Dr Anatol Kontush
email: anatol.kontush@upmc.fr

Identifying new risk markers and potential targets: The value of the proteome

Keywords: proteomics, biomarkers, cardiovascular disease

Several protein biomarkers, including cardiac troponin T, cardiac troponin I, Btype natriuretic peptide, C-reactive protein and apolipoprotein A-I, are widely employed in the evaluation of cardiovascular disease. Several of such potential biomarkers, or their multiscores, have been assessed over the last years for the prediction of cardiovascular risk but only a few of them have been validated for clinical use. Substantial improvement in the cardiovascular risk prediction and reclassification relative to traditional models therefore remains a difficult task presently unresolved. Hence, a potential importance of alternative approaches which may rely on novel proteomic biomarkers among others. Plasma or serum concentrations of numerous proteins were measured using proteomic approaches to establish their relationships with cardiovascular disease; none of them was however evaluated for cardiovascular risk prediction and subject stratification in rigorous large-scale studies. Thus, further research is needed to identify novel candidates that can improve cardiovascular risk prediction, subject stratification and standard care. Proteomics will undoubtedly remain a key approach to address this major clinical and scientific challenge.

Introduction

The concept of proteomics aims at characterizing and quantifying the proteome -all proteins present in a biological fluid, biological tissue or a given cell type. The term "proteome" was coined by Mark Wilkins as early as 1994 to describe total protein composition of a biological tissue or cell type [START_REF] Wilkins | Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it[END_REF]. Key technical advancements, including developments of soft ionisation techniques and specialized algorithms to analyse the proteome, occurred over this time to allow quantitative assessment of the proteome. As a result, proteomics has emerged as a tool to evaluate metabolic alterations present in a disease state or pathological condition. Multiple applications of proteomic analyses to the clinics have been developed to contribute to our understanding of molecular mechanisms of diseases and to identify potential biomarkers of disease presence and progression.

Biomarkers are molecules whose levels provide information about disease. Biomarkers are typically determined in biological fluids that can be readily obtained under clinical conditions, primarily in plasma, serum and urine. Measurements of biomarkers can be valuable to diagnose the disease, to assess its progression, to monitor effects of a therapy, to discover novel therapeutic targets and to understand molecular mechanisms underlying the disease development. The importance of the biomarker development in the field of cardiovascular (CV) disease, which represents the leading cause of death in industrialized countries, is highlighted by the need of improved strategies for the primary prevention of CV disease. Indeed, predictive value of current risk-assessment models remains limited, with a large proportion of patients without traditional risk factors (up to 20%) [START_REF] Khot | Prevalence of conventional risk factors in patients with coronary heart disease[END_REF][START_REF] Hozawa | Absolute and attributable risks of cardiovascular disease incidence in relation to optimal and borderline risk factors: comparison of African American with white subjects--Atherosclerosis Risk in Communities Study[END_REF]. Thus, novel diagnostic and prognostic biomarkers of CV risk bear a potential to improve the selection of individuals for preventative strategies and to ameliorate disease management. This review summarizes our current knowledge of the value of proteomics to discover new biomarkers and targets in the field of CV disease.

Novel protein biomarkers and CV risk prediction

Several protein biomarkers are widely employed in the evaluation of CV disease (Table 1). Troponin T represents the classical biomarker of MI possessing a high predictive value for the disease [START_REF] Tiwari | Cardiac troponins I and T: molecular markers for early diagnosis, prognosis, and accurate triaging of patients with acute myocardial infarction[END_REF]; elevated plasma levels of this molecule constitute the gold standard approach to detect MI [START_REF] Segraves | Highly Sensitive Cardiac Troponin Assays: A Comprehensive Review of Their Clinical Utility[END_REF]. Circulating concentrations of BNP reveal strong associations with CV risk under a range of different clinical conditions [START_REF] Angelantonio | B-type natriuretic peptides and cardiovascular risk: systematic review and meta-analysis of 40 prospective studies[END_REF]. Similarly, circulating levels of CRP are strongly associated with CV disease [START_REF] Buckley | C-reactive protein as a risk factor for coronary heart disease: a systematic review and meta-analyses for the U.S. Preventive Services Task Force[END_REF]. Finally, plasma levels of apoA-I, the major protein components of HDL, can be superior to HDL-cholesterol as a predictor of CV risk [START_REF] Walldius | Apolipoprotein A-I versus HDL cholesterol in the prediction of risk for myocardial infarction and stroke[END_REF].

However, the guidelines of the American College of Cardiology Foundation and the American Heart Association (ACCF/AHA) released in 2010 largely failed to add proteinderived measurements to the list of clinically useful biomarkers of CV disease [START_REF] Greenland | ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines[END_REF]. Indeed, only the evaluation of family history of CV disease received a class I recommendation, the highest possible level of a clinical importance. Remarkably, circulating levels of CRP and BNP firmly established to be strongly associated with CV disease were concluded to only modestly improve CV risk assessment when evaluated together with traditional CV risk factors [START_REF] Melander | Novel and conventional biomarkers for prediction of incident cardiovascular events in the community[END_REF][START_REF] Angelantonio | B-type natriuretic peptides and cardiovascular risk: systematic review and meta-analysis of 40 prospective studies[END_REF][START_REF] Shah | Critical appraisal of CRP measurement for the prediction of coronary heart disease events: new data and systematic review of 31 prospective cohorts[END_REF]. In a more recent report, levels of high-sensitivity troponin T (hsTnT) were associated with CV mortality but only marginally imporved a traditional risk factor model in the Dallas Heart Study [START_REF] De Lemos | Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population[END_REF]. In a similar fashion, plasma levels of apoA-I are not always superior to HDL-cholesterol as a predictor of CV risk [START_REF] Schmidt | Apolipoprotein B and apolipopotein A-I in vascular risk prediction -a review[END_REF]. Finally, growth-differentiation factor-15 (GDF-15) expressed by cardiomyocytes under conditions of ischaemia or pressure-related stress was a strong predictor of all-cause, CV and non-CV mortality but only moderately improved prediction of all-cause mortality in the Rancho Bernardo Study [14].

Such negative results may in part reflect the lack of causative relationships of a given biomarker with the disease. Thus, Mendelian randomization studies evaluating subjects with genetically determined CRP levels do not support a causative role of the protein in the development of CV, suggesting that CRP is a biomarker rather than a cause of atherosclerosis [START_REF] Elliott | Genetic Loci associated with C-reactive protein levels and risk of coronary heart disease[END_REF].

In order to improve risk predicition using individual protein biomarkers, they were proposed to be combined into a multi-biomarker score ("multiscore"). Such approach is based on the assumption that biomarkers to be combined reflect different pathophysiological pathways independently contributing to CV disease, which is not always valid. Thus, in the Framingham Offspring Study, five biomarkers, and notably BNP, CRP, urinary albumin/creatinine ratio, homocysteine and renin, out of ten selected on the basis of known associations with CV risk, were retained as predictors of death [START_REF] Wang | Multiple biomarkers for the prediction of first major cardiovascular events and death[END_REF]. In addition, two biomarkers (BNP and urinary albumin/creatinine ratio) were retained as predictors of CV events. A multiscore constructed using the ten biomarkers was significantly related to allcause mortality and CV events; however, assessment of CV risk was only minimally improved relative to the model containing traditional CV risk factors. Similarly, two of six biomarkers, and notably BNP and mid-region pro-adrenomedullin, were retained as predictors of CV events in the Swedish Malmo Diet and Cancer cohort [START_REF] Melander | Novel and conventional biomarkers for prediction of incident cardiovascular events in the community[END_REF]. A multiscore constructed using the biomarkers was significantly related to the risk of coronary events but the risk prediction was only marginally improved when the multiscore was added to traditional risk factors. Finally, four biomarkers (troponin I, N-terminal of the prohormone BNP (NT-proBNP), CRP and cystatin C) only modestly improved prediction of CV and all-cause mortality in elderly men without CV disease in the Uppsala Longitudinal Study of Adult Men when added to a traditional risk factor model [START_REF] Zethelius | Use of multiple biomarkers to improve the prediction of death from cardiovascular causes[END_REF]. An attempt to add a larger number of novel biomarkers to the traditional risk prediction model was similarly unsuccessful as revealed by the Women's Health Initiative cohort in which five (out of 18) biomarkers moderately improved CV risk prediction [START_REF] Kim | Multimarker prediction of coronary heart disease risk: the Women's Health Initiative[END_REF]. Simialr results were observed in the MORGAM study that evaluated 30 biomarkers, including NT-proBNP, CRP and troponin I, none of which alone was capable of improving discrimination in terms of CV risk [START_REF] Blankenberg | Contribution of 30 biomarkers to 10-year cardiovascular risk estimation in 2 population cohorts: the MONICA, risk, genetics, archiving, and monograph (MORGAM) biomarker project[END_REF]. A combination of NT-proBNP, CRP and troponin I only slightly improved the discrimination.

Potential novel proteomic biomarkers of CV disease

The data on the evaluation of CV risk with a help of circulating levels of individual proteins, or using a multiscore built of their combinations, reveal that substantial improvement in the risk prediction and reclassification relative to traditional models remains a difficult task presently unresolved. Hence, a potential importance of alternative approaches which may rely on novel proteomic biomarkers among others.

Plasma or serum concentrations of numerous proteins were measured using proteomic approaches to evaluate their relationships with CV disease (Table 1 and Figure 1); none of them was however evaluated for CV risk prediction and subject stratification in rigorous large-scale studies. Elevated plasma levels of several proteins were documented in MI (Figure 1). Thus, concentrations of creatine kinase CK-MB were increased in plasma following MI [START_REF] Robinson | Creatine kinase and its CK-MB isoenzyme: the conventional marker for the diagnosis of acute myocardial infarction[END_REF][START_REF] Zimmermann-Ivol | Fatty acid binding protein as a serum marker for the early diagnosis of stroke: a pilot study[END_REF], while concentrations of fatty acid-binding protein (FABP) were elevated in MI, stroke and coronary artery disease (CAD) [START_REF] Kleine | Release of heart fatty acid-binding protein into plasma after acute myocardial infarction in man[END_REF][START_REF] Zimmermann-Ivol | Fatty acid binding protein as a serum marker for the early diagnosis of stroke: a pilot study[END_REF]23]. Positive associations with MI were also observed for plasma levels of cyclophilin A, cluster of differentiation 5 molecule [CD5] antigen-like, mucin cell surface associated protein 18 [MUC-18], a cell-surface glycoprotein, collagen α-1(XVIII) chain, salivary α-amylase 1, CRP and multimerin-2 [24]. In another study, plasma concentrations of apoC-I, apoC-II and apoE were found to be elevated in ST segment elevated MI as revealed by multiple reaction monitoring (MRM) proteomic measurements [25]. In a simalr fashion, levels of myeloperoxidase (MPO) were capable of predicting CV events in patients with CAD [START_REF] Tang | Plasma myeloperoxidase predicts incident cardiovascular risks in stable patients undergoing medical management for coronary artery disease[END_REF]. In addition, cardiac myosinbinding protein C (cMyBP-C) was strongly increased in patients with non-ST segment elevated MI [START_REF] Kuster | Release kinetics of circulating cardiac myosin binding protein-C following cardiac injury[END_REF].

By contrast, onset of MI was associated with diminished concentrations of retinol-binding protein 4 (RBP4) [START_REF] Cubedo | Retinol-binding protein 4 levels and susceptibility to ischaemic events in men[END_REF][START_REF] Lambadiari | Serum levels of retinol-binding protein-4 are associated with the presence and severity of coronary artery disease[END_REF]. Another plasma protein whose levels can be negatively related to CV disease is exemplified by heat shock protein-27 (HSP27) that appears to be reduced in myocardial ischemia [START_REF] Duran | Identification by a differential proteomic approach of heat shock protein 27 as a potential marker of atherosclerosis[END_REF][START_REF] Seibert | Serum heat shock protein 27 levels represent a potential therapeutic target for atherosclerosis: observations from a human cohort and treatment of female mice[END_REF]. Indeed, HSP27 concentrations are decreased in the blood of patients with carotid stenosis relative to healthy subjects [START_REF] Duran | Identification by a differential proteomic approach of heat shock protein 27 as a potential marker of atherosclerosis[END_REF]. In addition, low circulating levels of HSP27 are associated with the presence of CAD and prognostic of future adverse clinical events [START_REF] Seibert | Serum heat shock protein 27 levels represent a potential therapeutic target for atherosclerosis: observations from a human cohort and treatment of female mice[END_REF]. In a prospective study of initially healthy women, baseline HSP27 concentrations in plasma were however not associated with incident CV events [START_REF] Kardys | Plasma concentration of heat shock protein 27 and risk of cardiovascular disease: a prospective, nested case-control study[END_REF].

Plasma levels of soluble tumor necrosis factor-like weak inducer of apoptosis (sTWEAK)

are equally decreased in patients with CAD [START_REF] Jelic-Ivanovic | Circulating sTWEAK improves the prediction of coronary artery disease[END_REF], abdominal aortic aneurysm [START_REF] Lindholt | Soluble TWEAK plasma levels predict expansion of human abdominal aortic aneurysms[END_REF] and chronic heart failure [START_REF] Chorianopoulos | Decreased soluble TWEAK levels predict an adverse prognosis in patients with chronic stable heart failure[END_REF]. Furthermore, sTWEAK concentrations were capable of predicting CV disease in renal transplant patients [START_REF] Turkmen | Soluble TWEAK independently predicts atherosclerosis in renal transplant patients[END_REF].

When proteomic analysis was applied to pooled plasma samples obtained from CAD patients and controls in order to identify low-abundance proteins, 95 differential protein signals were identified, including those of CA11, CD59, CHFR-1, collagen α-3(VI) chain, complement C1s, defensin 5, emilin 3/multimerin-2, fibrinogen γ chain, GST-omega-1, IGF binding complex acid labile chain, secreted phosphoprotein 24 and some other proteins involved in natural defenses, inflammation, growth and coagulation [23].

While a vast majority of the proteomic studies were performed in plasma or serum, urine was occasionally employed (Table 1). Thus, urine levels of collagen α-1(I) and collagen α-1(III) were augmented in CAD patients relative to controls [37]. In some studies, proteins were measured in isolated lipoproteins rather than in whole plasma; the presence of apoC-III in HDL [START_REF] Jensen | Apolipoprotein C-III as a Potential Modulator of the Association Between HDL-Cholesterol and Incident Coronary Heart Disease[END_REF] and LDL [START_REF] Mendivil | Low-density lipoproteins containing apolipoprotein C-III and the risk of coronary heart disease[END_REF] was thereby identified as a factor associated with CV disease. In addition, other proteins, including serum amyloid A and complement C3, were reported to be elevated in HDL from CAD patients relative to controls [START_REF] Vaisar | Shotgun proteomics implicates protease inhibition and complement activation in the antiinflammatory properties of HDL[END_REF][START_REF] Alwaili | The HDL proteome in acute coronary syndromes shifts to an inflammatory profile[END_REF]. Proteomic approaches have equally been applied to discover new biomarkers of cardiomyopathy and heart failure to improve risk assessment in these conditions (Table 1), which is presently based on the measurement of BNP firmly established as a gold-standard biomarker for this pathological condition. Thus, myosin heavy chain 7 (MYH7), desmin, insulin-like growth factor-binding protein 7 (IGFBP7) and annexin A2 were initially proposed to represent circulating biomarkers of cardiomyopathy-induced heaft failure in a transgenic mouse model to be subsequently validated in humans [42]. In another study, quiescin Q6 (QSOX1) was reported to represent a biomarker for acute heart failure [43].

A strength of proteomic analyses involves their capacity to quantitatively assay proteins displaying post-translational modifications, which are typically present in the circulation at low abundance and whose levels can be altered by CV disease. Major post-translational protein modifications include phosphorylation, glycation and oxidation. Altered phosphorylation of numerous proteins is observed in CV diseases; phosphorylation of cardiac Troponin I was documented as a potential biomarker of chronic heart failure [START_REF] Zhang | Top-down quantitative proteomics identified phosphorylation of cardiac troponin I as a candidate biomarker for chronic heart failure[END_REF]. Advanced glycation end products (AGEs) can accumulate on several proteins in patients with CV disorder [START_REF] Simm | Protein glycation during aging and in cardiovascular disease[END_REF]. In addition, key plasma proteins, including apoA-I, α-1-antitrypsin and fibrinogen, can also become oxidized, reflecting elevated oxidative stress in CV disease [START_REF] Heinecke | The role of myeloperoxidase in HDL oxidation and atherogenesis[END_REF][START_REF] Banfi | Oxidized proteins in plasma of patients with heart failure: role in endothelial damage[END_REF]. It remains however unclear as to whether post-translational protein modifications can serve as biomarkers of the disease.

Technical challenges

Despite generating wide-spread enthusiasm and providing first promising data, proteomic methods face considerable technical challenges [START_REF] Napoli | Recent advances in proteomic technologies applied to cardiovascular disease[END_REF][START_REF] Shen | Quantitative proteomics in cardiovascular research: global and targeted strategies[END_REF]. Thus, proteomic quantification of circulating protein concentrations is complicated by their wide dynamic range. This question can be addressed by protein fractionation and depletion before LC/MS analysis. High interindividual variability represents another issue, which can be resolved by enlarging study cohorts. Proteomics of post-translational modifications requires high-resolution and highsensitivity mass-spectrometrical methodology.

Next, there still remains a gap between candidate biomarkers and their clinical applications.

Numerous candidate proteins discovered in original proteomic studies often do not survive rigorous validation in follow-up investigations, implying a risk of false-positive reports and calling for stringent verification approaches.

In addition, biological importance of proteomic data obtained is frequently difficult to deduce from extensive numerical information reported, complicating data interpretation. Quantitative alterations discovered by proteomics need to be linked to biological function and integrated with other "omics" data; modern computational and visualization tools, including network models and protein interaction networks, can provide a major progress in this regard. Finally, "classical" issues of LC/MS/MS analyses need to be addressed to ameliorate resolution, sensitivity, specificity, throughput, precision and accuracy of the assays, to standardize procedures for sample collection, preparation and analysis, and to lower the assay costs.

Conclusions and perspectives

Proteomic studies have revealed that plasma concentrations of numerous proteins are associated with CV disease. Several of such potential biomarkers for the prediction of CV risk have been evaluated over the last years but only a few of them have been validated for clinical use. Such predominantly negative findings highlight inherent difficulties of the biomarker discovery and validation, calling for further research to identify novel candidates that can improve CV risk prediction, subject stratification and standard care. Assuming major technical issues successfully resolved, new and powerful high-throughput proteomic platforms can ensure translation of technical advancements into clinical practice to further reduce the burden of CV disease [START_REF] Lindsey | Transformative Impact of Proteomics on Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association[END_REF].

In addition to clinical biomarkers, proteomic strategies can provide a valuable tool to identify proteins involved in CV disease, which may become therapeutic targets. Computational approaches which combine protein identity with biological pathways and activities are critical to uncover the role of proteins as functional mediators of disease [START_REF] Menoret | Transition from identity to bioactivity-guided proteomics for biomarker discovery with focus on the PF2D platform[END_REF]. Alternative sources of biological material, such as atherosclerotic plaques, circulating cells and plasma extracellular vesicles, can deliver novel information on physiopathological mechanisms underlying associations discovered by proteomics of biological fluids [START_REF] Barderas | Vascular proteomics[END_REF]. These considerations leave little doubt that proteomics will both open wide diagnostic perspectives and drive basic science research in the field of CV disease in the near future. 1.

Table 1. Circulating proteomic biomarkers of CV disease

Protein

Disease Ref.

  Thus, cardiac troponin T and cardiac troponin I (cTnI) are used for the diagnosis of acute coronary syndromes and myocardial infarction (MI), B-type natriuretic peptide (BNP) and its N-terminal form are employed to diagnose congestive heart failure, C-reactive protein (CRP) is a useful biomarker of inflammation in atherosclerosis, and apolipoprotein A-I (apo A-I) is an excellent risk predictor of CV risk related to the metabolism of high-density lipoprotein (HDL).

Figure 1 .

 1 Figure 1. Alterations in circulating concentrations of proteomic biomarkers in patients

Acknowledgements

These studies were supported by National Institute for Health and Medical Research (INSERM), Paris, France.

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Erwan Le Merrer

Gilles Trédan

Retro-ing énierer les m étriques topologiques dans les algorithmes de peer-ranking

Détecter au plus tôt les utilisateurs importants dans les réseaux sociaux est un problème majeur. Les services de classement d'utilisateurs (peer ranking) sont maintenant des outils bien établis, par des sociétés comme PeerIndex, Klout ou Kred. Leur fonction est de "classer" les utilisateurs selon leur influence. Cette notion est néanmoins abstraite, et les méthodes algorithmiques de ces services pour obtenir ce classement sont opaques (les métriques et paramétrages utilisés sont des recettes internes dissimulées). Suivant le récent intérêt pour les outils permettant une plus grande transparence du web, nous proposons d'explorer le problème du retro-ingéniering de l'influence topologique dans ces services de classement. Comme ces services exploitent l'activité en ligne des utilisateurs pour inférer leur influence (dont leur connectivité sur les réseaux sociaux), nous proposons une approche permettant d'estimer précisément l'influence d'un ensemble de métriques topologiques (ou centralités) et leur taux de prise en compte dans le résultat final (classement utilisateur). Pour ce faire, nous modélisons l'algorithme de classement comme une boite noire, avec laquelle nous interagissons via des modifications topologiques, afin d'inférer quelles sont les centralités en jeu dans l'évolution du résultats de classement. Nous montrons que dans certains cas il est possible de déterminer quelles sont ces métriques, et ceci via des opérations sur la topologie par un utilisateur souhaitant connaitre ces paramètres.

Mots-clefs :

Reverve-engineering ; centrality metrics ; social graphs ; ranking functions As personal information concentrates in the cloud, so does the exploitation of this data. The need for an increased transparency in the functioning of web-services has recently arised, motivated by various use cases such as privacy or copyright control. For example, work such as [START_REF] Lécuyer | Xray : Enhancing the web's transparency with differential correlation[END_REF] proposes to retrieve which piece of information of a user-profile triggered advertisement to that user. Goal is thus to infer the internals of black-box services provided by companies on the web. Klout or PeerIndex propose to rank users based on their behavior on social networks (using their social connectivity and activity). They nevertheless keep secret the algorithms and parameters used for this ranking. This motivated some users to try reversing their internals [START_REF] Golliher | How i reverse engineered klout score to an r2 = 0.94[END_REF]. Sometimes information leaks about some ingredients of those hidden recipes ; CEO of PeerIndex admitted to use Pagerank (and thus graph topological-metrics), as a part of their ranking algorithm, to compute user influence.

Reverse engineering such black-box is a challenging task. Indeed, in this web-service paradigm, the user only has access to the output of the algorithm, and cannot extract any side-information. Moreover, in many cases such as peer ranking services, the user is only aware and able to act on a limited part of the algorithm input. Motivated by this challenge for transparency, we thus ask the following questions : can a user infer, from the results returned by such peer-ranking algorithms, what are the topological metrics in use, and if possible to what extent ? How difficult is such an attack ?

We first introduce the service we consider and model our actions, before warming-up on a toy example. We then generalize our approach, and illustrate it on a concrete attack example. We finally give perspectives.

Model and Warm Up : Reversing the Use of a Single Centrality

We model the problem through an observable "ranking" black-box function f , that takes a graph as input (noted G ∞ ) and returns a ranking > r of all graph nodes such that : ∀i, j ∈ V (G ∞ ) 2 , i > r j iff "node i is more important than node j".

As warm up, let us assume that f is one of the following classic centralities C base = {degree, eccentricity, betweeness, Pagerank, closeness} [START_REF] Koschützki | Network Analysis : Methodological Foundations, chapter Centrality Indices[END_REF]. To determine which is the used centrality, one attacker wants to build

G ∞ a 1 a 2 a 3 a 4 a 5
(a) A small attack graph G A , solving the single centrality reverse-engineering problem q q q q q q q q q q q q q q q q q q q q 2 4 6 1 2 3 4 5

Real Coeff.

Estimated Coeff. attackSize q q q q q 20 30 40 50 60

(b) Reversing a f with hidden coefficients from 1 to 5, with various attack sizes (node creations)

a small attack graph G A , attached G ∞ (then G ∞ = G ∞ ∪ G A
, as being the giant social network), in order to reverse f . She has at her disposal means to edit the social graph, by adding nodes and edges. In practice, this consists in opening false accounts on the social network, and to link those (with e.g., "friend" requests).

To get rid of the non-topological metrics that may be used in f , the attacker creates nodes ∈ G A that are strictly identical up to their connectivity (e.g., they have not Tweeted or posted any comment, etc).

Lemma 1. A small attack graph, of 5 nodes, is sufficient to reverse a function f that is based on a single centrality in C base , relatively to the other centralities in the same set C base . Graph is depicted on Figure 1a.

Proof Sketch. The proof requires showing that such G A is able to discriminate the centralities considered in the set C base . Consider graph G ∞ ∪ G A on Figure 1a. G A nodes are given the following ranking, for centralities in

C base : < degree, [a 1 = r a 2 > r a 3 = r a 4 = r a 5 ] >, < eccentricity, [a 1 > r a 2 = r a 3 > r a 4 = r a 5 ] >, < betweenness, [a 1 > r a 2 > r a 3 = r a 4 = r a 5 ] >, < Pagerank, [a 2 > r a 1 > r a 4 = r a 5 > r a 3 ] >, < closeness, [a 1 > r a 2 > r a 3 > r a 4 = r a 5 ] >.
All rankings are indeed unique, thus allowing to discriminate the centrality used, by simple observation at G A nodes' rankings produced by f . Note that this does not constitute a proof that a graph of size 5 is the minimal attack graph for such a problem and this centrality set C base , or that this is the unique graph solving this problem. The interest of minimizing the size of the constructed attack graph is for the attacker twofold : first, constructing a bigger graph requires a longer time, especially if actions on the service platform are rate limited. Second, the bigger the attack, the easier it can be detected by the targeted service, which can then react accordingly.

For instance the graph G A \ n5, of size 4 is not a solution, as degree and betweenness produce the same [a 1 > r a 2 > r a 3 = r a 4 ] ranking, as for both fringe nodes a 3 and a 4 , betweenness is 0, and degree is 1. Sketch for a formal proof is to find of pairs of centralities over certain attack graphs that produce equivalent ranking to discard them, until reaching one particular graph where such a pair cannot be found. Following a possible set of centrality definitions, one attacker then designs an attack graph that is minimal.

A General Reverse-Engineering Approach

We now propose a reversing method for extending to a f that is a linear combination of centralities. Note that we also extend the notion of centrality to the one presented in [START_REF] Borgatti | A graph-theoretic perspective on centrality[END_REF], that is of any node-level measure.

Rationale The objective of the attack is to reverse-engineer f 's internals, that is, to determine which centralities are used, and what is their relative importance in the linear combination that provides the ranking. G ∞ is not known to the attacker. As a social network user, she is able (through API calls) to create profiles, perform operations on these profiles, and observe the consequence of these operations on rankings in the peer-ranking service. As the space of possible centralities is theoretically infinite, we assume the attacker takes a bet on a list of d centralities in a set C, that are potentially involved in f .

In a nutshell, the attack proceeds as follows. The attacker leverages an arbitrary node a, already present in G ∞ . She then creates d identical nodes and connects them to a ; those d nodes end up with the same ranking. She applies to each node a different serie of API calls (i.e., topological updates, attaching them one node for instance). After each serie of API calls, ranking of those nodes is expected to change. Based Data: G ∞ , a target node a ∈ V (G ∞ ), operations {u 1 , . . . , u d } Result: An estimate of h (i.e., the vector containing the weight of each centrality in f ) 1 //initialization 2 Let k be a vector of size d -1 initialized to 0; 3 for 1 ≤ i ≤ d do 4 //attach an attack node to an existing node, and conduce operations over it

5 Create node a i : V (G ∞ ) ← V (G ∞ ) ∪ {a i }; 6 Add edge (a i , a) : E(G ∞ ) ← E(G ∞ ) ∪ {(a i , a)}; 7 Apply u i (a i );
8 W.l.o.g. assume u d is the operation with the highest impact (that is ∀ j < d, u j < r u d ); 9 (Reorder otherwise); 10 for i = 1 to d -1 do 11 //identify operation thresholds

12 k i ← max x≥1 (u x i (a i ) < r u d (a d
)); 13 //J is the matrix where each element (i, j) is the impact of k i applications of u i on the j th centrality of node a i , minus the impact of operation u d on a d ;

14 Let J i, j = c j (u k i i (a i )) -c j (u d (a d ))
; 15 Set J d,. = 0 d ; 16 //find h s.t. J.h = 0, thus is solution to the reverse-engineering of f 17 return Ker(J) FIGURE 2: General reverse-engineering algorithm : estimation of linear weight combination of f on those observed changes, she is able to sort the impact of those calls, and thus to describe the impact of one given call by a composition of smaller effect calls. This allows her to retrieve the weights assigned by f to the d centralities in set C, by solving a linear equation system.

Lets consider the following image : imagine you have an old weighing scale (that only answers "left is heavier than right" or vice-versa) and a set of fruits (say berries, oranges, apples and melons) you want to weigh. Since no "absolute" weighing system is available, the solution is to weighs the fruits relatively to each other, for instance by expressing each fruit as a fraction of the heaviest fruit, the melon. One straightforward approach is to directly test how many of each fruit weigh one melon. This is the approach adopted here. However, to continue on the analogy, the problem here is that in general, we are not able to individually weigh each fruit (centrality). Instead, we have a set of d different fruit salads. This is not a problem since the composition of each salad (i.e., the impact of API calls) is known ; one has to solve a linear system using d different combinations of coefficients yielding an equal impact.

A reverse-engineering algorithm For demonstration purposes, let us assume that f relies on some centralities included in set C of size d. Let c i ∈ R d be the d dimensional column vector representing each of the d involved centrality values for node i. We assume that f is linear in all directions (i.e., f is a weighted mean of all centralities) : ∃h ∈ R d s.t. f (i) = c i .h. Reverse-engineering the topological impact over the final ranking thus boils down to find h (and therefore directly obtain f ).

The attacker performs operations on G ∞ through API calls (or user operations). For instance, the following operation u 1 : G ∞ (V, E), i → u (V ∪ {a}, E ∪ {(i, a)}) simply adds a neighbor to i (we refer to it as u 1 (i)). Each operation u(i) impacts i's topological position, and therefore it's ranking by f . Let u ∈ R d be this impact on each centrality in C : c u(i) = c i + u. We assume the attacker is able to find d different operations denoted {u 1 , . . . , u d } that respect the following properties : i) she is able to determine the result of each u i 's impact on the target node's centrality values (i.e., compute u k i (i), ∀i ≤ d, where k > 0 is the number of applications of the operation). And ii) the d operations are mutually independent : each operation has a unique impact on set of considered centralities.

The attack proceeds as shown on Figure 2, where notations are defined. First, observe that by construction matrix J has not a full rank. The last update u d is our reference against which we compare other updates. Line 12 records the maximum number of same u i operation applications that lead to the same rank (or close) than a single u d operation : that is the number of u i operations needed to "negate" the effects of u d .

Consider a line i of J. Since at the end u i = r u d (or close), we have (c

a i + u k i i (a i ))h = (c a d + u d (a d ))h ± u i (a i ).
h. Since by construction c a i = c a d , therefore we seek h s.t. u k i i (a i )hu d (a i )h = 0. Or as matrix notation : J.h = 0 : h is in the kernel of J.

Intuitively, the fact that we get infinitely many solutions (α.h, ∀α ∈ R + ) comes from our observation method : we are never able to observe actual scores, but rather rankings. Since multiplying h by a constant does not change the final ranking, any vector co-linear to, e.g., h/||h|| is a solution.

One important remark is that one cannot precisely claim that one centrality metric is not in use in C with our attack. Assume for instance that one centrality, say number of tweets, is 10 6 times less important than another, say degree. Then we will not be able to witness its effect unless we produce 10 6 tweets. And after k < 10 6 tweets, the only possible conclusion is : number of tweets is at least k times less important that degree. One can reasonably assume that such an imbalance in practice means that one service operator will not compute a possibly costly centrality to use it to such a low extent in f ; this thus makes our attack able to discard barely or not used centralities in C. To limit the detectability of the attack however, the maximal k i after which u i is considered to have a negligible impact should be carefully chosen.

Finally, one interesting complexity metric is the total number of calls to the API. Let cost(u i ) be the number of calls issued by operation u i . Then the total number of operations is cost(u d ) + ∑ d-1 i=1 k i .cost(u i ). The precision of this estimation can be improved, but this approach is omitted due to space limitations.

An Illustration To illustrate our approach, let us assume a ranking function whose internals use a combination of c 1 : clustering centrality and c 2 : degree. W.l.o.g, assuming the coefficient for degree in h is h 1 = 1, we seek the corresponding coefficient h 2 = h. Let us consider the following two operations : u 1 that, applied to node a 1 , adds a new node and connects it to all the neighbors of a 1 (i.e., modifies clustering), and u 2 that only adds a new node at each call (i.e., grows a star graph around a 2 , thus modifying a 2 's degree). The attacker can compute the value of u k-1 1 (a 1 ) and u k-1 2 (a 2 ) at any time. We simulated the attack with a G ∞ being a 1, 000 nodes Barabási-Albert graph with an average degree of 5, estimating h using u 1 and u 2 operations. Figure 1b presents the obtained results : a point (x, y) means the real value of h is x and was estimated by the attack (using the algorithm presented on Figure 2) as y. Black dots plot the real coefficient values of h. Each colored area represents the estimated (reverse-engineered) coefficients, while each color represents the number of applications of the operations (refered to as the "attack size"). The larger the attack, the more precise the reverse-engineered results. We note that if the real values of coefficients to be estimated are bigger (e.g., 4 or 5 on the x-axis), estimations show lower precision (larger areas). Despite this remark, estimations are always correct.

Conclusion

The will for web-services transparency starts to trigger new research works. XRay [START_REF] Lécuyer | Xray : Enhancing the web's transparency with differential correlation[END_REF] for instance proposes a correlation algorithm, that aims at inferring to which data input is associated a personalized output to the user. This Bayesian-based algorithm returns data that are the cause of received ads, while we seek in this paper to retrieve the internals of a hidden ranking function, in order to assess what is the effect of topology on the output peer-ranking. We have presented a general framework. Based on the centralities that might be used by the ranking function, there remain work for the attacker, to find operations (i.e., topological updates) that will make the reverse-engineering possible. We also find this to be an interesting challenge for future research.


Israel Fernández-Cadenas

Anna Penalba

Cristina Boada

Caty Carrerra Msc

Santiago Rodriguez Bueno

Adoración Quiroga

Jasone Monasterio

Pilar Delgado

Eduardo Anglés-Cano

Joan Montaner

Caty Carrerra Msc

Exome Sequencing and Clot Lysis Experiments Demonstrate the R458C Mutation of the Alpha Chain of Fibrinogen to be Associated with Impaired Fibrinolysis in a Family with Thrombophilia

Keywords: Coagulation, Dysfibrinogenemia, Fibrinogen, Thrombosis, Genetics

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Exome sequencing is an efficient strategy to search for genetic variants underlying rare Mendelian disorders. Here we report a family with an autosomal dominant heritable disease characterized by venous thromboembolic events that has been studied since 9

Tests for Coagulation System

Coagulation factors activity was determined by measuring activated partial tromboplastin time (APTT) in citrate plasma samples using the semiautomated coagulometer ST4 (Diagnostica Stago-Roche, Asnières, France) following manufacturer's users guide. Factors diluent, APTT reagents, deficient serums, normal control and calibration plasma were from IZASA (Werfen Group, Barcelona, Spain). Activities are indicated as a percentage of normal control plasma.

Thrombin clotting times of plasma were done in a mechanical test system containing 75μl of plasma and 75μl of thrombin. For reptilase times, 50μl of plasma and 100μl of enzyme were used. The reptilase clotting time was measured at 37 ℃ during one minute and the thrombin time at 37 ℃ during two minutes.

Tests for Coagulation System Regulation

Thrombin-antithrombin complex: Citrate tubes were used to obtain plasma samples and commercial ELISA kit was used to determine TAT complex following manufacturer user guide (Enzygnost TAT micro, Dade Behring Marburg GmbH, Marburg, German).

Protein C activity: Protein C activity was assayed by ProtClot Kit, following manufacturer's users guide (IZASA, Spain).

Protein S activity: Protein S activity was assayed by ProS Kit, following manufacturer's users guide (IZASA, Spain).

Antithrombin Ⅲ activity was assayed by a commercial kit, following manufacturer's users guide (IZASA, Spain). sis activity of fibrinogen Bordeaux found in that family.

Materials and Methods

Study Population

Several members of a family had episodes of venous thromboembolism (VTE), mainly deep vein thrombosis of the lower extremities or pulmonary embolism as seen in Fig. 1. The proband (Ⅲ-6) suffered acute dyspnea when he was 36. A ventilationperfusion scan showed multiple bilateral perfusion defects, and an ultrasonography revealed a bilateral deep vein thrombosis of the lower limbs. Placement of a Greenfield cava filter was required. He also reported to have abundant epistaxis as the only accompanying symptom. The most aggressive cases were those of a male (Ⅲ-8) and his daughter. He suffered right leg deep vein thrombosis and pulmonary embolism in 1991, received anticoagulation therapy for 6 months, and was then shifted to aspirin; 10 years later, he suffered a lethal massive pulmonary embolism. His daughter (Ⅳ-7) suffered a pulmonary embolism in the early postnatal period of her second child. Patients signed an informed consent, and this study was approved by our Hospital Ethics committee.

Biochemical Laboratory Tests

Routine biochemical and coagulation tests were performed according to standard procedures. Biochemical studies consisted the evaluation of thrombin time; reptilase time; levels of fibrinogen, PAI-1, alpha-2-antiplasmin, protein C, protein S, antithrombin, plasminogen, thrombin-antithrombin complex, t-PA, D-dimer, and beta-tromboglobulin; Fearnley test, coagulation factors activity, and t-PA zymography [START_REF] Gaussem | Sodium dodecyl sulfate-induced dissociation of complexes between human tissue plasminogen activator and its specific inhibitor[END_REF][START_REF] Lacroix | Leukocyte-and endothelial-derived microparticles: a circulating source for[END_REF][START_REF] Chakrabarti | R: The fibrinolytic potential as a simple measure of spontaneous fibrinolysis[END_REF][4][START_REF] Clauss | Rapid physiological coagulation method in determination of fibrinogen[END_REF] . fication was performed by PCR. PCR was performed in a mixture containing 100-200 ng DNA, 5 μl (5 μM) forward primer 5′ -ATGTAAGTCCAGGGACAAGG-3′ , 5 μl (5 μM) reverse primer 5′ -GGTGAGAAGAA ACCTGGGAA-3′ , 5 μl 10× PCR buffer, 4 μl dNTP buffer, 0.5 μl (5 U μl -1 ) Taq polymerase (TaKaRaBio Inc.), and up to 50 μl water. The samples were subjected to denaturation at 95 ℃ for 5 min, followed by amplification consisting of 30 cycles of denaturation at 94 ℃ for 1 min, annealing at 55 ℃ for 30s, and extension at 72 ℃ for 1 min, with a final extension at 72 ℃ for 5 min, in a GeneAmp PCR system 2700 (Applied Biosystems, Foster City, CA, USA). Amplicons were purified with the QIAquick PCR purification kit and subjected to cycle sequencing on a Gene-Amp PCR System 2700 with BigDye-labeled terminators (Applied Biosystems) and analyzed in an ABI Prism_310 DNA sequencer.

Results

Biochemical laboratory tests detected normal levels or activity for fibrinogen, PAI-1, alpha-2-antiplasmin, protein C, protein S, antithrombin, plasminogen, thrombin-antithrombin complex, D-dimer, betatromboglobulin, and coagulation factors activity.

Thrombin time (21.5 s; normal range: 18-25 s) and reptilase time (14.2 s; normal range <22 s) were also normal for the proband and Ⅳ-6 patient (Fig. 1). Fearnley test results were abnormally prolonged, indicating an impaired lysis; however, t-PA antigen levels measured by ELISA were high in cases, including the proband (23.5 ng/ml), compared with the normal range (0.15-13.4 ng/ml). The majority of t-PA appeared to be in complex with PAI-1 as detected by zymography (Supplemental Fig. 2). However, after an extensive study, the cause of the disease remained undiagnosed.

After quality control analysis of exome sequencing results, 21.787 new mutations not previously described as polymorphisms in dbSNP or 1.000 Genomes project were identified. Sixty nine of these mutations were found in heterozygous in three cases and were not present in the control subject (Table 1) following an autosomal dominant heritability. In 6 out of 69 mutations an amino acid change was identified with a moderate or high impact on the protein function coded by the gene, based on Polyphen results (http://genetics.bwh.harvard.edu/pph2/) (Table 1). The six mutations and genes were: R458C of FGA gene, Splice-site of ARHGEF37 gene, V67G of TPRG1L, T612P of RARA, V265G of ATL3 and V1G of CELF2.

Tests for Fibrinolytic System

The presence and identity of t-PA and its inhibitors in circulating blood were analyzed using the euglobulin fraction of plasma by direct and reverse fibrin autography (zymography) following SDS-PAGE performed as described previously 1) (expanded methods of tPA zymography are in the supplemental material).

Fearnley test: Tests were carried out in duplicate. In each case blood dilutions were made with phosphate buffer pH 7.4 and clots were made with bovine or human thrombin at 10 units/ml. The tubes were kept in ice-cold water until firm clots appeared and then transferred to a water-bath at 37 ℃. When the clots were set the tubes were rolled between the palms of hands in order to loosen the clots. The tubes were left in a water-bath at 37 ℃ and the clots were observed for lysis at regular intervals.

The clot lysis assay was performed following previous methods [START_REF] Carter | EuroCLOT Investigators: Heritability of clot formation, morphology, and lysis: the EuroCLOT study[END_REF] . The slopes were calculated as follow. We selected three points in the graph. Point 1 (start of clot formation), Point 2 (OD maximum) and Point 3 (end of clot lysis). Each of these points is indicated by two coordinates (OD and time in seconds). The slope of the clot formation is represented by the formula (OD2-OD1) / (t2-t1). The slope of the fibrinolysis is represented by the formula (OD3-OD2) / (t3-t2).

PAI-1, t-PA measurements: PAI-1 and t-PA were assayed by ELISA methods using kits from Biopool-Menarini, PAI-1 and t-PA antigens were from Biopool-Menarini as well.

Alpha-2-antiplasmin and Plasminogen activity: Alpha-2-antiplasmin and Plasminogen were assayed by commercial kits, following manufacturer's users guide (IZASA, Spain).

Exome Sequencing

Exome sequencing was performed in three familial cases (Ⅳ-6, Ⅳ-7, and Ⅳ-11) and one familial control (Ⅲ-12) (Fig. 1). The results were validated in the same samples by Sanger sequencing (Supplemental Fig. 1). In the second validation phase, 13 new familial cases and controls were analyzed.

Exome sequencing was performed in the Centro Nacional de Analisis Genómicos (CNAG, Barcelona, Spain). The Illumina TruSeq DNA Sample Prep kit and the NimbleGen SeqCap EZ Exome were used for library preparation and exome enrichment, respectively.

PCR Primers and Conditions

For the validation study the Exon 5 of the FGA gene, where the R458C mutation was located, ampli- CHR: Chromosome number, Pos: Nucleotide position on the chromosome, Ref: Reference sequence at the position, Alt: Alternative sequence at this position, Effect_impact: Effects are categorized by 'impact': High, Moderate, Low and Modifier depending on results of Polyphen web tool. Functional_Class: the functional class of the variant, AA_change: the change in amino acid produced by a variant in a coding region, given in the following format using one letter amino acid codes: Amino acid in the reference-amino acid position in the protein-amino acid in the variant. Gene_Name: the name of the gene overlapping this position in HGNC gene symbol format, na: non applicable. The same variant position may appear in more than one row, since the same variant can affect different transcripts and could be located in different genes. result of controls, indicating an altered clot formation process.

T A A C C C T C G A A A G A T C T T A A A A G C A A T A T T T G C C G T T T G G A G T G C G C C C G A A C C T T C T C C G G C G G T G T MODERATE MODIFIER
C C C T A A A A T G A C C G T C T T G G C G A C T T C A G G T T G T A T G T A G G T G C C T T T A A T C C A A G A C T A A G T A A C C A G C MODIFIER MODIFIER
The internal fibrin lysis time process after t-PA administration was delayed in both abnormal fibrinogens from proband and Ⅳ-6 patient vs. three healthy controls (95.8±0.28 min vs. 62.3±4.04 min, respectively).

The lysis rate was markedly reduced. The slope of the turbidity decrease diminished approximately 3-4 times in proband and Ⅳ-6 compared with controls (patients: 1.65±0.07 OD/sec vs. controls: 6± 1.22 OD/sec). The diminished slopes and delayed fibrin lysis are associated with clot lysis difficulty, probably due to the difficult binding of t-PA to fibrin.

Discussion

Familial dysfibrinogenemia is a coagulation disorder with a bleeding tendency due to a functional One of these six mutations, R458C mutation in FGA, was previously described [START_REF] Hanss | A new electrophoretic variant of fibrinogen associated with venous thromboembolism, fibrinogen Bordeaux Aalpha Arg439[END_REF] . FGA codifies for the alpha chain subunit of fibrinogen, and mutations located in this gene have been associated with dysfibrinogenemia, a disease with VTE episodes in some rare cases. Validation tests in three cases and control subjects and the analysis of thirteen new family individuals confirmed the link between the R458C mutation and the disease (Supplemental Table 1). In addition, an asymptomatic subject (Ⅳ-10) (Fig. 1) harboring a R458C mutation was detected; the patient had no history of venous thromboembolic events at the time of evaluation.

The proband and Ⅳ-6 patient showed an impaired fibrin polymerization and an impaired fibrinolysis based on clot lysis experiments (Fig. 2 and Supplemental Table 2). The slope of the turbidity increase diminished approximately 2-3 times, and the final turbidity was 67% less than that of the mean In addition, the time for fibrinolysis was higher in cases compared with controls. Time was measured in hours: minutes: seconds. Abs: Absorbance at 450 nm. Slope was measured in absorbance units/time in seconds.

Advance Publication Journal of Atherosclerosis and Thrombosis

Accepted for publication: August 27, 2015 Published online: November 17, 2015 study.

The R458C mutation causes a fibrinogen variant previously identified as fibrinogen Bordeaux 7) . The base substitution found in fibrinogen Bordeaux implies the presence of an unpaired Cys residue. We observed in patients with the R458C mutation a delayed fibrin lysis process (the slope of the turbidity decrease was diminished in the clot-lysis experiments and the time that t-PA needs to do the complete fibrinolysis was prolonged), suggesting an impaired tPAinduced fibrinolysis. This impaired fibrinolysis was also observed with Fearnley tests. This effect has been previously observed in dysfibrinogenemia patients caused by a less permeable fibrin that is more difficult to lyse compared with normal fibrin [START_REF] Marchi | Fibrinogen Guarenas, an abnormal fibrinogen with an Aalpha-chain truncation due to a nonsense mutation at Aalpha 467 Glu (GAA)-->stop (TAA)[END_REF] . Hanss and collegues hypothesized that the R458C mutation produces an aberrant thrombus that blocked the correct binding of t-PA to fibrin, inhibiting fibrinolysis and causing thrombosis. We speculate that our clot lysis experiment suggests the same hypothesis of Hanss and colleagues. The region of the alpha chain of fibrinogen molecule from amino acid 392 to 610, where our mutation is located, participates in fibrinolysis regulation, providing sites for plasminogen and t-PA binding, as it has been previously described [START_REF] Tsurupa | Fibrinogen aC domains contain cryptic plasminogen and t-PA binding sites[END_REF][START_REF] Medved | Conformational changes upon conversion of fibrinogen into fibrin. The mechanisms of exposure of cryptic sites[END_REF] . Consequently, the R458C could modify plasminogen-tPA binding to fibrin delaying normal fibrin lysis, as we observed in our clot lysis experiment. We hypothesize that the increased t-PA levels in plasma (measured by ELISA and zymography) are due to an attempt to lyse the altered thrombus. This is the first time that has been described the biological mechanism of the impaired fibrinolysis in fibrinogen Bordeaux, however further studies are needed to confirm these hypotheses. In addition clot-lysis experiments could be a useful strategy to diagnose idiopathic thrombophilias. In our case the fibrinogen Bordeaux was associated with an impaired fibrinolysis detectable by clot-lysis experiments. However, experiments focused on the diagnostic usefulness of clot-lysis experiments in clinical practise await further demonstration.

In summary, we could use exome sequencing to diagnose a family with dysfibrinogenemia that remained undiagnosed for 9 years. We suggest that exome sequencing could be a quick and economically affordable technique to diagnose monogenic idiopathic thrombophilias when current clinical biochemistry tests fail to find the cause. and importantly, we have observed an impaired fibrinolysis associated with fibrinogen Bordeaux, which could be the mechanism associated with the thrombotic accidents. alteration of the circulating fibrinogen. In several dysfibrinogenemias, an altered fibrinogen generates aberrant thrombus that in some rare cases increases the risk of venous thrombosis.

Dysfibrinogenemia may be due to mutations in FGA, FGB, or FGG that code for the alpha, beta, and gamma chains of fibrinogen, respectively. More than 580 cases of abnormal fibrinogens have been reported [START_REF] Hanss | A database for human fibrinogen variants[END_REF] . In our study, the origin of idiopathic thrombophilia was impossible to elucidate using the routine biochemical tests. Only the exome sequencing analysis was useful to find the causes of the familial thrombophilia disease. We studied three affected cousins and a control subject of the family without thromboembolic events. We found a heterozygous R458C mutation in FGA, and we considered this as a pathological mutation on the following considerations. First, the mutation was previously described in a family with dysfibrinogenemia and venous thromboembolic disease [START_REF] Hanss | A new electrophoretic variant of fibrinogen associated with venous thromboembolism, fibrinogen Bordeaux Aalpha Arg439[END_REF] , and it is included in the Human Genome Variation database. Second, the mutation was present in five cases and was absent in 11 healthy family controls. Third, the mutation changes a conservative arginine to cysteine, probably affecting the secondary structure of the protein, as has been suggested previously [START_REF] Tsurupa | Fibrinogen aC domains contain cryptic plasminogen and t-PA binding sites[END_REF][START_REF] Medved | Conformational changes upon conversion of fibrinogen into fibrin. The mechanisms of exposure of cryptic sites[END_REF] .

The biochemical tests to determine the origin of venous thromboembolic events were performed according to the conventional protocol of our hospital. Only the t-PA antigen and zymography assays detected higher t-PA and tPA-PAI-1 complexes in cases compared with controls. In addition, the Fearnley test detected an impaired fibrinolysis. Other clinical biochemistry results obtained, including fibrinogen levels and reptilase and thrombin times, were within the control range.

Thrombin and reptilase times are the gold standard clinical tests to identify dysfibrinogenemias. Fibrinogen levels could be normal in dysfibrogenemias but thrombin and reptilase times are prolonged. However, this is not the case in some rare cases of dysfibrinogenemia. This problem was documented by Hanss and colleagues [START_REF] Hanss | A new electrophoretic variant of fibrinogen associated with venous thromboembolism, fibrinogen Bordeaux Aalpha Arg439[END_REF] who reported a patient with the R458C mutation and normal thrombin and reptilase times, as we observed in our study.

This type of dysfibrinogenemias is very difficult to diagnose due to the normal results of fibrinogen levels and normal thrombin and reptilase times. In cases with idiopathic genetic thrombophilias, when all biochemical tests have been inconclusive and familial heritability has been observed, the use of exome sequencing could be an important clinical tool to perform the diagnosis of the disease, as suggested by our Advance Publication Journal of Atherosclerosis and Thrombosis Accepted for publication: August 27, 2015 Published online: November 17, 2015 2.5% Triton X-100. After washing-off excess Triton X-100 with distilled water, the gel was carefully overlaid on 1% agarose gel containing 1 mg/mL of bovine fibrinogen, 100 nM plasminogen, and 0.2 NIH U/ mL of bovine thrombin. For reverse fibrin zymography, the fibrin gel was supplemented with 0.05 IU/ mL of urokinase. Zymograms were allowed to develop at 37 ℃ for 24 h and photographed at regular intervals using dark-ground illumination. Active proteins in the samples were identified by reference to the migration of known markers (u-PA and t-PA). When required, the fibrin-agarose indicator gel was supplemented with antibodies (10 μg/mL) directed against specific plasminogen activators.

Supplemental Material

Expanded Methods

Fibrin zymography. The presence and identity of t-PA and its inhibitors in circulating blood were analyzed using the euglobulin fraction of plasma by direct and reverse fibrin autography following SDS-PAGE performed as described previously (Supplemental reference 1). Briefly, MPs were lysed in 100 mM Tris-HCl buffer, pH 8.1, containing 1% Triton X-100. MPs lysates (10 μL from 2.10 5 MPs) and reference proteins (10 μL of t-PA 5 nM and uPA) were electrophoresed in a 7.5% polyacrylamide gel under nonreducing conditions. SDS was then exchanged with Advance Publication Journal of Atherosclerosis and Thrombosis Accepted for publication: August 27, 2015 Published online: November 17, 2015

Fig. 1 .

 1 Fig. 1. Pedigree of the family In black, subjects with documented venous thrombotic events, including pulmonary venous thrombosis. * indicates that the patient harbored the R458C mutation. The arrow indicates the proband.

Fig. 2 .

 2 Fig. 2. Clot lysis experiment of the proband C76, C77, and C79 were healthy controls. The graph shows the lower slope decrease of the case compared with three controls, indicating difficulty to lyse the clot. The slopes were calculated selecting three points in the graph. Point 1 (when the clot begins to form, white dot), Point 2 [point of maximum Absorbance (Abs.), black dot], and Point 3 (When just lyses, gray dot). Each of these points are indicated by two coordinates (OD and time in seconds). The slope of the clot formation is represented by the formula (Abs2-Abs1)/(t2-t1). The slope of the fibrinolysis is represented by the formula (Abs3-Abs2)/(t3-t2).In addition, the time for fibrinolysis was higher in cases compared with controls. Time was measured in hours: minutes: seconds. Abs: Absorbance at 450 nm. Slope was measured in absorbance units/time in seconds.

Table 1 .

 1 Mutations obtained after exome sequencing analysisThe table shows the mutations that have not been described previously in dbSNP or 1000 genomes project and were present in the three cases and absent in the familial control.

	CHR	POS	REF	ALT	Effefct_Impact	Functional_Class	AA_change	Gene_Name
	chr10 chr10 chr10 chr10 chr10 chr10 chr10 chr10 chr10 chr10 chr10 chr10 chr10 chr11 chr16 chr16 chr17 chr17 chr17 chr17 chr18 chr18 chr18 chr18 chr19 chr19 chr1 chr1 chr1 chr1 chr1 chr20 chr22 chr22	11299536 38466875 39108582 42355885 42369451 42369460 42380101 42380949 42398896 42398919 42532591 42947013 46187861 63410966 33497584 33866511 25264916 25267857 38512948 77770440 14187255 14187255 18512924 18515816 52568032 52568032 3542312 121355224 142538644 167063884 167063884 21213251 22707151 22730530

Acknowledgments and Notice

We want to thank the family members, especially Florencio and María del Pilar.

The Laboratory of Stroke Genetics and Pharmacogenomics is a part of the International Stroke Genetics Consortium (ISGC, www.strokegenetics. com) and coordinates the Spanish Stroke Genetics Consortium (Genestroke, www.genestroke.com). Ⅰ. F-C. is supported by the Miguel Servet programme (CP12/03298), Instituto de Salud Carlos Ⅲ. This study was funded by the Fundació Docència I Recerca MutuaTerrassa, Hospital Universitari Mutua de Terrassa grant (EXCLOP project).

Conflicts of Interest

The authors declare nothing to disclose.

Exome Sequencing for Thrombophilia Cadenas et al.

Supplemental Table 1. Genetic results of the exome study and validation analysis

Subjects

R458C Mutation Study

Ⅲ


Andrés Salamanca Lugo

Olga Manrique Chaparro

Some Strategic Aspects of Private Information: An Experimental Study $

Keywords: Repeated games, incomplete information, experiments. JEL Classification: C73, D83, C91

In this paper we experimentally analyze the use of private information in a class of zero-sum repeated games with incomplete information on one side and perfect monitoring. We analyze whether the information disclosure by the informed players, and its use by the uninformed players, matches the theoretical predictions. We consider two games that differ according to the amount of information that the informed player should optimally disclose: in the first game, the informed player should entirely conceal his information. In contrast, in the second game the informed player should fully disclose his information. We find that the flow of information is higher than predicted in the first game and lower than predicted in the second game. However, the use of information is strictly higher in the second game than in the first one. Uninformed subjects tend not to use the revealed information in the first game, and seem to misinterpret the revealed information in the second game.

Introduction

In this paper we experimentally analyze the use of private information in two examples from the class of undiscounted zero-sum repeated games with incomplete information on one side $ A preliminary version of this paper was written as part of Olga's doctoral dissertation at the Universitat Autónoma de Barcelona. The two authors carried out the experiment in 2011 and completed the present version of the paper in 2016. Financial support in the form of a Research Grant from the Fondo de Investigación Jesús Antonio Bejarano of the Faculty of Economics at the Universidad Nacional de Colombia (Bogotá) is gratefully acknowledged. We thank Nikos Georgantzís, Jordi Brandts, Rosemarie Nagel, Jonathan Schulz, Astrid Hopfensitz and Franc ¸oise Forges for extremely useful comments. We also benefited from numerous comments from participants at the International Meeting on Experimental and Behavioral Economics (2012) and the Second International Meeting on Experimental and Behavioral Social Sciences (2015).

Email addresses: asalamancal.tse@gmail.com (Andrés Salamanca Lugo), olmanriquec@unal.edu.co (Olga Manrique Chaparro) and perfect monitoring studied by Aumann and Maschler [START_REF] Aumann | Repeated Games with Incomplete Information[END_REF]. In the first example, the informed player cannot benefit in the long-run from his private information: behaving as if he were uninformed is optimal. In contrast, in the second example the informed player should optimally disclose his information by consistently playing the stage-dominant action.

Figure 1 presents the payoff matrices of the first repeated game we implement in the laboratory. Before the game starts, one of the two payoff matrices, G A or G B , is randomly chosen from an equiprobable distribution and only player 1 is informed about the matrix to be played. After that, both players repeatedly choose an action simultaneously: player 1 chooses a row, player 2 chooses a column. The game consists of a "long" number of finitely many repetition, but no player is certain about how many1 . At the end of every stage each player is informed about the choice made by his opponent (full monitoring), however, payoffs received by each one are credited (in the case of player 1) or debited (in the case of player 2) from their accounts without any of them to be able to see the amounts recorded.

G A W E N 1 , -1 0 , 0 S 0 , 0 0 , 0 G B W E N 0 , 0 0 , 0 S 0 , 0 1 , -1
By ignoring his private information, and playing both actions with equal probability, player 1 can guarantee an (undiscounted) expected payoff equal to 1/4. A strategy like that is called a non-revealing (NR) strategy, since the actions do not reveal in any way the choice of the chance. Aumann and Maschler [START_REF] Aumann | Repeated Games with Incomplete Information[END_REF] showed that the minmax value of this repeated game is 1/4, thus the NR strategy is optimal for player 1. If player 1 slightly alters his strategy to take some advantage of his knowledge, player 2 will eventually notice this and he will switch to his best response. In accordance with this, we shall refer to this game as the non-revealing game.

The following repeated game has the same description as the NR game except that the two possible matrices are now: By using the fully revealing (FR) strategy "always choose S if the matrix is G A and always choose N if the matrix is G B " player 1 guarantees a payoff of 0 at each stage of the game. Because the highest payoff that player 1 can get in both matrices is 0, it is clear that the minmax value of this game is 0 and the FR strategy is optimal for player 1. We refer to this game as the fully revealing game.

G A W E N -1 , 1 0 , 0 S 0 , 0 0 , 0 G B W E N 0 , 0 0 , 0 S 0 , 0 -1 , 1
Each one of these repeated games was implemented as a separate experiment. In total, 130 subjects participated. The NR (resp. FR) game was played by 33 (resp. 32) pairs of players, of which 16 (resp. 10) played the game A and 17 (resp. 22) the game B. Subjects were instructed on the rules of the game with written instructions2 and they played the repeated game for 17 rounds. No individual was informed about the number of repetitions. Subjects were only told that the game will be repeated for a "long" number of rounds, without any clear idea of how long. In each period, uninformed players were asked the following question: Which do you think is the matrix you are playing?. Further details on the experimental design are given in the appendix.

The experimental analysis shows that the flow of information is higher than predicted in the NR game and lower than predicted in the FR game. However, the use of information is strictly higher in the FR game than in the NR game. Uninformed subjects tend not to use the revealed information in the NR game, and seem to misinterpret the revealed information in the FR game (although their actions are consistent with their incorrect beliefs).

Our experiment is closely related to Jacquement and Koessler [START_REF] Jacquement | Using or hiding private information? An experimental study of zero-sum repeated games with incomplete information[END_REF] (JK henceforth), albeit both experiments were conducted independently and without mutual knowledge of each other. Both papers use a similar experimental protocol (although JK consider a partially revealing game in addition to NR and FR games), and measure the use of information (they also study the empirical value of information). The following are the main differences between both experimental designs: first, the payoff matrices employed by JK are constant-sum modified versions of the matrices we use. In particular, they consider a FR game in which the informed player cannot guarantee himself the maximum payoff of the game, so that the uninformed player has more incentives to exploit the information revealed by the informed player3 . Second, we focus on long-run games instead of the 1 to 5 periods used by JK. Finally, contrary to JK, we investigate the role of the beliefs guiding the decision making process of the uninformed players. For that, we elicit explicitly the beliefs of the individuals. This allow us to go further in the analysis of the uninformed players' behavior. While most of our results regarding the informed subjects are similar to those in JK, one result which is markedly different concerns the behavior of uninformed subjects in the FR game, i.e., having consistently wrong beliefs about the actual stage game, despite rather clear information revealed by the behavior of the informed players. We provide a possible explanation for this result, yet it remains puzzling.

Experimental Analysis

Non-revealing Game

In this section we present the results from our experiment in the NR game. Figure 3 depicts the relative frequency of the stage-dominant action in the actual game4 . According to the theoretical predictions, the informed subjects should play each action with a probability equal to 50%. However, we observe that the frequencies of the stage-dominant action are consistently larger than 50% at every stage (see panel (a) of figure 3 and table 1 in the appendix). Indeed, every informed player chose in average the stage-dominant action 66% of the times. This means that informed players made use of their information more than they should do. The ability of the experimental subjects to optimally ignore their private information is weak. Given the revelation pattern, we expect the uninformed players to optimally respond by playing more often the stage-dominant action of the actual game. However, the observed frequencies for the uninformed subjects suggest that they were unable to account for the information they received (see panel (b) of figure 3). In average, they played both actions equally likely (see table 2 in the appendix). In order to study the extent to which uninformed players reacted to the transmitted information, we estimated conditional fixed-effect5 logit regressions on the uninformed players' decisions. We denote I(E) the binary variable associated to decision E, Belief n a binary variable taking value 1 when the uninformed player thinks he is facing game A, and P1's choice n-1 a binary variable taking value 1 whenever he observes that player 1 chose action S in the previous stage. We separate regressions according to the actual stage game. Table 1 presents the regression results.

Regarding the players in stage game A, only beliefs were statistically significant. This is not surprising, since current beliefs are a sufficient statistic for all information contained in any history of the game. A positive estimated marginal effect for beliefs is consistent with an optimal response: as long as an uninformed player thought he was facing game A, the probability of playing action E was increased by about 30%. In contrast, for those subjects in stage game B neither the beliefs nor the informed player's choice were significant at any confidence level. Statistical evidence suggests that uninformed players in the stage game B faced difficulties to account for the revealed information. Marginal effects reported instead of estimated coefficients.

Marginal effects calculated at the multivariated point of means.

P1's choice

n-1 = 1 if S . Belief = 1 if game A.
Significance level: *** p < 0.01, ** p < 0.05, * p < 0.1

Fully Revealing Game

We now turn to the fully revealing game. Theoretically, informed players should play the stagedominant action at each stage of the game. Figure 4 displays the relative frequency of the dominant action in the actual payoff matrix. The observed frequencies suggest a strong correlation between decisions and private information. In average, the informed subjects played the stage-dominant action 75% of the times. Therefore, they used their information more than in the non-revealing game, but less than they should do. On the other hand, uninformed players accounted for the revealed information, but they reacted in an unexpected way: the most frequent action in every stage was the stage-dominated action. Indeed, the dominant action was only chosen 39% of the times. These frequencies are statistically different from 50% (see tables 3 and 4 in the appendix). We estimated conditional fixed-effect logit regressions on the uninformed players' decisions as in section 2.1. Regression results are summarized in table 2. Observed actions were consistent with a rational behavior given the elicited beliefs: the probability of choosing action W (resp. E) was increased once the player believed he faced the stage game A (resp. B). However, beliefs were not updated consistently with the revealed information. Whenever the actual stage game was A (resp. B), the average frequency of uninformed players that believed to be in game B (resp. A) was around 70% (resp. 50%)6 . This explains why the observed behavior of the uninformed individuals is not consistent with an optimal response given the revealed information. The question is why the uninformed subjects did not update their beliefs according to the information revealed on the actual stage game. We think that the uninformed individuals mainly focused on their own payoff matrices disregarding the payoff motivations of the informed individuals. This resulted in a lack of incentive compatibility: despite the fact that the informed individuals found profitable to release their private information, uninformed subjects could not believe in such a disclosure. Our hypothesis is that external motivations other than the strategic aspects led the uninformed players to believe that any revelation could only be for the purpose of misleading. Then, in anticipation of a deceiving behavior, uninformed players modified their beliefs in the opposite direction. In sharp contrast with the strategic motivations, social motivations like "mistrust" can be a leading factor guiding human decisions.

Figure 1 :

 1 Figure 1: Non-revealing game

Figure 2 :

 2 Figure 2: Fully revealing game

Figure 3 :

 3 Figure 3: Relative frequency of the stage-dominant action in the NR game

Figure 4 :

 4 Figure 4: Relative frequency of the stage-dominant action in the FR game

Table 1 :

 1 Probability of choosing E

	Conditional Fixed-Effects Logit Regression GAME A
	I(E)	(1)	(2)	(3)
	Belief n 0.347*** (0.080) P1's choice n-1	-0.010 (0.107)	0.325*** (0.101) 0.003 (0.090)
		GAME B
	I(E)	(1)	(2)	(3)
	Belief n P1's choice n-1	0.000 (0.226)	-0.017 (0.139)	0.058 (0.231) -0.015 (0.142)

Table 2 :

 2 Probability of choosing E

	Conditional Fixed-Effects Logit Regression GAME A
	I(E)	(1)	(2)	(3)
	Belief n -0.457*** (0.035) P1's choice n-1	0.212 (0.137)	-0.603*** (0.104) 0.240 (0.210)
		GAME B
	I(E)	(1)	(2)	(3)
	Belief n -0.493*** (0.007) P1's choice n-1	0.110 (0.092)	-0.513*** (0.062) 0.023 (0.077)

Marginal effects reported instead of estimated coefficients.

Marginal effects calculated at the multivariated point of means.

P1's choice

n-1 = 1 if S . Belief = 1 if game A.

Significance level: *** p < 0.01, **p < 0.05, * p < 0.1

This interpretation of a infinitely repeated game is consistent with the idea that players can approximately optimally play in any long finite game of duration n without making any essential use of n (see Aumann and Maschler[1, pp. 131]).

Instructions were written in Spanish. Translated instructions are provided in the appendix.

This difference may explain the differing results regarding the uniformed players' behavior in the FR game.

Dotted lines corresponds to 95% confidence intervals.

Fixed effects controls for non-observable heterogeneity among individuals.

Reported frequencies for stage game A are statistically different from 50% (see table 5 in the appendix). Figure1in the appendix provides a summary of the evolution of average beliefs on each actual stage game.