Saturday, August 25, 2018

Genomics And Metabolomics In Psychiatry As A Combined Tool - Predicting SSRI Response

From Reference 1
The last two posts here were an introduction to some recent work that combine aspects of genomics and metabolomics.  The paper that I will briefly discuss here is done by researchers from the same groups focused on studies in genomics and metabolomics.  They present work that is quite exciting because it illustrates the amount of information necessary to analyze the biological complexity present in brain science and they produce results that may prove very useful from a clinical standpoint.  The study involves serotonin metabolism in depression. I have discussed serotonin in many places on this blog as a significant neurotransmitter that refuses to go away despite the various critics.  This work will illustrate why that may be.  The first paper (1) looks at the relationship between baseline serotonin levels and levels after treatment with selective serotonin reuptake inhibitors and what those phenotypes may be associated with at a genetic level.  A second paper (2) that I will discuss in a subsequent post looks at kynurenine metabolism in depression and the association of that metabolite with symptoms severity and the genetic correlates.  Both papers offer a new look at serotonin metabolism in depression and its genetic basis.

In paper 1, the sample included 366 patients in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study (PGRN-AMPS). 918 samples from these patients after 4 and 8 weeks of SSRI therapy were used for the metabolomics part of the study.  Baseline serotonin and changes in these levels were measured.  Patients with higher baseline levels and/or greatest drops in serotonin levels were determined to have the best response to treatment with SSRI medications.

From Reference 1

When a GWAS study was performed looking at the baseline serotonin concentrations as the phenotype and at 4 and 8 weeks of treatment a significant single nucleotide polymorphism (SNP) cluster was noted at the Tetraspanin 5 (TSPAN5) gene on chromosome 4 and a number of SNPs were noted at the Glutamate-rich 3 (ERICH3) gene on chromosome 1.  Both are highlighted in the Manhattan plot at the top of this post. Both of these genes were noted to be novel genes involved in serotonin metabolism and plasma serotonin concentrations.

The SNPs 5' of the TSPAN5 gene were cis regulatory elements for that gene (referred to in the paper as cis-expression quantitative trait loci or eQTLs).  In the ERICH3 cluster SNPs, two were variants that were associated with proteosome mediated degradation of ERICH3.  Changes in the expression of this gene were correlated with plasma serotonin concentrations but the serotonin pathway expression was unaltered.  One of the SNPs was also associated with clinical response in the STAR*D study.

The study is interesting because of the plasma serotonin concentration phenotypes, positive treatment response, and identification of associated genes and SNPs. What has not been determined is the specific mechanism of the drop in serotonin levels and the specific genetic mechanisms.  In the experimental section of their paper they show how the serotonin concentrations in the periphery could also affect levels in the CNS by looking at TSPAN5 and ERICH3 expression in neuroblastoma cells.  The authors were able to demonstrate that both mRNA and protein fold changes of enzymes involved in the synthesis, degradation, and transport of serotonin were affected by knockdown (KD) - (a technique of gene silencing by the introduction of doublestranded interfering RNAs (siRNAs)) or overexpression (OE) (a technique leading to enhanced gene transcription by the introduction of regulatory elements for transcription.) genes.     

The paper is an excellent example of molecular psychiatry and a possible application in the field of precision medicine.  The ultimate goal is to determine the treatment in psychiatry that has a high probability of working as soon as possible and eliminate the long trials that many people have to endure before they find a medication that is effect for (in this case) depression.  Along the way it will be evident that just as clinical psychiatrists have know for some time -  the general categories of psychiatric disorders - just like all polygenic illnesses are really a collection of diverse disorders at the omic levels.

The rapid identification of these many subtypes will not only lead to more rapid and efficient treatment - but also prevent unnecessary exposure to medications that can be both intolerable and ineffective.

In closing, some will question the utility of reading papers that contain a lot of terminology from what we used to call molecular biology.  I like to read these papers because it continues to consolidate what I learned in medical school and add to that knowledge. In my biochemistry seminars back then - there was still a lot of emphasis on enzymatic pathways and protein function. We had to know the basics of nucleic acid structure, function, and analysis - but nothing like the details presented in this paper.  As an example, we knew the synthetic pathways and enzymes for serotonin biosynthesis discussed in this paper - but the idea of analyzing human DNA with a chip encompassing 7.5 million SNPs across hundreds of research subjects would have been mind blowing and in many ways it still is.  Reading papers like this one also assures that you are not stuck in serotonin metabolism and receptor theory from the 1980s.  If that is all you know these days - it is not enough!

I realize this is not for everybody - but for some of us it is very exciting stuff.

George Dawson, MD, DFAPA

Graphics Credit:

All of the above figures are from reference 1, per a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License.  The graphics are unaltered and are taken from the paper as they are printed in reference 1 below.  This is a non-commercial and not-for-profit blog.


1:  Gupta M, Neavin D, Liu D, Biernacka J, Hall-Flavin D, Bobo WV, Frye MA, Skime M, Jenkins GD, Batzler A, Kalari K, Matson W, Bhasin SS, Zhu H, Mushiroda T, Nakamura Y, Kubo M, Wang L, Kaddurah-Daouk R, Weinshilboum RM. TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics. Mol Psychiatry. 2016 Dec;21(12):1717-1725. doi: 10.1038/mp.2016.6. Epub 2016 Feb 23. PubMed PMID: 26903268

2: Liu D, Ray B, Neavin DR, Zhang J, Athreya AP, Biernacka JM, Bobo WV,Hall-Flavin DK, Skime MK, Zhu H, Jenkins GD, Batzler A, Kalari KR, Boakye-Agyeman F, Matson WR, Bhasin SS, Mushiroda T, Nakamura Y, Kubo M, Iyer RK, Wang L, Frye MA, Kaddurah-Daouk R, Weinshilboum RM. Beta-defensin 1, aryl hydrocarbon receptor and plasma kynurenine in major depressive disorder: metabolomics-informed genomics. Transl Psychiatry. 2018 Jan 10;8(1):10. doi: 10.1038/s41398-017-0056-8. PubMed PMID: 29317604.

3: Ji Y, Biernacka JM, Hebbring S, Chai Y, Jenkins GD, Batzler A, Snyder KA, Drews MS, Desta Z, Flockhart D, Mushiroda T, Kubo M, Nakamura Y, Kamatani N, Schaid D, Weinshilboum RM, Mrazek DA. Pharmacogenomics of selective serotonin reuptake inhibitor treatment for major depressive disorder: genome-wide associations and functional genomics. Pharmacogenomics J. 2013 Oct;13(5):456-63. doi: 10.1038/tpj.2012.32. Epub 2012 Aug 21. PubMed PMID: 22907730.

4: Mrazek DA, Biernacka JM, McAlpine DE, Benitez J, Karpyak VM, Williams MD, Hall-Flavin DK, Netzel PJ, Passov V, Rohland BM, Shinozaki G, Hoberg AA, Snyder KA, Drews MS, Skime MK, Sagen JA, Schaid DJ, Weinshilboum R, Katzelnick DJ. Treatment outcomes of depression: the pharmacogenomic research network antidepressant medication pharmacogenomic study. J Clin Psychopharmacol. 2014 Jun;34(3):313-7. doi: 10.1097/JCP.0000000000000099. Erratum in: J Clin Psychopharmacol. 2014 Oct;34(5):558. PubMed PMID: 24743713.

5: Ji Y, Schaid DJ, Desta Z, Kubo M, Batzler AJ, Snyder K, Mushiroda T, Kamatani N, Ogburn E, Hall-Flavin D, Flockhart D, Nakamura Y, Mrazek DA, Weinshilboum RM. Citalopram and escitalopram plasma drug and metabolite concentrations: genome-wide associations. Br J Clin Pharmacol. 2014 Aug;78(2):373-83. doi: 10.1111/bcp.12348. PubMed PMID: 24528284; PubMed Central PMCID: PMC4137829.

6: Athreya A, Iyer R, Neavin D, Wang L, Weinshilboum R, Kaddurah-Daouk R, Rush J, Frye M, Bobo W. Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE Comput Intell Mag. 2018 Aug;13(3):20-31. doi: 10.1109/MCI.2018.2840660. Epub 2018 Jul 20. PubMed PMID: 30467458; PubMed Central PMCID: PMC6241311.

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