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      Menstrual cycle rhythmicity: metabolic patterns in healthy women

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          Abstract

          The menstrual cycle is an essential life rhythm governed by interacting levels of progesterone, estradiol, follicular stimulating, and luteinizing hormones. To study metabolic changes, biofluids were collected at four timepoints in the menstrual cycle from 34 healthy, premenopausal women. Serum hormones, urinary luteinizing hormone and self-reported menstrual cycle timing were used for a 5-phase cycle classification. Plasma and urine were analyzed using LC-MS and GC-MS for metabolomics and lipidomics; serum for clinical chemistries; and plasma for B vitamins using HPLC-FLD. Of 397 metabolites and micronutrients tested, 208 were significantly (p < 0.05) changed and 71 reached the FDR 0.20 threshold showing rhythmicity in neurotransmitter precursors, glutathione metabolism, the urea cycle, 4-pyridoxic acid, and 25-OH vitamin D. In total, 39 amino acids and derivatives and 18 lipid species decreased (FDR < 0.20) in the luteal phase, possibly indicative of an anabolic state during the progesterone peak and recovery during menstruation and the follicular phase. The reduced metabolite levels observed may represent a time of vulnerability to hormone related health issues such as PMS and PMDD, in the setting of a healthy, rhythmic state. These results provide a foundation for further research on cyclic differences in nutrient-related metabolites and may form the basis of novel nutrition strategies for women.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Validation of new methods.

            Reliable analytical data are a prerequisite for correct interpretation of toxicological findings in the evaluation of scientific studies, as well as in daily routine work. Unreliable analytical data might not only be contested in court, but could also lead to unjustified legal consequences for the defendant or to wrong treatment of the patient. Therefore, new analytical methods to be used in forensic and/or clinical toxicology require careful method development and thorough validation of the final method. This is especially true in the context of quality management and accreditation, which have become matters of increasing relevance in analytical toxicology in recent years. In this paper, important considerations in analytical method validation will be discussed which may be used as guidance by scientists wishing to develop and validate analytical methods.
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              Recon 2.2: from reconstruction to model of human metabolism

              Introduction The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed. Objectives We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources. Methods Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions. Results Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources. Conclusion Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001). Electronic supplementary material The online version of this article (doi:10.1007/s11306-016-1051-4) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                colleen.draper@rd.nestle.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 October 2018
                1 October 2018
                2018
                : 8
                : 14568
                Affiliations
                [1 ]Nestle Institute of Health Sciences (NIHS), Lausanne, Switzerland
                [2 ]ISNI 0000 0001 2312 1970, GRID grid.5132.5, Mathematical Institute, Leiden University, ; Leiden, The Netherlands
                [3 ]ISNI 0000 0001 2312 1970, GRID grid.5132.5, Division of Analytical Biosciences, Leiden Academic Center for Drug Research, , Leiden University, ; Leiden, The Netherlands
                [4 ]ISNI 0000 0004 4678 3135, GRID grid.450196.f, Netherlands Metabolomics Centre, ; Leiden, The Netherlands
                [5 ]ISNI 0000 0001 0768 2743, GRID grid.7886.1, University College Dublin, School of Agriculture and Food Science, ; Belfield, Dublin 4 Ireland
                Author information
                http://orcid.org/0000-0002-2931-4295
                http://orcid.org/0000-0001-8170-8876
                Article
                32647
                10.1038/s41598-018-32647-0
                6167362
                30275458
                d3ed3eeb-8ecb-41cd-b228-41ef1034813c
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 12 February 2018
                : 12 September 2018
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