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Psaty BM, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73.

Qin J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature.2010;464:59.

R. D. C. Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. 2005

Scupham AJ, et al. Abundant and diverse fungal microbiota in the murine intestine. Appl Environ Microbiol. 2006;72:793.

Seow CH, et al. Novel anti-glycan antibodies related to inflammatory bowel disease diagnosis and phenotype. Am J Gastroenterol. 2009;104:1426.

Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001;68:978.

Stephens M, Donnelly P. A comparison of bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003;73:1162.

Taylor PR, et al. Dectin-1 is required for beta-glucan recognition and control of fungal infection. Nat Immunol. 2007;8:31.

Vijay-Kumar M, et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science. 2010;328:228.

Willing BP, et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology. 2010;139:1844.

A19

METAGENOMICS AND PERSONALIZED MEDICINE87

Herbert W. Virgin88,*and John A. Todd89,*

The microbiome is a complex community of Bacteria, Archaea, Eukarya, and viruses that infect humans and live in our tissues. It contributes the majority of genetic information to our metagenome and, consequently, influences our resistance and susceptibility to diseases, especially common inflammatory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn’s disease. Here we discuss how host–gene–microbial interactions are major determinants for the development of these multifactorial chronic disorders

________________

87 Reprinted from Cell 147(1), Virgin, H. V., J.A. Todd. 2011. Metagenomics and personalized medicine, pages 44-56, Copyright 2011, with permission from Elsevier.

88 Department of Pathology and Immunology, Department of Molecular Microbiology, and Midwest Regional Center of Excellence for Biodefense and Emerging Infectious Diseases Research, Washington University School of Medicine, St. Louis, MO, 63110, USA.

89 Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0XY, UK.

* Correspondence: virgin@wustl.edu (H.W.V.), john.todd@cimr.cam.ac.uk (J.A.T.).



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APPENDIX A 445 Psaty BM, et al. Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts. Circ Cardiovasc Genet. 2009;2:73. Qin J, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature.2010;464:59. R. D. C. Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical ­ Computing. 2005 Scupham AJ, et al. Abundant and diverse fungal microbiota in the murine intestine. Appl Environ Microbiol. 2006;72:793. Seow CH, et al. Novel anti-glycan antibodies related to inflammatory bowel disease diagnosis and phenotype. Am J Gastroenterol. 2009;104:1426. Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001;68:978. Stephens M, Donnelly P. A comparison of bayesian methods for haplotype reconstruction from popu- lation genotype data. Am J Hum Genet. 2003;73:1162. Taylor PR, et al. Dectin-1 is required for beta-glucan recognition and control of fungal infection. Nat Immunol. 2007;8:31. Vijay-Kumar M, et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science. 2010;328:228. Willing BP, et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology. 2010;139:1844. A19 Metagenomics and Personalized Medicine87 Herbert W. Virgin88,* and John A. Todd89,* The microbiome is a complex community of Bacteria, Archaea, ­ ukarya, E and viruses that infect humans and live in our tissues. It contributes the m ­ ajority of genetic information to our metagenome and, consequently, i ­nfluences our resistance and susceptibility to diseases, especially ­ ommon c inflam­ atory diseases, such as type 1 diabetes, ulcerative colitis, and Crohn’s m disease. Here we discuss how host–gene–microbial interactions are major determinants for the development of these multifactorial chronic disorders 87   Reprinted from Cell 147(1), Virgin, H. V., J.A. Todd. 2011. Metagenomics and personalized medicine, pages 44-56, Copyright 2011, with permission from Elsevier. 88   Department of Pathology and Immunology, Department of Molecular Microbiology, and Mid- west Regional Center of Excellence for Biodefense and Emerging Infectious Diseases Research, Washington University School of Medicine, St. Louis, MO, 63110, USA. 89  Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Labora- tory, Department of Medical Genetics, Cambridge Institute for Medical Research, University of C ­ ambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0XY, UK. * Correspondence: virgin@wustl.edu (H.W.V.), john.todd@cimr.cam.ac.uk (J.A.T.).

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446 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE and, thus, for the relationship between genotype and phenotype. We also explore how genome-wide association studies (GWAS) on autoimmune and inflammatory diseases are uncovering mechanism-based subtypes for these disorders. Applying these emerging concepts will permit a more complete understanding of the etiologies of complex diseases and underpin the devel- opment of both next-generation animal models and new therapeutic strate- gies for targeting personalized disease phenotypes. Recent advances in diverse areas of science and technology make this a unique time to study the genetics and pathogenesis of complex diseases, such type 1 diabetes (T1D) and inflammatory bowel disease (IBD), which includes Crohn’s disease (CD) and ulcerative colitis (UC). These distinct diseases are now understood to share important common characteristics and aspects of their disease mechanisms. In all three diseases, the immune system damages tissues: T1D is likely an autoimmune disease, whereas CD and UC are likely caused by inappro- priate inflammatory responses to components of our microbiome (see Box A19-1 for definition of key terms). Many genetic loci regulate the risk for each disease. Although a threshold dose of these susceptibility alleles provides the foundation for developing the disease, these alleles are not sufficient to cause the disease. It has been obvious for decades that complex gene–gene and gene–­ environment interactions govern these diseases, but not surprisingly, untangling this web of interactions has been extremely difficult (Figure A19-1). Despite the failure to identify single causal agents for each disease, there is strong evidence that ­ icrobes contribute to pathogenesis. Furthermore, genomewide association m s ­tudies (GWAS), which use large study populations and careful replication of r ­esults, have effectively identified many important loci in the host that increase one’s risk for the disease, and these results have fundamentally altered how we conceptualize these diseases (Stappenbeck et al., 2011; Khor et al., 2011; ­ nderson et al., 2011; Franke et al., 2010; Todd, 2010). Correlation of GWAS A data with genome-wide gene expression analyses (eQTLs), in combination with protein–protein interaction data, is greatly assisting the identification of candi- date causal genes within these loci (Anderson et al., 2011; Franke et al., 2010; ­ otsapas et al., 2011; Rossin et al., 2011; Fehrmann et al., 2011). Recently, numer- C ous approaches have been developed to start defining mechanisms for complex inflammatory diseases by using leads from GWAS and analyses of the micro­ biome. These promising approaches include the following: the introduction of mutations in GWAS-identified loci into the mouse genome (Cadwell et al., 2010; Bloom et al., 2011); the creation of induced pluripotent stem cells (iPSCs) from patients and their differentiation into relevant cell types (e.g., Rashid et al., 2010); and humanized mouse models in which the murine immune system is replaced by transplantation (e.g., Brehm et al., 2010; Esplugues et al., 2011) or human ­ icrobial communities are transplanted into formerly germ-free mice (Goodman m et al., 2011). Currently the great challenges in this field are to (1) understand how

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APPENDIX A 447 BOX A19-1 Definition of Terms Dysbiosis: Most commonly refers to a disruption in the normal homeostatic and beneficial relationship between microbes and their host, including disruptions in microbial community structure and function. Alterations in microbial community structure, involving Bacteria, Archaea, and/or Eukarya, can occur in any body h ­ abitat but have been best described in the gut where they have been associ- ated with a number of disease states including, for example, inflammatory bowel disease. Familial clustering: If a family member is diagnosed with a disease such as type 1 diabetes, ulcerative colitis, or Crohn’s disease, then the risk of other first- degree family members is much greater (perhaps as much as 50-fold for some multi­ ctorial disorders) than that for a person taken at random from the general a population. Familial clustering is caused by a combination of inherited genetic variants from the parents to the children and shared environmental factors within the families. Susceptibility variants are being discovered rapidly by GWAS, but the environmental factors remain unknown, although numerous candidates are recognized, most particularly a role for the microbiome and infections. GWAS: Analysis of common alleles (mostly single-nucleotide polymorphisms, SNPs) in a population that associates genetic loci with disease susceptibility. These loci contain “candidate” disease genes. Metagenetics: Approaching genetic and genomic studies by considering all of the genes in the metagenome as opposed to considering, in isolation, host genes or genes that confer particular properties (e.g., virulence or ­ommensalism) c upon an individual microbe. Importantly, the history of microbial inputs into the m ­ etagenomic profile of an individual is important for identifying the causes of complex disease, requiring expensive but essential longitudinal studies, including information from maternal and gestational exposures and phenotypes. Metagenome: As used here, metagenome is the sum of all genes and genetic elements and their modifications in the somatic and germ cells of a host plus all genes and genetic elements in all microorganisms that live on or in that host at a given time. The metagenome has transient elements (e.g., during infection with a pathogen) and more persistent elements (e.g., infection with latent eukaryotic virus; presence of commensal bacteria). Microbiome: As used here, the microbiome is the sum of all microbial organisms that live in or on the host at a given time. The microbiome includes members of Bacteria, Archaea, Eukarya, and the viruses of these organisms. In other articles this term may be used to refer to the genes of these organisms. Virome: The sum of all viruses living in the tissues of the host or infecting organ- isms in the microbiome. These viruses maybe further divided into viruses that infect members of each of the three domains of life (e.g., bacterial virome or bacterial phages or the eukaryotic virome).

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448 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE FIGURE A19-1  Perfect storms for developing Crohn’s disease and type 1 diabetes. A series of overlapping events and phenotypes driven by metagenetic and environmental pro- cesses that, in sum, contribute to the development and pathogenesis of type 1 ­ iabetes (A) d and Crohn’s disease (B). both microbiome and GWAS-identified genes contribute to disease; (2) elucidate the molecular mechanisms by which causal genes act during pathogenesis; and (3) validate biomarkers and druggable pathways via genotype- phenotype studies (e.g., Dendrou et al., 2009; Bloom et al., 2011; Cadwell et al., 2010). By peering through the lens of recent studies on CD, UC, and T1D, this review seeks to delineate emerging concepts in research on complex inflam- matory diseases and to comment on the implications of these concepts for the interpretation of genetic and pathogenetic data. Two concepts are emphasized and integrated herein: (1) that single disease diagnoses are unlikely to be single phenotypes and may instead be the sum of multiple mechanism-based disease subsets, and (2) that the interactions of individual microorganisms and their g ­ enomes with specific host genes or pathways underpin the relationship between genotype and phenotype in these complex diseases. In this view, disease genetics may be combinatorial with different host–gene–microbial interactions, contribut- ing to the pathogenesis of disease in subsets of patients. These two interrelated concepts, therefore, define T1D, CD, and UC as metagenetic (Box A19-1), rather than simply “genetic,” diseases. These concepts will guide the design and inter- pretation of future experiments that seek to dissect the pathophysiologic mecha- nisms underlying a number of complex diseases and to identify more effective approaches for their treatment and prevention. Host Genetic Grist for the Metagenetic Mill Recently, meta-analyses of GWAS of large cohorts of patients of European descent with UC or CD have been performed (Franke et al., 2010; Anderson et

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APPENDIX A 449 al., 2011; Khor et al., 2011). These studies identified 98 loci, and candidate genes within these loci, that have a putative role in IBD. Similar studies of T1D identified 53 disease susceptibility loci (Barrett et al., 2009) (http://www.t1dbase.org). Im- portantly, many disease susceptibility loci are shared among common auto­ mmune i and inflammatory diseases, including T1D, Graves’ disease, celiac disease, CD, UC, psoriasis, rheumatoid arthritis, alopecia areata, multiple sclerosis, and systemic lupus erythematosus (Cotsapas et al., 2011; Khor et al., 2011). It is striking that T1D and CD share 13/52 (25%) risk loci outside the human leukocyte antigen (HLA) gene complex despite the fact that these diseases are neither thought to be related diseases nor reported to be shared within families more often than expected by chance (http://www.t1dbase.org). Notably, the candidate causal genes in these 13 susceptibility loci regulate immunity. These include (Khor et al., 2011) PTPN22, which is involved in T and B cell signaling; IL10, encoding a powerful cytokine that suppresses inflammatory responses (including in specialized T regulatory cells in the gut) (Maloy and Powrie, 2011); BACH2, which regulates B cell gene expression and possibly IgA production; TAGAP, which is involved in T cell activation; IKZF1, which negatively regulates B cells; IL2RA, which controls T regulatory lymphocyte development and function; GSDMB/GSDMA/ORMDL3, which is involved in stress responses; FUT2, which controls microbial susceptibility (Smyth et al., 2011; Franke et al., 2010; McGovern et al., 2010); and IL27, which suppresses inflam- matory responses and regulates IL-10 signaling (Imielinski et al., 2009; Barrett et al., 2009). This is a remarkable concordance of involved genes for two unrelated diseases, indicating that different diseases can have common mechanistic compo- nents and that the immune system is key for both diseases. However, not withstanding all insights into disease mechanisms that the GWAS approach has already provided, the inheritance and the strong clustering of these multifactorial diseases within families (Box A19-1), which encompass both inherited genetic variants and intrafamilial environmental factors, remain only partially explained. Assuming a simple statistical model of gene interaction (Clayton, 2009), the numerous identified loci account for not more than 25% of the familial clustering of CD and UC (Anderson et al., 2011; Franke et al., 2010). This contrasts with T1D, in which the HLA effect is uniquely large and, together with 52 non-HLA loci, can account for almost all of the familial clus- tering (­ layton, 2009). For T1D, the massive effect of the HLA region, owing C to functional polymorphisms in the HLA class II and class I genes, contributes almost 50% of familial clustering on its own (Clayton, 2009; Todd, 2010). There are, however, probably hundreds of non-HLA loci affecting the risk of CD, UC, and T1D that remain unmapped owing to their very small effect sizes (Barrett et al., 2009; Anderson et al., 2011; Franke et al., 2010). These putative loci will be difficult to map unless they contain rare mutations of higher penetrance, an occurrence that is just beginning to yield informative findings (Nejentsev et al., 2009; Rivas et al., 2011) and holds continued promise with the rapid use of high- throughput next-generation sequencing.

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450 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE In humans, the HLA locus contains a large number of genes encoding the major histocompatibility complex (MHC) molecules (which are responsible for presenting antigens to cells of the immune system), along with a number of other genes that modulate immune responses. The remarkable contribution of HLA variations (Todd, 2010) to T1D risk is an unusual feature of a common disease. Nevertheless, HLA genotypes that greatly predispose individuals to T1D are not sufficient to cause the disease because only ~5% of high-risk HLA carriers develop T1D. HLAs are expressed by antigen-presenting cells (APCs), such as macro­ hages, B lymphocytes, and dendritic cells (DCs). DCs are highly p potent APCs that reside in the pancreas and its islets (i.e., collections of insulin-­ producing beta cells and other endocrine cells) and could initiate the autoimmune destruc­ion of beta cells by T cells (Calderon et al., 2011a, 2011b). Interestingly, t the pancreatic lymph nodes, where DC priming of T cells for the induction of T1D may occur, also drain parts of the intestine, providing a site where the micro- biome might influence the genesis of T1D (Turley et al., 2005; Wen et al., 2008). Because the insulin gene is one of the strongest non-HLA T1D susceptibility loci in the genome (Todd, 2010) (http://www.t1dbase.org), insulin and its precursors are likely primary autoantigens. These very strong associations with both HLA and this autoantigen gene are not a feature of CD or UC, in which no particular antigen is known to be targeted, hence their classification as inflammatory rather than autoimmune diseases. GWAS point to several other immunologic compo- nents of T1D etiology, including IL-2 production and receptor signaling (IL-2 gene, IL-2 receptors IL2RA [CD25] and IL2RB [CD132]; Todd, 2010), immune tolerance and T cell receptor signaling (PTPN2 [Long et al., 2011], PTPN22 [Arechiga et al., 2009; Bottini et al., 2006]), and recently, the immune response to viral infections and the type 1 interferon responses (IFIH1 [encoding MDA5], GPR183 [EBI2] [Heinig et al., 2010], TLR7 and TLR8, and FUT2 [Smyth et al., 2011]). Twenty-eight loci (28/71, 39%) of CD risk loci are shared with UC, indicat- ing that a set of core mechanisms participate in these diseases (Figure A19-2) (Khor et al., 2011). These diseases genes implicate numerous processes in both CD and UC, including T cell differentiation and function, autophagy, ­ ndoplasmic ­eticulum stress, oxidative stress, and mucosal immune defenses, e r among others. There are important gene–gene and pathway–pathway interactions within this core set of processes. For example, the CD risk gene NOD2 links to a ­ utophagy through interactions with the Nod2 protein and with Atg16L1, induc- tion of proinflammatory cytokines, control of bacterial infection, and sensing of p ­ athogen-associated molecular patterns (Levine et al., 2011). Particularly notable are pathways involving the cytokines IL-23 and IL-12, which regulate the devel- opment of TH1 and TH17 CD4 T cells, and IL-10, which is essential in the func- tion of certain regulatory T cells (Tregs) via its anti-inflammatory ­ ctivity. Rare a mutations in genes encoding IL-10 receptors confer susceptibility to early-onset IBD (Glocker et al., 2009). These genetic clues point to a key role for regulating

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APPENDIX A 451 FIGURE A19-2  Refining the relationship between genotype and phenotype in complex inflammatory diseases. (A) Traditionally, a disease is considered as a single phenotype, with genes or loci conferring risk to two diseases shown as overlapping in a Venn diagram. (B) We propose a new view of the genotype–phenotype relationship in which different sets of loci are responsible for mechanistically distinct subtypes of diseases, and the sum of these subtypes constitutes the overall diagnosis. Here two disease subtypes are indicated for simplicity, but many such subtypes may exist, and sets of overlapping risk loci may be associated with these multiple mechanistically distinct disease phenotypes.

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452 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE the balance between pro- and anti-inflammatory T cells in CD and UC. The regulation of T cell differentiation is also a key target for host-gene-microbial interactions, as discussed below. Mechanism-Based Disease Subtypes GWAS have revealed a wealth of genes potentially involved in T1D, UC, and CD, but no single gene or set of genes is prognostic. How can we interpret this observation? Here, we argue for an important contributor to this observation—the concept that “diagnosis” does not equal “single phenotype.” Without a distinct phenotype, genetic results are often difficult to interpret. This basic principle comes into sharp focus as one considers current genetic and pathogenesis studies of CD, UC, and T1D. Why is a diagnosed “disease” an imprecise phenotype? It is not because patients have been misdiagnosed—the diagnoses of UC, CD, or T1D have stood the test of time to predict patient prognosis. However, we believe that there are many pathways to the same diagnosis. A diagnosis may be “clinically” precise but “mechanistically” imprecise. Thus, clinical diagnoses are poor phenotypes for genetic studies unless a single mechanism is responsible for the diagnosis, as in the case of a rare gene mutation in a monogenic disease. The complexity of GWAS results is consistent with the existence of multiple disease subtypes within T1D, UC, or CD, each based on a specific mechanism (Figure A19-2). Support for this idea comes from the observation that subsets of IBD patients respond dif- ferentially to mechanistically distinct interventions (Melmed and Targan, 2010). Why do diagnostic categories group different mechanistic processes under the same moniker? Over many decades, pathologists have lumped patients with similar but nonidentical clinical and pathological signs and symptoms into diag- nostic categories that predict outcome and complications. Indeed, this has enor- mous value clinically, but it emphasizes similarities between patients in outcome rather than differences in pathways that lead to a common endpoint. Complex diseases are diagnosed by summing up multiple factors that may be causes or mere consequences of the disease process. Disease “diagnosis” does not require the presence in the tissue of all of the abnormalities that may be “classically” seen in a given disease (Gianani et al., 2010; Odze, 2003). For example, at the polar extreme, CD is easily distinguished from UC by its classical ileal involvement (i.e., involvement of tissue at the end of the small intestine), fissures, granulomas, transmural inflammation (i.e., inflammation through the entire intestinal wall), fat wrapping of the intestine, patchy pathology, skip lesions, and patient presenta- tion with bowel strictures or percutaneous fistulae. However, like UC, CD can be restricted to the colon, and the inflammatory infiltrates of CD and UC overlap. UC can be patchy, and the patient presentations of the two diseases can overlap extensively. Similarly, the genetics, pathology, and pathogenesis of IBD may differ between young and old patients with the same diagnosis (Imielinski et al.,

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APPENDIX A 453 2009; Odze, 2003). Even when all classical aspects of a disease are present, the mechanism responsible for the pathology observed may differ from one person to another. Based on these considerations, it is no surprise that the genetics of T1D, CD, and UC are complex because different phenotypes may have been grouped into a single analysis. This putative mechanistic heterogeneity is reflected in sometimes subtle, but quantifiable, characteristics of the disease process and pathology. Taking such differences into account can be used to identify disease subtypes that are more recognizable as molecularly defined pathological conditions and that more closely relate to specific pathogenetic mechanisms underpinned by distinct sets of genetic risk loci (Figure A19-3). For example, variations in the ATG16L1 gene (i.e., hypomorphic expression in the mouse and homozygosity for the T300A variant in humans) result in abnormalities in Paneth cell granules and secretion (Cadwell et al., 2008, 2010). Paneth cells are innate immune epithelial cells p ­ ositioned at the base of small intestinal crypts, where they secrete antimicrobial peptides and other factors that help shape the configuration of the intestine’s bacterial community. Abnormalities in Paneth cells are observed in the subset of CD patients homozygous for the T300A allele, thus defining a pathologic subtype of CD (Figure A19-2B). If one used criteria including Paneth cell abnormalities in CD diagnosis, the frequency of the ATG16L1 T300A allele would be higher in patients with the “Paneth cell subtype” of CD than in the CD population as a whole (Figure A19-3). If multiple risk loci contribute to such Paneth cell changes, one might be able to detect gene-gene interactions in this subset of patients com- pared to other subsets. A similar situation exists in T1D. Biopsy specimens of the pancreas are virtually impossible to obtain. Therefore, T1D is defined clinically by the down- stream consequences of destroying the insulin-secreting b cells of the pancreatic islets, namely, high blood glucose and absolute insulin dependence, rather than by the mechanisms for their destruction. It is, therefore, possible that several dif- ferent pathologic processes result in this disease. T1D patients diagnosed under age 10 years frequently exhibit islet inflammation or insulitis, whereas patients diagnosed over age 10 years exhibit insulitis less frequently. More recently, this histopathological heterogeneity has become even more evident (Gianani et al., 2010), thanks to the Juvenile Diabetes Research Foundation nPOD project (http:// www.jdrfnpod.org). As for CD, the diagnosis of T1D may reflect the presence of more than one pathogenetic mechanism and, thus, represent more than one dis- ease subtype, although in T1D the HLA effects are an essential common pathway. The concept that disease diagnoses include mechanism-based disease sub- types has many implications for interpreting human genetic studies and for under­ standing the relationship between the microbiome and genetic susceptibility, as discussed below. Including disease subtypes within a single diagnosis would de- crease the power to define causal alleles and to detect gene-gene interactions that contribute to a single disease subtype. In this view, the difficulty of interpreting

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454 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE FIGURE A19-3  The iterative redefinition of mechanism-based disease subtypes. Here we present a conceptual workflow for breaking a broad disease diagnosis into its component subtypes by the iterative application of genetics and mechanistic studies. One output would be therapeutics based on disease subtype and patient stratification into groups more likely to respond to a given therapy or preventive strategy (A). (B) shows specific challenges for this process for type 1 diabetes and Crohn’s disease.

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APPENDIX A 455 how multiple small genetic effects sum to predispose an individual to a clinical diagnosis may partly reflect insufficient precision in selection of specific pheno- types to study. It is important to recognize that it is the power and informativeness of GWAS themselves that drive the concept of mechanism-based disease subtypes (Figure A19-2). In the absence of candidate genetic mechanisms for defining disease subtypes, there is limited clinical utility in focusing on low-frequency characteristics or subtypes within a larger diagnostic category that predicts pa- tient outcome. We, therefore, argue for iterative high-precision phenotyping of patients into mechanism-based subtypes in future studies; this will allow more accurate interpretation of genetic, pathogenesis, outcome, and therapeutic studies ­ (Figure A19-3). Such definitions must be iteratively reassessed as risk alleles are defined and disease mechanisms are delineated so that the field is not limited by inflexible definitions of disease that may obscure mechanistic heterogeneity. This type of approach is a necessary presage to so-called stratified or personalized medicine. The genetic and pathological complexity of T1D, CD, and UC is par- ticularly well suited for testing whether iteratively redefining disease diagnoses can enhance the value of genetic and pathogenesis studies. Importantly, precision in disease categorization would make defining the impact of host-gene-microbial interactions on disease processes more robust. Host-Gene-Microbial Interactions in Metagenetics Metazoan organisms are complex communities that include a core organ- ism in combination with a veritable zoo of other organisms that live on or in the body—our microbiome. The microbiome includes eukaryotic viruses, E ­ ukarya, bacteria viruses, Bacteria, Archaea, and, for many, helminths (Virgin et al., 2009; Kau et al., 2011; Garrett et al., 2010b; Spor et al., 2011). The impor- tance of understanding the microbiome has been repeatedly emphasized, giving rise to a large number of international human microbiome projects (e.g., https://­ commonfund.nih.gov/hmp/, http://www.metahit.eu/) that have focused initially on the bacterial component of the microbiome. The host plus non-host genes of this polyglot and interactive community constitute our metagenome (Box A19-1). A critical emerging concept is that bacterial and viral interactions in the patho- genesis of inflammatory disease occur in a host gene-specific fashion (see below; Virgin et al., 2009; Cadwell et al., 2010; Bloom et al., 2011; Elinav et al., 2011). Understanding the metagenome is, therefore, highly relevant to understanding T1D, UC, CD, and other common multifactorial diseases. Intestinal bacteria play a role in driving IBD, and emerging data support a similar view for T1D (Wen et al., 2008; Giongo et al., 2011; Roesch et al., 2009). The evidence that bacteria play a role in IBD includes two major observations: that surgical diversion of the fecal stream ameliorates inflammation (Sartor, 2008), and that antibiotics help some patients. In mouse models of colitis, viruses,

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460 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE to self-antigens. This is particularly important because it has been reported that T lymphocytes migrate to the intestine to accept differentiation signals regulating autoimmune responses (Esplugues et al., 2011). It was also shown that injection of Staphylococcus aureus or its superantigen S. aureus enterotoxin B (SEB) was able to induce these intestinal regulatory TH17 cells, which is consistent with SEB injection being immune tolerogenic (Esplugues et al., 2011). These studies suggest that variation in the metagenome between individual humans, between mice in different research facilities, or even between animals from different cages within the same facility could have profound effects on many aspects of the im- mune response. This concept has key implications for the interpretation of mouse studies. The microbiome is maternally inherited in mice, but it can differ among research facilities; there may even be significant microenvironmental variation between cages of mice or between mice born of different dams. Given that the microbiome influences immunity so extensively, experiments must control for these factors. Currently, this is neither consistently performed nor required by peer reviewers. Host-Gene-Metagenome Interactions in UC and CD Correlations between communities of intestinal bacteria and CD or UC have led to the concept of dysbiosis (Box A19-1) as a contributor to these diseases (e.g., Sartor, 2008). This important hypothesis emphasizes the potential role that changes in the bacterial microbiota have on disease. However, now this hypothesis needs to expand and include both nonbacterial components of the metagenome and highly specific interactions between individual bacteria or viruses and host genes, which have recently been identified as contributors to disease pathogenesis. The relative contribution of dysbiosis versus the contribution of single organisms within the microbiome to the etiology of complex inflammatory diseases is un- resolved. A confounding element has been the reliance on antibiotic treatment to assess bacteria as causes for intestinal disease. Because antibiotics can treat enteric inflammatory disease triggered by viruses (Figure A19-4) (Cadwell et al., 2010), a broader approach—including proof that specific bacteria or viruses are both neces- sary and sufficient for a phenotype—will be required to understand metagenetics of disease. Specific risk alleles for CD or UC could affect IBD by altering bacte- rial populations or individual bacterial types (Maloy and Powrie, 2011; Garrett et al., 2010b; Spor et al., 2011). Data from numerous mouse models of transmis- sible colitis confirm this point and are discussed below. The complexity of these reciprocal interactions between host and non-host genes within the metagenome underlines the critical need for new concepts and methodologies in computational and systems biology that can deal with individual host-gene microbial interactions in the broader context of the metagenome. IBD in humans and mice is associated with alterations in the balance between TH1, TH17, and Treg cells, and this balance is dependent on the metagenome

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APPENDIX A 461 (Garrett et al., 2010b; Maloy and Powrie, 2011). The relevance of these studies to human CD and UC is strongly supported by the identification of genes regulating these pathways in GWAS on IBD (see above). The role of specific bacteria and helminths in regulating these T cell responses in both the small and large intestine is highly relevant to understanding the genetics and pathogenesis of IBD. Poly- saccharide A synthesized by the common colonic commensal Bacteroides fragilis induces Tregs that secrete IL-10 and inhibit intestinal inflammation (Round and Mazmanian, 2010; Mazmanian et al., 2008). Similarly, a protein antigen secreted by the intestinal helminth Heligmosomoides polygyrus induces Foxp3+ Treg cells in vitro and in vivo in mice (Grainger et al., 2010). Furthermore, enteric carriage of a community of Clostridium species induces IL-10-secreting Foxp3+ Tregs in the colon, likely via induction of TGF-β (Atarashi et al., 2011). These findings are interesting in light of the ubiquity of Bacteroides and Clostridia as commensal organisms in human and mouse, and differences in human carriage of helminths across the world. In mice, the presence of distant relatives of Clostridia, called SFB, drives resistance to the enteric pathogen Citrobacter rodentium and the induction of CD4+ TH17 cells in the lamina propria of the small intestine (Ivanov et al., 2009; Gaboriau-Routhiau et al., 2009). The discovery that SFB influence CD4+ T cell differentiation was made when investigators noticed differences in intestinal TH17 cell numbers between mice of the same strain purchased from different vendors, followed by the demonstration that co-housing of these mice resulted in induction of TH17 cells (Ivanov et al., 2009). SFB are highly evolved for their commensal relationship with the mouse intestine (Sczesnak et al., 2011). Similar organisms have not yet been reported in humans, but it seems likely that similarly coevolved organisms will play a role in human intestinal biology and immuno- regulation. The discovery of the role for SFB in CD4+ T cell responses is similar to the discovery of a virus-plus-gene trigger for an intestinal disease in mice with symptoms similar to those in CD (Cadwell et al., 2010). This finding occurred by comparing intestinal phenotypes in one strain of mice bred in two different facili- ties. Both of these findings underline the critical importance of directly analyzing the contributions of the entire microbiome, rather than individual components, in animal models of diseases. Transmissible Colitis and Host-Gene-Metagenome Interactions Recent studies have made the striking observation that genetically deter- mined colitis is transmissible, revealing a key role for host genes in defining the microbiome and for metagenomic contributions to enteric disease. Mice lacking both Rag2 and the transcription factor T-bet develop colitis that can be transmitted from a mutant mother to wild-type fosterling mice (Garrett et al., 2007, 2010a). Although there are expansions of specific bacterial types in these mice, includ- ing Klebsiella pneumoniae and Proteus mirabilis, another cofactor, in addition to

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462 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE these bacteria, is required to generate the colitis phenotype. This cofactor is not yet identified. Similarly, mice deficient in NLRP6, caspase- 1, IL-18, or ASC (all proteins that regulate the expression of proinflammatory cytokines such as IL-18) develop colitis that is transmissible to co-housed wild-type mice (Elinav et al., 2011). Recent studies in another mouse model of transmissible colitis, which has similarities to UC, provide an example of the specificity of host-gene-bacterial relationships and IBD (Figure A19-4) (Bloom et al., 2011; Kang et al., 2008). Mice lacking the IL-10 receptor and expressing a dominant-negative form of the TGF-β receptor in T lymphocytes develop IFN-γ- and TNF-α-dependent colitis (Kang et al., 2008). The disease is cured by antibiotic treatment and reinduced by co-housing diseased and cured animals or by simply feeding cured mice the common commensal bacteria Bacteroides thetaiotaomicron (B. theta) (Bloom et al., 2011). In the same mice, the related Bacteroides sp. TP5 induced a lympho- cytic inflammatory infiltrate different from that induced by B. theta, indicating the remarkable specificity of host-gene-bacterial interactions (Figure A19-4). The authors noted dysbiosis in diseased animals with increased numbers of ­ nterobacteriaceae, but these bacteria did not induce disease despite being pres- E ent in higher numbers in sick mice. This study shows that a single bacterial type can cause IBD-like pathology in the proper genetic setting, a bacteria-plus-gene interaction that triggers intestinal inflammation. Importantly, the observation that two closely related bacteria induce different pathologies in the same genetically susceptible host provides support for the concept that genes present in the non- host metagenome can determine a host phenotype. A similar observation, in this case of a virus-plus-gene interaction that trig- gers IBD-like pathology, has been described in mice mutant for the CD risk gene Atg16L1 (Figure A19-4) (Cadwell et al., 2008, 2010). Abnormal Paneth cells were observed in humans carrying the ATG16L1 T300A allele and mice hypomorphic for expression of Atg16L1 raised in a conventional clean barrier (Cadwell et al., 2008). Importantly, the phenotype of the mice varied between different facilities and could be induced in mutant mice, but not wild-type mice, by inoculation with a specific strain of murine norovirus (Karst et al., 2003; Thackray et al., 2007). When these mice were challenged with dextran sodium sulfate (DSS), they devel- oped inflammatory phenotypes specific to the combination of Atg16L1 mutation and an individual norovirus strain (Figure A19-4). Virus-triggered pathologies could be treated by blocking TNF-α or IFN-γ or by treatment with antibiotics. Interestingly, infection with murine norovirus enhances signaling through Nod1 and Nod2 via the induction of type 1 IFN, potentially providing a direct link be- tween enteric viral infection and NOD signaling pathways implicated in IBD risk (Kim et al., 2011). These data raise the possibility that patterns of viral infection and specific components of the bacterial metagenome act together to influence the penetrance of UC and CD susceptibility risk alleles in humans. Furthermore, these data show that closely related viruses can have quite different effects on the phenotype of a host genetically prone to a disease process. This finding further

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APPENDIX A 463 supports the concept that genes in the non-host metagenome can determine host phenotypes. Host-Gene-Metagenome Interactions in T1D For T1D, recent observations fit with a “perfect storm” scenario in which numerous events combine to increase susceptibility to disease development ­ in early childhood (Figure A19-1). These events include susceptibility alleles in HLA class II genes and INS that cause increased autoreactivity against insulin, its precursors, and other islet antigens; lowered IL-2, IL-10, and IL-27 production and signaling; altered T cell receptor signaling and regulation (via, for example, susceptibility alleles in PTPN2, PTPN22, CTLA4, and IL2RA); and increased type 1 IFN production and responsiveness (Todd, 2010; Robinson et al., 2011; B ­ luestone et al., 2010). The “perfect T1D storm” is generated when these fac- tors combine with a permissive, modern environment of widespread vitamin D deficiency (Cooper et al., 2011) and other still unidentified environmental fac- tors (Figure A19-5). In particular, the T1D susceptibility genes and candidates IFIH1 (Nejentsev et al., 2009), GPR183 (EBI2) (Heinig et al., 2010), TLR7, TLR8 (­ arrett et al., 2009), and FUT2 (Smyth et al., 2011) strongly suggest B an etiological role for virus-induced, type 1 interferon production. A common knockout mutation of FUT2 in several populations causes the nonsecretor status (i.e., a lack of shedding of the A and B blood group antigens into saliva and intestinal secretions). This T1D-predisposing FUT2 genotype is also associated with increased risk of CD (McGovern et al., 2010; Franke et al., 2010), providing another direct mechanistic link between these two diseases and microbial infec- tions. The FUT2 nonsecretor genotype is associated with resistance to certain strains of norovirus and Helicobacter pylori (Smyth et al., 2011). Investigations of the mechanisms involved in the FUT2 associations with chronic and infectious disease are ­ rgently required, as is the case for many of the newly identified u GWAS candidate genes. Defining the Metagenome Now and in the Future Technologies for analyzing human loci involved in complex diseases have, until recently, outstripped technologies for analyzing the metagenome. For ex- ample, single-nucleotide polymorphism (SNP)-based GWAS cover the entire human genome, although at low resolution, whereas most common tools and methods applied to the non-host metagenome focus on only one component, such as a particular bacteria, viruses, or phage. The non-host metagenome is so complex that researchers have focused on DNA sequencing, even though many organisms relevant to disease—including enteroviruses that have been linked to T1D and viruses that cause intestinal disease—have RNA genomes. Although our knowledge of the human gut metagenome is in its infancy, this metagenome can

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464 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE now be explored in detail by deep, next-generation sequencing of both RNA and DNA, then stratified by host genotype, disease risk, or disease status. Investiga- tors are increasingly using shotgun sequencing of RNA + DNA, which theoreti- cally can detect any organism (e.g., Finkbeiner et al., 2008). However, studies to date have often relied on the DNA sequencing of 16S rRNA genes of bacteria. This standard and reliable method has identified dysbiosis in IBD and T1D (Wen et al., 2008; Roesch et al., 2009; Giongo et al., 2011; Sartor, 2008; Garrett et al., 2010b). Whether these changes are causal or secondary to disease is unclear. An outstanding example of consequences of relying on the analysis of only a subset of the metagenome is the recent appreciation that bacterial phage viruses are a major and dynamic part of the intestinal microbiome (Reyes et al., 2010). This adds an enteric bacterial “virome” to the eukaryotic virome that lives in our tissues (Virgin et al., 2009). Bacteria are not the only cells, in addition to host cells, that can be infected by viruses with consequent changes in biology. For example, an RNA virus infects the eukaryotic pathogen Leishmania and regulates the host inflammatory responses during parasite infection (Ives et al., 2011). Thus, like bacteria and their phages, all Eukarya in the microbiome are candidates for viral infection that might alter biological processes. The tools to detect and quantify the entire non-host metagenome at a rea- sonable cost will undoubtedly develop rapidly as metagenomic sequencing tech- nologies and computational approaches to phylogeny and microbe detection are developed and applied. Similarly, sequencing the entire host genome is becoming more cost efficient and practical. This wealth of data will set the stage for meta- genetics, but meaningful and robust analyses of the complex interactions within the metagenome will require new computational tools and new conceptualizations of gene-gene and gene-microbe interactions. Conclusion: The Metagenetics of Mechanism-Based Disease Subtypes Here we have argued that two factors need to be considered as key contribu- tors to the genetics and pathogenesis of complex inflammatory diseases, such as T1D, CD, and UC: specific host-gene-microbial interactions and the mechanistic heterogeneity of phenotypes that constitute complex diseases. Although we have used the lens of T1D, CD, and UC research to support these concepts, it is clear that these ideas may apply to a broader array of diseases as well. The striking effects of the microbiome on systemic immunity and on diseases that affect both visceral and mucosal tissues suggest that any physiologic process may be altered by the microbiome and gene-specific interactions of the microbiome with the host. At a minimum, the diverse diseases that have been revealed by GWAS to share risk alleles are strong candidates for considering the metagenome, rather than only the host genome, as contributing to health or disease. The concepts of mechanism-defined disease subtypes and host-gene-­ microbial interactions cooperate in important ways. For example, if the single

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APPENDIX A 465 diagnosis of CD or T1D includes multiple mechanistic phenotypes (Figure A19-2 and Figure A19-3), a specific host-gene-microbial interaction (Figure A19-4) might contribute to only one of these phenotypes. In this setting, the impact of interactions between genes in the metagenome, of either microbial or host origin, would be obscured. This could, for example, obscure the role of a single microbe in causing one mechanism-based disease subtype rather than causing all cases of a disease. Failure to identify such an agent would prevent the use of approaches that treat or vaccinate against the agent (Figure A19-2 and Figure A19-5). It is logical and anticipated that stratifying patients for treatment with pathway-­ specific drugs will improve outcomes and success of phase II and III clinical trials (Figure A19-3). This paradigm is highly effective and increasingly used in the treatment of cancer, but it also seems likely to benefit those with germline-based predisposition to disease as well. Deconvoluting the complex matrix of interactions within the metagenome that contribute to disease will require more complete analyses of the metagenome. It also requires an iterative redefinition of disease subtypes using markers that distin- guish between patients based on the mechanism responsible for injury rather than the presence of tissue injury per se. This ambitious goal is daunting to consider, but data discussed herein from human studies, animal studies, and analyses of the microbiome lead us to the inescapable conclusion that complex interactions within the metagenome control phenotypes. We must face this complexity head-on to solve the puzzle of the etiology and pathogenesis of complex diseases. We, therefore, argue for the inclusion of the metagenome in human genetic studies for these diseases. We view complex diseases as “metagenetic,” reflecting the contributions of both host and non-host genes within the metagenome. The nonhost genes in the metagenome that are relevant to a disease might be viral, bacterial, or derived from additional members of the microbiome, which are still largely uncharacterized. Parasites likely play a critical role in some populations. These metagenetic interactions probably contribute to the development of disease at two levels (Figure A19-5). First, we envision the normal immune system de- veloping via harmonious relationships within the metagenome. For example, the level of innate immunity in mice is regulated by chronic herpesvirus infection (Barton et al., 2007; White et al., 2010), and therefore acquisition of a specific chronic virus might predispose the host to either helpful or harmful responses to other components of the microbiome. It will be important to develop quantita- tive and robust ways to identify such a “normal” immune system. Second, once a poorly balanced immune system is generated, host-gene interactions, with either other host genes or the non-host metagenome, likely synergize to generate inappro­ riate levels of inflammation in response to microbial products (e.g., CD p and UC) or to set the stage for development of HLA-dependent auto­mmunity i (T1D). Understanding this level of biological complexity will require the involve- ment of statisticians, computational biologists, geneticists, pathogenesis experts, virologists, bacteriologists, and parasitologists in an integrated fashion to identify

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466 MICROBIAL ECOLOGY IN STATES OF HEALTH AND DISEASE mechanistically important interactions. Such an integrated approach can then perhaps make sense of the metagenetics of complex diseases, to the advantage of us all. Acknowledgments The authors would like to acknowledge helpful conversations with Thad Stappenbeck, Ramnik Xavier, Emil Unanue, Jeff Gordon, Balfour Sartor, Adolfo Garcia-Sastre, and Dermot McGovern. We thank Tom Smith for providing the images of noroviruses used in Figure A19-4. H.W.V. is supported by the NCI, NIAID, NCRR, the Crohn’s and Colitis Foundation of America, and the Broad Medical Foundation. J.A.T. is supported by the NIDDK, NIHR, the Wellcome Trust, the Juvenile Diabetes Research Foundation International, and the European Union. References Anderson, C.A., Boucher, G., Lees, C.W., Franke, A., D’Amato, M., Taylor,K.D., Lee, J.C., Goyette, P., Imielinski, M., Latiano, A., et al. (2011). Meta-analysis identifies 29 additional ulcerative colitis risk loci, increasing the number of confirmed associations to 47. Nat. Genet. 43, 246–252. Arechiga, A.F., Habib, T., He, Y., Zhang, X., Zhang, Z.Y., Funk, A., and Buckner, J.H. (2009). ­Cutting edge: the PTPN22 allelic variant associated with autoimmunity impairs B cell signaling. J. I ­ mmunol. 182, 3343–3347. Atarashi, K., Tanoue, T., Shima, T., Imaoka, A., Kuwahara, T., Momose, Y., Cheng, G., Yamasaki, S., Saito, T., Ohba, Y., et al. (2011). Induction of colonic regulatory T cells by indigenous C ­ lostridium species. Science 331, 337–341. Bach, J.F. (2002). The effect of infections on susceptibility to autoimmune and allergic diseases. N. Engl. J. Med. 347, 911–920. Barrett, J.C., Clayton, D.G., Concannon, P., Akolkar, B., Cooper, J.D., Erlich, H.A., Julier, C., M ­ orahan, G., Nerup, J., Nierras, C., et al; Type 1 Diabetes Genetics Consortium. (2009). Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703–707. Barton, E.S., White, D.W., Cathelyn, J.S., Brett-McClellan, K.A., Engle, M., Diamond, M.S., Miller, V.L., and Virgin, H.W., 4th. (2007). Herpesvirus latency confers symbiotic protection from bacterial infection. Nature 447, 326–329. Benson, A., Pifer, R., Behrendt, C.L., Hooper, L.V., and Yarovinsky, F. (2009). Gut commensal bacteria direct a protective immune response against Toxoplasma gondii. Cell Host Microbe 6, 187–196. Benson, A.K., Kelly, S.A., Legge, R., Ma, F., Low, S.J., Kim, J., Zhang, M., Oh, P.L., Nehrenberg, D., Hua, K., et al. (2010). Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl. Acad. Sci. USA 107, 18933–18938. Bloom, S.M., Bijanki, V.N., Nava, G.M., Sun, L., Malvin, N.P., Donermeyer, D.L., Dunne, W.M., Jr., Allen, P.M., and Stappenbeck, T.S. (2011). Commensal Bacteroides species induce colitis in host-genotype-specific fashion in a mouse model of inflammatory bowel disease. Cell Host Microbe 9, 390–403. Bluestone, J.A., Herold, K., and Eisenbarth, G. (2010). Genetics, pathogenesis and clinical interven- tions in type 1 diabetes. Nature 464, 1293–1300.

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