Epistemic_Statements  

CORD-19:12af471f81b05440047e30b963b3733bd6f33693 JSONTXT 8 Projects

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Id Subject Object Predicate Lexical cue
T1 0-103 Epistemic_statement denotes Mechanistic and topological explanations in medicine: the case of medical genetics and network medicine
T2 114-274 Epistemic_statement denotes Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction.
T3 275-577 Epistemic_statement denotes In this paper, I argue that topological explanations, which have been described in ecology or in cognitive sciences, can also be found in medicine and I discuss the relationships between mechanistic and topological explanations in medicine, through the example of network medicine and medical genetics.
T4 770-1136 Epistemic_statement denotes My aim is to show how topological explanations in network medicine can help solving the conceptual issues that pure mechanistic explanations of the genetics of disease are currently facing, namely the crisis of the concept of genetic disease, the progressive geneticization of diseases and the dissolution of the distinction between monogenic and polygenic diseases.
T5 1137-1313 Epistemic_statement denotes However, I will also argue that topological explanations should not be considered as independent and radically different from mechanistic explanations for at least two reasons.
T6 1410-1545 Epistemic_statement denotes Second, they leave out some missing gaps in disease explanation that require, in turn, the development of new mechanistic explanations.
T7 1768-1836 Epistemic_statement denotes This last point may have major consequences for biomedical research.
T8 2258-2511 Epistemic_statement denotes This gave rise to a hot debate in philosophy of science revolving around whether topological explanations are real explanations or mere descriptions of biomedical phenomena and about the way mechanistic and topological explanations relate to each other.
T9 2512-2636 Epistemic_statement denotes My aim in this paper is to contribute to this debate by focusing on the case study of medical genetics and network medicine.
T10 2911-3070 Epistemic_statement denotes First, there are topological explanations in medicine whose impact on our understanding of disease in terms of robustness and functional redundancy is crucial.
T11 3071-3307 Epistemic_statement denotes Second, topological explanations and mechanistic explanations do constitute two distinct explanatory types, since they do not explain the same phenomenon in virtue of the same properties (topological properties vs. material properties).
T12 3308-3561 Epistemic_statement denotes However, they are not completely independent from each other: while pure mechanistic and pure topological explanations may exist, topological explanations often rely on mechanisms and raise new issues that, in turn, require new mechanistic explanations.
T13 3562-3904 Epistemic_statement denotes Third, I want to emphasize that in the case of medicine and medical genetics, the specific contribution of topological explanations is to foster a general explanation of disease and of the role of genes in disease, as opposed to pure mechanistic explanations that tend to focus on detailed explanations of the genetics of individual diseases.
T14 4196-4419 Epistemic_statement denotes In this neo-mechanistic trend, several philosophers distinguish between different concepts of mechanisms (Kuorikoski 2009; Nicholson 2012) or between different theses about why we need mechanistic explanations (Levy 2013) .
T15 4420-4626 Epistemic_statement denotes Still, some core ideas at the root of this concept can be spelled out: giving a mechanistic explanation of a phenomenon implies to identify the mechanism in virtue of which the given phenomenon is produced.
T16 4627-4926 Epistemic_statement denotes Identifying a mechanism thus implies to decompose a physical system, to individuate its components, including both its "parts" (also called "entities") and its "activities" (also called "operations"), and finally to describe the relationships between its components, namely its overall organization.
T17 5431-5662 Epistemic_statement denotes However, according to others, sometimes it appears that "less is more" and that abstracting away from the structural specifics of a mechanism is actually quite useful to understand its overall organization (Levy and Bechtel 2013) .
T18 5663-5848 Epistemic_statement denotes Since medical explanations have been often thought on the model of biological ones, this neo-mechanistic trend in philosophy of biology has progressively invaded philosophy of medicine.
T19 6187-6289 Epistemic_statement denotes In this view, disease is thought as the product of broken/dysfunctional/altered biological mechanisms.
T20 6341-6473 Epistemic_statement denotes It is possible to describe the mechanism of SARS infection by identifying the parts and the activities of the virus and of the host.
T21 6680-7029 Epistemic_statement denotes Of course, in the same way that there are many differences between mechanistic accounts in philosophy of biology, there are several disputes over what is a disease mechanism and whether diseases mechanisms should be viewed as fundamentally different from physiological mechanisms or not (on this controvery, see Moghaddam-Taaheri 2011; Nervi 2010) .
T22 7594-7979 Epistemic_statement denotes I will focus here on topological explanations that Philippe Huneman defines as "a kind of explanation that abstracts away from causal relationships and interactions in a system, in order to pick up some sort of "topological" properties of that system and draw from those properties mathematical consequences that explain the features of the system they target" (Huneman 2010, p. 214) .
T23 8194-8426 Epistemic_statement denotes It is then possible to use graph-theoretical concepts such as hubs, modules, motifs or coefficient clusters to derive topological properties from the location of the parts in the space and from the way the nodes are linked together.
T24 8647-8903 Epistemic_statement denotes If you want to explain how this given ecological community behaves and what happens to the system when one species (let's say species B) goes extinct, you may give a mechanistic explanation, based on the physical properties of the parts of the system (i.e.
T25 8947-9092 Epistemic_statement denotes the predation relationship, for instance) in order to explain how the disappearance of the species B affects the ecological community as a whole.
T26 9093-9361 Epistemic_statement denotes Such an explanation would constitute of a linear and organized sequence of causal-mechanistic interactions: "Species B usually preys on species C that preys on species D. In the absence of species B, species C will multiply and prey both species D and species A, etc."
T27 9362-9757 Epistemic_statement denotes However, another way to understand the behavior of the ecological community S when species B goes extinct is to choose one relevant mechanistic relationship between the species of your ecological community, for example, the predation-relationship and to represent it on a graph S', each species being a node and two nodes being connected by an oriented edge if one species prey on the other one.
T28 11172-11527 Epistemic_statement denotes Whether species B finds another species to prey on or whether species B also goes extinct, it will not change the material properties according to which species B has sharp canines and is usually a predator of species C. On the contrary, topological properties of a given object are derived from its spatial relationships with the other parts of a system.
T29 11528-11846 Epistemic_statement denotes It is not a property constituent of a given object, but a property that concerns "how, to put it vaguely, it fills the space; how parts of the system are located regarding one another and whether those relations can still hold under some continuous deformations of the system (and which ones)" (Huneman 2010, p. 214) .
T30 12258-12360 Epistemic_statement denotes When put like this, the contrast between mechanistic and topological explanations seems quite obvious.
T31 12361-12860 Epistemic_statement denotes First, whereas mechanistic explanations consist in breaking down a system into entities and activities in order to consider the causal relationships that are responsible for the production of regular changes in this system, topological expla-nations abstract away from the physical and material features of its parts and rely on the topological properties of a system, i.e, on the location that these parts occupy in a given space (i.e, in our example, species B being a hub (highly-connected node).
T32 12861-13023 Epistemic_statement denotes Second, while mechanistic explanations are firmly ground in temporal conditions, topological explanations may be (and usually are) completely independent of them.
T33 13024-13339 Epistemic_statement denotes 1 Third, instead of explaining the causal mechanistic interactions between the parts of the system, topological properties provide an explanation for the robustness of a system against different perturbations (how does the system react to the extinction of species B versus the extinction of species C for example).
T34 13340-13621 Epistemic_statement denotes However, in spite of theses apparently clear-cut distinct features, the status of topological explanations in biomedical sciences and the extent to which they actually differ from mechanistic explanations has became a hot topic in philosophy of science, for at least three reasons.
T35 13800-14018 Epistemic_statement denotes In this view, other types of explanations can either be considered as extensions of mechanistic explanations or should be denied the status of "explanations" and be only considered as mere descriptions of a phenomenon.
T36 14240-14396 Epistemic_statement denotes They defend the existence of a continuum between these two types of explanations, going from pure mechanistic explanations to pure topological explanations.
T37 14397-14621 Epistemic_statement denotes Indeed, topological explanations frequently build on mechanistic information and usually entail that some causal mechanistic interactions of the system have been considered explanatorily relevant enough to enter the network.
T38 14830-14992 Epistemic_statement denotes Nonetheless, to build such a network implies a choice between what would count as explanatory relevant relationships, i.e., in this case, predation relationships.
T39 14993-15326 Epistemic_statement denotes According to Huneman, it is thus possible to define a continuum between pure topological explanations, "when all the relations are explanatorily equivalent and enter into S' as nodes, vertices, points or sides" and pure mechanistic explanations when "all differences between causal interactions are relevant" (Huneman 2010, p. 225) .
T40 15327-15568 Epistemic_statement denotes As a consequence of this continuum, these philosophers do not necessarily consider topological and mechanistic explanations as competing or mutually exclusive from one another, but rather as complementary explanations of the same phenomenon.
T41 15569-15901 Epistemic_statement denotes So, in this view, the debate should not be about whether we should choose between a mechanistic and a topological explanation of a given biomedical phenomenon, but whether we need both types of explanations to explain the same phenomenon, depending on which features we are the most interested in Brigandt (2013) , Woodward (2013) .
T42 15902-16194 Epistemic_statement denotes Finally, another reason why the debate is so complicated and threatens to be a mere "semantic" one is that, as I mentioned at the beginning of this paper, there are many ways to define mechanisms and there seems to co-exist today at least one strict definition of mechanism and a broader one.
T43 16195-16669 Epistemic_statement denotes 2 Following this liberalization of the concept of mechanism, it became obvious that the more one might want to defend a strong and strict concept of mechanism, the more topological explanations and mechanistic explanations may be seen as two radically different ways of explaining a phenomenon, while the more liberal one might be with the concept of mechanism and the easier it would be to consider that mechanistic explanations can somewhat encompass topological analyses.
T44 16670-16878 Epistemic_statement denotes It is precisely in these terms that Woodward analyses the controversy between Craver, Kaplan and Bechtel over what should be considered mechanistic explanations and what should be considered topological ones.
T45 16879-17072 Epistemic_statement denotes 3 To sum it up, the current controversy on topological and mechanistic explanations raises three issues: are topological explanations real explanations and do they exist in biomedical sciences?
T46 17073-17149 Epistemic_statement denotes In what sense topological explanations differ from mechanistic explanations?
T47 17150-17297 Epistemic_statement denotes And what is the specific contribution of topological explanations to our understanding of a given phenomenon, compared to mechanistic explanations?
T48 17298-17412 Epistemic_statement denotes In order to explore these three interrelated issues, I focus on the case of network medicine and medical genetics.
T49 17413-17754 Epistemic_statement denotes I will first point out three main shortcomings of the current mechanistic explanations of genetic diseases in contemporary medical genetics, namely the collapse of the mechanistic definition of monogenic disease, the progressive geneticization of every disease and the dissolution of the distinction between monogenic and polygenic diseases.
T50 17896-18094 Epistemic_statement denotes I will especially focus on one of the main tools of network medicine: the diseasome whose aim is to represent as a network the relationships between every human disease gene and every human disease.
T51 18095-18245 Epistemic_statement denotes Third, I will show how the topological properties of the diseasome partially renew the traditional mechanistic explanation of the genetics of disease.
T52 18246-18447 Epistemic_statement denotes However, I will argue that network medicine does not provide pure topological explanations, since topological explanations developed by network medicine are highly dependent on mechanistic information.
T53 18448-18626 Epistemic_statement denotes I will also point that some gaps remain in our understanding of the genetics of diseases and that new mechanistic explanations are needed in order to fill these explanatory gaps.
T54 19057-19252 Epistemic_statement denotes Among the reasons why philosophers of medicine are interested in mechanistic explanations of diseases, some of them, such as Thagard (2000 Thagard ( , 2006 , highlight their classificatory power.
T55 19253-19570 Epistemic_statement denotes In this view, the identification of mechanisms can be used for classificatory purposes, thus moving away from pure phenotypic characterization of disease towards mechanism-based characterization of diseases and allowing us to distinguish between disease classes (such as infectious diseases, autoimmune diseases, etc.
T56 19886-20074 Epistemic_statement denotes For example, vitamin C is crucial for collagen synthesis and the metabolism and synthesis of various chemical structures, which explains why its deficiency produces the symptoms of scurvy.
T57 20288-20465 Epistemic_statement denotes Other diseases such as cystic fibrosis are directly caused by genetic factors, and the connection between mutated genes and defective metabolism is increasingly well understood.
T58 20693-20999 Epistemic_statement denotes (Thagard 2008, p. 384) This is of tremendous interest in medicine, since there seems to be a very intuitive link between identifying the parts and the activities of the mechanisms responsible for the disease and finding a treatment aiming at restoring the dysfunctional mechanism or at altering its course.
T59 21000-21265 Epistemic_statement denotes Now, such a seemingly simplistic classification of diseases classes in contemporary medicine is probably debatable, since, for example, this way of classifying diseases does not mirror the categories presented in the International Classification of Disease -ICD 10.
T60 21266-21578 Epistemic_statement denotes But the point that I want to make and what Thagard has in mind here, is that, once a general mechanism has been identified for a disease class, each individual disease belonging to this class can get a detailed mechanistic explanation, where the parts and activities involved in this given disease are specified.
T61 21579-21906 Epistemic_statement denotes However and more importantly, while I will not assert that each disease class is identified with a mechanistic explanation in medicine, it is true that in the specific case of the history of medical genetics, mechanistic explanations have played an important classificatory role, with major consequences on biomedical research.
T62 21907-22269 Epistemic_statement denotes In order to understand the current conceptual challenges of medical genetics, one needs to go back to the 1960s, when genetic diseases were considered to be monogenic diseases, when genetic diseases were a specific class of rare, inherited, Mendelian, monogenic disorders and when the distinction between monogenic and polygenic diseases was strongly delineated.
T63 22440-22530 Epistemic_statement denotes Indeed, phenylketonuria is a rare disease whose prevalence varies from 1/4000 to 1/40,000.
T64 23259-23360 Epistemic_statement denotes However, a simple diet without phenylalanine, administered from birth, prevents the onset of disease.
T65 23361-23657 Epistemic_statement denotes On the model of this mechanistic explanation of phenylketonuria, the mechanistic explanation of monogenic disease in the 1960s can thus be described as: one inherited Mendelian mutation in one gene causes one dysfunctional protein that, in turn, causes the symptoms and the states of one disease.
T66 24052-24222 Epistemic_statement denotes However, since the establishment of phenylketonuria as a paradigmatic example of genetic disease, a double shift has occurred in medical genetics (Melendro-Oliver 2004) .
T67 24460-24835 Epistemic_statement denotes The discovery of susceptibility genes in the 1970s (genes that are associated to the occurrence of a disease but whose presence is not sufficient to cause it) and the discovery of oncogenes and anti-oncogenes in the 1980s (genes whose activation or repression plays a major part in the development of cancer) have drawn attention to the genetics of polygenic common diseases.
T68 24836-25022 Epistemic_statement denotes The rise of DNA sequencing and genetic engineering techniques has allowed the development of various methods for identifying allelic variants and an upsurge of gene-disease associations.
T69 25023-25229 Epistemic_statement denotes In the contemporary biomedical literature, every disease whose occurrence is statistically associated to an allelic variant (a variation of one or more nucleotides in a gene) tends to be considered genetic.
T70 25304-25537 Epistemic_statement denotes These diseases are not hereditary, but due to de novo mutations (mutations that appear in a gamete of one of the parents or in the fertilized egg itself) or to acquired mutations (mutations due to environmental effects, for example).
T71 25538-25748 Epistemic_statement denotes Their transmission does not necessarily follow Mendel's laws and they are said to be polygenic or complex, because their physiopathology implies the joint action of several genes and many environmental factors.
T72 25837-26038 Epistemic_statement denotes "Major gene effect" designates a mechanism where one main genetic mutation with a major effect on the phenotype is associated to several other genes with a low effect and several environmental factors.
T73 26039-26237 Epistemic_statement denotes "Oligogenic" disease designates a mechanism where a few genes have a major effect on the disease occurrence but are associated to several other genes with minor effects and to environmental factors.
T74 26590-26709 Epistemic_statement denotes This phenomenon is usually called "the geneticization of diseases" and has been well explored in sociology of medicine.
T75 26710-26919 Epistemic_statement denotes 5 On the other hand, several scientific discoveries have disrupted our understanding of monogenic disease and blurred the distinction between simple monogenic diseases and those that are complex and polygenic.
T76 26920-27521 Epistemic_statement denotes Indeed, three major new mechanisms have been recently revealed in the pathophysiology of phenylketonuria (Scriver and Waters 1999; Scriver 1995 Scriver , 2007 Scriver and Waters 1999; Scriver 1995 Scriver , 2007 : allelic heterogeneity (over 500 mutations of the PAH gene can cause phenylketonuria), genetic heterogeneity (when the gene PAH is normal, a mutation of the BH4 gene that codes for its receptor can be sufficient to cause the disease) and modifier genes (the BH4 gene influences the expression of the PAH gene and the consequences of its mutations on severity and variability of symptoms).
T77 27713-28152 Epistemic_statement denotes It is now widely acknowledged that these three new mechanisms, namely allelic heterogeneity (several mutations in the same gene can cause the same disease), genetic heterogeneity (several genes can cause the same disease) and modifier genes (one or more gene(s) can influence the disease phenotype) are at play in monogenic diseases and have called into question the apparent simplicity of monogenic diseases (Dipple and McCabe 2000a, b) .
T78 28220-28360 Epistemic_statement denotes On the one hand, every disease seems to be considered genetic and we have discovered several mechanisms involved in the genetics of disease.
T79 28559-28663 Epistemic_statement denotes I do not claim here that mechanistic explanations of individual genetic diseases are vain or irrelevant.
T80 28664-28867 Epistemic_statement denotes From a mechanistic point of view, our understanding of phenylketonuria is definitely much more detailed now than it was in the 1960s and the same can be claimed about many so-called "monogenic" diseases.
T81 28868-29223 Epistemic_statement denotes What I claim is that there is no longer a unified schematic mechanistic account (such as the "one mutation in one gene > one dysfunctional protein > one disease") that would hold for every monogenic disease and that would successfully discriminate between genetic disease and non-genetic diseases or even between monogenic diseases and polygenic diseases.
T82 29224-29420 Epistemic_statement denotes So, mechanistic genetic explanations do not allow us to identify a mechanism-based disease class called "genetic diseases", since the physiopathology of every disease can imply genetic mechanisms.
T83 29421-29921 Epistemic_statement denotes And they do not allow us to distinguish between monogenic diseases and polygenic diseases, since the difference between some mechanisms The distinction between monogenic and polygenic diseases is blurry exhibited in monogenic diseases (such as modifier genes) and polygenic diseases (such as "major gene effect") seems to be highly relative and since most mechanisms at play in monogenic diseases (allelic heterogeneity, genetic heterogeneity, modifier genes) can also be found in polygenic diseases.
T84 29922-30274 Epistemic_statement denotes Therefore, even if mechanistic explanations in medical genetics still are needed in medical genetics, they do not fulfill anymore their classificatory or unifying purpose and they struggle to answer three research questions, namely what a monogenic disease is, the geneticization of diseases and the difference between monogenic and polygenic diseases.
T85 30275-30368 Epistemic_statement denotes There have been some attempts to integrate these shifts in regional mechanistic explanations.
T86 30556-30905 Epistemic_statement denotes This theory aims to explain interindividual variability to infections by identifying four genetic mechanisms at play in infectious diseases: Mendelian monogenic predisposition to one infection, Mendelian monogenic predisposition to several infections, major gene/resistance to one infection and polygenic predisposition to one infection ( Table 2 ).
T87 30906-31113 Epistemic_statement denotes The strength of their theory relies on the fact that every mechanism does not correspond to a subclass of infectious diseases, but that several mechanisms can be at play in the same disease (Darrason 2013 ).
T88 31115-31368 Epistemic_statement denotes For example, the genetics of tuberculosis can involve, depending on individuals, either Mendelian monogenic predisposition to several infections or major gene/resistance to one infection or polygenic predisposition (Abel and Casanova 2000; Alcaïs et al.
T89 31399-31788 Epistemic_statement denotes While these mechanisms might be extrapolated to other disease classes and while their identification constitutes a progress in the explanation of the genetics of infectious diseases, they still rely on oversimplifications of the underlying mechanisms since, as I have previously discussed, the difficulty lies precisely in distinguishing between Mendelian monogenic and polygenic diseases.
T90 31789-32145 Epistemic_statement denotes One way to solve this situation would be to acknowledge that it is very difficult to get general genetic mechanisms in disease explanations and that we should stick at localizing and decomposing the specific genetic mechanisms at play in each individual disease and eventually at finding very schematic regional genetic mechanisms for some disease classes.
T91 32146-32281 Epistemic_statement denotes However, these shortcomings of mechanistic explanations have very concrete consequences on clinical research in medical genetics today.
T92 32282-32712 Epistemic_statement denotes Indeed, while the clear-cut mechanistic explanation of monogenic diseases in the sixties led to the development of gene identification techniques and to many successes in reverse genetics, the increasing complexity of mechanistic explanations of polygenic diseases made it more difficult to develop similarly successful and efficient gene identification techniques for polygenic diseases (Botstein and Risch 2003; Feingold 2005) .
T93 32713-33047 Epistemic_statement denotes To some extent, the final outcome of this increasing complexity precisely led to the development of genome-wide association studies, which is a gene identification technique that is specifically designed in order to require as least biological hypotheses as possible about the underlying mechanisms of the disease under investigation.
T94 33048-33272 Epistemic_statement denotes While genome-wide association studies raised great hopes, they were also quite deceptive, since many of the disease-gene associations they identify were not confirmed (Feingold 2005; Hirschhorn and Daly 2005; Visscher et al.
T95 33281-33613 Epistemic_statement denotes In other words, I claim that the current complexity and concreteness of mechanistic explanations in the genetics of diseases lead genomic research in a corner, with the seemingly insurmountable task to decipher the molecular mechanisms of thousand of individual diseases, without the help of general identification research methods.
T96 33614-33943 Epistemic_statement denotes However, there is another way to solve this current paradox of medical genetics: it is to look for a different type of disease explanation, that abstracts away from the complex mechanistic explanations of the role of individuals genes in individual diseases in order to consider the general role of genes in disease explanations.
T97 33944-33999 Epistemic_statement denotes This is precisely what network medicine suggests doing.
T98 34000-34361 Epistemic_statement denotes Network medicine is a recent research program, mainly developed by the team of Albert-László Barabási Barabási and Oltvai 2004; Barabási 2007) and born from the synthesis between the concept of "human disease genes", the development of systems biology and medicine and the formalization of network theory-three theoretical pillars that I am now going to detail.
T99 34637-34744 Epistemic_statement denotes The point is that human disease genes may have specific characteristics that differ from non-disease genes.
T100 34806-35063 Epistemic_statement denotes 2008; Griffiths and Gray 2005; Kitano 2002 Kitano , 2007 is an interdisciplinary research program, that emphasizes that the study of the individual components of a system is not sufficient to get a full understanding of its complexity and of its properties.
T101 35064-35291 Epistemic_statement denotes It relies on bioinformatics and mathematical modeling to represent and explore the interlevel and intralevel interactions between the components of complex systems and aims at finding general organizing principles in organisms.
T102 35292-35423 Epistemic_statement denotes The definition of systems medicine and its relationships with systems biology have been the subject of many debates Clermont et al.
T103 35536-35756 Epistemic_statement denotes In systems medicine, disease is not only a biological event, it is a very complex system composed of many interlevel components, going from DNA strands and tissue and organs to socio-economic factors, just to name a few.
T104 36462-36677 Epistemic_statement denotes Depending on the degree distribution of a network, that is, on the probability distribution of these degree P(k) over the whole network, it is possible to distinguish between random networks and scale-free networks.
T105 37474-37581 Epistemic_statement denotes Indeed, one of the main hypotheses of the network medicine is the interconnectivity of the cell components.
T106 37582-37710 Epistemic_statement denotes Based on this interconnectivity property, disease can never been understood as the result of a single mutation in a single gene.
T107 37711-37876 Epistemic_statement denotes On the contrary, disease is defined as a perturbation in a complex network of intra and extracellular components in a tissue specific or in an organ specific system.
T108 37877-38132 Epistemic_statement denotes In this framework, it is very likely that diseases are not discrete and clinically defined entities but have intertwined relationships with each other, since different diseases may share a same functional module of components, disrupted in different ways.
T109 38133-38285 Epistemic_statement denotes Therefore, the aim of network medicine is both to identify the pathological network of each disease and to identify which diseases share which networks.
T110 39031-39177 Epistemic_statement denotes A complete human interactome that would roughly incorportate 25,000 human genes, around 10 6 proteins, and their interactions, is yet to be drawn.
T111 39344-39431 Epistemic_statement denotes Disease networks may include disease genes networks (Goh and Choi 2012; Loscalzo et al.
T112 39558-39729 Epistemic_statement denotes However, since the aim of this paper is to discuss how network medicine deals with conceptual issues in contemporary medical genetics, I will focus especially here on Fig.
T113 40412-40463 Epistemic_statement denotes 2007 ) and on its relationships to the interactome.
T114 40464-40565 Epistemic_statement denotes The diseasome intends to represent the relationships between human diseases and diseasecausing genes.
T115 40961-41102 Epistemic_statement denotes The list of disorders, disease genes, and gene-disease association was obtained from the Online Mendelian Inheritance on Man (OMIM) database.
T116 41309-41441 Epistemic_statement denotes Once this bipartite graph is built, it is possible to construct two projections, which are basically the two faces of the same coin.
T117 42943-43107 Epistemic_statement denotes Before analyzing the topological properties of the diseasome, let us make some general remarks on how the diseasome was built and on the robustness of its analysis.
T118 43108-43375 Epistemic_statement denotes Although OMIM is the most up-to-date repository on the genetics of human disease, it is important to specify that it was originally restricted to monogenic disorders and has only in recent years expanded to include complex traits and the associated genetic mutations.
T119 43527-43632 Epistemic_statement denotes It is however worth noting that there are several reasons to believe in the robustness of these analyses.
T120 43633-43954 Epistemic_statement denotes Indeed, first, the researchers that built the first version of the diseasome simulated the inclusion of additional (but more noisy) gene-disease associations (thus going from 1,777 to 2,765 gene-disease associations): this in silico expansion of the diseasome did not affect the general structure of the obtained network.
T121 44298-44434 Epistemic_statement denotes This new version of the diseasome does not only take into consideration gene-disease associations but also protein-disease associations.
T122 44703-44755 Epistemic_statement denotes Three main analyses can be drawn from the diseasome.
T123 44756-44857 Epistemic_statement denotes The first one is a global analysis whose aim is to characterize the general structure of the network.
T124 45056-45279 Epistemic_statement denotes The third one consists in comparing topological properties of the human disease genes network with topological properties of the interactome, which represents the set of possible biological interactions in a human organism.
T125 45280-45475 Epistemic_statement denotes The first analysis of the diseasome is global and topological: the main aim is to qualify the general behavior and the topological properties of both networks, using the network theory's toolbox.
T126 45476-45947 Epistemic_statement denotes In the human disease network, as in the human disease gene network, it appears that the nodes (respectively, the diseases and the genes) are highly interconnected (meaning there are very few nodes that have no connections at all to the general network) and that the degree distribution in both networks follows a power law distribution (meaning that a few nodes have far more connections in the network than the others and that they play the role of hubs in the network).
T127 46564-46651 Epistemic_statement denotes For example, deafness is associated to 41 genes, leukemia to 31 and colon cancer to 34.
T128 46928-47091 Epistemic_statement denotes This first topological analysis might seem quite simple: it still points toward a first strong hypothesis: the hypothesis of the common genetic origin of diseases.
T129 47092-47366 Epistemic_statement denotes Indeed, would each human disease have a distinct genetic origin, the human disease network would either only exhibit disconnected sub-networks, composed of few isolated nodes, each one corresponding to a disease, or would be composed of small subgroups of similar disorders.
T130 47367-47649 Epistemic_statement denotes But since the distribution of both networks significantly differs from these hypotheses and from the distribution of a random network, it suggests that most diseases share some interconnected genes and that genes involved in the same disease may be involved in some common pathways.
T131 47650-47870 Epistemic_statement denotes The second analysis is a local analysis: the aim is to test this hypothesis of a functional clustering of human disease genes and to analyze the behavior and the properties of genes that are involved in the same disease.
T132 48113-48365 Epistemic_statement denotes Testing this "local hypothesis" requires characterizing whether two genes involved in the same disease produce interacting proteins, whether they are co-expressed at the same time and in the same tissues and whether they have close molecular functions.
T133 48997-49329 Epistemic_statement denotes By comparing these biological data to the diseasome, it was possible to conclude that genes involved in the same disease tend to (a) interact via protein-protein interactions, (b) be expressed in the same specific tissues (c) be strongly co-expressed, (d) exhibit synchronized expression as a group (e) share the same Gene Ontology.
T134 49330-49577 Epistemic_statement denotes Based on this confirmation of the local hypothesis, they develop the concept of disease functional module: Cellular networks are modular, consisting of groups of highly interconnected proteins responsible for specific cellular functions (21, 22) .
T135 49805-50161 Epistemic_statement denotes 2007) This is a major hypothesis of network medicine: when diseases share genes or when several genes are associated to the same disease, they belong to the same functional module, that is, to a set of molecular elements consisting of transcription factors, genes, proteins, that interact in a certain way to achieve a given cellular or molecular function.
T136 50213-50313 Epistemic_statement denotes The primary disease genome G is the set of molecular anomalies that are associated to the phenotype.
T137 50959-51106 Epistemic_statement denotes The concept of "essential genes" is intrinsically linked to gene knockout experiments, in which an organism's gene is selectively made inoperative.
T138 51107-51270 Epistemic_statement denotes A gene is considered to be essential for an organism if it is necessary for its survival, i.e., if a knockout of the corresponding gene leads to the lethal mutant.
T139 51271-51521 Epistemic_statement denotes Since such experiments cannot be conducted on humans, a human gene is considered essential if the knockout of its murine orthologue leads to the death of the mutant (in the embryonary state, in the prenatal state or in the immediate postnatal state).
T140 51780-51986 Epistemic_statement denotes Indeed, a first topological analysis of the interactome seems to prove that proteins produced by human diseases genes have a higher connectivity than proteins whose genes are not involved in human diseases.
T141 51987-52156 Epistemic_statement denotes So, if centrality (the capacity to be a hub) is taken as a proxy for being essential, this first analysis seems to confirm that human diseases genes are essential genes.
T142 52157-52454 Epistemic_statement denotes However, when using murin orthologues of the human disease genes to determine a given gene's essentiality, the situation appears to be more complex: over the 7,533 genes of the reconstructed interactome, the researchers identified 1267 essential genes that are not associated to any known disease.
T143 52618-52891 Epistemic_statement denotes To put it shortly, it seems that the vast majority of human disease genes are not essential genes, do not encode hubs and are located at the periphery of the interactome, while a few of them are essential genes, encode hubs and are located at the center of the interactome.
T144 53171-53376 Epistemic_statement denotes Not all genes can be diseases genes: some genes would be too essential for the development of the organism; were they mutated, there simply would not be an individual to pass on the mutations to offspring.
T145 53377-53552 Epistemic_statement denotes As I have pointed out previously, pure mechanistic explanations in medical genetics nowadays struggle with three issues: how to account for the role of genes in every disease?
T146 53553-53630 Epistemic_statement denotes How to account for a unifying description of the genetics of a given disease?
T147 53631-53730 Epistemic_statement denotes And, how to account for the relativity of the distinction between monogenic and polygenic diseases?
T148 53731-53843 Epistemic_statement denotes On each of these issues, based on the results I have described, network medicine provides some new explanations.
T149 53844-54081 Epistemic_statement denotes First, the local hypothesis, that relies on the global and local analyses of the diseasome, provides an explanation for three phenomena linked to the geneticization of diseases, namely syndrome families, comorbidity and diseases classes.
T150 54223-54347 Epistemic_statement denotes Syndrome families are a group of disorders that seem to have some symptoms in common but whose main cause is not understood.
T151 54348-54534 Epistemic_statement denotes The local hypothesis means that, if syndrome families have some symptoms in common, it is because they share interconnected genes that interact in overlapping disease functional modules.
T152 54774-54958 Epistemic_statement denotes A way to explain this phenomenon is to make the hypothesis that diseases that tend to happen together imply the same genes encoding interacting proteins in the same metabolic pathways.
T153 54959-55134 Epistemic_statement denotes To put it differently, if, being obese, an individual is more likely to get diabetes; it is partially because obesity and diabetes share common genes in their physiopathology.
T154 55135-55416 Epistemic_statement denotes Finally, if diseases belong to the same disease class, whether this one is based on an etiological category (such as cancer) or on an anatomical localization (such as cardiovascular diseases), it is because they share some common genes that interact in overlapping disease modules.
T155 55972-56096 Epistemic_statement denotes For example, in phenylketonuria, the primary genome would be the PAH gene, which codes the phenylalanine hydroxylase enzyme.
T156 56097-56226 Epistemic_statement denotes The secondary genome would be the BH4 gene and all the modifier genes that are known to influence the expression of the PAH gene.
T157 56227-56857 Epistemic_statement denotes The intermediate phenotypes would be all the physiopathological phenomena that lead from hyperphenylalanemia to brain damages: (a) direct toxicity of phenylalanine on brain cells, (b) the fact that, since the PAH enzyme aims at converting phenylalanine into tyrosine, a deficit in PAH enzyme also results in a deficit in tyrosine, which is a precursor of very important neurotransmitters, such as dopamine, adrenaline and noradrenaline (c) the fact that, in phenylketonuria, phenylalanine competes with other amino acids to enter the brain, since it shares the same transporters, thus altering the intracerebral protein synthesis.
T158 56858-56982 Epistemic_statement denotes Finally, environmental determinants would include the amount of phenylalanine intake depending on the diet, treatments, etc.
T159 57413-57475 Epistemic_statement denotes So, some disease modules would not include any primary genome.
T160 57700-57878 Epistemic_statement denotes Eventually, the concept of disease module explains how the difference between monogenic and polygenic diseases can be understood in terms of functional redundancy and robustness.
T161 57879-58023 Epistemic_statement denotes In a system, for a given function, there is functional redundancy when several independent pathways in the system can achieve the same function.
T162 58097-58394 Epistemic_statement denotes Based on these two properties, monogenic diseases can be redefined as diseases whose modules exhibit low functional redundancy and consequently, low robustness, while polygenic diseases are diseases whose modules exhibit high functional redundancy, and consequently, high robustness (Debret et al.
T163 58940-59048 Epistemic_statement denotes What do we learn from this case study on the relationships between topological and mechanistic explanations?
T164 59304-59491 Epistemic_statement denotes In this specific case study, I have shown how topological explanations can help solving issues in medical genetics that pure mechanistic explanations of disease have been struggling with.
T165 59676-59864 Epistemic_statement denotes Second, they allow us to abandon the concept of genetic disease in order to understand the various roles that genes can play in every disease through the identification of disease modules.
T166 59865-60116 Epistemic_statement denotes Finally, they explain the difference between monogenic diseases and polygenic diseases not as a mechanistic difference, but as a difference in the structure of the disease module that can be understood in terms of robustness and functional redundancy.
T167 60117-60386 Epistemic_statement denotes However, and this my second point, it is obvious that network medicine does not rely only on pure topological explanations, or, to put it differently, that topological explanations in network medicine highly depend on mechanistic explanations, for at least two reasons.
T168 60387-60599 Epistemic_statement denotes First, the relationships that are represented in networks are mechanistic, even though it is in virtue of the features of the network and not of the details of these relationships that an explanation is provided.
T169 60600-60855 Epistemic_statement denotes Indeed, the diseasome is an abstract representation of gene-diseases associations, that is, of the mechanistic relationships that are, if not always proved, at least strongly supported, between a given gene and the occurrence of the corresponding disease.
T170 60856-61009 Epistemic_statement denotes 8 So, in this sense, topological explanations cannot be understood as completely independent from mechanistic explanations, at least in network medicine.
T171 61152-61652 Epistemic_statement denotes For example, the local hypothesis, according to which genes and gene products that are involved in the same disease have an increased tendency to interact together and to belong to the same disease module, depends highly both on a topological property of the diseasome (the scale-free network property) and on mechanistic information on the human disease genes represented in the diseasome (about protein-protein interactions, about the level, time and place of human disease genes expression, etc.).
T172 61876-61996 Epistemic_statement denotes My point is not to claim that topological explanations and mechanistic explanations can always be seen as complementary.
T173 61997-62507 Epistemic_statement denotes But in the case of network medicine, not only the network itself is an abstract representation of facts about mechanistic relationships, it needs to be interpreted in mechanistic terms: the local hypothesis and the concept of disease module are fundamentally mechanistic concepts about the relationships between various components (genes, proteins, transcription factors, metabolic reactions, phenotypes, symptoms…) and about how the way that these components are organized or disorganized can lead to disease.
T174 62508-62674 Epistemic_statement denotes Another point worth noting is that, if network medicine provides some interesting explanations about the genetics of disease, at least two types of major gaps remain.
T175 62675-62963 Epistemic_statement denotes First, we may wonder why the diseasome has such topological properties, how it has evolved to be a scale-free network, why some functional disease modules are more robust than others to external perturbations and why the human disease genes are mostly at the periphery of the interactome.
T176 62964-63161 Epistemic_statement denotes I have mentioned some evolutionary hypotheses about this last point: human disease genes would be located at the periphery of the interactome, because their mutations do not lead to death in utero.
T177 63162-63328 Epistemic_statement denotes Second, there are still missing gaps in our understanding of what a disease module is, how it works and what kind of interactions cause the occurrence of the disease.
T178 63329-63510 Epistemic_statement denotes From this point of view, it seems obvious that topological explanations are not enough to explain diseases but provide a strong incentive to search for new mechanistic explanations.
T179 63511-63719 Epistemic_statement denotes Indeed, once diseases are defined as disease modules, the next step is to identify and localize the parts of each disease module and to understand the mechanistic relationships that link these parts together.
T180 63720-63963 Epistemic_statement denotes For instance, once phenylketonuria has been redefined as a disease module, the next step is to understand how its primary genome, secondary genome, intermediate phenotype and environmental determinants interact together to produce the disease.
T181 63964-64329 Epistemic_statement denotes So, to put in a nutshell, following Huneman and Woodward, I claim that topological explanations and mechanistic explanations are different because they do not explain the same phenomenon (the genetics of disease, in this case) in virtue of the same properties (topological vs. material properties) and because they capture different features of the same phenomenon.
T182 64330-64603 Epistemic_statement denotes Indeed, and this will be my final point, while mechanistic explanations can attain some level of generality, their main aim is to get concrete details about the way parts and activities are organized in a set of spatial-temporal conditions in order to produce a phenomenon.
T183 64604-64849 Epistemic_statement denotes Topological explanations, on the other hand, are concerned about more general properties of the system, such as robustness and functional redundancy: their aim is to explain how a phenomenon can resist or react to a set of various perturbations.
T184 65032-65382 Epistemic_statement denotes Instead of considering single individual diseases as completely distinct entities, whose genetic mechanisms need to be investigated separately, topological explanations push us to understand diseases as intertwined phenomena that are linked together from a genetic point of view and that need to be investigated from a common and general perspective.
T185 65612-65752 Epistemic_statement denotes This may have major consequences on biomedical research and has already led to the development of new ways of identifying new disease genes.
T186 66290-66495 Epistemic_statement denotes For example, if two modules are involved in the same pathway by a common gene product, the genes belonging to the neighbor module are considered potential candidate disease genes (Chan and Loscalzo 2012) .
T187 66496-66664 Epistemic_statement denotes Although these techniques are quite recent, they already meet some success in unraveling new disease genes in diseases as different and complex as asthma (Sharma et al.
T188 66762-66930 Epistemic_statement denotes In this paper, my aim was to examine the relationships between mechanistic and topological explanations through the case study of network medicine and medical genetics.
T189 67634-67746 Epistemic_statement denotes However, topological explanations cannot be seen as independent from mechanistic explanations for three reasons.
T190 67817-67924 Epistemic_statement denotes Second, interpreting the topological properties of the network depends on external mechanistic information.
T191 67925-68046 Epistemic_statement denotes Third, topological explanations are not complete explanations: they provide an incentive to new mechanistic explanations.
T192 68047-68389 Epistemic_statement denotes To put it in a nutshell, topological explanations in medicine challenge the way we traditionally explain diseases but should not be seen as independent and radically different from mechanistic explanations: instead of looking for specific mechanisms for each individual disease, topological explanations push us to explain disease in general.