Information sciences to study biological systems (example of the aging of the immune system)

Le laboratoire i3 et le laboratoire LGIPH, utilisent des approches à haut débit pour l’étude du système immunitaire et ces disfonctionnements. Des limites ont été observées quant à l’utilisation des approches classiques pour l’annotation des signatures d’expression des gènes. L’objectif principal a été de développer une approche d’annotation pour répondre à ce besoin. L’approche que nous avons développée est une approche basée sur la contextualisation des gènes et de leurs produits puis sur la modélisation des voies biologiques pour la production de bases de connaissances pour l’étude de l’expression des gènes. Nous définissons ici un contexte d’expression des gènes comme suit : population cellulaire+compartiment anatomique+état pathologique. Pour connaitre ces contextes, nous avons opté pour la fouille de la littérature et nous avons développé un package Python, qui permet d’annoter les textes automatiquement en fonction de trois ontologies choisies en fonction de notre définition du contexte. Nous montrons ici que notre package a des performances meilleures que un outil de référence. Nous avons l’avons utilisé pour le criblage d’un corpus sur le vieillissement du système immunitaire dont on présente ici les résultats. Pour la modélisation des voies biologiques nous avons développé en collaboration avec le LIPAH une méthode de modélisation basée sur un algorithme génétique qui permet de combiner les résultats de mesure de la proximité sémantique sur la base des annotations des gènes et les données d’interactions. Nous avons réussis retrouver des réseaux de références avec un taux d’erreur de 0,47.High-throughput experimental approaches for gene expression study involve several processing steps for the quantification, the annotation and interpretation of the results. The i3 lab and the LGIPH, applies these approaches in various experimental setups. However, limitations have been observed when using conventional approaches for annotating gene expression signatures. The main objective of this thesis was to develop an alternative annotation approach to overcome this problem. The approach we have developed is based on the contextualization of genes and their products, and then biological pathways modeling to produce a knowledge base for the study of gene expression. We define a gene expression context as follows: cell population+ anatomical compartment+ pathological condition. For the production of gene contexts, we have opted for the massive screening of literature. We have developed a Python package, which allows annotating the texts according to three ontologies chosen according to our definition of the context. We show here that it ensures better performance for text annotation the reference tool. We used our package to screen an aging immune system text corpus. The results are presented here. To model the biological pathways we have developed, in collaboration with the LIPAH lab a modeling method based on a genetic algorithm that allows combining the results semantics proximity using the Biological Process ontology and the interactions data from db-string. We were able to find networks with an error rate of 0.47

Sciences de l'information pour l'étude des systèmes biologiques (exemple du vieillissement du système immunitaire)

High-throughput experimental approaches for gene expression study involve several processing steps for the quantification, the annotation and interpretation of the results. The i3 lab and the LGIPH, applies these approaches in various experimental setups. However, limitations have been observed when using conventional approaches for annotating gene expression signatures. The main objective of this thesis was to develop an alternative annotation approach to overcome this problem. The approach we have developed is based on the contextualization of genes and their products, and then biological pathways modeling to produce a knowledge base for the study of gene expression. We define a gene expression context as follows: cell population+ anatomical compartment+ pathological condition. For the production of gene contexts, we have opted for the massive screening of literature. We have developed a Python package, which allows annotating the texts according to three ontologies chosen according to our definition of the context. We show here that it ensures better performance for text annotation the reference tool. We used our package to screen an aging immune system text corpus. The results are presented here. To model the biological pathways we have developed, in collaboration with the LIPAH lab a modeling method based on a genetic algorithm that allows combining the results semantics proximity using the Biological Process ontology and the interactions data from db-string. We were able to find networks with an error rate of 0.47.Le laboratoire i3 et le laboratoire LGIPH, utilisent des approches à haut débit pour l’étude du système immunitaire et ces disfonctionnements. Des limites ont été observées quant à l’utilisation des approches classiques pour l’annotation des signatures d’expression des gènes. L’objectif principal a été de développer une approche d’annotation pour répondre à ce besoin. L’approche que nous avons développée est une approche basée sur la contextualisation des gènes et de leurs produits puis sur la modélisation des voies biologiques pour la production de bases de connaissances pour l’étude de l’expression des gènes. Nous définissons ici un contexte d’expression des gènes comme suit : population cellulaire+compartiment anatomique+état pathologique. Pour connaitre ces contextes, nous avons opté pour la fouille de la littérature et nous avons développé un package Python, qui permet d’annoter les textes automatiquement en fonction de trois ontologies choisies en fonction de notre définition du contexte. Nous montrons ici que notre package a des performances meilleures que un outil de référence. Nous avons l’avons utilisé pour le criblage d’un corpus sur le vieillissement du système immunitaire dont on présente ici les résultats. Pour la modélisation des voies biologiques nous avons développé en collaboration avec le LIPAH une méthode de modélisation basée sur un algorithme génétique qui permet de combiner les résultats de mesure de la proximité sémantique sur la base des annotations des gènes et les données d’interactions. Nous avons réussis retrouver des réseaux de références avec un taux d’erreur de 0,47

Caractérisation et comparaison fonctionnelle de listes de gènes : apport de la sémantique.

International audienc

Genetic Algorithm for Community Detection in Biological Networks

International audienceWe are interested in the detection of communities in biological networks. We focus more precisely on gene interaction networks. They represent protein-protein or gene-gene interactions. A community in such networks corresponds to a set of proteins or genes that collaborate at the same cellular function. Our goal is to identify such network or community from gene annotation sources such as Gene Ontology (GO). In this paper, we propose a Genetic Algorithm (GA) based approach to discover communities in a gene interaction network. Special solution coding and mutation operator are introduced. Otherwise, we propose a specific fitness function based on similarity measure and interaction value between genes. Experiments on real data extracted from the well-known Kyoto Encyclopedia of Genes and Genomes (KEGG) database show the ability of the proposed method to successfully detect existing or even new communities

OntoContext, a new python package for gene contextualization based on the annotation of biomedical texts

Motivation : The automatic mining for bibliography exploitation in given contexts is a challenge according to the increasing number of scientific publications and new concepts. Several indexing systems were developed for biomedical literature. However, such systems have failed to produce contextualised research of genes and proteins and automatically group texts according to shared concepts. In this paper, we present OntoContext, a contextualization system crossing the use of biomedical ontologies to annotate texts containing terms related to cell populations, anatomical locations and diseases and to extract gene, RNA or protein names in these contexts. Results : OntoContext, a new python package contains two modules. The “annot” module for “annotation” function, is based on combination of morphosyntactic labelling and exact matching and on dictionaries derived from the Cell Ontology, the UBERON Ontology (anatomical context), the Human Disease Ontology and geniatagger, (which contains particular tags for gene-related names). The “annot” output is used as input for the second module “crisscross” generating lists of gene-related names obtained by crossing annotations from the three mentioned ontologies. OntoContext showed better performances than NCBO Annotator after evaluation on two text corpuses. OntoContext is freely available in the pypi

PTEN Loss and Cyclin A2 Upregulation Define a PI3K/AKT Pathway Activation in Helicobacter pylori–induced MALT and DLBCL Gastric Lymphoma With Features of MALT

International audienceHelicobacter pylori infection is strongly associated with primary gastric diseases, such as extranodal mucosa-associated lymphoid tissue (MALT) lymphoma, diffuse large B-cell lymphoma (DLBCL) with histologic evidence of MALT origin, and gastric carcinoma. The cytotoxin-associated gene A (CagA) protein behaves as a bacterial oncoprotein, promoting tumorigenesis via dysregulation of the phosphatidylinositol 3-kinase/AKT pathway (PI3K/AKT). We investigated the molecular mechanisms of PI3K/AKT pathway dysregulation in H. pylori-induced MALT and DLBCL gastric lymphoma. Immunohistochemical assays for CagA, phospho(p)-S473-AKT, PTEN, SHIP, and cyclin A2 proteins were performed on samples from 23 patients with H. pylori-positive MALT lymphoma and 16 patients with H. pylori-positive gastric DLBCL. We showed that CagA localization is correlated with the activation of the AKT pathway in both MALT and DLBCL lymphoma cells. Interestingly, we found a close association between the loss of PTEN, the overexpression of cyclin A2, and the phosphorylation of AKT in gastric MALT and DLBCL tumor cells

Involvement of IL17A, IL17F and IL23R Polymorphisms in Colorectal Cancer Therapy.

IL23/IL17 pathway plays an important role in the development of inflammatory bowel diseases (IBD). In general, the genes encoding the cytokines are genetically polymorphic and polymorphisms in genes IL23R and IL17 have been proved to be associated with its susceptibility to inflammatory diseases as well as cancer including colorectal cancer. Moreover, it has been shown that these interleukins are involved in anti-tumor or pro-tumor effects of various cancers. Previously, we showed that there is a significant association between IL17A, IL17F and IL23R polymorphisms as well as the occurrence of colorectal cancer and the clinical features of the disease. The purpose of the present work is to investigate an association between IL17A, IL17F and IL23R polymorphisms in 102 Tunisian patients with colorectal cancer treatment. The association was analyzed by statistical tools. We found that patients with mutated genotypes of IL17A G197A SNP could be a risk factor for the inefficiency of chemotherapy and radiotherapy. Unlike IL17F variant, patients with wild type genotypes require surgery and adjuvant chemotherapy. On the one hand, we found no evidence that supports a significant association between IL23R polymorphism and the combined genotypes of these three genes and the colorectal cancer treatment. On the other hand, we showed that there is an important interaction between IL17A/IL17F polymorphisms and the stage of the disease as well as its treatment. Finally, patients with IL17F wild type genotype highlighted that there is a valid longer OS without all treatments and with radiotherapy and a neoadjuvant chemotherapy. In contrast, we observed that there are no relationships between IL17A, IL23R and the survival of these patients neither with nor without the treatment. Our results suggest that polymorphisms in IL17A and IL17F genes may be a predictive source of colorectal cancer therapy type. Therefore, IL17F may serve as an independent prognostic factor for overall survival in patients with colorectal cancer

Association between combined genotype of IL23R/IL17A and IL17F polymorphisms and CRC treatments.

<p>Association between combined genotype of IL23R/IL17A and IL17F polymorphisms and CRC treatments.</p

IL17F genotypes impact on overall survival stratified by surgery.

<p>(A) palliative surgery and (B) radical surgery; in CRC patients according to IL17F genotype (IL17F AA vs. IL17F AGGG). P-values from the log-rank test are indicated. OS: overall survival.</p

IL17F genotypes impact on overall survival stratified by neoadjuvant chemotherapy.

<p>(A) withoutneoadjuvant chemotherapy (<b>p = 0,003</b>) and (B) with neoadjuvant chemotherapy (<b>p = 0,026</b>); in CRC patients according to IL17F genotype (IL17F AA vs. IL17F AGGG). P-values from the log-rank test are indicated. OS: overall survival.</p