Defining a robust biological prior from Pathway Analysis to drive Network Inference

Inferring genetic networks from gene expression data is one of the most challenging work in the post-genomic era, partly due to the vast space of possible networks and the relatively small amount of data available. In this field, Gaussian Graphical Model (GGM) provides a convenient framework for the discovery of biological networks. In this paper, we propose an original approach for inferring gene regulation networks using a robust biological prior on their structure in order to limit the set of candidate networks. Pathways, that represent biological knowledge on the regulatory networks, will be used as an informative prior knowledge to drive Network Inference. This approach is based on the selection of a relevant set of genes, called the "molecular signature", associated with a condition of interest (for instance, the genes involved in disease development). In this context, differential expression analysis is a well established strategy. However outcome signatures are often not consistent and show little overlap between studies. Thus, we will dedicate the first part of our work to the improvement of the standard process of biomarker identification to guarantee the robustness and reproducibility of the molecular signature. Our approach enables to compare the networks inferred between two conditions of interest (for instance case and control networks) and help along the biological interpretation of results. Thus it allows to identify differential regulations that occur in these conditions. We illustrate the proposed approach by applying our method to a study of breast cancer's response to treatment

Agrárgazdasági Figyelő = Agricultural Economics Monitor

Gazdasági folyamatok és statisztikai eredmények Az Agrárgazdasági Kutató Intézet "Agrárgazdasági Figyelő" címmel negyedévenként áttekinti a főbb gazdasági folyamatokat és statisztikai eredményeket. A periodika négy állandó területre fokuszál: Az elmúlt negyedévben megjelent információk. Mi történt az agrárgazdaságban? Az AKI kiadványainak fontosabb megállapításai. Az agrárgazdaságot jellemző adatok ("statisztikai zsebkönyv"). Gyakran feltett kérdések (Esetenként szerepeltetjük azokat az alapkérdéseket, amelyek igénylik a közszereplők és érdeklődők tájékoztatását

Early and accurate detection of cholangiocarcinoma in patients with primary sclerosing cholangitis by methylation markers in bile

Background and Aims Primary sclerosing cholangitis (PSC) is associated with increased risk of cholangiocarcinoma (CCA). Early and accurate CCA detection represents an unmet clinical need as the majority of patients with PSC are diagnosed at an advanced stage of malignancy. In the present study, we aimed at establishing robust DNA methylation biomarkers in bile for early and accurate diagnosis of CCA in PSC. Approach and Results Droplet digital PCR (ddPCR) was used to analyze 344 bile samples from 273 patients with sporadic and PSC-associated CCA, PSC, and other nonmalignant liver diseases for promoter methylation of cysteine dioxygenase type 1, cannabinoid receptor interacting protein 1, septin 9, and vimentin. Receiver operating characteristic (ROC) curve analyses revealed high AUCs for all four markers (0.77-0.87) for CCA detection among patients with PSC. Including only samples from patients with PSC diagnosed with CCA 36 months) as controls, and remained high (83%) when only including patients with PSC and dysplasia as controls (n = 23). Importantly, the bile samples from the CCA-PSCPeer reviewe

Should We Abandon the t-Test in the Analysis of Gene Expression Microarray Data: A Comparison of Variance Modeling Strategies

High-throughput post-genomic studies are now routinely and promisingly investigated in biological and biomedical research. The main statistical approach to select genes differentially expressed between two groups is to apply a t-test, which is subject of criticism in the literature. Numerous alternatives have been developed based on different and innovative variance modeling strategies. However, a critical issue is that selecting a different test usually leads to a different gene list. In this context and given the current tendency to apply the t-test, identifying the most efficient approach in practice remains crucial. To provide elements to answer, we conduct a comparison of eight tests representative of variance modeling strategies in gene expression data: Welch's t-test, ANOVA [1], Wilcoxon's test, SAM [2], RVM [3], limma [4], VarMixt [5] and SMVar [6]. Our comparison process relies on four steps (gene list analysis, simulations, spike-in data and re-sampling) to formulate comprehensive and robust conclusions about test performance, in terms of statistical power, false-positive rate, execution time and ease of use. Our results raise concerns about the ability of some methods to control the expected number of false positives at a desirable level. Besides, two tests (limma and VarMixt) show significant improvement compared to the t-test, in particular to deal with small sample sizes. In addition limma presents several practical advantages, so we advocate its application to analyze gene expression data

Microbe-host interplay in atopic dermatitis and psoriasis

Despite recent advances in understanding microbial diversity in skin homeostasis, the relevance of microbial dysbiosis in inflammatory disease is poorly understood. Here we perform a comparative analysis of skin microbial communities coupled to global patterns of cutaneous gene expression in patients with atopic dermatitis or psoriasis. The skin microbiota is analysed by 16S amplicon or whole genome sequencing and the skin transcriptome by microarrays, followed by integration of the data layers. We find that atopic dermatitis and psoriasis can be classified by distinct microbes, which differ from healthy volunteers microbiome composition. Atopic dermatitis is dominated by a single microbe (Staphylococcus aureus), and associated with a disease relevant host transcriptomic signature enriched for skin barrier function, tryptophan metabolism and immune activation. In contrast, psoriasis is characterized by co-occurring communities of microbes with weak associations with disease related gene expression. Our work provides a basis for biomarker discovery and targeted therapies in skin dysbiosis.Peer reviewe

Méthodes statistiques pour une analyse robuste du transcriptome à travers l'intégration d'a priori biologique

Au cours de la dernière décennie, les progrès en Biologie Moléculaire ont accéléré le développement de techniques d'investigation à haut-débit. En particulier, l'étude du transcriptome a permis des avancées majeures dans la recherche médicale. Dans cette thèse, nous nous intéressons au développement de méthodes statistiques dédiées au traitement et à l'analyse de données transcriptomiques à grande échelle. Nous abordons le problème de sélection de signatures de gènes à partir de méthodes d'analyse de l'expression différentielle et proposons une étude de comparaison de différentes approches, basée sur plusieurs stratégies de simulations et sur des données réelles. Afin de pallier les limites de ces méthodes classiques qui s'avèrent peu reproductibles, nous présentons un nouvel outil, DiAMS (DIsease Associated Modules Selection), dédié à la sélection de modules de gènes significatifs. DiAMS repose sur une extension du score-local et permet l'intégration de données d'expressions et de données d'interactions protéiques. Par la suite, nous nous intéressons au problème d'inférence de réseaux de régulation de gènes. Nous proposons une méthode de reconstruction à partir de modèles graphiques Gaussiens, basée sur l'introduction d'a priori biologique sur la structure des réseaux. Cette approche nous permet d'étudier les interactions entre gènes et d'identifier des altérations dans les mécanismes de régulation, qui peuvent conduire à l'apparition ou à la progression d'une maladie. Enfin l'ensemble de ces développements méthodologiques sont intégrés dans un pipeline d'analyse que nous appliquons à l'étude de la rechute métastatique dans le cancer du sein.Recent advances in Molecular Biology have led biologists toward high-throughput genomic studies. In particular, the investigation of the human transcriptome offers unprecedented opportunities for understanding cellular and disease mechanisms. In this PhD, we put our focus on providing robust statistical methods dedicated to the treatment and the analysis of high-throughput transcriptome data. We discuss the differential analysis approaches available in the literature for identifying genes associated with a phenotype of interest and propose a comparison study. We provide practical recommendations on the appropriate method to be used based on various simulation models and real datasets. With the eventual goal of overcoming the inherent instability of differential analysis strategies, we have developed an innovative approach called DiAMS, for DIsease Associated Modules Selection. This method was applied to select significant modules of genes rather than individual genes and involves the integration of both transcriptome and protein interactions data in a local-score strategy. We then focus on the development of a framework to infer gene regulatory networks by integration of a biological informative prior over network structures using Gaussian graphical models. This approach offers the possibility of exploring the molecular relationships between genes, leading to the identification of altered regulations potentially involved in disease processes. Finally, we apply our statistical developments to study the metastatic relapse of breast cancer

Méthodes statistiques pour une analyse robuste du transcriptome à travers l'intégration d'a priori biologique

Au cours de la dernière décennie, les progrès en Biologie Moléculaire ont accéléré le développement de techniques d'investigation à haut-débit. En particulier, l'étude du transcriptome a permis des avancées majeures dans la recherche médicale. Dans cette thèse, nous nous intéressons au développement de méthodes statistiques dédiées au traitement et à l'analyse de données transcriptomiques à grande échelle. Nous abordons le problème de sélection de signatures de gènes à partir de méthodes d'analyse de l'expression différentielle et proposons une étude de comparaison de différentes approches, basée sur plusieurs stratégies de simulations et sur des données réelles. Afin de pallier les limites de ces méthodes classiques qui s'avèrent peu reproductibles, nous présentons un nouvel outil, DiAMS (DIsease Associated Modules Selection), dédié à la sélection de modules de gènes significatifs. DiAMS repose sur une extension du score-local et permet l'intégration de données d'expressions et de données d'interactions protéiques. Par la suite, nous nous intéressons au problème d'inférence de réseaux de régulation de gènes. Nous proposons une méthode de reconstruction à partir de modèles graphiques Gaussiens, basée sur l'introduction d'a priori biologique sur la structure des réseaux. Cette approche nous permet d'étudier les interactions entre gènes et d'identifier des altérations dans les mécanismes de régulation, qui peuvent conduire à l'apparition ou à la progression d'une maladie. Enfin l'ensemble de ces développements méthodologiques sont intégrés dans un pipeline d'analyse que nous appliquons à l'étude de la rechute métastatique dans le cancer du sein.Recent advances in Molecular Biology have led biologists toward high-throughput genomic studies. In particular, the investigation of the human transcriptome offers unprecedented opportunities for understanding cellular and disease mechanisms. In this PhD, we put our focus on providing robust statistical methods dedicated to the treatment and the analysis of high-throughput transcriptome data. We discuss the differential analysis approaches available in the literature for identifying genes associated with a phenotype of interest and propose a comparison study. We provide practical recommendations on the appropriate method to be used based on various simulation models and real datasets. With the eventual goal of overcoming the inherent instability of differential analysis strategies, we have developed an innovative approach called DiAMS, for DIsease Associated Modules Selection. This method was applied to select significant modules of genes rather than individual genes and involves the integration of both transcriptome and protein interactions data in a local-score strategy. We then focus on the development of a framework to infer gene regulatory networks by integration of a biological informative prior over network structures using Gaussian graphical models. This approach offers the possibility of exploring the molecular relationships between genes, leading to the identification of altered regulations potentially involved in disease processes. Finally, we apply our statistical developments to study the metastatic relapse of breast cancer.EVRY-Bib. électronique (912289901) / SudocSudocFranceF

A robust internal control for high-precision DNA methylation analyses by droplet digital PCR

Background Droplet digital PCR (ddPCR) allows absolute quantification of nucleic acids and has potential for improved non-invasive detection of DNA methylation. For increased precision of the methylation analysis, we aimed to develop a robust internal control for use in methylation-specific ddPCR. Methods Two control design approaches were tested: (a) targeting a genomic region shared across members of a gene family and (b) combining multiple assays targeting different pericentromeric loci on different chromosomes. Through analyses of 34 colorectal cancer cell lines, the performance of the control assay candidates was optimized and evaluated, both individually and in various combinations, using the QX200™ droplet digital PCR platform (Bio-Rad). The best-performing control was tested in combination with assays targeting methylated CDO1, SEPT9, and VIM. Results A 4Plex panel consisting of EPHA3, KBTBD4, PLEKHF1, and SYT10 was identified as the best-performing control. The use of the 4Plex for normalization reduced the variability in methylation values, corrected for differences in template amount, and diminished the effect of chromosomal aberrations. Positive Droplet Calling (PoDCall), an R-based algorithm for standardized threshold determination, was developed, ensuring consistency of the ddPCR results. Conclusion Implementation of a robust internal control, i.e., the 4Plex, and an algorithm for automated threshold determination, PoDCall, in methylation-specific ddPCR increase the precision of DNA methylation analysis

Spatial analysis and CD25-expression identify regulatory T cells as predictors of a poor prognosis in colorectal cancer

Regulatory T cells (Tregs) are a heterogeneous cell population that can either suppress or stimulate immune responses. Tumor-infiltrating Tregs are associated with an adverse outcome from most cancer types, but have generally been found to be associated with a good prognosis in colorectal cancer (CRC). We investigated the prognostic heterogeneity of Tregs in CRC by co-expression patterns and spatial analyses with diverse T cell markers, using multiplex fluorescence immunohistochemistry and digital image analysis in two consecutive series of primary CRCs (total n = 1720). Treg infiltration in tumors, scored as FOXP3+ or CD4+/CD25+/FOXP3+ (triple-positive) cells, was strongly correlated to the overall amount of CD3+ and CD8+ T cells, and consequently associated with a favorable 5-year relapse-free survival rate among patients with stage I–III CRC who underwent complete tumor resection. However, high relative expression of the activation marker CD25 in triple-positive Tregs was independently associated with an adverse outcome in a multivariable model incorporating clinicopathological and known molecular prognostic markers (hazard ratio = 1.35, p = 0.028). Furthermore, spatial marker analysis based on Voronoi diagrams and permutation testing of cellular neighborhoods revealed a statistically significant proximity between Tregs and CD8+-cells in 18% of patients, and this was independently associated with a poor survival (multivariable hazard ratio = 1.36, p = 0.017). These results show prognostic heterogeneity of different Treg populations in primary CRC, and highlight the importance of multi-marker and spatial analyses for accurate immunophenotyping of tumors in relation to patient outcome

Evaluation of commercial kits for isolation and bisulfite conversion of circulating cell-free tumor DNA from blood

Abstract Background DNA methylation biomarkers in circulating cell-free DNA (cfDNA) have great clinical potential for cancer management. Most methods for DNA methylation analysis require bisulfite conversion, causing DNA degradation and loss. This is particularly challenging for cfDNA, which is naturally fragmented and normally present in low amounts. The aim of the present study was to identify an optimal combination of cfDNA isolation and bisulfite conversion kits for downstream analysis of DNA methylation biomarkers in plasma. Results Of the five tested bisulfite conversion kits (EpiJET Bisulfite Conversion Kit, EpiTect Plus DNA Bisulfite Kit (EpiTect), EZ DNA Methylation-Direct Kit, Imprint DNA Modification Kit (Imprint) and Premium Bisulfite Kit), the highest and lowest DNA yield and recovery were achieved using the EpiTect kit and the Imprint kit, respectively, with more than double the amount of DNA for the EpiTect kit. Of the three tested cfDNA isolation kits (Maxwell RSC ccfDNA Plasma Kit, QIAamp Circulating Nucleic Acid Kit (CNA) and QIAamp MinElute ccfDNA Mini Kit), the CNA kit yielded around twice as much cfDNA compared to the two others kits, although with more high molecular weight DNA present. When comparing various combinations of cfDNA isolation kits and bisulfite conversion kits, the CNA kit and the EpiTect kit were identified as the best-performing combination, resulting in the highest yield of bisulfite converted cfDNA from normal plasma, as measured by droplet digital PCR (ddPCR). As a proof of principle, this kit combination was used to process plasma samples from 13 colorectal cancer patients for subsequent ddPCR methylation analysis of BCAT1 and IKZF1. Methylation of BCAT1 and/or IKZF1 was identified in 6/10 (60%) stage IV patients and 1/3 (33%) stage III patients. Conclusions Based on a thorough evaluation of five bisulfite conversion kits and three cfDNA isolation kits, both individually and in combination, the CNA kit and the EpiTect kit were identified as the best-performing kit combination, with highest DNA yield and recovery across a range of DNA input amounts. The combination was successfully used for detection of clinically relevant DNA methylation biomarkers in plasma from cancer patients