Mathieu Emily

Using biological networks to search for interacting loci in genome-wide association studies.

Genome-wide association studies have identified a large number of single-nucleotide polymorphisms (SNPs) that individually predispose to diseases. However, many genetic risk factors remain unaccounted for. Proteins coded by genes interact in the cell, and it is most likely that certain variants mainly affect the phenotype in combination with other variants, termed epistasis. An exhaustive search for epistatic effects is computationally demanding, as several billions of SNP pairs exist for typical genotyping chips. In this study, the experimental knowledge on biological networks is used to narrow the search for two-locus epistasis. We provide evidence that this approach is computationally feasible and statistically powerful. By applying this method to the Wellcome Trust Case–Control Consortium data sets, we report four significant cases of epistasis between unlinked loci, in susceptibility to Crohns disease, bipolar disorder, hypertension and rheumatoid arthritis.

MetAmyl: A METa-Predictor for AMYLoid Proteins

The aggregation of proteins or peptides in amyloid fibrils is associated with a number of clinical disorders, including Alzheimers, Huntingtons and prion diseases, medullary thyroid cancer, renal and cardiac amyloidosis. Despite extensive studies, the molecular mechanisms underlying the initiation of fibril formation remain largely unknown. Several lines of evidence revealed that short amino-acid segments (hot spots), located in amyloid precursor proteins act as seeds for fibril elongation. Therefore, hot spots are potential targets for diagnostic/therapeutic applications, and a current challenge in bioinformatics is the development of methods to accurately predict hot spots from protein sequences. In this paper, we combined existing methods into a meta-predictor for hot spots prediction, called MetAmyl for METapredictor for AMYLoid proteins. MetAmyl is based on a logistic regression model that aims at weighting predictions from a set of popular algorithms, statistically selected as being the most informative and complementary predictors. We evaluated the performances of MetAmyl through a large scale comparative study based on three independent datasets and thus demonstrated its ability to differentiate between amyloidogenic and non-amyloidogenic polypeptides. Compared to 9 other methods, MetAmyl provides significant improvement in prediction on studied datasets. We further show that MetAmyl is efficient to highlight the effect of point mutations involved in human amyloidosis, so we suggest this program should be a useful complementary tool for the diagnosis of these diseases.

Walking up and down in immersive virtual worlds: Novel interactive techniques based on visual feedback

We introduce novel interactive techniques to simulate the sensation of walking up and down in immersive virtual worlds based on visual feedback. Our method consists in modifying the motion of the virtual subjective camera while the user is really walking in an immersive virtual environment. The modification of the virtual viewpoint is a function of the variations in the height of the virtual ground. Three effects are proposed: (1) a straightforward modification of the cameras height, (2) a modification of the cameras navigation velocity, (3) a modification of the cameras orientation. They were tested in an immersive virtual reality setup in which the user is really walking. A Desktop configuration where the user is seated and controls input devices was also tested and compared to the real walking configuration. Experimental results show that our visual techniques are very efficient for the simulation of two canonical shapes: bumps and holes located on the ground. Interestingly, a strong ¿orientation-height illusion¿ is found, as changes in pitch viewing orientation produce perception of height changes (although cameras height remains strictly the same in this case). Our visual effects could be applied in various virtual reality applications such as urban or architectural project reviews or training, as well as in videogames, in order to provide the sensation of walking on uneven grounds.

Impact of human milk pasteurization on the kinetics of peptide release during in vitro dynamic term newborn digestion

Holder pasteurization (62.5°C, 30 min) ensures sanitary quality of donors human milk but also denatures beneficial proteins. Understanding whether this further impacts the kinetics of peptide release during gastrointestinal digestion of human milk was the aim of the present paper. Mature raw (RHM) or pasteurized (PHM) human milk were digested (RHM, n = 2; PHM, n = 3) by an in vitro dynamic system (term stage). Label‐free quantitative peptidomics was performed on milk and digesta (ten time points). Ascending hierarchical clustering was conducted on “Pasteurization × Digestion time” interaction coefficients. Preproteolysis occurred in human milk (159 unique peptides; RHM: 91, PHM: 151), mostly on β‐casein (88% of the endogenous peptides). The predicted cleavage number increased with pasteurization, potentially through plasmin activation (plasmin cleavages: RHM, 53; PHM, 76). During digestion, eight clusters resumed 1054 peptides from RHM and PHM, originating for 49% of them from β‐casein. For seven clusters (57% of peptides), the kinetics of peptide release differed between RHM and PHM. The parent protein was significantly linked to the clustering (p‐value = 1.4 E‐09), with β‐casein and lactoferrin associated to clusters in an opposite manner. Pasteurization impacted selectively gastric and intestinal kinetics of peptide release in term newborns, which may have further nutritional consequences.

Novel interactive techniques for bimanual manipulation of 3d objects with two 3dof haptic interfaces

This paper presents a set of novel interactive techniques adapted to two-handed manipulation of objects with dual 3DoF single-point haptic devices. We first propose the double bubble for bimanual haptic exploration of virtual environments through hybrid position/rate controls, and a bimanual viewport adaptation method that keeps both proxies on screen in large environments. We also present two bimanual haptic manipulation techniques that facilitate pick-and-place tasks: the joint control, which forces common control modes and control/display ratios for two interfaces grabbing an object, and the magnetic pinch, which simulates a magnet-like attraction between both hands to prevent unwanted drops of that object. An experiment was conducted to assess the efficiency of these techniques for pick-and-place tasks, by comparing the double bubble with viewport adaptation to the clutching technique for extending the workspaces, and by measuring the benefits of the joint control and magnetic pinch.

IndOR: a new statistical procedure to test for SNP–SNP epistasis in genome-wide association studies

Epistasis is often cited as the biological mechanism carrying the missing heritability in genome-wide association studies. However, there is a very few number of studies reported in the literature. The low power of existing statistical methods is a potential explanation. Statistical procedures are also mainly based on the statistical definition of epistasis that prevents from detecting SNP-SNP interactions that rely on some classes of epistatic models. In this paper, we propose a new statistic, called IndOR for independence-based odds ratio, based on the biological definition of epistasis. We assume that epistasis modifies the dependency between the two causal SNPs, and we develop a Wald procedure to test such hypothesis. Our new statistic is compared with three statistical procedures in a large power study on simulated data sets. We use extensive simulations, based on 45 scenarios, to investigate the effect of three factors: the underlying disease model, the linkage disequilibrium, and the control-to-case ratio. We demonstrate that our new test has the ability to detect a wider range of epistatic models. Furthermore, our new statistical procedure is remarkably powerful when the two loci are linked and when the control-to-case ratio is higher than 1. The application of our new statistic on the Wellcome Trust Case Control Consortium data set on Crohns disease enhances our results on simulated data. Our new test, IndOR, catches previously reported interaction with more power. Furthermore, a new combination of variant has been detected by our new test as significantly associated with Crohns disease.

Spatial Correlation of Gene Expression Measures in Tissue Microarray Core Analysis

Tissue microarrays (TMAs) make possible the screening of hundreds of different tumour samples for the expression of a specific protein. Automatic features extraction procedures lead to a series of covariates corresponding to the averaged stained scores. In this article, we model the random geometry of TMA cores using voronoi tesselations. This formalism enables the computation of indices of spatial correlation of stained scores using both classical and novel approaches. The potential of these spatial statistics to correctly discriminate between diseased and non-diseased cases is evaluated through the analysis of a TMA containing samples of breast carcinoma data. The results indicate a significant improvement in the breast cancer prognosis.

Risk Assessment for Hospital-Acquired Diseases: A Risk-Theory Approach

We introduce a new approach to hospital-acquired disease risk assessment from public health databases. In a spirit similar to actuarial risk theory, we define an adjustment coefficient that can quantify the risk associated with a hospital department, allowing comparisons of similar departments. The adjustment coefficient characterizes the tail of the distribution of the total patient length of stay in a department before the first disease event occurs. We show that this coefficient is the solution of a Lundberg-like equation, and we provide a nonparametric estimation procedure for this measure, based on a Cramér-Lundberg approximation for the tail of the distribution. Using simulations, we provide evidence of the robustness of the approximation to various individual risk models. In addition, we illustrate the relevance of this approach by evaluating the risk associated with a standard patient safety indicator in 20 hospitals of southeastern France.

A statistical approach to estimating the strength of cell-cell interactions under the differential adhesion hypothesis

BackgroundThe Differential Adhesion Hypothesis (DAH) is a theory of the organization of cells within a tissue which has been validated by several biological experiments and tested against several alternative computational models.ResultsIn this study, a statistical approach was developed for the estimation of the strength of adhesion, incorporating earlier discrete lattice models into a continuous marked point process framework. This framework allows to describe an ergodic Markov Chain Monte Carlo algorithm that can simulate the model and reproduce empirical biological patterns. The estimation procedure, based on a pseudo-likelihood approximation, is validated with simulations, and a brief application to medulloblastoma stained by beta-catenin markers is given.ConclusionOur model includes the strength of cell-cell adhesion as a statistical parameter. The estimation procedure for this parameter is consistent with experimental data and would be useful for high-throughput cancer studies.

Conditional Coalescent Trees With Two Mutation Rates and Their Application to Genomic Instability

Humans have invested several genes in DNA repair and fidelity replication. To account for the disparity between the rarity of mutations in normal cells and the large number of mutations present in cancer, an hypothesis is that cancer cells must exhibit a mutator phenotype (genomic instability) during tumor progression, with the initiation of abnormal mutation rates caused by the loss of mismatch repair. In this study we introduce a stochastic model of mutation in tumor cells with the aim of estimating the amount of genomic instability due to the alteration of DNA repair genes. Our approach took into account the difficulties generated by sampling within tumoral clones and the fact that these clones must be difficult to isolate. We provide corrections to two classical statistics to obtain unbiased estimators of the raised mutation rate, and we show that large statistical errors may be associated with such estimators. The power of these new statistics to reject genomic instability is assessed and proved to increase with the intensity of mutation rates. In addition, we show that genomic instability cannot be detected unless the raised mutation rates exceed the normal rates by a factor of at least 1000.