Inferring Regulatory Networks from Expression Data Using Tree-Based Methods

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions

First report of AURORA, the breast international group (BIG) molecular screening initiative for metastatic breast cancer (MBC) patients (pts)

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Predicting gene essentiality from expression patterns in Escherichia coli

peer reviewedEssential genes are genes whose loss of function causes lethal- ity. In the case of pathogen organisms, the identification of these genes is of considerable interest, as they provide targets for the development of novel antibiotics. Computational analyses have revealed that the posi- tions of the encoded proteins in the protein-protein interaction network can help predict essentiality, but this type of data is not always avail- able. In this work, we investigate prediction of gene essentiality from expression data only, using a genome-wide compendium of expression patterns in the bacterium Escherichia coli, by using single decision trees and random forests. We first show that, based on the original expression measurements, it is possible to identify essential genes with good accu- racy. Next, we derive, for each gene, higher level features such as average, standard deviation and entropy of its expression pattern, as well as fea- tures related to the correlation of expression patterns between genes. We find that essentiality may actually be predicted based only on the two most relevant ones among these latter.We discuss the biological meaning of these observations

Supervised learning with decision tree-based methods in computational and systems biology

At the intersection between artificial intelligence and statistics, supervised learning provides algorithms to automatically build predictive models only from observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the paper is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the paper provides a survey of their applications in the context of computational and systems biology. The supplementary material provides information about various non-standard extensions of the decision tree-based approach to modeling, some practical guidelines for the choice of parameters and algorithm variants depending on the practical ob jectives of their application, pointers to freely accessible software packages, and a brief primer going through the different manipulations needed to use the tree-induction packages available in the R statistical tool

Identification of a microRNA landscape targeting the PI3K/Akt signaling pathway in inflammation-induced colorectal carcinogenesis

peer reviewedInflammation can contribute to tumor formation; however, markers that predict progression are still lacking. In the present study, the well-established azoxymethane (AOM)/dextran sulfate sodium (DSS)-induced mouse model of colitis-associated cancer was used to analyze microRNA (miRNA) modulation accompanying inflammation-induced tumor development and to determine whether inflammation-triggered miRNA alterations affect the expression of genes or pathways involved in cancer. A miRNA microarray experiment was performed to establish miRNA expression profiles in mouse colon at early and late time points during inflammation and/or tumor growth. Chronic inflammation and carcinogenesis were associated with distinct changes in miRNA expression. Nevertheless, prediction algorithms of miRNA-mRNA interactions and computational analyses based on ranked miRNA lists consistently identified putative target genes that play essential roles in tumor growth or that belong to key carcinogenesis-related signaling pathways. We identified PI3K/Akt and the insulin growth factor-1 (IGF-1) as major pathways being affected in the AOM/DSS model. DSS-induced chronic inflammation downregulates miR-133a and miR-143/145, which is reportedly associated with human colorectal cancer and PI3K/Akt activation. Accordingly, conditioned medium from inflammatory cells decreases the expression of these miRNA in colorectal adenocarcinoma Caco-2 cells. Overexpression of miR-223, one of the main miRNA showing strong upregulation during AOM/DSS tumor growth, inhibited Akt phosphorylation and IGF-1R expression in these cells. Cell sorting from mouse colons delineated distinct miRNA expression patterns in epithelial and myeloid cells during the periods preceding and spanning tumor growth. Hence, cell-type-specific miRNA dysregulation and subsequent PI3K/Akt activation may be involved in the transition from intestinal inflammation to cancer

Various Activating TIE2 Tyrosine Kinase Domain Mutations, Including the Recurrent R849W Substitution, Cause Cutaneomucosal Venous Malformation (VMCM) in a Paradominant Fashion.

Venous malformations are the most frequent vascular anomalies among patients in specialized centers, characterized by localized bluish lesions in the skin and the mucosae. While these are predominantly sporadic in nature, 1 to 2% occur as an autosomal dominantly inherited trait termed cutaneomucosal venous malformation (VMCM). The angiopoeitin receptor TIE2 has been identified as the causative gene for VMCM in five families. In each family, one of two specific substitutions (R849W or Y897S) in the kinase domain of TIE2 co-segregates with the disorder. These mutations result in increased ligand-independant phosphorylation of the receptor, and altered downstream signaling. Here, we studied twelve new families with VMCM: six bear the R849W substitution, five have a novel mutation in the tyrosine kinase domain and one in the carboxy-terminal end of the receptor. Overexpression of each of these mutants in COS-7 cells resulted in ligand independent hyper-phosphorylation of the receptor, suggesting that this is a general feature of VMCM-causing TIE2 mutations. Interestingly, we also discovered a somatic deletion in a resected VM tissue sample from one patient with the inherited R849W mutation. This somatic alteration partially deletes the ligand binding domain of the receptor in the allele that does not bear the R849W mutation. This is the first report of a double-hit mutation in VMCM. Moreover, we demonstrate that the somatic deletion does not result in hyper-phosphorylation, nor does it cause an increase in the phosphorylation of the R849W allele. Thus, this deletion rather seems to cause an elimination of the wild-type allele that is able to protect from the deleterious effects of the mutant, inherited allele. In conclusion, the focal development of VMCM is likely due to the combination of various predisposing germline mutations that cause hyperphosphorylation of the receptor, and a somatic second-hit, hallmarks of paradominant inheritance. ([email protected]

Myelin-Derived Lipids Modulate Macrophage Activity by Liver X Receptor Activation

Multiple sclerosis is a chronic, inflammatory, demyelinating disease of the central nervous system in which macrophages and microglia play a central role. Foamy macrophages and microglia, containing degenerated myelin, are abundantly found in active multiple sclerosis lesions. Recent studies have described an altered macrophage phenotype after myelin internalization. However, it is unclear by which mechanisms myelin affects the phenotype of macrophages and how this phenotype can influence lesion progression. Here we demonstrate, by using genome wide gene expression analysis, that myelin-phagocytosing macrophages have an enhanced expression of genes involved in migration, phagocytosis and inflammation. Interestingly, myelin internalization also induced the expression of genes involved in liver-X-receptor signaling and cholesterol efflux. In vitro validation shows that myelin-phagocytosing macrophages indeed have an increased capacity to dispose intracellular cholesterol. In addition, myelin suppresses the secretion of the pro-inflammatory mediator IL-6 by macrophages, which was mediated by activation of liver-X-receptor b. Our data show that myelin modulates the phenotype of macrophages by nuclear receptor activation, which may subsequently affect lesion progression in demyelinating diseases such as multiple sclerosis