Perhaps most important, systems pathobiology can be used to revise and refine the definition of human disease. The classification of human disease used in this and all medical textbooks derives from the correlation between pathologic analysis and clinical syndromes that began in the nineteenth century. Although this approach has been very successful, serving as the basis for the development of many effective therapies, it has major shortcomings. Those shortcomings include a lack of sensitivity in defining preclinical disease, a primary focus on overtly manifest disease, failure to recognize different and potentially differentiable causes of common late-stage pathophenotypes, and a limited ability to incorporate the growing body of molecular and genetic determinants of pathophenotype into the conventional classification scheme.
Two examples will illustrate the weakness of simple correlation analyses grounded in the reductionist principle of simplification (Occam’s razor) in defining human disease. Sickle cell anemia, the “classic” Mendelian disorder, is caused by a Val6Gln substitution in the β chain of hemoglobin. If conventional genetic teaching holds, this single mutation should lead to a single phenotype in patients who harbor it (genotype-phenotype correlation). This assumption is, however, false, as patients with sickle cell disease manifest a variety of pathophenotypes, including hemolytic anemia, stroke, acute chest syndrome, bony infarction, and painful crisis, as well as an overtly normal phenotype. The reasons for these different phenotypic presentations include the presence of disease-modifying genes or gene products (e.g., hemoglobin F, hemoglobin C, glucose-6-phosphate dehydrogenase), exposure to adverse environmental factors (e.g., hypoxia, dehydration), and the genetic and environmental determinants of common intermediate pathophenotypes (i.e., variations in those generic pathologic mechanisms underlying all human disease—inflammation, thrombosis/hemorrhage, fibrosis, cell proliferation, apoptosis/necrosis, immune response).
A second example of note is familial pulmonary arterial hypertension. This disorder is associated with over 100 different mutations in three members of the transforming growth factor β (TGF-β) superfamily: bone morphogenetic protein receptor-2 (BMPR-2), activin receptor-like kinase-1 (Alk-1), and endoglin. All these different genotypes are associated with a common pathophenotype, and each leads to that pathophenotype by molecular mechanisms that range from haploinsufficiency to dominant negative effects. As only approximately one-fourth of individuals in families that harbor these mutations manifest the pathophenotype, other disease-modifying genes (e.g., the serotonin receptor 5-HT2B, the serotonin transporter 5-HTT), genomic and environmental determinants of common intermediate pathophenotypes, and environmental exposures (e.g., hypoxia, infective agents [HIV], anorexigens) probably account for the incomplete penetrance of the disorder.
On the basis of these and many other related examples, one can approach human disease from a systems pathobiology perspective in which each “disease” can be depicted as a network that includes the following modules: the primary disease-determining elements of the genome (or proteome, if posttranslationally modified), the disease-modifying elements of the genome or proteome, environmental determinants, and genomic and environmental determinants of the generic intermediate pathophenotypes. Figure 87e-2 graphically depicts these genotype-phenotype relationships as modules for the six common disease types with specific examples for each type. Figure 87e-3 shows a network-based depiction of sickle cell disease using this kind of modular approach.
Examples of modular representations of human disease. D, secondary human disease genome or proteome; E, environmental determinants; G, primary human disease genome or proteome; I, intermediate phenotype; P, pathophenotype. (Reproduced with permission from J Loscalzo et al: Molec Syst Biol 3:124, 2007.)
A. Theoretical human disease network illustrating the relationships among genetic and environmental determinants of the pathophenotypes. Key: D, secondary disease genome or proteome; E, environmental determinants; G, primary disease genome or proteome; I, intermediate phenotype; PS, pathophysiologic states leading to P, pathophenotype. B. Example of this theoretical construct applied to sickle cell disease. Key: Red, primary molecular abnormality; gray, disease-modifying genes; yellow, intermediate phenotypes; green, environmental determinants; blue, pathophenotypes. (Reproduced with permission from J Loscalzo et al: Molec Syst Biol 3:124, 2007.)
Goh and colleagues developed the concept of a human disease network (Fig. 87e-4) in which they used a systems approach to characterize the disease-gene associations listed in the Online Mendelian Inheritance in Man database. Their analysis showed that genes linked to similar disorders are more likely to have products that physically associate and greater similarity between their transcription profiles than do genes not associated with similar disorders. In addition, proteins associated with the same pathophenotype are significantly more likely to interact with one another than with other proteins not associated with the pathophenotype. Finally, these authors showed that the great majority of disease-associated genes are not highly connected genes (i.e., not hubs) and are typically weakly linked nodes within the functional periphery of the network in which they operate.
A. Human disease network. Each node corresponds to a specific disorder colored by class (22 classes, shown in the key to B). The size of each node is proportional to the number of genes contributing to the disorder. Edges between disorders in the same disorder class are colored with the same (lighter) color, and edges connecting different disorder classes are colored gray, with the thickness of the edge proportional to the number of genes shared by the disorders connected by it. B. Disease gene network. Each node is a single gene, and any two genes are connected if implicated in the same disorder. In this network map, the size of each node is proportional to the number of specific disorders in which the gene is implicated. (Reproduced with permission from KI Goh et al: Proc Natl Acad Sci USA 104:8685, 2007.)
This type of analysis validates the potential importance of defining disease on the basis of its systems pathobiologic determinants. Clearly, doing this will require a more careful dissection of the molecular elements in the relevant pathways (i.e., more precise molecular pathophenotyping), less reliance on overt manifestations of disease for their classification, and an understanding of the dynamics (not just the static architecture) of the pathobiologic networks that underlie pathophenotypes defined in this way. Figure 87e-5 illustrates the elements of a molecular network within which a disease module is contained. This network is first identified by determining the interactions (physical or regulatory) among the proteins or genes that comprise it (the “interactome”). These interactions then define a topologic module within which exists functional modules (pathways) and disease modules. One approach to constructing this module is illustrated in Fig. 87e-6. Examples of the use of this approach in defining novel determinants of disease are given in Table 87e-1.
TABLE 87e-1Examples of Systems Biology Application to Disease ||Download (.pdf) TABLE 87e-1Examples of Systems Biology Application to Disease
|Disease ||Analysis ||Reference |
|Hereditary ataxias ||Many ataxia-causing proteins share interacting partners that affect neurodegeneration ||Lim et al: Cell 125:801-814, 2006 |
|Diabetes mellitus ||Metabolite-protein network analysis links three unique metabolite abnormalities in prediabetics to seven type 2 diabetes genes through four enzymes ||Wang-Sattler et al: Mol Syst Biol 8:615, 2012 |
|Ebstein-Barr virus infection ||Viral proteome exerts its effects through linking to host interactome ||Gulbahce et al: PLoS One 8:e1002531, 2012 |
|Pulmonary arterial hypertension ||Network analysis indicates adaptive role for microRNA 21 in suppressing rho kinase pathway ||Parikh et al: Circulation 125:1520-1532, 2012 |
The elements of the interactome. The interactome includes topologic modules (genes or gene products that are closely associated with one another through direct interactions), functional modules (genes or gene products that work together to define a pathway), and disease modules (genes or gene products that interact to yield a pathophenotype). (Reproduced with permission from AL Barabasi et al: Nat Rev Genet 12:56, 2011.)
Approaches to identifying disease modules in molecular networks. A strategy for defining disease modules involves (i) reconstructing the interactome; (ii) ascertaining potential seed (disease) genes from the curated literature, the Online Mendelian Inheritance in Man (OMIM) database, or genomic analyses (genome-wide association studies [GWAS] or transcriptional profiling); (iii) identifying the disease module using different modeling or statistical approaches; (iv) identifying pathways and the role of disease genes or modules in those pathways; and (v) disease module validation and prediction. (Reproduced with permission from AL Barabasi et al: Nat Rev Genet 12:56, 2011.)
As yet another potential consideration, one can argue that disease reflects the later-stage consequences of the predilection of an organ system to manifest a particular intermediate pathophenotype in response to injury. This paradigm reflects a reverse causality view in which a disease is defined as a tendency to heightened inflammation, thrombosis, or fibrosis after an injurious perturbation. Where the process is manifest (i.e., the organ in which it occurs) is less important than that it occurs (with the exception of the organ-specific pathophysiologic consequences that may require acute attention). For example, from this perspective, acute myocardial infarction (AMI) and its consequences are a reflection of thrombosis (in the coronary artery), inflammation (in the acutely injured myocardium), and fibrosis (at the site or sites of cardiomyocyte death). In effect, the major therapies for AMI address these intermediate pathophenotypes (e.g., antithrombotics, statins) rather than any organ-specific disease-determining process. This paradigm would argue for a systems-based analysis that would first identify the intermediate pathophenotypes to which a person is predisposed, then determine how and when to intervene to attenuate that adverse predisposition, and finally limit the likelihood that a major organ-specific event will occur. Evidence for the validity of this approach is found in the work of Rzhetsky and colleagues, who reviewed 1.5 million patient records and 161 diseases and found that these disease phenotypes form a network of strong pairwise correlations. This result is consistent with the notion that underlying genetic predispositions to intermediate pathophenotypes form the predicate basis for conventionally defined end organ diseases.
Regardless of the specific nature of the systems pathobiologic approach used, these analyses will lead to a drastic revision of the way human disease is defined and treated, establishing the discipline of network medicine. This will be a lengthy and complicated process but ultimately will lead to better disease prevention and therapy and probably do so from an increasingly personalized perspective. The analysis of pathobiology from a systems-based perspective is likely to help define specific subsets of patients more likely to respond to particular interventions based on shared disease mechanisms. Although it is unlikely that the extreme of “individualized medicine” will ever be practical (or even desirable), complex diseases can be mechanistically subclassified and interventions may be tailored to those settings in which they are more likely to work.