1. The Train of PIFAS and associated Subclinical Disorder and Clinical Illness PPPM (Predictive, Preventive and Personalized Medicine) as a Novel Avenue and a Source of New Translational Tools to Predict and to Prevent Chronic Autoimmune Disorders Dr Sergey Suchkov, MD, PhD Professor in Immunology & Medicine I.M.Sechenov First Moscow State Medical University and A.I.Evdokimov Moscow State Medical & Dental University, Moscow, Russia ISPM (International Society for Personalized Medicine), Tokyo, Japan PMC (Personalized Medicine Coalition), Washington, DC, USA EPMA (European Association for Predictive, Preventive and Personalized Medicine), Brussels, EU Dr Sergey Suchkov, MD, PhD Professor in Immunology & Medicine I.M.Sechenov First Moscow State Medical University and A.I.Evdokimov Moscow State Medical & Dental University, Moscow, Russia ISPM (International Society for Personalized Medicine), Tokyo, Japan PMC (Personalized Medicine Coalition), Washington, DC, USA EPMA (European Association for Predictive, Preventive and Personalized Medicine), Brussels, EU Dr Trevor Marshall, MD, PhD Professor in Immunology Autoimmunity Foundation, LA, CA, USA

2. Autoimmune diseases (ADs) are a family of complex heterogeneous disorders with similar underlying mechanisms characterized by immune responses against self. Autoimmune diseases are the third leading cause of morbidity and mortality in the industrialized world! Most of the autoimmune diseases follow a chronic course that can be compared with a lengthy train ride. And a substantial need does exist for developing and validating a range of biomarkers that would address a number of important unmet clinical needs in the autoimmunity field. The journey mentioned begins based on pre-existing genetic equipment followed by the onset of a self directed autoimmune response. And the earliest evidence that the journey has begun is often found in the form of rising autoAbs. The pace of the journey can be ascertained by the soluble mediators of autoimmunity found in the blood in the form of cytokines and autoAbs again as well. If the train spirals out of control, a train wreck may follow. We may think of the autoimmune disease as the train wreck that follows this long journey of autoimmunity. It is a journey that can be timed in hours or days, but more frequently re-quires weeks, months or years. Generally it is only when the train wreck has occurred that the disease reaches the threshold of attention by the patient and the physician. Too often diagnosis and therapy of an autoimmune disease is delayed only after irreversible damage has taken place.

4. There are many risk factors for autoimmune diseases, other than cytokines and autoAbs; some of them genetic and others related to the patients lifestyle, diet and environment. Moreover, autoimmune diseases have a genetic predisposition. The abnormal immune response probably depends upon interactions between susceptibility genes to secure surveillance over the immune control, and various environmental factors.

6. The most potent genetic influence on susceptibility to autoimmunity is the major histocompatibility complex (MHC/HLA), which has been known for over two decades to affect susceptibility to a variety of autoimmune disorders. Different HLA alleles are linked to different autoimmune diseases. Among those alleles are: ● A1 and DQW1 associated with SLE; ● DQ3-DR4, DQ3-DR9, DQ5-DR1, DQ5-DR10 associated with RA; ● DR3 and B8 associated with RA, SLE, autoimmune thyroiditis, celiac, multiple sclerosis, and myasthenia gravis, etc. Other genetic risk factors confirmed are polymorphisms in specific genes encoding molecules involved in antigen presentation, such as Tap-1 and proteosomes, as well as many other candidate genes.

7. More precisely, autoimmunity results from disruption of different combinations of genes participating in the same immunologic function designated as tolerance. Meanwhile, some susceptibility loci are shared by several autoimmune diseases, whereas others are more disease-specific. Neverltheless, due to the highly multifactorial nature of most autoimmune diseases, individual loci do not seem significant enough to be used as biomarkers for susceptibility or as diagnostic tools. In this sense, many exciting things are happening, including an expanded understanding of the genetic basis of human health and autoimmune disease through research efforts, the rapid lowering of the cost of genetic sequencing, new precision drugs for a number of the diseases based on genetic targets, including autoimmune conditions. Let me stress the the link that might exert reliable control over morbidity, mortality and disabling rates and significantly optimize the cost of treatment for those who had fallen ill whilst having either of the autoimmune disorder. The name of the link is Predictive, Preventive and Personalized Medicine (PPPM) (slide)

8. PPPM associated with Subclinical and Predictive Diagnostics

9. To achieve the practical implementation of PPPM concept, it is necessary to create a fundamentally new strategy based upon the pre-early (subclinical) recognition of biomarkers of hidden imbalances and defects long before the illness clinically manifests itself. This strategy would give a real opportunity to secure preventive measures whose personalization could have a significantly positive influence on demographics! (slide)

10. to predict the likelihood of developing disease to estimate the length of the asymptomatic period to provide predictive information about disease course, severity, and complications to serve as a warning to avoid potential disease- triggering factors identify high-risk individuals who might be suitable candidates for preventive intervention trials Impacts to be assumed for the practical implementation of PPPM into the practice

11. PPPM as thus the big change to forecast, to predict and to prevent is rooted in a big and new science to be rooted from the achievements of genomics, proteomics, metabolomics and bioinformatics which are being implemented into the daily practice to secure visualizing of lesion foci that was previously unknown to clinicians (slides x2)

14. In reality, Genomics as a set of molecular tools to probe genome and to thus identify and to select genomic biomarkers has allowed for identifying newer genetic variations that affect health to form subclinical and predictive risks to be screened and unveiled, and then the subclinical pathology to be diagnosed, monitored and terminated to prevent tolerance breakage and thus the clinical illness

16. Over the last decade genome-wide association studies (GWASs) have identified many loci associated with late-onset autoimmune conditions including T1D, MS, SLE, RA, AIM and others

18. For this model, about half of the total risk is genetically predisposed, and about half of the risk is in the HLA and other regions to be useful for gene-based predictive testing! HLA-I HLA-III HLA-II

19. The genetic map of MS: the location of genes and SNPs on the chromosome

21. Gene products associated with systemic lupus erythematosus (SLE) that might affect the adaptive immune system

22. Gene products associated with rheumatoid arthritis (RA) that might affect the adaptive immune system

23. Well, genes can say a lot about an individual’s predisposition to an autoimmune disorder, but cannot reveal what is happening in cells at the protein level. The latter would attribute to proteomics to identify individual proteins and their epitopes to be valuable for predictive diagnosing and thus may eventually have a great impact on PPPM Predictive & Prognostic Diagnosing Predictive & Prognostic Diagnosing Predictive & Prognostic Diagnosing

24. Meanwhile, a combination of genomic and proteomic biomarkers are becoming of great significance to predict risks of the chronification!!! LOOK AT:

25. Proteomic biomarkers Proteomic biomarkers Proteomic biomarkers Proteomic biomarkers Genomic biomarkers

26. And thus to predict risks of disabling since chronic autoimmune diseases are preceded by a long subclinical (symptom-free) phase or a period of latency (slides xx)

27. Stage of subclinical autoaggression Stage of full-term autoaggression Clinical illness Subclinical (cryptic) latency A stepwise (subclinical-clinical) course to be developed A stepwise progression of autoaggression

29. Notwithstanding the underlying mechanism of disease, almost all autoimmune disorders are associated with circulating autoantibodies (immunomics as a portion of the proteomics) which bind self protein. Some of those antibodies can be detected in the patients serum long before clinical disease manifests itself. Several studies showed the predictive ability of anticyclic- citrullinated-peptide (anti-CCP) for rheumatoid arthritis (RA).

30. Anticyclic-citrullinated-peptide (anti-CCP) in rheumatoid arthritis (RA).

31. In reality, proteomics per se is the continuation of functional genomics and, at the same time, a prologue to metabolomics Genome Proteome Genome Proteome Transcriptome Metabolome 10 6 human proteins 25,000 human genes Posttranslational modifications (PTMs) of proteins Alternative splicing (mRNA) Transcriptomic modifications

32. The latter (metabolomics) illustrates the functional state of the cell at the level of metabolism on a real time basis, requiring the use of the term 'metabolome', demonstrating a set of metabolic pathways in the cell at a given time point

33. Tissue-derived information we would accumulate from the sources including: ● molecular architectonics and profiling of the autoimmune disorder at its clinical and subclinical stages; ● laboratory and instrumental armamentarium; ● predictive testing; ● susceptibility and resistance testing

34. might be combined with the: ● individual's medical records; ● family history; ● data from imaging; ●instrumental and laboratory tests to develop personalized and preventive treatments. But, in this sense, how is the whole databank provided by omics- technologies could be comprehended?

35. It is bioinformatics to suit the goal by applying mathematical modeling techniques to thus secure constructing and maintaining unified biobanks and datasets necessary for personal health monitoring based on principles of genotyping and phenotyping. As a result, the patient becomes a data carrier, whilst learning about possible risks of a disease, and the physician can reasonably select a kind of preventive and personalized protocol rooting from the predictive assays made (slides).

36. The diagram shows the integrated “knowledge environment” that enables clinicians to query critical information from across disparate data sources to find relationships between an individual patient’s EMR information of persons-at-risk family records Bioinformatics would service PPPM

37. By integrating bioinformatics and clinical healthcare informatics, both offers unique infrastructure, tools, techniques and applications to bridge those areas. This facilitates the sharing of data and information across diverse disciplines and professional sectors to unveil the clinical case illustrating the autoimmunity condition

39. Individuals, selected in the first (genotypic) stage, undergo the second stage, which uses a panel of phenotypic biomarkers, whilst monitoring every: ● potential patients, ● persons-at-risks predisposed to the autoimmune disease, and/or ● persons at subclinical stages of the disease.

40. Biobanks would thus supplement the proper information and datasets about patient's proteomic, genomic and metabolic profiles to be used to tailor medical care due to the individual's needs and personalized scenarios. An understanding of the factors underlying the burden of a disorder and later on of the clinical illness would provide policymakers, healthcare providers and administrators and medical educators with an opportunity to guide preventive initiatives at both individual and community levels (next Fig)

41. Impact on Sustainability in Three Main Dimensions of Biobanking Biobanking as applicable to PPPM PPPM

42. PPPM will require the integration of clinical information, stable and dynamic genomics, and molecular phenotyping. And you might understand that bioinformatics will be crucial in translating these data into useful applications, leading to improved diagnosis, prediction, prognostication and treatment. So, our viewpoint would support the contributions of genomic and proteomic approaches in developing a preventive and personalized approach to manage chronic autoimmune diseases and to thus monitor the patients and persons-at-risk

43. PPPM thus uses diagnostic and predictive tests of newer generations based on biomarkers, to individually determine the health conditions a person is predisposed to the autoimmune conditions and to reveal biomarkers of the probable or the already existing pathological processes, and thus to select the targets. PPPM-oriented survey should be based on biomarkers and algorithms to differ essentially from those employed in traditional clinical strategies, namely, (i)algorithms for predictive and subclinical diagnostics on one hand, and (ii) algorithms for preventive therapy, on the other one

44. Integrating High-Throughput Measurements with the Phenotype: from Science to Medicine (Omenn & Athey, NCIBI, 2010)

45. In the era of personalized medicine there is increased interest in the incorporation of individual biomarkers in risk score algorithms in order to improve autoimmune disorders-related risk stratification followed by the prompt initiation of preventive measures whilst evaluated the predictive and prognostic value of currently used biomarkers like canonical autoAbs, cytokines, specific proteins and ligands as well as promising future-affiliated biomarkers like noncoding RNA, silencing sequences, pathway-associated biomarkers, and combinatorial biomarkers as the latest ones!!!

46. Most of the latter are directly associated with development of chronic autoimmune diseases. So, we would need absolute proofs to confirm clinical applications of the biomarkers mentioned, evaluating not only their diagnostic and prognostic value but also their integrability into routine practice Although a single biomarker is a tangible entity that can be easily understood, aggregate measures comprised of multiple genes are also informative as the combinatorial biomarkers.

47. The simultaneous addition of combinatorial biomarkers of autoimmune disorders to the score index panel substantially improves the risk stratification for death and disabling. And clinical potential may be evaluated by asking three fundamental questions: (1) Can the clinician measure the biomarker as applicable to chronic autoimmune diseases? (2) Does it add new information? (3) Does it help the clinician to manage the patients? The incorporation of the multiplexed but consolidated biomarker-based panels mentioned into clinical practice for the prediction of death and disabling causes could be accomplished quickly, since the measurement of these biomarkers is already well established for diagnostic use.

48. And there is urgent clinical need to identify predictive combinatorial biomarkers for subclinical PIFAS (post-infectious autoimmune syndrome) to allow preventive/treatment strategies to be instituted early in the disease running.

49. A schematic representation of the postinfectious autoimmune syndrome (PIFAS) The primary microbial pathogen triggers PIFAS through activation of two different mechanisms: (i)depletion of intrinsic (antigenic) molecular mimicry pools of cross-reacting (mimicking) epitopes of the pathogen (red arrows); (ii)generation, by the infectious pathogen, of antigen-nonspecific signals (blue arrows) able to induce inflammation and thus enhance immune responsiveness (so-called adjuvant effect)

50. A crucial role in formation of PIFAS and progression to develop clinical illness is played by inborn (in the first place, HLA-associated) predisposition coupled with impaired immune responsiveness of the human body invaded and/or having had signs of dysbiosis since recent data has stressed the importance of the endomicrobiota in immune homeostasis. In this sense, then how the endomicrobiota influences host immune responses to provoke autoaggression?

51. When microbes are critical for development of PIFAS, several mechanisms can be discriminated! Which ones, in specific terms? The mimicry is not necessary if APCs are activated by microbes through innate receptor signaling and happen to express self-Ag when a self-reactive T cell is present. This is “bystander activation” (right). The third mechanism is “amplification of autoimmunity by cytokines” elicited by microbial activation of professional APCs and the so-called innate lymphoid cells (ILCs), which together produce or elicit cytokine production by T cells. These cytokines target self-reactive T cells and their target tissues (bottom). They can also reduce the efficiency of regulatory cells, which are responsible for active tolerance to self Mechanisms by which microbes contribute to initiation or severity of PIFAS In all cases the end point is the destruction of tissue target cells (center) by self-reactive cells (in red) or antibodies (not shown). Microbial peptides could be identical or similar enough to self- peptides (left) to induce cross-reactivity to self- tissues - “molecular mimicry.” Innate-adaptive connec- tion mechanisms (Ag presentation and co- stimulation; green arrows) are required to activate self-reactive cells.

52. As you know, antimicrobial immunity is based on sequential reactivity of the innate and adaptive immune systems. And the final version of PIFAS is controlled by the coordinated functioning of innate and adaptive arms on one hand, and the interaction of the arms with endomicrobiota, on the other hand.

53. As you see, the host’s genetics defines the interactions with the endomicrobiota, and the outcome of those interactions affects the autoimmunity risks

54. Indeed, autoaggression provoked by insufficient coordination between two immune arms and hyperactivity of the adaptive one is a dominant feature of PIFAS to serve as a combinatorial biomarker to monitor PIFAS and then the PIFAS- dependent autoimmune disorder. Its unique feature is a broad repertoire of autoAbs responsible for multiseropositivity and thus to autoimmune inflammation biomarkers, e.g., anti-B7-HI autoAbs

55. Several examples. Autoimmune myocarditis (AIM) usually develops in genetically predisposed individuals infected with the CVB3 or related viruses to represent typical manifestations of molecular mimicry.

56. Initiation and progression of autoimmune myocarditis PIFAS is initialized moderate PIFAS full-term PIFAS

57. The presence of CM-AR CTLs and anti-CM autoAbs is prerequisite to AIM development to initiate myocardial lesions. Clinical manifestations of AIM, with distinct onset, vary from asymptomatic to fatal. But the biomarkers to predict the course at initial presentation have not yet been established. Meanwhile, an improved knowledge of the mechanisms of infections to preceed the illness should help to get PIFAS defined and then be used as a combinatorial biomarker to develop preventive strategies for quenching PIFAS at the subclinical stages

58. T1D is a chronic autoimmune inflammation comprising stages of subclinical pathology and clinical manifestations and resulting in a destruction of pancreatic beta-cells capable of producing insulin T1D Subclinical stage Clinical stage

59. Subclinical stages are usually determined by identification of proteomic biomarkers, i.e., antiislet autoAbs as early as 5-10 years before the clinical onset of disease

60. Stage of subclinical autoaggression Stage of full-term autoaggression Clinical illness Subclinical (cryptic) latency A stepwise (subclinical-clinical) course to be developed A stepwise progression of autoaggression But! Look again at the evolution of Chronic Autoimmune Disorder – a lot of factors are involved into the subclinical stage! And PIFAS as a Combinatorial Biomarker may serve to suit the needs!

61. A stepwise development of T1D Genetic predisposition Stage 1 НТГНТГ Population of β-cells to function Population of β-cells to function Factors to provoke T1DFactors T1D T1D clinical manifestations T1D Clinical illness Stage 2 Stage 3 Stage 4 Stage 5 Stage 6 100% death of β-cells Benign autoimmunity ( autoimmune insulitis) Benign autoimmunity ( autoimmune insulitis) Pathogenic autoimmunity (latent or asymptomatic insulin deficiency Pathogenic autoimmunity (latent or asymptomatic insulin deficiency GlucoseintoleranceGlucoseintolerance A subclinical stage is characterized by depletion of β-cells and fall in insulin secretion levels to have a biased burst. Clinical manifestations link to β-cell death to illustrate ceasing in insulin secretion.

62. Thus, the need for biomarkers of newer generations, newer algorithms and software applications to support integrative data analysis and to secure the databanks becomes critical to the discovery of linkages and concordance between different data types such as genomics-, proteomics-, immunomics-, metabolomics- and clinically-rooted!

63. Multifactorial risk factor modification and control can have a profound and favorable impact on decreasing the incidence of initial and recurrent autoimmunity-related events. Meanwhile, because the adult tissues and organs have negligible regenerative capacity, healing of the latter damaged and affected is dependent on sequential activation of anti-inflammatory and anti-fibrogeneic signals. So, a strategy of preventive treatment should contain, at least, two critical steps for managing autoimmune disorders: (i)quenching of inflammation and degeneration; and, (ii)restoration of the tissue damaged and affected.

64. PPPM is thus a model of healthcare services being tailored to the individual and dictates a construction of PPPM-based algorithms to diagnose, to predict, and to prevent autoimmune conditions in time!

65. Due to our viewpoint, all healthcare professionals of the future should be educated to deliver patient-centric care as members of interdisciplinary teams, emphasizing evidence-based practice, quality improvement approaches and bioinformatics. That concerns the need for novel training programs since the society is in bad need of large-scale dissemination of novel systemic thinking and minding. And upon construction of the new educational platforms in the rational proportions, there would be not a primitive physician created but a medical artist to be able to enrich flow-through medical standards with creative elements to gift for a patient a genuine hope to survive but, in turn, for a person- at-risk – a trust for being no diseased. So, the existing medical education would strongly need to be restructured to involve along with traditional graduate and post-graduate training, pre-graduate preliminaries to disclose for schoolchildren the mysteries of the evidence-based medicine and PPPM as the entity

66. EPMA-World Congress 2011 September 15 th 19th, Bonn, Germany International Research Team of Youngers

67. Based on current trends and own experience, we have tried a non-canonical approach towards reshuffling the traditional educational tandem “School-University” to create a team of talented and gifted teenagers to be engaged into PPPM-related areas. The Team has been given a roof under the aegis of European Association of Predictive, Preventive and Personalized Medicine (EPMA), Brussels, EU, and started up to move ahead now The First Anglo-Russian Students’ Workshop on PPPM and Translational Medicine Lancaster University 4 th September 2012 Location: TR1/TR2 Gordon Manley building Chairs:Professors Frank Martin, PhD (UK) Director, Environmental and Biophotonics Center, and Chairman, Dept for Biochemistry, Lancaster University, UK Professor Sergey Suchkov, MD, PhD (Russia) Dept of Pathology, School of Pharmacy, I.M.Sechenov First Moscow State Medical University, and Dept of Clinical Immunology, Moscow State Medical & Dentistry University, First Vice-President and Dean, School of PPPM, University of World Politics and Law, Moscow, Russia

68. EPMA World Congress 2013 Europarliament, Brussels, EU, Sep 2013 Section For Young Professionals (Session)

69. And, finally, in Aug-Sep 2015, on the invitation of American and Canadian universities The EPMA-YPC Team of Young Researchers and Clinicians were participating in a series of the Workshops and Summits organized jointly with young students of the inviting universities whilst demonstrating their creative potential and activity

70. The First US-EPMA-Russian Students’ Workshop “Chronic Disorders through the View of Preventive, Personalized and Translational Medicine” UCSD, San Diego, CA, USA

72. The First US-EPMA-Russian Students’ Workshop “Chronic Disorders through the View of Preventive, Personalized and Translational Medicine” UCSF, San Francisco, CA, USA

74. SENIOR YOUNG MEMBERS of THE EPMA TEAM Georgia Israel Russia

75. Austria Italy USA JUNIOR YOUNG MEMBERS of THE EPMA TEAM

76. Russia Australia RussiaGermany Russia SLovakia VIRGIN YOUNG MEMBERS of THE EPMA TEAM

77. This approach should be based on postulates which will change the incarnate culture and social mentality.

78. Our global challenge is that the new guidelines should create the robust juristic, social, ethical and economic platforms for advanced medical services utilizing the cost-effective models of risk assessments followed by tailored preventive treatments focused on the precursor stages of chronic autoimmune diseases, in particular!