Can Genetics Predict Risk for Alcohol Dependence?
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Neurotransmitter Resource Guide
NEUROTRANSMITTER RESOURCE GUIDE Science + Insight doctorsdata.com Doctor’s Data, Inc. Neurotransmitter RESOURCE GUIDE Table of Contents Sample Report Sample Report ........................................................................................................................................................................... 1 Analyte Considerations Phenylethylamine (B-phenylethylamine or PEA) ................................................................................................. 1 Tyrosine .......................................................................................................................................................................................... 3 Tyramine ........................................................................................................................................................................................4 Dopamine .....................................................................................................................................................................................6 3, 4-Dihydroxyphenylacetic Acid (DOPAC) ............................................................................................................... 7 3-Methoxytyramine (3-MT) ............................................................................................................................................... 9 Norepinephrine ........................................................................................................................................................................ -
Thyroid Hormone Influences Brain Gene Expression Programs And
Molecular Psychiatry https://doi.org/10.1038/s41380-018-0281-4 ARTICLE Thyroid hormone influences brain gene expression programs and behaviors in later generations by altering germ line epigenetic information 1 2,3 1 1 1 M. Elena Martinez ● Christine W. Duarte ● J. Patrizia Stohn ● Aldona Karaczyn ● Zhaofei Wu ● 1 1,3,4 Victoria E DeMambro ● Arturo Hernandez Received: 30 March 2018 / Revised: 16 August 2018 / Accepted: 26 September 2018 © Springer Nature Limited 2018 Abstract Genetic factors do not fully account for the relatively high heritability of neurodevelopmental conditions, suggesting that non-genetic heritable factors contribute to their etiology. To evaluate the potential contribution of aberrant thyroid hormone status to the epigenetic inheritance of neurological phenotypes, we examined genetically normal F2 generation descendants of mice that were developmentally overexposed to thyroid hormone due to a Dio3 mutation. Hypothalamic gene expression profiling in postnatal day 15 F2 descendants on the paternal lineage of ancestral male and female T3-overexposed mice 1234567890();,: 1234567890();,: revealed, respectively, 1089 and 1549 differentially expressed genes. A large number of them, 675 genes, were common to both sets, suggesting comparable epigenetic effects of thyroid hormone on both the male and female ancestral germ lines. Oligodendrocyte- and neuron-specific genes were strongly overrepresented among genes showing, respectively, increased and decreased expression. Altered gene expression extended to other brain regions and was associated in adulthood with decreased anxiety-like behavior, increased marble burying and reduced physical activity. The sperm of T3-overexposed male ancestors revealed significant hypomethylation of CpG islands associated with the promoters of genes involved in the early development of the central nervous system. -
Review Paper Monoamine Oxidase Inhibitors: a Review Concerning Dietary Tyramine and Drug Interactions
PsychoTropical Commentaries (2016) 1:1 – 90 © Fernwell Publications Review Paper Monoamine Oxidase Inhibitors: a Review Concerning Dietary Tyramine and Drug Interactions PK Gillman PsychoTropical Research, Bucasia, Queensland, Australia Abstract This comprehensive monograph surveys original data on the subject of both dietary tyramine and drug interactions relevant to Monoamine Oxidase Inhibitors (MAOIs), about which there is much outdated, incorrect and incomplete information in the medical literature and elsewhere. Fewer foods than previously supposed have problematically high tyramine levels because international food hygiene regulations have improved both production and handling. Cheese is the only food that has, in the past, been associated with documented fatalities from hypertension, and now almost all ‘supermarket’ cheeses are perfectly safe in healthy-sized portions. The variability of sensitivity to tyramine between individuals, and the sometimes unpredictable amount of tyramine content in foods, means a little knowledge and care are still advised. The interactions between MAOIs and other drugs are now well understood, are quite straightforward, and are briefly summarized here (by a recognised expert). MAOIs have no apparently clinically relevant pharmaco-kinetic interactions, and the only significant pharmaco-dynamic interaction, other than the ‘cheese reaction’ (caused by indirect sympatho-mimetic activity [ISA], is serotonin toxicity ST (aka serotonin syndrome) which is now well defined and straightforward to avoid by not co-administering any drug with serotonin re-uptake inhibitor (SRI) potency. There are no therapeutically used drugs, other than SRIs, that are capable of inducing serious ST with MAOIs. Anaesthesia is not contra- indicated if a patient is taking MAOIs. Most of the previously held concerns about MAOIs turn out to be mythical: they are either incorrect, or over-rated in importance, or stem from apprehensions born out of insufficient knowledge. -
S41598-021-90243-1.Pdf
www.nature.com/scientificreports OPEN Metabolomics and computational analysis of the role of monoamine oxidase activity in delirium and SARS‑COV‑2 infection Miroslava Cuperlovic‑Culf1,2*, Emma L. Cunningham3, Hossen Teimoorinia4, Anuradha Surendra1, Xiaobei Pan5, Stefany A. L. Bennett2,6, Mijin Jung5, Bernadette McGuiness3, Anthony Peter Passmore3, David Beverland7 & Brian D. Green5* Delirium is an acute change in attention and cognition occurring in ~ 65% of severe SARS‑CoV‑2 cases. It is also common following surgery and an indicator of brain vulnerability and risk for the development of dementia. In this work we analyzed the underlying role of metabolism in delirium‑ susceptibility in the postoperative setting using metabolomic profling of cerebrospinal fuid and blood taken from the same patients prior to planned orthopaedic surgery. Distance correlation analysis and Random Forest (RF) feature selection were used to determine changes in metabolic networks. We found signifcant concentration diferences in several amino acids, acylcarnitines and polyamines linking delirium‑prone patients to known factors in Alzheimer’s disease such as monoamine oxidase B (MAOB) protein. Subsequent computational structural comparison between MAOB and angiotensin converting enzyme 2 as well as protein–protein docking analysis showed that there potentially is strong binding of SARS‑CoV‑2 spike protein to MAOB. The possibility that SARS‑CoV‑2 infuences MAOB activity leading to the observed neurological and platelet‑based complications of SARS‑CoV‑2 infection requires further investigation. COVID-19 is an ongoing major global health emergency caused by severe acute respiratory syndrome coro- navirus SARS-CoV-2. Patients admitted to hospital with COVID-19 show a range of features including fever, anosmia, acute respiratory failure, kidney failure and gastrointestinal issues and the death rate of infected patients is estimated at 2.2%1. -
Mouse Adh4 Knockout Project (CRISPR/Cas9)
https://www.alphaknockout.com Mouse Adh4 Knockout Project (CRISPR/Cas9) Objective: To create a Adh4 knockout Mouse model (C57BL/6J) by CRISPR/Cas-mediated genome engineering. Strategy summary: The Adh4 gene (NCBI Reference Sequence: NM_011996 ; Ensembl: ENSMUSG00000037797 ) is located on Mouse chromosome 3. 9 exons are identified, with the ATG start codon in exon 1 and the TGA stop codon in exon 9 (Transcript: ENSMUST00000013458). Exon 2~7 will be selected as target site. Cas9 and gRNA will be co-injected into fertilized eggs for KO Mouse production. The pups will be genotyped by PCR followed by sequencing analysis. Note: Exon 2 starts from about 1.68% of the coding region. Exon 2~7 covers 84.17% of the coding region. The size of effective KO region: ~9976 bp. The KO region does not have any other known gene. Page 1 of 8 https://www.alphaknockout.com Overview of the Targeting Strategy Wildtype allele 5' gRNA region gRNA region 3' 1 2 3 4 5 6 7 9 Legends Exon of mouse Adh4 Knockout region Page 2 of 8 https://www.alphaknockout.com Overview of the Dot Plot (up) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 2000 bp section upstream of Exon 2 is aligned with itself to determine if there are tandem repeats. No significant tandem repeat is found in the dot plot matrix. So this region is suitable for PCR screening or sequencing analysis. Overview of the Dot Plot (down) Window size: 15 bp Forward Reverse Complement Sequence 12 Note: The 936 bp section downstream of Exon 7 is aligned with itself to determine if there are tandem repeats. -
The Effects of Phenelzine and Other Monoamine Oxidase Inhibitor
British Journal of Phammcology (1995) 114. 837-845 B 1995 Stockton Press All rights reserved 0007-1188/95 $9.00 The effects of phenelzine and other monoamine oxidase inhibitor antidepressants on brain and liver 12 imidazoline-preferring receptors Regina Alemany, Gabriel Olmos & 'Jesu's A. Garcia-Sevilla Laboratory of Neuropharmacology, Department of Fundamental Biology and Health Sciences, University of the Balearic Islands, E-07071 Palma de Mallorca, Spain 1 The binding of [3H]-idazoxan in the presence of 106 M (-)-adrenaline was used to quantitate 12 imidazoline-preferring receptors in the rat brain and liver after chronic treatment with various irre- versible and reversible monoamine oxidase (MAO) inhibitors. 2 Chronic treatment (7-14 days) with the irreversible MAO inhibitors, phenelzine (1-20 mg kg-', i.p.), isocarboxazid (10 mg kg-', i.p.), clorgyline (3 mg kg-', i.p.) and tranylcypromine (10mg kg-', i.p.) markedly decreased (21-71%) the density of 12 imidazoline-preferring receptors in the rat brain and liver. In contrast, chronic treatment (7 days) with the reversible MAO-A inhibitors, moclobemide (1 and 10 mg kg-', i.p.) or chlordimeform (10 mg kg-', i.p.) or with the reversible MAO-B inhibitor Ro 16-6491 (1 and 10 mg kg-', i.p.) did not alter the density of 12 imidazoline-preferring receptors in the rat brain and liver; except for the higher dose of Ro 16-6491 which only decreased the density of these putative receptors in the liver (38%). 3 In vitro, phenelzine, clorgyline, 3-phenylpropargylamine, tranylcypromine and chlordimeform dis- placed the binding of [3H]-idazoxan to brain and liver I2 imidazoline-preferring receptors from two distinct binding sites. -
Gene Expression Studies in Depression Development and Treatment
Mariani et al. Translational Psychiatry (2021) 11:354 https://doi.org/10.1038/s41398-021-01469-6 Translational Psychiatry REVIEW ARTICLE Open Access Gene expression studies in Depression development and treatment: an overview of the underlying molecular mechanisms and biological processes to identify biomarkers Nicole Mariani 1, Nadia Cattane2,CarminePariante 1 and Annamaria Cattaneo 2,3 Abstract A combination of different risk factors, such as genetic, environmental and psychological factors, together with immune system, stress response, brain neuroplasticity and the regulation of neurotransmitters, is thought to lead to the development of major depressive disorder (MDD). A growing number of studies have tried to investigate the underlying mechanisms of MDD by analysing the expression levels of genes involved in such biological processes. These studies have shown that MDD is not just a brain disorder, but also a body disorder, and this is mainly due to the interplay between the periphery and the Central Nervous System (CNS). To this purpose, most of the studies conducted so far have mainly dedicated to the analysis of the gene expression levels using postmortem brain tissue as well as peripheral blood samples of MDD patients. In this paper, we reviewed the current literature on candidate gene expression alterations and the few existing transcriptomics studies in MDD focusing on inflammation, neuroplasticity, neurotransmitters and stress-related genes. Moreover, we focused our attention on studies, which have investigated 1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; mRNA levels as biomarkers to predict therapy outcomes. This is important as many patients do not respond to antidepressant medication or could experience adverse side effects, leading to the interruption of treatment. -
Supplementary Material
Supplementary Material Table S1: Significant downregulated KEGGs pathways identified by DAVID following exposure to five cinnamon- based phenylpropanoids (p < 0.05). p-value Term: Genes (Benjamini) Cytokine-cytokine receptor interaction: FASLG, TNFSF14, CXCL11, IL11, FLT3LG, CCL3L1, CCL3L3, CXCR6, XCR1, 2.43 × 105 RTEL1, CSF2RA, TNFRSF17, TNFRSF14, CCNL2, VEGFB, AMH, TNFRSF10B, INHBE, IFNB1, CCR3, VEGFA, CCR2, IL12A, CCL1, CCL3, CXCL5, TNFRSF25, CCR1, CSF1, CX3CL1, CCL7, CCL24, TNFRSF1B, IL12RB1, CCL21, FIGF, EPO, IL4, IL18R1, FLT1, TGFBR1, EDA2R, HGF, TNFSF8, KDR, LEP, GH2, CCL13, EPOR, XCL1, IFNA16, XCL2 Neuroactive ligand-receptor interaction: OPRM1, THRA, GRIK1, DRD2, GRIK2, TACR2, TACR1, GABRB1, LPAR4, 9.68 × 105 GRIK5, FPR1, PRSS1, GNRHR, FPR2, EDNRA, AGTR2, LTB4R, PRSS2, CNR1, S1PR4, CALCRL, TAAR5, GABRE, PTGER1, GABRG3, C5AR1, PTGER3, PTGER4, GABRA6, GABRA5, GRM1, PLG, LEP, CRHR1, GH2, GRM3, SSTR2, Chlorogenic acid Chlorogenic CHRM3, GRIA1, MC2R, P2RX2, TBXA2R, GHSR, HTR2C, TSHR, LHB, GLP1R, OPRD1 Hematopoietic cell lineage: IL4, CR1, CD8B, CSF1, FCER2, GYPA, ITGA2, IL11, GP9, FLT3LG, CD38, CD19, DNTT, 9.29 × 104 GP1BB, CD22, EPOR, CSF2RA, CD14, THPO, EPO, HLA-DRA, ITGA2B Cytokine-cytokine receptor interaction: IL6ST, IL21R, IL19, TNFSF15, CXCR3, IL15, CXCL11, TGFB1, IL11, FLT3LG, CXCL10, CCR10, XCR1, RTEL1, CSF2RA, IL21, CCNL2, VEGFB, CCR8, AMH, TNFRSF10C, IFNB1, PDGFRA, EDA, CXCL5, TNFRSF25, CSF1, IFNW1, CNTFR, CX3CL1, CCL5, TNFRSF4, CCL4, CCL27, CCL24, CCL25, CCL23, IFNA6, IFNA5, FIGF, EPO, AMHR2, IL2RA, FLT4, TGFBR2, EDA2R, -
Variation in Protein Coding Genes Identifies Information
bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 1 1 2 3 4 5 Variation in protein coding genes identifies information flow as a contributor to 6 animal complexity 7 8 Jack Dean, Daniela Lopes Cardoso and Colin Sharpe* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Institute of Biological and Biomedical Sciences 25 School of Biological Science 26 University of Portsmouth, 27 Portsmouth, UK 28 PO16 7YH 29 30 * Author for correspondence 31 [email protected] 32 33 Orcid numbers: 34 DLC: 0000-0003-2683-1745 35 CS: 0000-0002-5022-0840 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Abstract bioRxiv preprint doi: https://doi.org/10.1101/679456; this version posted June 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Animal complexity and information flow 2 1 Across the metazoans there is a trend towards greater organismal complexity. How 2 complexity is generated, however, is uncertain. Since C.elegans and humans have 3 approximately the same number of genes, the explanation will depend on how genes are 4 used, rather than their absolute number. -
Identification of Key Genes and Pathways Involved in Response To
Deng et al. Biol Res (2018) 51:25 https://doi.org/10.1186/s40659-018-0174-7 Biological Research RESEARCH ARTICLE Open Access Identifcation of key genes and pathways involved in response to pain in goat and sheep by transcriptome sequencing Xiuling Deng1,2†, Dong Wang3†, Shenyuan Wang1, Haisheng Wang2 and Huanmin Zhou1* Abstract Purpose: This aim of this study was to investigate the key genes and pathways involved in the response to pain in goat and sheep by transcriptome sequencing. Methods: Chronic pain was induced with the injection of the complete Freund’s adjuvant (CFA) in sheep and goats. The animals were divided into four groups: CFA-treated sheep, control sheep, CFA-treated goat, and control goat groups (n 3 in each group). The dorsal root ganglions of these animals were isolated and used for the construction of a cDNA= library and transcriptome sequencing. Diferentially expressed genes (DEGs) were identifed in CFA-induced sheep and goats and gene ontology (GO) enrichment analysis was performed. Results: In total, 1748 and 2441 DEGs were identifed in CFA-treated goat and sheep, respectively. The DEGs identi- fed in CFA-treated goats, such as C-C motif chemokine ligand 27 (CCL27), glutamate receptor 2 (GRIA2), and sodium voltage-gated channel alpha subunit 3 (SCN3A), were mainly enriched in GO functions associated with N-methyl- D-aspartate (NMDA) receptor, infammatory response, and immune response. The DEGs identifed in CFA-treated sheep, such as gamma-aminobutyric acid (GABA)-related DEGs (gamma-aminobutyric acid type A receptor gamma 3 subunit [GABRG3], GABRB2, and GABRB1), SCN9A, and transient receptor potential cation channel subfamily V member 1 (TRPV1), were mainly enriched in GO functions related to neuroactive ligand-receptor interaction, NMDA receptor, and defense response. -
Stem Cells and Ion Channels
Stem Cells International Stem Cells and Ion Channels Guest Editors: Stefan Liebau, Alexander Kleger, Michael Levin, and Shan Ping Yu Stem Cells and Ion Channels Stem Cells International Stem Cells and Ion Channels Guest Editors: Stefan Liebau, Alexander Kleger, Michael Levin, and Shan Ping Yu Copyright © 2013 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “Stem Cells International.” All articles are open access articles distributed under the Creative Com- mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board Nadire N. Ali, UK Joseph Itskovitz-Eldor, Israel Pranela Rameshwar, USA Anthony Atala, USA Pavla Jendelova, Czech Republic Hannele T. Ruohola-Baker, USA Nissim Benvenisty, Israel Arne Jensen, Germany D. S. Sakaguchi, USA Kenneth Boheler, USA Sue Kimber, UK Paul R. Sanberg, USA Dominique Bonnet, UK Mark D. Kirk, USA Paul T. Sharpe, UK B. Bunnell, USA Gary E. Lyons, USA Ashok Shetty, USA Kevin D. Bunting, USA Athanasios Mantalaris, UK Igor Slukvin, USA Richard K. Burt, USA Pilar Martin-Duque, Spain Ann Steele, USA Gerald A. Colvin, USA EvaMezey,USA Alexander Storch, Germany Stephen Dalton, USA Karim Nayernia, UK Marc Turner, UK Leonard M. Eisenberg, USA K. Sue O’Shea, USA Su-Chun Zhang, USA Marina Emborg, USA J. Parent, USA Weian Zhao, USA Josef Fulka, Czech Republic Bruno Peault, USA Joel C. Glover, Norway Stefan Przyborski, UK Contents Stem Cells and Ion Channels, Stefan Liebau, -
A Yeast Phenomic Model for the Influence of Warburg Metabolism on Genetic Buffering of Doxorubicin Sean M
Santos and Hartman Cancer & Metabolism (2019) 7:9 https://doi.org/10.1186/s40170-019-0201-3 RESEARCH Open Access A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin Sean M. Santos and John L. Hartman IV* Abstract Background: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. Methods: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. Results: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context.