Literature DB >> 33998853

An Approach to Biomarker Discovery of Cannabis Use Utilizing Proteomic, Metabolomic, and Lipidomic Analyses.

Jesse D Hinckley1, Laura Saba2, Kristen Raymond1, Karsten Bartels1,3, Jost Klawitter3,4, Uwe Christians3,4, Christian Hopfer1.   

Abstract

Introduction: Relatively little is known about the molecular pathways influenced by cannabis use in humans. We used a multi-omics approach to examine protein, metabolomic, and lipid markers in plasma differentiating between cannabis users and nonusers to understand markers associated with cannabis use.
Methods: Eight discordant twin pairs and four concordant twin pairs for cannabis use completed a blood draw, urine and plasma toxicology testing, and provided information about their past 30-day cannabis use and other substance use patterns. The 24 twins were all non-Hispanic whites. Sixty-six percent were female. Median age was 30 years. Fifteen participants reported that they had used cannabis in the last 30 days, including eight participants that used every day or almost every day (29-30 of 30 days). Of these 15 participants, plasma 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH) and total tetrahydrocannabinol (THC) concentrations were detectable in 12 participants. Among the eight "heavy users" the amount of total THC (sum of THC and its metabolites) and plasma THC-COOH concentrations varied widely, with ranges of 13.1-1713 ng/mL and 2.7-284 ng/mL, respectively. A validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay measured plasma THC-COOH, THC, and other cannabinoids and metabolites. Plasma THC-COOH was used as the primary measure. Expression levels of 1305 proteins were measured using SOMAScan assay, and 34 lipid mediators and 314 metabolites were measured with LC-MS/MS. Analyses examined associations between markers and THC-COOH levels with and without taking genetic relatedness into account.
Results: Thirteen proteins, three metabolites, and two lipids were identified as associated with THC-COOH levels. Myc proto-oncogene was identified as associated with THC-COOH levels in both molecular insight and potential marker analyses. Five pathways (interleukin-6 production, T lymphocyte regulation, apoptosis, kinase signaling pathways, and nuclear factor kappa-light-chain-enhancer of activated B cells) were linked with molecules identified in these analyses. Conclusions: THC-COOH levels are associated with immune system-related pathways. This study presents a feasible approach to identify additional molecular markers associated with THC-COOH levels.

Entities:  

Keywords:  Myc proto-oncogene; cannabis; markers

Mesh:

Substances:

Year:  2020        PMID: 33998853      PMCID: PMC8864439          DOI: 10.1089/can.2020.0002

Source DB:  PubMed          Journal:  Cannabis Cannabinoid Res        ISSN: 2378-8763


  82 in total

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2.  Excretion patterns of cannabinoid metabolites after last use in a group of chronic users.

Authors:  G M Ellis; M A Mann; B A Judson; N T Schramm; A Tashchian
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3.  Determination of delta 1-tetrahydrocannabinol in human fat biopsies from marihuana users by gas chromatography-mass spectrometry.

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4.  Role of MSK1 in the Induction of NF-κB by the Chemokine CX3CL1 in Microglial Cells.

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Review 5.  Cannabinoids as novel anti-inflammatory drugs.

Authors:  Prakash Nagarkatti; Rupal Pandey; Sadiye Amcaoglu Rieder; Venkatesh L Hegde; Mitzi Nagarkatti
Journal:  Future Med Chem       Date:  2009-10       Impact factor: 3.808

6.  CX3CL1/fractalkine is released from apoptotic lymphocytes to stimulate macrophage chemotaxis.

Authors:  Lucy A Truman; Catriona A Ford; Marta Pasikowska; John D Pound; Sarah J Wilkinson; Ingrid E Dumitriu; Lynsey Melville; Lauren A Melrose; Carol Anne Ogden; Robert Nibbs; Gerard Graham; Christophe Combadiere; Christopher D Gregory
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7.  20-Hydroxyeicosatetraenoic acid stimulates nuclear factor-kappaB activation and the production of inflammatory cytokines in human endothelial cells.

Authors:  Tsuneo Ishizuka; Jennifer Cheng; Harpreet Singh; Marco D Vitto; Vijay L Manthati; John R Falck; Michal Laniado-Schwartzman
Journal:  J Pharmacol Exp Ther       Date:  2007-10-18       Impact factor: 4.030

8.  Amino acids in a targeted versus a non-targeted metabolomics LC-MS/MS assay. Are the results consistent?

Authors:  Jacek Klepacki; Jost Klawitter; Jelena Klawitter; Anis Karimpour-Fard; Joshua Thurman; Gordon Ingle; Dharmesh Patel; Uwe Christians
Journal:  Clin Biochem       Date:  2016-06-08       Impact factor: 3.281

9.  Does marijuana use have residual adverse effects on self-reported health measures, socio-demographics and quality of life? A monozygotic co-twin control study in men.

Authors:  Seth A Eisen; Sunanta Chantarujikapong; Hong Xian; Michael J Lyons; Rosemary Toomey; William R True; Jeffrey F Scherrer; Jack Goldberg; Ming T Tsuang
Journal:  Addiction       Date:  2002-09       Impact factor: 6.526

10.  MAPK14 and CNR1 gene variant interactions: effects on brain volume deficits in schizophrenia patients with marijuana misuse.

Authors:  O E Onwuameze; K W Nam; E A Epping; T H Wassink; S Ziebell; N C Andreasen; B-C Ho
Journal:  Psychol Med       Date:  2012-07-31       Impact factor: 7.723

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Authors:  Sharlene D Newman; Ashley M Schnakenberg Martin; David Raymond; Hu Cheng; Landon Wilson; Stephen Barnes; Brian F O'Donnell
Journal:  PLoS One       Date:  2022-06-02       Impact factor: 3.752

Review 2.  A Proteomic View of Cellular and Molecular Effects of Cannabis.

Authors:  Morteza Abyadeh; Vivek Gupta; Joao A Paulo; Veer Gupta; Nitin Chitranshi; Angela Godinez; Danit Saks; Mafruha Hasan; Ardeshir Amirkhani; Matthew McKay; Ghasem H Salekdeh; Paul A Haynes; Stuart L Graham; Mehdi Mirzaei
Journal:  Biomolecules       Date:  2021-09-27

3.  Genetic basis of cannabis use: a systematic review.

Authors:  Alannah Hillmer; Caroul Chawar; Stephanie Sanger; Alessia D'Elia; Mehreen Butt; Raveena Kapoor; Flavio Kapczinski; Lehana Thabane; Zainab Samaan
Journal:  BMC Med Genomics       Date:  2021-08-12       Impact factor: 3.063

  3 in total

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