MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions.

Summary: Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data. Availability and implementation: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online.

Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies.

BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established metabolic quantitative trait loci. RESULTS: Run day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable. CONCLUSION: Missing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.

LocTree3 prediction of localization.

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18=80±3% for eukaryotes and a six-state accuracy Q6=89±4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3.

Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva.

Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.

A systems view of type 2 diabetes-associated metabolic perturbations in saliva, blood and urine at different timescales of glycaemic control.

AIMS/HYPOTHESIS: Metabolomics has opened new avenues for studying metabolic alterations in type 2 diabetes. While many urine and blood metabolites have been associated individually with diabetes, a complete systems view analysis of metabolic dysregulations across multiple biofluids and over varying timescales of glycaemic control is still lacking. METHODS: Here we report a broad metabolomics study in a clinical setting, covering 2,178 metabolite measures in saliva, blood plasma and urine from 188 individuals with diabetes and 181 controls of Arab and Asian descent. Using multivariate linear regression we identified metabolites associated with diabetes and markers of acute, short-term and long-term glycaemic control. RESULTS: Ninety-four metabolite associations with diabetes were identified at a Bonferroni level of significance (p < 2.3 × 10(-5)), 16 of which have never been reported. Sixty-five of these diabetes-associated metabolites were associated with at least one marker of glycaemic control in the diabetes group. Using Gaussian graphical modelling, we constructed a metabolic network that links diabetes-associated metabolites from three biofluids across three different timescales of glycaemic control. CONCLUSIONS/INTERPRETATION: Our study reveals a complex network of biochemical dysregulation involving metabolites from different pathways of diabetes pathology, and provides a reference framework for future diabetes studies with metabolic endpoints.

Phenotype-driven identification of modules in a hierarchical map of multifluid metabolic correlations.

The identification of phenotype-driven network modules in complex, multifluid metabolomics data poses a considerable challenge for statistical analysis and result interpretation. This is the case for phenotypes with only few associations ('sparse' effects), but, in particular, for phenotypes with a large number of metabolite associations ('dense' effects). Herein, we postulate that examining the data at different layers of resolution, from metabolites to pathways, will facilitate the interpretation of modules for both the sparse and the dense cases. We propose an approach for the phenotype-driven identification of modules on multifluid networks based on untargeted metabolomics data of plasma, urine, and saliva samples from the German Study of Health in Pomerania (SHIP-TREND) study. We generated a hierarchical, multifluid map of metabolism covering both metabolite and pathway associations using Gaussian graphical models. First, this map facilitates a fundamental understanding of metabolism within and across fluids for our study, and can serve as a valuable and downloadable resource. Second, based on this map, we then present an algorithm to identify regulated modules that associate with factors such as gender and insulin-like growth factor I (IGF-I) as examples of traits with dense and sparse associations, respectively. We found IGF-I to associate at the rather fine-grained metabolite level, while gender shows well-interpretable associations at pathway level. Our results confirm that a holistic and interpretable view of metabolic changes associated with a phenotype can only be obtained if different layers of metabolic resolution from multiple body fluids are considered.

Sex-specific metabolic profiles of androgens and its main binding protein SHBG in a middle aged population without diabetes.

The role of androgens in metabolism with respect to sex-specific disease associations is poorly understood. Therefore, we aimed to provide molecular signatures in plasma and urine of androgen action in a sex-specific manner using state-of-the-art metabolomics techniques. Our study population consisted of 430 men and 343 women, aged 20-80 years, who were recruited for the cross-sectional population-based Study of Health in Pomerania (SHIP-TREND), Germany. We used linear regression models to identify associations between testosterone, androstenedione and dehydroepiandrosterone-sulfate (DHEAS) as well as sex hormone-binding globulin and plasma or urine metabolites measured by mass spectrometry. The analyses revealed major sex-specific differences in androgen-associated metabolites, particularly for levels of urate, lipids and metabolic surrogates of lifestyle factors, like cotinine or piperine. In women, in particular in the postmenopausal state, androgens showed a greater impact on the metabolome than in men (especially DHEAS and lipids were highly related in women). We observed a novel association of androstenedione on the metabolism of biogenic amines and only a small sex-overlap of associations within steroid metabolism. The present study yields new insights in the interaction between androgens and metabolism, especially about their implication in female metabolism.

Alterations in Lipid and Inositol Metabolisms in Two Dopaminergic Disorders.

BACKGROUND: Serum metabolite profiling can be used to identify pathways involved in the pathogenesis of and potential biomarkers for a given disease. Both restless legs syndrome (RLS) and Parkinson`s disease (PD) represent movement disorders for which currently no blood-based biomarkers are available and whose pathogenesis has not been uncovered conclusively. We performed unbiased serum metabolite profiling in search of signature metabolic changes for both diseases. METHODS: 456 metabolites were quantified in serum samples of 1272 general population controls belonging to the KORA cohort, 82 PD cases and 95 RLS cases by liquid-phase chromatography and gas chromatography separation coupled with tandem mass spectrometry. Genetically determined metabotypes were calculated using genome-wide genotyping data for the 1272 general population controls. RESULTS: After stringent quality control, we identified decreased levels of long-chain (polyunsaturated) fatty acids of individuals with PD compared to both RLS (PD vs. RLS: p = 0.0001 to 5.80x10-9) and general population controls (PD vs. KORA: p = 6.09x10-5 to 3.45x10-32). In RLS, inositol metabolites were increased specifically (RLS vs. KORA: p = 1.35x10-6 to 3.96x10-7). The impact of dopaminergic drugs was reflected in changes in the phenylalanine/tyrosine/dopamine metabolism observed in both individuals with RLS and PD. CONCLUSIONS: A first discovery approach using serum metabolite profiling in two dopamine-related movement disorders compared to a large general population sample identified significant alterations in the polyunsaturated fatty acid metabolism in PD and implicated the inositol metabolism in RLS. These results provide a starting point for further studies investigating new perspectives on factors involved in the pathogenesis of the two diseases as well as possible points of therapeutic intervention.

Metabolic Fingerprints of Circulating IGF-1 and the IGF-1/IGFBP-3 Ratio: A Multifluid Metabolomics Study.

OBJECTIVE: IGF-1 is known for its various physiological and severe pathophysiological effects on human metabolism; however, underlying molecular mechanisms still remain unsolved. To reveal possible molecular mechanisms mediating these effects, for the first time, we associated serum IGF-1 levels with multifluid untargeted metabolomics data. METHODS: Plasma/urine samples of 995 nondiabetic participants of the Study of Health in Pomerania were characterized by mass spectrometry. Sex-specific linear regression analyses were performed to assess the association of IGF-1 and IGF-1/IGF binding protein 3 ratio with metabolites. Additionally, the predictive ability of the plasma and urine metabolome for IGF-1 was assessed by orthogonal partial least squares analyses. RESULTS AND CONCLUSIONS: We revealed a multifaceted image of associated metabolites with large sex differences. Confirming previous reports, we detected relations between IGF-1 and steroid hormones or related intermediates. Furthermore, various associated metabolites were previously mentioned regarding IGF-1-associated diseases, eg, betaine and cortisol in cardiovascular disease and metabolic syndrome, lipid disorders, and diabetes, or have previously been found to associate with differentiation and proliferation or mitochondrial functionality, eg, phospholipids. bradykinin, fatty acid derivatives, and cortisol, which were inversely associated with IGF-1, might establish a link of IGF-1 with inflammation. For the first time, we showed an association between IGF-1 and pipecolate, a metabolite linked to amino acid metabolism. Our study demonstrates that IGF-1 action on metabolism is tractable, even in healthy subjects, and that the findings provide a solid basis for further experimental/clinical investigation, eg, searching for inflammatory or cardiovascular disease- or metabolic syndrome-associated biomarkers and therapeutic targets.

Gender-specific pathway differences in the human serum metabolome.

The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.