An equivalence test between features lists, based on the Sorensen-Dice index and the joint frequencies of GO term enrichment

Background: In integrative bioinformatic analyses, it is of great interest to stablish the equivalence between gene or (more in general) feature lists, up to a given level and in terms of their annotations in the Gene Ontology. The aim of this article is to present an equivalence test based on the proportion of GO terms which are declared as enriched in both lists simultaneously. Results: On the basis of these data, the dissimilarity between gene lists is measured by means of the Sorensen-Dice index. We present two flavours of the same test: One of them based on the asymptotic normality of the test statistic and the other based on the bootstrap method. Conclusions: The accuracy of these tests is studied by means of simulation and their possible interest is illustrated by using them over two real datasets: A collection of gene lists related to cancer and a collection of gene lists related to kidney rejection after transplantation

An equivalence approach to the integrative analysis of feature lists

Background Although a few comparison methods based on the biological meaning of gene lists have been developed, the goProfiles approach is one of the few that are being used for that purpose. It consists of projecting lists of genes into predefined levels of the Gene Ontology, in such a way that a multinomial model can be used for estimation and testing. Of particular interest is the fact that it may be used for proving equivalence (in the sense of 'enough similarity') between two lists, instead of proving differences between them, which seems conceptually better suited to the end goal of establishing similarity among gene lists. An equivalence method has been derived that uses a distance-based approach and the confidence interval inclusion principle. Equivalence is declared if the upper limit of a one-sided confidence interval for the distance between two profiles is below a pre-established equivalence limit. Results In this work, this method is extended to establish the equivalence of any number of gene lists. Additionally, an algorithm to obtain the smallest equivalence limit that would allow equivalence between two or more lists to be declared is presented. This algorithm is at the base of an iterative method of graphic visualization to represent the most to least equivalent gene lists. These methods deal adequately with the problem of adjusting for multiple testing. The applicability of these techniques is illustrated in two typical situations: (i) a collection of cancer-related gene lists, suggesting which of them are more reasonable to combine -as claimed by the authors- and (ii) a collection of pathogenesis-based transcript sets, showing which of these are more closely related. The methods developed are available in the goProfiles Bioconductor package. Conclusions The method provides a simple yet powerful and statistically well-grounded way to classify a set of genes or other feature lists by establishing their equivalence at a given equivalence threshold. The classification results can be viewed using standard visualization methods. This may be applied to a variety of problems, from deciding whether a series of datasets generating the lists can be combined to the simplification of groups of lists

On equivalence and bioequivalence testing.

Equivalence testing is the natural approach to many statistical problems. First, its main application, bioequivalence testing, is reviewed. The basic concepts of bioequivalence testing (2×2 crossover designs, TOST, interval inclusion principle, etc.) and its problems (TOST biased character, the carryover problem, etc.) are considered. Next, equivalence testing is discussed more generally. Some applications and methods are reviewed and the relation of equivalence testing and distance-based inference is highlighted. A new distance-based method to determine whether two gene lists are equivalent in terms of their annotations in the Gene Ontology illustrates these ideas. We end with a general discussion and some suggestions for future research

FOBI: An ontology to represent food intake data and associate it with metabolomic data

Nutrition research can be conducted by using two complementary approaches: 1) traditional self-reporting methods or 2) via metabolomics techniques to analyze food intake biomarkers in biofluids. However, the complexity and heterogeneity of these two very different types of data often hinder their analysis and integration. To manage this challenge, we have developed a novel ontology that describes food and their associated metabolite entities in a hierarchical way. This ontology uses a formal naming system, category definitions, properties and relations between both types of data. The ontology presented is called FOBI (Food-Biomarker Ontology) and it is composed of two interconnected sub-ontologies. One is a 'Food Ontology' consisting of raw foods and multi-component foods while the second is a 'Biomarker Ontology' containing food intake biomarkers classified by their chemical classes. These two sub-ontologies are conceptually independent but interconnected by different properties. This allows data and information regarding foods and food biomarkers to be visualized in a bidirectional way, going from metabolomics to nutritional data or vice versa. Potential applications of this ontology include the annotation of foods and biomarkers using a well-defined and consistent nomenclature, the standardized reporting of metabolomics workflows (e.g. metabolite identification, experimental design), or the application of different enrichment analysis approaches to analyze nutrimetabolomic data. Availability: FOBI is freely available in both OWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) formats at the project's Github repository (https://github.com/pcastellanoescuder/FoodBiomarkerOntology) and FOBI visualization tool is available in https://polcastellano.shinyapps.io/FOBI_Visualization_Tool/

POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis

Metabolomics and proteomics, like other omics domains, usually face a data mining challenge in providing an understandable output to advance in biomarker discovery and precision medicine. Often, statistical analysis is one of the most difficult challenges and it is critical in the subsequent biological interpretation of the results. Because of this, combined with the computational programming skills needed for this type of analysis, several bioinformatic tools aimed at simplifying metabolomics and proteomics data analysis have emerged. However, sometimes the analysis is still limited to a few hidebound statistical methods and to data sets with limited flexibility. POMAShiny is a web-based tool that provides a structured, flexible and user-friendly workflow for the visualization, exploration and statistical analysis of metabolomics and proteomics data. This tool integrates several statistical methods, some of them widely used in other types of omics, and it is based on the POMA R/Bioconductor package, which increases the reproducibility and flexibility of analyses outside the web environment. POMAShiny and POMA are both freely available at https://github.com/nutrimetabolomics/POMAShiny and https://github.com/nutrimetabolomics/POMA, respectively

Making sense of metabolomic data: comprehensive analysis of altered metabolic pathways in diabetes and obesity

Podeu consultar el III Workshop anual INSA-UB complet a: http://hdl.handle.net/2445/118993Sessió 1. Pòster núm.

Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data

Background: Bioinformatic tools for the enrichment of 'omics' datasets facilitate interpretation and understanding of data. To date few are suitable for metabolomics datasets. The main objective of this work is to give a critical overview, for the first time, of the performance of these tools. To that aim, datasets from metabolomic repositories were selected and enriched data were created. Both types of data were analysed with these tools and outputs were thoroughly examined. Results: An exploratory multivariate analysis of the most used tools for the enrichment of metabolite sets, based on a non-metric multidimensional scaling (NMDS) of Jaccard's distances, was performed and mirrored their diversity. Codes (identifiers) of the metabolites of the datasets were searched in different metabolite databases (HMDB, KEGG, PubChem, ChEBI, BioCyc/HumanCyc, LipidMAPS, ChemSpider, METLIN and Recon2). The databases that presented more identifiers of the metabolites of the dataset were PubChem, followed by METLIN and ChEBI. However, these databases had duplicated entries and might present false positives. The performance of over-representation analysis (ORA) tools, including BioCyc/HumanCyc, ConsensusPathDB, IMPaLA, MBRole, MetaboAnalyst, Metabox, MetExplore, MPEA, PathVisio and Reactome and the mapping tool KEGGREST, was examined. Results were mostly consistent among tools and between real and enriched data despite the variability of the tools. Nevertheless, a few controversial results such as differences in the total number of metabolites were also found. Disease-based enrichment analyses were also assessed, but they were not found to be accurate probably due to the fact that metabolite disease sets are not up-to-date and the difficulty of predicting diseases from a list of metabolites. Conclusions: We have extensively reviewed the state-of-the-art of the available range of tools for metabolomic datasets, the completeness of metabolite databases, the performance of ORA methods and disease-based analyses. Despite the variability of the tools, they provided consistent results independent of their analytic approach. However, more work on the completeness of metabolite and pathway databases is required, which strongly affects the accuracy of enrichment analyses. Improvements will be translated into more accurate and global insights of the metabolome

Chitinase 3-like 1 is neurotoxic in primary cultured neurons

Chitinase 3-like 1 (CHI3L1) is known to play a role as prognostic biomarker in the early stages of multiple sclerosis (MS), and patients with high cerebrospinal fluid CHI3L1 levels have an increased risk for the development of neurological disability. Here, we investigated its potential neurotoxic effect by adding recombinant CHI3L1 in vitro to primary cultures of mouse cortical neurons and evaluating both neuronal functionality and survival by immunofluorescence. CHI3L1 induced a significant neurite length retraction after 24 and 48 hours of exposure and significantly reduced neuronal survival at 48 hours. The cytotoxic effect of CHI3L1 was neuron-specific and was not observed in mouse immune or other central nervous system cells. These results point to a selective neurotoxic effect of CHI3L1 in vitro and suggest a potential role of CHI3L1 as therapeutic target in MS patients

Memory improvement in the AβPP/PS1 mouse model of familial Alzheimer's disease induced by carbamylated-erythropoietin is accompanied by modulation of synaptic genes

Neuroprotection of erythropoietin (EPO) following long-term administration is hampered by the associated undesirable effects on hematopoiesis and body weight. For this reason, we tested carbamylated-EPO (CEPO), which has no effect on erythropoiesis, and compared it with EPO in the AβPP/PS1 mouse model of familial Alzheimer's disease. Groups of 5-month-old wild type (WT) and transgenic mice received chronic treatment consisting of CEPO (2,500 or 5,000 UI/kg) or EPO (2,500 UI/kg) 3 days/week for 4 weeks. Memory at the end of treatment was assessed with the object recognition test. Microarray analysis and quantitative-PCR were used for gene expression studies. No alterations in erythropoiesis were observed in CEPO-treated WT and AβPP/PS1 transgenic mice. EPO and CEPO improved memory in AβPP/PS1 animals. However, only EPO decreased amyloid-β (Aβ) plaque burden and soluble Aβ40. Microarray analysis of gene expression revealed a limited number of common genes modulated by EPO and CEPO. CEPO but not EPO significantly increased gene expression of dopamine receptors 1 and 2, and adenosine receptor 2a, and significantly down-regulated adrenergic receptor α1D and gastrin releasing peptide. CEPO treatment resulted in higher protein levels of dopamine receptors 1 and 2 in WT and AβPP/PS1 animals, whereas the adenosine receptor 2a was reduced in WT animals. The present results suggest that the improved behavior observed in AβPP/PS1 transgenic mice after CEPO treatment may be mediated, at least in part, by the observed modulation of the expression of molecules involved in neurotransmission

Comparative transcriptomic profile of tolerogenic dendritic cells differentiated with vitamin D3, dexamethasone and rapamycin

Tolerogenic dendritic cell (tolDC)-based therapies have become a promising approach for the treatment of autoimmune diseases by their potential ability to restore immune tolerance in an antigen-specific manner. However, the broad variety of protocols used to generate tolDC in vitro and their functional and phenotypical heterogeneity are evidencing the need to find robust biomarkers as a key point towards their translation into the clinic, as well as better understanding the mechanisms involved in the induction of immune tolerance. With that aim, in this study we have compared the transcriptomic profile of tolDC induced with either vitamin D3 (vitD3-tolDC), dexamethasone (dexa-tolDC) or rapamycin (rapa-tolDC) through a microarray analysis in 5 healthy donors. The results evidenced that common differentially expressed genes could not be found for the three different tolDC protocols. However, individually, CYP24A1, MUCL1 and MAP7 for vitD3-tolDC; CD163, CCL18, C1QB and C1QC for dexa-tolDC; and CNGA1 and CYP7B1 for rapa-tolDC, constituted good candidate biomarkers for each respective cellular product. In addition, a further gene set enrichment analysis of the data revealed that dexa-tolDC and vitD3-tolDC share several immune regulatory and anti-inflammatory pathways, while rapa-tolDC seem to be playing a totally different role towards tolerance induction through a strong immunosuppression of their cellular processes