Male gender is an independent risk factor for coronary artery disease, and androgen administration has been associated with increased atherosclerosis in experimental animals. Since endothelial dysfunction is an important event in the atherogenic process, we hypothesized that androgen deprivation in adult men might be associated with enhanced arterial endothelial function. Using external vascular ultrasound, brachial artery diameter was measured at rest, after flow increase (causing endothelium-dependent dilatation) and after nitroglycerin (an endothelium-independent dilator). We studied 30 adult males aged 40 to 70 years: 10 had had bilateral orchidectomy and/or maximal androgen blockade for > or = 6 months for treatment of prostate cancer, and all were in complete remission (group 1). Ten healthy controls (group 2) and 10 controls who had remission from nonprostate cancers (group 3) were matched for age and smoking history. Testosterone levels were lower in men in group 1 versus groups 2 or 3 (0.8 +/- 0.1 versus 19.2 +/- 8.4 or 16.1 +/- 4.9 nmol/L, P < .001). By contrast, endothelium-dependent dilatation was markedly higher in group 1 than in groups 2 or 3 (6.2 +/- 3 versus 2.7 +/- 2 or 2.0 +/- 1.9%, P < .001). The nitroglycerin response was similar in all three groups (P = .92). On multivariate analysis, increased endothelium-dependent dilatation was significantly associated with low serum testosterone levels (P = .001) but not with cholesterol levels or with a past history of malignancy (P > .25). The withdrawal of male sex hormones may be associated with enhanced endothelial function in adult men. This is consistent with a deleterious effect of physiologic levels of male sex steroids on the arterial wall.

BackgroundMetabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution.FindingsPhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm.ConclusionsPhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.

Background: Alzheimer’s disease (AD) is diagnosed based on a clinical evaluation as well as analyses of classical biomarkers: Aβ 42 , total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). Although the sensitivities and specificities of the classical biomarkers are fairly good for detection of AD, there is still a need to develop novel biochemical markers for early detection of AD. Objective: We explored if integration of novel proteins with classical biomarkers in CSF can better discriminate AD from non-AD subjects. Methods: We applied ELISA, mass spectrometry, and multivariate modeling to investigate classical biomarkers and the CSF proteome in subjects ( n = 206) with 76 AD patients, 74 mild cognitive impairment (MCI) patients, 11 frontotemporal dementia (FTD) patients, and 45 non-dementia controls. The MCI patients were followed for 4–9 years and 21 of these converted to AD, whereas 53 remained stable. Results: By combining classical CSF biomarkers with twelve novel markers, the area of the ROC curves (AUROCS) of distinguishing AD and MCI/AD converters from non-AD were 93% and 96%, respectively. The FTDs and non-dementia controls were identified versus all other groups with AUROCS of 96% and 87%, respectively. Conclusions: Integration of new and classical CSF biomarkers in a model-based approach can improve the identification of AD, FTD, and non-dementia control subjects.

To better understand the pathophysiological differences between secondary progressive multiple sclerosis (SPMS) and relapsing-remitting multiple sclerosis (RRMS), and to identify potential biomarkers of disease progression, we applied high-resolution mass spectrometry (HRMS) to investigate the metabolome of cerebrospinal fluid (CSF). The biochemical differences were determined using partial least squares discriminant analysis (PLS-DA) and connected to biochemical pathways as well as associated to clinical and radiological measures. Tryptophan metabolism was significantly altered, with perturbed levels of kynurenate, 5-hydroxytryptophan, 5-hydroxyindoleacetate, and N-acetylserotonin in SPMS patients compared with RRMS and controls. SPMS patients had altered kynurenine compared with RRMS patients, and altered indole-3-acetate compared with controls. Regarding the pyrimidine metabolism, SPMS patients had altered levels of uridine and deoxyuridine compared with RRMS and controls, and altered thymine and glutamine compared with RRMS patients. Metabolites from the pyrimidine metabolism were significantly associated with disability, disease activity and brain atrophy, making them of particular interest for understanding the disease mechanisms and as markers of disease progression. Overall, these findings are of importance for the characterization of the molecular pathogenesis of SPMS and support the hypothesis that the CSF metabolome may be used to explore changes that occur in the transition between the RRMS and SPMS pathologies.

Supplementary data is available at Bioinformatics online.

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS).Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability.Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate.The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression.Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

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Motivation Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed using the Kubernetes container orchestrator. Results We developed a Virtual Research Environment (VRE) which facilitates rapid integration of new tools and developing scalable and interoperable workflows for performing metabolomics data analysis. The environment can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry, one nuclear magnetic resonance spectroscopy and one fluxomics study. We showed that the method scales dynamically with increasing availability of computational resources. We demonstrated that the method facilitates interoperability using integration of the major software suites resulting in a turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, statistics and identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science. Availability and implementation The PhenoMeNal consortium maintains a web portal (https://portal.phenomenal-h2020.eu) providing a GUI for launching the Virtual Research Environment. The GitHub repository https://github.com/phnmnl/ hosts the source code of all projects. Supplementary information Supplementary data are available at Bioinformatics online.