**3.2 MHC: Genes of instability**

196 Autoimmune Disorders – Current Concepts and Advances from Bedside to Mechanistic Insights

the organism to diseases, drugs and/or environmental factors. A deeper insight into gene structure and regulatory mechanisms can significantly facilitate diagnosis and treatment of individuals at risk and, in a more distant future, provide the physician with potent tools for diagnosing diseases, preventing their progression and implementing effective therapy as

Rapid progress in science and technology created necessary prerequisites for highthroughput screening of several hundreds of thousands of SNP variants and enabled adequate involvement of all human DNA blocks in selection of the disease associated variant provided the latter is present in the genome. From theoretical standpoint, linking of genotyping data to epidemiological findings provides a way to identification and/or characterization of gene sequences and gene interactions with the environment determining the susceptibility of various body cells and tissues to normal genetic variations and/or the

Genomewide association studies represent an effective tool for detecting genetic associations between specific genetic variations and complex pathological conditions in large cohorts of the general population and provides a deeper insight into mechanisms

The contribution of SNP's to the pathogenesis of many common diseases is relatively small and does not exceed 5–10%, which significantly restricts their application as markers for predicting disease risks. However, today well-established associations number in hundreds and their panel grows with every passing week. Taking into account considerable investments in the search for hitherto unidentified sources of inherited risks, it may be expected that existing (both genomic and nongenomic) models for estimating potential risks

The current need for highly multiplexed tests increases with every passing day. Innovative gene chip- and sequencing-based technologies displace rapidly traditional methods for establishing variations and mutations in the human genome. In future, the advent of improved nanotechnological sequencing protocols may further increase the accuracy and reduce the cost of genetic analysis. The idea of complete sequencing of the human genome at the cost of \$1000 is becoming more and more realistic. The project, which got the name "\$1000 genome", is expected to improve existing protocols through direct sequencing of individual DNA molecules. This approach is potentially oriented at elimination of the amplification step, further reduction of chemical reagents expenditure and construction of a

The feasibility of reliable and low-cost estimation of human genetic variations put forward the idea of personalized medicine as an indispensable element of modern-day public health care. The key principle of personalized medicine is in that the health status of any human individual is most effectively controlled through implementation of individual preventive and curative treatment schedules. Although unsolvable controversies between principles of personalized medicine and populational (probative) medicine really exist, they are not inconsistent. Novel decisions are being taken in the private and public sectors, and those would enable progressive studies to provide the linkage between personalized and

All-round cognition of gene structure and genetic regulatory mechanisms is extremely important not only from theoretical, but also from practical point of view, particularly, for

high-precision database of genetic sequences in the foreseeable future.

early as the preclinical stage.

underlying disease.

probative medicine.

underlying genetic predisposition to various diseases.

will soon be improved and rationalized.

As can be seen, identical genes can simultaneously trigger a variety of body-related autoimmune disorders. The latter form a disease-based cluster, which further develops into a polyglandular autoimmune syndrome. (Fernando MM et al., 2008)

Fig. 2. The role of MHC genes in the development of diseases the key pathogenetic role in which is played by genetic predisposition. Some genes (DR7, DR8) determine the risk for only one disease (DR7, DR8), while others are responsible for two (DR1, DR10), three or even more (DR4) diseases. However, their presence is not prerequisite to the development of pathological processes, but, rather, significantly increases the likelihood of their early occurrence during the patient's lifetime.

MHC represents a large family of genes encoding molecules of three major HLA classes, viz., HLA class I, HLA class II and HLA class III. MHC plays an essential role in the functional activity of the immune system being directly involved in presentation of peptide antigens to APCs and formation of the so-called MHC restriction phenomenon. To-date, MHC is the most thoroughly investigated gene family in the human genome by virtue of its extremely close linkage to autoimmune diseases, hypersensitivity to infections and hyperbolic immune responsiveness. These genes are usually present in patients with severe autoimmune disorders and/or imbalances, e.g., rheumatoid arthritis (RA), multiple sclerosis

Preclinical and Predictive Algorithms in Monitoring

**3.4 HLA class II: A wheelhorse or a time bomb?** 

supported by promoting effects of HLA class II Ags.

of patients with primary biliary cirrhosis. (Béatrice Faideau et al. 2005)

et al. 2009)

Patients with Autoimmune Diseases and Their Relatives-at-Risks 199

Some methods for early diagnosis of T1D e.g., *ex vivo* detection of GAD65 autoreactive T cell CD8(+) by HLA class I tetramers, are based on the use of HLA class 1 I antigens. (Giuliani L

HLA class II constitute a family of genes localized on the short arm of the 6th chromosome. These genes encode glycoproteins with an Ig-like structure and are predominantly localized on the APCs surface. Their functional role consists in presentation of Ags peptides to CD4(+) T helper cells type I. There exist several autoimmune diseases (including T1D)

Presumably, MHC glycoproteins modify positive and negative selection in the thymus allowing some immature autoAg-reactive T cells to escape from immune surveillance and thus avoid negative selection. However, presentation of morbid peptides to cytotoxic T lymphocytes (CTLs) or T helper cells by mature MHC seems to be more likely. Polymorphic variants (cytokin-related genes) residing in the vicinity of MHC classes I, II and III and non-MHC significantly increase the predisposition to autoimmune diseases and impart high (in comparison with healthy individuals) genomic instability. When the cells switch over their modality from Ag expression to MHC class II production, the molecules localized on the cell surface begin to form potentially autoreactive complexes and thus provoke self-reactive immune responses. Such nondiscriminating Ags were identified on the surface of beta cells of patients with T1D, on thyroid cells of patients with Grave's disease and on bile duct cells

Fig. 4. The distribution of diabetogenic and protective potentials of HLA class II in different racial populations. Green columns: low risk (diabetoprotective); yellow columns: medium risk (moderately diabetogenic); red columns: highly diabetogenic. The diabetogenicity of the same alleles in different populations is either similar or radically different. It is not excluded

that this parameter is controlled by environment factors.

(MS), Crohn's disease, aneurisms of large vessels (ALV), ulcerative colitis (UC), systemic lupus erythematosus (SLE) and type 1 diabetes (T1D).

Fig. 3. The role of various MHC classes I, II and III alleles in the development of T1D. The x axis designates the continuous arrangement of some MHC 123 regions. The vertical graph segment indicates the association of an allele with a specific MHC region and the typical scatter of diabetogenicity probabilities for the given allele.

#### **3.3 HLA class I: Role in T1D**

Molecules of HLA class 1, jointly with HLA class II molecules and in closest association with one another afford effective protection against T1D and risks thereof. The HLA class I compartment contains both diabetoprotective genotypes (A\*1101, A\*3201, A\*6601, B\*0702, B\*4403, B\*3502, C\*1601, C\*0401) and highly associative genes (B\*5701, B\*3906). The diabetogenic alleles of MHC class I genes display age-related features. For example, HLA-E\*0101 is predominant in patients in whom T1D developed during the first 10 years of life, while HLA-E\*0103 is found in children under 10. (Hodgkinson AD et al. 2000) Apart from borderline (diabetogenic or diabetoprotective) genes, there exist several intermediate types (A\*2402, A\*0201, B\*1801, C\*0501). All of them increase the risk of diabetes, but their role in triggering autoimmune responses is insignificant. (Noble JA et al., 2010)

The increasing number of publications devoted to genes associated with T1D (Howson JM et al., 2009; Viken MK et al., 2009) and identification of reactions stimulating or potentiating beta cell destruction testify to the fact that HLA class I initiate and potentiate autoimmune destruction of beta cells and manifest close linkage to HLA class II. (Lipponen K et al., 2010) Systemic autotolerance of homogeneic CD8(+) T cells is one of the patterns subject to regulatory control of HLA class I. It is well known that T1D is concomitant with disturbances in coordinated interactions between HLA-E CD8(+) T cells and HSP60sp that are specific to them. This phenomenon is responsible for disturbances in so-called "friend or foe" identification during a switchover of normal immune processes to self-destruction. (Jiang H et al. 2010)

Some methods for early diagnosis of T1D e.g., *ex vivo* detection of GAD65 autoreactive T cell CD8(+) by HLA class I tetramers, are based on the use of HLA class 1 I antigens. (Giuliani L et al. 2009)

### **3.4 HLA class II: A wheelhorse or a time bomb?**

198 Autoimmune Disorders – Current Concepts and Advances from Bedside to Mechanistic Insights

(MS), Crohn's disease, aneurisms of large vessels (ALV), ulcerative colitis (UC), systemic

Fig. 3. The role of various MHC classes I, II and III alleles in the development of T1D. The x axis designates the continuous arrangement of some MHC 123 regions. The vertical graph segment indicates the association of an allele with a specific MHC region and the typical

Molecules of HLA class 1, jointly with HLA class II molecules and in closest association with one another afford effective protection against T1D and risks thereof. The HLA class I compartment contains both diabetoprotective genotypes (A\*1101, A\*3201, A\*6601, B\*0702, B\*4403, B\*3502, C\*1601, C\*0401) and highly associative genes (B\*5701, B\*3906). The diabetogenic alleles of MHC class I genes display age-related features. For example, HLA-E\*0101 is predominant in patients in whom T1D developed during the first 10 years of life, while HLA-E\*0103 is found in children under 10. (Hodgkinson AD et al. 2000) Apart from borderline (diabetogenic or diabetoprotective) genes, there exist several intermediate types (A\*2402, A\*0201, B\*1801, C\*0501). All of them increase the risk of diabetes, but their role in

The increasing number of publications devoted to genes associated with T1D (Howson JM et al., 2009; Viken MK et al., 2009) and identification of reactions stimulating or potentiating beta cell destruction testify to the fact that HLA class I initiate and potentiate autoimmune destruction of beta cells and manifest close linkage to HLA class II. (Lipponen K et al., 2010) Systemic autotolerance of homogeneic CD8(+) T cells is one of the patterns subject to regulatory control of HLA class I. It is well known that T1D is concomitant with disturbances in coordinated interactions between HLA-E CD8(+) T cells and HSP60sp that are specific to them. This phenomenon is responsible for disturbances in so-called "friend or foe" identification during a

lupus erythematosus (SLE) and type 1 diabetes (T1D).

scatter of diabetogenicity probabilities for the given allele.

triggering autoimmune responses is insignificant. (Noble JA et al., 2010)

switchover of normal immune processes to self-destruction. (Jiang H et al. 2010)

**3.3 HLA class I: Role in T1D** 

HLA class II constitute a family of genes localized on the short arm of the 6th chromosome. These genes encode glycoproteins with an Ig-like structure and are predominantly localized on the APCs surface. Their functional role consists in presentation of Ags peptides to CD4(+) T helper cells type I. There exist several autoimmune diseases (including T1D) supported by promoting effects of HLA class II Ags.

Presumably, MHC glycoproteins modify positive and negative selection in the thymus allowing some immature autoAg-reactive T cells to escape from immune surveillance and thus avoid negative selection. However, presentation of morbid peptides to cytotoxic T lymphocytes (CTLs) or T helper cells by mature MHC seems to be more likely. Polymorphic variants (cytokin-related genes) residing in the vicinity of MHC classes I, II and III and non-MHC significantly increase the predisposition to autoimmune diseases and impart high (in comparison with healthy individuals) genomic instability. When the cells switch over their modality from Ag expression to MHC class II production, the molecules localized on the cell surface begin to form potentially autoreactive complexes and thus provoke self-reactive immune responses. Such nondiscriminating Ags were identified on the surface of beta cells of patients with T1D, on thyroid cells of patients with Grave's disease and on bile duct cells of patients with primary biliary cirrhosis. (Béatrice Faideau et al. 2005)

Fig. 4. The distribution of diabetogenic and protective potentials of HLA class II in different racial populations. Green columns: low risk (diabetoprotective); yellow columns: medium risk (moderately diabetogenic); red columns: highly diabetogenic. The diabetogenicity of the same alleles in different populations is either similar or radically different. It is not excluded that this parameter is controlled by environment factors.

Preclinical and Predictive Algorithms in Monitoring

Patients with Autoimmune Diseases and Their Relatives-at-Risks 201

Fig. 5. A comparison of some diabetogenic non-HLA genes having mean values for HLA. The height of the column reflects the average probability of the clinical stage of type 1 diabetes (IDDM1) for the given allele. at the same time, MHC genes manifest a high degree

**TNFAIP3 (A20),** tumor necrosis factor, alpha-induced protein 3. In the pancreas, this gene performs miscellaneous functions to include inactivation of NF-kappa B signals, prevention of inflammatory lesions of pancreatic cells, deceleration or delayed recruitment of immunocompetent cells into target organs, retardation of intercellular matrix restructuring, and so on. Studies by Liuwantara D et al. established that expression of the A20 gene is an effective mechanism of beta cell protection from TNF-induced apoptosis. Mutations in this gene and formation of SNPs initiate functional disturbances in NF-kappa B and represent the most common mechanism of disregulation and disorganization of immune reactions resulting in autoimmunity. Yet another salient feature of A20 is its ability to stimulate angiogenesis. Knockout of A20 shortens the tubule area and length in mice *in vitro.* (Grey ST

A crucial role in specification of APCs and pathogenesis of T1D is played by the **ERBB3** gene known under the official name "v-erb-b2 erythroblastic leukemia viral oncogen homolog 3 (avian)". This gene encodes the family of specific receptors to the epidermal

Mutations in ERBB3 lead to immunoregulatory collapses coupled with continuous emergence of autoreactive cells. By virtue of its ability to provide linkage between genetic predisposition, infectious diseases and adaptive immune reactions, ERBB3 has every right to be regarded as a central molecular constituent element of the T1D-inducing complex.

**PTPN22 (LYP),** known under the official name "protein tyrosine phosphatase, non-receptor type 22 (lymphoid)", encodes lymphoid-specific intracellular phosphatase able to bind to the molecular adaptor protein CBL and thus controls its activity in the signaling pathway of

PTPN22 contains several SNPs disturbing normal operation of immune mechanisms. SNP rs2476601 is a valuable biomarker of susceptibility to autoimmune diseases, but its role in NK cell biology is not yet finally elucidated. The fact that SNP rs2476601 upsets the balance between T and NK cells *in vitro* points to the involvement of PTPN22 in immune regulation

of variation and, in some special cases, diabetoprotective activity.

et al., 2003)

growth factor (EGFR).

(Hongjie Wang et al., 2010)

the T cell receptor (TCR).

This bar chart displays the most important T1D-predetermining haplotypes and their distribution in different populations. As can be seen, the presence of the same haplotype in two different populations contributes differentially to T1D-associated risks. For example, in Russians DQA1\*0301 elicits a nearly 85% risk of T1D, whereas in Latinos the risk is less than 75%. Moreover, haplotypes spreading risks of diabetes in one population can be nonspecific for other populations. As regards DQA1\*0301 its effect on acquisition of sensitivity to T1D is negligibly small in Brazilians, while in Chinese, Japanese, Arabians, Finns and Caucasians this haplotype is not associated with diabetes. These findings suggest that genetic features, haplotype frequency and contribution of specific haplotypes to susceptibility to T1D vary widely in different populations indicating different diabetogenic or protective orientation and high risk of T1D development.

Analysis of specific domains of the human genome made it possible to establish their roles in pathological processes and to get a deeper insight into molecular mechanisms responsible for instability of the human biome. The clue to the practical solution of problems in this area is to discover novel genetic markers, to secure low cost of the analysis and to ensure high accuracy of the methods employed. The totality of these factors may culminate in the construction of unique tools for subclinical diagnosis and preventive medicine.

#### **3.5 HLA class III: Role in genetic predisposition**

Far fewer (compared to HLA classes I and II) messages deal with contribution of HLA class III to background predisposition to T1D. In constructing a basic screening algorithm with special reference to early diagnosis potentials, one should take into consideration the crucial role of this region's genes in predisposition to T1D.

There exist about a dozen HLA class III genes manifesting a diagnostically significant association with T1D. These include NOTCH4 (rs2395106) responsible for susceptibility to rheumatoid arthritis and MSH5 (rs707915) associated with a high risk of T1D. As an overall trend, HLA classes II and III provoke diabetes at the highest levels of the odds ratio, while the effect of HLA class I on T1D is much less expressed (see Figure 3). (Valdes AM et al. 2006; Yamaji K et al.2006)

### **3.6 Non-MHC genes and their contribution to T1D**

Any systemic approach to T1D diagnosis demands a large set of complementary and mutually specifying biomarkers. In addition to screening of circulating autoAbs and MHC Ags, systemic analysis of non-MHC genes is extremely important for validating the diagnosis. Though the odds ratios of the overwhelming majority of MHC genes are by one order of magnitude lower than that of MHC, identification of these gene clusters allows a qualitative description of risks for various autoimmune disorders including generation and progression of insulitis and, in a more distant perspective, objective prognosis of T1D outcomes.

In all probability (and not too surprisingly), each individual gene does not act specifically upon every component of the immune system or cell metabolism, but, rather, exerts a complex action by forming a kind of a pathological system. An immense variety of genes responsible for susceptibility to T1D are known, but their functional capabilities are either obscure or poorly investigated. Some SNPs whose role in etiology and pathogenesis of T1D leaves no doubt are described below. (Barret et. al., 2009)

This bar chart displays the most important T1D-predetermining haplotypes and their distribution in different populations. As can be seen, the presence of the same haplotype in two different populations contributes differentially to T1D-associated risks. For example, in Russians DQA1\*0301 elicits a nearly 85% risk of T1D, whereas in Latinos the risk is less than 75%. Moreover, haplotypes spreading risks of diabetes in one population can be nonspecific for other populations. As regards DQA1\*0301 its effect on acquisition of sensitivity to T1D is negligibly small in Brazilians, while in Chinese, Japanese, Arabians, Finns and Caucasians this haplotype is not associated with diabetes. These findings suggest that genetic features, haplotype frequency and contribution of specific haplotypes to susceptibility to T1D vary widely in different populations indicating different diabetogenic or protective orientation

Analysis of specific domains of the human genome made it possible to establish their roles in pathological processes and to get a deeper insight into molecular mechanisms responsible for instability of the human biome. The clue to the practical solution of problems in this area is to discover novel genetic markers, to secure low cost of the analysis and to ensure high accuracy of the methods employed. The totality of these factors may culminate in the

Far fewer (compared to HLA classes I and II) messages deal with contribution of HLA class III to background predisposition to T1D. In constructing a basic screening algorithm with special reference to early diagnosis potentials, one should take into consideration the crucial

There exist about a dozen HLA class III genes manifesting a diagnostically significant association with T1D. These include NOTCH4 (rs2395106) responsible for susceptibility to rheumatoid arthritis and MSH5 (rs707915) associated with a high risk of T1D. As an overall trend, HLA classes II and III provoke diabetes at the highest levels of the odds ratio, while the effect of HLA class I on T1D is much less expressed (see Figure 3). (Valdes AM et al.

Any systemic approach to T1D diagnosis demands a large set of complementary and mutually specifying biomarkers. In addition to screening of circulating autoAbs and MHC Ags, systemic analysis of non-MHC genes is extremely important for validating the diagnosis. Though the odds ratios of the overwhelming majority of MHC genes are by one order of magnitude lower than that of MHC, identification of these gene clusters allows a qualitative description of risks for various autoimmune disorders including generation and progression of insulitis and, in a more distant perspective, objective prognosis of T1D

In all probability (and not too surprisingly), each individual gene does not act specifically upon every component of the immune system or cell metabolism, but, rather, exerts a complex action by forming a kind of a pathological system. An immense variety of genes responsible for susceptibility to T1D are known, but their functional capabilities are either obscure or poorly investigated. Some SNPs whose role in etiology and pathogenesis of T1D

construction of unique tools for subclinical diagnosis and preventive medicine.

and high risk of T1D development.

2006; Yamaji K et al.2006)

outcomes.

**3.5 HLA class III: Role in genetic predisposition** 

role of this region's genes in predisposition to T1D.

**3.6 Non-MHC genes and their contribution to T1D** 

leaves no doubt are described below. (Barret et. al., 2009)

Fig. 5. A comparison of some diabetogenic non-HLA genes having mean values for HLA. The height of the column reflects the average probability of the clinical stage of type 1 diabetes (IDDM1) for the given allele. at the same time, MHC genes manifest a high degree of variation and, in some special cases, diabetoprotective activity.

**TNFAIP3 (A20),** tumor necrosis factor, alpha-induced protein 3. In the pancreas, this gene performs miscellaneous functions to include inactivation of NF-kappa B signals, prevention of inflammatory lesions of pancreatic cells, deceleration or delayed recruitment of immunocompetent cells into target organs, retardation of intercellular matrix restructuring, and so on. Studies by Liuwantara D et al. established that expression of the A20 gene is an effective mechanism of beta cell protection from TNF-induced apoptosis. Mutations in this gene and formation of SNPs initiate functional disturbances in NF-kappa B and represent the most common mechanism of disregulation and disorganization of immune reactions resulting in autoimmunity. Yet another salient feature of A20 is its ability to stimulate angiogenesis. Knockout of A20 shortens the tubule area and length in mice *in vitro.* (Grey ST et al., 2003)

A crucial role in specification of APCs and pathogenesis of T1D is played by the **ERBB3** gene known under the official name "v-erb-b2 erythroblastic leukemia viral oncogen homolog 3 (avian)". This gene encodes the family of specific receptors to the epidermal growth factor (EGFR).

Mutations in ERBB3 lead to immunoregulatory collapses coupled with continuous emergence of autoreactive cells. By virtue of its ability to provide linkage between genetic predisposition, infectious diseases and adaptive immune reactions, ERBB3 has every right to be regarded as a central molecular constituent element of the T1D-inducing complex. (Hongjie Wang et al., 2010)

**PTPN22 (LYP),** known under the official name "protein tyrosine phosphatase, non-receptor type 22 (lymphoid)", encodes lymphoid-specific intracellular phosphatase able to bind to the molecular adaptor protein CBL and thus controls its activity in the signaling pathway of the T cell receptor (TCR).

PTPN22 contains several SNPs disturbing normal operation of immune mechanisms. SNP rs2476601 is a valuable biomarker of susceptibility to autoimmune diseases, but its role in NK cell biology is not yet finally elucidated. The fact that SNP rs2476601 upsets the balance between T and NK cells *in vitro* points to the involvement of PTPN22 in immune regulation

Preclinical and Predictive Algorithms in Monitoring

treatment schedules in a real-time mode.

**4. Proteomics: A powerful tool for predictive medicine** 

elevation always points to apoptosis of pancreatic beta cells. (Lee et al., 2011)

Transcortin (Corticosteroid-binding globulin, CBG) and Lumican are capable to induce pronounced (1.5–2-fold against control) upregulation. Transcortin fulfils the function of a glucocorticoid transporter and is strongly inhibited by insulin; hence, insulin deficiency is always associated with hyperproduction of Transcortin. (Fernández-Real et al., 1999) However, this protein is hardly effective as a selective biomarker for T1D, since it emerges exclusively at the latest stages of autoimmune aggression and its role in T1D etiogenesis is

young children);

anamnesis vita.

Patients with Autoimmune Diseases and Their Relatives-at-Risks 203

 Supporting high risks of autoimmune processes (HLA-DQ-related T1D) (NB: HLA-DR3-DQ2 and HLA-DR4-DQ8 genes are among the most popular T1D inducers in

Diabetoprotective function (HLA-DR2, DR6, DR7) (**NB**: HLA-DR1, DR5, DR8 and DR9

From the foregoing it follows that the first step of preclinical diagnosis must include identification of classical genetic biomarkers for each concrete pathology and acquisition of information from three basic resources: (i) genealogic tree, (ii) anamnesis morbid and (iii)

This approach would enable identification of individuals predisposed to a concrete disease and their distribution into risk groups with further transition to the second stage where patients are subject to investigation, using target panels of genotypic and phenotypic biomarkers and continuous monitoring of potentially affected cohorts and those predisposed to the preclinical pathology stage. The primary testing approach demands validated procedures for detecting molecular and cellular shifts in one or another cell and/or tissue function in the paradigm of the most pathogenetically significant targets. The methods employed thereupon include high-performance genomic and metagenomic scanning as well as proteomic and metabolic analyses in the paradigm of microbial colonyforming populations. Moreover, biomonitoring is based on the use of a vast array of nanotools as well as visualization and biosensoric facilities allowing comprehensive examination of "suspected" individuals and elaboration of wide-range activity-oriented

The role of proteomic technologies in the study of autoimmune diseases can hardly be overestimated. Virtually all currently known autoimmune diseases including diabetes mellitus, multiple sclerosis, systemic lupus erythematosus and other severe autoimmune disorders have proteomic markers of their own. At the same time, the advent of efficient high-precision diagnostic technologies opened up new opportunities in the search for novel preclinical diagnostic markers. Identification of autoAbs to immunoglobulins GADA, IAA, ICA, IA-2A and ZnT8 has become a routine procedure in T1D diagnosis. In this chapter, the main emphasis will be laid on some characteristics of specific proteins for progressive T1D. Clusterin (apolipoprotein J) holds considerable promise as a candidate biomarker; its main function is traditionally recognized as a tool to control apoptosis. It should be noted, however, that clusterin exhibits the behaviour of an antiapoptotic chaperone when used at low concentrations; at higher concentrations ( 12% of control), it causes disruption of mitochondria and initiates cell apoptosis by a mitochondrial mechanism. Recent reports highlighted a high regenerative potential of Clusterin, particularly, with respect to beta cells. The functional activity of this protein demands further verification and analysis, but its

genes are usually identified in individuals for whom T1D is uncommon).

of NK function. (Douroudis et al., 2010) The PTPN22 allele 1858T worsens the function of beta cells. 1858T is associated with IAA, an autoAb participating in pancreas destruction. The 1858TT and 1858CT genotypes exhibit a steadily increasing risk for the appearance of additional autoAbs and clinical manifestations of the disease.

The primary mechanism of PTPN22 SNPs is launched upon triggering of insulin-specific autoimmune responses. SNPs produce multifarious effects: they disturb functional activity and suppress metabolic responses of beta cells to changing blood glucose levels, stimulate the transition from prediabetes to type 1 diabetes, and so on. (Fichna et al., 2010; Taniyama et al., 2010; Hermann et al., 2006)

The **IFIH1** gene (interferon induced by helicase C domain 1, also known as MDA5) encodes the DNA receptor associated with viral infections with a concomitant formation of autoreactive T cells and induction of autoimmune diabetes. Moreover, IFIH1 fulfils a protective function in hypomorphic expression of IFIH1. (Downes K et al., 2010) IFIH1 is directly involved in the destruction of Langerhans islets due to pooling and mobilization of autoreactive cells in response to viral invasion. This circumstance aggravates immune dissonance and promotes self-restructuring of targeted organs by provoking persistent deficiency of the pancreas and accelerating insulin failure. IFIH1 disturbs cell-mediated and humoral immunity by initiating selective deficiency of IgA. (Ferreira et al., 2010)

**IL2RA** (interleukin 2 receptor, alpha, also known as CD25, T1D0, TGGFR) represents, along with IL2RB and the γ-chain IL2RG, a fragment of the high-affinity receptor IL-2 (homodimerization of α-chains yields low-affinity receptors, while homodimerization of βchains gives receptors with medium affinity). By virtue of its structural and functional peculiarities, IL2RA makes the greatest contribution to the progression of T1D. It regulates immune and inflammatory responses, exerts negative control over cell proliferation and favors differentiation of T cells. In addition, IL2RA controls apoptosis via a positive feedback mechanism. Mutations in the IL2RA gene point to IL2RA insufficiency. Genetic variations in IL2-IL21 and IL2RA/CD25 regions predetermine the susceptibility to T1D by interfering with the transcription and/or splicing of mRNA. In this way, IL2 and IL2RA exert genetic control over protein expression in different cell subpopulations. (Dendrou et al., 2008)

The **INS** (ILPR, IRDN, IDDM2, MODY) gene is a key participant in the synthesis of insulin molecules. In patients with T1D, the mutation frequency of this gene does not exceed 0.1%. (Rajasalu et al., 2007)

**CD226** (rs763361) SNPs regulate the activity of certain cells involved in immune mechanisms mediating beta cell destruction. The susceptibility to T1D is associated with SNPs rs763361 (genotype TT, OR = 2.29) and allele T (OR = 1.48). (Douroudis et al., 2009; Hafler et al., 2009)

In conclusion, we can state with assurance that nearly the overall repertoire of genes whose mutations are known to increase the risk of T1D development has been identified and characterized in terms of functional activity, which includes:


of NK function. (Douroudis et al., 2010) The PTPN22 allele 1858T worsens the function of beta cells. 1858T is associated with IAA, an autoAb participating in pancreas destruction. The 1858TT and 1858CT genotypes exhibit a steadily increasing risk for the appearance of

The primary mechanism of PTPN22 SNPs is launched upon triggering of insulin-specific autoimmune responses. SNPs produce multifarious effects: they disturb functional activity and suppress metabolic responses of beta cells to changing blood glucose levels, stimulate the transition from prediabetes to type 1 diabetes, and so on. (Fichna et al., 2010; Taniyama

The **IFIH1** gene (interferon induced by helicase C domain 1, also known as MDA5) encodes the DNA receptor associated with viral infections with a concomitant formation of autoreactive T cells and induction of autoimmune diabetes. Moreover, IFIH1 fulfils a protective function in hypomorphic expression of IFIH1. (Downes K et al., 2010) IFIH1 is directly involved in the destruction of Langerhans islets due to pooling and mobilization of autoreactive cells in response to viral invasion. This circumstance aggravates immune dissonance and promotes self-restructuring of targeted organs by provoking persistent deficiency of the pancreas and accelerating insulin failure. IFIH1 disturbs cell-mediated and

**IL2RA** (interleukin 2 receptor, alpha, also known as CD25, T1D0, TGGFR) represents, along with IL2RB and the γ-chain IL2RG, a fragment of the high-affinity receptor IL-2 (homodimerization of α-chains yields low-affinity receptors, while homodimerization of βchains gives receptors with medium affinity). By virtue of its structural and functional peculiarities, IL2RA makes the greatest contribution to the progression of T1D. It regulates immune and inflammatory responses, exerts negative control over cell proliferation and favors differentiation of T cells. In addition, IL2RA controls apoptosis via a positive feedback mechanism. Mutations in the IL2RA gene point to IL2RA insufficiency. Genetic variations in IL2-IL21 and IL2RA/CD25 regions predetermine the susceptibility to T1D by interfering with the transcription and/or splicing of mRNA. In this way, IL2 and IL2RA exert genetic control over protein expression in different cell subpopulations. (Dendrou et

The **INS** (ILPR, IRDN, IDDM2, MODY) gene is a key participant in the synthesis of insulin molecules. In patients with T1D, the mutation frequency of this gene does not exceed 0.1%.

**CD226** (rs763361) SNPs regulate the activity of certain cells involved in immune mechanisms mediating beta cell destruction. The susceptibility to T1D is associated with SNPs rs763361 (genotype TT, OR = 2.29) and allele T (OR = 1.48). (Douroudis et al., 2009;

In conclusion, we can state with assurance that nearly the overall repertoire of genes whose mutations are known to increase the risk of T1D development has been identified and

General immunity (ERBB3, IL2RA, PTPN22, PTPN2, SH2B3, CTLA4, SUMO, ICOS;,

Generation of autoAbs to insulin and formation of the insulin resistance syndrome

characterized in terms of functional activity, which includes: Protection of beta cells from apoptosis (TNFAIP3);

Undefined function (CTSH, CLEC16A, IL7RA, CIQTNF6);

Generation of autoAbs to beta cells (mostly, in adults) (HLA-DR3);

Secretion and metabolism of insulin (INS);

(mostly, in adolescents)(HLA-DR4);

humoral immunity by initiating selective deficiency of IgA. (Ferreira et al., 2010)

additional autoAbs and clinical manifestations of the disease.

et al., 2010; Hermann et al., 2006)

al., 2008)

(Rajasalu et al., 2007)

Hafler et al., 2009)

etc.);


From the foregoing it follows that the first step of preclinical diagnosis must include identification of classical genetic biomarkers for each concrete pathology and acquisition of information from three basic resources: (i) genealogic tree, (ii) anamnesis morbid and (iii) anamnesis vita.

This approach would enable identification of individuals predisposed to a concrete disease and their distribution into risk groups with further transition to the second stage where patients are subject to investigation, using target panels of genotypic and phenotypic biomarkers and continuous monitoring of potentially affected cohorts and those predisposed to the preclinical pathology stage. The primary testing approach demands validated procedures for detecting molecular and cellular shifts in one or another cell and/or tissue function in the paradigm of the most pathogenetically significant targets. The methods employed thereupon include high-performance genomic and metagenomic scanning as well as proteomic and metabolic analyses in the paradigm of microbial colonyforming populations. Moreover, biomonitoring is based on the use of a vast array of nanotools as well as visualization and biosensoric facilities allowing comprehensive examination of "suspected" individuals and elaboration of wide-range activity-oriented treatment schedules in a real-time mode.
