Spectral analysis of gene expression profiles using gene networks

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. Here we propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We applied the method to the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. It performed at least as well as the usual classification but provides much more biologically relevant results and allows a direct biological interpretation

Calcul du rayon de courbure d'une séquence d'ADN

Stage de DEA. Rapport de stage.Pour certains gènes, un facteur important dans la formation et la stabilisation ADN-protéine est la courbure de l'ADN. L'objectif de ce stage est de calculer la courbure de l'ADN avec des techniques issues de la géométrie discrète

Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data

A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth

Determining Frequent Patterns of Copy Number Alterations in Cancer

Cancer progression is often driven by an accumulation of genetic changes but also accompanied by increasing genomic instability. These processes lead to a complicated landscape of copy number alterations (CNAs) within individual tumors and great diversity across tumor samples. High resolution array-based comparative genomic hybridization (aCGH) is being used to profile CNAs of ever larger tumor collections, and better computational methods for processing these data sets and identifying potential driver CNAs are needed. Typical studies of aCGH data sets take a pipeline approach, starting with segmentation of profiles, calls of gains and losses, and finally determination of frequent CNAs across samples. A drawback of pipelines is that choices at each step may produce different results, and biases are propagated forward. We present a mathematically robust new method that exploits probe-level correlations in aCGH data to discover subsets of samples that display common CNAs. Our algorithm is related to recent work on maximum-margin clustering. It does not require pre-segmentation of the data and also provides grouping of recurrent CNAs into clusters. We tested our approach on a large cohort of glioblastoma aCGH samples from The Cancer Genome Atlas and recovered almost all CNAs reported in the initial study. We also found additional significant CNAs missed by the original analysis but supported by earlier studies, and we identified significant correlations between CNAs

JAK2/IDH-mutant–driven myeloproliferative neoplasm is sensitive to combined targeted inhibition

Patients with myeloproliferative neoplasms (MPNs) frequently progress to bone marrow failure or acute myeloid leukemia (AML), and mutations in epigenetic regulators such as the metabolic enzyme isocitrate dehydrogenase (IDH) are associated with poor outcomes. Here, we showed that combined expression of Jak2V617Fand mutant IDH1R132Hor Idh2R140Q induces MPN progression, alters stem/progenitor cell function, and impairs differentiation in mice. Jak2V617FIdh2R140Q–mutant MPNs were sensitive to small-molecule inhibition of IDH. Combined inhibition of JAK2 and IDH2 normalized the stem and progenitor cell compartments in the murine model and reduced disease burden to a greater extent than was seen with JAK inhibition alone. In addition, combined JAK2 and IDH2 inhibitor treatment also reversed aberrant gene expression in MPN stem cells and reversed the metabolite perturbations induced by concurrent JAK2 and IDH2 mutations. Combined JAK2 and IDH2 inhibitor therapy also showed cooperative efficacy in cells from MPN patients with both JAK2mutand IDH2mutmutations. Taken together, these data suggest that combined JAK and IDH inhibition May offer a therapeutic advantage in this high-risk MPN subtype.Damon Runyon Cancer Research Foundation (DRG-2241-15)Howard Hughes Medical Institute (Faculty Scholars Award)Stand Up To CancerNational Cancer Institute (U.S.) (P50CA165962)National Cancer Institute (U.S.) (P30CA14051)Koch Institute for Integrative Cancer Research ( Dana-Farber Harvard Cancer Center Bridge Project)Leukemia & Lymphoma Society of America. Specialized Center of Research (SCOR) ProgramNational Institutes of Health (U.S.) (grant U54OD020355-01)National Institutes of Health (U.S.) (grant NCI R01CA172636)National Institutes of Health (U.S.) (grant R35CA197594)National Cancer Institute (U.S.) (Cancer Center Support Grant (P30 CA008747)

Classification of microarray data using gene networks

BACKGROUND: Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. RESULTS: We propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We illustrate the method with the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. CONCLUSION: Including a priori knowledge of a gene network for the analysis of gene expression data leads to good classification performance and improved interpretability of the results

Human OTULIN haploinsufficiency impairs cell-intrinsic immunity to staphylococcal alpha-toxin

The molecular basis of interindividual clinical variability upon infection with Staphylococcus aureus is unclear. We describe patients with haploinsufficiency for the linear deubiquitinase OTULIN, encoded by a gene on chromosome 5p. Patients suffer from episodes of life-threatening necrosis, typically triggered by S. aureus infection. The disorder is phenocopied in patients with the 5p- (Cri-du-Chat) chromosomal deletion syndrome. OTULIN haploinsufficiency causes an accumulation of linear ubiquitin in dermal fibroblasts, but tumor necrosis factor receptor-mediated nuclear factor kappa B signaling remains intact. Blood leukocyte subsets are unaffected. The OTULIN-dependent accumulation of caveolin-1 in dermal fibroblasts, but not leukocytes, facilitates the cytotoxic damage inflicted by the staphylococcal virulence factor alpha-toxin. Naturally elicited antibodies against alpha-toxin contribute to incomplete clinical penetrance. Human OTULIN haploinsufficiency underlies life-threatening staphylococcal disease by disrupting cell-intrinsic immunity to alpha-toxin in nonleukocytic cells.Peer reviewe