ORAL PROGESTERONE (OMP): Best Practices
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PLGG1, a Plastidic Glycolate Glycerate Transporter, Is Required for Photorespiration and Defines a Unique Class of Metabolite Transporters
PLGG1, a plastidic glycolate glycerate transporter, is required for photorespiration and defines a unique class of metabolite transporters Thea R. Picka,1, Andrea Bräutigama,1, Matthias A. Schulza, Toshihiro Obatab, Alisdair R. Fernieb, and Andreas P. M. Webera,2 aInstitute of Plant Biochemistry, Cluster of Excellence on Plant Sciences, Heinrich Heine University, 40225 Düsseldorf, Germany; and bMax-Planck Institute for Molecular Plant Physiology, Department of Molecular Physiology, 14476 Potsdam-Golm, Germany Edited by Wolf B. Frommer, Carnegie Institution for Science, Stanford, CA, and accepted by the Editorial Board January 8, 2013 (received for review September 4, 2012) Photorespiratory carbon flux reaches up to a third of photosyn- (PGLP). Glycolate is exported from the chloroplasts to the per- thetic flux, thus contributes massively to the global carbon cycle. oxisomes, where it is oxidized to glyoxylate by glycolate oxidase The pathway recycles glycolate-2-phosphate, the most abundant (GOX) and transaminated to glycine by Ser:glyoxylate and Glu: byproduct of RubisCO reactions. This oxygenation reaction of glyoxylate aminotransferase (SGT and GGT, respectively). Glycine RubisCO and subsequent photorespiration significantly limit the leaves the peroxisomes and enters the mitochondria, where two biomass gains of many crop plants. Although photorespiration is molecules of glycine are deaminated and decarboxylated by the a compartmentalized process with enzymatic reactions in the glycine decarboxylase complex (GDC) and serine hydroxymethyl- chloroplast, the peroxisomes, the mitochondria, and the cytosol, transferase (SHMT) to form one molecule each of serine, ammo- nia, and carbon dioxide. Serine is exported from the mitochondria no transporter required for the core photorespiratory cycle has to the peroxisomes, where it is predominantly converted to glyc- been identified at the molecular level to date. -
The Self-Inhibitory Nature of Metabolic Networks and Its Alleviation Through Compartmentalization
ARTICLE Received 30 Oct 2016 | Accepted 23 May 2017 | Published 10 Jul 2017 DOI: 10.1038/ncomms16018 OPEN The self-inhibitory nature of metabolic networks and its alleviation through compartmentalization Mohammad Tauqeer Alam1,2, Viridiana Olin-Sandoval1,3, Anna Stincone1,w, Markus A. Keller1,4, Aleksej Zelezniak1,5,6, Ben F. Luisi1 & Markus Ralser1,5 Metabolites can inhibit the enzymes that generate them. To explore the general nature of metabolic self-inhibition, we surveyed enzymological data accrued from a century of experimentation and generated a genome-scale enzyme-inhibition network. Enzyme inhibition is often driven by essential metabolites, affects the majority of biochemical processes, and is executed by a structured network whose topological organization is reflecting chemical similarities that exist between metabolites. Most inhibitory interactions are competitive, emerge in the close neighbourhood of the inhibited enzymes, and result from structural similarities between substrate and inhibitors. Structural constraints also explain one-third of allosteric inhibitors, a finding rationalized by crystallographic analysis of allosterically inhibited L-lactate dehydrogenase. Our findings suggest that the primary cause of metabolic enzyme inhibition is not the evolution of regulatory metabolite–enzyme interactions, but a finite structural diversity prevalent within the metabolome. In eukaryotes, compartmentalization minimizes inevitable enzyme inhibition and alleviates constraints that self-inhibition places on metabolism. 1 Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK. 2 Division of Biomedical Sciences, Warwick Medical School, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK. 3 Department of Food Science and Technology, Instituto Nacional de Ciencias Me´dicas y Nutricio´n Salvador Zubira´n, Vasco de Quiroga 15, Tlalpan, 14080 Mexico City, Mexico. -
Increased and Mistimed Sex Hormone Production in Night Shift Workers
Published OnlineFirst March 3, 2015; DOI: 10.1158/1055-9965.EPI-14-1271 Research Article Cancer Epidemiology, Biomarkers Increased and Mistimed Sex Hormone Production & Prevention in Night Shift Workers Kyriaki Papantoniou1,2,3,4, Oscar J. Pozo2, Ana Espinosa1,2,3,4, Josep Marcos2,3, Gemma Castano-Vinyals~ 1,2,3,4, Xavier Basagana~ 1,2,3,4, Elena Juanola Pages 5, Joan Mirabent6,7, Jordi Martín8, Patricia Such Faro9, Amparo Gasco Aparici10, Benita Middleton11, Debra J. Skene11, and Manolis Kogevinas1,2,3,4,12 Abstract Background: Night shift work has been associated with Results: Night workers had higher levels of total progestagens an increased risk for breast and prostate cancer. The effect [geometric mean ratio (GMR) 1.65; 95% confidence intervals of circadian disruption on sex steroid production is a pos- (CI), 1.17–2.32] and androgens (GMR: 1.44; 95% CI, 1.03–2.00), sible underlying mechanism, underinvestigated in hum- compared with day workers, after adjusting for potential con- ans. We have assessed daily rhythms of sex hormones founders. The increased sex hormone levels among night and melatonin in night and day shift workers of both shift workers were not related to the observed suppression of sexes. 6-sulfatoxymelatonin. Peak time of androgens was significantly Methods: We recruited 75 night and 42 day workers, ages later among night workers, compared with day workers (testos- 22 to 64 years, in different working settings. Participants terone: 12:14 hours; 10:06-14:48 vs. 08:35 hours; 06:52-10:46). collected urine samples from all voids over 24 hours on a Conclusions: We found increased levels of progestagens and working day. -
Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice
pharmaceutics Review Mechanisms of CYP450 Inhibition: Understanding Drug-Drug Interactions Due to Mechanism-Based Inhibition in Clinical Practice Malavika Deodhar 1, Sweilem B Al Rihani 1 , Meghan J. Arwood 1, Lucy Darakjian 1, Pamela Dow 1 , Jacques Turgeon 1,2 and Veronique Michaud 1,2,* 1 Tabula Rasa HealthCare Precision Pharmacotherapy Research and Development Institute, Orlando, FL 32827, USA; [email protected] (M.D.); [email protected] (S.B.A.R.); [email protected] (M.J.A.); [email protected] (L.D.); [email protected] (P.D.); [email protected] (J.T.) 2 Faculty of Pharmacy, Université de Montréal, Montreal, QC H3C 3J7, Canada * Correspondence: [email protected]; Tel.: +1-856-938-8697 Received: 5 August 2020; Accepted: 31 August 2020; Published: 4 September 2020 Abstract: In an ageing society, polypharmacy has become a major public health and economic issue. Overuse of medications, especially in patients with chronic diseases, carries major health risks. One common consequence of polypharmacy is the increased emergence of adverse drug events, mainly from drug–drug interactions. The majority of currently available drugs are metabolized by CYP450 enzymes. Interactions due to shared CYP450-mediated metabolic pathways for two or more drugs are frequent, especially through reversible or irreversible CYP450 inhibition. The magnitude of these interactions depends on several factors, including varying affinity and concentration of substrates, time delay between the administration of the drugs, and mechanisms of CYP450 inhibition. Various types of CYP450 inhibition (competitive, non-competitive, mechanism-based) have been observed clinically, and interactions of these types require a distinct clinical management strategy. This review focuses on mechanism-based inhibition, which occurs when a substrate forms a reactive intermediate, creating a stable enzyme–intermediate complex that irreversibly reduces enzyme activity. -
Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling
H OH metabolites OH Article Consistency, Inconsistency, and Ambiguity of Metabolite Names in Biochemical Databases Used for Genome-Scale Metabolic Modelling Nhung Pham , Ruben G. A. van Heck † , Jesse C. J. van Dam , Peter J. Schaap , Edoardo Saccenti and Maria Suarez-Diez * Laboratory of Systems and Synthetic Biology; Wageningen University & Research, 6708 WE, Wageningen, The Netherlands; [email protected] (N.P.); [email protected] (R.G.A.v.H.); [email protected] (J.C.J.v.D.); [email protected] (P.J.S.); [email protected] (E.S.) * Correspondence: [email protected] † Current address: EDP Patent Attorneys, 6708 WH, Wageningen, The Netherlands. Received: 28 December 2018; Accepted: 31 January 2019; Published: 6 February 2019 Abstract: Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. -
Ranking Metabolite Sets by Their Activity Levels
H OH metabolites OH Article Ranking Metabolite Sets by Their Activity Levels Karen McLuskey 1,† , Joe Wandy 1,† , Isabel Vincent 2 , Justin J. J. van der Hooft 3 , Simon Rogers 4 , Karl Burgess 5 and Rónán Daly 1,* 1 Glasgow Polyomics, University of Glasgow, Glasgow G61 1QH, UK; [email protected] (K.M.); [email protected] (J.W.) 2 IBioIC, Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow G1 1XQ, UK; [email protected] 3 Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The Netherlands; [email protected] 4 School of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK; [email protected] 5 Centre for Synthetic and Systems Biology, School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3JG, UK; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Abstract: Related metabolites can be grouped into sets in many ways, e.g., by their participation in series of chemical reactions (forming metabolic pathways), or based on fragmentation spectral similarities or shared chemical substructures. Understanding how such metabolite sets change in relation to experimental factors can be incredibly useful in the interpretation and understanding of complex metabolomics data sets. However, many of the available tools that are used to perform this analysis are not entirely suitable for the analysis of untargeted metabolomics measurements. Here, we present PALS (Pathway Activity Level Scoring), a Python library, command line tool, and Web application that performs the ranking of significantly changing metabolite sets over different experimental conditions. -
Metabolite Secretion in Microorganisms: the Theory of Metabolic Overflow Put to the Test
Metabolomics (2018) 14:43 https://doi.org/10.1007/s11306-018-1339-7 REVIEW ARTICLE Metabolite secretion in microorganisms: the theory of metabolic overflow put to the test Farhana R. Pinu1 · Ninna Granucci2 · James Daniell2,3 · Ting‑Li Han2 · Sonia Carneiro4 · Isabel Rocha4 · Jens Nielsen5,6 · Silas G. Villas‑Boas2 Received: 10 November 2017 / Accepted: 7 February 2018 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Introduction Microbial cells secrete many metabolites during growth, including important intermediates of the central car- bon metabolism. This has not been taken into account by researchers when modeling microbial metabolism for metabolic engineering and systems biology studies. Materials and Methods The uptake of metabolites by microorganisms is well studied, but our knowledge of how and why they secrete different intracellular compounds is poor. The secretion of metabolites by microbial cells has traditionally been regarded as a consequence of intracellular metabolic overflow. Conclusions Here, we provide evidence based on time-series metabolomics data that microbial cells eliminate some metabo- lites in response to environmental cues, independent of metabolic overflow. Moreover, we review the different mechanisms of metabolite secretion and explore how this knowledge can benefit metabolic modeling and engineering. Keywords Microbial metabolism · Microorganisms · Active efflux · Secretion · Metabolic engineering · Metabolic modeling · Systems biology 1 Introduction Microorganisms have been used for the production of indus- trially relevant compounds for many years. Corynebacte- rium glutamicum and its mutants have been employed for the large-scale industrial production of glutamate, lysine Electronic supplementary material The online version of this and other flavor active amino acids (Hermann and Krämer article (https ://doi.org/10.1007/s1130 6-018-1339-7) contains 1996; Krämer 1994, 2004). -
PDF Datasheet
Product Datasheet Progesterone Antibody NB100-62847 Unit Size: 0.5 ml Store at 4C short term. Aliquot and store at -20C long term. Avoid freeze-thaw cycles. Protocols, Publications, Related Products, Reviews, Research Tools and Images at: www.novusbio.com/NB100-62847 Updated 5/17/2019 v.20.1 Earn rewards for product reviews and publications. Submit a publication at www.novusbio.com/publications Submit a review at www.novusbio.com/reviews/destination/NB100-62847 Page 1 of 2 v.20.1 Updated 5/17/2019 NB100-62847 Progesterone Antibody Product Information Unit Size 0.5 ml Concentration 5.0 mg/ml Storage Store at 4C short term. Aliquot and store at -20C long term. Avoid freeze-thaw cycles. Clonality Polyclonal Preservative 0.09% Sodium Azide Isotype IgG Purity Protein G purified Buffer PBS Product Description Host Rabbit Species All Species Reactivity Notes Broad Specificity/Sensitivity NB100-62847 is specific for progesterone, a steroid hormone synthesized from the cholesterol derivative, pregnenolone, in the cortex of the adrenal gland. Cross reactivity: 100% Progesterone 4% 11-deoxycoritcosterone 4% Corticosterone 3% 11 Hydroxyprogesterone 2.5% 20 Dihydroprogesterone 1.5% 17A Hydroxyprogesterone Less than 0.005% pregnanetriol, dihydrotestosterone, androstanedione, 11-deoxycortisol, 21 deoxycortisol, testostserone, 17b oestradiol, 17a oestradiol, dehydroepiandrosterone, androsterone, pregnanediol, pregnenolone, oestrone, oestriol, 16-Epi oestriol, and 6 keto oestradiol. Progesterone is secreted by the corpus luteum and acts to prepare the endometrium for the implantation of a fertilized egg. During pregnancy, it is secreted by the placenta in order to prevent spontaneous abortion and to stimulate the development of mammary tissue to produce milk. -
Quantification of Hair Corticosterone, DHEA and Testosterone As
animals Article Quantification of Hair Corticosterone, DHEA and Testosterone as a Potential Tool for Welfare Assessment in Male Laboratory Mice Alberto Elmi 1 , Viola Galligioni 2, Nadia Govoni 1 , Martina Bertocchi 1 , Camilla Aniballi 1 , Maria Laura Bacci 1 , José M. Sánchez-Morgado 2 and Domenico Ventrella 1,* 1 Department of Veterinary Medical Sciences, University of Bologna, 40064 Ozzano dell’Emilia, BO, Italy; [email protected] (A.E.); [email protected] (N.G.); [email protected] (M.B.); [email protected] (C.A.); [email protected] (M.L.B.) 2 Comparative Medicine Unit, Trinity College Dublin, D02 Dublin, Ireland; [email protected] (V.G.); [email protected] (J.M.S.-M.) * Correspondence: [email protected]; Tel.: +39-051-2097-926 Received: 11 November 2020; Accepted: 14 December 2020; Published: 16 December 2020 Simple Summary: Mice is the most used species in the biomedical research laboratory setting. Scientists are constantly striving to find new tools to assess their welfare, in order to ameliorate husbandry conditions, leading to a better life and scientific data. Steroid hormones can provide information regarding different behavioral tracts of laboratory animals but their quantification often require stressful sampling procedures. Hair represents a good, less invasive, alternative in such scenario and is also indicative of longer timespan due to hormones’ accumulation. The aim of the work was to quantify steroid hormones in the hair of male laboratory mice and to look for differences imputable to age and housing conditions (pairs VS groups). Age influenced all analysed hormones by increasing testosterone and dehydroepiandrosterone (DHEA) levels and decreasing corticosterone. -
Effect of Maternal Intrahepatic Cholestasis on Fetal Steroid Metabolism
Effect of Maternal Intrahepatic Cholestasis on Fetal Steroid Metabolism Timo J. Laatikainen, … , Jari I. Peltonen, Pekka L. Nylander J Clin Invest. 1974;53(6):1709-1715. https://doi.org/10.1172/JCI107722. Research Article Estriol, estriol sulfate, progesterone, and 17 neutral steroid sulfates, including estriol precursors and progesterone metabolites, were determined in 27 cord plasma samples collected after pregnancies complicated by intrahepatic cholestasis of the mother. The levels of these steroids were compared with those in the cord plasma of 42 healthy controls. In the cord plasma, the steroid profile after pregnancies complicated by maternal intrahepatic cholestasis differed greatly from that seen after uncomplicated pregnancy. Two main differences were found. In the disulfate fraction, the concentrations of two pregnanediol isomers, 5α-pregnane-3α,20α-diol and 5β-pregnane-3α,20α-diol, were high after cholestasis. Other investigators have shown that, as a result of cholestasis, these pregnanediol sulfates circulate in greatly elevated amounts in the maternal plasma. Our results indicate that in cholestasis these steroids cross the placenta into the fetal compartment, where they circulate in elevated amounts as disulfates. Secondly, the concentrations of several steroid sulfates known to be synthesized by the fetus were significantly lower in the cholestasis group than in the healthy controls. This was especially true of 16α-hydroxydehydroepiandrosterone sulfate and 16α- hydroxypregnenolone sulfate. These results suggest that, in pregnancies complicated by maternal intrahepatic cholestasis, impairment of fetal steroid synthesis, and especially of 16α-hydroxylation, occurs in the fetal compartment. Thus, the changes in maternal steroid metabolism caused by cholestasis are reflected in the steroid profile of the fetoplacental circulation. -
Metabolism of Testoterone and Related Steroids in Metastatic Interstitial Cell Carcinoma of the Testis
Metabolism of testoterone and related steroids in metastatic interstitial cell carcinoma of the testis. M B Lipsett, … , C W Bardin, L M Fishman J Clin Invest. 1966;45(11):1700-1709. https://doi.org/10.1172/JCI105476. Research Article Find the latest version: https://jci.me/105476/pdf Journal of Clinical Investigation Vol. 45, No. 11, 1966 Metabolism of Testosterone and Related Steroids in Metastatic Interstitial Cell Carcinoma of the Testis * M. B. LIPSETT,t G. A. SARFATY, H. WILSON, C. WAYNE BARDIN, AND L. M. FISHMAN (From the Endocrinology Branch, National Cancer Institute, Bethesda, Md.) Interstitial cell carcinoma of the testis is a singu- production rate has been shown to be a conse- larly rare steroid-producing cancer. Of the seven quence of metabolism of dehydroepiandrosterone reported cases (1-7), urinary 17-ketosteroid (17- sulfate. KS) excretion was high in the four cases in which it was measured. Abelson, Bulaschenko, Trom- Methods mer, and Valdes-Dapena (7) fractionated the uri- Routine methods were used to analyze the following: nary 17-ketosteriods and corticoids in one recently urinary 17-KS (8), urinary 17-hydroxycorticoids (9), reported case. There is, however, no comprehen- plasma Silber-Porter chromogens (10), and plasma tes- tosterone (11). sive study of either the production of androgens Gas-liquid chromatography. We carried out gas-liquid or related steroids by this tumor. We have had chromatography (GLC) in a Glowell Chromolab gas the opportunity to study a patient with metastatic chromatograph utilizing a 'Sr ionization detector oper- interstitial cell carcinoma, and we have examined ating at 1,050 v. -
PDG) Enzyme Immunoassay Kit
DetectX® Pregnanediol-3-Glucuronide (PDG) Enzyme Immunoassay Kit 1 Plate Kit Catalog Number K037-H1 5 Plate Kit Catalog Number K037-H5 Species Independent Sample Types Validated: Dried Fecal Extracts, Urine, Extracted Serum/Plasma, and Tissue Culture Media Please read this insert completely prior to using the product. For research use only. Not for use in diagnostic procedures. www.ArborAssays.com K037-H WEB 210301 TABLE OF CONTENTS Background 3 Assay Principle 4 Related Products 4 Supplied Components 5 Storage Instructions 5 Other Materials Required 6 Precautions 6 Sample Types 7 Sample Preparation 7 Reagent Preparation 8 Assay Protocol 9 Calculation of Results 10 Typical Data 10-11 Validation Data Sensitivity, Linearity, etc. 11-13 Samples Values and Cross Reactivity 14 Warranty & Contact Information 15 Plate Layout Sheet 16 ® 2 EXPECT ASSAY ARTISTRY™ K037-H WEB 210301 BACKGROUND Pregnanediol Glucuronide, C27H44O8, also known as PDG (5β-Pregnan-3a,20a-diol 3-glucosiduronate) is the major metabolite of progesterone1-4. Progesterone is the hormone involved in the female menstrual cycle, gestation and embryogenesis of humans and other species. Progesterone belongs to a class of hormones called progestogens, and is the major naturally occurring human progestogen5,6. Progesterone is an essential regulator of human female reproductive function in the uterus, ovary, mammary gland and brain, and plays an important role in non-reproductive tissues such as the cardiovascular system, bone and the central nervous system. Progesterone action is conveyed by two isoforms of the nuclear progesterone receptor (PR), PRA and PRB. PRA and B are expressed in a variety of normal breast tissue from humans, rats and mice and is also expressed in breast cancer cells7,8.