Inferring gene networks using a sparse factor model approach, Statistical Learning and Data Science

The availability of genome-wide expression data to complement the measurements of a phenotypic trait opens new opportunities for identifying biologic processes and genes that are involved in trait expression. Usually differential analysis is a preliminary step to identify the key biological processes involved in the variability of the trait of interest. However, this variability shall be viewed as resulting from a complex combination of genes individual contributions. In other words, exploring the interactions between genes viewed in a network structure which vertices are genes and edges stand for inhibition or activation connections gives much more insight on the internal structure of expression profiles. Many currently available solutions for network analysis have been developed but an efficient estimation of the network from high-dimensional data is still a questioning issue. Extending the idea introduced for differential analysis by Friguet et al. (2009) [1] and Blum et al. (2010) [2], we propose to take advantage of a factor model structure to infer gene networks. This method shows good inferential properties and also allows an efficient testing strategy for the significance of partial correlations, which provides an interesting tool to explore the community structure of the networks. We illustrate the performance of our method comparing it with competitors through simulation experiments. Moreover, we apply our method in a lipid metabolism study that aims at identifying gene networks underlying the fatness variability in chickens

DECONbench: a benchmarking platform dedicated to deconvolution methods for tumor heterogeneity quantification

Quantifcation of tumor heterogeneity is essential to better understand cancer progression and to adapt therapeutic treatments to patient specifcities. Bioinformatic tools to assess the diferent cell populations from single-omic datasets as bulk transcriptome or methylome samples have been recently developed, including reference-based and reference-free methods. Improved methods using multi-omic datasets are yet to be developed in the future and the community would need systematic tools to perform a comparative evaluation of these algorithms on controlled data

A factor model to analyze heterogeneity in gene expression

<p>Abstract</p> <p>Background</p> <p>Microarray technology allows the simultaneous analysis of thousands of genes within a single experiment. Significance analyses of transcriptomic data ignore the gene dependence structure. This leads to correlation among test statistics which affects a strong control of the false discovery proportion. A recent method called FAMT allows capturing the gene dependence into factors in order to improve high-dimensional multiple testing procedures. In the subsequent analyses aiming at a functional characterization of the differentially expressed genes, our study shows how these factors can be used both to identify the components of expression heterogeneity and to give more insight into the underlying biological processes.</p> <p>Results</p> <p>The use of factors to characterize simple patterns of heterogeneity is first demonstrated on illustrative gene expression data sets. An expression data set primarily generated to map QTL for fatness in chickens is then analyzed. Contrarily to the analysis based on the raw data, a relevant functional information about a QTL region is revealed by factor-adjustment of the gene expressions. Additionally, the interpretation of the independent factors regarding known information about both experimental design and genes shows that some factors may have different and complex origins.</p> <p>Conclusions</p> <p>As biological information and technological biases are identified in what was before simply considered as statistical noise, analyzing heterogeneity in gene expression yields a new point of view on transcriptomic data.</p

Combined MEK and PI3K/p110β Inhibition as a Novel Targeted Therapy for Malignant Mesothelioma Displaying Sarcomatoid Features

Among malignant mesotheliomas (MM), the sarcomatoid subtype is associated with higher chemoresistance and worst survival. Due to its low incidence, there has been little progress in the knowledge of the molecular mechanisms associated with sarcomatoid MM, which might help to define novel therapeutic targets. In this work, we show that loss of PTEN expression is frequent in human sarcomatoid MM and PTEN expression levels are lower in sarcomatoid MM than in the biphasic and epithelioid subtypes. Combined Pten and Trp53 deletion in mouse mesothelium led to nonepithelioid MM development. In Pten;Trp53-null mice developing MM, the Gαi2-coupled receptor subunit activated MEK/ERK and PI3K, resulting in aggressive, immune-suppressed tumors. Combined inhibition of MEK and p110β/PI3K reduced mouse tumor cell growth in vitro. Therapeutic inhibition of MEK and p110β/PI3K using selumetinib (AZD6244, ARRY-142886) and AZD8186, two drugs that are currently in clinical trials, increased the survival of Pten;Trp53-null mice without major toxicity. This drug combination effectively reduced the proliferation of primary cultures of human pleural (Pl) MM, implicating nonepithelioid histology and high vimentin, AKT1/2, and Gαi2 expression levels as predictive markers of response to combined MEK and p110β/PI3K inhibition. Our findings provide a rationale for the use of selumetinib and AZD8186 in patients with MM with sarcomatoid features. This constitutes a novel targeted therapy for a poor prognosis and frequently chemoresistant group of patients with MM, for whom therapeutic options are currently lacking.[Significance] Mesothelioma is highly aggressive; its sarcomatoid variants have worse prognosis. Building on a genetic mouse model, a novel combination therapy is uncovered that is relevant to human tumors.This work was supported, in part, by grants from Asociación Española Contra el Cáncer (F.X. Real), Spanish Ministry of Economy and Competitivity, Plan Estatal de I+D+I 2013-2016, ISCIII (FIS PI15/00045 to A. Carnero), RTICC (Instituto de Salud Carlos III, grants RD12/0036/0034 to F.X. Real and A. Carnero, respectively), and CIBERONC (CB16/12/00453 and CD16/12/00275 to F.X. Real and A. Carnero, respectively), cofunded by FEDER from Regional Development European Funds (European Union) and Inserm (Institut national de la santé et de la recherche médicale). M. Marqués was supported by a Sara Borrell Fellowship from Instituto de Salud Carlos III. CNIO is supported by Ministerio de Ciencia, Innovación y Universidades as a Centro de Excelencia Severo Ochoa SEV-2015-0510

Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort

Background: The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study.Results: We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota–metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state.Conclusions: Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host–gut microbiota relationships

Complex trait subtypes identification using transcriptome profiling reveals an interaction between two QTL affecting adiposity in chicken

<p>Abstract</p> <p>Background</p> <p>Integrative genomics approaches that combine genotyping and transcriptome profiling in segregating populations have been developed to dissect complex traits. The most common approach is to identify genes whose eQTL colocalize with QTL of interest, providing new functional hypothesis about the causative mutation. Another approach includes defining subtypes for a complex trait using transcriptome profiles and then performing QTL mapping using some of these subtypes. This approach can refine some QTL and reveal new ones.</p> <p>In this paper we introduce Factor Analysis for Multiple Testing (FAMT) to define subtypes more accurately and reveal interaction between QTL affecting the same trait. The data used concern hepatic transcriptome profiles for 45 half sib male chicken of a sire known to be heterozygous for a QTL affecting abdominal fatness (AF) on chromosome 5 distal region around 168 cM.</p> <p>Results</p> <p>Using this methodology which accounts for hidden dependence structure among phenotypes, we identified 688 genes that are significantly correlated to the AF trait and we distinguished 5 subtypes for AF trait, which are not observed with gene lists obtained by classical approaches. After exclusion of one of the two lean bird subtypes, linkage analysis revealed a previously undetected QTL on chromosome 5 around 100 cM. Interestingly, the animals of this subtype presented the same q paternal haplotype at the 168 cM QTL. This result strongly suggests that the two QTL are in interaction. In other words, the "q configuration" at the 168 cM QTL could hide the QTL existence in the proximal region at 100 cM. We further show that the proximal QTL interacts with the previous one detected on the chromosome 5 distal region.</p> <p>Conclusion</p> <p>Our results demonstrate that stratifying genetic population by molecular phenotypes followed by QTL analysis on various subtypes can lead to identification of novel and interacting QTL.</p

Gene expression profiling of patient‐derived pancreatic cancer xenografts predicts sensitivity to the BET bromodomain inhibitor JQ1: implications for individualized medicine efforts

Abstract c‐MYC controls more than 15% of genes responsible for proliferation, differentiation, and cellular metabolism in pancreatic as well as other cancers making this transcription factor a prime target for treating patients. The transcriptome of 55 patient‐derived xenografts show that 30% of them share an exacerbated expression profile of MYC transcriptional targets (MYC‐high). This cohort is characterized by a high level of Ki67 staining, a lower differentiation state, and a shorter survival time compared to the MYC‐low subgroup. To define classifier expression signature, we selected a group of 10 MYC target transcripts which expression is increased in the MYC‐high group and six transcripts increased in the MYC‐low group. We validated the ability of these markers panel to identify MYC‐high patient‐derived xenografts from both: discovery and validation cohorts as well as primary cell cultures from the same patients. We then showed that cells from MYC‐high patients are more sensitive to JQ1 treatment compared to MYC‐low cells, in monolayer, 3D cultured spheroids and in vivo xenografted tumors, due to cell cycle arrest followed by apoptosis. Therefore, these results provide new markers and potentially novel therapeutic modalities for distinct subgroups of pancreatic tumors and may find application to the future management of these patients within the setting of individualized medicine clinics

Analyse génétique d'un caractère complexe à l'aide de données transcriptomiques: Apport de la modélisation de réseaux de gènes

For the past ten years, many projects on functional genomics have been developed with the aim of better understanding complex traits of socio-economical interest in order to better control them. These traits are called complex traits because they are controlled by multiple factors: genetics, food, health status... One strategy commonly used to analyze such traits involves localizing QTL (Quantitative Trait Loci), i.e. chromosomic regions controlling their variability. In parallel to this work, new technologies (microarrays) have emerged, which allow the high throughput measurement of gene expression through the quanti cation of transcripts (transcriptomic data). Genetical genomic approaches combining functional genomic methods and QTL mapping have been developed with the aim of facilitating the identi cation of causal mutations underlying detected QTL. In this new context, an original aspect of my thesis is to take into account the heterogeneity existing in transcriptomic data and due to known or unknown factors independently of the trait of interest. Through several studies, we show that signal heterogeneity or expression pro le heterogeneity most of the time hides the detection of genes or genome regions associated with the trait of interest. A second aspect of the thesis concerns gene network inference using transcriptomic data. Gene network modeling is a promising solution to better understand regulatory mechanisms of genes involved in the trait variability. We develop here new methods to estimate such structures based on a factor model. These methods are applied in the context of the genetic analysis of a complex trait. They allow characterizing key regulators and biological processes underlying the trait variability, giving new functional information about the sought causal mutations.Depuis une dizaine d'années, de nombreux projets de génomique fonctionnelle se sont développés, avec pour objectif de mieux comprendre des caractères complexes d'intérêt socio- économique en vue de mieux les maîtriser. Ces caractères sont dits complexes car contrôles par de multiples facteurs : g en étique, alimentation, état de santé... Une stratégie couramment utilisée pour l'étude de tels caractères consiste à localiser des QTL (Quantitative Trait Loci), c'est- à-dire des régions chromosomiques contrôlant leur variabilité. Parallèlement au développement de ces travaux, de nouvelles technologies ont émergé (puces a ADN) permettant de mesurer a haut débit l'expression de l'ensemble des gènes d'un organisme via la quanti cation des transcrits (données transcriptomiques). Des stratégies dites de "génétique génomique" combinant des approches de génomique fonctionnelle et de cartographie de QTL ont alors et e développées avec comme objectif de faciliter l'identification des mutations causales sous-jacentes aux QTL détectés. Dans ce contexte nouveau, une originalité de la thèse est de prendre en compte l'hétérogénéité existante dans les données transcriptomiques et causée par des facteurs connus ou inconnus indépendamment au caractère d'intérêt. Au travers de plusieurs études, on montre que l’hétérogénéité du signal d'expression ou des profils d'expression masque bien souvent la détection des gènes et des régions du génome liés au caractère d'intérêt. Un deuxième volet de la thèse concerne l'inférence de réseaux de gènes à partir de données transcriptomiques. La modélisation de réseaux géniques semble être une solution prometteuse pour mieux comprendre les mécanismes de régulation des gènes impliqués dans la variabilité d'un caractère. Nous développons ici de nouvelles méthodes pour l'estimation de telles structures basées sur un modèle a facteurs. Ces méthodes sont appliquées dans le cadre de l'analyse génétique d'un caractère complexe, et permettent de caractériser les régulateurs cl es et les processus biologiques sous-jacents, apportant de nouvelles informations fonctionnelles quant aux mutations causales recherchées