Search

The inclusion of omic data into constraint-based modeling (CBM) has improved metabolic network characterization of biological models. However, the integration of semi-quantitative metabolomic data into CBM remains challenging. Here, we present MAMBA (Metabolic Adjustment via Multiomic Blocks Aggregation), a CBM approach that enables the use of semi-quantitative metabolomic data together with a gene-centric omic data type (e.g. transcriptomics, ChIP-seq or chromatin accessibility data, among others), and the combination of different time points and conditions. MAMBA outperformed other methods in terms of metabolic network characterization and metabolite prediction accuracy. As a case study, we applied MAMBA to a yeast multiomic dataset with time series design where two different yeast strains where exposed to heat stress. MAMBA captured known biology of heat stress in yeast and identified novel affected metabolic pathways. MAMBA was implemented as an integer linear programming (ILP) problem to guarantee efficient computation, and coded for MATLAB (freely available at github.com/ConesaLab/MAMBA).

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/., Spanish Ministry of Science and Innovation [BIO2015-1658-R and PID2020-119537RB-100 to A.C. and S.T.]; National Institutes of Health [R03 CA222444 to A.C. and L.M.M.]; Slovene Research Agency Program [P4-0165]; European Union’s Horizon 2020 [GA 2020 862-858]; COST Action CA15110 STSM travel grant supported by European Cooperation in Science and Technology; Generalitat Valenciana [ACIF2019/081 to P.S.]. Spanish Ministry of Science and Innovation., Funding for open access charge: Spanish Ministry of Science and Innovation [PID2020-119537RB-100].

14 páginas, Diversity of omic technologies has expanded in the last years together with the number of omic data integration strategies. However, multiomic data generation is costly, and many research groups cannot afford research projects where many different omic techniques are generated, at least at the same time. As most researchers share their data in public repositories, different omic datasets of the same biological system obtained at different labs can be combined to construct a multiomic study. However, data obtained at different labs or moments in time are typically subjected to batch effects that need to be removed for successful data integration. While there are methods to correct batch effects on the same data types obtained in different studies, they cannot be applied to correct lab or batch effects across omics. This impairs multiomic meta-analysis. Fortunately, in many cases, at least one omics platform-i.e. gene expression- is repeatedly measured across labs, together with the additional omic modalities that are specific to each study. This creates an opportunity for batch analysis. We have developed MultiBaC (multiomic Multiomics Batch-effect Correction correction), a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. Our strategy is based on the existence of at least one shared data type which allows data prediction across omics. We validate this approach both on simulated data and on a case where the multiomic design is fully shared by two labs, hence batch effect correction within the same omic modality using traditional methods can be compared with the MultiBaC correction across data types. Finally, we apply MultiBaC to a true multiomic data integration problem to show that we are able to improve the detection of meaningful biological effects.

[EN] Multi-omics approaches have become a reality in both large genomics projects and small laboratories. However, the multi-omics research community still faces a number of issues that have either not been sufficiently discussed or for which current solutions are still limited. In this Perspective, we elaborate on these limitations and suggest points of attention for future research. We finally discuss new opportunities and challenges brought to the field by the rapid development of single-cell high-throughput molecular technologies., This work has been funded by the Spanish Ministry of Science and Innovation with grant number BES-2016-076994 to A.A.-L.

B- and T-cell receptor (immune) repertoires can represent an individual’s immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters (e.g., clonal diversity, germline usage). Here, we introduce immuneREF: a quantitative multi-dimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. immuneREF is implemented in an R package and was validated based on detection sensitivity of immune repertoires with known similarities and dissimilarities. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2400 datasets from individuals with varying immune states (healthy, [autoimmune] disease and infection [Covid-19], immune cell population). Importantly we discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF implements population-wide analysis of immune repertoire similarity and thus enables the study of the adaptive immune response across health and disease states., Support was provided from The Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO WorldLeading Research Community (to VG), UiO:LifeSciences Convergence Environment Immunolingo (to VG and GKS), EU Horizon 2020 iReceptorplus (#825821) (to VG), a Research Council of Norway FRIPRO project (#300740, to VG), a Research Council of Norway IKTPLUSS project (#311341, to VG and GKS), a Norwegian Cancer Society grant (#215817, to VG), and Stiftelsen Kristian Gerhard Jebsen (K.G. Jebsen Coeliac Disease Research Centre) (to GKS), Swiss National Science Foundation (Project 31003A to S.T.R), the Norwegian Research Council, Helse Sør-Øst, and the University of Oslo through the Centre for Molecular Medicine Norway (#187615 to MLK).

T-cell receptor (TCR) analysis is relevant for the study of immune system diseases. The expression of TCRs is usually measured with targeted sequencing approaches where TCR genes are selectively amplified. However, many non-targeted RNA-seq experiments also contain reads of TCR genes, which could be leveraged for TCR expression analysis while reducing sample requirements and costs. Moreover, a step-by-step pipeline for the processing of transcriptome RNA-seq reads to deliver immune repertoire data is missing, and these types of analyses are usually not included in RNA-seq studies of immunological conditions. This represents a missed opportunity for complementing them with the analysis of the immune repertoire., We present a Nextflow pipeline for T-cell receptor repertoire reconstruction and analysis from RNA sequencing data. We used a case study where TCR repertoire profiles were recovered from bulk RNA-seq of isolated CD4 T cells from control patients, cirrhotic patients without and with Minimal Hepatic Encephalopathy (MHE). MHE is a neuropsychiatric syndrome, mediated by peripheral inflammation, that may affect cirrhotic patients. After the recovery of 498-1,114 distinct TCR beta chains per patient, repertoire analysis of patients resulted in few public clones, high diversity and elevated within-repertoire sequence similarity, independently of immune status. Additionally, TCRs associated with celiac disease and inflammatory bowel disease were significantly overrepresented in MHE patient repertoires. The provided computational pipeline functions as a resource to facilitate TCR profiling from RNA-seq data boosting immunophenotype analyses of immunological diseases., We acknowledge generous support by The Leona M. and Harry B. Helmsley Charitable Trust (#2019PG-T1D011, to VG), UiO World-Leading Research Community (to VG), UiO:LifeScience Convergence Environment Immunolingo (to VG), EU Horizon 2020 iReceptorplus (#825821) (to VG), a Research Council of Norway FRIPRO project (#300740, to VG), a Research Council of Norway IKTPLUSS project (#311341, to VG), a Norwegian Cancer Society Grant (#215817, to VG). This work was also supported in part by Fundación Ramón Areces (to CM), the Ministerio de Ciencia e Innovación Spain (SAF2017-82917-R and PID2020-113388RB-I00 to VF; FIS PI18/00150 to CM), Consellería Educación Generalitat Valenciana (PROMETEOII/2018/051 to VF), Ministerio de Economía y Competitividad (BIO2015-71658-R to AC), Centro de Investigación Príncipe Felipe (Ayudas para proyectos de investigación intergrupos to TR) and co-funded with European Regional Development Funds (ERDF to VF, CM, AC).

Alternative splicing (AS) is a highly-regulated post-transcriptional mechanism known to modulate isoform expression within genes and contribute to cell-type identity. However, the extent to which alternative isoforms establish co-expression networks that may relevant in cellular function has not been explored yet. Here, we present acorde, a pipeline that successfully leverages bulk long reads and single-cell data to confidently detect alternative isoform co-expression relationships. To achieve this, we developed and validated percentile correlations, a novel approach that overcomes data sparsity and yields accurate co-expression estimates from single-cell data. Next, acorde uses correlations to cluster co-expressed isoforms into a network, unraveling cell type-specific alternative isoform usage patterns. By selecting same-gene isoforms between these clusters, we subsequently detect and characterize genes with co-differential isoform usage (coDIU) across neural cell types. Finally, we predict functional elements from long read-defined isoforms and provide insight into biological processes, motifs and domains potentially controlled by the coordination of post-transcriptional regulation.

Patients with liver cirrhosis may develop covert or minimal hepatic encephalopathy (MHE). Hyperammonemia (HA) and peripheral inflammation play synergistic roles in inducing the cognitive and motor alterations in MHE. The cerebellum is one of the main cerebral regions affected in MHE. Rats with chronic HA show some motor and cognitive alterations reproducing neurological impairment in cirrhotic patients with MHE. Neuroinflammation and altered neurotransmission and signal transduction in the cerebellum from hyperammonemic (HA) rats are associated with motor and cognitive dysfunction, but underlying mechanisms are not completely known. The aim of this work was to use a multi-omic approach to study molecular alterations in the cerebellum from hyperammonemic rats to uncover new molecular mechanisms associated with hyperammonemia-induced cerebellar function impairment. We analyzed metabolomic, transcriptomic, and proteomic data from the same cerebellums from control and HA rats and performed a multi-omic integrative analysis of signaling pathway enrichment with the PaintOmics tool. The histaminergic system, corticotropin-releasing hormone, cyclic GMP-protein kinase G pathway, and intercellular communication in the cerebellar immune system were some of the most relevant enriched pathways in HA rats. In summary, this is a good approach to find altered pathways, which helps to describe the molecular mechanisms involved in the alteration of brain function in rats with chronic HA and to propose possible therapeutic targets to improve MHE symptoms.

[EN] The Universitat Politècnica de València and its Faculty of Business Administration and Management have created a new intensification, named, "Intelligent Data Analysis", that provides the student with sufficient knowledge to integrate data analysis in the sometimes routine tasks of a company.The statistical, computer and ICT-related skills obtained through the Business Administration and Management degree are enhanced with more advanced statiscal models for multivariate data analysis and with R language programming, which is very suitable for such data analysis. All these skills are acquired under the Project-Based Learning methodology.This project's main achievement has been the coordination between the different subjects of the intensification to use the same software, which has resulted in a continuity for the way in which students work with RStudio, R, and Rmakdown. This has provided them a high level of management and integration of data analysis in the students’ work routines which will later aid them to become more qualified professionals., [ES] La Universitat Politècnica de Valencia y, en concreto, su Facultad de Administración y Dirección de Empresas ha decidido crear una nueva intensificación llamada "Análisis Inteligente de Datos" que proporciona al estudiante conocimientos suficientes para integrar el análisis de datos en las tareas en ocasiones rutinarias de la empresa. Las habilidades estadísticas, informáticas y relacionadas con las TIC que pueden obtenerse en el grado en Administración y Dirección de Empresas, se potencian con modelos estadísticos más avanzados para el análisis de datos multivariantes y con la programación en lenguaje R, que es muy adecuado para el análisis de datos. Todas las habilidades mencionadas se adquieren bajo la metodología de Aprendizaje Basado en Proyectos. El principal logro del presente proyecto ha sido la coordinación entre asignaturas de la intensificación a través de la utilización del mismo software, ya que ello ha supuesto una continuidad en la forma de trabajar del alumno con RStudio, R y Rmarkdown. Todo ello ha redundado en un alto nivel de manejo e integración del análisis de datos en las rutinas de trabajo de los estudiantes que les ayudará a convertirse posteriormente en profesionales más cualificados., Este trabajo ha sido financiado con un proyecto de la convocatoria Aprendizaje + Docencia: Proyectos de Innovación y Mejora educativa (PIME/20-21/200) de la Universitat Politècnica de València.

Patients with liver cirrhosis may develop minimal hepatic encephalopathy (MHE) which affects their quality of life and life span. It has been proposed that a shift in peripheral inflammation triggers the appearance of MHE. However, the mechanisms involved in this immune system shift remain unknown. In this work we studied the broad molecular changes involved in the induction of MHE with the goal of identifying (1) altered genes and pathways in peripheral blood cells associated to the appearance of MHE, (2) serum metabolites and cytokines with modified levels in MHE patients and (3) MHE-regulated immune response processes related to changes in specific serum molecules. We adopted a multi-omic approach to profile the transcriptome, metabolome and a panel of cytokines of blood samples taken from cirrhotic patients with or without MHE. Transcriptomic analysis supports the hypothesis of alternations in the Th1/Th2 and Th17 lymphocytes cell populations as major drivers of MHE. Cluster analysis of serum molecules resulted in six groups of chemically similar compounds, suggesting that functional modules operate during the induction of MHE. Finally, the multi-omic integrative analysis suggested a relationship between cytokines CCL20, CX3CL1, CXCL13, IL-15, IL-22 and IL-6 with alteration in chemotaxis, as well as a link between long-chain unsaturated phospholipids and the increased fatty acid transport and prostaglandin production. We found altered immune pathways that may collectively contribute to the mild cognitive impairment phenotype in MHE. Our approach is able to combine extracellular and intracellular information, opening new insights to the understanding of the disease.

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

[EN] Multi-omic studies combine measurements at different molecular levels to build comprehensive models of cellular systems. The success of a multi-omic data analysis strategy depends largely on the adoption of adequate experimental designs, and on the quality of the measurements provided by the different omic platforms. However, the field lacks a comparative description of performance parameters across omic technologies and a formulation for experimental design in multi-omic data scenarios. Here, we propose a set of harmonized Figures of Merit (FoM) as quality descriptors applicable to different omic data types. Employing this information, we formulate the MultiPower method to estimate and assess the optimal sample size in a multi-omics experiment. MultiPower supports different experimental settings, data types and sample sizes, and includes graphical for experimental design decision-making. MultiPower is complemented with MultiML, an algorithm to estimate sample size for machine learning classification problems based on multi-omic data., This work has been funded by FP7 STATegra project agreement 306000 and Spanish MINECO grant BIO2012-40244. In addition, work in the Imhof lab has been funded by the (DFG; CIPSM and SFB1064). The work of L.B.-N. has been funded by the University of Florida Startup funds.

Background: Inflammatory and nutritional indexes are prognostic factors in non-small cell lung cancer (NSCLC). Furthermore, a low grade of chronic inflammation has been described in the older population (inflammaging). We aimed to evaluate the neutrophil-to-lymphocyte ratio (NLR), the Prognostic Nutritional Index (PNI), the advanced lung cancer inflammation index (ALI), the platelet-to-lymphocyte ratio (PLR), and the Glasgow Prognostic Score (GPS) in young and older patients diagnosed with locally advanced NSCLC to determine if significant differences between these groups exist. Methods: We conducted a retrospective study analyzing the impact of age on the NLR, PNI, ALI, PLR, and GPS among patients diagnosed with stage III NSCLC at Hospital Universitario Doctor Peset between 2010 and 2015. Results: We included 124 patients (84 young, 40 older patients). The median hemoglobin level and leukocyte count were lower in the older patients (p = 0.0158 and p = 0.001, respectively). A higher median C-reactive protein level was also found in this group (p = 0.0095). Regarding specific inflammatory indexes, the PNI, comprising inflammatory and nutritional parameters, was lower among the older patients (p = 0.0463). The median NLR, ALI, and PLR were similar in both age groups. Moreover, no differences between the age groups were found in the percentage of patients showing high versus low NLR (cutoff point, 5) or ALI (cutoff point, 18) or in the different GPS groups. Conclusions: The baseline PNI, hemoglobin level, and lymphocyte count were lower among the older patients; furthermore, CRP was higher, possibly, because of a more prominent inflammatory status in older patients with lung cancer. No other immunological or nutritional analytical variables were different between the age groups.

Recent advances in long-read sequencing solve inaccuracies in alternative transcript identification of full-length transcripts in short-read RNA-Seq data, which encourages the development of methods for isoform-centered functional analysis. Here, we present tappAS, the first framework to enable a comprehensive Functional Iso-Transcriptomics (FIT) analysis, which is effective at revealing the functional impact of context-specific post-transcriptional regulation. tappAS uses isoform-resolved annotation of coding and non-coding functional domains, motifs, and sites, in combination with novel analysis methods to interrogate different aspects of the functional readout of transcript variants and isoform regulation. tappAS software and documentation are available at https://app.tappas.org.

10 páginas, 8 figuras, 1 tabla. Material suplementario accesible en: http//dx.doi.org/10.1186/s12915-020-0748-z. Preprocessing scripts for each of the omics datasets are available at the Github repository (https://github.com/ConesaLab/MultiMip6), Gene expression is a biological process regulated at different molecular levels, including chromatin accessibility, transcription, and RNA maturation and transport. In addition, these regulatory mechanisms have strong links with cellular metabolism. Here we present a multi-omics dataset that captures different aspects of this multi-layered process in yeast. We obtained RNA-seq, metabolomics, and H4K12ac ChIP-seq data for wild-type and mip6Δ strains during a heat-shock time course. Mip6 is an RNA-binding protein that contributes to RNA export during environmental stress and is informative of the contribution of post-transcriptional regulation to control cellular adaptations to environmental changes. The experiment was performed in quadruplicate, and the different omics measurements were obtained from the same biological samples, which facilitates the integration and analysis of data using covariance-based methods. We validate our dataset by showing that ChIP-seq, RNA-seq and metabolomics signals recapitulate existing knowledge about the response of ribosomal genes and the contribution of trehalose metabolism to heat stress. Raw data, processed data and preprocessing scripts are made available., This work is part of a research project funded by Generalitat Valenciana through PROMETEO grants programme for excellence research groups (PROMETEO 2016/093).

Traditionally, the functional analysis of gene expression data has used pathway and network enrichment algorithms. These methods are usually gene rather than transcript centric and hence fall short to unravel functional roles associated to posttranscriptional regulatory mechanisms such as Alternative Splicing (AS) and Alternative PolyAdenylation (APA), jointly referred here as Alternative Transcript Processing (AltTP). Moreover, short-read RNA-seq has serious limitations to resolve full-length transcripts, further complicating the study of isoform expression. Recent advances in long-read sequencing open exciting opportunities for studying isoform biology and function. However, there are no established bioinformatics methods for the functional analysis of isoform-resolved transcriptomics data to fully leverage these technological advances. Here we present a novel framework for Functional Iso-Transcriptomics analysis (FIT). This framework uses a rich isoform-level annotation database of functional domains, motifs and sites –both coding and non-coding- and introduces novel analysis methods to interrogate different aspects of the functional relevance of isoform complexity. The Functional Diversity Analysis (FDA) evaluates the variability at the inclusion/exclusion of functional domains across annotated transcripts of the same gene. Parameters can be set to evaluate if AltTP partially or fully disrupts functional elements. FDA is a measure of the potential of a multiple isoform transcriptome to have a functional impact. By combining these functional labels with expression data, the Differential Analysis Module evaluates the relative contribution of transcriptional (i.e. gene level) and post-transcriptional (i.e. transcript/protein levels) regulation on the biology of the system. Measures of isoform relevance such as Minor Isoform Filtering, Isoform Switching Events and Total Isoform Usage Change contribute to restricting analysis to biologically meaningful changes. Finally, novel methods for Differential Feature Inclusion, Co-Feature Inclusion, and the combination of UTR-lengthening with Alternative Polyadenylation analyses carefully dissects the contextual regulation of functional elements resulting from differential isoforms usage. These methods are implemented in the software tappAS, a user-friendly Java application that brings FIT to the hands of non-expert bioinformaticians supporting several model and non-model species. tappAS complements statistical analyses with powerful browsing tools and highly informative gene/transcript/CDS graphs.We applied tappAS to the analysis of two mouse Neural Precursor Cells (NPCs) and Oligodendrocyte Precursor Cells (OPCs) whose transcriptome was defined by PacBio and quantified by Illumina. Using FDA we confirmed the high potential of AltTP regulation in our system, in which 90% of multi-isoform genes presented variation in functional features at the transcript or protein level. The Differential Analysis module revealed a high interplay between transcriptional and AltTP regulation in neural development, mainly controlled by differential expression, but where AltTP acts the main driver of important neural development biological mechanisms such as vesicle trafficking, signal transduction and RNA processing. The DFI analysis revealed that, globally, AltTP increased the availability of functional features in differentiated neural cells. DFI also showed that AltTP is a mechanism for altering gene function by changing cellular localization and binding properties of proteins, via the differential inclusion of NLS, transmembrane domains or DNA binding motifs, for example. Some of these findings were experimentally validated by others and us.In summary, we propose a novel framework for the functional analysis of transcriptomes at isoform resolution. We anticipate the tappAS tool will be an important resource for the adoption of the Functional Iso-Transcriptomics analysis by functional genomics community.

The Yeast Metabolic Cycle (YMC) is a model system in which levels of around 60% of the yeast transcripts cycle over time. The spatial and temporal resolution provided by the YMC has revealed that changes in the yeast metabolic landscape and chromatin status can be related to cycling gene expression. However, the interplay between histone modifications and transcription factor activity during the YMC is still poorly understood. Here we apply an innovative statistical approach to integrate chromatin state (ChIP-seq) and gene expression (RNA-seq) data to investigate the transcriptional control during the YMC. By using the multivariate regression models N-PLS (Partial Least Squares) and MORE (Multi-Omics REgulation) methodologies, we assessed the contribution of histone marks and transcription factors to the regulation of gene expression in the YMC. We found that H3K18ac and H3K9ac were the most important histone modifications, whereas Sfp1, Hfi1, Pip2, Mig2, and Yhp1 emerged as the most relevant transcription factors. A significant association in the co-regulation of gene expression was found between H3K18ac and the transcription factors Pip2 (involved in fatty acids metabolism), Xbp1 (cyclin implicated in the regulation of carbohydrate and amino acid metabolism), and Hfi1 (involved in the formation of the SAGA complex). These results evidence the crucial role of histone lysine acetylation levels in the regulation of gene expression in the YMC through the coordinated action of transcription factors and lysine acetyltransferases.

[EN] The differentiation of self-renewing progenitor cells requires not only the regulation of lineage- and developmental stage-specific genes but also the coordinated adaptation of housekeeping functions from a metabolically active, proliferative state toward quiescence. How metabolic and cell-cycle states are coordinated with the regulation of cell type-specific genes is an important question, because dissociation between differentiation, cell cycle, and metabolic states is a hallmark of cancer. Here, we use a model system to systematically identify key transcriptional regulators of Ikaros-dependent B cell-progenitor differentiation. We find that the coordinated regulation of housekeeping functions and tissue-specific gene expression requires a feedforward circuit whereby Ikaros down-regulates the expression of Myc. Our findings show how coordination between differentiation and housekeeping states can be achieved by interconnected regulators. Similar principles likely coordinate differentiation and housekeeping functions during progenitor cell differentiation in other cell lineages., EU FP7-Health STATegra project (grant number 36000). AG, VL, RR, AC, GP, ST, SF, PN, ME, JT, AM, MM, DGC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Wellcome (grant number 099276/Z/12/Z). MM. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Medical Research Council, UK (grant number Institute Core Funding). AGF, BL, MM. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Bloodwise (grant number 09036). LC. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Wellcome (grant number). BL. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. European Research Council (grant number Repleniche). AGF. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. European Research Council (grant number 617393-CAUSALPATH). IT, VL, GP. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. MINECO (grant number BIO2015-71658-R). AC, ST. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Exposure to low levels of environmental contaminants, including pesticides, induces neurodevelopmental toxicity. Environmental and food contaminants can reach the brain of the fetus, affecting brain development and leading to neurological dysfunction. The pesticide endosulfan is a persistent pollutant, and significant levels still remain detectable in the environment although its use is banned in some countries. In rats, endosulfan exposure during brain development alters motor activity, coordination, learning, and memory, even several months after uptake, and does so in a sex-dependent way. However, the molecular mechanisms driving these effects have not been studied in detail. In this work, we performed a multiomics study in cerebellum from rats exposed to endosulfan during embryonic development. Pregnant rats were orally exposed to a low dose (0.5 mg/kg) of endosulfan, daily, from gestational day 7 to postnatal day 21. The progeny was evaluated for cognitive and motor functions at adulthood. Expression of messenger RNA and microRNA genes, as well as protein and metabolite levels, were measured on cerebellar samples from males and females. An integrative analysis was conducted to identify altered processes under endosulfan effect. Effects between males and females were compared. Pathways significantly altered by endosulfan exposure included the phosphatidylinositol signaling system, calcium signaling, the cGMP-PKG pathway, the inflammatory and immune system, protein processing in the endoplasmic reticulum, and GABA and taurine metabolism. Sex-dependent effects of endosulfan in the omics results that matched sex differences in cognitive and motor tests were found. These results shed light on the molecular basis of impaired neurodevelopment and contribute to the identification of new biomarkers of neurotoxicity.

MotivationAs new integrative methodologies are being developed to analyse multi-omic experiments, validation strategies are required for benchmarking. In silico approaches such as simulated data are popular as they are fast and cheap. However, few tools are available for creating synthetic multi-omic data sets.ResultsMOSim is a new R package for easily simulating multi-omic experiments consisting of gene expression data, other regulatory omics and the regulatory relationships between them. MOSim supports different experimental designs including time series data.AvailabilityThe package is freely available under the GPL-3 license from the Bitbucket repository (https://bitbucket.org/ConesaLab/mosim/).Contactcmartinez@cipf.esSupplementary informationSupplementary material is available at bioRxiv online.

[EN] The intrinsic characteristics of the tumor microenvironment (TME), including acidic pH and overexpression of hydrolytic enzymes, offer an exciting opportunity for the rational design of TME-drug delivery systems (DDS). We developed and characterized a pH-responsive biodegradable poly-L-glutamic acid (PGA)-based combination conjugate family with the aim of optimizing anticancer effects. We obtained combination conjugates bearing Doxorubicin (Dox) and aminoglutethimide (AGM) with two Dox loadings and two different hydrazone pH sensitive linkers that promote the specific release of Dox from the polymeric backbone within the TME. Low Dox loading coupled with a short hydrazone linker yielded optimal effects on primary tumor growth, lung metastasis (-90% reduction), and toxicological profile in a preclinical metastatic triple-negative breast cancer (TNBC) murine model. The use of transcriptomic analysis helped us to identify the molecular mechanisms responsible for such results including a differential immunomodulation and cell death pathways among the conjugates. This data highlights the advantages of targeting the TME, the therapeutic value of polymer-based combination approaches, and the utility of -omits-based analysis to accelerate anticancer DDS., The authors would like to thank Dr. Stuart P. Atkinson for his collaboration in manuscript preparation and English revision, and Irene Borreda for essential immunohistological support. This work has been supported by the European Research Council (grant ERC-CoG-2014-648831 "MyNano") and the Spanish Ministry of Science and Innovation (CTQ2010-18195, SAF2013-44848-R, BES-2008-006801, IPT-2012-0712-010000, Programa I3, and BIO2015-71658-R). LBN is funded through a University of South Florida-Helmsley Foundation award. FHL is funded through NIH grant. Part of the equipment employed in this work has been funded by Generalitat Valenciana and co-financed with FEDER funds (PO FEDER of Comunitat Valenciana 2014-2020).

[EN] The increasing availability of multi-omic platforms poses new challenges to data analysis. Joint visualization of multi-omics data is instrumental in better understanding interconnections across molecular layers and in fully utilizing the multi-omic resources available to make biological discoveries. We present here PaintOmics 3, a web-based resource for the integrated visualization of multiple omic data types onto KEGG pathway diagrams. PaintOmics 3 combines server-end capabilities for data analysis with the potential of modern web resources for data visualization, providing researchers with a powerful framework for interactive exploration of their multi-omics information. Unlike other visualization tools, PaintOmics 3 covers a comprehensive pathway analysis workflow, including automatic feature name/identifier conversion, multi-layered feature matching, pathway enrichment, network analysis, interactive heatmaps, trend charts, and more. It accepts a wide variety of omic types, including transcriptomics, proteomics and metabolomics, as well as region-based approaches such as ATAC-seq or ChIP-seq data. The tool is freely available at www.paintomics.org, European Union Seventh Framework Programme [FP7/2007-2013] under the grant agreement [306000-STATegra]; Marie Curie International Research Staff Exchange Scheme under grant agreement [612583-DEANN]; Spanish MINECO [BIO2012-40244]. INB Grant [PT17/0009/0015 - ISCIII-SGEFI / ERDF]. Funding for open access charge: MINECO [BIO2012-40244].

Motivation As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Results Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. Availability and implementation The package is freely available under the LGPL license from the Bioconductor web site. Supplementary information Supplementary data are available at Bioinformatics online.

Studying biological systems or diseases from the multiomic perspective have been shown to improve the biological understanding of the underlying mechanisms causing the phenotypes. However, data integration analysis is not straightforward and added difficulties arise regarding experimental design, the statistical strategy to be applied and the interpretation and visualization of the results. In this chapter, we discuss the potential issues of integrating multiomic datasets. We describe some useful and widely applicable supervised and unsupervised statistical techniques for the analysis of multiomic experiments and also for the visualization and interpretation of the results. The use of some of these methodologies is illustrated with examples focused on time series data.

Pneumocystis species are ascomycete fungi adapted to live inside the lungs of mammals. These ascomycetes show extensive stenoxenism, meaning that each species of Pneumocystis infects a single species of host. Here, we study the effect exerted by natural selection on gene evolution in the genomes of three Pneumocystis species. We show that genes involved in host interaction evolve under positive selection. In the first place, we found strong evidence of episodic diversifying selection in Major surface glycoproteins (Msg). These proteins are located on the surface of Pneumocystis and are used for host attachment and probably for immune system evasion. Consistent with their function as antigens, most sites under diversifying selection in Msg code for residues with large relative surface accessibility areas. We also found evidence of positive selection in part of the cell machinery used to export Msg to the cell surface. Specifically, we found that genes participating in glycosylphosphatidylinositol (GPI) biosynthesis show an increased rate of nonsynonymous substitutions (dN) versus synonymous substitutions (dS). GPI is a molecule synthesized in the endoplasmic reticulum that is used to anchor proteins to membranes. We interpret the aforementioned findings as evidence of selective pressure exerted by the host immune system on Pneumocystis species, shaping the evolution of Msg and several proteins involved in GPI biosynthesis. We suggest that genome evolution in Pneumocystis is well described by the Red-Queen hypothesis whereby genes relevant for biotic interactions show accelerated rates of evolution., This project has received funding from the Marie Curie International Research Staff Exchange Scheme within the 7th European Community Framework Program under grant agreement No 612583-DEANN. Part of this work was done during an internship of L.D. as invited professor at the Universidad de Valencia. Support from CONACYT (grant 454938) is gratefully acknowledged. This work was supported by grants to A.M. from the Spanish Ministry of Science and Competitivity (projects SAF 2012-31187, SAF2013-49788-EXP, SAF2015-65878-R), Carlos III Institute of Health (projects PIE14/00045, AC 15/00022 and AC15/00042), Generalitat Valenciana (project PrometeoII/2014/065) and cofinanced by FEDER.

[EN] Systems medicine and systems biology have inherent educational challenges. These have largely been addressed either by providing new masters programs or by redesigning undergraduate programs. In contrast, short courses can respond to a different need: they can provide condensed updates for professionals across academia, the clinic, and industry. These courses have received less attention. Here, we share our experiences in developing and providing such courses to current and future leaders in systems biology and systems medicine. We present guidelines for how to reproduce our courses, and we offer suggestions for how to select students who will nurture an interdisciplinary learning environment and thrive there., The authors would like to acknowledge helpful comments from the editor. J.T. was supported by EU FP7 305033 CASyM and King Abdullah University for Science and Technology. D.G., F.M., S.T, A.C., and J.T. were supported by EU FP7 306000 STATegra

As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. The package is freely available under the LGPL license from the Bioconductor web site (http://bioconductor.org).

[EN] Non-coding RNA transcripts such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are important genetic regulators. However, the functions of many of these transcripts are still not clearly understood. Recently, it has become apparent that there is significant crosstalk between miRNAs and lncRNAs and that this creates competition for binding between the miRNA, a lncRNA and other regulatory targets. Indeed, various competitive endogenous RNAs (ceRNAs) have already been identified where a lncRNA acts by sequestering miRNAs. This implies the down-regulation in the interaction of the miRNAs with their mRNA targets, what has been called a sponge effect. Multiple approaches exist for the prediction of miRNA targets in mRNAs. However, few methods exist for the prediction of miRNA response elements (MREs) in lncRNAs acting as ceRNAs (sponges). Here, we present spongeScan (http://spongescan.rc.ufl.edu), a graphical web tool to compute and visualize putative MREs in lncRNAs, along with different measures to assess their likely behavior as ceRNAs., FP7 STATegra project [agreement number 36000]; MINECO, co-funded with European Regional Development Funds (ERDF) [BIO2012-40244]; European Molecular Biology Laboratory and the European Union and ERC Seventh Framework Programme (FP7/2007-2013) [ERC-2012-StG-310325]. Funding for open access charge: University of Florida funds.

[EN] High-throughput transcriptome studies are breaking new ground to investigate the responses that organisms deploy in alternative environments. Nevertheless, much remains to be understood about the genetic basis of host plant adaptation. Here, we investigate genome-wide expression in the fly Drosophila buzzatii raised in different conditions. This species uses decaying tissues of cactus of the genus Opuntia as primary rearing substrate and secondarily, the necrotic tissues of the columnar cactus Trichocereus terscheckii. The latter constitutes a harmful host, rich in mescaline and other related phenylethylamine alkaloids. We assessed the transcriptomic responses of larvae reared in Opuntia sulphurea and T. terscheckii, with and without the addition of alkaloids extracted from the latter. Whole-genome expression profiles were massively modulated by the rearing environment, mainly by the presence of T. terscheckii alkaloids. Differentially expressed genes were mainly related to detoxification, oxidation–reduction and stress response; however, we also found genes involved in development and neurobiological processes. In conclusion, our study contributes new data onto the role of transcriptional plasticity in response to alternative rearing environments., We thank Dr. A. Ruiz for early access to reference genome, S. Szajnman for the invaluable chemistry technical assistance and Lic. J. Vrdoljak for his assistance in DT measurements. This work was supported by Argentinian grants from Universidad de Buenos Aires (UBACyT 20020130100058BA), CONICET (PIP 11220150100029CO) and ANPCyT (PICT 2795/2010 and 1121/2013), by Spanish MINECO grants (PIB2010AR-00266 and BIO2012-40244) and cofunded with European Regional Development Funds and Marie Curie IRSES Project DEANN (PIRSES-GA-2013-612583).

[EN] RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics., The authors would like to thank Michael Love and Harold Pimentel for helpful suggestions on the initial draft of the manuscript. AC, ST, AM, DGC were supported by the FP7 STATegra project (grant 36000). Research in AC’s laboratory was supported by MINECO grant BIO2012-40244 and co-funded with European Regional Development Funds (ERDF). Research in PM’s laboratory is supported by ERC starting grant Relieve-IMDs and by a core support grant from the Wellcome Trust and MRC to the Wellcome TrustMedical Research Council Cambridge Stem Cell Institute. XZ was supported by the National Basic Research Program of China (2012CB316504). LLE was supported by JDRF (grant number 2-2013-32) and by the Sigrid Juselius Foundation. ACe was supported by the Academy of Finland (Center of Excellence in Cancer Genetics Research).

[EN] Background: The integrative analysis of multiple genomics data often requires that genome coordinates-based signals have to be associated with proximal genes. The relative location of a genomic region with respect to the gene (gene area) is important for functional data interpretation; hence algorithms that match regions to genes should be able to deliver insight into this information. Results: In this work we review the tools that are publicly available for making region-to-gene associations. We also present a novel method, RGmatch, a flexible and easy-to-use Python tool that computes associations either at the gene, transcript, or exon level, applying a set of rules to annotate each region-gene association with the region location within the gene. RGmatch can be applied to any organism as long as genome annotation is available. Furthermore, we qualitatively and quantitatively compare RGmatch to other tools. Conclusions: RGmatch simplifies the association of a genomic region with its closest gene. At the same time, it is a powerful tool because the rules used to annotate these associations are very easy to modify according to the researcher’s specific interests. Some important differences between RGmatch and other similar tools already in existence are RGmatch’s flexibility, its wide range of user options, compatibility with any annotatable organism, and its comprehensive and user-friendly output., The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under the grant agreement 306000 and the MINECO (Economy and Competitiveness Ministry) BIO2012-40244 grant.

This paper discusses the potential of multi-way projection methods for analysing multifactorial data structures to identify underlying components of variability that interconnect different blocks of omics variables. We explore their suitability for explorative and variable selection analysis of systems biology data where different types of biological parameters are studied together. These methodologies were applied to the integrative analysis of a functional genomics dataset where transcriptomics, metabolomics and physiological data are available. Our results show that multiway methods are suited to accommodate multifactorial omics experiments and to analyse relationships between different biochemical layers. Additionally, strategies are presented for variable selection in the context of omics data and for interpreting results at the level of cellular pathways.

Background Invasive aspergillosis is started after germination of Aspergillus fumigatus conidia that are inhaled by susceptible individuals. Fungal hyphae can grow in the lung through the epithelial tissue and disseminate hematogenously to invade into other organs. Low fungaemia indicates that fungal elements do not reside in the bloodstream for long. Results We analyzed whether blood represents a hostile environment to which the physiology of A. fumigatus has to adapt. An in vitro model of A. fumigatus infection was established by incubating mycelium in blood. Our model allowed to discern the changes of the gene expression profile of A. fumigatus at various stages of the infection. The majority of described virulence factors that are connected to pulmonary infections appeared not to be activated during the blood phase. Three active processes were identified that presumably help the fungus to survive the blood environment in an advanced phase of the infection: iron homeostasis, secondary metabolism, and the formation of detoxifying enzymes. Conclusions We propose that A. fumigatus is hardly able to propagate in blood. After an early stage of sensing the environment, virtually all uptake mechanisms and energy-consuming metabolic pathways are shut-down. The fungus appears to adapt by trans-differentiation into a resting mycelial stage. This might reflect the harsh conditions in blood where A. fumigatus cannot take up sufficient nutrients to establish self-defense mechanisms combined with significant growth. Electronic supplementary material The online version of this article (doi10.1186/s12864-015-1853-1) contains supplementary material, which is available to authorized users.

[EN] As the use of RNA-seq has popularized, there is an increasing consciousness of the importance of experimental design, bias removal, accurate quantification and control of false positives for proper data analysis. We introduce the NOISeq R-package for quality control and analysis of count data. We show how the available diagnostic tools can be used to monitor quality issues, make pre-processing decisions and improve analysis. We demonstrate that the non-parametric NOISeqBIO efficiently controls false discoveries in experiments with biological replication and outperforms state-of-the-art methods. NOISeq is a comprehensive resource that meets current needs for robust data-aware analysis of RNA-seq differential expression., European Union Seventh Framework Programme [FP7/2007-2013, 306000]; Spanish Ministry of Science and Innovation [MICINN, BIO2008-04638-E], in the framework of ERA-Net Pathogenomics; MICINN [DPI2008-06880-C03-03/DPI]. Funding for open access charge: University of Florida, Publication Funds.

Background Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is one of the main challenges in the analysis of genomic data and is on the basis of the future implementation of precision medicine. Results Here we propose a simple probabilistic model in which signaling pathways are separated into elementary sub-pathways or signal transmission circuits (which ultimately trigger cell functions) and then transforms gene expression measurements into probabilities of activation of such signal transmission circuits. Using this model, differential activation of such circuits between biological conditions can be estimated. Thus, circuit activation statuses can be interpreted as biomarkers that discriminate among the compared conditions. This type of mechanism-based biomarkers accounts for cell functional activities and can easily be associated to disease or drug action mechanisms. The accuracy of the proposed model is demonstrated with simulations and real datasets. Conclusions The proposed model provides detailed information that enables the interpretation disease mechanisms as a consequence of the complex combinations of altered gene expression values. Moreover, it offers a framework for suggesting possible ways of therapeutic intervention in a pathologically perturbed system. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0121-3) contains supplementary material, which is available to authorized users.