REVIEW doi: 10.1111/sji.12338 ..................................................................................................................................................................

New Insights and Biomarkers for Type 1 Diabetes: Review for Scandinavian Journal of Immunology M. T. Heinonena, R. Mouldera & R. Lahesmaa

Abstract Turku Centre for Biotechnology, University of Turku,
Abo Akademi University, Turku, Finland

Received 21 June 2015; Accepted in revised form 25 June 2015 Correspondence to: R. Lahesmaa, Turku Centre for Biotechnology, University of Turku and
Abo Akademi University, Tykist€okatu 6 FI-20520 Turku, Finland. E-mail: [email protected] a

These authors have contributed equally to this work.

The increasing incidence of type 1 diabetes observed in the past 60 years has spawned massive efforts in multiple research fields to elucidate the aetiology of this disease. While GWAS studies provide a good genetic basis for the current knowledge, it is clear that environmental triggers and their influence in disease prevalence and origin are highly important. The realization of disease heterogeneity has created a requirement for better biomarkers to complement the known autoantibody markers and to more successfully predict the severity and onset time of the disease. Such biomarkers would be needed both for prevention as well as for monitoring disease activity and response to preventive and therapeutic measures. Systematic holistic approaches concentrating on the triggering molecular mechanisms, pancreatic beta cells, immune response, as well as the influence of diet and environment, are necessary to understand the disease pathogenesis and find a cure. The current genomic knowledge is being broadened with accompanying studies in epigenetics and transcriptomic regulation, metabolomics, proteomics and lipidomics, covering the whole system from beta cells, the profile and cellular balance of the infiltrating lymphocytes, to gut microbiota and viral infections. Here we highlight interesting recent findings in type 1 diabetes research.

Introduction Type 1 diabetes (T1D) is an autoimmune disease that occurs when the body can no longer produce insulin due to the destruction of the insulin producing b cells of the Islet of Langerhans in the pancreas [1]. The disease is associated with genetic susceptibility and there is a large body of evidence supporting the influence of early childhood diet, infections, environmental components and other nongenetic factors in its aetiology. Alarmingly there has been a steady increase in the incidence of T1D during the past 60 years [2, 3], leading to increased efforts to understand the disease phenotype. A number of longitudinal sample collections from at risk subjects have thus been established, particularly in geographic regions where the risk is highest. The Diabetes Auto Immunity Study in the Young (DAISY) was started in July 1993 to study how genes and the environment interact to trigger the onset of T1D. Similarly, the Diabetes Prediction and Prevention study (DIPP) was established in Finland in 1994 [4], and has been since mirrored in other regions, e.g. Sweden (DIPIS [5]), Germany (BabyDiab, [6]) and the USA (The Environmental Determinants of Diabetes in the Young, TEDDY [7]). In these studies, newborns are screened for

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T1D risk and at risk children are recruited to periodically visit the clinic where biological samples are collected (every 3 or 6 months). Notably, due to the level of participation and adherence to the study and sample collection, there is age, risk, gender and geographically matched samples for diseased and healthy children. These unique longitudinal sample series provide an exceptional opportunity to find markers that would reflect the disease process from the birth to the diagnosis, give insights into potential pathogenetic mechanisms as well as biomarkers that could be used to predict the progression to T1D and monitor its activity at different stages. Moreover, studying such follow-up samples from children who represent distinct clinical outcomes will enable us to identify markers that can be used for stratification of children according to different disease subsets. This will be particularly important for preventive and therapeutic strategies and trials to test them. Recent studies using these biobanks and exploiting a range of methods including transcriptomics, lipidomics, metabolomics, proteomics and metagenomics have provided compelling new insights into disease process and pathogenesis. In this review, we consider recent progress in the identification of biomarkers and understanding the influence of the diet and environment on the pathogenesis

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of T1D, with particular focus on the use of data from these cohorts, in addition to recent data from pancreatic organ donors and autopsies. Figure 1 provides an illustration of the triggering factors and potential sources of biomarkers that have received recent attention in T1D research.

T1D associated autoantibodies To date the only truly established markers that have been detected in association with the autoimmune attack are a panel of serum borne autoantibodies that include islet cell autoantibodies (ICA), antibodies to insulin (IAA), glutamic acid decarboxylase (GAD), protein tyrosine phosphatase (IA2) and zinc transporter 8 (ZnT8). These have been evaluated in terms the underlying risks of their order of presentation and their different combinations. For instance, in the evaluation of data from the Finnish DIPP cohort, the influence of the identity of the first primary antibody and the age of seroconversion was assessed in 520 children who progressed to T1D. IAA was the most common (320 children), which appeared in the second year of life [8]. Recently, the data from three major long term studies in Finland, Germany and United States were combined to evaluate their collective observations. In keeping with earlier conclusions, the majority of children at risk of T1D

who displayed multiple autoantibody seroconversion progressed to diabetes within 15 years [9]. However, as in the order of 2–5% of patients clinically diagnosed with T1D are found negative for known antibodies, there may still be other autoantibodies to be found. Although efforts to detect additional autoantibodies have been made, the former five autoantibodies remain as the key markers [10, 11]. In addition, the effort to find SNP markers associated with autoantibody appearance continues. In the frame of the TEDDY study, recent results revealed new insights on this matter [12]. T€orn et al. reported that among the high-risk genotype carriers aged under 9 years, they could verify four T1D and seroconversion linked susceptibility loci from the eight previously reported ones; PTPN22, ERBB3, SH2B3 and INS. They showed that although all of these are associated with T1D, ERBB3, SH2B3 are especially associated with autoantibody appearance. They also suggested that some SNPs are rather predictive for autoantibody appearance, whereas others are for T1D, and this could be caused by the environmental factors associated with the disease.

From the genome to the transcriptome and beyond The risk for developing T1D can be assessed by genotyping, whereby polymorphisms of the human leucocyte

Figure 1 Summary of triggering factors and biomarkers in type 1 Diabetes. There is a multitude of epidemiological factors that may trigger the onset of type 1 diabetes. Suspected environmental factors include some that may begin their influence already in utero, as well as dietary elements, such as introduction of cow’s milk and wheat, and lack of exposure to sunlight/ low vitamin D levels. The pancreas, in which the beta cells are destroyed by autoimmune reaction, has a major role in the disease. The lymphocyte driven destruction is mostly mediated by the imbalanced immune homeostasis. The effect of viral infections has recently been supported by new data rising in the field. The gut microbiome has also been shown to have an impact. The quest to find more accurate biomarkers, causative genes and molecular mechanisms, besides the classical autoantibody markers and GWAS studies, has created new and noteworthy transcriptomic, epigenetic and proteomic data. Recent discoveries are paving the way to improved biomarkers to predict and prevent T1D and to stratify patients for therapeutic and prevention trials.

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246 New Insights and Biomarkers for Type 1 Diabetes M. T. Heinonen et al. .................................................................................................................................................................. antigen genes (HLA-DQB1) have been found to be the most common linkage. Notably, however, only 10% of the subjects displaying these genetic traits develop disease. A wider panel of (less frequent) risk alleles has been identified and GWAS studies have been conducted in search of other genetic variants and combinations that may either increase or protect from the risk [13]. Altogether, the past era of GWAS has produced close to 60 T1D susceptibility loci in various chromosomal regions. Currently the focus is upon analysis and combination of the vast resource of data produced in the GWAS field, from which the aims are to pinpoint the causative genes and their function in disease aetiology, and to discover mechanistic pathways. How T1D susceptibility loci influence the function of the immune system, pancreatic beta cells and their interactions with environmental triggers is an active area of investigation. In this review, we will highlight selected recent findings. For a broader historical perspective of what has been discovered in the past from GWAS studies, the recent detailed reviews, such as the one by Bakay et al. [14], are recommended. As an example where researchers have undertaken the re-analysis of GWAS data and combined these with other data types, Guo et al. [15] recently used Bayesian colocalization analysis to study whether genetic associations to different immune-mediated diseases, including T1D, were consistent with a causal variant that regulates gene expression in B cells and monocytes. They integrated disease GWAS with eQTL data and highlighted candidate causal genes and cells. In the course of their study they also refined the earlier finding that regulation of the CTSH gene is contributory to T1D aetiology, and defined the putative associations of this marker in detail [16]. CTSH overexpression has been shown to protect the insulinsecreting cells against cytokine-induced apoptosis, making this a valuable finding with regard to potential treatment and prevention. In further work by the same group, Guo and Wallace [15] used a new bioinformatic approach for variant set enrichment analysis, VSEAMS, to identify novel transcription regulators contributing to T1D. They extended a non-parametric SNP set enrichment method and tested for enrichment of GWAS signals in functionally defined loci. Their results revealed enrichment of T1D GWAS associations near genes targeted by IKZF3, which localizes within a known T1D susceptibility region, BATF and ESRRA, which are associated with other autoimmune disorders. Another interesting approach has been to focus on the genes in GWAS studies rather than just for the SNP markers. New susceptibility genes were found by Qiu et al. [17] with a gene-based GWAS study. Finally, regarding gene regulation, the epigenetic factors together with emerging knowledge about non-coding RNAs as transcriptional regulators have also been integrated into T1D research [18–20]. A recently reported major study, fine-

mapped the T1D susceptibility loci and discovered new markers that localize in active thymic gene enhancer regions, in addition to active T and B cell enhancer regions [21]. They indicated that a potential follow-up from this would be to discover causal genes and regulators by analysing which promoter regions are interacting with the enhancer regions. In parallel with the development of new approaches for the analysis of susceptibility loci and causative genes, pathway analysis approaches have been used in order to elucidate the T1D disease mechanisms. In a recent study by Evangelou et al. [22], they used summary level GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) project and succeeded in finding new associations and reported more refined pathways. In comparison to previous pathway analyses, e.g. Carbonetto and Stephens [23], differences in these results were notable in terms of the larger sample size and the removal of the MHC genes from the analysis. Nevertheless, the results were consistent with earlier findings. Additionally, in the context of future studies that might aim to define the mechanistic interactions between the causative genes or gene regulators and/or build a holistic view about the network causing the disease phenotype, it is worth considering genetic epistasis i.e. how gene interactions can affect the phenotype [24]. Moreover, there are a number of genes important for the proper function of the immune system that should be taken into consideration, even though they have not been highlighted by GWAS studies. This is especially applicable for studies of gene–gene interactions and disease promoting pathways. In our recent study, we investigated the role of T helper cell balance regulators, GIMAP genes, in human immune-mediated diseases [25]. We discovered an association of GIMAP5 in the Finnish South-Western subpopulation, but most importantly, we observed a GIMAP5-INS interaction in the data set. The GIMAP5 genotype provided a protective effect against an INS conferred risk, and this was further emphasized by the putative regulation of GIMAP5 by the insulin induced transcription factor FOXO1. In addition to immune system specific genes not indicated by GWAS, the genes involved in beta cell function are also an important piece of the puzzle. Sartori et al. [26] found GATA factors, which are known to be central in tissue development and survival, to have an important role in beta cell function and T1D pathogenesis. Their functional studies were conducted in mice, but they also showed that multiple polymorphisms in the GATA4 region are associated with human T1D. In their summary, Santin and Eizrik [27] indicated that the polymorphisms in T1D candidate genes might affect the function and inflammation of beta cells in response to environmental cues. Stroling et al. [28] reviewed the status of beta cell function in T1D, and using a published RNA sequencing data set made a systematic human islet expression analysis of all genes located in 50

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T1D-associated GWAS loci, reporting that that 336 out of the 857 genes they tested were expressed in human islets and that many of these interact in protein networks. Beyond sequencing the genome as a potential template for risk-associated aberrations, researchers have investigated how the risk and onset is reflected in the dynamics of the whole-blood transcriptome in the pre-diabetic phases [29– 32]. Following on from preliminary studies [29], Kallionpaa et al. [31] compared the transcriptome of healthy controls with children on route to T1D and seroconverted children. The activity of genes and pathways related to innate immunity functions was observed, and notably type 1 interferon (IFN) response genes and IFN response factors were identified as central mediators of the IFN-related transcriptional changes observed prior to seroconversion. In keeping with this, and other observations from seroconverted children [30], Ferreira et al. [32] similarly reported differential abundance of transcripts associated IFN response in the PBMCs of children before seroconversion, while linking this to be the likely influence of viral infection. Kallionpaa et al. [31] also considered the influence of disease associated SNPs, including Immunochip measurements of the subjects. Differences were detected between children progressing to diabetes and their matched healthy controls with the detection of variants of a number of genes that have been previously linked to several autoimmune diseases. In terms of the regulation of gene expression, Stefan et al. [20] compared the DNA methylation patterns of lymphocyte cell lines from monozygotic twin pairs who were discordant and concordant for T1D and (3 versus 6 pairs). With the integration of this and GWAS data, significant differences were observed in the DNA-methylation of several known T1D associated genes, i.e. HLA, INS, IL-2RB, CD226.

compared serum profiles from longitudinal sample series from birth to the onset of T1D from 19 prediabetic children with control children in the same genetic risk group, and detected new markers for prediciting and monitoring the disease progression [40]. The work indicated that the protein profiles could distinguish healthy controls from children en route to diabetes and included proteomic differences detected before seroconversion. The detected differences and profiles emphasized alteration in the relative abundance of proteins associated with inflammation and immune responses as the children progressed to T1D. In the future, it will be of great interest to find out which of the findings can be generalized through crossvalidation in independent cohorts and which are more specific to a given cohort. Inflammation of the beta cells is accompanied by elevated levels of pro-inflammatory cytokines, and altered levels of chemokines and cytokines have been observed in the serum of children with a high T1D risk [41, 42]. Increased levels of IL6, IL-16, TNFa and IL-8 were detected in children with T1D associated antibodies long before the diagnosis of T1D [43]. Moreover, in relation to viral triggers for T1D (see below), distinct cytokine profiles have been observed with islet autoimmunity and enterovirus infection [44]. Purohit et al. [45] recently presented an overview on the scope for multiplex cytokine assays in the study of environmental triggers of T1D, citing their advantages in terms of throughput and cost, although emphasizing the importance of thorough assay development. Another viable route to protein markers from serum may be from the analysis of extracellular vesicles, such as the micro-particulate fraction [46], exosomes or other microvesicles [47].

Serum proteomics

Inflammation plays an important role for beta cell function in different ways at various stages of T1D development (reviewed in Eizirik et al.) [48]. The efforts of the Network for Pancreatic Organ Donors with Diabetes (nPOD) project have provided fresh insights into beta cell biology and its role in T1D. On the basis of the results from the nPOD project, Reddy et al. [49] observed a population of putatively pathogenic leucocytes among insulin producing islet cells, suggesting that the leucocytes detected in the exocrine region possibly may be pathogenic to residual beta cells in long-standing diabetic individuals and participate in the disease pathogenesis. Similarly, immune cell infiltrates have been detected also in exocrine pancreas pointing out that the underlining aetiology of T1D may not be restricted to beta cells [50, 51]. In the Diabetes Virus Detection (DiViD) project, pancreatic tissue from patients recently diagnosed with diabetes and organ donors (diabetic and no diabetic) has been collected to study the aetiology of T1D. Importantly, for patients where glucose-induced insulin secretion could

The targeted measurement of serum protein markers is regularly used in the clinic as general evaluation of health status, where for instance glycated haemoglobin (HbA1c) is monitored to assess blood sugar levels and the diabetic condition [33]. Serum potentially carries thousands of proteins. It is a complex mixture with significant interindividual variation and dominated by several highly abundant proteins [34]. As with the case of transcripts, changes in the serum proteome could reflect or predict the onset of T1D. However, while a number of studies have compared the serum proteome of T1D patients [35–37], only a few reports have considered markers prior to diagnosis [38–40]. McGuire et al. [38] targeted analysis of the cord blood of T1D developing children, and although discriminating patterns were discerned between healthy controls and children who later developed T1D, the distinguishing peaks were not identified. Recently, we

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Pancreatic beta cells

248 New Insights and Biomarkers for Type 1 Diabetes M. T. Heinonen et al. .................................................................................................................................................................. be still observed after the onset of T1D, a normal glucoseinduced insulin response could be observed after several days in a non-diabetogenic environment in vitro [52]. All in all, these publications, among others, have shown the importance of the pancreas and considerations of the roles of both endocrine and exocrine cells in the disease aetiology. Other important aspect rising from the beta cell research is the role of viral infections [53], which is discussed later in this review.

proposed that low phospholipid levels at birth could contribute to early induction of islet autoimmunity [61]. With similar measurements from the German BABYDIAB study, distinct metabolic profiles were associated with age and islet autoimmunity [58]. The observations from the cord blood measurements were taken as an indication of the development of a metabolomics phenotype in utero. Notably, data considering regional difference in developed and emerging societies have similarly supported the role of in utero effects on the offspring (see below) [62].

Proteomics of the pancreas, islets and beta cells The progress made using proteomics to study beta cells has been recently reviewed by Crevecoeur et al. [39]. In brief the developments in this area of research has followed a course consistent with the evolution of proteomics technologies of the past 10 years. From initial studies using 2D gel electrophoresis to create reference maps of the pancreas and islets, to studies using multidimensional chromatography together with mass spectrometry. Studies of the beta cell proteome have generally investigated the effect of stimulation with inflammatory cytokines (IL-1b and IFNϒ) [39], although as with the other work with samples of pancreatic origin, the use of human material has been the exception rather than the rule. Studies of the stimulation of human beta cells indicated changes in proteins involved in cytoskeleton remodelling and vesicle motility. Investigations have also considered T cell reactivity against beta cell peptides [54], and post-translationally modified beta cell autoantigens have been reported that demonstrate recognition by T cells of T1D patients [55, 56].

Metabolomics The interaction of an organism and its environment is primarily reflected by changes in its metabolic profile. In this manner, the detection of perturbations in the metabolome or distinctive patterns can provide early signatures of T1D risk. Studies of the serum metabolome of subjects developing T1D have included global analysis of polar metabolites and lipids in longitudinal samples from infancy (including cord blood) through to diagnosis [57–61]. Data from the cord blood studies have suggested early metabolite traits may predispose the subjects to a diseased outcome. Finnish children who progressed to T1D had decreased phosphatidylcholines (PCs) at birth (cord blood) and diminished ether phospholipids throughout the followup [57]. Increased levels of pro-inflammatory lysophosphatidylcholine, glutamate, and branched chain amino acids, and decreased levels of several TCA cycle metabolites were also observed in serum samples collected before the appearance of autoantibodies. In related metabolomics studies of umbilical cord blood from a Swedish cohort, it was similarly indicated that the profiles could be used to identify children at increased risk for T1D. The authors

Gut microbiome Beyond the makeup of the human genome, the body is host to a subpopulation of microbiota that outnumbers its own cells 10 times over. From the moment of birth there is a colonization of the gut microbiome, and thus our immediate environment and the manner of delivery can influence its diversity [63, 64]. Gut microbiota affect both the innate and adaptive immune system. Colonization of the gut can thus serve to educate the immune system, and while some gastrointestinal infections can be protective others may predispose to risk [65, 66]. Evaluation of how aberrations of this symbiosis can precipitate or prevent diabetes has included analysis of stool samples prospectively collected T1D risk cohorts (i.e. DIPP and DIABIMMUNE) [67–69]. Alterations in the gut microbiome of infants in the period between seroconversion and diagnosis were interpolated from these data. Decreased alpha diversity (how many types of species of microbiota there are in a sample) was observed in seroconverted children prior to diagnosis, although not in the seroconverted children who were not diagnosed with T1D during the time frame of the study [67]. Higher levels of human b-defensin 2 (hBD2) were also found in the early samples of children who developed T1D. In keeping with emerging evidence of the immuno-metabolic effects of butyrate and other short-chain fatty acids, de Goffau et al. [68] observed that lactate-producing and butyrate-producing bacteria were less abundant in autoantibody positive children and thus implicated in b cell autoimmunity. Similar observations have also been made in studies of microbiota and type 2 diabetes [70]. Davis-Richardson et al. studied the early development of the gut microbiomes from stool samples of 76 children at high genetic risk for T1D. A higher abundance of Bacteroides dorei was observed before seroconversion, indicating that changes in the composition of the microbiome could be useful in the prediction of T1D autoimmunity in genetically susceptible infants [69]. In summary, there is much evidence supporting the correlations of the diversity of microbiota in the gut with the balance of the immune responses. To complement the information obtained from stool samples, analysis of gut biopsies if available would be valuable. Moreover, in terms of proteomics measurements, powerful new technologies have recently been combined such that detailed character-

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ization of tissue from biopsy samples and their associated microbiota can be achieved in a time efficient manner with minimal sample requirements [71].

Viral triggers of T1D The role of viral infection has long been considered as a potential trigger for the development of T1D [72]. The candidate list of risk-associated viruses has ranged from mumps to Rubella and one hypothesis has been that the observed trend for increasing T1D could reflect the growth of widespread vaccination programs. Overall, enteroviruses have remained as a main candidate [72, 73]. Enterovirus RNA has been detected in the pancreatic islets from newborn babies who died from fulminant coxsackievirus infection [74], and enteroviral capsid protein has been detected in the beta cells of patients who had either been recently diagnosed with or had established T1D [75, 76]. In keeping with evidence that the effect of enterovirus infection on the risk of T1D development is strain specific [77], it was shown that Coxsackievirus B1 is associated with an increased risk of beta cell autoimmunity [78]. In the latter study, serum samples were screened for neutralizing antibodies against 41 different enterovirus types in longitudinal sample series from the DIPP study. The strongest risk was observed when the infection occurred a few months before autoantibodies appeared, while the presence of maternal antibodies against the virus reduced the risk, as did the coxsackieviruses B3 and B6. On the basis of evaluation of the effect of enterovirus infection in pregnancy it was concluded that this was not a major risk factor but might play a role in some susceptible subjects. Further consideration of the data from other European nations supported the notion of the diabetogenic nature of the CBV1 virus [79]. The detection of enteroviral RNA in stool samples from T1D children has been previously reported, and recent investigations have considered changes in the gut virome at the onset of autoimmunity. In this study (19 versus 19) no dramatic changes were observed [80].

Diet environment and prevalence The accumulation of several prospective longitudinal sample collections has provided a valuable resource to study the regional differences, changes and the influence of diet and various interventions. Nested in these studies has been the evaluation of variables such as breast feeding [81], food diversity during the first year of life, the intakes of cow milk and fruit and berry juices [82, 83], early introduction of gluten [84], different types of wheat, rye, cereals, fish, fish oil and egg [82]. These data have thus been considered in terms of T1D/beta cell immunity as well as for the risks of asthma and allergies in childhood. Clinical trials conducted in parallel have considered nasal

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and oral insulin as well as hydrolyzed formula milk [85– 87]. To date these have not proved effective. Evaluation of the cumulative data from Finland has shown that the growth of T1D incidence in children below the age of 15 years has now plateaued [88]. Further data suggest that the observed trend could be associated with the more frequent use of vitamin D supplements [89]. Conflicting evidence has also been reported [90, 91]. Further evaluations of environmental influences upon T1D and allergy have extended to the comparison of cohorts from neighbouring geographic regions. The DIABIMMUNE study has compared and followed newborns from the towns of Espoo (Finland), Tartu (Estonia) and Petrovoska (Russia). The comparison represents the contrast between developing and developed society, and city and rural environment. The interpretations of this study follow many of the considerations of the hygiene hypothesis, i.e. that recent changes in living conditions, family sizes, exposure to animals and an increasing hygiene may influence the maturation of the immune system [92]. The data from the DIABIMMUNE study support the contrasting rates of T1D incidence between these regions, and implicates the plasticity in the developing immune system towards environmental factors that may begin already in utero [62]. Notably, in the cord blood samples from Petrovoska, several pivotal innate immune response mediators were upregulated and the gene expression profiles were clearly different than those of cord blood samples of babies born in Estonia or Finland. The observed profiles were compared with published data comparing immune responses in newborns versus young children, bacterially challenged cells from children, and children with sepsis [62]. This analysis demonstrated that the immune responses of newborns from Petrovoska resembled that of 1 year old children. Such an early maturation of immune response already in utero could thus be protective against the development of immune-mediated disease in later life.

Cellular profiles The human leucocyte antigen gene encodes for major histocompatibility complex (MHC) proteins, which are responsible for regulation of the immune system in humans. The genes of the MHC locus include MHC class I and MHC class II. MHC class I molecules present antigens binding to CD8+ cells, and MHC class II molecules are expressed on antigen presenting cells and present antigens to T helper cells (CD4+). Polymorphisms in these genes may thus affect the nature of the antigen presented. There has been a long established association of T cells with T1D, most notably with involvement of autoreactive CD4+ T helper cells and CD8+ T cells in destruction of the beta cells. Recent studies have isolated the cellular subtypes from at risk

250 New Insights and Biomarkers for Type 1 Diabetes M. T. Heinonen et al. .................................................................................................................................................................. subjects/diseased and studied for instance the diversity of the T cell receptor in patients [93]. With the recent recognition of other Th cells subtypes, Th17 cells have also come into focus as a player in the events leading to autoimmunity. The observation of increasing Th1/Th17 plasticity with progression from b cell autoantibody positivity to type 1 diabetes, was reported, suggesting a Th1/Th17 influence on tissue destruction in autoimmune conditions [94]. The emerging new area of Tfh cells and their role in autoimmunity has produced interesting new insights into the mechanisms of T1D [95]. Kenefeck et al. reported that in comparison to healthy controls, the memory CD4+ T cells (CD4+ CD45RA ) of T1D patients have increased levels of molecules characteristic for Tfh cells and have a higher percentage of the CXCR5 + ICOS+ effector cells. The role of Treg cells has also received much attention [96, 97]. Ryba-Stanislawowska et al. have shown that the balance between Treg cells and Th17 cells is disturbed in T1D patients [98]. A major line of investigation also includes the interplay of IL2 signalling and Treg cells. The genetic variation in IL2Ra is known to be causative for T1D, but it has also been shown that it correlates with defects in Treg function [99]. This has been taken into consideration in clinical trials, as low doses of IL2 stimulate Treg cell proliferation without an effect on effector T cells [100]. It has also been shown that Treg cells inhibit inflammation and contribute to the disease progression via IL10 and TGF-b secretion [101]. IL10 production by Treg cells was shown to be essential in the immune response at environmental interfaces, such as lung and intestine, and Th1 responses can be suppressed by the TGF-b produced by Treg cells [102, 103]. Beyond investigations of the causative nature of cellular types, research has also been made towards prevention by the use of expanded peripheral or cord blood T regulatory cells for immune cell therapy to stop immune-mediated destruction of the beta cells [104].

Conclusion While a universal phenotype or trigger has yet to be identified for T1D, its aetiology remains under investigation. The aspects of genetic risk together with autoimmunity remain as predictors of risk and onset, and yet the evidence for such triggering elements as limited exposure to sunlight/vitamin D or dietary factors, such as cow’s milk remain inconclusive. Re-occurring evidence has supported the influence of viral factors for several decades, thus presenting the question if indeed T1D is triggered by a virus, (at least in some cases/regions), would vaccination reduce the incidence? Similarly, if an imbalance in the gut microflora may precipitate the illness, could diet, probiotics, other microbial supplements, or specific factors or metabolites released from microbes reduce the risk of T1D.

Evidence suggesting in utero effects for protection and predisposition has also emerged, with further indication that these are dependent on the environment. However, any subsequent health/preventative directives related to these would be less subtle. Better biomarkers for classifying the heterogeneous patient population and for the prediction, prevention and monitoring the disease are urgently needed. Analysis of longitudinal samples linked to carefully collected clinical information with cutting-edge emerging technologies and advanced data analysis and integration methods provides an enormous opportunity for breakthroughs in T1D research. The near future is likely to provide further important insights into disease pathogenesis, new practical assays for predictive and preventive medicine and for clinical trials to find cure for T1D.

Acknowledgment This work was supported by the Academy of Finland Centre of Excellence in Molecular Systems Immunology and Physiology Research, 2012–2017, Decision No. 250114, JDRF, The Sigrid Juselius Foundation and the National Technology Agency of Finland.

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