"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling.

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

INFRAFRONTIER: mouse model resources for modelling human diseases.

Over the last decade, INFRAFRONTIER has positioned itself as a world-class Research Infrastructure for the generation, phenotyping, archiving, and distribution of mouse models in Europe. The INFRAFRONTIER network consists of 22 partners from 15 countries, and is continuously enhancing and broadening its portfolio of resources and services that are offered to the research community on a non-profit basis. By bringing together European rodent model expertise and providing valuable disease model services to the biomedical research community, INFRAFRONTIER strives to push the accessibility of cutting-edge human disease modelling technologies across the European research landscape. This article highlights the latest INFRAFRONTIER developments and informs the research community about its extensively utilised services, resources, and technical developments, specifically the intricacies of the INFRAFRONTIER database, use of Curated Disease Models, overview of the INFRAFRONTIER Cancer and Rare Disease resources, and information about its main state-of-the-art services.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

Principles and application of LIMS in mouse clinics.

Large-scale systemic mouse phenotyping, as performed by mouse clinics for more than a decade, requires thousands of mice from a multitude of different mutant lines to be bred, individually tracked and subjected to phenotyping procedures according to a standardised schedule. All these efforts are typically organised in overlapping projects, running in parallel. In terms of logistics, data capture, data analysis, result visualisation and reporting, new challenges have emerged from such projects. These challenges could hardly be met with traditional methods such as pen & paper colony management, spreadsheet-based data management and manual data analysis. Hence, different Laboratory Information Management Systems (LIMS) have been developed in mouse clinics to facilitate or even enable mouse and data management in the described order of magnitude. This review shows that general principles of LIMS can be empirically deduced from LIMS used by different mouse clinics, although these have evolved differently. Supported by LIMS descriptions and lessons learned from seven mouse clinics, this review also shows that the unique LIMS environment in a particular facility strongly influences strategic LIMS decisions and LIMS development. As a major conclusion, this review states that there is no universal LIMS for the mouse research domain that fits all requirements. Still, empirically deduced general LIMS principles can serve as a master decision support template, which is provided as a hands-on tool for mouse research facilities looking for a LIMS.

Proteomic and metabolomic profiling reveals time-dependent changes in hippocampal metabolism upon paroxetine treatment and biomarker candidates.

Most of the commonly used antidepressants block monoamine reuptake transporters to enhance serotonergic or noradrenergic neurotransmission. Effects besides or downstream of monoamine reuptake inhibition are poorly understood and yet presumably important for the drugs' mode of action. In the present study we aimed at identifying hippocampal cellular pathway alterations in DBA/2 mice using paroxetine as a representative Selective Serotonin Reuptake Inhibitor (SSRI). Furthermore we identified biomarker candidates for the assessment of antidepressant treatment effects in plasma. Hippocampal protein levels were compared between chronic paroxetine- and vehicle-treated animals using in vivo(15)N metabolic labeling combined with mass spectrometry. We also studied the time course of metabolite level changes in hippocampus and plasma using a targeted polar metabolomics profiling platform. In silico pathway analyses revealed profound alterations related to hippocampal energy metabolism. Glycolytic metabolite levels acutely increased while Krebs cycle metabolite levels decreased upon chronic treatment. Changes in energy metabolism were influenced by altered glycogen metabolism rather than by altered glycolytic or Krebs cycle enzyme levels. Increased energy levels were reflected by an increased ATP/ADP ratio and by increased ratios of high-to-low energy purines and pyrimidines. In the course of our analyses we also identified myo-inositol as a biomarker candidate for the assessment of antidepressant treatment effects in the periphery. This study defines the cellular response to paroxetine treatment at the proteome and metabolome levels in the hippocampus of DBA/2 mice and suggests novel SSRI modes of action that warrant consideration in antidepressant development efforts.

The (15)N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model.

Stable isotope labeling techniques hold great potential for accurate quantitative proteomics comparisons by MS. To investigate the effect of stable isotopes in vivo, we metabolically labeled high anxiety-related behavior (HAB) mice with the heavy nitrogen isotope (15)N. (15)N-labeled HAB mice exhibited behavioral alterations compared to unlabeled ((14)N) HAB mice in their depression-like phenotype. To correlate behavioral alterations with changes on the molecular level, we explored the (15)N isotope effect on the brain proteome by comparing protein expression levels between (15)N-labeled and (14)N HAB mouse brains using quantitative MS. By implementing two complementary in silico pathway analysis approaches, we were able to identify altered networks in (15)N-labeled HAB mice, including major metabolic pathways such as the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. Here, we discuss the affected pathways with regard to their relevance for the behavioral phenotype and critically assess the utility of exploiting the (15)N isotope effect for correlating phenotypic and molecular alterations.

Segmentation of multi-isotope imaging mass spectrometry data for semi-automatic detection of regions of interest.

Multi-isotope imaging mass spectrometry (MIMS) associates secondary ion mass spectrometry (SIMS) with detection of several atomic masses, the use of stable isotopes as labels, and affiliated quantitative image-analysis software. By associating image and measure, MIMS allows one to obtain quantitative information about biological processes in sub-cellular domains. MIMS can be applied to a wide range of biomedical problems, in particular metabolism and cell fate [1], [2], [3]. In order to obtain morphologically pertinent data from MIMS images, we have to define regions of interest (ROIs). ROIs are drawn by hand, a tedious and time-consuming process. We have developed and successfully applied a support vector machine (SVM) for segmentation of MIMS images that allows fast, semi-automatic boundary detection of regions of interests. Using the SVM, high-quality ROIs (as compared to an expert's manual delineation) were obtained for 2 types of images derived from unrelated data sets. This automation simplifies, accelerates and improves the post-processing analysis of MIMS images. This approach has been integrated into "Open MIMS," an ImageJ-plugin for comprehensive analysis of MIMS images that is available online at http://www.nrims.hms.harvard.edu/NRIMS_ImageJ.php.

Proteomic and metabolomic profiling of a trait anxiety mouse model implicate affected pathways.

Depression and anxiety disorders affect a great number of people worldwide. Whereas singular factors have been associated with the pathogenesis of psychiatric disorders, growing evidence emphasizes the significance of dysfunctional neural circuits and signaling pathways. Hence, a systems biology approach is required to get a better understanding of psychiatric phenotypes such as depression and anxiety. Furthermore, the availability of biomarkers for these disorders is critical for improved diagnosis and monitoring treatment response. In the present study, a mouse model presenting with robust high versus low anxiety phenotypes was subjected to thorough molecular biomarker and pathway discovery analyses. Reference animals were metabolically labeled with the stable (15)N isotope allowing an accurate comparison of protein expression levels between the high anxiety-related behavior versus low anxiety-related behavior mouse lines using quantitative mass spectrometry. Plasma metabolomic analyses identified a number of small molecule biomarkers characteristic for the anxiety phenotype with particular focus on myo-inositol and glutamate as well as the intermediates involved in the tricarboxylic acid cycle. In silico analyses suggested pathways and subnetworks as relevant for the anxiety phenotype. Our data demonstrate that the high anxiety-related behavior and low anxiety-related behavior mouse model is a valuable tool for anxiety disorder drug discovery efforts.

Proteomics and metabolomics analysis of a trait anxiety mouse model reveals divergent mitochondrial pathways.

BACKGROUND: Although anxiety disorders are the most prevalent psychiatric disorders, no molecular biomarkers exist for their premorbid diagnosis, accurate patient subcategorization, or treatment efficacy prediction. To unravel the neurobiological underpinnings and identify candidate biomarkers and affected pathways for anxiety disorders, we interrogated the mouse model of high anxiety-related behavior (HAB), normal anxiety-related behavior (NAB), and low anxiety-related behavior (LAB) employing a quantitative proteomics and metabolomics discovery approach. METHODS: We compared the cingulate cortex synaptosome proteomes of HAB and LAB mice by in vivo (15)N metabolic labeling and mass spectrometry and quantified the cingulate cortex metabolomes of HAB/NAB/LAB mice. The combined data sets were used to identify divergent protein and metabolite networks by in silico pathway analysis. Selected differentially expressed proteins and affected pathways were validated with immunochemical and enzymatic assays. RESULTS: Altered levels of up to 300 proteins and metabolites were found between HAB and LAB mice. Our data reveal alterations in energy metabolism, mitochondrial import and transport, oxidative stress, and neurotransmission, implicating a previously nonhighlighted role of mitochondria in modulating anxiety-related behavior. CONCLUSIONS: Our results offer insights toward a molecular network of anxiety pathophysiology with a focus on mitochondrial contribution and provide the basis for pinpointing affected pathways in anxiety-related behavior.

Proteome scale turnover analysis in live animals using stable isotope metabolic labeling.

At present most quantitative proteomics investigations are focused on the analysis of protein expression differences between two or more sample specimens. With each analysis a static snapshot of a cellular state is captured with regard to protein expression. However, any information on protein turnover cannot be obtained using classic methodologies. Protein turnover, the result of protein synthesis and degradation, represents a dynamic process, which is of equal importance to understanding physiological processes. Methods employing isotopic tracers have been developed to measure protein turnover. However, applying these methods to live animals is often complicated by the fact that an assessment of precursor pool relative isotope abundance is required. Also, data analysis becomes difficult in case of low label incorporation, which results in a complex convolution of labeled and unlabeled peptide mass spectrometry signals. Here we present a protein turnover analysis method that circumvents this problem using a (15)N-labeled diet as an isotopic tracer. Mice were fed with the labeled diet for limited time periods and the resulting partially labeled proteins digested and subjected to tandem mass spectrometry. For the interpretation of the mass spectrometry data, we have developed the ProTurnyzer software that allows the determination of protein fractional synthesis rates without the need of precursor relative isotope abundance information. We present results validating ProTurnyzer with Escherichia coli protein data and apply the method to mouse brain and plasma proteomes for automated turnover studies.

Phenome-transcriptome correlation unravels anxiety and depression related pathways.

The identification of pathways pertinent to human diseases is critical for gaining a better understanding of their pathophysiology. Pathway knowledge in turn can provide disease marker information required for diagnosis, drug development and improved patient treatment. Psychiatric disorders including anxiety and depression are complex diseases and are caused by a combination of multiple genetic and environmental factors affecting certain brain circuits. Here we used a systems biology approach to identify molecular pathways that affect anxiety- and depression-like phenotypes. For this purpose we screened pathways for stable enrichment in a great number of publicly available transcriptome data from the Gene Expression Omnibus related to anxiety- and depression-like phenotypes. In case of anxiety our analysis implicate a dysregulation of carbohydrate metabolism, tight junction and the phosphatidylinositol signaling system, whereas for depression gap junction, gonadotropin-releasing hormone signaling and ubiquitin-mediated proteolysis pathways are affected. Furthermore, both anxiety and depression show a dysregulation of VEGF signaling, long term potentiation and the glycolysis pathway. Molecular entities that are part of the identified pathways can serve as biomarkers and potential therapeutic targets for diagnosis and treatment of depression and anxiety disorders.

Proteome analysis of the thalamus and cerebrospinal fluid reveals glycolysis dysfunction and potential biomarkers candidates for schizophrenia.

Schizophrenia (SCZ) is the result of DNA alterations and environmental factors, which together lead to differential protein expression and ultimately to the development of the illness. The diagnosis is based on clinical symptoms, and the molecular background of SCZ is not completely understood. The thalamus, whose dysfunction has been associated with SCZ based in diverse lines of evidences, plays for instance a pivotal role in the central nervous system as a relay center by re-distributing auditory and visual stimuli from diverse brain regions to the cerebral cortex. We analyzed the proteome of postmortem mediodorsal thalamus (MDT) samples from 11 SCZ patients and 8 non-SCZ individuals by using quantitative shotgun-mass spectrometry and two-dimensional gel electrophoresis. Our analyses identified 551 proteins, 50 of which showed significant differential expression. The main pathways affected by the differentially expressed proteins include energy metabolism, oligodendrocyte metabolism, and cytoskeleton assembly. The potential protein biomarkers candidates myelin basic protein and myelin oligodendrocyte protein were validated by Western blot in the MDT samples and also in cerebrospinal fluid from a separate set of samples of 17 first-episode SCZ patients and 10 healthy controls. The differential expression of µ-crystallin, protein kinase C-gamma, and glial fibrillary acidic protein were confirmed in MDT. Because we found several glycolysis enzymes to be differentially expressed, we measured the levels of pyruvate and NADPH and found them to be altered in MDT. The protein changes described here corroborate the importance of myelin/oligodendrocyte and energy metabolism in SCZ and highlight new potential biomarkers candidates that may contribute to the understanding of the pathogenesis of this complex disease.

Proteome analysis of schizophrenia brain tissue.

OBJECTIVES: Proteome analysis has emerged as a promising strategy to the identification of potential biomarkers and to further confirm the importance of certain pathways in the schizophrenia (SCZ) pathophysiology. Reviewing the results of 13 proteome studies in SCZ brain tissue, we aimed to provide information regarding potential proteins biomarkers as well as information about the pathophysiology of the disease. METHODS AND RESULTS: Using two-dimensional gel electrophoresis and shotgun mass spectrometry, 31 proteins were consistently found differentially expressed in the brains of SCZ patients. The most frequent protein alterations reported in SCZ were related to brain energy metabolism, brain plasticity, and synaptic function, processes that are thought to belong to the core of the biology of this disease. The recurrent identification and validation of inter-related protein clusters, determined in different samples and approaches, strongly reinforces the putative involvement of certain pathways in SCZ. CONCLUSIONS: The availability of reliable markers not only paves the way to the development of new therapeutic strategies but also points out the possibility of their use as peripheral blood markers that may potentially contribute to the early SCZ detection and early therapeutic intervention, both of which can reduce the social and cognitive consequences of the disease.