TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics

Gupta, Milli; Neavin, Drew; Liu, Duan; Biernacka, Joanna M.; Hall‐Flavin, Daniel K.; Bobo, William V.; Frye, Mark A.; Skime, Michelle; Jenkins, Gregory D.; Batzler, Anthony; Kalari, Krishna R.; Matson, Wayne R.; Bhasin, Swati S.; Zhu, Hongjie; Mushiroda, Taisei; Nakamura, Yusuke; Kubo, Michiaki; Wang, L; Kaddurah‐Daouk, Rima; Weinshilboum, Richard M.

doi:10.1038/mp.2016.6

Cited by 102 publications

(138 citation statements)

References 74 publications

Supporting

Mentioning

126

Contrasting

Order By: Relevance

“…Previous microarray analysis reported the expression of ERICH3 in the brain, eye, lung, mammary gland, muscle, pituitary gland, testis, trachea, and uterus (61). In addition, a recent study suggests that ERICH3 is associated with plasma serotonin concentrations and may have a role in selective serotonin reuptake inhibitor (SSRI) therapy treatment outcomes in patients with major depressive disorder (62). …”

Section: Resultsmentioning

confidence: 99%

Quantitative Proteomic Analysis of Human Airway Cilia Identifies Previously Uncharacterized Proteins of High Abundance

et al. 2017

View full text Add to dashboard Cite

Cilia are essential to many diverse cellular processes. Although many major axonemal components have been identified and studied, how they interact to form a functional axoneme is not completely understood. To further our understanding of the protein composition of human airway cilia, we performed a semi-quantitative analysis of ciliary axonemes using label-free LC/MSE which identified over 400 proteins with high confidence. Tubulins were the most abundant proteins identified, with evidence for 20 different isoforms obtained. Twelve different isoforms of axonemal dynein heavy chain were also identified. Absolute quantification of the non-tubulin components demonstrated a greater than 75-fold range of protein abundance (RSPH9;1850 fmol vs CCDC103;24 fmol), adding another level of complexity to axonemal structure. Of the identified proteins, approximately 70% are known axonemal proteins. In addition, many previously uncharacterized proteins were identified. Unexpectedly, several of these, including ERICH3, C1orf87, and CCDC181, were present at high relative abundance in the cilia. RT-PCR analysis and immunoblotting confirmed cilia specific expression for eight uncharacterized proteins, and fluorescent microscopy demonstrated unique axonemal localizations. These studies have provided the first quantitative analysis of the ciliary proteome and have identified and characterized several previously unknown proteins as major constituents of human airway cilia.

show abstract

Section: Resultsmentioning

confidence: 99%

Quantitative Proteomic Analysis of Human Airway Cilia Identifies Previously Uncharacterized Proteins of High Abundance

et al. 2017

View full text Add to dashboard Cite

show abstract

“…All PGRN‐AMPS and ISPC subjects also had CYP2C19 metabolizer genotype data at baseline, and plasma drug levels at 4 and 8 weeks. Details of genotyping and GWAS of the PGRN‐AMPS, STAR*D, and ISPC subjects have been previously published …”

Section: Methodsmentioning

confidence: 99%

“…Each of these SNPs were the “top” SNP in its respective genomewide association study (GWAS) SNP signal, except that for DEFB1 , we included the “top” SNP as well as two others in different haplotype blocks. The GWAS had used as phenotypes plasma serotonin and kynurenine concentrations assayed in PGRN‐AMPS samples . Of the plasma metabolites assayed in those patients, serotonin and kynurenine concentrations were the most highly associated with SSRI outcomes at 8 weeks or with baseline depressive symptom severity—one of the most important predictors of eventual antidepressant treatment response .…”

mentioning

confidence: 99%

Pharmacogenomics‐Driven Prediction of Antidepressant Treatment Outcomes: A Machine‐Learning Approach With Multi‐trial Replication

Athreya

Neavin

Carrillo-Roa

et al. 2019

Clin Pharma and Therapeutics

View full text Add to dashboard Cite

We set out to determine whether machine learning–based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN‐AMPS; n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D; n = 467), and International SSRI Pharmacogenomics Consortium (ISPC; n = 165) trials. A genomewide association study for PGRN‐AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine‐learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN‐AMPS patients, with comparable prediction accuracies > 69% (P ≤ 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

show abstract

“…In an attempt to overcome that limitation, there have been recent attempts to “inform” genomic analyses by beginning with metabolomics data, determine which metabolite is most highly associated with the psychiatric phenotype (eg., Hamilton D Scores for patients with Major Depressive Disorder), perform a GWAS to identify genes associated with the concentration of the metabolite and then functionally validate the genes/SNPs that were identified during the GWAS. 36 These examples have begun to show us that, in the future, biomarkers for drug response might well be composed jointly of genomic data, transcriptomic data and metabolomic data, ie that we will be using “pharmaco-omics” to help us individualize and optimize drug therapy.…”

Section: Pharmacogenomics: Future Prospectsmentioning

confidence: 99%

Pharmacogenomics: Precision Medicine and Drug Response

Weinshilboum

Wang

2017

Mayo Clinic Proceedings

178

115

View full text Add to dashboard Cite

Pharmacogenomics is the use of genomic and other “omic” information to individualize drug selection and drug use in order to avoid adverse drug reactions and to maximize drug efficacy. The science underlying pharmacogenomics has evolved rapidly over the 50 years since it was first suggested that genetics might influence drug response phenotypes. That process has occurred in parallel with advances in DNA sequencing and other molecular technologies, with striking increases in our understanding of the human genome. There are now many validated examples of the clinical utility of pharmacogenomics, and this type of clinical genomic information is increasingly being generated in clinical laboratories, incorporated into electronic health records (EHRs) and used to “tailor” or individualize drug therapy. This review will survey the origins and development of pharmacogenomics; it will address some of the challenges associated with the clinical implementation of pharmacogenomics; and it will attempt to foresee future advances in this important genomic discipline, one that almost certainly will be among the earliest and most widely adopted aspects of clinical genomics.

show abstract

TSPAN5, ERICH3 and selective serotonin reuptake inhibitors in major depressive disorder: pharmacometabolomics-informed pharmacogenomics

Cited by 102 publications

References 74 publications

Quantitative Proteomic Analysis of Human Airway Cilia Identifies Previously Uncharacterized Proteins of High Abundance

Quantitative Proteomic Analysis of Human Airway Cilia Identifies Previously Uncharacterized Proteins of High Abundance

Pharmacogenomics‐Driven Prediction of Antidepressant Treatment Outcomes: A Machine‐Learning Approach With Multi‐trial Replication

Pharmacogenomics: Precision Medicine and Drug Response

Contact Info

Product

Resources

About