Increased amino acids levels and the risk of developing of hypertriglyceridemia in a 7-year follow-up

BACKGROUND: Recently, five branched-chain and aromatic amino acids were shown to be associated with the risk of developing type 2 diabetes (T2D). AIM: We set out to examine whether amino acids are also associated with the development of hypertriglyceridemia. MATERIALS AND METHODS: We determined the serum amino acids concentrations of 1,125 individuals of the KORA S4 baseline study, for which follow-up data were available also at the KORA F4 7 years later. After exclusion for hypertriglyceridemia (defined as having a fasting triglyceride level above 1.70 mmol/L) and diabetes at baseline, 755 subjects remained for analyses. RESULTS: Increased levels of leucine, arginine, valine, proline, phenylalanine, isoleucine and lysine were significantly associated with an increased risk of hypertriglyceridemia. These associations remained significant when restricting to those individuals who did not develop T2D in the 7-year follow-up. The increase per standard deviation of amino acid level was between 26 and 40 %. CONCLUSIONS: Seven amino acids were associated with an increased risk of developing hypertriglyceridemia after 7 years. Further studies are necessary to elucidate the complex role of these amino acids in the pathogenesis of metabolic disorders

Metabolomics enables precision medicine: “A White Paper, Community Perspective”

Introduction: Background to metabolomics: Metabolomics is the comprehensive study of the metabolome, the repertoire of biochemicals (or small molecules) present in cells, tissues, and body fluids. The study of metabolism at the global or “-omics” level is a rapidly growing field that has the potential to have a profound impact upon medical practice. At the center of metabolomics, is the concept that a person’s metabolic state provides a close representation of that individual’s overall health status. This metabolic state reflects what has been encoded by the genome, and modified by diet, environmental factors, and the gut microbiome. The metabolic profile provides a quantifiable readout of biochemical state from normal physiology to diverse pathophysiologies in a manner that is often not obvious from gene expression analyses. Today, clinicians capture only a very small part of the information contained in the metabolome, as they routinely measure only a narrow set of blood chemistry analytes to assess health and disease states. Examples include measuring glucose to monitor diabetes, measuring cholesterol and high density lipoprotein/low density lipoprotein ratio to assess cardiovascular health, BUN and creatinine for renal disorders, and measuring a panel of metabolites to diagnose potential inborn errors of metabolism in neonates. Objectives of White Paper—expected treatment outcomes and metabolomics enabling tool for precision medicine: We anticipate that the narrow range of chemical analyses in current use by the medical community today will be replaced in the future by analyses that reveal a far more comprehensive metabolic signature. This signature is expected to describe global biochemical aberrations that reflect patterns of variance in states of wellness, more accurately describe specific diseases and their progression, and greatly aid in differential diagnosis. Such future metabolic signatures will: (1) provide predictive, prognostic, diagnostic, and surrogate markers of diverse disease states; (2) inform on underlying molecular mechanisms of diseases; (3) allow for sub-classification of diseases, and stratification of patients based on metabolic pathways impacted; (4) reveal biomarkers for drug response phenotypes, providing an effective means to predict variation in a subject’s response to treatment (pharmacometabolomics); (5) define a metabotype for each specific genotype, offering a functional read-out for genetic variants: (6) provide a means to monitor response and recurrence of diseases, such as cancers: (7) describe the molecular landscape in human performance applications and extreme environments. Importantly, sophisticated metabolomic analytical platforms and informatics tools have recently been developed that make it possible to measure thousands of metabolites in blood, other body fluids, and tissues. Such tools also enable more robust analysis of response to treatment. New insights have been gained about mechanisms of diseases, including neuropsychiatric disorders, cardiovascular disease, cancers, diabetes and a range of pathologies. A series of ground breaking studies supported by National Institute of Health (NIH) through the Pharmacometabolomics Research Network and its partnership with the Pharmacogenomics Research Network illustrate how a patient’s metabotype at baseline, prior to treatment, during treatment, and post-treatment, can inform about treatment outcomes and variations in responsiveness to drugs (e.g., statins, antidepressants, antihypertensives and antiplatelet therapies). These studies along with several others also exemplify how metabolomics data can complement and inform genetic data in defining ethnic, sex, and gender basis for variation in responses to treatment, which illustrates how pharmacometabolomics and pharmacogenomics are complementary and powerful tools for precision medicine. Conclusions: Key scientific concepts and recommendations for precision medicine: Our metabolomics community believes that inclusion of metabolomics data in precision medicine initiatives is timely and will provide an extremely valuable layer of data that compliments and informs other data obtained by these important initiatives. Our Metabolomics Society, through its “Precision Medicine and Pharmacometabolomics Task Group”, with input from our metabolomics community at large, has developed this White Paper where we discuss the value and approaches for including metabolomics data in large precision medicine initiatives. This White Paper offers recommendations for the selection of state of-the-art metabolomics platforms and approaches that offer the widest biochemical coverage, considers critical sample collection and preservation, as well as standardization of measurements, among other important topics. We anticipate that our metabolomics community will have representation in large precision medicine initiatives to provide input with regard to sample acquisition/preservation, selection of optimal omics technologies, and key issues regarding data collection, interpretation, and dissemination. We strongly recommend the collection and biobanking of samples for precision medicine initiatives that will take into consideration needs for large-scale metabolic phenotyping studie

The High-Acceptance Dielectron Spectrometer HADES

HADES is a versatile magnetic spectrometer aimed at studying dielectron production in pion, proton and heavy-ion induced collisions. Its main features include a ring imaging gas Cherenkov detector for electron-hadron discrimination, a tracking system consisting of a set of 6 superconducting coils producing a toroidal field and drift chambers and a multiplicity and electron trigger array for additional electron-hadron discrimination and event characterization. A two-stage trigger system enhances events containing electrons. The physics program is focused on the investigation of hadron properties in nuclei and in the hot and dense hadronic matter. The detector system is characterized by an 85% azimuthal coverage over a polar angle interval from 18 to 85 degree, a single electron efficiency of 50% and a vector meson mass resolution of 2.5%. Identification of pions, kaons and protons is achieved combining time-of-flight and energy loss measurements over a large momentum range. This paper describes the main features and the performance of the detector system

CYGD: the Comprehensive Yeast Genome Database

The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/

Superior outcomes of nodal metastases compared to visceral sites in oligometastatic colorectal cancer treated with stereotactic ablative radiotherapy

Background: Stereotactic ablative radiotherapy (SBRT) is a radical option for oligometastatic colorectal cancer (CRC) patients, but most data relate to visceral metastases. Methods: A prospective, multi-centre database of CRC patients treated with SBRT was interrogated. Inclusion criteria were ECOG PS 0–2, ≤3 sites of disease, a disease free interval of >6 months unless synchronous liver metastases. Primary endpoints were local control (LC), progression free survival (PFS) and overall survival (OS). Results: 163 patients (172 metastases) were analysed. The median FU was 16 months (IQR 12.2–22.85). The LC at 1 year was 83.8% (CI 76.4%−91.9%) with a PFS of 55% (CI 47%−64.7%) respectively. LC at 1 year was 90% (CI 83%−99%) for nodal metastases (NM), 75% (63%−90%) for visceral metastases (VM). NM had improved median PFS (9 vs 19 months) [HR 0.6, CI 0.38–0.94, p = 0.032] and median OS (32 months vs not reached) [HR 0.28, CI 0.18–0.7, p = 0.0062] than VM, regardless of whether the NM were located inside or outside the pelvis. On multivariate analysis, NM and ECOG PS 0 were significant good prognostic factors. An exploratory analysis suggests KRAS WT is also a good prognostic factor. Conclusion: Nodal site is an important prognostic determinant of SBRT that should incorporated into patient selection. We hypothesise this may have an immunoediting basis

Bile acids targeted metabolomics and medication classification data in the ADNI1 and ADNIGO/2 cohorts

Alzheimer’s disease (AD) is the most common cause of dementia. The mechanism of disease development and progression is not well understood, but increasing evidence suggests multifactorial etiology, with a number of genetic, environmental, and aging-related factors. There is a growing body of evidence that metabolic defects may contribute to this complex disease. To interrogate the relationship between system level metabolites and disease susceptibility and progression, the AD Metabolomics Consortium (ADMC) in partnership with AD Neuroimaging Initiative (ADNI) is creating a comprehensive biochemical database for patients in the ADNI1 cohort. We used the Biocrates Bile Acids platform to evaluate the association of metabolic levels with disease risk and progression. We detail the quantitative metabolomics data generated on the baseline samples from ADNI1 and ADNIGO/2 (370 cognitively normal, 887 mild cognitive impairment, and 305 AD). Similar to our previous reports on ADNI1, we present the tools for data quality control and initial analysis. This data descriptor represents the third in a series of comprehensive metabolomics datasets from the ADMC on the ADNI

A network-based conditional genetic association analysis of the human metabolome

Background: Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits;however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics ("omics"), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods. Results: To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites. Conclusions: We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement

Induction of Noxa-Mediated Apoptosis by Modified Vaccinia Virus Ankara Depends on Viral Recognition by Cytosolic Helicases, Leading to IRF-3/IFN-β-Dependent Induction of Pro-Apoptotic Noxa

Viral infection is a stimulus for apoptosis, and in order to sustain viral replication many viruses are known to carry genes encoding apoptosis inhibitors. F1L, encoded by the orthopoxvirus modified vaccinia virus Ankara (MVA) has a Bcl-2-like structure. An MVA mutant lacking F1L (MVAΔF1L) induces apoptosis, indicating that MVA infection activates and F1L functions to inhibit the apoptotic pathway. In this study we investigated the events leading to apoptosis upon infection by MVAΔF1L. Apoptosis largely proceeded through the pro-apoptotic Bcl-2 family protein Bak with some contribution from Bax. Of the family of pro-apoptotic BH3-only proteins, only the loss of Noxa provided substantial protection, while the loss of Bim had a minor effect. In mice, MVA preferentially infected macrophages and DCs in vivo. In both cell types wt MVA induced apoptosis albeit more weakly than MVAΔF1L. The loss of Noxa had a significant protective effect in macrophages, DC and primary lymphocytes, and the combined loss of Bim and Noxa provided strong protection. Noxa protein was induced during infection, and the induction of Noxa protein and apoptosis induction required transcription factor IRF3 and type I interferon signalling. We further observed that helicases RIG-I and MDA5 and their signalling adapter MAVS contribute to Noxa induction and apoptosis in response to MVA infection. RNA isolated from MVA-infected cells induced Noxa expression and apoptosis when transfected in the absence of viral infection. We thus here describe a pathway leading from the detection of viral RNA during MVA infection by the cytosolic helicase-pathway, to the up-regulation of Noxa and apoptosis via IRF3 and type I IFN signalling

APOE ε2 resilience for Alzheimer’s disease is mediated by plasma lipid species: Analysis of three independent cohort studies

Introduction The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer\u27s disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood. Methods We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer\u27s Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species. Results A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer\u27s disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively. Discussion Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer\u27s disease and as such represent a potential therapeutic target