, C4BP, HP, F12, F5, and PF4, were enriched in complement and coagulation cascades (Supplemental Figure 10B). The increased gene expression but decreased protein expression of this subset may indicate excessive activation and consumption of these molecules. Interestingly, a reduction in C3 protein has also been observed in COVID-19 nonsurvivors compared with survivors (25). We previously identified TRBV11-2 skewing in MIS-C patients, which correlated with disease severity and cytokine storm (17). As this study utilizes the same patient samples, we compared TRBV11-2 usage between the 2 MIS-C clusters identified by RNA-seq analysis (Figure 4B). The patients with TRBV11-2 expansion were restricted to cluster 1, which contained primarily severe MIS-C patients (Figure 4H). We also examined titers of antibodies against Spike protein between the 2 groups and found that cluster 1 MIS-C patients had higher levels of anti-Spike IgG antibodies than patients in cluster 2 (Figure 4I). This is similar to observations in adult COVID-19 patients, in which increased antibody titers against SARS-CoV-2 are associated with disease severity (26). Overall, these data indicate that the patients in MIS-C cluster 1 exhibited increased inflammatory makers, increased neutrophil responses, reduced lymphocytes, increased SARS-CoV-2 antibodies, and TRBV11-2 T cell expansion. MIS-C autoantibodies are targeted to a diverse set of intracellular autoantigens and are enhanced in MIS-C cluster 1 patients. We next sought to characterize the levels of autoantibodies in our patient cohort and determine how these relate to the hyperinflammatory cluster 1 identified by RNA-seq analysis. Autoantibody analysis was performed using the HuProt array (CDI Labs) with serum from febrile controls (n = 5) and MIS-C patients (n = 11: 3 mild and 8 severe). The MIS-C patient group included 6 samples identified by RNA-seq as belonging to cluster 1, and 5 MIS-C samples from cluster 2 (Figure 5A). Candidate autoantibody targets were identified (P < 0.05, FC > 2) based on differential expression analysis of MIS-C samples, or RNA clusters, compared with febrile controls (Figure 5, B and C). Figure 5Autoantibody analysis of MIS-C. (A) Autoantibody analysis was performed on serum from febrile controls (n = 5) and MIS-C patients (n = 11) using HuProt array. MIS-C samples correspond to RNA cluster 1 (n = 6) and RNA cluster 2 (n = 5) identified in Figure 4. (B) Venn diagram of candidate IgG autoantibody targets in MIS-C and RNA clusters (P < 0.05, FC > 2). (C) Venn diagram of candidate IgA autoantibody targets in MIS-C and RNA clusters (P < 0.05, FC > 2). (D) IgG autoantibody targets identified in MIS-C (n = 11) compared with febrile controls (n = 5). The bar represents log2(FC). Each symbol represents 1 MIS-C patient presented as log2(FC) above the mean of febrile controls. (E) IgA autoantibody targets identified in MIS-C (n = 11) compared with febrile controls (n = 5). The bar represents log2(FC). Each symbol represents 1 MIS-C patient presented as log2(FC) above the mean of febrile controls. (F) IgG autoantibody targets separated based on RNA cluster 1 (n = 6) and RNA cluster 2 (n = 5). Data are presented as log2(FC) above the mean of febrile controls. (G) IgA autoantibody targets separated based on RNA cluster 1 (n = 6) and RNA cluster 2 (n = 5). Data are presented as log2(FC) above the mean of febrile controls. For box-and-whisker plots, the bounds of the boxes represent the interquartile range (IQR, Q1 to Q3) and the whiskers represent the minimum and maximum values. The median values are marked with a horizontal line within the box. *FDR < 0.05 compared with febrile controls. While the majority of IgG autoantibodies that significantly increased in MIS-C compared with febrile controls were targeted to ubiquitously expressed antigens, we identified a number of tissue-specific antigens from the GI tract and cardiovascular, skeletal muscle, and brain tissues, reflecting the systemic nature of MIS-C and the involvement of specific organs in clinical presentation of disease (Figure 5D). GI tract autoantigens included ATPase H+/K+-transporting α subunit (ATP4A), SRY-box 6 (SOX6), family with sequence similarity 84 member A (FAM84A), and RAB11 family interacting protein 1 (RAB11FIP1) (Figure 5D). Cardiovascular autoantigens included PDZ and LIM domain 5 (PDLIM5) and eukaryotic translation initiation factor 1A, Y linked (EIF1AY) (Figure 5D). Skeletal muscle autoantigens included RNA-binding motif protein 38 (RBM38) and skeletal troponin C2 (TNNC2) (Figure 5D). Brain autoantigens included microtubule-associated protein 9 (MAP9) and NSF attachment protein β (NAPB). Interestingly, several antigens highly expressed in neutrophils were identified, including endothelin-converting enzyme 1 (ECE1), SOX6, and RAB11FIP1 (Figure 5D). Autoantibodies were predominantly targeted to intracellular antigens, suggesting they may result from a secondary immune response to cell damage. We identified 3 IgA autoantibodies that were significantly increased in MIS-C compared with febrile controls, namely FAM84A, which is highly expressed in GI tissues, TNNC2, which, as mentioned above is highly expressed in skeletal muscle, and guanylate-binding protein family member 6 (GBP6) (Figure 5E). FAM84A and TNNC2 were significantly increased in RNA cluster 1, but not RNA cluster 2, compared with febrile controls. We next examined how the 2 RNA clusters differed in autoantibody responses (Figure 5, F and G). Overall, patients in RNA cluster 1 had greater autoantibody responses than those in RNA cluster 2 (Figure 5, B and C), with the largest differences identified in IgG autoantibodies against ATP4A, UBE3A, FOXK2, SATB1, and MAOA (Figure 5F), and in IgA autoantibodies against FAM84A (Figure 5G). Overall, our data suggest systemic tissue damage and cell death may contribute to excessive antigenic drive against a diverse set of tissue-specific and ubiquitously expressed antigens. The enhanced levels of autoantibodies in RNA cluster 1 link autoantibody development to hyperinflammation, myeloid cell activation, lymphopenia, increased SARS-CoV-2 antibodies, and TRBV11-2 T cell expansion. Patients belonging to RNA cluster 1 show BCR repertoires with highly connected networks of CDR3 sequences. To further study B cell repertoire metrics and antigenic selection in our cohort, we performed BCR-seq on extracted RNA from blood samples of patients with mild (n = 4) or severe (n = 8) MIS-C, and age-matched febrile control patients (n = 15). We found a trend toward higher richness and a lower fraction of antigen-experienced B cells with somatic hypermutation in MIS-C patients than in febrile control patients. However, repertoire richness was distributed quite heterogeneously across patients, and the high richness pattern appeared to apply more to the small group of individuals with mild MIS-C without reaching statistical significance due to the small group size (Supplemental Figure 11). These distinct immune metrics were observable in all Ig chains — heavy chain (IGH) as well as κ (IGK) and λ (IGL) light chains — arguing in favor of the specificity of this finding. These findings were also consistent with the previously reported increased richness in T cell receptor repertoires in patients with mild MIS-C (17). Since our RNA-seq analysis revealed 2 clusters of MIS-C patients, with cluster 1 correlated with high levels of SARS-CoV-2 antibodies and autoantibodies as well as TRBV11-2 T cell expansion, we subdivided our MIS-C cohort into these clusters for the following analyses of BCR repertoires. Our aim was to determine imprints of (auto)antigenic selection or other B cell repertoire features that discriminate cluster 1 from cluster 2. MIS-C patients from cluster 1 showed lower B cell richness than febrile control patients and cluster 2 (Figure 6A), consistent with the contracted B cell compartment suggested by the transcriptome analysis of this cluster. Interestingly, although no differences in the level of somatic hypermutation were detectable between MIS-C patients of RNA cluster 1 and 2, the BCRs of all cluster 1 patients converged toward networks of highly similar CDR3 amino acid sequences (Figure 6B). The degree of connected sequences was significantly enriched in cluster 1 repertoires and comprised up to 99% of BCR clones when Levenshtein distances of 1 and 3 were used for network construction (Figure 6B). Thus, MIS-C patients in cluster 1 showed strong imprints of antigenic selection in their B cell repertoires. Figure 6B cell repertoire metrics, connectivity characteristics, and skewing of IGHV-J usage of MIS-C patients in RNA clusters 1 and 2. (A) Richness and somatic hypermutation of productive IGH repertoires of MIS-C patients of RNA cluster 1 (n = 5) and RNA cluster 2 (n = 6) compared with age-matched febrile control patients (n = 15). Bars indicate mean ± SD. Statistical analysis: ordinary 1-way ANOVA for global analysis and unpaired Student’s t test for paired comparison. (B) Petri dish plots of IGH repertoire networks of MIS-C patients of RNA cluster 1 and 2. A sample of 1000 unique CDR3 amino acid clones per repertoire were subjected to imNet network analysis (75). Petri dish plots are shown for Levenshtein distance 1. Percentages of connected sequences of MIS-C patients of RNA cluster 1 and 2 obtained from networks with Levenshtein distance 1 and 3 are shown as bar plots. Bars indicate mean ± SD. Statistical analysis: unpaired Student’s t test. (C) PCA of differential IGHV-J gene usage in MIS-C patients of RNA cluster 1 (n = 5) versus cluster 2 (n = 6) versus age-matched febrile controls (n = 15). Statistical analysis: Pillai-Bartlett test of MANOVA of all principal components. Frequencies per repertoire of the 10 most skewed IGHV genes in MIS-C and febrile control patients are shown as box-and-whisker plots. The boxes extend from the 25th to 75th percentiles, whiskers from minimum to maximum, and the line within the box indicates the median. (D) BAFF expression in MIS-C cluster 1 and cluster 2, using the RNA-seq data in Figure 5. Data are presented as mean ± SEM. (E) IL-6 and IL-10 levels in serum of MIS-C cluster 1 and cluster 2 patients, using cytokine data from Supplemental Figure 1. Data are presented as mean ± SEM. Statistical analysis: Mann-Whitney test (D and E). **P < 0.01. Higher frequency of autoantibody-associated IGHV genes IGHV4-34 and IGHV4-39 in MIS-C. The higher autoantibody levels in RNA cluster 1 patients prompted us to investigate whether specific IGHV sequences known to be involved in the formation of autoantibodies are overrepresented in this cluster. To globally investigate IGHV-J gene usage in RNA cluster 1 versus cluster 2, we studied the repertoires by PCA. This revealed a significant skewing of IGHV-J gene usage between cluster 1 and cluster 2 (Figure 6C). Among the genes preferentially used in B cell repertoires of patients from RNA cluster 1 was IGHV4-39, which has been previously reported to be used by autoreactive lymphocytes in multiple sclerosis (27, 28). Moreover, IGHV4-34, a gene extensively studied for its usage in autoreactive B cells (29), was preferentially used in B cells from MIS-C patients in general, with a numerically higher expansion in cluster 1 repertoires. Furthermore, IGHV1-69, which is preferentially used in autoreactive B cells (30, 31), was also overrepresented in cluster 1 (Figure 6C). We next asked which factors drive B lineage repertoires in MIS-C patients toward autoreactivity. The majority of patients from RNA cluster 1, where imprints of antigenic selection beyond SARS-CoV-2 reactivity were most obvious, showed superantigenic T cell interactions, which could be one driver promoting autoreactive B lymphocytes. The RNA transcriptomics data pointed to increased BAFF expression in RNA cluster 1, and cytokine analysis pointed to increased IL-6 and IL-10 levels in the serum (Figure 6, D and E). These results indicated that B cell dysregulation in MIS-C patients from RNA cluster 1 may not only be driven by superantigenic T cell interactions, but also by soluble factors derived from the pronounced myeloid/innate cell compartment in these patients.

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS), with characteristic inflammatory lesions and demyelination. The clinical benefit of cell-depleting therapies targeting CD20 has emphasized the role of B cells and autoantibodies in MS pathogenesis.

Abnormal liver function tests are frequently seen in patients with multiple sclerosis (MS) and their origin at times is attributed to the possible co-occurrence or the de novo induction of autoimmune liver diseases (AILD), namely autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC), but comprehensive analysis of AILD-related autoantibody has not been carried out. To assess the presence of AILD-related autoantibodies in a well-defined cohort of MS patients, and to assess their clinical significance. 133 MS (93 female) patients (102 RRMS, 27 SPMS, and 5 PPMS), mean age 42.7 ± 11.9 SD years, mean duration of disease 11.2 ± 7.2 years were studied. 150 age and sex-matched healthy individuals were tested as normal controls (NCs).Autoantibody testing was performed by indirect immunofluorescence (IF) using triple tissue and HEp-2, a multiparametric line immunoassay detecting anti-LKM1(anti-CYP2D6), anti-LC1(anti-FTCD), soluble liver antigen/liver-pancreas(anti-SLA/LP), AMA-M2, and AMA-MIT3 (BPO), PBC-specific ANA (anti-gp210, anti-sp100 and anti-PML), and ELISA for anti-F-actin SMA and anti-dsDNA antibodies. Reactivity to at least one autoantibody was more frequent in MS patients compared to NCs (30/133, 22.6% vs 12/150, 8%) NCs (p = 0.00058). SMAs by IIF were more frequent in MS patients (18/133, 13.53%) compared to NCs (6/150, 4%, p = 0.002%). The AIH-1 related anti-F-actin SMA by ELISA were present in 21 (15.8%), at relatively low titres (all but three of the SMA-VG pattern by IF); anti-dsDNA in 3 (2.3%), and anti-SLA/LP in none; AIH-2 anti-LKM1 autoantibodies in 1 (0.8%, negative by IF), and anti-LC1 in none; PBC-specific AMA-M2 in 2 (1.5%, both negative for AMA-MIT3 and AMA by IF) and PBC-specific ANA anti-PML in 6 (4.5%), anti-sp100 in 1 (0.8%) and anti-gp210 in 1 (0.8%). Amongst the 30 MS patients with at least one autoantibody positivity, only 4 (3%) had overt AILD (2 AIH-1 and 2 PBC). Autoantibody positivity did not differ between naïve MS patients and patients under treatment. Despite the relatively frequent presence of liver autoantibodies, tested either by IF or molecular assays, overt AILD is rather infrequent discouraging autoantibody screening strategies of MS patients in the absence of clinical suspicion.

Via Krishan Maggon

Abstract Epidemiological data suggest that previous infections can alter an individual's susceptibility to unrelated diseases. Nevertheless, the underlying mechanisms are not completely understood. Substantial research efforts have expanded the classical concept of immune memory to also include long‐lasting changes in innate immunity and antigen‐independent reactivation of adaptive immunity. Collectively, these processes provide possible explanations on how acute infections might induce long‐term changes that also affect immunity to unrelated diseases. Here, we review lasting changes the immune compartment undergoes upon infection and how infection experience alters the responsiveness of immune cells towards universal signals. This heightened state of alert enhances the ability of the immune system to combat even unrelated infections but may also increase susceptibility to autoimmunity. At the same time, infection‐induced changes in the regulatory compartment may dampen subsequent immune responses and promote pathogen persistence. The concepts presented here outline how infection‐induced changes in the immune system may affect human health. 1 Introduction People react differently to immunological challenges, such as infections or cancer, and show large variance in their susceptibility to inflammatory diseases, such as allergies or autoimmunity. The responsiveness of the immune system is naturally to a large degree determined by genetic factors. However, a series of recent studies have revealed that genetic factors can only explain about 50–75% of the immune trait variance and the immune profile of an individual is thus to an astonishingly large degree determined by environmental factors.1, 2 There is robust epidemiologic evidence that the decreasing frequency of infections in developed countries correlates with a rising prevalence of allergies and other inflammatory disorders, as already put forward in the hygiene hypothesis almost 30 years ago.3 The hypothesis has since been expanded and adapted to account also for the rise in autoimmunity in developed countries.4 Indeed, reduced incidence of infections and lower microbiotic diversity in developed countries correlate with an increased prevalence of clinical conditions that are caused by inappropriate immune responses, such as allergy and autoimmunity—and possibly even cancer.5 These data indicate that both pathogenic and nonpathogenic microorganisms play a fundamental role in educating the immune system.6 While the specific mechanisms regulating how the immune response to one pathogen alters the response to a later infection with another pathogen or the susceptibility to autoimmunity, allergy, or cancer have been studied in some individual combinations, a global picture of how infection history affects disease susceptibility is still lacking. Twin studies have been used to determine heritable versus nonheritable influences in immune responses elicited by vaccinations and the development of autoimmune diseases and found that both aspects strongly affect the immunological status of an individual.7 The microbes (commensal and pathogenic) an individual encounters throughout his or her life are most likely a major determinant for the nonheritable factors. Indeed monozygous twin pairs, from which only one twin acquired cytomegalovirus (CMV) infection, show greatly enhanced variation for immune parameters after the infection.1 Chronic infections, like CMV, and continuous interactions with commensal microbiota thus shape the immune system. Intriguingly, accumulating evidence suggests that transient challenges, such as acute infections and vaccinations, also have nonspecific effects on the ability of the immune system to react to other diseases.8, 9 Several adaptations of the immune system that could contribute to this altered reactivity have been reported, which include changes in the innate and adaptive arm of the immune system as well as alterations in its regulatory mechanisms. The following sections illustrate the long‐term changes that pathogen encounters elicit in these different parts of the immune system (Figure 1), how the sum of these changes contributes to shaping the immune system over time and how this may affect susceptibility to unrelated diseases. 2 Infection‐induced Changes in the Immune System 2.1 Infections Generate Pathogen‐Specific and Heterologous Immunity The capability to memorize previous pathogenic encounters and thereby confer superior protection if the same pathogen is re‐encountered represents a well‐established core feature of the vertebrate adaptive immune system. Memory T cells can rapidly re‐expand and are activated upon engagement of their cognate antigen with their T‐cell receptor (TCR). Antibody production by memory B cells furthermore contributes to rapid and more robust responses upon re‐encounter with a specific pathogen. Although these mechanisms constitute the basis for vaccinations, early observations have noted that certain vaccines such as Bacillus Calmette‐Guérin (BCG), which protects against tuberculosis, can improve overall childhood survival.10 These observations have hinted that the immune system cannot only learn to defend its host against re‐encounter with the same pathogens but might also become superior in fighting other, unrelated pathogens under certain circumstances. To date, a number of mechanisms have been described that are believed to contribute to heterologous immunity (Table 1). These include cross‐reactivity and bystander activation of T cells as well as trained immunity of innate immune cells. A general feature of innate and adaptive immune cells that have participated in an immune response is an altered epigenetic landscape.28, 29 These alterations could put them in a “poised state” and add an additional layer of responsiveness towards inflammatory cues, such as cytokines and the engagement of germline‐encoded receptors, during heterologous immune challenges. To date, a number of studies have shown how mechanisms of heterologous immunity can, on the one hand, contribute to protection against newly encountered infections of their host but, on the other hand, pose a potential risk for immunopathology or the establishment of autoimmune disorders. Process Protection against heterologous infection Reference Memory Cross‐reactivity 42-44, 46 or [36], 45 Bystander activation 56, 57 Trained immunity 11, 14, 57 Microbiota/chronic infection 27, 61 or 72 Physical remodeling 9, 64 Tregs 73, 75 2.2 How Selective Are Adaptive Immune Cells? The most striking feature of T cells and antibodies produced by B cells is their unique specificity for their cognate antigen. This enables the formation of immunological memory, directed responses, and protects from the emergence of autoreactive T cells. But just how specific are T cells and antibodies really? About two decades ago, Mason30 argued that cross‐reactivity of the TCR is required for robust immunological protection. He supported this hypothesis with a simple but conclusive model, in which the number of monospecific naïve T cells required to cover all possible foreign peptides would be impossible to generate and maintain in a physiological context. Indeed, cross‐reactivity has emerged as a common, or even necessary, feature of T cells and antibodies with potentially beneficial and adverse consequences for the host.31 Whether a T cell or an antibody cross‐reacts with different antigens is determined by the sequence and structural similarities of these antigens. Such similarities, also known as molecular mimicry, can occur between self‐ and foreign antigens.32 Importantly, molecular mimicry can result in autoimmunity through inappropriate responses of cross‐reactive T cells that were primed during microbial encounters and are subsequently activated by recognition of self‐antigens.32, 33 Conversely, T‐cell clones with a strong affinity towards a specific self‐antigen will be deleted during their maturation process and thereby the T‐cell pool responsive towards a similar microbial or tumor antigen might be reduced.34 In addition to this, cross‐reactivity towards different foreign antigens was also frequently observed in subsequent infections with related but also unrelated viruses. Some studies have shown that cross‐reactive memory T‐cell clones can be favored over non‐cross‐reactive clones during heterologous infections, thereby altering the relative contributions of different T‐cell clonotypes, also known as immunodominance (Figure 2).35 In extreme cases this could skew the immune response towards cross‐reactive epitopes and thereby facilitate viral escape by mutation of the respective epitope.36 Although cross‐reactive memory T cells could help to confer protective immune responses towards unrelated pathogens, they also pose a risk for immunopathology.37, 38 This is showcased by a study, in which lymphocytic choriomeningitis virus (LCMV)‐immune mice were challenged with influenza A virus (IAV) infection and the degree of IAV‐specific T cells cross‐reacting with LCMV‐peptides correlated with the degree of lung injury.39 Several studies have also found evidence for the significance of cross‐reactive T cells in human infectious diseases. Epstein–Barr virus (EBV) is a common infection in humans and well known as the causative agent of infectious mononucleosis (IM). IM involves pronounced activation and proliferation of CD8+ T cells and can vary considerably in severity. T‐cell clones recognizing IAV epitopes were identified among the T cells activated during IM.40 Moreover, the frequency of IAV and EBV cross‐reactive CD8+ T cells was reported to correlate with disease severity during IM.41 Conversely, a more recent study suggested that some IAV‐specific T‐cell clones might be protective against infection with EBV.42 These contrasting results highlight that the relationship between cross‐reactive T cells and the outcome of heterologous infections may be very complex and not only depend on the respective pathogens but also on the specific cross‐reactive antigens or T‐cell clones. Antibodies and T cells are also commonly cross‐reactive to different species of flavivirus, including the Zika virus (ZIKV) and the dengue virus (DENV). CD8+ T cells from DENV‐immune mice can contribute to immunity against experimental ZIKV infection.43 In line with these reports, a stronger T‐cell response and altered immunodominance pattern were also observed in DENV‐pre‐exposed patients upon ZIKV infection.44 In contrast to the potentially protective role of cross‐reactive CD8+ T cells, several publications have indicated that cross‐reactive antibodies from DENV‐exposed humans or mice can enhance ZIKV infection.45 Thus, it is exceedingly difficult to dissect and determine the overall contribution of previous DENV exposure to infection with ZIKV or disease pathogenesis. Nevertheless, a recent epidemiological study of local residents in Brazil found that previous exposure to DENV is associated with a lower risk of ZIKV infection.46 Overall, the evidence for protective functions of cross‐reactive T cells or antibodies in humans is still rare. This does not seem surprising, as it would likely not result in any clinical symptoms. However, memory‐phenotype CD4+ T cells with specificities for human immunodeficiency virus (HIV), CMV, and herpes simplex virus (HSV) have been found in the blood of donors who were never infected with these viruses.16 The same study also showed that IAV‐specific T cells that expanded in individuals following flu vaccination were able to recognize other microbial peptides, thus indicating that vaccinations could induce long‐lived T cells that cross‐react with peptides derived from unrelated pathogens. 2.3 Training Shapes Innate Immunity The long‐standing dogma that memory features can only be acquired by adaptive immune cells has been overthrown by a number of studies within the last decade. Innate immune cells such as natural killer cells (NK cells) and myeloid cells can also form a type of memory that has been termed “trained immunity.”47 For instance, NK cells that were preactivated had a higher cytokine response upon reactivation.48 Furthermore, preactivated NK cells showed higher proliferative capacity than nonactivated controls and could thereby maintain enhanced function.49 Importantly, it was demonstrated that preactivation of NK cells can enable them to respond more potently towards tumor cells.50 In contrast to other innate cells, NK cells are also able to form antigen‐dependent memory by expression of the germline‐encoded receptor Ly49H, which specifically recognizes a mouse cytomegalovirus (MCMV)‐encoded glycoprotein.51 Through this mechanism, MCMV‐specific NK cells can generate a pool of long‐lived memory cells. Myeloid cells are also able to acquire a “trained” phenotype, which enables them to respond more efficiently to inflammatory stimuli. Trained monocytes were able to confer protection from reinfection with the opportunistic fungal pathogen Candida albicans in mice lacking adaptive immune cells.11 This study also showed that β‐glucans, a component of the fungal cell wall, could train the monocytes and induced changes in their histone methylation. Indeed, epigenetic changes have been shown to be a hallmark of trained monocytes.28 Training of monocytes could also contribute to a potential heterologous protection through BCG vaccinations, which has been frequently discussed.12 In a study addressing this hypothesis, monocytes from BCG‐vaccinated donors acquired epigenetic modifications and exhibited higher functional responsiveness to heterologous inflammatory stimuli when compared to monocytes that were collected before vaccination.13 Furthermore, a BCG vaccination trial in humans showed that epigenetic remodulation of monocytes correlated with cross‐protection against experimental yellow fever virus (YFV) challenge.14 Different functional states in immune cells are accompanied and supported by changes in cell metabolism.17 In accordance with this observation, metabolic and epigenetic changes associated with trained immunity are closely interlinked. BCG‐induced trained immunity in monocytes is dependent on alterations of cellular metabolism, most notably the induction of glycolysis.18 More evidence for the connection between trained immunity and metabolism was provided by a recent study, which showed that the metabolite mevalonate can induce trained immunity in monocytes.19 Because monocytes are rather short‐lived cells, the question arose whether their precursor cells in the bone marrow might acquire a different phenotype through immune training and thereby influence myelopoiesis. A combination of three studies approaching the subject with different murine models of immune training—β‐glucan administration, BCG vaccination, and sterile inflammation induced by a high‐fat western diet—all found that the hematopoetic precursors of myeloid cells were modified and thereby constitute an important component of trained immunity.20 However, trained immune cells may also be detrimental to host fitness in certain settings and contribute to disease manifestation. Monocytes isolated from patients with symptomatic atherosclerosis showed epigenetic modifications and expression levels of glycolytic enzymes that could be attributed to trained immunity.15 Innate immune cells in the brain can also acquire a trained state and thereby affect central nervous system inflammation. In mice, inflammation‐mediated modulation of brain‐resident macrophages (microglia) affected neuropathology in diseases like an experimental Alzheimer's model.52 Interestingly, the authors of the latter study found pronounced, microglia‐dependent differences in brain cytokine content depending on the administration of the inflammatory stimulus. Repeated injection resulted in a tolerant state, whereas a single administration was connected to a training effect and therefore a lower threshold for activation. This highlights the ability of the innate immune system to adapt to different kinds of stimuli and thereby provides important insights for the design of immunotherapies. 2.4 T Cells Show Unexpected Talents As previously mentioned, T cells express a TCR that is unique for each clonotype and can recognize its cognate antigen in the context of MHC presentation. Two additional signals are required in order to direct the T cell to an appropriate response or differentiation: the engagement of costimulatory receptors and signals provided by cytokines. However, depending on the differentiation status of T cells, they can also be activated independent of TCR stimulation and therefore respond in a nonspecific manner during a heterologous immune challenge. This phenomenon has been termed “bystander” or “innate‐like” activation of T cells. While TCR‐dependent activation of T cells is rather well described, the understanding of T‐cell activation by cytokines or germline‐encoded receptors without recognition of cognate antigen is still quite limited. The first observations that memory‐phenotype CD8+ T cells can be activated by cytokines in the absence of TCR signaling were already made about 20 years ago. In particular, the proliferation of memory‐phenotype CD8+ T cells in mice could be induced with cytokine interleukin‐15 (IL‐15).53 Some groups that observed bystander activation or proliferation of T cells have argued that the biological relevance would be rather minimal.37, 54 Although this may be true for the models used in the respective studies, other groups have argued for a significant contribution of bystander T cells in various settings. It has become clear that innate‐like activation of memory T cells depends on a number of variables like the cytokine environment, tissue homing or residency, and ligand expression of other cells, which are likely not met by all commonly examined virus infections or inflammation models. Importantly, the cytokine profile that is induced by innate cells during a heterologous challenge needs to fit the requirements for innate‐like activation of T cells. Some cytokines, in particular IL‐12, IL‐18, or IL‐15, were shown to potently induce interferon‐γ (IFN‐γ) production or cytolytic activity in memory CD8+ T cells.55 In mouse models, activation of memory CD8+ T cells in an antigen‐independent manner contributed to protection against heterologous infection with Listeria monocytogenes.56 In addition to cytokines, engagement of germline‐encoded receptors can also contribute to TCR‐independent activation of memory CD8+ T cells. Such receptors can be expressed by innate as well as adaptive immune cells and detect a wide range of signals that are associated with infections or malignant cells. Some adaptive immune cells can also be activated in the absence of their cognate antigen through the recognition of these general signals in combination with cytokine stimuli. NK receptors like NKG2D are broadly expressed on memory CD8+ T cells in mice and humans. Antigen‐independent activation of memory T cells through NKG2D engagement could have beneficial effects for early pathogen control and thereby support innate immunity.57 In contrast to this protective role, engagement of NKG2D in bystander memory T cells may also result in immunopathology under certain conditions, as shown in the context of Leishmania major infection in mice.58 In humans, NKG2D engagement on CD8+ T cells is thought to contribute to immunopathology in celiac disease.25 Furthermore, a recent report described how TCR‐independent activation of memory CD8+ T cells by IL‐15 and NK‐receptor engagement might significantly contribute to liver injury in patients during acute hepatitis A virus infection.26 Overall, these findings reveal that innate‐like activation of bystander T cells might have a significant impact on disease outcome depending on the inflammatory context (Figure 3). It has become clear that cytokines released by innate cells and engagement of NK receptors can trigger TCR‐independent activation of memory or effector T cells and thereby add an additional layer to the immune response. Nevertheless, many important questions concerning this mode of T‐cell activation remain to be answered: for instance, which specific T‐cell subsets can be activated independent of antigen recognition. 2.5 Microbe Exposure Shapes the Immune System The human body is not just challenged by infections; it is also colonized by a diverse collection of viruses, as well as the microbiota, which is composed of microorganisms like bacteria, fungi, and protists. The microbiota mostly resides at barrier sites such as the skin or the gut and plays an important role in training and shaping the host immune system, allowing for induction of protective immunity to combat infections but also the establishment of immune tolerance.59 However, changes in the microbiota composition or diversity induced by hygiene conditions, overuse of antibiotics, or diet can prevent the maturation and maintenance of a healthy and balanced immune system and predispose to inflammatory diseases and autoimmunity. Furthermore, depending on the context, commensal microorganisms can become pathogenic and vice versa.60 Microbiota as well as ongoing chronic infections continuously condition cells of the immune system and thereby enable a rapid response to infectious challenges through trained immunity and bystander activation. This is illustrated by the observation that antibiotic treatment, which leads to a transient elimination of the bacterial microbiota, markedly reduces the efficacy of vaccinations as well as parasite‐specific immune responses.27 Similarly, several chronic infections were shown to enhance immune responses to unrelated pathogens.61 At the same time, the heightened state of alert in persistent virus infections can also enhance immune responses that are harmful to the host as, e.g., those causing colitis or other inflammatory disorders and thus exacerbate disease.62 2.6 Infections Can Cause Long‐Lasting Physical Changes In addition to changing the composition and function of immune cells, infections often also induce physical changes in the host that, in some cases, even persist after the infection has been cleared. These changes can alter the microenvironment of the affected organ as outlined here for sepsis or they can induce persisting structural changes as seen in Yersinia pseudotuberculosis (Y. pseudotuberculosis) infection. These changes have a large impact on subsequent immune responses as they alter the ability of the host to counter infections but also influence the development of inflammatory disorders and therefore affect susceptibility to a broad spectrum of diseases that are not related to the initial infectious agent. Severe, life‐threatening infections can trigger massive immune responses that are known as sepsis. Severe sepsis entails multiorgan dysfunction and is often accompanied by sepsis‐induced immunosuppression and a high risk of developing pneumonia. This immunosuppressed state persists for weeks even after patients appear to have “recovered” from sepsis itself.63 A recent study revealed that the primary infection induces lasting changes in the local microenvironment in that it promotes the induction of regulatory T cells (Tregs). These dampen the immune response and compromise effective immune responses to a secondary challenge, leaving the host susceptible to infections.64 This suggests that intervention strategies that interfere with Treg induction or function may be able to revert the generalized immune suppression in sepsis patients and improve their survival. Indeed, blockade of the PD‐1/PD‐L1 pathway, which interferes with Treg function, can restore immune function and improve survival of sepsis patients.65 Overall, this suggests that preceding infections induce long‐lasting changes in the local cytokine milieu, immune cell composition, and function that can alter the susceptibility to subsequent immune challenges. The potent immune responses induced to rapidly eliminate the infectious agents are often also accompanied by a certain degree of collateral tissue damage. Once the infection is cleared, tissue damage is usually repaired and function is restored. However, in some instances, such as in Y. pseudotuberculosis infection, extensive structural changes occur and persist even after the infection is cleared.9 Acute infection with Y. pseudotuberculosis results in the relocation of dendritic cells that persists even beyond the clearance of Yersinia and leads to a markedly reduced efficacy of subsequent oral vaccinations.9 At the same time, the sustained inflammation might set the stage for chronic disorders, such as inflammatory bowel disease or celiac disease, which share many features with the inflammation induced by Yersinia infection.66 Infection‐induced structural changes can thus serve as a direct link between acute infections and the development of chronic inflammatory disorders. In both examples listed here, sepsis and Yersinia challenge, the infection leads to long‐lasting changes in the local cytokine milieu that can be immune suppressive or stimulating and persists beyond the clearance of the infection. Given their constant exposure to external challenges as well as constant stimulation through commensals, barrier tissues might be at particularly high risk of accumulating these kinds of immunological scars, predisposing for inflammatory disorders such as inflammatory bowel or celiac disease, psoriasis, allergies, or asthma but also infectious diseases such as pneumonia or gastrointestinal infections. 2.7 Pathogen Encounter Shapes the Regulatory Immune Response Pathogen encounter not only initiates a proinflammatory immune response to eliminate the infection, but also induces a regulatory response that balances these effects to limit immunopathology resulting from excessive activity. At the same time, these regulatory responses can favor pathogen persistence and chronic infections. Myeloid suppressor cells, regulatory B cells, and Tregs are the mediators of this regulatory response. Tregs act through diverse mechanisms,67 and like all T cells, are activated through the engagement of their TCR by antigen together with costimulatory signals.68 However, in contrast to conventional T cells, the TCR repertoire of Tregs is shifted towards self‐antigens.69 This implies that Tregs can be activated independently of foreign antigen in all settings that enhance costimulatory signals. Once activated, their suppressive mechanisms allow Tregs to inhibit responder T cells irrespective of their antigen specificity, a mechanism known as bystander suppression. Furthermore, cytokines released upon tissue damage, such as IL‐33 and IL‐18, have been shown to be able to activate Tregs even in the absence of a TCR signal,70 allowing Tregs to rapidly respond to tissue damage in a bystander fashion and limit immune pathology by dampening the immune response. Pathogen‐ or microbiota‐induced changes in Treg frequency or function could therefore have a strong impact on immune responses to heterologous challenges. The composition of the microbiota has been shown to be a major determinant for the induction of pro‐ versus anti‐inflammatory T‐cell responses. While segmented filamentous bacteria promote proinflammatory T‐cell responses and systemic autoimmunity,71 Clostridia and their metabolic by‐products induce Tregs that have systemic suppressive effects and contribute to maintaining immune homeostasis.72 Similarly, persistent pathogens often induce an increase in Tregs and thereby limit the pathogen‐specific immune response, allowing for pathogen persistence (Figure 4). This extends to all classes of persistent pathogens, including chronic viral, bacterial, and parasitic infections, such as tuberculosis, leprosy, or malaria, where patients with poor anti‐pathogen responses and persistent disease show an increase in Tregs.73 An extensive body of work also outlines the central role of Tregs in parasite persistence in helminth infections and the extensive interplay between the parasites and the immune system.74 Importantly, the generalized suppression of immunity by helminth‐induced Tregs also extends to modulation of unrelated immune responses, as helminth‐infected patients show reduced immune responses towards childhood vaccines or other parasites.75 Given that coinfections with helminths and malaria and/or tuberculosis are still frequent in many low‐income countries, it will be important to determine whether the immune‐suppressive effect of pathogen‐induced Tregs continues to increase with increasing infectious burden and how this could be counteracted to allow for pathogen clearance and efficient vaccination strategies against prevalent diseases. Interestingly, the high levels of parasitic infections in low‐income countries also seem to have beneficial effects in that inflammatory and autoimmune disorders are much less frequent than in the developed world.74 In a cohort of Argentinian multiple sclerosis patients, individuals who unintentionally acquired a helminth infection displayed elevated Treg frequencies and remission from the symptomatic disease. This was reversed upon anti‐helminth treatment and accompanied by a loss of the regulatory response and clinical relapse.21 These findings could also be recapitulated in experimental animal models where helminth infection protects mice from inflammatory disorders such as celiac disease and allergy and this protection can be transferred to naïve animals via Treg transfer.22 These findings have led to therapeutic approaches in clinical trials that capitalize on the ability of parasite extracts to induce Tregs and thereby dampen inflammatory immune responses. The stimulation of Treg activity has thus emerged as a central concept that explains the beneficial effects of certain microbiota and parasitic infections in ameliorating inflammatory diseases such as allergy and autoimmune disorders as well as improving transplantation tolerance.23 Recent research has also revealed that Tregs themselves represent a mixture of functionally diverse subsets. Furthermore, infectious challenges can transiently alter the composition and function of Tregs to allow for the induction of anti‐pathogen responses but also efficiently shutting down the effector response once the infection has been cleared (Figure 4).24 Most likely, these temporary changes in Treg composition not only affect immune responses against the ongoing infection but also would alter suppression of autoreactive immune cells or cells specific for persistent pathogens. Indeed, epidemiological data revealed that several autoimmune diseases show a correlation with certain pathogens, although these do not induce the disease. In the case of multiple sclerosis, many different viruses were investigated as a possible cause, most recently EBV. However, there is no proof that any of them causes multiple sclerosis.76 Similarly, there is a strong correlation between enterovirus species and type 1 diabetes, but again, there is no indication that the virus can actually cause islet destruction.77 As such, the viral infections that are associated with the diseases likely serve as their environmental triggers without being the causative agent. We propose that in addition to infection‐induced training of innate immunity, bystander activation of memory cells and reactivation of cross‐reactive lymphocytes, changes in Treg composition and function may allow an autoreactive immune response to unfold and thus contribute to the manifestation of autoimmune diseases. In contrast, infection‐induced changes in Treg composition could also result in a more potent suppression of immune responses and thereby promote pathogen or tumor persistence. 2.8 Speaking the Same Language—Educated Immune Cells Respond to Universal Signals As outlined in the preceding sections, infections trigger long‐lasting changes in both the innate and adaptive arm of the immune system that go far beyond classical memory. Infection experience seems to equip both types of immune cells with an altered responsiveness towards two types of universal signals: cytokines and the ligation of germline‐encoded receptors. Innate cells are thus able to respond more quickly and potently as observed in trained immunity. Adaptive immune cells are able to rapidly participate in an immune response even in the absence of their cognate antigen as observed in bystander activation. Following infection, immune cells adopt a state of alertness that persists even beyond pathogen clearance and is maintained by epigenetic modifications.28, 29 This education of the immune system likely re‐enforces a first line of defense in heterologous challenges and thereby reduces the magnitude of the immune response required to control the secondary infection and allows the host to preserve its resources. In addition, this heightened responsiveness might serve as a means to compensate for the loss of clonal diversity that is observed with age and contributes to the higher susceptibility of older people to infections.78 Indeed, a recent study found that the effect of non‐heritable factors on the immune response becomes more dominant with age, most likely due to the accumulating effect of environmental influences.1, 79 Interestingly, the frequency of Tregs was more strongly determined by environmental factors with progressing age,1 suggesting that infection‐induced changes in Treg frequencies and function might be an important factor in shaping the immune response and determining susceptibility to infectious and inflammatory diseases. Trained immunity and bystander activation both show positive effects on pathogen control and maybe even anti‐cancer immunity as outlined above (Table 2).50 At the same time, heterologous immunity can also have negative consequences for the host such as increased tissue damage as seen in hepatitis26 and likely also contributes to disease in the context of autoimmunity. Most of the changes observed in an experienced immune system reported to date result in enhanced inflammatory responses and increased cytotoxicity (through NK and cytotoxic T cells). Whether similar mechanisms also exist in cells contributing to allergic responses or anti‐helminth immunity, such as mast cells, eosinophils, or T helper cells, remains to be determined (see Table 2). Similarly, how cells participating in a heterologous response are regulated is still completely unclear. While Tregs have been shown to have tissue protective effects upon antigen‐independent activation,70 it is unclear which cell type might dampen and limit heterologous immunity to prevent immune pathology. Although many questions still remain open, recent developments have made it clear that immune cells are more versatile than previously appreciated. In addition to their classical functions, infection‐experienced immune cells rapidly respond to stimulation by cytokines or germline‐encoded receptors and initiate potent heterologous immune responses that contribute to pathogen control but might also enhance immune pathology. Branch Cell type Mechanisms Possible disease associations Potential impact on host Innate Macrophages ● Epigenetic changes in progenitor cells ● Heterologous protection27, 60 Beneficial ● Proinflammatory phenotype ● Neuropathology68 Harmful ● Enhanced responsiveness ● Atherosclerosis67 NK cells ● Cytokine‐induced epigenetic changes ● Antitumor responses56-58 Beneficial ● Cytokine production ● Heterologous protection? ● Cytotoxicity ● Tissue damage? (mechanism may be similar to memory bystander CD8+ T cells) Harmful Granulocytes ● Epigenetic changes in progenitor cells? ● Heterologous protection? Beneficial ● Allergic disease? Harmful Mast cells ● Epigenetic changes? ● Heterologous protection? Beneficial ● Allergic disease? Harmful Adaptive Effector and memory CD8+ T cells ● Epigenetic changes ● Heterologous protection21-24 Beneficial ● Cytokine production ● Antitumor responses? ● Cytotoxicity ● Tissue damage76-78 Harmful Effector and memory CD4+ T‐helper cells ● Epigenetic changes ● Heterologous protection? Beneficial ● Cytokine production ● Autoimmunity? Harmful Regulatory ● Epigenetic changes? ● Tissue protection? Beneficial T cells ● Tissue abundance? ● Immune homeostasis? ● Enhanced responsiveness? ● Promoting pathogen persistence? Harmful ● Hampered antitumor responses? 3 Conclusions and Outlook The concepts presented here outline how pathogens and commensals induce long‐term changes in the immune system that affect subsequent immune responses and impact human health. Animal models and clinical studies show that vaccinations, prior infections, or coinfections can modify the immune response to unrelated pathogens. Additionally, a number of inflammatory diseases have been linked to infections but finding direct associations between pathogens and the initiation of these disorders has remained difficult. This might in part be due to the fact that infections can induce long‐term changes in the immune system that may persist even once the infection is cleared. These effects likely accumulate over time and the onset of inflammatory disease does not have to coincide with the infectious trigger(s). Similarly, infection history can also induce changes that enhance the regulatory properties of the immune system and thus favor the establishment of chronic infections and prevent protective immunity against cancer. Future work should be aimed at further unraveling the mechanistic basis for these changes in the immune system and most importantly also at bringing these concepts together to reveal a more comprehensive picture of the mechanisms underlying heterologous immunity. Each new infection or immune trigger an individual is exposed to during a lifetime will educate and potentially alter the dynamics of their immune system and it is important that this is taken into account in preclinical models. Future research should identify the factors that shape the immune system in these processes and determine how heterologous secondary immunity affects vaccine efficiency and immune therapies targeting inflammatory disorders or cancer to be able to optimize preventive and therapeutic interventions in the future. Conflict of Interest The authors declare no conflict of interest. References

Researchers have analyzed the bones and teeth of approximately 5,000 humans who lived across Western Europe and Asia up to 34,000 years ago to create the world’s largest ancient human gene bank. The findings show the historical spread of genes and diseases over time as populations migrated. The study pinpoints the introduction of MS risk genes into north-western Europe by herders from the east around 5,000 years ago, having a beneficial impact despite increasing the risk of MS. The gene bank resulted in a new understanding of genetic markers associated with autism, ADHD, schizophrenia, bipolar disorder, and depression. This research was supported by the Lundbeck Foundation and is a great leap forward in the understanding of immune systems and their impact on autoimmune diseases today. Source link

The pathogenic role of the interleukin 21 (IL-21) in different autoimmune diseases, such as multiple sclerosis (MS), has been extensively studied. However, its pleiotropic nature makes it a cytokine that may exhibit different activity depending on the immunological stage of the disease.

Abstract Autoimmune optic neuritis (AON), a model of multiple sclerosis‐associated optic neuritis, is accompanied by degeneration of retinal ganglion cells (RGCs) and optic nerve demyelination and ...

Celiac disease (CeD) is a common gastrointestinal disorder that can be diagnosed at any age. The disease is associated with intake of cereal gluten proteins, and the diagnostic scheme initially relied on elimination-provocation diets, typical of food intolerances. This has changed. Currently, diagnosis in pediatric patients can be made solely on the basis of the presence of high serum concentrations of autoantibodies (antibodies that recognize “self” antigens) to transglutaminase 2 (TG2, also known as TGM2 ), a cytosolic enzyme with broad tissue expression. Accumulating evidence indicates that these autoantibodies are formed as a result of an adaptive immune response to gluten and that interactions between gluten-specific T cells and TG2-specific B cells are important for development of CeD. No other human autoantibodies are better diagnostic markers for disease than TG2-specific antibodies. Without knowledge of disease dependence on dietary gluten, the presence of these autoantibodies would categorize CeD as an archetypical autoimmune disease rather than a food intolerance. Although autoimmune disorders are a collection of heterogeneous conditions, for which a single unifying mechanism is unlikely, some autoimmune diseases might share key pathogenic processes with CeD. Indeed, a provocative idea is that immune reactions to exogenous antigens can drive autoimmune diseases other than CeD (1). Environmental factors such as viral or bacterial infections have often been associated with development of autoimmunity. Yet, CeD is the only autoimmune disease for which pathogenic T cell epitopes originating from an exogenous antigen (gluten) have been identified. CeD shows strong association to certain genetic variants (allotypes) of major histocompatibility complex (MHC) class II molecules that mediate antigen presentation to CD4+ T cells. Hence, gluten-reactive CD4+ T cells are considered key pathogenic players in CeD. These CD4+ T cells reside in the gut lamina propria and release proinflammatory cytokines in response to gluten in the diet. As recently demonstrated in a mouse model of CeD, cytokines from CD4+ T cells control the action of cytotoxic intraepithelial lymphocytes (2). The result is release of cytotoxic molecules that kill epithelial cells and thus cause the typical destruction of the small intestinal tissue structure that is seen in CeD. The gluten epitopes that are recognized by the CD4+ T cells uniformly contain negatively charged glutamate residues that are important for binding to the CeD-associated MHC class II molecules. The glutamate residues are introduced into gluten peptides through deamidation, a posttranslational modification that is catalyzed by TG2. The dual role of TG2 in CeD as the target of autoantibodies and responsible for creating T cell epitopes can be explained in the context of T cell–B cell collaboration. TG2-specific B cells can take up TG2-gluten complexes through B cell receptor (BCR)–mediated endocytosis. Deamidated gluten peptides may then be presented to CD4+ T cells in a complex with MHC class II molecules on the surface of the B cells. The outcome is mutual activation of B cells and T cells, resulting in production of TG2-specific antibodies by the B cells and release of proinflammatory cytokines by the T cells (see the figure). The generation of autoantibodies in CeD implies breaking of B cell self-tolerance to TG2. However, a recent study in genetically modified mice expressing a CeD patient–derived, TG2-specific BCR suggested that there is no active induction of B cell tolerance to TG2 under normal conditions (3). The reason for the lack of tolerance induction is probably that TG2 is a cytosolic enzyme and that TG2-reactive B cells are therefore not exposed to extracellular antigen during their development. Hence, TG2-reactive naïve B cells are most likely continuously present both in CeD and in healthy individuals. In CeD, such B cells receive activation signals from gluten-reactive effector T cells. We hypothesize that once efficient T cell–B cell collaboration has been established, autoimmunity and tissue damage could ensue, in CeD and likely also in other autoimmune diseases. Although T cell–B cell interactions have been implicated in other autoimmune diseases, the well-characterized target epitopes in CeD offer distinct possibilities for studying pathogenic mechanisms. Hence, collaboration between TG2-specific B cells and gluten-specific T cells has been demonstrated both in vitro and in vivo (3). Gluten presentation by TG2-specific B cells was shown to depend on the TG2 epitope that is recgnized by the BCR (4). Thus, targeting of some TG2 epitopes allowed more efficient presentation of gluten on MHC class II than others, and antibody production against those epitopes correlated with the onset of clinical disease. Efficient T cell–B cell interactions therefore seem to be important for CeD development, and B cells are likely to be the main antigenpresenting cells (APCs) for pathogenic CD4+ T cells in inductive lymphoid structures. In addition, B-lineage cells may be involved in antigen presentation in nonlymphoid tissues. Plasma cells were recently identified as the main cell type presenting an immunodominant gluten epitope in gut biopsies of CeD patients (5). Plasma cells are terminally differentiated B cells whose main function is to secrete antibodies. However, plasma cells secreting immunoglobulin A (IgA) and IgM antibodies also express cell-surface immunoglobulins (6), which serve as functional BCRs (7), allowing receptor-mediated uptake of cognate antigen. Although the ability of plasma cells to stimulate CD4+ T cells has yet to be demonstrated, these observations suggest that they may act as APCs for tissue-resident CD4+ effector T cells in CeD. A role of B cells as the main APCs in CeD is supported by characteristics of gluten-reactive CD4+ T cells in blood and gut biopsies of CeD patients. These cells were recently described to have a distinct phenotype with features resembling those of T follicular helper (TFH) cells that are specialized for providing activation signals to B cells and that rely on B cell interactions for their differentiation (8). Thus, the CD4+ T cells expressed high amounts of the cytokine interleukin-21 and C-X-C motif chemokine ligand 13 (CXCL13), which are important for activating and attracting B cells. But, they lacked expression of the chemokine receptor CXCR5, which is required for homing to B cell follicles. A similar expression profile was observed in CD4+ T cells of patients with other autoimmune dieseases, including systemic lupus erythematosus (SLE) (8) and rheumatoid arthritis (9). The lack of CXCR5 expression suggests that inductive T cell–B cell interactions take place not in conventional germinal centers (GCs) in secondary lymphoid organs, but rather at extrafollicular sites such as the border between the T cell zone and the B cell follicle in lymph nodes or Peyer's patches of the gut. Extrafollicular activation of B cells in CeD is supported by the observation that TG2-specific antibodies rapidly disappear when patients start a gluten-free diet, indicating that GC-dependent long-lived plasma cells are not generated. Furthermore, these antibodies contain relatively few mutations, consistent with B cells being activated extrafollicularly rather than in GCs (6). Curiously, activated B cells in SLE patients were also found to lack CXCR5, consistent with a non–GC-dependent origin (10). Similarities between CeD and other autoimmune diseases may not be restricted to immune cell phenotypes and interactions. Common mechanisms could also guide the targeting of antigens. Similar to the deamidation of gluten in CeD, posttranslational modifications of antigens have been implicated in other autoimmune diseases. Yet, it remains to be established whether T cells specific to modified (self )-peptides are controlling tissue destruction in patients or if posttranslational modifications are a side effect of autoimmune reactions. Posttranslational modifications can potentially create neoepitopes that the immune system perceives as foreign, thereby facilitating escape of autoreactive cells from tolerance. The underlying mechanisms of neoepitope formation and their potential role in autoimmunity are poorly understood. Environmental factors such as smoking and viral infections are candidate triggers that may induce inflammatory tissue alterations, accompanied by dysregulation of posttranslational modifications and formation of neoantigens that could lead to autoimmunity. A prominent example of the connection between viral infections and autoimmunity is the association of Epstein-Barr virus (EBV) infection with development of multiple sclerosis (MS) (11)—a demyelinating autoimmune disease that affects the central nervous system. EBV persists in a latent state in memory B cells, which could serve as a permanent reservoir of viral antigens that can stimulate other immune cells. The disease is traditionally considered T cell mediated. Nevertheless, B cell depletion therapy with CD20-specific antibodies has a beneficial effect and limits relapses in MS, suggesting that B cells play an important role. If persistent viral antigens are important drivers of autoimmunity, a possible explanation for the clinical observations is that EBV-infected B cells are depleted by anti-CD20 therapy, thereby effectively removing the driving antigen (12). In this case, B cell depletion in MS would resemble the exclusion of gluten from the diet of CeD patients. Although intriguing, it has not been proven experimentally that viral antigens can drive autoimmune disease. Thus, the exact role of EBV in MS remains unclear, and it is not established whether EBV-specific T cells are pathogenic or if EBV-infected B cells in the central nervous system give rise to inflammation. Antibody production is typically considered the main function of B cells. However, B cells can play additional roles in regulation of immune reactions through secretion of cytokines or antigen presentation. Indeed, because anti-CD20 therapy does not deplete plasma cells, the clinical benefits of the treatment in MS strongly suggest that B cells have pathogenic involvement independent of antibody production. Circulating B cells in MS patients were shown to stimulate autoreactive, potentially pathogenic T cells that home to the brain (13). In addition, ablation of MHC class II expression specifically on B cells in mice resulted in amelioration of symptoms in experimental autoimmune encephalomyelitis, the primary mouse model of MS (14). Similar findings were also obtained in a mouse model of SLE (15), suggesting that antigen presentation by B cells to CD4+ T cells plays a key role in development of destructive immune reactions, at least in some autoimmune diseases. Dendritic cells are usually credited as the main APCs for T cells during an immune response. However, B cells are receiving increased attention as potent APCs in several autoimmune diseases. The main limitation to our understanding of pathogenic T cell–B cell interactions is the lack of well-defined target antigens in most autoimmune disorders. Characterization of T cell and B cell specificities will allow the study of disease-relevant immune cells that potentially can be targeted. Another major challenge is to understand why some people develop autoimmunity. Genetic predisposition is part of the answer, but environmental factors also play a role, possibly both by triggering and driving autoimmune reactions. Defining such factors is crucial for efficient treatment and prevention of autoimmune diseases. It is important to note that autoimmune disorders are a heterogeneous group of diseases with different manifestations and etiologies. Nevertheless, the mechanisms that are beginning to be unraveled in CeD could be relevant for other autoimmune conditions. http://www.sciencemag.org/about/science-licenses-journal-article-reuse This is an article distributed under the terms of the Science Journals Default License. References and Notes ↵ L. M. Sollid, B. Jabri, Nat. Rev. Immunol. 13, 294 (2013).OpenUrlCrossRefPubMed ↵ V. Abadie et al., Nature 578, 600 (2020).OpenUrl ↵ M. F. du Pré et al., J. Exp. Med. 217, e20190860 (2020).OpenUrl ↵ R. Iversen et al., Proc. Natl. Acad. Sci. U.S.A. 116, 15134 (2019). ↵ L. S. Høydahl et al., Gastroenterology 156, 1428 (2019).OpenUrl ↵ R. Di Niro et al., Nat. Med. 18, 441 (2012).OpenUrlCrossRefPubMed ↵ D. Pinto et al., Blood 121, 4110 (2013). ↵ A. Christophersen et al., Nat. Med. 25, 734 (2019).OpenUrlCrossRef ↵ D. A. Rao et al., Nature 542, 110 (2017).OpenUrlCrossRef ↵ S. A. Jenks et al., Immunity 49, 725 (2018).OpenUrl ↵ L. I. Levin, K. L. Munger, E. J. O'Reilly, K. I. Falk, A. Ascherio, Ann. Neurol. 67, 824 (2010). ↵ U. C. Meier et al., Clin. Exp. Immunol. 167, 1 (2012).OpenUrlCrossRefPubMed ↵ I. Jelcic et al., Cell 175, 85 (2018).OpenUrlCrossRef ↵ N. Molnarfi et al., J. Exp. Med. 210, 2921 (2013). ↵ J. R. Giles et al., J. Immunol. 195, 2571 (2015). Acknowledgments: The authors are supported by the University of Oslo World-leading research program on human immunology (WL-IMMUNOLOGY) and by grants from the South-Eastern Norway Regional Health Authority (project 2016113), the European Commission (project ERC-2010-Ad-268541), and Stiftelsen KG Jebsen (SKGJ-MED-017).

One of the more perplexing questions in biomedical research is—why does the body's protective shield against infections, the immune system, attack its own vital cells, organs, and tissues? The answer to this question is central to understanding an array of autoimmune diseases, such as rheumatoid arthritis, type 1 diabetes, systemic lupus erythematosus, and Sjogren's syndrome. When some of the body's cellular proteins are recognized as "foreign" by immune cells called T lymphocytes, a destructive cascade of inflammation is set in place. Current therapies to combat these cases of cellular mistaken identity dampen the body's immune response and leave patients vulnerable to life-threatening infections. Research on stem cells is now providing new approaches to strategically remove the misguided immune cells and restore normal immune cells to the body. Presented here are some of the basic research investigations that are being guided by adult and embryonic stem cell discoveries. Introduction The body's main line of defense against invasion by infectious organisms is the immune system. To succeed, an immune system must distinguish the many cellular components of its own body (self) from the cells or components of invading organisms (nonself). "Nonself" should be attacked while "self" should not. Therefore, two general types of errors can be made by the immune system. If the immune system fails to quickly detect and destroy an invading organism, an infection will result. However, if the immune system fails to recognize self cells or components and mistakenly attacks them, the result is known as an autoimmune disease. Common autoimmune diseases include rheumatoid arthritis, systemic lupus erythematosis (lupus), type 1 diabetes, multiple sclerosis, Sjogren's syndrome and inflammatory bowel disease. Although each of these diseases has different symptoms, they share the unfortunate reality that, for some reason, the body's immune system has turned against itself (see Box 6.1. Immune System Components: Common Terms and Definitions). How Does the Immune System Normally Keep Us Healthy? The "soldiers" of the immune system are white blood cells, including T and B lymphocytes, which originate in the bone marrow from hematopoietic stem cells. Every day the body comes into contact with many organisms such as bacteria, viruses, and parasites. Unopposed, these organisms have the potential to cause serious infections, such as pneumonia or AIDS. When a healthy individual is infected, the body responds by activating a variety of immune cells. Initially, invading bacteria or viruses are engulfed by an antigen presenting cell (APC), and their component proteins (antigens) are cut into pieces and displayed on the cell's surface. Pieces of the foreign protein (antigen) bind to the major histocompatibility complex (MHC) proteins, also known as human leukocyte antigen (HLA) molecules, on the surface of the APCs (see Figure 6.1 Immune Response to Self or Foreign Antigens). This complex, formed by a foreign protein and an MHC protein, then binds to a T cell receptor on the surface of another type of immune cell, the CD4 helper T cell. They are so named because they "help" immune responses proceed and have a protein called CD4 on their surface. This complex enables these T cells to focus the immune response to a specific invading organism. The antigen-specific CD4 helper T cells divide and multiply while secreting substances called cytokines, which cause inflammation and help activate other immune cells. The particular cytokines secreted by the CD4 helper T cells act on cells known as the CD8 "cytotoxic" T cells (because they can kill the cells that are infected by the invading organism and have the CD8 protein on their surface). The helper T cells can also activate antigen-specific B cells to produce antibodies, which can neutralize and help eliminate bacteria and viruses from the body. Some of the antigen-specific T and B cells that are activated to rid the body of infectious organisms become long-lived "memory" cells. Memory cells have the capacity to act quickly when confronted with the same infectious organism at later times. It is the memory cells that cause us to become "immune" from later reinfections with the same organism. Figure 6.1. Immune Response to Self or Foreign Antigens. (© 2001 Terese Winslow) How Do the Immune Cells of the Body Know What to Attack and What Not To? All immune and blood cells develop from multipotent hematopoietic stem cells that originate in the bone marrow. Upon their departure from the bone marrow, immature T cells undergo a final maturation process in the thymus, a small organ located in the upper chest, before being dispersed to the body with the rest of the immune cells (e.g., B cells). Within the thymus, T cells undergo an important process that "educates" them to distinguish between self (the proteins of their own body) and nonself (the invading organism's) antigens. Here, the T cells are selected for their ability to bind to the particular MHC proteins expressed by the individual. The particular array of MHCs varies slightly between individuals, and this variation is the basis of the immune response when a transplanted organ is rejected. MHCs and other less easily characterized molecules called minor histocompatibility antigens are genetically determined and this is the reason why donor organs from relatives of the recipient are preferred over unrelated donors. In the bone marrow, a highly diverse and random array of T cells is produced. Collectively, these T cells are capable of recognizing an almost unlimited number of antigens. Because the process of generating a T cell's antigen specificity is a random one, many immature T cells have the potential to react with the body's own (self) proteins. To avoid this potential disaster, the thymus provides an environment where T cells that recognize self-antigens (autoreactive or self-reactive T cells) are deleted or inactivated in a process called tolerance induction. Tolerance usually ensures that T cells do not attack the "autoantigens" (self-proteins) of the body. Given the importance of this task, it is not surprising that there are multiple checkpoints for destroying or inactivating T cells that might react to auto-antigens. Autoimmune diseases arise when this intricate system for the induction and maintenance of immune tolerance fails. These diseases result in cell and tissue destruction by antigen-specific CD8 cytotoxic T cells or autoantibodies (antibodies to self-proteins) and the accompanying inflammatory process. These mechanisms can lead to the destruction of the joints in rheumatoid arthritis, the destruction of the insulinproducing beta cells of the pancreas in type 1 diabetes, or damage to the kidneys in lupus. The reasons for the failure to induce or maintain tolerance are enigmatic. However, genetic factors, along with environmental and hormonal influences and certain infections, may contribute to tolerance and the development of autoimmune disease [4, 7]. Hematopoietic Stem Cell Therapy for Autoimmune Diseases The current treatments for many autoimmune diseases include the systemic use of anti-inflammatory drugs and potent immunosuppressive and immunomodulatory agents (i.e., steroids and inhibitor proteins that block the action of inflammatory cytokines). However, despite their profound effect on immune responses, these therapies are unable to induce clinically significant remissions in certain patients. In recent years, researchers have contemplated the use of stem cells to treat autoimmune disorders. Discussed here is some of the rationale for this approach, with a focus on experimental stem cell therapies for lupus, rheumatoid arthritis, and type 1 diabetes. The immune-mediated injury in autoimmune diseases can be organ-specific, such as type 1 diabetes which is the consequence of the destruction of the pancreatic beta islet cells or multiple sclerosis which results from the breakdown of the myelin covering of nerves. These autoimmune diseases are amenable to treatments involving the repair or replacement of damaged or destroyed cells or tissue (see Chapter 7. Stem Cells and Diabetes and Chapter 11. Use of Genetically Modified Stem Cells in Experimental Gene Therapies). In contrast, non-organ-specific autoimmune diseases, such as lupus, are characterized by widespread injury due to immune reactions against many different organs and tissues. One approach is being evaluated in early clinical trials of patients with poorly responsive, life-threatening lupus. This is a severe disease affecting multiple organs in the body including muscles, skin, joints, and kidneys as well as the brain and nerves. Over 239,000 Americans, of which more than 90 percent are women, suffer from lupus. In addition, lupus disproportionately afflicts African-American and Hispanic women [11]. A major obstacle in the treatment of non-organ-specific autoimmune diseases such as lupus is the lack of a single specific target for the application of therapy. The objective of hematopoietic stem cell therapy for lupus is to destroy the mature, long-lived, and auto-reactive immune cells and to generate a new, properly functioning immune system. In most of these trials, the patient's own stem cells have been used in a procedure known as autologous (from "one's self") hematopoietic stem cell transplantation. First, patients receive injections of a growth factor, which coaxes large numbers of hematopoietic stem cells to be released from the bone marrow into the blood stream. These cells are harvested from the blood, purified away from mature immune cells, and stored. After sufficient quantities of these cells are obtained, the patient undergoes a regimen of cytotoxic (cell-killing) drug and/or radiation therapy, which eliminates the mature immune cells. Then, the hematopoietic stem cells are returned to the patient via a blood transfusion into the circulation where they migrate to the bone marrow and begin to differentiate to become mature immune cells. The body's immune system is then restored. Nonetheless, the recovery phase, until the immune system is reconstituted represents a period of dramatically increased susceptibility to bacterial, fungal, and viral infection, making this a high-risk therapy. Recent reports suggest that this replacement therapy may fundamentally alter the patient's immune system. Richard Burt and his colleagues [18] conducted a long-term follow-up (one to three years) of seven lupus patients who underwent this procedure and found that they remained free from active lupus and improved continuously after transplantation, without the need for immunosuppressive medications. One of the hallmarks of lupus is that during the natural progression of disease, the normally diverse repertoire of T cells become limited in the number of different antigens they recognize, suggesting that an increasing proportion of the patient's T cells are autoreactive. Burt and colleagues found that following hematopoietic stem cell transplantation, levels of T cell diversity were restored to those of healthy individuals. This finding provides evidence that stem cell replacement may be beneficial in reestablishing tolerance in T cells, thereby decreasing the likelihood of disease reoccurrence. Development of Hematopoietic Stem Cell Lines for Transplantation The ability to generate and propagate unlimited numbers of hematopoietic stem cells outside the body—whether from adult, umbilical cord blood, fetal, or embryonic sources—would have a major impact on the safety, cost, and availability of stem cells for transplantation. The current approach of isolating hematopoietic stem cells from a patient's own peripheral blood places the patient at risk for a flare-up of their autoimmune disease. This is a potential consequence of repeated administration of the stem cell growth factors needed to mobilize hematopoietic stem cells from the bone marrow to the blood stream in numbers sufficient for transplantation. In addition, contamination of the purified hematopoietic stem cells with the patient's mature autoreactive T and B cells could affect the success of the treatment in some patients. Propagation of pure cell lines in the laboratory would avoid these potential drawbacks and increase the numbers of stem cells available to each patient, thus shortening the at-risk interval before full immune reconstitution. Whether embryonic stem cells will provide advantages over stem cells derived from cord blood or adult bone marrow hematopoietic stem cells remains to be determined. However, hematopoietic stem cells, whether from umbilical cord blood or bone marrow, have a more limited potential for self-renewal than do pluripotent embryonic stem cells. Although new information will be needed to direct the differentiation of embryonic stem cells into hematopoietic stem cells, hematopoietic cells are present in differentiated cultures from human embryonic stem cells [9] and from human fetal-derived embryonic germ stem cells [17]. One potential advantage of using hematopoietic stem cell lines for transplantation in patients with autoimmune diseases is that these cells could be generated from unaffected individuals or, as predisposing genetic factors are defined, from embryonic stem cells lacking these genetic influences. In addition, use of genetically selected or genetically engineered cell types may further limit the possibility of disease progression or reemergence. One risk of using nonself hematopoietic stem cells is of immune rejection of the transplanted cells. Immune rejection is caused by MHC protein differences between the donor and the patient (recipient). In this scenario, the transplanted hematopoietic stem cells and their progeny are rejected by the patient's own T cells, which are originating from the patient's surviving bone marrow hematopoietic stem cells. In this regard, embryonic stem cell-derived hematopoietic stem cells may offer distinct advantages over cord blood and bone marrow hematopoietic stem cell lines in avoiding rejection of the transplant. Theoretically, banks of embryonic stem cells expressing various combinations of the three most critical MHC proteins could be generated to allow close matching to the recipient's MHC composition. Additionally, there is evidence that embryonic stem cells are considerably more receptive to genetic manipulation than are hematopoietic stem cells (see Chapter 11. Use of Genetically Modified Stem Cells in Experimental Gene Therapies). This characteristic means that embryonic stem cells could be useful in strategies that could prevent their recognition by the patient's surviving immune cells. For example, it may be possible to introduce the recipient's MHC proteins into embryonic stem cells through targeted gene transfer. Alternatively, it is theoretically possible to generate a universal donor embryonic stem cell line by genetic alteration or removal of the MHC proteins. Researchers have accomplished this by genetically altering a mouse so that it has little or no surface expression of MHC molecules on any of the cells or tissues. There is no rejection of pancreatic beta islet cells from these genetically altered mice when the cells are transplanted into completely MHC-mismatched mice [13]. Additional research will be needed to determine the feasibility of these alternative strategies for prevention of graft rejection in humans [6]. Jon Odorico and colleagues have shown that expression of MHC proteins on mouse embryonic stem cells and differentiated embryonic stem cell progeny is either absent or greatly decreased compared with MHC expression on adult cells [8]. These preliminary findings raise the intriguing possibility that lines derived from embryonic stem cells may be inherently less susceptible to rejection by the recipient's immune system than lines derived from adult cells. This could have important implications for the transplantation of cells other than hematopoietic stem cells. Another potential advantage of using pure populations of donor hematopoietic stem cells achieved through stem cell technologies would be a lower incidence and severity of graft-versus-host disease, a potentially fatal complication of bone marrow transplantation. Graft-versus-host disease results from the immune-mediated injury to recipient tissues that occurs when mature organ-donor T cells remain within the organ at the time of transplant. Such mature donor alloreactive T cells would be absent from pure populations of multipotent hematopoietic stem cells, and under ideal conditions of immune tolerance induction in the recipient's thymus, the donor-derived mature T cell population would be tolerant to the host. Gene Therapy and Stem Cell Approaches for the Treatment of Autoimmune Diseases Gene therapy is the genetic modification of cells to produce a therapeutic effect (see Chapter 11. Use of Genetically Modified Stem Cells in Experimental Gene Therapies). In most investigational protocols, DNA containing the therapeutic gene is transferred into cultured cells, and these cells are subsequently administered to the animal or patient. DNA can also be injected directly, entering cells at the site of the injection or in the circulation. Under ideal conditions, cells take up the DNA and produce the therapeutic protein encoded by the gene. Currently, there is an extensive amount of gene therapy research being conducted in animal models of autoimmune disease. The goal is to modify the aberrant, inflammatory immune response that is characteristic of autoimmune diseases [15, 19]. Researchers most often use one of two general strategies to modulate the immune system. The first strategy is to block the actions of an inflammatory cytokine (secreted by certain activated immune cells and inflamed tissues) by transferring a gene into cells that encodes a "decoy" receptor for that cytokine. Alternatively, a gene is transferred that encodes an anti-inflammatory cytokine, redirecting the auto-inflammatory immune response to a more "tolerant" state. In many animal studies, promising results have been achieved by using these approaches, and the studies have advanced understanding of the disease processes and the particular inflammatory cytokines involved in disease progression [15, 19]. Serious obstacles to the development of effective gene therapies for humans remain, however. Foremost among these are the difficulty of reliably transferring genetic material into adult and slowly dividing cells (including hematopoietic stem cells) and of producing long-lasting expression of the intended protein at levels that can be tightly controlled in response to disease activity. Importantly, embryonic stem cells are substantially more permissive to gene transfer compared with adult cells, and embryonic cells sustain protein expression during extensive self-renewal. Whether adult-derived stem cells, other than hematopoietic stem cells, are similarly amenable to gene transfer has not yet been determined. Ultimately, stem cell gene therapy should allow the development of novel methods for immune modulation in autoimmune diseases. One example is the genetic modification of hematopoietic stem cells or differentiated tissue cells with a "decoy" receptor for the inflammatory cytokine interferon gamma to treat lupus. For example, in a lupus mouse model, gene transfer of the decoy receptor, via DNA injection, arrested disease progression [12]. Other investigators have used a related but distinct approach in a mouse model of type 1 diabetes. Interleukin-12 (IL-12), an inflammatory cytokine, plays a prominent role in the development of diabetes in these mice. The investigators transferred the gene for a modified form of IL-12, which blocks the activity of the natural IL-12, into pancreatic beta islet cells (the target of autoimmune injury in type 1 diabetes). The islet cell gene therapy prevented the onset of diabetes in these mice [20]. Theoretically, embryonic stem cells or adult stem cells could be genetically modified before or during differentiation into pancreatic beta islet cells to be used for transplantation. The resulting immune-modulating islet cells might diminish the occurrence of ongoing autoimmunity, increase the likelihood of long-term function of the transplanted cells, and eliminate the need for immunosuppressive therapy following transplantation. Researchers are exploring similar genetic approaches to prevent progressive joint destruction and loss of cartilage and to repair damaged joints in animal models of rheumatoid arthritis. Rheumatoid arthritis is a debilitating autoimmune disease characterized by acute and chronic inflammation, in which the immune system primarily attacks the joints of the body. In a recent study, investigators genetically transferred an anti-inflammatory cytokine, interleukin-4 (IL-4), into a specialized, highly efficient antigen-presenting cell called a dendritic cell, and then injected these IL-4-secreting cells into mice that can be induced to develop a form of arthritis similar to rheumatoid arthritis in humans. These IL-4-secreting dendritic cells are presumed to act on the CD4 helper T cells to reintroduce tolerance to self-proteins. Treated mice showed complete suppression of their disease and, in addition to its immune-modulatory properties, IL-4 blocked bone resorption (a serious complication of rheumatoid arthritis), making it a particularly attractive cytokine for this therapy [10]. However, one obstacle to this approach is that human dendritic cells are difficult to isolate in large numbers. Investigators have also directed the differentiation of dendritic cells from mouse embryonic stem cells, indicating that a stem cell-based approach might work in patients with rheumatoid arthritis [5]. Longer-term follow-up and further characterization will be needed in animal models before researchers proceed with the development of such an approach in humans. In similar studies, using other inhibitors of inflammatory cytokines such as a decoy receptor for tumor necrosis factor-α (a prominent inflammatory cytokine in inflamed joints), an inhibitor of nuclear factor-κB (a protein within cells that turns on the production of many inflammatory cytokines), and interleukin-13 (an anti-inflammatory cytokine), researchers have shown promising results in animal models of rheumatoid arthritis [19]. Because of the complexity and redundancy of immune system signaling networks, it is likely that a multifaceted approach involving inhibitors of several different inflammatory cytokines will be successful, whereas approaches targeting single cytokines might fail or produce only short-lived responses. In addition, other cell types may prove to be even better vehicles for the delivery of gene therapy in this disease. Chondrocytes, cells that build cartilage in joints, may provide another avenue for stem cell-based treatment of rheumatoid arthritis. These cells have been derived from human bone marrow stromal stem cells derived from human bone marrow [14]. Little is known about the intermediate cells that ultimately differentiate into chondrocytes. In addition to adult bone marrow as a source for stromal stem cells, human embryonic stem cells can differentiate into precursor cells believed to lead ultimately to the stromal stem cells [16]. However, extensive research is needed to reliably achieve the directed derivation of the stromal stem cells from embryonic stem cells and, subsequently, the differentiation of chondrocytes from these stromal stem cells. The ideal cell for optimum cartilage repair may be a more primitive cell than the chondrocyte, such as the stromal cell, or an intermediate cell in the pathway (e.g., a connective tissue precursor) leading to the chondrocyte. Stromal stem cells can generate new chondrocytes and facilitate cartilage repair in a rabbit model [3]. Such cells may also prove to be ideal targets for the delivery of immune-modulatory gene therapy. Like hematopoietic stem cells, stromal stem cells have been used in animal models for delivery of gene therapy [1]. For example, a recent study demonstrated that genetically engineered chondrocytes, expressing a growth factor, can enhance the function of transplanted chondrocytes [2]. Two obstacles to the use of adult stromal stem cells or chondrocytes are the limited numbers of these cells that can be harvested and the difficulties in propagating them in the laboratory. Embryonic stem cells, genetically modified and expanded before directed differentiation to a connective tissue stem cell, may be an attractive alternative. Collectively, these results illustrate the tremendous potential these cells may offer for the treatment of rheumatoid arthritis and other autoimmune diseases. Conclusion Stem cell-based therapies offer many exciting possibilities for the development of novel treatments, and perhaps even cures, for autoimmune diseases. A challenging research effort remains to fully realize this potential and to address the many remaining questions, which include how best to direct the differentiation of specific cell types and determine which particular type of stem cell will be optimum for each therapeutic approach. Gene therapy with cytokines or their inhibitors is still in its infancy, but stem cells or their progeny may provide one of the better avenues for future delivery of immune-based therapies. Ultimately, the potential to alleviate these devastating chronic diseases with the use of stem cell-based technologies is enormous. References Allay, J.A., Dennis, J.E., Haynesworth, S.E., Majumdar, M.K., Clapp, D.W., Shultz, L.D., Caplan, A.I., and Gerson, S.L. (1997). LacZ and interleukin-3 expression in vivo after retroviral transduction of marrow-derived human osteogenic mesenchymal progenitors. Hum. Gene Ther. 8, 1417–1427. Brower-Toland, B.D., Saxer, R.A., Goodrich, L.R., Mi, Z., Robbins, P.D., Evans, C.H., and Nixon, A.J. (2001). Direct adenovirus-mediated insulin-like growth factor I gene transfer enhances transplant chondrocyte function. Hum. Gene Ther. 12, 117–129. Caplan, A.I., Elyaderani, M., Mochizuki, Y., Wakitani, S., and Goldberg, V.M. (1997). Principles of cartilage repair and regeneration. Clin. Orthop. 342, 254–269. Cooper, G.S., Dooley, M.A., Treadwell, E.L., St Clair, E.W., Parks, C.G., and Gilkeson, G.S. (1998). Hormonal, environmental, and infectious risk factors for developing systemic lupus erythematosus. Arthritis Rheum. 41, 1714–1724. Fairchild, P.J., Brook, F.A., Gardner, R.L., Graca, L., Strong, V., Tone, Y., Tone, M., Nolan, K.F., and Waldmann, H. (2000). Directed differentiation of dendritic cells from mouse embryonic stem cells. Curr. Biol. 10, 1515–1518. Gearhart, J. (1998). New potential for human embryonic stem cells. Science. 282, 1061–1062. Grossman, J.M. and Tsao, B.P. (2000). Genetics and systemic lupus erythematosus. Curr. Rheumatol. Rep. 2, 13–18. Harley, C.B., Gearhart, J., Jaenisch, R., Rossant, J., and Thomson, J. (2001). Keystone Symposia. Pluripotent stem cells: biology and applications. Durango, CO. Itskovitz-Eldor, J., Schuldiner, M., Karsenti, D., Eden, A., Yanuka, O., Amit, M., Soreq, H., and Benvenisty, N. (2000). Differentiation of human embryonic stem cells into embryoid bodies comprising the three embryonic germ layers. Mol. Med. 6, 88–95. Kim, S.H., Kim, S., Evans, C.H., Ghivizzani, S.C., Oligino, T., and Robbins, P.D. (2001). Effective treatment of established murine collagen-induced arthritis by systemic administration of dendritic cells genetically modified to express IL-4. J. Immunol. 166, 3499–3505. Lawrence, R.C., Helmick, C.G., Arnett, F.C., Deyo, R.A., Felson, D.T., Giannini, E.H., Heyse, S.P., Hirsch, R., Hochberg, M.C., Hunder, G.G., Liang, M.H., Pillemer, S.R., Steen, V.D., and Wolfe, F. (1998). Estimates of the prevalence of arthritis and selected musculoskeletal disorders in the United States. Arthritis Rheum. 41, 778–799. Lawson, B.R., Prud'homme, G.J., Chang, Y., Gardner, H.A., Kuan, J., Kono, D.H., and Theofilopoulos, A.N. (2000). Treatment of murine lupus with cDNA encoding IFN-gammaR/Fc. J. Clin. Invest. 106, 207–215. Osorio, R.W., Ascher, N.L., Jaenisch, R., Freise, C.E., Roberts, J.P., and Stock, P.G. (1993). Major histocompatibility complex class I deficiency prolongs islet allograft survival. Diabetes. 42, 1520–1527. Pittenger, M.F., Mackay, A.M., Beck, S.C., Jaiswal, R.K., Douglas, R., Mosca, J.D., Moorman, M.A., Simonetti, D.W., Craig, S., and Marshak, D.R. (1999). Multilineage potential of adult human mesenchymal stem cells. Science. 284, 143–147. Prud'homme, G.J. (2000). Gene therapy of autoimmune diseases with vectors encoding regulatory cytokines or inflammatory cytokine inhibitors. J. Gene. Med. 2, 222–232. Schuldiner, M., Yanuka, O., Itskovitz-Eldor, J., Melton, D., and Benvenisty, N. (2000). Effects of eight growth factors on the differentiation of cells derived from human embryonic stem cells. Proc. Natl. Acad. Sci. U. S. A. 97, 11307–11312. Shamblott, M.J., Axelman, J., Littlefield, J.W., Blumenthal, P.D., Huggins, G.R., Cui, Y., Cheng, L., and Gearhart, J.D. (2000). Human embryonic germ cell derivatives express a broad range of develpmentally distinct markers and proliferate extensively in vitro. Proc. Natl. Acad. Sci. U. S. A. 98, 113–118. Traynor, A.E., Schroeder, J., Rosa, R.M., Cheng, D., Stefka, J., Mujais, S., Baker, S., and Burt, R.K. (2000). Treatment of severe systemic lupus erythematosus with high-dose chemotherapy and haemopoietic stem-cell transplantation: a phase I study. Lancet. 356, 701–707. Tsokos, G.C. and Nepom, G.T. (2000). Gene therapy in the treatment of autoimmune diseases. J. Clin. Invest. 106, 181–183. Yasuda, H., Nagata, M., Arisawa, K., Yoshida, R., Fujihira, K., Okamoto, N., Moriyama, H., Miki, M., Saito, I., Hamada, H., Yokono, K., and Kasuga, M. (1998). Local expression of immunoregulatory IL-12p40 gene prolonged syngeneic islet graft survival in diabetic NOD mice. J. Clin. Invest. 102, 1807–1814.  Chapter 5 | Table of Contents | Chapter 7  Historical content: June 17, 2001

Via Krishan Maggon

A machine learning approach using high-dimensional phenotypic and functional profiling data identifies a multiple sclerosis-specific T cell population that is reduced following treatment.

Full text KEYWORDS Antiphospholipid syndrome, antiphospholipid antibodies, thrombosis, pregnancy morbidity, catastrophic antiphospholipid syndrome, treatment INTRODUCTION  The antiphospholipid syndrome (APS) is defined by the occurrence of venous and/or arterial thrombotic events and/or pregnancy-related morbidity (≥ 3 unexplained consecutive spontaneous abortions < 10 weeks with exclusion of chromosomal causes, foetal death or severe pre-eclampsia before 34th week of gestation), combined with the presence of circulating antiphospholipid antibodies (aPL) and/or a lupus anticoagulant (LAC), see table 1.1 Formally, LAC is the result of aPL binding to plasma proteins, mainly β2-glycoprotein, that have affinity for the negatively-charged phospholipids; therefore, the pathologic auto-antibodies are not directed against phospholipids. In this paper, the term ‘aPL’ refers to both LAC and anticardiolipin (aCL)/anti-β2-glycoprotein-I (anti-β2GPI) antibodies, following clinical practice and literature. APS is considered a primary autoimmune disease, but is often diagnosed as being secondary to other auto-immune diseases; 30-40% of patients with systemic lupus erythematodes (SLE) also have APS, and APS can be diagnosed secondary to rheumatoid arthritis, systemic sclerosis, dermatomyositis or other autoimmune diseases.2 Large, controlled, randomised clinical trials for interventions in APS are limited, and the quality of current data is not sufficient to develop a structured guideline or assess evidence-based management strategies. However, clinically-relevant questions about diagnostic criteria and treatment of APS patients arise on a daily basis. We aim to provide clinicians with an expert consensus on the management of APS, with a focus on classification, diagnostics, risk stratification, and treatment.  METHODS A literature review was performed (KdL and ML) with the following Pubmed search terms: ‘antiphospholipid syndrome’, "Antiphospholipid Syndrome"[Mesh], ‘antiphospholipid antibodies’, "Antibodies, Antiphospholipid"[Mesh], ‘obstetric antiphospholipid syndrome’, ‘catastrophic antiphospholipid syndrome’, ‘laboratory diagnostics’, ‘Clinical Laboratory Techniques"[Mesh] ‘diagnosis’, "Diagnosis"[Mesh], ‘treatment’ and "Therapeutics"[Mesh]; papers focusing on diagnostics and/or treatment for APS were included. Non-English papers and papers published before 2000 were excluded. The literature search was performed on April 3rd, 2018; papers published after that date but with high impact according to the writing committee were included until September 1st, 2018. Statements on APS diagnostics and treatment were extracted from the papers selected, and a first draft of the consensus paper was written (KdL and ML). This draft was circulated amongst all authors and comments were collected. On June 6th, 2018, a first consensus meeting was held in Utrecht, The Netherlands. Based on the outcomes of this meeting, a second draft of the paper was written (JS, ML) and circulated again for comments. On November 5th, 2018, a second consensus meeting was held in Utrecht, The Netherlands. Degree of consensus on statements was assessed by a Delphi procedure and a final report was written afterwards (see table 3). Diagnosis, classification, risk stratification Diagnosis of APS No diagnostic criteria for APS exist. If a patient meets the classification criteria – developed for research purposes – for APS (e.g., a thrombotic event and/or pregnancy morbidity, combined with repeated presence of antiphospholipid antibodies (aPL); see table 1), most likely a clinical diagnosis of APS will be made, although thrombotic and pregnancy complications are not necessarily causally related to circulating aPL. In addition to the thrombotic or pregnancy-related events listed in the classification criteria, APS may be associated with a variety of non-criteria manifestations, such as superficial vein thrombosis, thrombocytopenia, renal microangiopathy, heart valve disease, livedo reticularis or racemosa, migraine, chorea, seizures and myelitis; see table 2. As a result, a clinical diagnosis of APS can be made in patients who do not fulfil the classification criteria.3 An even more complicating factor is the concept of seronegative APS, a term coined to include patients with clinical (criteria and non-criteria) features suggestive of APS, but who are persistently negative for aPL.4 The existence of seronegative APS is a point of international discussion among experts. If the suspicion of seronegative APS arises, we suggest referral to an expert centre. Catastrophic antiphospholipid syndrome (CAPS) is the most extreme APS variant that includes simultaneous multiple organ thrombosis and develops in a short period of time with a high mortality rate. Although strongly associated with the presence of LAC, no other laboratory or clinical determinants are known to be associated with CAPS.2 Risk stratification in APS Several assessment tools to risk-stratify patients have been proposed, mainly focusing on presence and levels of aPL and/or on clinical parameters. For aPL, the presence of LAC is the strongest risk factor for both arterial and venous thrombosis in APS.5,6 For aCL and anti-β2GPI, the association between (levels of) antibodies and thrombosis is less clear. It has been suggested that anti-β2GPIdependent LAC has a strong association with thrombotic risk.7 In several studies, it has been demonstrated that the risk of arterial and venous thrombosis increases with the number of positive tests for aPL, with the highest risks in patients with both LAC, aCL and anti-β2GPI antibodies, so-called ‘triple positive patients’.6,8 In clinical practice, risk stratification does not affect treatment decisions in most situations. The antiphospholipid score (aPL-S) has been developed to predict the risk of APS-related clinical events in patients with APS and other autoimmune diseases (such as SLE, rheumatoid arthritis, and Sjögren’s syndrome) based on the presence of aPL.9 The global APS score (GAPSS) is another clinical score, including both aPL and conventional cardiovascular risk factors, predicting the risk of thrombotic events in patients with SLE.10 The GAPSS has been validated in a cohort of APS patients, and a correlation between higher GAPSS values and recurrence of thrombotic events was observed.11 However, these scores are not sufficient to design treatment strategies for the individual patient. We consider triple positive patients to be at highest risk for recurrent thrombosis. Laboratory diagnostics in APS The classification criteria for APS indicate three different antibody subsets of aPL. For two of these, the antigen is well-defined: aCL antibodies recognize the plasma glycoprotein β2GPI in complex with the anionic phospholipid cardiolipin, and anti-β2GPI antibodies recognize the protein β2GPI in the absence of cardiolipin. Both antibody subtypes can be detected with quantitative solid phase assays in which the antigen is immobilized on a surface. The third aPL subtype, known as LAC, is detected with a functional assay: these antibodies manifest as phospholipid-dependent inhibitors of in vitro coagulation. They are detected with phospholipid sensitive coagulation assays. Although the exact antigen to which LAC are directed is currently unclear, there is ample evidence that antibodies against β2GPI as well as antibodies against prothrombin can induce the LAC phenomenon.12,13 The diagnosis of APS is made in cases of persistent presence of aPL (titre > 99th percentile), assessed in two separate samples taken with an arbitrarily defined interval of at least twelve weeks. This is an important distinction, as several aPL occur transiently in relation to viral and bacterial infections and are of uncertain clinical relevance.14 Moreover, since aPL is prevalent (1-5%) in the general population,15 aPL status should only be tested in patients considered at risk of having APS, such as those < 50 years of age, unprovoked arterial or venous thrombosis, thrombosis at an unusual site, recurrent thrombosis, and thrombotic/pregnancy complications with or without association with a systemic autoimmune disease.16-18 Unfortunately, gold standards for aPL detection are lacking, although aCL assays based on pooled human serum have been in use for over 20 years.19 Reports have been published of new standards based on human(ized) monoclonal antibodies against β2GPI and purified patient-derived polyclonal antibody preparations, but these are not yet available.20 No such standards exist for LAC-positive plasmas. For further harmonization of results between diagnostic laboratories, centres performing these tests participate in diagnostic surveys as part of laboratory accreditation. Measurement of anticardiolipin and anti-β2-glycoprotein I antibodies The classification criteria for APS specify that both immunoglobin G (IgG) and IgM class immunoglobulins against cardiolipin or β2GPI should be measured. Several commercial entities supply kits to measure these antibodies and many laboratories have developed their own solid phase assays. To minimize the effect of the lack of gold standards and the large number of assays in use for detection of aPL on assay standardization, guidance on assay characteristics has been provided by the Scientific Standardization Committee of the International Society on Thrombosis and Haemostasis.16 Detection of aCL and β2GPI antibodies can be performed in both trisodium citrate anticoagulated plasma and in serum. Since aCL antibodies associated with APS are β2GPI-dependent, diagnostic laboratories should use assays in which cardiolipin is saturated with human β2GPI. Samples should be considered positive when the value obtained in these assays exceeds the 99th percentile of the normal population, rather than antibody levels exceeding 40 arbitrary units as indicated in the classification criteria, as this appears to be more specific for APS.21 We recommend that the laboratory report mentions both a cut-off value (< 99th or > 99th percentile, e.g., negative or positive) and a continuous numeric value. Detection of lupus anticoagulant LAC are phospholipid-dependent coagulation inhibitors and are detected with sensitive coagulation assays in trisodium citrate anticoagulated plasma. Plasma samples should be double centrifuged to minimize contamination with platelets, as they are a major source of phospholipid and might therefore interfere with LAC detection.17,18 LAC can be detected with any phospholipid-dependent coagulation assay, however, no gold standard for LAC testing exists. For this reason, it is warranted to perform two tests based on a different assay principle for LAC detection, preferably a dilute Russell’s viper venom time (dRVVT) and a LAC-sensitive activated partial thromboplastin time (APTT).17 In order to be deemed LAC-positive, a sample should have a prolonged clotting time when a reagent with a low phospholipid content is used (screening test), which should correct when a reagent with a high phospholipid content is used (confirmatory test), indicating phospholipid-dependence of the prolongation. The presence of a coagulation inhibitor as the cause of the prolongation should be shown with a mixing test, in which patient plasma is mixed with an equal volume of pooled normal plasma. This will normalise any coagulation factor deficiencies that are present, and any remaining prolongation of the clotting time is therefore caused by an inhibitor. Cut-off values for LAC should be determined locally in each diagnostic laboratory, based on the 99th percentile of the local normal population or alternatively, on the mean + 2 standard deviation (SD) of the clotting time of the normal population. The strength of the LAC should be expressed as a ratio between screen and confirm clotting times, preferably normalized on the mean of the normal population, according to the following equation:   Samples are deemed positive for LAC when one or both tests for LAC detection (APTT and dRVVT-based tests) indicate the presence of LAC. The use of anticoagulant drugs interferes with detection of LAC, possibly resulting in false-positive test results. Samples for LAC-detection should therefore be collected before treatment with anticoagulants has started, or sufficiently long after cessation of treatment to minimize confounding effects. However, it should be avoided to use samples obtained in the acute phase after a thrombotic event or during infection, as this is associated with high Factor VIII levels, which might interfere with LAC detection by APTT-based assays. Although LAC can be determined in samples from patients receiving vitamin K antagonists when they are mixed with an equal volume of pooled normal plasma, the outcome of LAC tests in samples with an INR within therapeutic range (INR 2-3) should be interpreted with caution and measurement of LAC in samples with an international normalised ratio (INR) > 3 is not recommended. Mixing antagonists with normal plasma dilutes the titre of LAC and thus reduces the sensitivity of the assay. On the other hand, the mixing test may not completely correct the clotting time for samples in the high INR range and may lead to false-positive interpretation. Temporary discontinuation of vitamin K antagonists (or co-administration of vitamin K and continuing vitamin K antagonists) and bridging with low molecular weight heparin is possible. Unfractionated heparin, however, is incompatible with LAC testing, as is the use of direct oral anticoagulants, even at trough levels of factor Xa or thrombin inhibitors, with factor Xa inhibitors producing false-positive LAC.22 No alternatives exist for LAC detection in samples containing direct thrombin inhibitors. A possible means to detect LAC in samples containing rivaroxaban is the Taipan snake venom time/Ecarin clotting time combination, as these tests are insensitive to factor Xa-inhibitors.23 More studies on the specificity and sensitivity for LAC of this test combination are required before these tests will be widely adopted for LAC detection. Non-criteria aPL There are several reports on the association between various non-criteria aPL subtypes and thrombosis. Amongst these are IgA anti-β2GPI antibodies,24-26 antibodies against the phospholipid phosphatidylethanolamine (PE),27 antibodies against the complex between the phospholipid phosphatidylserine, and the coagulation factor prothrombin (aPS/PT).28 Currently, however, there is insufficient evidence of their clinical relevance to warrant routine detection of these antibodies. Treatment Venous thrombosis in APS A first venous thrombotic event (VTE; amongst others including deep vein thrombosis and pulmonary embolism, abdominal vein thrombosis, cerebral vein thrombosis) should be treated according to the current guidelines for treatment of VTE; no routine testing for aPL is indicated in the general population. In cases of recurrent thrombosis (both provoked and unprovoked), or patients with a pre-existing autoimmune disease (and in particular, SLE), additional testing for aPL should be performed. Low molecular weight heparin (LMWH) in therapeutic doses and subsequent vitamin K antagonists (VKA) are first-line treatments for a first or recurrent APS-related venous thrombotic event (VTE). Treatment with direct oral anticoagulants (DOACs; see separate section below) is not recommended. For APS patients with a first VTE, life-long anticoagulation is recommended. After treatment with LMWH in the acute phase, treatment will be switched to VKA, with an INR target range of 2.0-3.0 for venous events.29,30 High-intensity treatment with an INR ≥ 3.0 after a first VTE is not recommended.31,32 Arterial thrombosis in APS Optimal long-term treatment for arterial thrombosis (other than cerebral arterial thrombosis; see below) is still a matter of debate; either anti-platelet therapy such as aspirin or clopidogrel, VKA with an INR target range of 2.5-3.5 or combined therapy with VKA with an INR target range of 2.0-3.0 and anti-platelet therapy has been recommended.30 As combined therapy, VKA and anti-platelet therapy has not been shown to be superior to anti-platelet therapy alone, and since more major bleeding complications were observed in the combination group,33 we do not recommend up-front combined treatment with both VKA and anti-platelet therapy. Based on expert opinion and in slight contrast with international recommendations, we prefer treatment with either clopidogrel or VKA with an INR target range of 2.0-3.0 in these patients.33,34 In patients with a cerebral ischemic event (transient ischemic attack (TIA) or ischemic stroke) as clinical manifestation, there is no evidence supporting one therapy over the other. A prospective, comparative study in aPL-positive stroke patients showed no benefit of warfarin over aspirin (325 mg/day) on recurrent events; more (minor) haemorrhagic complications in the warfarin group were observed. However, these patients did not necessarily fulfil APS criteria.35 A small (n = 20), randomized, controlled trial in APS patients with ischemic stroke compared VKA and low-dose aspirin with low-dose aspirin alone, and demonstrated less recurrent stroke in the combined-therapy group.36 In APS patients with a first ischemic stroke or TIA without any cause other than APS on work-up, we propose treatment with either VKA with an INR target range of 2.0-3.0 or clopidogrel (since this is superior to aspirin for stroke prevention in a general population suffering from atherosclerotic cerebrovascular disease).37 Position of direct oral anticoagulants in APS DOACs such as dabigatran, rivaroxaban, apixaban, and edoxaban, were shown to be non-inferior to VKAs for treatment and secondary prevention of venous thromboembolic events and prevention of stroke and systemic embolism in patients with nonvalvular atrial fibrillation.38 Furthermore, these drugs have some advantages compared to VKAs as it is a fixed dose, no monitoring is required, interaction is limited, and there is a lower risk of intracranial and other major bleeding. Nonetheless, few studies are published concerning the use of DOACs in known APS. A recent review summarised the available data, including one randomized controlled trial (RAPS trial) and case series.39 From all available case series and case reports, 122 patients were analysed.39 High-risk APS patients with triple positivity or with several clinical criteria for definite APS, developed recurrent thrombosis more frequently while on DOACs in comparison to warfarin. The RAPS trial, comparing 54 APS patients treated with rivaroxaban to 56 APS patients treated with warfarin, demonstrated that the in vitro anticoagulant effect of rivaroxaban may be inferior to that of warfarin.40 A randomized controlled clinical trial in triple positive patients, comparing VKA treatment with rivaroxaban, was terminated early due to more thrombotic events in the rivaroxaban-arm, with particularly more ischemic strokes in the DOAC group.41 At this moment VKAs, compared to DOACs, remain the standard of care in the treatment of APS, especially in high-risk APS patients. DOACs might be an alternativetreatment modality for only those patients with instable INR or poor adherence to INR monitoring, and we anticipate more data from future trials using DOACs in thrombotic APS. Recurrent venous thrombotic events while using anticoagulation therapy Recurrent thrombosis in APS patients, despite adequate anticoagulation therapy (INR target range, 2.0-3.0) is common and occurs in up to one-third of all patients.2 However, recurrent thrombosis is uncommon in APS patients with a higher INR target range of 2.5-3.5 and most patients treated with a VKA with recurrent thrombosis, appear to have an INR target < 3.0.42 Therefore, anticoagulation therapy should be intensified with a INR target range of 2.5-3.5 in case of a recurrent venous event despite adequate INR. When a recurrent venous thrombosis is diagnosed with a suboptimal INR (< 2.0), therapeutic LMWH should be given for a period of two weeks and the INR treatment range should not be changed. Note that the INR value can be underestimated due to interference of thromboplastin and LAC, although this is mostly problematic with higher INR values, i.e. > 4.0.43 If the target INR cannot be reached and/or maintained with VKA, referral to an expert centre is recommended. Based on expert opinion, in patients with recurrent venous thrombosis, despite an adequate INR target range of 2.5-3.5 and after two weeks of therapeutic LMWH, additional long-term use of aspirin can be  recommended,  or  intensifying of  VKA  therapy with to an INR  target  range  of 3-4. Alternatively, a permanent switch to therapeutic LMWH can be considered. Combining VKA with hydroxychloroquine may also decrease the risk of recurrent venous thrombosis, although randomized studies with hydroxychloroquine in APS are still lacking.44,45 Until now, no clinical support for the use of statins in patients with recurrent venous thrombosis despite anticoagulation exists. However, based on in vitro work and surrogate endpoints, a beneficial role of statins has been suggested.46,47 Recurrent arterial thrombotic events while using anticoagulation therapy No consensus on treatment of recurrent arterial thrombosis was reached at the last meeting of the APS task force.30 Optimal treatment strategies for recurrent arterial thrombotic events have not been studied. In recurrent arterial thrombosis, including TIA or ischemic stroke, despite treatment with clopidogrel, we suggest switching treatment to VKA with an INR target range of 2.0-3.0. If recurrent arterial thrombosis occurs despite adequate treatment with VKA, referral to an expert centre is recommended. We do not recommend to routinely perform brain magnetic resonance imaging (MRI) in APS patients with an ischemic stroke. However, some experts believe that if neurological symptoms, including migraine or cognitive performance, deteriorate, a repeat brain MRI is indicated and new white matter abnormalities may lead to intensifying treatment. The possible benefits of this strategy have not been confirmed in clinical studies, and for patients with deterioration of neurological symptoms, referral to an expert centre is recommended.  Catastrophic antiphospholipid syndrome (CAPS) CAPS is a rare complication of APS and occurs in approximately 1% of APS patients. CAPS is a severe condition, including massive (mostly arterial) thrombosis of small vessels, causing multi-organ failure. Mortality in CAPS is high, up to 50%. Adequate and fast treatment initiation slightly improves clinical outcomes (mortality 20-40%) and treatment should be carried out in an expert centre.48,49 A combination of anticoagulant drugs (mostly unfractionated heparin with APTT ratio 2-2.5), intravenous corticosteroids (methylprednisolone 500-1000 mg/day for 3-5 days), therapeutic plasma exchange (TPE) and/or intravenous immunoglobulins (IVIG) (1 g/kg, for a time period of 3 days) is associated with highest survival rates. Combination of heparin-corticosteroids-plasmapheresis with or without additional IVIG results in 69-78% patient survival.50-52 TPE should be started when the clinical suspicion of CAPS arises, within a minimum of 5 days.52 Clinical response dictates the duration of TPE and no single clinical or laboratory parameter is used to determine when to discontinue treatment. For patients with CAPS and underlying SLE, treatment with cyclophosphamide (750 mg/m2 monthly) has been proposed.48,53 Rituximab (anti-CD20) has been administered to patients with refractory CAPS and may be of adjunctive value in a selected population of patients. Eculizumab (anti-complement 5) has been reported in case reports as a last resort, and could be considered in those patients refractory to all other therapies.54,55 Pregnancy and antiphospholipid antibodies Pregnancy complications of APS include recurrent first trimester pregnancy loss, intrauterine growth restriction (IUGR), preeclampsia (PET), premature birth, and intrauterine death (IUD). Early miscarriages are reported in 26-35% and aPL-related PET, premature birth or foetal death are seen in 10–20% of APS pregnancies.2,56 Women with positive aPL do not all carry the same obstetric risk. The following parameters are associated with an increased risk: the presence of LAC, more than one aPL (especially triple positivity), IgG aPL (instead of IgM), previous thrombosis, previous pregnancy complications, associated autoimmune condition, and hypocomplementemia.57 At present, however, risk stratification does not direct different treatment strategies. The current standard treatment, based on low-dose aspirin and LMWH, increases the percentage of a successful pregnancy from 20% to 54-80%.58 In patients with obstetrical APS, aspirin and a prophylactic dose of LMWH should be administered. Depending on the context (concurrent SLE, maternal age, non-criteria clinical features), treatment of patients with fewer than three miscarriages, and therefore, formally not classified as having APS, can be considered. To prevent overtreatment in these cases, confirmation of miscarriage by ultrasound is recommended. During pregnancy, regular clinical pregnancy follow-up is sufficient; only in specified subpopulations (e.g. patients with SLE, chronic kidney disease, morbid obesity, triple positive antibody profile, and patients with a thrombotic event during pregnancy), follow-up (of foetal growth and hypertensive pregnancy complications) should be intensified. Platelets should be checked at least once every 10-14 days after starting LMWH to exclude heparin-induced thrombocytopenia. If pregnancy complications (foetal death or miscarriage) still occur despite combined treatment with low-dose aspirin and prophylactic dose of LMWH, the dose of LMWH can be increased to a therapeutic dose; reverting back to a prophylactic dose of LMWH after 36 weeks of gestation can be considered. Based on expert opinion, patients with APS who already use therapeutic anticoagulation before pregnancy, should be switched to therapeutic dose of LMWH and low-dose aspirin should be added. Anti-factor Xa levels should be periodically monitored (at least once every trimester) in patients receiving therapeutic doses of LMWH, depending on the context. If this strategy fails, further treatment should take place in an expert centre. The recommendations are again based on expert opinion and retrospective cohort studies and include, for example, the addition of hydroxychloroquine or intravenous immunoglobulins. This should however, be confirmed in randomized controlled trials.59-61 In the postpartum period there is an increased risk for thrombosis. Patients with APS with an indication of therapeutic anticoagulation before pregnancy, should be switched to VKA or continue with therapeutic LMWH. Obstetrical APS patients should receive prophylactic or intermediate doses (a randomized clinical trial to investigate optimal dosing is currently underway62) of LMWH during a period of six weeks postpartum.57 Withdrawal of antithrombotic treatment in APS As mentioned above it is recommended to treat thrombotic APS patients lifelong. However, as long-term anticoagulation therapy is associated with haemorrhagic complications, the question arises whether it is possible to withdraw this therapy in selected patients, especially those who have a long-term event free period or who no longer have positive aPL, also called seroconversion. Only a few (uncontrolled and small) studies have addressed this question. Criado-Garcia et al described the effects of anticoagulation withdrawal in six primary APS patients in whom aPL had disappeared.63 None of these patients experienced a thrombotic event during a follow-up of 21 ± 4.9 months. It should be mentioned, however, that these patients were at low risk for recurrence, as they only had a venous thrombosis history. Similar results were found by Coloma Bazan et al, who reported no thrombotic recurrences after anticoagulation withdrawal durign a median follow-up of 20 months in 11 primary APS patients (seven with history of venous thrombosis and four with obstetrical APS).64 However, in a more recent study, 30 APS patients with anticoagulation withdrawal were compared to a thrombotic APS control population without withdrawal. During a median follow-up of 51 months, anticoagulation withdrawal was associated with a higher risk of thrombotic relapse (HR 4.82). Predictive factors were male gender, anti-β2GPI-positivity and triple positivity at onset as well as persistence positivity over time. Predictive factors for low risk of relapse were aspirin prescription and aPL disappearance during follow-up.65 In conclusion, data is insufficient to draw firm conclusions concerning anticoagulation withdrawal in APS patients. The available data suggests that anticoagulation could be withdrawn in APS patients with a single provoked venous thrombotic event in the presence of a known transient precipitating risk factor (such as smoking, disease activity, oral contraceptive use) together with disappearance of aPL. In all other situations, the high risk of thrombotic relapse favours the continuance of anticoagulation treatment. Randomized studies with larger sample sizes are still needed to confirm these statements.66,67 The decision to withdraw anticoagulation in APS patients should be made in an expert centre. Position of rituximab, hydroxychloroquine, intravenous immunoglobulins Several immunomodulatory drugs are suggested for the treatment for patients with APS, although their exact use in the treatment of APS is unclear; these include. rituximab, hydroxychloroquine (HCQ), and IVIG. Most evidence is based on cohort studies, case series or expert opinion. Only a few randomised controlled trials (RCTs) are published. Furthermore, their application is strongly dependent on the different clinical situations. For example, in secondary APS in SLE patients, HCQ is strongly recommended, as it has been shown that HCQ has ‘thrombo-protective’ effects, resulting in fewer venous and arterial thrombotic events.68 In primary APS, limited data is available. One retrospective cohort study demonstrated strong reduction of aPL titres and a decrease in the incidence of arterial thrombosis recurrence by using HCQ.69,70 One RCT randomizing 40 APS patients to VKAs versus VKAs with HCQ showed a protective effect of adding HCQ upon venous thrombotic events.45 At this moment, HCQ is not included in standard care of primary APS, but refractory cases, including refractory obstetrical APS, might benefit from this treatment. Treatment with HCQ (200-400 mg per day) is safe during pregnancy and lactation. Rituximab and IVIG may be used in difficult-totreat APS patients, especially in those with CAPS or recurrent haematological non-criteria manifestations as thrombocytopenia, but not as standard of care of APS.71,72 Prophylaxis in aPL-positive patients without earlier events The question remains whether patients with obstetric APS or individuals with positive aPL without thrombotic events should be treated to prevent thrombosis (primary thromboprophylaxis). At present, there is insufficient evidence to support prophylactic treatment for all of these patients.8,73 However, in patients with more risk factors for thrombosis (such as obesity, smoking, higher age) and/ or high-risk aPL profile (e.g., triple positive), low dose aspirin might be beneficial. In any case, attention should be paid to avoid or to treat any associated cardiovascular risk factors, e.g. using antihypertensives or cholesterollowering agents and avoidance of smoking, etc. Also, the administration of oral contraceptives should be used with caution and with counselling.74 Lastly, prophylaxis of venous thrombosis using LMWH is required for patients in situations associated with increased risk of thrombosis, such as surgical procedures, plaster casts, and those requiring bed rest.  CONCLUSION  APS is a rare and heterogeneous disease and as a result, well-designed and well-conducted clinical trials are scarce and the development of a formal guideline is difficult. However, by combining data from completed clinical intervention trials together with observational data and data from research in other thrombotic and/ or inflammatory conditions, recommendations for clinical practice can be formulated. Current national and international initiatives – such as the Dutch Arthritis Research and Collaboration Hub (ARCH) and the European Reference Network on Rare and Complex Connective Tissue and Musculoskeletal Diseases (ERN-ReConnet) – are aiming to structure the care for APS patients to offer a unique future opportunity to collect longitudinal clinical data on APS treatment and outcomes. Until a formal guideline has been made, this consensus paper fills the gap between evidence-based medicine and daily clinical practice for the care of APS patients. ACKNOWLEDGEMENTS KdL and ML performed the literature search and wrote the first article draft. All authors reviewed and commented on the first article draft and attended the first consensus meeting. JS and ML wrote the second article draft. All authors reviewed and commented on the second article draft. ML wrote the final report. All authors read the final report and agreed with the text. DISCLOSURES All authors declare no conflicts of interest. FUNDING This work was supported by the Arthritis Research and Collaboration Hub (ARCH) Foundation. REFERENCES Myakis S, Lockshin MD, Atsumi T, et al. International consensus statement on an update of the classification criteria for definite antiphospholipid syndrome (APS). J Thromb Hemost. 2006;4:295-306. Cervera R, Piette JC, Font J et al. Antiphospholipid syndrome: clinical and immunologic manifestations and patterns of disease expression in a cohort of 1,000 patients. Arthritis Rheum. 2002;46:1019-27. Abreu MM, Danowski A, Wahl DG, et al. The relevance of "non-criteria" clinical manifestations of antiphospholipid syndrome: 14th International Congress on Antiphospholipid Antibodies Technical Task Force Report on Antiphospholipid Syndrome Clinical Features. Autoimmun Rev. 2015;14:401-14. Hughes GRV, Khamashta MA. Seronegative antiphospholipid syndrome. Ann Rheum Dis. 2003;62:1127. Galli M, Luciani D, Bertolini G, Barbui T. 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Research ArticleImmunology Free access | 10.1172/jci.insight.126471 Stem cell–derived tissue-associated regulatory T cells suppress the activity of pathogenic cells in autoimmune diabetes Mohammad Haque,1 Fengyang Lei,2 Xiaofang Xiong,1 Jugal Kishore Das,1 Xingcong Ren,3 Deyu Fang,4 Shahram Salek-Ardakani,5 Jin-Ming Yang,3 and Jianxun Song1 First published February 19, 2019 - More info Abstract The autoantigen-specific Tregs from pluripotent stem cells (PSCs), i.e., PSC-Tregs, have the ability to suppress autoimmunity. PSC-Tregs can be programmed to be tissue associated and to infiltrate into local inflamed tissues to suppress autoimmune responses after adoptive transfer. Nevertheless, the mechanisms by which the autoantigen-specific PSC-Tregs suppress the autoimmune response remain to be fully elucidated. In this study, we generated functional autoantigen-specific Tregs from the induced PSC (iPSCs), i.e., iPSC-Tregs, and investigated the underlying mechanisms of autoimmunity suppression by these Tregs in a type 1 diabetes (T1D) murine model. A double-Tg mouse model of T1D was established in F1 mice, in which the first generation of RIP-mOVA Tg mice that were crossed with OT-I T cell receptor (TCR) Tg mice was challenged with vaccinia viruses expressing OVA (VACV-OVA). We show that adoptive transfer of OVA-specific iPSC-Tregs greatly suppressed autoimmunity in the animal model and prevented the insulin-secreting pancreatic β cells from destruction. Further, we demonstrate that the adoptive transfer significantly reduced the expression of ICAM-1 in the diabetic pancreas and inhibited the migration of pathogenic CD8+ T cells and the production of the proinflammatory IFN-γ in the pancreas. These results indicate that the stem cell–derived tissue-associated Tregs can robustly accumulate in the diabetic pancreas, and, through downregulating the expression of ICAM-1 in the local inflamed tissues and inhibiting the production of proinflammatory cytokine IFN-γ, suppress the migration and activity of the pathogenic immune cells that cause T1D. Graphical Abstract Introduction Type 1 diabetes (T1D) develops due to autoimmune self-destruction of pancreatic β cells that produce insulin, and life-time administration of insulin is required for treatment of this disease (1). Although accumulating knowledge has contributed greatly to our understanding of the autoimmune pathogenesis of T1D, the precise causes remain unclear. It has been generally appreciated that autoimmune diseases arise from the breakdown of immune tolerance (2, 3). A number of studies found the massive infiltration of CD8+ T cells into the pancreases of newly diagnosed T1D patients whereas the number of CD4+ T cells was greatly reduced (4, 5). Infiltrated CD8+ T cells are pathogenic to the islets, can cause the destruction of pancreatic β cells, and ultimately reduce the secretion of insulin. Currently, there is no definitive treatment for controlling blood glucose level in T1D except durable insulin therapy. As a result, generation and transplantation of exogenous β cells to replace dead or dysfunctional endogenous β cells are considered as a promising strategy for controlling blood glucose level in patients with T1D. However, since the autoimmune disease is a continuous process, after β cell transplantation, diabetes may develop and progress again through destruction of the pancreatic islets by pathogenic T cells. Therefore, β cell transplantation might not be a long-lasting solution for the control of blood glucose level in T1D. Over the past several years, there has been an increasing interest in Tregs, which play a fundamental role in controlling various autoimmune responses. Numerous preclinical studies suggest that adoptive transfer of Tregs can prevent or cure T cell–mediated autoimmune diseases, such as T1D and arthritis (6, 7). There are several advantages of the Treg adoptive transfer over conventional treatments. These advantages include (a) the potential of antigen (Ag) specificity without general immunosuppression; (b) the option of inducing “physiological” long-lasting regulation in vivo; and (c) the possibility of Treg-based immunotherapy as a customized or personally designed agent for each patient, with reduced side effects (8, 9). It is now generally believed that adoptive transfer of in vitro–generated Tregs can reduce the hazards of complicated surgical events throughout the life. However, the use of Tregs has been complicated due to difficulties in expanding and characterizing this minor subset of T cells. Here, we report the development of a robust technique for producing a large amount of autoantigen-specific Tregs from induced pluripotent stem cells (iPSCs), i.e., iPSC-Tregs that retain all the quintessential characteristics of this T cell subset, including expressions of CD25, CTLA-4, and FoxP3 and production of IL-10. We show that adoptive transfer of these autoantigen-specific iPSC-Tregs significantly reduces the high ratio of CD8+ to CD4+ T cells in the pancreases of diabetic mice and markedly decreases the expression of intracellular adhesion molecule-1 (ICAM-1) in the pancreases of prediabetic and diabetic mice. These results demonstrate the great potential of stem cell–derived autoantigen-specific Tregs in T1D immunotherapy. Moreover, we show that the stem cell–derived tissue-associated Tregs control autoimmune diabetes via preventing the ICAM-1–mediated migration of pathogenic CD8+ T cells in the pancreas and suppressing the production of proinflammatory cytokine IFN-γ. Results Characterization of autoantigen-specific naturally occurring Treg–like iPSC-Tregs. Although FoxP3 is a master regulator and a specific molecular marker for naturally occurring Tregs (nTregs), there is evidence that FoxP3 expression is not a distinct and reliable marker or a sole regulator of functionally stable Tregs. In addition, recent evidence showed that various transcription factors are critical for the development of FoxP3+ Tregs, including Nr4a1, Ikzf4, Tnfrsf18, and Tbx21 (10). These genes, which are substantially expressed in nTregs, are essential for regulating Treg transcriptional programs and maintaining the lineage stability in Tregs. We previously showed the generation of functional Ag-specific iPSC-Tregs, which had the ability to suppress the development of autoimmune arthritis after adoptive transfer in murine models (11, 12). Because the iPSC-Tregs are retrovirally transduced with FoxP3 and TCR genes and have a similar phenotype to iPSC-Tregs in expression of IL-10 and TGF-β, we examined the Treg signature genes in the autoantigen-specific iPSC-Tregs. We adoptively transferred Rag 1–/– mice with pre-iPSC-Tregs that had been cocultured with OP9-DL1/DL4/I-Ab cells for 7 days. Six weeks later, mice were sacrificed and their spleens and lymph nodes (LNs) were removed for the isolation of CD4+CD25+ cells. Conversely, CD4+CD25+ cells were sorted from the spleens and LNs of normal C57BL/6 mice. RT-PCR analysis of the Treg signature genes showed that there were no significant differences between the mice in gene expression of the transcription factors (Nr4a1, Il2ra, Ikzf4, Tnfrsf18, and Bcl6) involved in the regulation of Treg functions (all P > 0.05, Figure 1). Collectively, these results and suggest that the autoantigen-specific iPSC-Tregs are nTreg-like suppressive cells. Figure 1 Characterization of autoantigen-specific nTreg-like iPSC-Tregs. PCR analysis was performed by TaqMan real-time PCR. Primers for sequences were used as follows: (A) Nr4a1, forward 5′-TGTGAGGGCTGCAAGGGCTTC-3′, reverse 5′-AAGCGGCAGAACTGGCAGCGG-3′; (B) Il2ra, forward 5′-AACTGCCAGTGCACCAGCAAC-3′, reverse 5′ GAGGTGGCTCCCTGCAGTGAC-3′; (C) Ikzf4, forward 5′-CAATCTGCTTCGCCACATCAAG-3′, reverse 5′-GCCACAGTAGTTGCACTTGTAG-3′; (D) Tbx21, forward 5′-GGAGCCCACTGGATGCGCCAG-3′, reverse 5′ AGGCAGCCTCTGGCTCTCCATC-3′, Tnfrsf18, forward 5′-CCTGCCAACCAGGCCAGAGGG-3′, reverse 5′-GTCCAAAGTCTGCAGTGACCG-3′; and (E) Bcl6, forward 5′-CACACCCGTCCATCATTGAA-3′, reverse 5′-TGTCCTCACGGTGCCTTTTT-3′. Data shown are representative of 3 identical experiments. The values represent mean ± SEM (n = 3). ns, P > 0.05, Student’s 1-tailed t test. Accumulation of activated CD8+ T cells in autoantigen-expressing pancreases of diabetic mice. To show the expression of autoantigen in the pancreases of double-Tg mice (B6-mOVA with OT-I), we performed immunofluorescence examination. The results showed the expression of OVA autoantigen in the double-Tg mice but not in the B6 or OT-I control mice (Figure 2A). Because the OVA-specific CD8+ T cells have the ability to migrate into the pancreases of the F1 generation that results from crossing B6 mOVA with OT-I TCR Tg mice and to cause destruction of the islets, we examined the fate of OVA-specific CD8+ T cells in vivo (Figure 2B). The migrations of CD8+ T cells in diabetic mice were further confirmed by flow cytometry in which more numbers of both CD8+ and TCRVβ5+ cells were present in diabetic mice (Figure 2C). In F1 mice after vaccinia virus (VACV) infection (Supplemental Figure 3; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.126471DS1), the draining LNs and spleens of the F1 mice were analyzed for the presence of OT-I CD8+ T cells. The proportions of OVA-specific CD8+ cells among the total CD8+ cells in the pancreatic LNs were significantly higher than in the pyloric, mesenteric, inguinal, cervical, or splenic LNs. No apparent accumulation or homing was observed in the non-Tg control B6 mice (Figure 2D). Additionally, all F1 mice after VACV infection developed autoimmune diabetes at age of 9 weeks, which was confirmed by the measurement of blood sugar (Figure 2E). These results verify that autoantigen-specific CD8+ T cells are the main pathogenic immune cells that induce autoimmune diabetes in the murine model. Figure 2 Accumulation of activated CD8+ T cells in autoantigen-expressing pancreases in diabetic mice. Pancreases were isolated from B6-mOVA × OT-I TCR double-Tg mice and B6 mice at 9 weeks of age, including 1 week in which mice were challenged with vaccinia viruses expressing OVA (VACV-OVA). (A) Detection of OVA expression by immunohistochemically staining. OVA expression (arrow) is indicated (original magnification, ×200). Data are representative of 5 mice per group in 3 independent experiments. Scale bar: 20.1 μm. (B) CD8+ T cell infiltration in the pancreas. CD8+ T cells (arrow) are indicated. Data are representative of 5 mice per group in 3 independent experiments. Scale bar: 20.1 μm. (C) OVA-specific CD8+ T cells in the pancreas. OVA-specific TCRVβ5 was analyzed by flow cytometry, after gating on CD8+ populations from the pancreas. Data shown are representative of 3 independent experiments (P < 0.001, Student’s 1-tailed t test). (D) Summarized analyses of OVA-specific CD8+ T cells in various locations. Data shown are representative of 5 mice per group in 3 independent experiments. Data shown are representative of 3 individual experiments. The values represent mean ± SD. **P < 0.01; ns, P > 0.05, multiple Student’s 1-tailed t test. (E) Blood glucose measurement. Data shown are representative of 3 individual experiments (n = 5). The values represent mean ± SD. **P < 0.01, Student’s 1-tailed t test. In vivo specificity of nTreg-like autoantigen-specific iPSC-Tregs. To exert their suppressive effects, Tregs need to migrate to specific tissues or organs, and this requires Ag specificity (8). We and others have previously reported that Tregs are detected within inflamed tissues and transplanted grafts, suggesting that these Tregs could control effector T cells in the peripheral tissues at sites of ongoing immune responses (12–14). Tregs may block the pathogenic cells from filling up their appropriate niche by taking up the space. The Treg population was reduced in autoimmune prone animals and patients, and Tregs were defective in NOD mice (15). In this study, we observed that mice receiving autoantigen OVA-specific iPSC-Tregs maintained a high population of both CD4+ and FoxP3+ cells in the pancreas, as compared with diabetic mice receiving control cells (iPSC-derived cells with empty vector) or nontissue-associated SM1-specific iPSC-Tregs by immunofluorescence examination (Figure 3A) and by flow cytometric analysis (90.1% vs. 12.5% or 16.8% at week 13, Figure 3B). These results suggest that the autoantigen-specific iPSC-Tregs are organ/tissue-associated nTreg-like suppressive cells. Figure 3 Accumulation of autoantigen-specific iPSC-Tregs in the pancreases of diabetic mice following adoptive transfer. B6-mOVA Tg × OT-I TCR double-Tg mice were immunized with VACV-OVA. At week 10, control cells or OVA- or SM1-specific pre-iPSC-Tregs were transferred into diabetic mice. Before or after the cell transfer, mice were sacrificed and their pancreases were isolated for analysis of CD4 and FoxP3. (A) Immunohistology at week 13 (original magnification, ×200). Scale bar: 20.1 μm. (B) Flow cytometric analysis at week 10 (before the cell transfer), 13, and 16. Data are representative of 5 mice per group in 3 independent experiments. Autoantigen-specific iPSC-Tregs can prevent destruction of pancreaticβcells and reduce autoimmune diabetes. Next, we injected diabetic mice with autoantigen-specific iPSC-Tregs. Two weeks after the cell transfer, blood glucose levels were measured. This cell transfer with autoantigen OVA-specific iPSC-Tregs but not control cells or nontissue-associated SM1-specific iPSC-Tregs significantly reduced blood glucose level with urine discharge in all of the diabetic mice (P < 0.0001, Figure 4A and Supplemental Figure 3). T1D develops due to the destructive autoreactive immune response in which CD8+ T cells play a critical role. CD8+ T cells infiltrated into the pancreatic islets of the T1D patients at both initial and final destructive phases of autoimmune β cells attack (16). The blood glucose level was increased because of the destruction of β cells in the islets by pathogenic CD8+ T cells in the diabetic pancreas, which was evidenced in the above mouse diabetic model. More CD8+ T cells were accumulated in the pancreases of mice receiving control cells or nontissue-associated Ag-specific iPSC-Tregs than autoantigen-specific iPSC-Tregs by immunofluorescence examination (Figure 4B) and by flow cytometric analysis (49.8% or 46.1% vs. 6.5%, Figure 4C). Only mice receiving autoantigen-specific iPSC-Tregs had a significantly reduced percentage of incidence of diabetes (P < 0.001, Figure 4D). A large number of inflammatory cells had infiltrated into the pancreases of mice receiving control cells, whereas the infiltration was substantially reduced in mice receiving autoantigen-specific iPSC-Tregs, as shown by H&E staining (Figure 4E). The total number of the islets was also reduced in mice receiving control cells as compared with autoantigen-specific iPSC-Tregs (P < 0.01 or P < 0.001, Figure 4F). To detect the destruction of β cells in the islets of diabetic mice, we stained pancreases with insulin. The islets were reduced in size and number in mice receiving control cells, whereas mice receiving autoantigen-specific iPSC-Tregs were protected from the destruction, by immunofluorescence examination (Figure 4G). The islets were partially destructed by pathogenic CD8+ T cells, which caused the increase of blood sugar and developed autoimmune diabetes, but some insulin+ staining in diabetic mice was still present. Collectively, these results indicate that autoantigen-specific iPSC-Tregs are specific and effective in protecting the hosts from islet destruction and in promoting insulin secretion to prevent the mice from diabetes mellitus. Figure 4 Prevention of destruction of pancreatic β cells and suppression of autoimmune diabetes by autoantigen-specific iPSCs-Tregs. Control cells or pre-iPSC-Tregs were adoptively transferred into diabetic mice, as described in Figure 3. (A) Blood sugar measurement at various weeks. Data shown are representative of 5 mice per group in 3 independent experiments. **P < 0.001; ***P < 0.0001, 2-way ANOVA analysis. (B) Immunofluorescence detection of pathogenic immune cells. Mice were sacrificed and pancreases were isolated for immunohistochemistry staining with CD8+ T cells (original magnification, ×200). Scale bar: 20.1 μm. Data are representative of 5 mice per group in 3 independent experiments. (C) Flow cytometric analysis of pathogenic immune cells. Pancreatic lymph nodes (LNs) were isolated, and single-cell suspensions were prepared for OVA-specific CD8+ TCRVβ5+ staining and analyzed by flow cytometry. Data shown are representative of 3 identical experiments (P < 0.001, Student’s 1-tailed t test). (D) Percentage of incidence of diabetes at week 22. Data shown are representative of 5 mice per group in 3 independent experiments. The values represent mean ± SD. **P < 0.001; ***P < 0.001, 1-way ANOVA analysis. (E) Representative photomicrographs (H&E staining) of the islet inflammation (original magnification, ×200). Scale bar: 20.4 μm. Cellular infiltrations (arrow) are indicated. Data are representative of 5 mice per group in 3 independent experiments. (F) Islet count from sections of 5 individual pancreases in each group. Data are represented as the mean ± SD of 3 independent experiments (***P < 0.001, 1-way ANOVA analysis). (G) Representative photomicrographs (immunofluorescence staining) of islet destruction (original magnification, ×200). Scale bar: 20.1 μm. Insulin-producing cells (arrow) are indicated. Data are representative of 5 mice per group in 3 independent experiments. Autoantigen-specific iPSC-Tregs can decrease the expression of ICAM-1. A number of mononuclear cells, including macrophages and CD4+ T cells, were found to be initially predominant in the autoimmune process taking place in T1D (17, 18). In autoimmune diabetes, these cells played an important role in the early infiltrating process and were shown to express ICAM-1, which served to bind to lymphocytes and possibly to monocytes and polymorphonuclear leukocytes (19). The infiltrated ICAM-1 can promote the release of several cytokines that modulate the expression of the adhesion molecules, thus increasing the adhesion of leukocytes and other autoreactive T cells (20). These autoreactive T cells, after migrating into the pancreas, start to destroy β cells. We thus examined the expression of ICAM-1 in the pancreases. The high expression of ICAM-1 in the pancreases of the diabetic mice, which was undetectable in the normal pancreases, was significantly reduced in diabetic mice receiving autoantigen-specific iPSC-Tregs but not other control cells (Figure 5, A and B). In addition, the high expression of ICAM-1 on the effector CD4+ T cells in the pancreases of the diabetic mice receiving control cells (84.4% or 78.9%), which was undetectable in the normal pancreases (0.35%), was markedly reduced in those of mice receiving autoantigen-specific iPSC-Tregs (18.4%) (Figure 5C). In addition, the expression of ICAM-1 on CD11b+ cells in the pancreases of diabetic mice receiving control cells (15.64% or 12.83%), which was at a low level in the normal pancreases (4.89%), was considerably reduced in those of mice receiving autoantigen-specific iPSC-Tregs (5.68%) (Figure 5D). Because CD11b is expressed on the surface of various leukocytes, including monocytes/macrophages, granulocytes, neutrophils, NK cells, and dendritic cells, the reduction of CD11b+ICAM-1+ cells indicated the inhibition of leukocyte adhesion and migration to mediate the inflammatory response. Taken together, these results suggest that the autoantigen-specific iPSC-Tregs can reduce the expression of ICAM-1 in diabetic mice, inhibit the migration of autoreactive CD8+ T cells into the pancreas, and, ultimately, protect the pancreatic β cells from destruction. Figure 5 Reduction of ICAM-1 expression in the diabetic pancreas by autoantigen-specific iPSC-Tregs. Mice were sacrificed 4 weeks after adoptive cell transfer, and the pancreases were isolated from nondiabetic, diabetic (cell transfer control), and diabetic (Ag-specific iPSC-Treg transfer) mice. Samples were prepared for immunohistochemistry staining. (A) ICAM-1+ zones (arrow) are indicated (original magnification, ×200). Data are representative of 5 mice per group in 3 independent experiments. (B) Quantification of ICAM-1 expression from sections of 5 individual pancreases in each group. Data are represented as the mean ± SD from 3 independent experiments (**P < 0.01; ***P < 0.001, 1-way ANOVA). (C and D) Flow cytometry analysis of ICAM-1 expression in pancreatic CD4+ T or CD11b+ cells. The pancreatic lymph nodes (LNs) were isolated, and single-cell suspensions were prepared for ICAM-1 staining on CD4+ or CD11b+ cells and analyzed by flow cytometry. Data shown are representative of 3 identical experiments. Autoantigen-specific iPSC-Tregs can migrate to the pancreas and produce a large amount of suppressive cytokines. In BDC2.5/NOD mice, FoxP3 was highly expressed in insulitic lesions where the function was impaired due to alterations in their gene expression profile (21, 22). We previously showed that the iPSC-Tregs exhibited a similar expression of Treg signature genes and produced IL-10 and TGF-β (12). In the current study, we observed very few numbers of FoxP3-expressing T cells in the pancreases of diabetic mice, and these cells did not secret IL-10 and TGF-β, which are the two key cytokines that are the hallmark for functional Tregs and secreted by activated and functional Tregs. We further showed that the pancreatic CD4+ T cells from mice receiving autoantigen-specific iPSC-Tregs produced substantially more IL-10 (66.1% vs. 12.9% or 15.1%) and TGF-β (5.61% vs. 0.62% or 1.68%), as compared with those from mice receiving control cells (Figure 6). These results support the assumption that autoantigen-specific iPSC-Tregs accumulating in the pancreas can secrete large amounts of suppressive cytokines (IL-10 and TGF-β), reduce the expression of ICAM-1, and prevent the migration of pathogenic CD8+ T cells into the pancreas, thus protecting the pancreas from destruction. Figure 6 Induction of IL-10 and TGF-β by autoantigen-specific iPSC-Tregs. Mice were sacrificed 4 weeks after adoptive cell transfer, and single-cell suspension was prepared from the pancreatic lymph nodes (LNs). Cells were stimulated with plate-coated CD3 and soluble CD28 antibodies and then stained with CD4, TGF-β, and IL-10. The CD4 population was gated, and the production of TGF-β and IL-10 was analyzed. Data shown are representative of 3 identical experiments. Autoantigen-specific iPSC-Tregs can decrease the production of IFN-γby pathogenic immune cells in the diabetic pancreatic islets. Treatment of NOD mice with an anti-IFN-γ monoclonal antibody could block diabetes (23); conversely, overproduction of IFN-γ in pancreatic islets provoked the disease (24). We next determined the secretion of IFN-γ in the diabetic islet. We found more IFN-γ–producing pancreatic CD8 and CD4 T cells in mice receiving control cells (31.4% and 30.4% or 29.7% and 28.6%); by contrast, both of the IFN-γ–producing pancreatic CD8 and CD4 T cells were significantly reduced in mice receiving autoantigen-specific iPSC-Tregs (14.6% and 10.9%) (Figure 7). These results indicate that the autoantigen-specific iPSC-Tregs can protect the islets from destruction by suppressing the secretion of IFN-γ produced by the pathogenic immune cells. Figure 7 Downregulation of IFN-γ by autoantigen-specific iPSC-Tregs in the diabetic pancreatic islets. Mice were sacrificed 4 weeks after adoptive cell transfer, and single cell suspension was prepared from the pancreatic lymph nodes (LNs). Cells were stimulated with plate-coated CD3 and soluble CD28 antibodies, and then stained with CD4, CD8, and IFN-γ for flow cytometric analysis. (A) IFN-γ production in CD4+ or CD8+ population. Data shown are the representative of 3 identical experiments. (B) Quantification of IFN-γ production. Data shown are representative of 3 identical experiments (**P < 0.01; ***P < 0.001, 1-way ANOVA). Discussion We and others have previously reported that adoptive transfer of stem cells has or stem cell–derived Tregs has the ability to suppress the development of autoimmunity in various animal models (11, 12, 25, 26). However, the suppressive mechanisms behind this suppression remain to be fully defined. By investigating the adoptive transfer of autoantigen-specific iPSC-Tregs in a murine model of T1D, we demonstrate that adoptive transfer significantly reduced the high ratio of CD8+ to CD4+ T cells in the pancreases of diabetic mice. We also show a critical role of this transfer in reducing the expression of ICAM-1 in inflamed pancreatic tissues and preventing the accumulation of pathogenic CD8+ T cells in the pancreases and diabetic pathogenesis. We further demonstrate that autoantigen-specific iPSC-Tregs associated with inflamed tissue and protected the islets from destruction through suppressing the production of proinflammatory cytokine IFN-γ. These findings may help us better understand the pathogenesis of autoimmunity in T1D and provide a foundation for therapeutic use of the stem cell–derived tissue-associated Tregs in the treatment of T1D (27). Among animal models of autoimmune diabetes, the NOD mouse is a widely used animal of autoimmune T cell–mediated T1D (spontaneously nonobese diabetic). The NOD mouse model clearly shows the leukocytic infiltration into the pancreatic islets (insulitis) and autoimmune destruction of the pancreatic β cell in female mice (2–4 weeks), which occurs later in male mice (5–7 weeks). Insulitis in NOD mice shows a combination of T cells (both CD4+ and CD8+), B cells (28), and inconstant numbers of macrophages/dendritic cells. In addition, a dominant-negative mutation in the mouse insulin 2 gene (Ins2Akita) produces a severe insulin deficiency syndrome exclusive of autoimmune participation and various transgenes overexpressed in β cells. Moreover, pharmacologically induced T1D (without autoimmunity) by alloxan or streptozotocin produces hyperglycemia in most strains of mice. Several low doses of streptozotocin combing direct β cell toxicity with local inflammation also elicit T1D in a male sex-specific fashion (29). To determine the suppressive mechanisms of stem cell–derived tissue-associated Tregs, we employed a mouse model of T1D by crossing B6-mOVA Tg mice with OT-I TCR Tg mice. Around 8 weeks of age, blood glucose levels were measured in F1 mice. Approximately 20%–40% of the mice were found to be diabetic; one potential explanation for this occurrence is that the OT-I CD8+ T cells may be tolerated during T cell development in the thymuses of F1 mice. As we propose in our model in which mice will develop autoimmune diabetes due to a large amount and the autoreactivity of CD8+ T cells, we activated tissue-associated CD8+ T cells by inoculating VACV-OVA into the mice in which the viruses-induced CD8+ T cells became highly polyfunctional (30). All mice developed diabetes with high blood glucose levels (Supplemental Figure 3) and more urine discharge, for which mice needed additional animal care. In this mouse model of T1D, OVA protein serves as inflamed tissue–associated autoantigen and is highly expressed in the pancreas. OVA-specific CD8+ T cells act as central pathogenic immune cells that accumulate in the pancreas and cause pancreatic β cell destruction and T1D (Figure 2). In addition, in this mouse model of T1D, we observed a non-OVA-specific CD4+ population existing in the pancreas (Figure 3), and these CD4+ T cells produced IFN-γ (Figure 7), which might also involve to the disease pathogenesis. These FoxP3–CD4+ T cells in the pancreas were TCRVα2–/Vβ5–, indicating that T cell activation was not through OVA-specific TCR. Of note, the number of these effector CD4+ T cells was dramatically reduced in mice receiving OVA-specific but not SM1-specific iPSC-Tregs (Supplemental Figure 4). These results also indicate that tissue-associated (OVA-specific) iPSC-Tregs are more effective than nontissue-associated (SM1-specific) iPSC-Tregs. Because of the challenge of VACV-OVA, these non-OVA-specific CD4+ T cells mainly originating from the RIP-mOVA descent may undergo bystander activation (31, 32). However, addition studies are needed on the pathways leading to bystander T cell activation under the condition. Tregs are an important component of self-tolerance, and numerous studies have demonstrated the effects of Tregs on natural and induced autoimmune diseases in various mouse models (33, 34). Targeting Tregs as a treatment for autoimmune disorders is an attractive approach, as there is an emerging consensus that many patients suffering from autoimmune disease have dysfunctional Tregs (35, 36). Given the manageability of murine models, the evidence for Tregs in control of autoimmune diseases has recently become clearer, particularly as a therapeutic intervention. In well-established models of rheumatoid arthritis (37), multiple sclerosis (38), and systemic lupus erythematosus (39), adoptive transfer of polyclonal Tregs could prevent or slow disease progression when administered prior to disease occurrence. In multiple sclerosis, Ag-specific Tregs targeting the disease-associated Ags were highly efficacious in reverting the ongoing disease (8, 40). Specifically, the nonobese T1D NOD.Cd28–/– mouse model deficient in Tregs developed diabetes at an accelerated rate as compared with NOD mice. Injection of NOD Tregs into NOD.Cd28–/– mice could delay and, in some cases, prevent the development of diabetes (41). In the current study, we have confirmed that adoptive transfer of stem cell–derived autoantigen-specific Tregs but not control cells, nonspecific iPSC-Tregs, or nontissue-associated Ag-specific iPSC-Tregs caused a high level of accumulation of these cells in the pancreas and reversed the disease process of autoimmune diabetes in the mouse model of T1D that was caused by pathogenic CD8+ T cells (Figure 4 and Supplemental Figure 3). A small number of Ag-specific iPSC-Tregs was observed in LNs and spleen (Supplemental Figure 5). A possible working mechanism of stem cell–derived autoantigen-specific Tregs is mediated through the secretion of large amounts of suppressive cytokines (e.g., IL-10 and TGF-β) that were identified (Figure 6). Of note, Ag-specific iPSC-Tregs have a functional stability for which we had previously demonstrated in an autoimmune condition (12), and FoxP3 expression in these cells persisted for 6 weeks after adoptive transfer in the diabetic mice (Figure 3). We further show that ICAM-1 (also known as CD54), a member of the Ig-like superfamily of adhesion proteins, plays a critical role in the accumulation of the pathogenic CD8+ T cells in the pancreases of the mice with autoimmune diabetes. ICAM-1 is a cell surface glycoprotein expressed in endothelial cells and some immune cells. ICAM-1 expressed in diabetic pancreatic tissues can bind to integrins of type CD11a/CD18 or CD11b/CD18 on pathogenic CD8+ T cells, and this binding facilitates the accumulation of pathogenic CD8+ T cells in the panaceas. The expression of ICAM-1 in the pancreases of diabetic mice is much higher than that in normal mice. In contrast, in the pancreases of diabetic mice, the adoptive transfer of autoantigen-specific iPSC-Tregs can dramatically reduce the expression of ICAM-1. This cell transfer can also reduce the accumulation of autoreactive CD8+ T cells in the pancreases and eventually protect the pancreatic β cells from further devastation (Figure 5). This conclusion is in line with those of previous studies showing that the expression of ICAM-1 is critical for the development of experimental autoimmune encephalomyelitis (42, 43). The role of IFN-γ, a prototypical Th1 cytokine, has been appreciated in the development of T1D, because the treatment of NOD mice with an anti-IFN-γ monoclonal antibody prevented the development of the autoimmune diabetes (23, 24). We detected an elevated amount of IFN-γ in both effector CD8 and CD4 T cells of the diabetic islets and showed that adoptive transfer of stem cell–derived autoantigen-specific Tregs markedly decreased the production of the proinflammatory cytokine in the pathogenic T cells. We demonstrated that the autoantigen-specific iPSC-Tregs protected the islets from mass destruction via suppressing the production of pathogenic IFN-γ (Figure 7). As other proinflammatory cytokines, such as IL-21 (44), IL-17 (45), TNF-α (46), and IL-1β (47) also play roles in the pathogenesis of T1D, studies on these proinflammatory cytokines would be needed to better exploit the stem cell–derived tissue-associated Tregs as a therapeutic approach against autoimmune disorders. The Tg model used in this study highly expressed OVA as surrogate autoantigen and received approximately 2 million of OVA-specific iPSC-Tregs for the treatment, which may be much higher than in the clinical setting with diverse low-abundance target peptides. However, the understanding of islet Ags has previously resulted in translation into new strategies by targeting tissue-specific immune interventions to prevent disease progression as well as to reverse T1D. A big advantage of our approach is the development of large numbers of autoantigen-specific nTreg-like suppressive cells (Figure 1 and Supplemental Figures 1 and 2), which can accumulate in the inflamed tissues and suppress local pathogenic immune cells. In addition, the stem cell–derived Tregs maintain stability for up to 3 months (11, 12), because the overexpression of FoxP3 has the ability to suppress the potential switch of Tregs to Th17 cells. Of note, the selection of optimal autoantigens for the development of tissue-associated Tregs is critical for the success of the Treg-based T1D immunotherapy. Taken together, the current study provides insight into the mechanisms by which stem cell–derived tissue-associated Tregs suppress autoimmunity in T1D and suggests that stem cell–derived autoantigen-specific Tregs have a great potential to be adapted as an immunotherapy for T1D. Methods Cell lines and mice. The mouse iPS-MEF-Ng-20D-17 cell line was obtained from the RIKEN Cell Bank (48). The OP9-DL1/DL4/I-Ab cell line was generated by a retroviral transduction of the OP9 cells (12). The SNL76/7 cell line (ATCC SCRC-1049) was purchased from ATCC. C57BL/6 (B6), Rag1–/–, B6 mOVA RIP (RIP-mOVA) Tg, and OT-I TCR Tg mice were purchased from The Jackson Laboratory. Cell culture. iPSCs were maintained on feeder layers of irradiated SNL76/7 cells in 6-well culture plates (Nunc) and were passaged every 3 days (48). Retroviral transduction and generation of OVA-specific iPSC-Tregs. cDNA for FoxP3 with OVA 322–339 (ISQAVHAAHAEINEAGR)-specific I-Ab-restricted TCR genes (Vα2 and Vβ5; obtained from Dario A. Vignali, University of Pittsburgh, Pittsburgh, Pennsylvania, USA) or LCMV (SMARTA1; SM1) gp61 (GLKGPDIYKGVYQFKSVEFD)-specific I-Ab-restricted TCR genes (Vα2 and Vβ8; obtained from Matthew A. Williams, University of Utah, Salt Lake City, Utah, USA) was used for retroviral transduction of mouse iPSCs and the generation of OVA- or SM1-specific iPSC-Tregs (12). Antibodies. PE-, PE/Cy7-, Alexa Fluor 647–, APC-, or APC/Cy7-conjugated anti-mouse TCRVβ5 (MR9-4), CD4 (GK1.5), CD11b (M1/70), TGF-β1 (TW7-16B4), and FoxP3 (MF-14) were obtained from Biolegend. FITC- or PE-conjugated anti-mouse CD8 (6A242) was obtained from Santa Cruz Biotechnology. APC-conjugated IL-10 (JES5-16E3) was purchased from BD Biosciences. Rabbit insulin antibody (C27C9; 3014) was purchased from Cell Signaling and anti-ICAM1 (YN1/1.7.4; ab25375) antibody was obtained from Abcam. Flow cytometric analysis. T cells from the pancreatic LNs were collected and intracellular IL-10 and TGF-β were analyzed by flow cytometry after gating on live CD4+ or CD8+ cells. RT-PCR. MiDR OTII 2A FoxP3-transduced mouse iPSCs were cocultured with OP9-DL1-DL4-I-Ab in the presence of mFlt-3L for 7 days. Differentiated iPSCs were separated and adoptively transferred into Rag1–/– mice. Mice were housed for 6 weeks for in vivo maturation of the iPSC-Tregs. Six weeks later, spleens and LNs were collected from normal B6 mice and cell transferred Rag1–/– mice. CD4+CD25+ cells were sorted and total RNA was extracted from sorted cells using QIAgen RNeasy mini kits. Samples were subjected to reverse transcription using a high-capacity cDNA synthesis kit (Applied Biosystems). PCR analysis was performed by TaqMan real-time PCR (Thermo Fisher Scientific). Ex vivo stimulation assay. Mice were sacrificed 4 weeks after adoptive cell transfer and single-cell suspension was then prepared from the pancreatic LNs. Cells were stimulated with plate-coated CD3 plus soluble CD28 antibodies. Productions of IL-10 and TGF-β were determined by intracellular cytokine staining (12). Murine autoimmune diabetes model. Autoimmune diabetes was induced in F1 mice that were crossed between RIP-mOVA Tg with OT-I TCR Tg mice by i.p. injection with VACV-OVA (2 × 106 PFU/mouse) (49). All of the mice developed diabetes after viral injection. Blood glucose was measured 1 week after the VACV-OVA injection. Blood glucose measurement. Blood glucose levels were determined using a Glucometer Ascensia Elite XL (Bayer). Six hundred milligrams per deciliter is the maximum measurable glucose reading. Mice were typically considered diabetic with readings of >250 mg/dl. Adoptive cell transfer. B6-mOVA Tg × OT-I TCR double-Tg mice (F1) were immunized with VACV-OVA for 1 week. At week 10, OVA-specific pre-iPSC-Tregs or iPSC-derived control cells (3 × 106) were i.v. transferred into diabetic mice. Four to six weeks later, mice were euthanized, and the pancreatic tissues were removed for histopathological examination. Histology and immunohistochemistry. For H&E staining, pancreatic tissues were fixed with 10% neutral formalin solution (VWR), and the fixed samples were prepared and stained as described previously (48). For immunofluorescent microscopy, the pancreatic tissues were frozen in cryovials on dry ice immediately after resection. Cryosectioning and immunofluorescent staining were performed as described previously (48). Statistics. Multiple Student’s 1-tailed t test or 1-way ANOVA or 2-way ANOVA analysis was performed to analyze the differences between the groups, using GraphPad Prism. A P value of less than 0.05 was considered significant. Study approval. The present studies in mice were reviewed and approved by The Texas A&M University Animal Care Committee and were in accordance with the guidelines of the Association for the Assessment and Accreditation of Laboratory Animal Care. Author contributions JS and JMY designed the experiments, analyzed data, and contributed to the writing of the paper. MH, FL, and JKD performed the experiments. DF and SSA provided reagents in the animal model. XX and XR analyzed data. Supplemental material View Supplemental data Acknowledgments JS is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. This work was supported by National Institutes of Health grants R01AI121180, R01CA221867, and R21AI109239 and the American Diabetes Association (1-16-IBS-281 to JS). Footnotes Conflict of interest: The authors have declared that no conflict of interest exists. Copyright: © 2019 American Society for Clinical Investigation Reference information: JCI Insight. 2019;4(7):e126471. https://doi.org/10.1172/jci.insight.126471. References Delovitch TL, Singh B. The nonobese diabetic mouse as a model of autoimmune diabetes: immune dysregulation gets the NOD. Immunity. 1997;7(6):727–738. 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