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A classifier driven approach to find biomarkers for affective disorders from transcription profiles in blood

Wiktor Mazin, Joseph A Tamm, Irina A Antonijevic, Aicha Abdourahman, Munish Das, Roman Artymyshyn, Birgitte Søgaard, Mary Walker, Danka Savic, Gordana Matic, Svetozar Damjanović, Ulrik Gether, Thomas Werge, Lars V Kessing, Henrik Ullum, Eva Haastrup, Eric Vermetten, Paul Markovitz, Erik Mosekilde, Christophe PG Gerald

Abstract

Gene expression profiles in blood are increasingly being used to identify biomarkers for different affective disorders. We have selected a set of 29 genes to generate expression profiles for healthy control subjects as well as for patients diagnosed with acute post-traumatic stress disorder (PTSD) and with borderline personality disorder (BPD). Measurements were performed by quantitative polymerase chain reaction (qPCR). Using the actual data in an anonym-ous form we constructed a series of artificial data sets with known gene expression profiles. These sets were used to test 14 classification algorithms and feature selection methods for their ability to identify the correct expression patterns. Application of the three most effective algorithms to the actual expression data showed that control subjects can be dis-tinguished from BPD patients based on differential expression levels of the gene transcripts Gi2, GR and MAPK14, targets that may have links to stress related diseases. Controls can also be distinguished from acute PTSD patients by differential expression levels of the transcripts for ERK2 and RGS2 that are known to be associated with mood disord-ers and social anxiety. We conclude that it is possible to identify informative transcription profiles in blood samples from individuals with affective disorders.

Keywords

feature selection; mental disorders; gene expressions; gene panel

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References

Antonijevic I, Artymyshyn R, Forray C, et al. 2009, Perspectives for an integrated biomarker approach to drug discovery and development, in Turck C, ed. Biomarkers for Psychiatric Disorders. Springer.

Tylee D S, Kawaguchi D M and Glatt S J, 2013, On the outside, looking in: a review and evaluation of the comparability of blood and brain "-omes". American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, vol.162B(7): 595–603.

Segman R H, Shefi N, Goltser-Dubner T, et al. 2005, Peripheral blood mononuclear cell gene expression profiles identify emergent post-traumatic stress disorder among trauma survivors. Molecular Psychiatry, vol.10(5): 500–513, 425.

Zieker J, Zieker D, Jatzko A, et al. 2007, Differential gene expression in peripheral blood of patients suffering from post-traumatic stress disorder. Molecular Psychiatry, vol.12(2): 116–118.

O'Donovan A, Sun B, Cole S, et al. 2011, Transcriptional control of monocyte gene expression in post-traumatic stress disorder. Disease Markers, vol.30(2–3): 123–132.

Tylee D S, Chandler S D, Nievergelt C M, et al. 2015, Blood-based gene-expression biomarkers of post-trau-matic stress disorder among deployed marines: apilot study. Psychoneuroendocrinology, vol.51: 472–494.

Le-Niculescu H, Kurian S M, Yehyawi N, et al. 2008, Identifying blood biomarkers for mood disorders using convergent functional genomics. Molecular Psychiatry, vol.14(2): 156–174.

Middleton F A, Pato C N, Gentile K L, et al. 2005, Gene expression analysis of peripheral blood leukocytes from discordant sib-pairs with schizophrenia and bipolar disorder reveals points of convergence between genetic and functional genomic approaches. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, vol.136(1): 12–25.

Munkholm K, Vinberg M, Berk M, et al. 2012, State- related alterations of gene expression in bipolar disorder: a systematic review. Bipolar Disorder, vol.14(7): 684–696.

Munkholm K, Peijs L, Vinberg M, et al. 2015, A composite peripheral blood gene expression measure as a potential diagnostic biomarker in bipolar disorder. Translational Psychiatry, vol.5: e614.

Iga J, Ueno S, Yamauchi K, et al. 2005, Serotonin transporter mRNA expression in peripheral leukocytes of patients with major depression before and after treatment with paroxetine. Neurosci Letters, vol.389(1): 12–16.

Iga J, Ueno S, Yamauchi K, et al. 2007, Altered HDAC5 and CREB mRNA expressions in the peripheral leukocytes of major depression. Progress in Neuro-psy-chopharmacol & Biological Psychiatry, vol.31(3): 628– 632.

Hepgul N, Cattaneo A, Zunszain P A, et al. 2013, Depression pathogenesis and treatment: what can we learn from blood mRNA expression? BMC Medicine, vol.11: 28.

Redei E E and Mehta N S, 2015, Blood transcriptomic markers for major depression: from animal models to clinical settings. Annals of the New York Academy of Sciences, vol.1344: 37–49.

Mazin W, 2008, Exploring the biological basis of affe-ctive disorders, thesis, Technical University of Denmark.

Andersen C L, Jensen J L and Orntoft T F, 2004, Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Research, vol.64(15): 5245–5250.

Jin P, Zhao Y, Ngalame Y, et al. 2004, Selection and validation of endogenous reference genes using a high throughput approach. BMC Genomics, vol.5(1): 55.

Huggett J, Dheda K, Bustin S, et al. 2005, Real-time RT-PCR normalisation; strategies and considerations. Genes & Immunity, vol.6(4): 279–284.

Vandesompele J, De P K, Pattyn F, et al. 2002, Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology, vol.3(7): RESEARCH0034.

Livak K J and Schmittgen T D, 2001, Analysis of relative gene expression data using real-time quantita-tive PCR and the 2(-Delta Delta C(T)) method. Methods, vol.25(4): 402–408.

Boulesteix A-L, Strobl C, Augustin T, et al. 2008, Evaluating microarray-based classifiers: an overview. Cancer Informatics, vol.4: 77–97.

supclust, R project —www r-project org,

Dettling M and Bühlmann P, 2004, Finding predictive gene groups from microarray data. Journal of Multivar-iate Analysis, vol.90(1): 106–131.

VR bundle - MASS, R project —www r-project org,

Weber G, Vinterbo S and Ohno-Machado L 2004, Multivariate selection of genetic markers in diagnostic classification. Artificial Intelligence in Medicine, vol.31(2): 155– 167.

Plr, R project —www r-project org,

RPART package, R project —www r-project org,

Naive Bayes, Wikipedia,

klaR, R project —www r-project org,

LDA, Wikipedia,

Knudsen S, 2004, Guide to Analysis of DNA Microarray Data, 2nd Edition.Wiley-Liss.

KNN, Wikipedia,

random forest, R project —www r-project org,

Random forest — wikipedia, Wikipedia,

Diaz-Uriarte R and Alvarez de A S 2006, Gene selection and classification of microarray data using random forest. BMC Bioinformatics, vol.7: 3.

QDA, Wikipedia,

caMassClass, R project —www r-project org,

SVM, Wikipedia,

Boosting, Wikipedia,

varselrf, R project —www r-project org,

Diaz-Uriarte R, 2007, GeneSrF and varSelRF: a web- based tool and R package for gene selection and classification using random forest. BMC Bioinformatics, vol.8: 328.

Accuracy, Wikipedia,

Kohavi R, A study of cross-validation and bootstrap for accuracy estimation and model selection, IJCAI,

Jaccard, Wikipedia,

Mukherjee S, Golland P and Panchenko D, 2003, Permutation tests for classification, MIT,

Shapiro-Wilk test, Wikipedia,

Mann–Whitney U, Wikipedia,

SVM in R, R project —www r-project org,

Matthews correlation coefficient, Wikipedia,

Han S B, Moratz C, Huang N N, et al. 2005, Rgs1 and Gnai2 regulate the entrance of B lymphocytes into lymph nodes and B cell motility within lymph node follicles. Immunity, vol.22(3): 343–354.

Delston R B, Matatall K A, Sun Y, et al. 2011, p38 phosphorylates Rb on Ser567 by a novel, cell cycle- independent mechanism that triggers Rb-Hdm2 intera-ction and apoptosis. Oncogene, vol.30(5): 588–599.

Zhao W, Liu M and Kirkwood K L, 2008, p38alpha stabilizes interleukin-6 mRNA via multiple AU-rich elements. Journal of Biological Chemistry, vol.283(4): 1778–1785.

Wingenfeld K and Wolf O T, 2011, HPA axis alterations in mental disorders: Impact on memory and its rele-vance for therapeutic interventions. CNS Neuroscience & Therapeutics, vol.17(6): 714–722.

Garoflos E, Panagiotaropoulos T, Pondiki S, et al. 2005, Cellular mechanisms underlying the effects of an early experience on cognitive abilities and affective states. Annals General Psychiatry, vol.4(1): 8.

Kai S, Goto S, Tahara K, et al. 2004, Indoleamine 2,3-dioxygenase is necessary for cytolytic activity of natural killer cells. Scandinavian Journal of Immuno-logy, vol.59(2): 177–182.

Muller N, Myint A M and Schwarz M J, 2011, Kynurenine pathway in schizophrenia: pathophysiolo-gical and therapeutic aspects. Current Pharmaceutical Design, vol.17(2): 130–136.

van Zuiden M, Geuze E, Willemen H L, et al. 2011, Pre- existing high glucocorticoid receptor number predicting development of posttraumatic stress symptoms after military deployment. The American Journal of Psychiatry, vol.168(1): 89–96.

van Zuiden M, Heijnen C J, van de S R, et al. 2011, Cytokine production by leukocytes of military personnel with depressive symptoms after deployment to a combat- zone: a prospective, longitudinal study. PLoS One, vol.6(12): e29142.

van Zuiden M, Geuze E, Willemen H L, et al. 2012, Glucocorticoid receptor pathway components predict posttraumatic stress disorder symptom development: A prospective study. Biological Psychiatry, vol.71(4): 309– 316.

2010, Posters. Basic & Clinical Pharmacology & Toxi-cology, vol.107(s1): 162–692.

Sullivan P F, Fan C and Perou C M, 2006, Evaluating the comparability of gene expression in blood and brain. American Journal of Medical Genetics Part B: Neurop-sychiatric Genetics, vol.141(3): 261–268.

Gladkevich A, Kauffman H F and Korf J, 2004, Lymphocytes as a neural probe: potential for studying psychiatric disorders. Progress in Neuro-psychophar-macology & Biological Psychiatry, vol.28(3): 559–576.


DOI: http://dx.doi.org/10.18063/APM.2016.01.003
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Copyright (c) 2016 Wiktor Mazin, Joseph A Tamm, Irina A Antonijevic, Aicha Abdourahman, Munish Das, Roman Artymyshyn, Birgitte Søgaard, Mary Walker, Danka Savic, Gordana Matic, Svetozar Damjanović, Ulrik Gether, Thomas Werge, Lars V Kessing, Henrik Ullum, Eva Haastrup, Eric Vermetten, Paul Markovitz, Erik Mosekilde, Christophe PG Gerald

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