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Aims/hypothesis Previous metabolomics studies suggest that type 1 diabetes is preceded by specific metabolic disturbances. The aim of this study was to investigate whether distinct metabolic patterns occur in peripheral blood mononuclear cells (PBMCs) of children who later develop pancreatic beta cell autoimmunity or overt type 1 diabetes. Methods In a longitudinal cohort setting, PBMC metabolomic analysis was applied in children who (1) progressed to type 1 diabetes (PT1D, n = 34), (2) seroconverted to ≥1 islet autoantibody without progressing to type 1 diabetes (P1Ab, n = 27) or (3) remained autoantibody negative during follow-up (CTRL, n = 10). Results During the first year of life, levels of most lipids and polar metabolites were lower in the PT1D and P1Ab groups compared with the CTRL group. Pathway over-representation analysis suggested alanine, aspartate, glutamate, glycerophospholipid and sphingolipid metabolism were over-represented in PT1D. Genome-scale metabolic models of PBMCs during type 1 diabetes progression were developed by using publicly available transcriptomics data and constrained with metabolomics data from our study. Metabolic modelling confirmed altered ceramide pathways, known to play an important role in immune regulation, as specifically associated with type 1 diabetes progression. Conclusions/interpretation Our data suggest that systemic dysregulation of lipid metabolism, as observed in plasma, may impact the metabolism and function of immune cells during progression to overt type 1 diabetes. Data availability The GEMs for PBMCs have been submitted to BioModels (www.ebi.ac.uk/biomodels/), under accession number MODEL1905270001. The metabolomics datasets and the clinical metadata generated in this study were submitted to MetaboLights (https://www.ebi.ac.uk/metabolights/), under accession number MTBLS1015.

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We call upon the research community to standardize efforts to use daily self-reported data about COVID-19 symptoms in the response to the pandemic and to form a collaborative consortium to maximize global gain while protecting participant privacy.

Aims/hypothesisVirally induced inflammatory responses, beta cell destruction and release of beta cell autoantigens may lead to autoimmune reactions culminating in type 1 diabetes. Therefore, viral capability to induce beta cell death and the nature of virus-induced immune responses are among key determinants of diabetogenic viruses. We hypothesised that enterovirus infection induces a specific gene expression pattern that results in islet destruction and that such a host response pattern is not shared among all enterovirus infections but varies between virus strains.MethodsThe changes in global gene expression and secreted cytokine profiles induced by lytic or benign enterovirus infections were studied in primary human pancreatic islet using DNA microarrays and viral strains either isolated at the clinical onset of type 1 diabetes or capable of causing a diabetes-like condition in mice.ResultsThe expression of pro-inflammatory cytokine genes (IL-1-α, IL-1-β and TNF-α) that also mediate cytokine-induced beta cell dysfunction correlated with the lytic potential of a virus. Temporally increasing gene expression levels of double-stranded RNA recognition receptors, antiviral molecules, cytokines and chemokines were detected for all studied virus strains. Lytic coxsackievirus B5 (CBV-5)-DS infection also downregulated genes involved in glycolysis and insulin secretion.Conclusions/interpretationThe results suggest a distinct, virus-strain-specific, gene expression pattern leading to pancreatic islet destruction and pro-inflammatory effects after enterovirus infection. However, neither viral replication nor cytotoxic cytokine production alone are sufficient to induce necrotic cell death. More likely the combined effect of these and possibly cellular energy depletion lie behind the enterovirus-induced necrosis of islets.

Multi-view learning studies how several views, different feature representations, of the same objects could be best utilized in learning. In other words, multi-view learning is analysis of co-occurrence data, where the observations are co-occurrences of samples in the views. Standard multi-view learning such as joint density modeling cannot be done in the absence of co-occurrence, when the views are observed separately and the identities of objects are not known. As a practical example, joint analysis of mRNA and protein concentrations requires mapping between genes and proteins. We introduce a data-driven approach for learning the correspondence of the observations in the different views, in order to enable joint analysis also in the absence of known co-occurrence. The method finds a matching that maximizes statistical dependency between the views, which is particularly suitable for multi-view methods such as canonical correlation analysis which has the same objective. We apply the method to translational metabolomics, to identify differences and commonalities in metabolic processes in different species or tissues. The metabolite identities and roles in the different species are not generally known, and it is necessary to search for a matching. In this paper we show, using different metabolomics measurement batches as the views so that the ground truth is known, that the metabolite identities can be reliably matched by a consensus of several matching solutions.

Aim:The Wnt/β-catenin signaling network offers potential targets to diagnose and uncouple obesity from its metabolic complications. In this study, we investigate the role of the Wnt antagonist, secreted frizzled-related protein 1 (SFRP1), in promoting adipogenesis in vitro and adipose tissue expansion in vivo.Methods:We use a combination of human and murine, in vivo and in vitro models of adipogenesis, adipose tissue expansion and obesity-related metabolic syndrome to profile the involvement of SFRP1.Results:SFRP1 is expressed in both murine and human mature adipocytes. The expression of SFRP1 is induced during in vitro adipogenesis, and SFRP1 is preferentially expressed in mature adipocytes in human adipose tissue. Constitutive ectopic expression of SFRP1 is proadipogenic and inhibits the Wnt/β-catenin signaling pathway. In vivo endogenous levels of adipose SFRP1 are regulated in line with proadipogenic states. However, in longitudinal studies of high-fat-diet-fed mice, we observed a dynamic temporal but biphasic regulation of endogenous SFRP1. In agreement with this profile, we observed that SFRP1 expression in human tissues peaks in patients with mild obesity and gradually falls in morbidly obese subjects.Conclusions:Our results suggest that SFRP1 is an endogenous modulator of Wnt/β-catenin signaling and participates in the paracrine regulation of human adipogenesis. The reduced adipose expression of SFRP1 in morbid obesity and its knock-on effect to prevent further adipose tissue expansion may contribute to the development of metabolic complications in these individuals.

Mass spectrometry (MS)-based metabolomics studies often require handling of both identified and unidentified metabolite data. In order to avoid bias in data interpretation, it would be of advantage for the data analysis to include all available data. A practical challenge in exploratory metabolomics analysis is therefore how to interpret the changes related to unidentified peaks. In this paper, we address the challenge by predicting the class membership of unknown peaks by applying and comparing multiple supervised classifiers to selected lipidomics datasets. The employed classifiers include k-nearest neighbours (k-NN), support vector machines (SVM), partial least squares and discriminant analysis (PLS-DA) and Naive Bayes methods which are known to be effective and efficient in predicting the labels for unseen data. Here, the class label predictions are sought for unidentified lipid profiles coming from high throughput global screening in Ultra Performance Liquid Chromatography Mass Spectrometry (UPLCTM/MS) experimental setup. Our investigation reveals that k-NN and SVM classifiers outperform both PLS-DA and Naive Bayes classifiers. Naive Bayes classifier perform poorly among all models and this observation seems logical as lipids are highly co-regulated and do not respect Naive Bayes assumptions of features being conditionally independent given the class. Common label predictions from k-NN and SVM can serve as a good starting point to explore full data and thereby facilitating exploratory studies where label information is critical for the data interpretation.

BackgroundLittle is known about the effect of fish consumption on gene expression of inflammation-related genes in immune cells in coronary heart disease (CHD).Aim of the studyWe sought to evaluate the effect of a fatty fish (FF) or a lean fish (LF) diet on the modulation of inflammatory and endothelial function-related genes in peripheral blood mononuclear cells (PBMCs) of subjects with CHD, and its association with serum fatty acid (FA) profile and lipid metabolic compounds.MethodsData from 27 patients randomized into an 8-week FF (n = 10; mean ± SD: 4.3 ± 0.4 portions of fish per week), LF (n = 11; 4.7 ± 1.1 portions of fish per week), or control diet (n = 6; 0.6 ± 0.4 portions of fish per week) were analyzed. The mRNA expression was measured using real-time PCR.ResultsThe effect of the intervention on the mRNA expression of the genes studied did not differ among groups. In the FF group, however, the decrease in arachidonic acid to eicosapentaenoic acid (AA:EPA) ratio in cholesterol ester and phospholipid fractions strongly correlated with the change in IL1B mRNA levels (r s = 0.60, P = 0.06 and r s = 0.86, P = 0.002, respectively). In the LF group, the decrease in palmitic acid and total saturated FAs in cholesterol esters correlated with the change in intercellular cell adhesion molecule-1 (ICAM1) expression (r s = 0.64, P = 0.04 for both). Circulating levels of soluble ICAM-1 decreased only in the LF group (P ConclusionsThe intake of FF or LF diet did not alter the expression of inflammatory and endothelial function-related genes in PBMCs of patients with CHD. However, the decrease in AA:EPA ratio in serum lipids in the FF group may induce an anti-inflammatory response at mRNA levels in PBMCs. A LF diet might benefit endothelial function, possibly mediated by the changes in serum FA composition.

Multifactorial diseases present a significant challenge for functional genomics. Owing to their multiple compartmental effects and complex biomolecular activities, such diseases cannot be adequately characterised by changes in single components, nor can pathophysiological changes be understood by observing gene transcripts alone. Instead, a pattern of subtle changes is observed in multifactorial diseases across multiple tissues and organs with complex associations between corresponding gene, protein and metabolite levels.This article presents methods for exploratory and integrative analysis of pathophysiological changes at the biomolecular level. In particular, novel approaches are introduced for the following challenges: (i) data processing and analysis methods for proteomic and metabolomic data obtained by electrospray ionisation (ESI) liquid chromatography-tandem mass spectrometry (LC/MS); (ii) association analysis of integrated gene, protein and metabolite patterns that are most descriptive of pathophysiological changes; and (iii) interpretation of results obtained from association analyses in the context of known biological processes.These novel approaches are illustrated with the apolipoprotein E3-Leiden transgenic mouse model, a commonly used model of atherosclerosis. We seek to gain insight into the early responses of disease onset and progression by determining and identifying — well in advance of pathogenic manifestations of disease — the sets of gene transcripts, proteins and metabolites, along with their putative relationships in the transgenic model and associated wild-type cohort. Our results corroborate previous findings and extend predictions for three processes in atherosclerosis: aberrant lipid metabolism, inflammation, and tissue development and maintenance.

Background Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2. Results A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms. Conclusions MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

Background Lipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available. Results We present computational and informatics approaches to study lipid molecular profiles in the context of known metabolic pathways and established pathophysiological responses, utilizing information obtained from modern analytical technologies. In order to facilitate identification of lipids, we compute the scaffold of theoretically possible lipids based on known lipid building blocks such as polar head groups and fatty acids. Each compound entry is linked to the available information on lipid pathways and contains the information that can be utilized for its automated identification from high-throughput UPLC/MS-based lipidomics experiments. The utility of our approach is demonstrated by its application to the lipidomic characterization of the fatty liver of the genetically obese insulin resistant ob/ob mouse model. We investigate the changes of correlation structure of the lipidome using multivariate analysis, as well as reconstruct the pathways for specific molecular species of interest using available lipidomic and gene expression data. Conclusion The methodology presented herein facilitates identification and interpretation of high-throughput lipidomics data. In the context of the ob/ob mouse liver profiling, we have identified the parallel associations between the elevated triacylglycerol levels and the ceramides, as well as the putative activated ceramide-synthesis pathways.

Background: Saccharomyces cerevisiae is able to adapt to a wide range of external oxygen conditions. Previously, oxygen-dependent phenotypes have been studied individually at the transcriptional, metabolite, and flux level. However, the regulation of cell phenotype occurs across the different levels of cell function. Integrative analysis of data from multiple levels of cell function in the context of a network of several known biochemical interaction types could enable identification of active regulatory paths not limited to a single level of cell function.Results: The graph theoretical method called Enriched Molecular Path detection (EMPath) was extended to enable integrative utilization of transcription and flux data. The utility of the method was demonstrated by detecting paths associated with phenotype differences of S. cerevisiae under three different conditions of oxygen provision: 20.9%, 2.8% and 0.5%. The detection of molecular paths was performed in an integrated genome-scale metabolic and protein-protein interaction network.Conclusions: The molecular paths associated with the phenotype differences of S. cerevisiae under conditions of different oxygen provisions revealed paths of molecular interactions that could potentially mediate information transfer between processes that respond to the particular oxygen availabilities.

BACKGROUND: Persons with schizophrenia and other psychotic disorders have a high prevalence of obesity, impaired glucose tolerance, and lipid abnormalities, particularly hypertriglyceridemia and low high-density lipoprotein. More detailed molecular information on the metabolic abnormalities may reveal clues about the pathophysiology of these changes, as well as about disease specificity. METHODS: We applied comprehensive metabolomics in serum samples from a general population-based study in Finland. The study included all persons with DSM-IV primary psychotic disorder (schizophrenia, n = 45; other non-affective psychosis (ONAP), n = 57; affective psychosis, n = 37) and controls matched by age, sex, and region of residence. Two analytical platforms for metabolomics were applied to all serum samples: a global lipidomics platform based on ultra-performance liquid chromatography coupled to mass spectrometry, which covers molecular lipids such as phospholipids and neutral lipids; and a platform for small polar metabolites based on two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). RESULTS: Compared with their matched controls, persons with schizophrenia had significantly higher metabolite levels in six lipid clusters containing mainly saturated triglycerides, and in two small-molecule clusters containing, among other metabolites, (1) branched chain amino acids, phenylalanine and tyrosine, and (2) proline, glutamic, lactic and pyruvic acids. Among these, serum glutamic acid was elevated in all psychoses (P = 0.0020) compared to controls, while proline upregulation (P = 0.000023) was specific to schizophrenia. After adjusting for medication and metabolic comorbidity in linear mixed models, schizophrenia remained independently associated with higher levels in seven of these eight clusters (P < 0.05 in each cluster). The metabolic abnormalities were less pronounced in persons with ONAP or affective psychosis. CONCLUSIONS: Our findings suggest that specific metabolic abnormalities related to glucoregulatory processes and proline metabolism are specifically associated with schizophrenia and reflect two different disease-related pathways. Metabolomics, which is sensitive to both genetic and environmental variation, may become a powerful tool in psychiatric research to investigate disease susceptibility, clinical course, and treatment response., Funding agencies:NARSAD Maltz InvestigatorAward Academy of Finland 129434