CORD-19-Sentences  

CORD-19:12af471f81b05440047e30b963b3733bd6f33693 JSONTXT 8 Projects

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Id Subject Object Predicate Lexical cue
TextSentencer_T1 0-103 Sentence denotes Mechanistic and topological explanations in medicine: the case of medical genetics and network medicine
TextSentencer_T2 105-113 Sentence denotes Abstract
TextSentencer_T3 114-274 Sentence denotes Medical explanations have often been thought on the model of biological ones and are frequently defined as mechanistic explanations of a biological dysfunction.
TextSentencer_T4 275-577 Sentence 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.
TextSentencer_T5 578-769 Sentence denotes Network medicine is a recent discipline that relies on the analysis of various disease networks (including diseasegene networks) in order to find organizing principles in disease explanation.
TextSentencer_T6 770-1136 Sentence 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.
TextSentencer_T7 1137-1313 Sentence 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.
TextSentencer_T8 1314-1409 Sentence denotes First, in network medicine, topological explanations depend on and use mechanistic information.
TextSentencer_T9 1410-1545 Sentence denotes Second, they leave out some missing gaps in disease explanation that require, in turn, the development of new mechanistic explanations.
TextSentencer_T10 1546-1767 Sentence denotes Finally, I will insist on the specific contribution of topological explanations in medicine: they push us to develop an explanation of disease in general, instead of focusing on single explanations of individual diseases.
TextSentencer_T11 1768-1836 Sentence denotes This last point may have major consequences for biomedical research.
TextSentencer_T12 1838-2041 Sentence denotes B Marie Darrason marie.darrason@gmail.com Keywords Philosophy of science · Philosophy of medicine · Mechanistic explanation · Topological explanation · Network medicine · Genetic disease · Geneticization
TextSentencer_T13 2042-2257 Sentence denotes While most biomedical explanations have been considered to be mechanistic, some philosophers have recently pointed out the existence of more abstract or ideal types of explanations, such as topological explanations.
TextSentencer_T14 2258-2511 Sentence 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.
TextSentencer_T15 2512-2636 Sentence denotes My aim in this paper is to contribute to this debate by focusing on the case study of medical genetics and network medicine.
TextSentencer_T16 2637-2848 Sentence denotes Indeed, network medicine is a new discipline that relies on topological explanations to answer some research questions that traditional mechanistic explanations of medical genetics are currently struggling with.
TextSentencer_T17 2849-2910 Sentence denotes By focusing on this example, I aim at defending three claims.
TextSentencer_T18 2911-3070 Sentence denotes First, there are topological explanations in medicine whose impact on our understanding of disease in terms of robustness and functional redundancy is crucial.
TextSentencer_T19 3071-3307 Sentence 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).
TextSentencer_T20 3308-3561 Sentence 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.
TextSentencer_T21 3562-3904 Sentence 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.
TextSentencer_T22 3905-4157 Sentence denotes Since Bechtel and Richardson's book «Discovering complexity» (Bechtel and Richardson 1993) , there has been a strong focus on mechanisms and on mechanistic explanations in biology (Bechtel and Abrahamsen 2005; Craver 2006; Glennan 2005; Machamer et al.
TextSentencer_T23 4158-4195 Sentence denotes 2000; Machamer 2004; Woodward 2013) .
TextSentencer_T24 4196-4419 Sentence 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) .
TextSentencer_T25 4420-4626 Sentence 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.
TextSentencer_T26 4627-4926 Sentence 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.
TextSentencer_T27 4927-5136 Sentence denotes It is the way these entities and activities are organized in a continuous and temporal process in order to produce "regular changes" that gives explanatory power to the mechanistic explanation of a phenomenon.
TextSentencer_T28 5137-5430 Sentence denotes Some philosophers have heavily stressed concreteness and completeness as major features of a good mechanistic explanation: according to them, the more detailed, the more fine-grained a mechanism is, the more explanatory it is of the exhibited phenomenon (Craver 2006; Kaplan and Craver 2011) .
TextSentencer_T29 5431-5662 Sentence 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) .
TextSentencer_T30 5663-5848 Sentence 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.
TextSentencer_T31 5849-5994 Sentence denotes For example, Paul Thagard explicitly refers in his 2006 article to the Machamer Darden and Craver characterization of mechanisms (Machamer et al.
TextSentencer_T32 5995-6180 Sentence denotes 2000) and defines medical explanations as "the representation, […] , of mechanisms whose proper and improper functioning generate the states and symptoms of a disease" (Thagard 2006, p.
TextSentencer_T33 6181-6186 Sentence denotes 59) .
TextSentencer_T34 6187-6289 Sentence denotes In this view, disease is thought as the product of broken/dysfunctional/altered biological mechanisms.
TextSentencer_T35 6290-6340 Sentence denotes Thagard takes the example of the SARS coronavirus.
TextSentencer_T36 6341-6473 Sentence 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.
TextSentencer_T37 6474-6679 Sentence denotes It is the way these different parts and activities are organized in a continuous spatial-temporal process that allows the SARS coronavirus to infect the host cell, then to generate and cause SARS symptoms.
TextSentencer_T38 6680-7029 Sentence 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) .
TextSentencer_T39 7030-7225 Sentence denotes Nonetheless, most medical explanations are considered mechanistic explanations: in order to explain a disease, you need to localize and decompose the mechanism that produces the disease symptoms.
TextSentencer_T40 7226-7528 Sentence denotes While mechanistic explanation are pervasive in biology and medicine, some authors (philosophers as well as scientists) have recently insisted on the existence of more abstract, mathematical or ideal types of explanations (Batterman 2010; Brigandt 2013; Huneman 2010) in biology, ecology (Montoya et al.
TextSentencer_T41 7529-7593 Sentence denotes 2006) or neurosciences (Bullmore and Sporns 2009; Sporns 2012) .
TextSentencer_T42 7594-7972 Sentence 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.
TextSentencer_T43 7973-7979 Sentence denotes 214) .
TextSentencer_T44 7980-8193 Sentence denotes In order to provide a topological explanation for a given system, the system shall first be represented in an idealized space (usually, a graph or a network) where the parts of the system are represented as nodes.
TextSentencer_T45 8194-8426 Sentence 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.
TextSentencer_T46 8427-8646 Sentence denotes To illustrate this definition, Huneman takes the example of an ecological community S, composed of various species (A, B, C, D), tied together by many different kinds of relationships, including predation relationships.
TextSentencer_T47 8647-9092 Sentence 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. the organisms) and on the activities (i.e. the predation relationship, for instance) in order to explain how the disappearance of the species B affects the ecological community as a whole.
TextSentencer_T48 9093-9263 Sentence 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.
TextSentencer_T49 9264-9757 Sentence denotes In the absence of species B, species C will multiply and prey both species D and species A, etc." 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.
TextSentencer_T50 9758-10097 Sentence denotes Now if species B is connected to many other species by a predation relationship and if you remove it from your network (removal corresponding to extinction), it is easy to understand that this is going to affect your global network (the ecological community) in a different way than if species B was loosely connected to the whole network.
TextSentencer_T51 10098-10493 Sentence denotes In doing so, you explain the behavior of the ecological community not in virtue of the material and physical properties of the ecological community, but in virtue of the topological properties of the ecological community, once it has been represented as an abstract system, once that its parts (the species) and its activities (the predation relationships) have been stripped of any materiality.
TextSentencer_T52 10494-10652 Sentence denotes To be perfectly clear, let's specify that material properties and topological properties are not merely distinct but completely different kinds of properties.
TextSentencer_T53 10653-10747 Sentence denotes Material properties are directly related to the physical and concrete properties of an object.
TextSentencer_T54 10748-10952 Sentence denotes In my example, the fact that species B preys on species C depends on many properties, some of which being the material properties of the individuals of species B, such as having sharp canines for example.
TextSentencer_T55 10953-11093 Sentence denotes Material properties of an object are somewhere independent from the interactions of a given object with the system in which you consider it.
TextSentencer_T56 11094-11171 Sentence denotes Let's say that species B preys on species C, but that species C goes extinct.
TextSentencer_T57 11172-11392 Sentence 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.
TextSentencer_T58 11393-11527 Sentence denotes On the contrary, topological properties of a given object are derived from its spatial relationships with the other parts of a system.
TextSentencer_T59 11528-11839 Sentence 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.
TextSentencer_T60 11840-11846 Sentence denotes 214) .
TextSentencer_T61 11847-12005 Sentence denotes Here, the term "space" refers to the technical, abstract and mathematical notion of space used in graph theory and not to the vulgar notion of physical space.
TextSentencer_T62 12006-12257 Sentence denotes Thus, topological properties of a system have nothing to do with physical distances, but with the ability of the system and its parts to resist some types of spatial perturbations (such as removing a highly connected species in an ecological network).
TextSentencer_T63 12258-12360 Sentence denotes When put like this, the contrast between mechanistic and topological explanations seems quite obvious.
TextSentencer_T64 12361-12860 Sentence 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).
TextSentencer_T65 12861-13023 Sentence denotes Second, while mechanistic explanations are firmly ground in temporal conditions, topological explanations may be (and usually are) completely independent of them.
TextSentencer_T66 13024-13339 Sentence 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).
TextSentencer_T67 13340-13621 Sentence 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.
TextSentencer_T68 13622-13799 Sentence denotes First, some philosophers, such as Kaplan and Craver, claimed that there are no explanations in biomedical sciences other than mechanistic explanations (Kaplan and Craver 2011) .
TextSentencer_T69 13800-14018 Sentence 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.
TextSentencer_T70 14019-14239 Sentence denotes Second, even the proponents of the existence of topological explanations (Huneman 2010; Silberstein and Chemero 2013; Woodward 2013) claim that there is no dichotomy between topological explanations and mechanistic ones.
TextSentencer_T71 14240-14396 Sentence denotes They defend the existence of a continuum between these two types of explanations, going from pure mechanistic explanations to pure topological explanations.
TextSentencer_T72 14397-14621 Sentence 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.
TextSentencer_T73 14622-14829 Sentence denotes If we take back the example of the ecological community, it is true that what explains the behavior of the system in the absence of B is the fact that B is a major hub (highly connected node in the network).
TextSentencer_T74 14830-14992 Sentence 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.
TextSentencer_T75 14993-15319 Sentence 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.
TextSentencer_T76 15320-15326 Sentence denotes 225) .
TextSentencer_T77 15327-15568 Sentence 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.
TextSentencer_T78 15569-15901 Sentence 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) .
TextSentencer_T79 15902-16194 Sentence 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.
TextSentencer_T80 16195-16669 Sentence 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.
TextSentencer_T81 16670-16878 Sentence 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.
TextSentencer_T82 16879-17072 Sentence 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?
TextSentencer_T83 17073-17149 Sentence denotes In what sense topological explanations differ from mechanistic explanations?
TextSentencer_T84 17150-17297 Sentence denotes And what is the specific contribution of topological explanations to our understanding of a given phenomenon, compared to mechanistic explanations?
TextSentencer_T85 17298-17412 Sentence denotes In order to explore these three interrelated issues, I focus on the case of network medicine and medical genetics.
TextSentencer_T86 17413-17754 Sentence 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.
TextSentencer_T87 17755-17895 Sentence denotes Second, I will introduce network medicine, a recent discipline born form the synthesis between genomics, systems biology and network theory.
TextSentencer_T88 17896-18094 Sentence 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.
TextSentencer_T89 18095-18245 Sentence denotes Third, I will show how the topological properties of the diseasome partially renew the traditional mechanistic explanation of the genetics of disease.
TextSentencer_T90 18246-18447 Sentence 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.
TextSentencer_T91 18448-18626 Sentence 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.
TextSentencer_T92 18627-18859 Sentence denotes Finally, I will conclude on the specific contribution of topological explanations to our understanding of diseases: instead of focusing on the explanation of single diseases, they push us to develop a general explanation of disease.
TextSentencer_T93 18860-18988 Sentence denotes 4 2 In this paper, I will stick to the most widely accepted, but also stricter conception of mechanisms, namely the MDC account.
TextSentencer_T94 18989-19056 Sentence denotes 3 To get a detailed account of this analysis, see Woodward (2013) .
TextSentencer_T95 19057-19252 Sentence 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.
TextSentencer_T96 19253-19641 Sentence 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.), each disease class being defined by one or a series of mechanism(s):
TextSentencer_T97 19642-19748 Sentence denotes Not all diseases are caused by germs, but other major kinds have been amenable to mechanistic explanation.
TextSentencer_T98 19749-19885 Sentence denotes Nutritional diseases such as scurvy are caused by deprivation of vitamins, and the mechanisms by which vitamins work are now understood.
TextSentencer_T99 19886-20074 Sentence 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.
TextSentencer_T100 20075-20287 Sentence denotes Some diseases are caused by the immune system becoming overactive and attacking parts of the body, as when white blood cells remove myelin from axons between neurons, producing the symptoms of multiple sclerosis.
TextSentencer_T101 20288-20465 Sentence 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.
TextSentencer_T102 20466-20710 Sentence denotes The final major category of human disease is cancer, and the genetic mutations that convert a normal cell into an invasive carcinoma, as well as the biochemical pathways that are thereby affected, are becoming well mapped out. (Thagard 2008, p.
TextSentencer_T103 20711-20999 Sentence denotes 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.
TextSentencer_T104 21000-21265 Sentence 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.
TextSentencer_T105 21266-21578 Sentence 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.
TextSentencer_T106 21579-21906 Sentence 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.
TextSentencer_T107 21907-22269 Sentence 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.
TextSentencer_T108 22270-22439 Sentence denotes Phenylketonuria then embodied this concept of genetic disease viewed as synonymous with monogenic disease (Lindee 2000 (Lindee , 2002 Paul 1994 Paul , 2000 Paul , 2013 .
TextSentencer_T109 22440-22530 Sentence denotes Indeed, phenylketonuria is a rare disease whose prevalence varies from 1/4000 to 1/40,000.
TextSentencer_T110 22531-22605 Sentence denotes It is an inherited disease, as it is passed down from parents to children.
TextSentencer_T111 22606-22742 Sentence denotes It is a Mendelian disease, with an autosomal recessive transmission; meaning two mutated alleles are necessary for the disease to occur.
TextSentencer_T112 22743-22882 Sentence denotes It is a monogenic disease, that is, caused by the mutation of one gene: the PAH gene, which codes for the phenylalanine hydroxylase enzyme.
TextSentencer_T113 22883-23021 Sentence denotes Phenylalanine hydroxylase is necessary to convert phenylalanine, an essential amino acid found in food, into another amino acid, tyrosine.
TextSentencer_T114 23022-23184 Sentence denotes When this enzyme is mutated, phenylalanine cannot be converted in tyrosine and builds up in the blood, thus exerting a toxic effect on the central nervous system.
TextSentencer_T115 23185-23258 Sentence denotes When untreated, phenylketonuria (PKU) leads to severe mental retardation.
TextSentencer_T116 23259-23360 Sentence denotes However, a simple diet without phenylalanine, administered from birth, prevents the onset of disease.
TextSentencer_T117 23361-23657 Sentence 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.
TextSentencer_T118 23658-24051 Sentence denotes This mechanistic explanation of genetic disease had a huge impact on the development of specific research methods for identifying the genes involved in monogenic diseases, giving rise to the development of monozygotic twin studies, linkage analysis, candidate-gene approach to name a few, and leading to major successes in reverse genetic (Badano and Katsanis 2002; Jordan 1988 Jordan , 2006 .
TextSentencer_T119 24052-24222 Sentence 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) .
TextSentencer_T120 24223-24358 Sentence denotes On the one hand, the concept of genetic disease has extended far beyond the concept of monogenic disease, which it was synonymous with.
TextSentencer_T121 24359-24459 Sentence denotes Several scientific discoveries have contributed to this extension of the concept of genetic disease.
TextSentencer_T122 24460-24835 Sentence 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.
TextSentencer_T123 24836-25022 Sentence 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.
TextSentencer_T124 25023-25229 Sentence 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.
TextSentencer_T125 25230-25303 Sentence denotes Nowadays, the concept of genetic disease thus applies to common diseases.
TextSentencer_T126 25304-25537 Sentence 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).
TextSentencer_T127 25538-25748 Sentence 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.
TextSentencer_T128 25749-26237 Sentence denotes There are several mechanistic models of polygenic diseases (Badano and Katsanis 2002) . "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. "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.
TextSentencer_T129 26238-26384 Sentence denotes Finally, "true" polygenic diseases are diseases whose occurrence depends on multiple genes with a minor effect and multiple environmental factors.
TextSentencer_T130 26385-26589 Sentence denotes Thus, cancer, diabetes, hypertension and even tuberculosis-usually considered a paradigmatic example of environmental diseases, as an infectious agent causes it-have progressively been considered genetic.
TextSentencer_T131 26590-26709 Sentence denotes This phenomenon is usually called "the geneticization of diseases" and has been well explored in sociology of medicine.
TextSentencer_T132 26710-26919 Sentence 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.
TextSentencer_T133 26920-27521 Sentence 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).
TextSentencer_T134 27522-27712 Sentence denotes These new mechanisms have undermined the linear and specific correspondence between a mutation in the PAH gene, the production of a mutated PAH protein and the occurrence of phenylketonuria.
TextSentencer_T135 27713-28152 Sentence 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) .
TextSentencer_T136 28153-28219 Sentence denotes A paradox thus lies at the heart of contemporary medical genetics.
TextSentencer_T137 28220-28360 Sentence denotes On the one hand, every disease seems to be considered genetic and we have discovered several mechanisms involved in the genetics of disease.
TextSentencer_T138 28361-28558 Sentence denotes On the other hand, there is no consensual definition of what is a genetic disease and the distinction between monogenic disease and polygenic disease keeps getting blurrier and blurrier (Table 1) .
TextSentencer_T139 28559-28663 Sentence denotes I do not claim here that mechanistic explanations of individual genetic diseases are vain or irrelevant.
TextSentencer_T140 28664-28867 Sentence 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.
TextSentencer_T141 28868-29223 Sentence 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.
TextSentencer_T142 29224-29420 Sentence 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.
TextSentencer_T143 29421-29921 Sentence 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.
TextSentencer_T144 29922-30274 Sentence 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.
TextSentencer_T145 30275-30368 Sentence denotes There have been some attempts to integrate these shifts in regional mechanistic explanations.
TextSentencer_T146 30369-30530 Sentence denotes For example, Casanova and Abel, two French geneticists at the Necker Hospital, have successfully developed a genetic theory of infectious diseases (Alcaïs et al.
TextSentencer_T147 30531-30555 Sentence denotes 2009; Abel 2007, 2013) .
TextSentencer_T148 30556-30696 Sentence denotes This theory aims to explain interindividual variability to infections by identifying four genetic mechanisms at play in infectious diseases:
TextSentencer_T149 30697-30905 Sentence denotes 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 ).
TextSentencer_T150 30906-31113 Sentence 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 ).
TextSentencer_T151 31115-31368 Sentence 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.
TextSentencer_T152 31369-31390 Sentence denotes 2005; Baghdadi et al.
TextSentencer_T153 31391-31398 Sentence denotes 2006) .
TextSentencer_T154 31399-31788 Sentence 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.
TextSentencer_T155 31789-32145 Sentence 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.
TextSentencer_T156 32146-32281 Sentence denotes However, these shortcomings of mechanistic explanations have very concrete consequences on clinical research in medical genetics today.
TextSentencer_T157 32282-32712 Sentence 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) .
TextSentencer_T158 32713-33047 Sentence 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.
TextSentencer_T159 33048-33272 Sentence 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.
TextSentencer_T160 33273-33280 Sentence denotes 2012) .
TextSentencer_T161 33281-33613 Sentence 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.
TextSentencer_T162 33614-33943 Sentence 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.
TextSentencer_T163 33944-33999 Sentence denotes This is precisely what network medicine suggests doing.
TextSentencer_T164 34000-34361 Sentence 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.
TextSentencer_T165 34362-34452 Sentence denotes The concept of "human disease genes" rests on a double distinction (Jimenez-Sanchez et al.
TextSentencer_T166 34453-34460 Sentence denotes 2001) .
TextSentencer_T167 34461-34564 Sentence denotes First of all, it aims at distinguishing between human genes and non-human genes (such as animal genes).
TextSentencer_T168 34565-34636 Sentence denotes Secondly, it distinguishes between disease genes and non-disease genes.
TextSentencer_T169 34637-34744 Sentence denotes The point is that human disease genes may have specific characteristics that differ from non-disease genes.
TextSentencer_T170 34745-34805 Sentence denotes Systems biology (Bruggeman and Westerhoff 2007; Conti et al.
TextSentencer_T171 34806-35063 Sentence 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.
TextSentencer_T172 35064-35291 Sentence 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.
TextSentencer_T173 35292-35423 Sentence denotes The definition of systems medicine and its relationships with systems biology have been the subject of many debates Clermont et al.
TextSentencer_T174 35424-35459 Sentence denotes 2009; Wolkenhauer and Green 2013) .
TextSentencer_T175 35460-35535 Sentence denotes Systems medicine aims at discovering some organizing principles in disease.
TextSentencer_T176 35536-35756 Sentence 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.
TextSentencer_T177 35757-35902 Sentence denotes It thus partially rests on the results and findings developed by systems biology, but also requires developing its own specific tools and models.
TextSentencer_T178 35903-36202 Sentence denotes Finally, network theory has developed solid mathematical and computer-based methods to decipher the underlying architecture behind apparently anarchic networks such as the World Wide Web, social networks and biological networks (Barabási and Bonabeau 2003; Barabási 2011 Barabási , 2012 Jeong et al.
TextSentencer_T179 36203-36210 Sentence denotes 2000) .
TextSentencer_T180 36211-36289 Sentence denotes The basic components of a network are nodes, connected together through edges.
TextSentencer_T181 36290-36461 Sentence denotes The basic properties of a network are the total numbers of nodes in the network (N) and the degree of a node (k), that is the number of nodes a given node is connected to.
TextSentencer_T182 36462-36677 Sentence 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.
TextSentencer_T183 36678-36809 Sentence denotes In random networks, nodes follow a Poisson distribution, meaning that every node has on average the same number of connected nodes.
TextSentencer_T184 36810-37052 Sentence denotes Scale-free networks have a very different structure: their nodal distribution follows a Power law, meaning that there are both some highly interconnected nodes (that are called "hubs") and very sparsely connected nodes in scale-free networks.
TextSentencer_T185 37053-37212 Sentence denotes This is a "the rich-get-richer distribution": in this kind of network, the more a node is connected, the most connected he is going to become (Barabási 1999) .
TextSentencer_T186 37213-37473 Sentence denotes Combined together, these three disciplines naturally led to network medicine that aims to develop network-based approaches to disease by analyzing the interactions between different kinds of networks in a given disease and between apparently distinct diseases.
TextSentencer_T187 37474-37581 Sentence denotes Indeed, one of the main hypotheses of the network medicine is the interconnectivity of the cell components.
TextSentencer_T188 37582-37710 Sentence denotes Based on this interconnectivity property, disease can never been understood as the result of a single mutation in a single gene.
TextSentencer_T189 37711-37876 Sentence 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.
TextSentencer_T190 37877-38132 Sentence 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.
TextSentencer_T191 38133-38285 Sentence 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.
TextSentencer_T192 38286-38427 Sentence denotes In order to do so, network medicine relies on the systematic comparison between the human interactome and various disease networks (Fig. 1) .
TextSentencer_T193 38428-38549 Sentence denotes In a narrow sense, the interactome is the whole set of molecular interactions existing in a giving cell at a giving time.
TextSentencer_T194 38550-38690 Sentence denotes So, it is the whole set of the gene-gene, gene-protein, protein-protein, transcription factors-protein interactions, and so on and so forth.
TextSentencer_T195 38691-38843 Sentence denotes In a broader sense, the interactome designates the whole set of molecular interactions existing in an organism under specified conditions (Cusick et al.
TextSentencer_T196 38844-38862 Sentence denotes 2005; Vidal et al.
TextSentencer_T197 38863-38870 Sentence denotes 2011 ).
TextSentencer_T198 38871-39030 Sentence denotes The differences between the interactome of a particular cell and the interactome of an organism are huge, since an organism consists in several cellular types.
TextSentencer_T199 39031-39177 Sentence 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.
TextSentencer_T200 39178-39343 Sentence denotes The partial human interactome that are used nowadays, roughly incorporates 50,000 unique proteins, involved in around 200,000 interactions (Janjic and Przulj 2012) .
TextSentencer_T201 39344-39431 Sentence denotes Disease networks may include disease genes networks (Goh and Choi 2012; Loscalzo et al.
TextSentencer_T202 39432-39491 Sentence denotes 2007 ), protein-protein interactions networks (Zhang et al.
TextSentencer_T203 39492-39532 Sentence denotes 2011) , metabolic networks (Jeong et al.
TextSentencer_T204 39533-39549 Sentence denotes 2000; Lee et al.
TextSentencer_T205 39550-39557 Sentence denotes 2008) .
TextSentencer_T206 39558-39776 Sentence 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. 1 The theoretical pillars of network medicine.
TextSentencer_T207 39777-39977 Sentence denotes On the left side is represented the interactome, that is, the set of every physiological network of an individual, including gene-gene interactions, protein-protein interaction and metabolic networks.
TextSentencer_T208 39978-40085 Sentence denotes On the right side are represented the different pathological networks, including for example the diseasome.
TextSentencer_T209 40086-40216 Sentence denotes Network medicine consists in comparing these two sets of networks in order to understand the specificity of pathological networks.
TextSentencer_T210 40217-40411 Sentence denotes In order to do so, network medicine relies on three theoretical pillars, namely systems biology and medicine, the concept of human disease genes and network theory the diseasome (Loscalzo et al.
TextSentencer_T211 40412-40463 Sentence denotes 2007 ) and on its relationships to the interactome.
TextSentencer_T212 40464-40565 Sentence denotes The diseasome intends to represent the relationships between human diseases and diseasecausing genes.
TextSentencer_T213 40566-40622 Sentence denotes The construction of the diseasome is a two-step process.
TextSentencer_T214 40623-40718 Sentence denotes First, the researchers constructed a bipartite graph, consisting of two disjoint sets of nodes.
TextSentencer_T215 40719-40852 Sentence denotes One set corresponds to all known genetic disorders, whereas the other set corresponds to all known disease genes in the human genome.
TextSentencer_T216 40853-40960 Sentence denotes A disorder and a gene are then connected by an edge if mutations in this gene are involved in the disorder.
TextSentencer_T217 40961-41102 Sentence denotes The list of disorders, disease genes, and gene-disease association was obtained from the Online Mendelian Inheritance on Man (OMIM) database.
TextSentencer_T218 41103-41205 Sentence denotes OMIM represents the most complete and up-todate repository of all known disease genes (Amberger et al.
TextSentencer_T219 41206-41228 Sentence denotes 2009; McKusick 2007) .
TextSentencer_T220 41229-41308 Sentence denotes As of December 2005 the list contained 1,284 disorders and 1,777 disease genes.
TextSentencer_T221 41309-41441 Sentence denotes Once this bipartite graph is built, it is possible to construct two projections, which are basically the two faces of the same coin.
TextSentencer_T222 41442-41619 Sentence denotes On the one hand, there is the human disease network (HDN), where diseases are nodes and two diseases are connected if they share a same gene in their physiopathology (Fig. 2a) .
TextSentencer_T223 41620-41810 Sentence denotes On the other hand, there is the human disease gene network (DGN) where genes are nodes and two genes are connected if they are involved in the physiopathology of the same disease (Fig. 2b) .
TextSentencer_T224 41811-42075 Sentence denotes The first purpose of the diseasome is to pinpoint some unnoticed interactions between two types of diseases to direct more effectively the search of genes candidates and the understanding of the functional and topological modules in which the given genes interact.
TextSentencer_T225 42076-42255 Sentence denotes The second one is to characterize the specific properties of the "human diseases genes" by adding biological information on these genes to the topological analysis of the network.
TextSentencer_T226 42256-42277 Sentence denotes Goh et al. (2007 , p.
TextSentencer_T227 42278-42338 Sentence denotes 8687), Copyright (2007 , National Academy of Sciences, USA].
TextSentencer_T228 42339-42399 Sentence denotes A node's size is proportional to its degree of connectivity.
TextSentencer_T229 42400-42559 Sentence denotes The color code allows for the distinction between different disease classes. b The disease gene network [reproduced with permission, from Goh et al. (2007 , p.
TextSentencer_T230 42560-42620 Sentence denotes 8687), Copyright (2007 , National Academy of Sciences, USA].
TextSentencer_T231 42621-42681 Sentence denotes A node's size is proportional to its degree of connectivity.
TextSentencer_T232 42682-42942 Sentence denotes The color code allows for the distinction between different disease classes. (Color figure online) Finally, the third aim of the disease is to be compared to the interactome, in order to find some general organizing principles of the genetics of human disease.
TextSentencer_T233 42943-43107 Sentence 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.
TextSentencer_T234 43108-43375 Sentence 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.
TextSentencer_T235 43376-43526 Sentence denotes Moreover, the diseasome on which were performed the first topological analyses that I am now going to describe, only contains the OMIM data from 2005.
TextSentencer_T236 43527-43632 Sentence denotes It is however worth noting that there are several reasons to believe in the robustness of these analyses.
TextSentencer_T237 43633-43954 Sentence 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.
TextSentencer_T238 43955-44207 Sentence denotes Second, the properties of scale-free networks, such as the diseasome, are called "overdetermined properties": theoretically speaking, it is not necessary to know the total number of nodes in the network to identify its general structure and properties.
TextSentencer_T239 44208-44289 Sentence denotes Finally, an expanded version of the diseasome was performed in 2012 (Zhang et al.
TextSentencer_T240 44290-44297 Sentence denotes 2011) .
TextSentencer_T241 44298-44434 Sentence denotes This new version of the diseasome does not only take into consideration gene-disease associations but also protein-disease associations.
TextSentencer_T242 44435-44566 Sentence denotes In order to create this expanded version, the researchers used a different database, called the Genetic Association Database (GAD).
TextSentencer_T243 44567-44702 Sentence denotes The properties of this new version of the diseasome are still very stable compared to the early version that I am now going to analyze.
TextSentencer_T244 44703-44755 Sentence denotes Three main analyses can be drawn from the diseasome.
TextSentencer_T245 44756-44857 Sentence denotes The first one is a global analysis whose aim is to characterize the general structure of the network.
TextSentencer_T246 44858-45055 Sentence denotes The second one is a local analysis that compares topological properties from the human disease genes network with biological information on the pathophysiological role of these human disease genes.
TextSentencer_T247 45056-45279 Sentence 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.
TextSentencer_T248 45280-45475 Sentence 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.
TextSentencer_T249 45476-45947 Sentence 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).
TextSentencer_T250 45948-46100 Sentence denotes To put it differently, from a topological point of view, this means that the human disease network and the disease gene network are scale-free networks.
TextSentencer_T251 46101-46240 Sentence denotes Indeed, over 1,284 diseases, 867 are connected to another disease and 516 (around 40 % of the represented diseases) form one giant cluster.
TextSentencer_T252 46241-46420 Sentence denotes Among the hub diseases, cancer is particularly well represented, with colon cancer being linked to fifty other diseases, while breast cancer is connected to thirty other diseases.
TextSentencer_T253 46421-46563 Sentence denotes There is a strong heterogeneity in gene-disease associations: some diseases involve around thirty genes, while others involve only one or two.
TextSentencer_T254 46564-46651 Sentence denotes For example, deafness is associated to 41 genes, leukemia to 31 and colon cancer to 34.
TextSentencer_T255 46652-46816 Sentence denotes Conversely, some genes are involved in many diseases (and play the role of hubs in the disease gene network), while others are involved in only one or two diseases.
TextSentencer_T256 46817-46927 Sentence denotes For example, TP53, which is an extremely important gene in oncogenesis, is involved in more than ten diseases.
TextSentencer_T257 46928-47091 Sentence 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.
TextSentencer_T258 47092-47366 Sentence 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.
TextSentencer_T259 47367-47649 Sentence 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.
T79875 47650-47870 Sentence 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.
T22787 47871-48000 Sentence denotes In other words: when two genes are involved in the same disease, does that mean that they interact in the same functional module?
T50093 48001-48112 Sentence denotes And when two diseases involve the same gene, does that mean that they share some pathophysiological mechanisms?
T76163 48113-48365 Sentence 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.
T2922 48366-48502 Sentence denotes In order to do so, it is necessary to include some biological information about the genes and the diseases represented in the diseasome.
T6309 48503-48996 Sentence denotes Part of this biological information was retrieved from OMIM, but the researchers also retrieved information from (a) a network of physical protein-protein interactions derived from high-quality systematic interactome mapping and literature curation and (b) GO 6 annotations for each gene (c) data on the time, place and importance of the expression of the genes represented in the diseasome derived from DNA and RNA biochips results inventoried in the database Entrez Gene ID (linked to OMIM).
T39732 48997-49329 Sentence 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.
T56254 49330-49436 Sentence denotes Based on this confirmation of the local hypothesis, they develop the concept of disease functional module:
T3472 49437-49577 Sentence denotes Cellular networks are modular, consisting of groups of highly interconnected proteins responsible for specific cellular functions (21, 22) .
T1168 49578-49804 Sentence denotes A disorder then represents the perturbation or breakdown of a specific functional module caused by variation in one or more of the components producing recognizable developmental and/or physiological abnormalities. (Goh et al.
T44738 49805-50161 Sentence 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.
T45372 50162-50212 Sentence denotes A disease module consists of four main components.
T1070 50213-50313 Sentence denotes The primary disease genome G is the set of molecular anomalies that are associated to the phenotype.
T59313 50314-50437 Sentence denotes The secondary disease genome D is the set of modifiers genes that are susceptible to influence the primary genetic anomaly.
T7279 50438-50583 Sentence denotes The intermediate phenotype I is the set of polymorphisms that are susceptible to influence each of the generic answers of the organism to stress.
T71913 50584-50656 Sentence denotes Finally, E stands for the environmental determinants of a given disease.
T31595 50657-50863 Sentence denotes The third analysis gets to another level, since it aims at comparing the diseasome with the interactome 7 and at characterizing human disease genes properties compared to human non-disease genes properties.
T55990 50864-50958 Sentence denotes One of the main topological properties of interest is the question of centrality-essentiality.
T82877 50959-51106 Sentence denotes The concept of "essential genes" is intrinsically linked to gene knockout experiments, in which an organism's gene is selectively made inoperative.
T33247 51107-51270 Sentence 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.
T46930 51271-51521 Sentence 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).
T82305 51522-51674 Sentence denotes Since previous analyses of the yeast protein interaction network seemed to prove that essential genes constitute central hubs in the yeast (Jeong et al.
T7262 51675-51779 Sentence denotes 2001) , human disease genes were expected to be essential genes and to constitute hubs in the diseasome.
T99610 51780-51986 Sentence 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.
T5891 51987-52156 Sentence 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.
T11986 52157-52454 Sentence 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.
T64064 52455-52617 Sentence denotes Over the 1,777 human disease genes represented in the diseasome, there are 398 essential human disease genes and 1,379 human disease genes that are not essential.
T11196 52618-52891 Sentence 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.
T91124 52892-53170 Sentence denotes To explain this surprising result, the researchers made an evolutionary hypothesis: the vast majority of human disease genes are non-essential and centered at the periphery of the interactome because, when mutated, they only lead to disease instead of leading to death in utero.
T60908 53171-53376 Sentence 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.
T89313 53377-53552 Sentence 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?
T9221 53553-53630 Sentence denotes How to account for a unifying description of the genetics of a given disease?
T81348 53631-53730 Sentence denotes And, how to account for the relativity of the distinction between monogenic and polygenic diseases?
T8578 53731-53843 Sentence denotes On each of these issues, based on the results I have described, network medicine provides some new explanations.
T81984 53844-54081 Sentence 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.
T95907 54082-54222 Sentence denotes A syndrome disorder is usually a disorder that has no identified cause and that associates various symptoms without apparent links together.
T67055 54223-54347 Sentence denotes Syndrome families are a group of disorders that seem to have some symptoms in common but whose main cause is not understood.
T33251 54348-54534 Sentence 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.
T74949 54535-54565 Sentence denotes The same goes for comorbidity.
T59510 54566-54669 Sentence denotes In its narrow sense, comorbidity means that two or more diseases occur together in the same individual.
T14470 54670-54773 Sentence denotes In a broader sense, comorbidity is the fact that having disease A raises your risk of having disease B.
T27137 54774-54958 Sentence 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.
T67389 54959-55134 Sentence 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.
T48860 55135-55416 Sentence 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.
T69720 55417-55474 Sentence denotes The second point concerns the concept of genetic disease.
T6370 55475-55843 Sentence denotes Although not explicitly, network medicine abandons the concept of genetic disease to focus on an explanation of the genetics of every disease based on the identification of the disease functional module, which is defined by the four types of components that I have described (primary genome, secondary genome, intermediate pathophenotypes, environmental determinants).
T20440 55844-55971 Sentence denotes These four modules interact together to produce pathophysiological states (P), which are basically the symptoms of the disease.
T27490 55972-56096 Sentence denotes For example, in phenylketonuria, the primary genome would be the PAH gene, which codes the phenylalanine hydroxylase enzyme.
T17355 56097-56226 Sentence 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.
T62982 56227-56857 Sentence 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.
T49886 56858-56982 Sentence denotes Finally, environmental determinants would include the amount of phenylalanine intake depending on the diet, treatments, etc.
T22964 56983-57045 Sentence denotes Not every disease includes a primary Loscalzo et al. (2007, p.
T53828 57046-57050 Sentence denotes 6) .
T98046 57051-57143 Sentence denotes Different types of diseases are identified based on the components of their disease modules.
T65579 57144-57412 Sentence denotes G primary disease genome, D secondary disease genome, I intermediate phenotypes, E environmental determinants, P pathophenotypes (i.e., symptoms of the disease) genome-typically, in true polygenic diseases, no gene is necessary and sufficient for the disease to occur.
T77370 57413-57475 Sentence denotes So, some disease modules would not include any primary genome.
T34097 57476-57699 Sentence denotes But such an explanation of the genetics of disease allows the description of several models for several kinds of diseases, from diseases that are closer to classic Mendelian disorders to "true" polygenic diseases (Fig. 3) .
T17022 57700-57878 Sentence 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.
T51412 57879-58023 Sentence denotes In a system, for a given function, there is functional redundancy when several independent pathways in the system can achieve the same function.
T67931 58024-58096 Sentence denotes The more a system exhibits functional redundancy, the more robust it is.
T8659 58097-58394 Sentence 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.
T45935 58395-58402 Sentence denotes 2011) .
T43090 58403-58708 Sentence denotes To put it differently, if the functional module of a given disease depends on one fundamental pathway to achieve the corresponding function, then any disruption (for example, one genetic mutation) is enough to inactivate the given function and disorganize the module to the point where the disease occurs.
T10181 58709-58939 Sentence denotes If, on the contrary, the functional module of a disease consists of several redundant sub-modules, then a conjunction of several events is necessary to inactivate several modules and cause the occurrence of the disease (Table 3) .
T22147 58940-59048 Sentence denotes What do we learn from this case study on the relationships between topological and mechanistic explanations?
T31821 59049-59303 Sentence denotes My first conclusion is quite simple but not so trivial, given the current debates about the existence and the relevance of non-mechanistic explanations in biomedical sciences: there are topological explanations in medicine and they can be quite powerful.
T83265 59304-59491 Sentence 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.
T6338 59492-59675 Sentence denotes First, topological explanations in network medicine help to understand three phenomena linked to the geneticization of diseases (syndrome families, comorbidities and disease classes).
T48658 59676-59864 Sentence 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.
T24129 59865-60116 Sentence 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.
T41914 60117-60386 Sentence 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.
T51219 60387-60599 Sentence 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.
T36518 60600-60855 Sentence 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.
T67398 60856-61009 Sentence denotes 8 So, in this sense, topological explanations cannot be understood as completely independent from mechanistic explanations, at least in network medicine.
T11323 61010-61151 Sentence denotes But, in a stronger sense, I claim that even interpreting the topological properties of the network highly depends on mechanistic information.
T20512 61152-61652 Sentence 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.).
T95541 61653-61875 Sentence denotes In a similar way, interpreting the degree of essentiality-centrality of human diseases genes is also highly dependent on the import of external mechanistic information on the systematic knockout of their murin orthologues.
T60826 61876-61996 Sentence denotes My point is not to claim that topological explanations and mechanistic explanations can always be seen as complementary.
T61294 61997-62507 Sentence 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.
T85438 62508-62674 Sentence 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.
T53159 62675-62963 Sentence 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.
T48827 62964-63161 Sentence 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.
T47495 63162-63328 Sentence 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.
T77549 63329-63510 Sentence 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.
T95520 63511-63719 Sentence 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.
T71943 63720-63963 Sentence 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.
T72872 63964-64329 Sentence 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.
T30036 64330-64603 Sentence 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.
T60545 64604-64849 Sentence 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.
T29071 64850-65031 Sentence denotes In the case of medicine, the specific contribution of topological explanations is to completely shift the conceptual background of our understanding of the role of genes in disease.
T42971 65032-65382 Sentence 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.
T59796 65383-65611 Sentence denotes In other words, topological explanations in network medicine push us towards the search for organizing principles in the genetics of disease, instead of focusing on mechanistic genetic explanations of single individual diseases.
T86590 65612-65752 Sentence denotes This may have major consequences on biomedical research and has already led to the development of new ways of identifying new disease genes.
T84030 65753-65787 Sentence denotes Three methods have been developed.
T72781 65788-65958 Sentence denotes In the linkage-based method, candidate disease nodes (i.e. candidate disease genes) are identified by direct interaction with known disease node (i.e known disease gene).
T21532 65959-66133 Sentence denotes In disease module based methods, algorithms are used to group highly interconnected genes, in the hope of identifying potential functional disease modules in the interactome.
T48768 66134-66289 Sentence denotes In the disease-module based methods, algorithms or functional information are used in order to identify genes that closely neighbor a known disease module.
T80287 66290-66495 Sentence 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) .
T22219 66496-66664 Sentence 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.
T16494 66665-66753 Sentence denotes 2015) , breast cancer (Erler and Linding 2012) or cardiovascular diseases (Sharma et al.
T68373 66754-66761 Sentence denotes 2013 ).
T72646 66762-66930 Sentence 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.
T14210 66931-67062 Sentence denotes Indeed, medical genetics has developed pure mechanistic explanations of the genetics of disease that meet with some serious issues.
T27461 67063-67261 Sentence denotes These pure mechanistic explanations cannot give a unifying and satisfying explanation of the concept of genetic disease, the geneticization of disease and the distinction between monogenic diseases.
T23923 67262-67633 Sentence denotes By relying on the topological properties of the human disease gene network, network medicine provides an explanation of the common genetic origin of diseases, reinterprets the concept of genetic diseases through the identification of disease modules and explains the difference between monogenic and polygenic diseases as a matter of functional redundancy and robustness.
T63820 67634-67746 Sentence denotes However, topological explanations cannot be seen as independent from mechanistic explanations for three reasons.
T36729 67747-67816 Sentence denotes First, the network itself is an abstract representation of mechanism.
T62127 67817-67924 Sentence denotes Second, interpreting the topological properties of the network depends on external mechanistic information.
T13411 67925-68046 Sentence denotes Third, topological explanations are not complete explanations: they provide an incentive to new mechanistic explanations.
T49390 68047-68389 Sentence 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.