Concepcion Arenas

Exome sequencing in multiplex autism families suggests a major role for heterozygous truncating mutations

Autism is a severe neurodevelopmental disorder, the aetiology of which remains mainly unknown. Family and twin studies provide strong evidence that genetic factors have a major role in the aetiology of this disease. Recently, whole exome sequencing (WES) efforts have focused mainly on rare de novo variants in singleton families. Although these studies have provided pioneering insights, de novo variants probably explain only a small proportion of the autism risk variance. In this study, we performed exome sequencing of 10 autism multiplex families with the aim of investigating the role of rare variants that are coinherited in the affected sibs. The pool of variants selected in our study is enriched with genes involved in neuronal functions or previously reported in psychiatric disorders, as shown by Gene Ontology analysis and by browsing the Neurocarta database. Our data suggest that rare truncating heterozygous variants have a predominant role in the aetiology of autism. Using a multiple linear regression model, we found that the burden of truncating mutations correlates with a lower non-verbal intelligence quotient (NVIQ). Also, the number of truncating mutations that were transmitted to the affected sibs was significantly higher (twofold) than those not transmitted. Protein–protein interaction analysis performed with our list of mutated genes revealed that the postsynaptic YWHAZ is the most interconnected node of the network. Among the genes found disrupted in our study, there is evidence suggesting that YWHAZ and also the X-linked DRP2 may be considered as novel autism candidate genes.

Evaluation of Aminoglycoside and Non-Aminoglycoside Compounds for Stop-Codon Readthrough Therapy in Four Lysosomal Storage Diseases.

Nonsense mutations are quite prevalent in inherited diseases. Readthrough drugs could provide a therapeutic option for any disease caused by this type of mutation. Geneticin (G418) and gentamicin were among the first to be described. Novel compounds have been generated, but only a few have shown improved results. PTC124 is the only compound to have reached clinical trials. Here we first investigated the readthrough effects of gentamicin on fibroblasts from one patient with Sanfilippo B, one with Sanfilippo C, and one with Maroteaux-Lamy. We found that ARSB activity (Maroteaux-Lamy case) resulted in an increase of 2–3 folds and that the amount of this enzyme within the lysosomes was also increased, after treatment. Since the other two cases (Sanfilippo B and Sanfilippo C) did not respond to gentamicin, the treatments were extended with the use of geneticin and five non-aminoglycoside (PTC124, RTC13, RTC14, BZ6 and BZ16) readthrough compounds (RTCs). No recovery was observed at the enzyme activity level. However, mRNA recovery was observed in both cases, nearly a two-fold increase for Sanfilippo B fibroblasts with G418 and around 1.5 fold increase for Sanfilippo C cells with RTC14 and PTC124. Afterwards, some of the products were assessed through in vitro analyses for seven mutations in genes responsible for those diseases and, also, for Niemann-Pick A/B. Using the coupled transcription/translation system (TNT), the best results were obtained for SMPD1 mutations with G418, reaching a 35% recovery at 0.25 μg/ml, for the p.W168X mutation. The use of COS cells transfected with mutant cDNAs gave positive results for most of the mutations with some of the drugs, although to a different extent. The higher enzyme activity recovery, of around two-fold increase, was found for gentamicin on the ARSB p.W146X mutation. Our results are promising and consistent with those of other groups. Further studies of novel compounds are necessary to find those with more consistent efficacy and fewer toxic effects.

ICGE: an R package for detecting relevant clusters and atypical units in gene expression

BackgroundGene expression technologies have opened up new ways to diagnose and treat cancer and other diseases. Clustering algorithms are a useful approach with which to analyze genome expression data. They attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. An important problem associated with gene classification is to discern whether the clustering process can find a relevant partition as well as the identification of new genes classes. There are two key aspects to classification: the estimation of the number of clusters, and the decision as to whether a new unit (gene, tumor sample...) belongs to one of these previously identified clusters or to a new group.ResultsICGE is a user-friendly R package which provides many functions related to this problem: identify the number of clusters using mixed variables, usually found by applied biomedical researchers; detect whether the data have a cluster structure; identify whether a new unit belongs to one of the pre-identified clusters or to a novel group, and classify new units into the corresponding cluster. The functions in the ICGE package are accompanied by help files and easy examples to facilitate its use.ConclusionsWe demonstrate the utility of ICGE by analyzing simulated and real data sets. The results show that ICGE could be very useful to a broad research community.

Towards Application of One-Class Classification Methods to Medical Data

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

The Depth Problem: Identifying the Most Representative Units in a Data Group

This paper presents a solution to the problem of how to identify the units in groups or clusters that have the greatest degree of centrality and best characterize each group. This problem frequently arises in the classification of data such as types of tumor, gene expression profiles or general biomedical data. It is particularly important in the common context that many units do not properly belong to any cluster. Furthermore, in gene expression data classification, good identification of the most central units in a cluster enables recognition of the most important samples in a particular pathological process. We propose a new depth function that allows us to identify central units. As our approach is based on a measure of distance or dissimilarity between any pair of units, it can be applied to any kind of multivariate data (continuous, binary or multiattribute data). Therefore, it is very valuable in many biomedical applications, which usually involve noncontinuous data, such as clinical, pathological, or biological data sources. We validate the approach using artificial examples and apply it to empirical data. The results show the good performance of our statistical approach.

Loop-closing: A typicality approach

Loop-closing has long been identified as a critical issue when building maps from local observations. Topological mapping methods abstract the problem of how loops are closed from the problem of how to determine the metrical layout of places in the map and how to deal with noisy sensors. The typicality problem refers to the identification of new classes in a general classification context. This typicality concept is used in this paper to help a robot acquire a topological representation of the environment during its exploration phase. The problem is addressed using the INCA statistic which follows a distance-based approach. In this paper we describe a place recognition approach based on match testing by means of the INCA test. We describe the theoretical basis of the approach and present extensive experimental results performed in both a simulated and a real robot-environment system; Behaviour Based philosophy is used to construct the whole control architecture. Obtained results show the validity of the approach.

Microarray Time Course Experiments: Finding Profiles

Time course studies with microarray techniques and experimental replicates are very useful in biomedical research. We present, in replicate experiments, an alternative approach to select and cluster genes according to a new measure for association between genes. First, the procedure normalizes and standardizes the expression profile of each gene, and then, identifies scaling parameters that will further minimize the distance between replicates of the same gene. Then, the procedure filters out genes with a flat profile, detects differences between replicates, and separates genes without significant differences from the rest. For this last group of genes, we define a mean profile for each gene and use it to compute the distance between two genes. Next, a hierarchical clustering procedure is proposed, a statistic is computed for each cluster to determine its compactness, and the total number of classes is determined. For the rest of the genes, those with significant differences between replicates, the procedure detects where the differences between replicates lie, and assigns each gene to the best fitting previously identified profile or defines a new profile. We illustrate this new procedure using simulated data and a representative data set arising from a microarray experiment with replication, and report interesting results.

Extreme Observations in Biomedical Data

We present a new procedure to detect extreme observations which can be applied to low or high-dimensional data sets. Continuous features, a known underlying distribution or parameter estimations are not required. The procedure offers a ranking by assigning a value to each observation that reflects its degree of outlyingness. A short computation time is needed.

Identifying Extreme Observations, Outliers and Noise in Clinical and Genetic Data

Background: Currently, a major challenge is the treatment and interpretation of actual data. Data sets are often high-dimensional, have small number of observations and are noisy. Furthermore, in recent years, many approaches have been suggested for integrating continuous with categorical/ordinal data, in order to capture the information which is lost in independent studies. Objective: The aim of this paper is to develop a statistical tool for the detection of outliers adapted to any kind of features and to high-dimensional data. Method: Data is an nxp data matrix (n<<p) where the rows correspond to observations, the columns correspond to any kind of features. The new procedure is based on the distances between all the observations and offers a ranking by assigning each observation a value reflecting its degree of outlyingness. It was evaluated by simulation and by using actual data from clinical and genetic studies. Results: The simulation studies showed that the procedure correctly identified the outliers, was robust in front of the masking effect and was useful in the detection of noise. With simulated two-sample microarray data sets, it correctly detected outliers, especially when many genes showed increased expression only for a small number of samples. The method was applied to adult lymphoid malignancies, human liver cancer and autism multiplex families’ data sets obtaining good and valuable results. Conclusion: The actual and simulation studies show the efficiency of the procedure, offering a useful tool in those applications where the detection of outliers or noise is relevant. A R T I C L E H I S T O R Y Received: June 4, 2015 Revised: July 24, 2015 Accepted: July 26, 2015 DOI: 10.2174/15748936116661606061610 31

Diagnosis using clinical/pathological and molecular information.

In diagnosis and classification diseases multiple outcomes, both molecular and clinical/pathological are routinely gathered on patients. In recent years, many approaches have been suggested for integrating gene expression (continuous data) with clinical/pathological data (usually categorical and ordinal data). This new area of research integrates both clinical and genomic data in order to improve our knowledge about diseases, and to capture the information which is lost in independent clinical or genomic studies. The related metric scaling distance is a not well-known, but very valuable distance to integrate clinical/pathological and molecular information. In this article, we present the use of the related metric scaling distance in biomedical research. We describe how this distance works, and we also explain why it may sometimes be preferred. We discuss the choice of the related metric scaling distance and compare it with other proximity measures to include both clinical and genetic information. Furthermore, we comment the choice of the related metric scaling distance when classical clustering or discriminant analysis based on distances are performed and compare the results with more complex cluster or discriminant procedures specially constructed for integrating clinical and molecular information. The use of the related metric scaling distance is illustrated on simulated experimental and four real data sets, a heart disease, and three cancer studies. The results present the flexibility and availability of this distance which gives competitive results.