Genome-wide microarray analysis of gene expression profiling in major
depression and antidepressant therapy
Eugene Lin1,2, Shih-Jen Tsai3,4
1Institute of Clinical Medical Science, China Medical University, Taichung, Taiwan
2Vita Genomics, Inc., Taipei, Taiwan
3Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
4Division of Psychiatry, National Yang-Ming University, Taipei, Taiwan
Corresponding author: Dr. Shih-Jen Tsai, Department of Psychiatry, Taipei Veterans General Hospital, No. 201, Shih-Pai Road, Sec. 2, 11217, Taipei, Taiwan
E-mail addresses: [email protected] Phone: 886-2-2875 7027, ext. 276
Fax: 886-2-2872 5643
Abstract
Major depressive disorder (MDD) is a serious health concern worldwide. Currently there are no predictive tests for the effectiveness of any particular antidepressant in an individual patient. Thus, doctors must prescribe antidepressants based on educated guesses. With the recent advent of scientific research, genome-wide gene expression microarray studies are widely utilized to analyze hundreds of thousands of biomarkers by high-throughput technologies. In addition to the candidate-gene approach, the genome-wide approach has recently been employed to investigate the determinants of MDD as well as antidepressant response to therapy. In this review, we mainly focused on gene expression studies with genome-wide approaches using RNA derived from peripheral blood cells. Furthermore, we reviewed their limitations and future directions with respect to the genome-wide gene expression profiling in MDD pathogenesis as well as in antidepressant therapy.
Keywords: antidepressants, genome-wide gene expression profiling, genome-wide
microarray analysis, major depressive disorder, transcriptional profiling.
Abbreviations: CAPRIN1, cell cycle associated protein 1; CD3D, CD3d molecule delta;
domain family 4, member A; EDN1, endothelin 1; ELK3, ELK3 ETS-domain protein;
GZMA, granzyme A; HIST1H1E, histone cluster 1 H1e; IFITM3, interferon induced
transmembrane protein 3; IL1B, interleukin 1 beta; IRF7, interferon regulatory factor 7;
ITGB3, integrin beta 3; KRT23, keratin 23; LCLs, lymphoblastoid cell lines; lncRNAs, long
noncoding RNAs; LPS, lipopolysaccharide; MATR3, matrin 3; MDD, major depressive disorder; MDE, major depressive episode; MLC1, megalencephalic leukoencephalopathy with subcortical cysts 1; NESDA, Netherlands Study of Depression and Anxiety; NMDA, N-methyl-D-aspartate; ncRNAs, noncoding RNAs; mRNAs, messenger RNAs; miRNAs, microRNAs; PAQR6, progestin and adipoQ receptor family member VI; PBMCs, Peripheral blood mononuclear cells; PLSCR1, phospholipid scramblase 1; PPM1K, protein phosphatase Mg2+/Mn2+ dependent 1K; PPT1: palmitoyl-protein thioesterase 1; PROK2, prokineticin 2; PSMA4, proteasome subunit alpha type 4; PSMA6, proteasome subunit alpha type 6; RGS7BP, G-protein signaling 7 binding protein; RPL5, ribosomal protein L5;
RPL9, ribosomal protein L9; RPL17, ribosomal protein L17; RPL24, ribosomal protein
L24; TAGLN2, transgelin 2; TIMP1, TIMP metallopeptidase inhibitor 1; TNF, tumor necrosis factor; ZBTB16, zinc finger and BTB domain containing 16;
1. Introduction
Major depressive disorder (MDD), one of the most prevalent and complex mental disorders worldwide, is estimated to be the second leading cause of disability by 2030 (Mathers and Loncar, 2006). Currently there are no predictive tests for disease state and antidepressant treatment remission in MDD beforehand so that doctors can only take a trial and error approach to prescribe antidepressants, the first line of medication for lifting MDD (Lin and Chen, 2008). In clinical association studies, gene expression can be employed to determine the contribution of genes to pathogenesis of MDD because accumulating evidence suggests that patients with MDD exhibit an altered pattern of expression in relevant genes when compared with healthy controls (Hepgul et al., 2013). Similarly, the analysis of gene expression promises a new approach to cope with the complexity of personalized medication on antidepressant treatment (Menke, 2013). Although further findings in support of this hypothesis are needed, more and more biomarkers in gene expression profiling are being discovered to be associated with MDD as well as antidepressant response (Labermaier et al., 2013). In this paper, we briefly reviewed the few existing transcriptomics studies in assessing and understanding MDD pathogenesis and antidepressant therapy.
With the Human Genome Project completed, a new era for scientific and medical research is set to develop revolutionary technologies such as genome-wide gene expression microarrays, which are different from the candidate-gene approach of hypothesis-driven biomarker search (Seifuddin et al., 2013). An obvious strength of the genome-wide gene expression microarray studies is the hypothesis-free nature about the relevant genes. The genome-wide approach employs high-throughput technologies to analyze biomarkers across the entire human genome in order to find associations with observable traits. This genome-wide approach faces several challenges, such as how to account for the issue of multiple comparisons, which occurs when multiple statistical tests are considered simultaneously (Watanabe, 2011).
Many researchers have investigated MDD-related and transcriptome-based genome-wide approach in blood tissue, postmortem human brain tissue, and animal brain tissue (Mehta et al., 2010). Blood as a target tissue is readily accessible, and the gene expression levels using blood have shown to be comparable with the ones using prefrontal cortex in MDD-related transcriptomic research (Sullivan et al., 2006). In this review, we provided a synopsis of the genome-wide microarray studies published recently for MDD and antidepressants, mostly with a focus on studies using blood as a target tissue.
First, we surveyed the gene expression profiles and genes that were identified as biomarkers and were linked with MDD in the genome-wide gene expression microarray studies. Furthermore, we assessed some potential gene expression profiles and genes that were investigated in genome-wide gene expression microarray studies and were shown to be associated with drug efficacy for antidepressant medications. Finally, we summarized the limitations and future perspectives with respect to the genome-wide gene expression profiling studies. Future replication studies in large and independent samples are needed to confirm the role of the biomarkers discovered in the genome-wide transcriptional profiling studies in MDD as well as antidepressant treatment response.
2. RNA molecules
Noncoding RNAs (ncRNAs), including small ncRNAs and long noncoding RNAs (lncRNAs), are different from their counterpart messenger RNAs (mRNAs) owing to the fact that the sequence of nucleotides within ncRNAs does not encode proteins (Nagano and Fraser, 2011). Small ncRNAs, including the microRNAs (miRNAs), are smaller than 200 nucleotides in length. On the other hand, lncRNAs are transcripts over 200 nucleotides in length.
The miRNAs control gene expression by modulating mRNA degradation, translation, or stability (Dwivedi 2014). The role of mRNAs, miRNAs, and lncRNAs in disease pathogenesis and in monitoring therapeutic responses for MDD is emerging rapidly. Studies are now being geared to examine if gene expression profiles such as mRNAs, miRNAs, and lncRNAs can be developed as possible biomarkers for MDD as well as antidepressant response.
3. Genome-wide gene expression microarray studies in MDD
Table 1 summarizes the relevant gene expression profile and genes associated with MDD in the genome-wide microarray studies. This is by no means a comprehensive review of all potential biomarkers reported in the literature. As mentioned previously, increasing numbers of biomarkers are being identified as researchers continue to pay much attention to genome-wide microarray studies in MDD.
3.1. Study by Spijker and colleagues (2010)
In a stimulated blood-based genome-wide approach, Spijker and colleagues explored the possibility that the gene expression profile can distinguish patients suffering from MDD from controls (Spijker et al., 2010). They recruited 21 MDD patients and 21 matched controls from the Netherlands Study of Depression and Anxiety (NESDA) cohort. They chose a lipopolysaccharide (LPS) stimulus to induce gene expression in whole blood and used Agilent 44 K Human whole genome arrays (CA, USA) for genome-wide expression analysis. They employed a microarray analysis tool and constructed a classifier for MDD by using a set of 7 genes including the megalencephalic leukoencephalopathy with subcortical cysts 1 (MLC1), prokineticin 2 (PROK2), cell cycle associated protein 1
(CAPRIN1), C-type lectin domain family 4, member A (CLEC4A), keratin 23 (KRT23), phospholipid scramblase 1 (PLSCR1), and zinc finger and BTB domain containing 16 (ZBTB16) genes (Spijker et al., 2010). In their report, the classifier was shown to discriminate MDD patients from control subjects with sensitivity of 76.9% and specificity of 71.8% (Spijker et al., 2010). Their results were limited by the small sample size and would need a much larger cohort of patients to better evaluate sensitivity and specificity of the proposed candidate genes.
The MLC1 gene encodes a protein highly expressed in brainstem, cerebellum, olfactory tract, and thalamus, and has been linked with bipolar disorder and schizophrenia in a Southern Indian population (Verma et al., 2005). The protein encoded by the PROK2 gene is expressed in the circadian clock, and the PROK2 receptor (PROKR2) gene was found to be associated with MDD in a Japanese population (Kishi et al., 2009). To our knowledge, the MLC1, PROK2, CAPRIN1, CLEC4A, KRT23, PLSCR1, and ZBTB16 genes had not been indicated in MDD in other previous studies.
3.2. Study by Belzeaux and colleagues (2012)
In a transcriptome-based genome-wide approach, Belzeaux and colleagues tested the hypothesis that a convergent analysis of the gene expression profile of both mRNAs and
miRNAs can distinguish patients suffering from a major depressive episode (MDE) from controls (Belzeaux et al., 2012). They recruited 16 MDE patients and 13 matched controls and employed SurePrint G3 Human GE 8x60 K (Agilent Technologies, USA) for genome-wide expression analysis. In their analysis for transcripts, about 200 candidate genes were identified as dysregulated when MDE patients were compared with controls (including the regulator of endothelin 1 (EDN1), ELK3 ETS-domain protein (ELK3), progestin and adipoQ receptor family member VI (PAQR6), protein phosphatase Mg2+/Mn2+ dependent 1K (PPM1K), and G-protein signaling 7 binding protein (RGS7BP) genes). The EDN1,
ELK3, PAQR6, PPM1K, and RGS7BP gene also have been previously identified as
dysregulated in brain tissues with MDD (Sequeira et al., 2006; Sequeira et al., 2007; Guilloux et al., 2012).
Belzeaux and colleagues also tested the hypothesis that the gene expression profile (including mRNA and miRNA expression) can predict response to antidepressants with MDE patients (Belzeaux et al., 2012). We will discuss their report in the next section.
3.3. Study by Garbett and colleagues (2014)
In another transcriptome-based genome-wide approach, Garbett and colleagues studied dermal fibroblasts from patients with MDD and tested whether gene expression
profiling could be used as peripheral biomarkers of MDD (Garbett et al., 2014). They assayed dermal fibroblast samples (n = 32) from MDD patients and matched controls using genome-wide mRNA expression analysis (Garbett et al., 2014). In order to investigate the relationship between the mRNA and miRNA expression changes, they also performed quantitative polymerase chain reaction-based experiments of miRNA species (Garbett et al., 2014). Garbett and colleagues revealed that there was a strong mRNA gene expression pattern change in multiple molecular pathways, such as cell proliferation, cell-to-cell communication, and innate and adaptive immunity, when they compared MDD fibroblasts with matched controls. Furthermore, the miRNA and mRNA expression changes were found to be functionally correlated with each other.
3.4. Study by Liu and colleagues (2014)
Recently, the characterization of lncRNAs, which are highly expressed in brain, has become a fruitful area of research in MDD due to their importance in gene regulatory networks (Nagano and Fraser, 2011; Ng et al., 2013). In a similar transcriptome-based genome-wide approach, Liu and colleagues explored whether genome-wide lncRNA expression and co-expression with mRNA patterns may serve as tentative biomarkers for
MDD (Liu et al., 2014). They recruited 10 MDD patients and 10 matched controls. Affymetrix Glue Grant Human Transcriptome Array (CA, USA), which detected 34834 lncRNAs and 39224 mRNAs in peripheral blood, was employed for genome-wide expression analysis (Liu et al., 2014). In their report, there were 1556 upregulated lncRNAs and 441 down-regulated lncRNAs as well as 759 up-regulated mRNAs and 1007 downregulated mRNAs between MDD patients and controls. Furthermore, in co-expression analysis of lncRNAs and mRNAs, the lncRNAs located at chr10:874695-874794, chr10:75873456-75873642, and chr3:47048304-47048512 were each connected to four differentially regulated mRNAs in the MDD sub-network. However, these connections were not found in the control’s sub-network. Therefore, Liu et al. (2014) suggested these three lncRNAs as potential regulatory factors in MDD.
4. Genome-wide gene expression microarray studies in antidepressants
Table 2 summarizes the relevant gene expression profile and genes associated with antidepressant response in the genome-wide microarray studies. This is by no means a comprehensive review of all potential biomarkers reported in the literature. As mentioned previously, increasing numbers of biomarkers are being discovered as researchers continue to pay much attention to genome-wide microarray studies of antidepressants in MDD.
4.1. Study by Mamdani and colleagues (2011)
In the first transcriptome-based genome-wide study, Mamdani and colleagues explored whether genome-wide RNA expression may serve as potential biomarkers to antidepressant treatment in MDD (Mamdani et al., 2011). They recruited 63 MDD patients treated with citalopram for 8 weeks and employed Affymetrix HG-U133 Plus 2.0 microarrays (CA, USA) for genome-wide expression analysis (Mamdani et al., 2011). In their report, there were 32 differentially expressed probesets shown to be associated with response to citalopram treatment (Mamdani et al., 2011). Among the 32 probesets, the probeset 208436_s_at, which maps to the interferon regulatory factor 7 (IRF7) gene, was the most significant differentially expressed one (Mamdani et al., 2011). To our knowledge,
no other previous studies had implicated IRF7 in antidepressant treatments or in MDD.
4.2. Study by Morag and colleagues (2011)
In the second transcriptome-based genome-wide study, Morag and colleagues studied gene expression profiling as a biomarker of antidepressant drug treatment (Morag et al., 2011). They used drug-effect phenotypes in human lymphoblastoid cell lines (LCLs) and selected 14 human LCLs from healthy adult female individuals with relatively high versus low sensitivities to antidepressant paroxetine (Morag et al., 2011). Affymetrix GeneChip Human Gene 1.0 ST arrays (CA, USA) were employed for genome-wide expression analysis (Morag et al., 2011). Morag and colleagues revealed the cell adhesion molecule L1-like (CHL1) gene as a predictor of antidepressant drug treatment due to the lower levels of CHL1 expression by 6.3-fold (p = 0.0000256) in the paroxetine-sensitive cell lines when they compared the two phenotypic groups (Morag et al., 2011). However, a major drawback for the study by Morag and colleagues is that the observations were based on cell lines representing healthy individuals instead of major depression patients’ peripheral blood lymphocytes (Morag et al., 2011). The implication of gene expression profiling should ideally be assessed with the blood samples of much larger cohorts of MDD patients, both
before and after several weeks of antidepressant treatment, by comparing transcriptomic changes between good and poor responders.
The protein encoded by the CHL1 gene is a member of the neural cell adhesion molecules of the immunoglobulin superfamily, which is a neural recognition molecule and may play a key role in signal transduction pathways and structural reorganization indicated in learning and memory (Maness and Schachner, 2007). Clark and colleagues suggested further evidence of an association between the CHL1 gene and adverse effects to antidepressant medication in major depressive disorder from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (Clark et al., 2012).
Human LCLs consist of a promising model system in the pharmacogenomics studies of drug response including chemotherapeutics, statins, and antidepressants (Wheeler and Dolan, 2012). The main advantage of LCLs is the ease of experimental manipulation; on the other hand, a major disadvantage is that the complexity of drug effects in the human body can not be characterized by a single-model system such as LCLs (Wheeler and Dolan, 2012).
4.3. Study by Oved and colleagues (2012)
response to therapy, Oved and colleagues expanded the previous study by Morag and colleagues (2011) to investigate whether miRNAs may serve as tentative biomarkers for antidepressant response (Oved et al., 2012). They chose 8 human LCLs from healthy adult female individuals with reproducible high or low sensitivities to growth inhibition by antidepressant paroxetine (Oved et al., 2012). Affymetrix GeneChip miRNA 2.0 arrays (CA, USA) were employed for genome-wide expression analysis (Oved et al., 2012). In their report, the miRNA miR-151-3p was identified as a tentative biomarker because it exhibited 6.71-fold higher expression in LCLs with higher paroxetine sensitivity (Oved et al., 2012). Their results were in agreement with those of the study by Morag and colleagues (2011) such that the CHL1 gene was identified as a leading tentative biomarker for antidepressant sensitivity because CHL1 is a target of miR-151-3p (Oved et al., 2012). Again, a major drawback for the study by Oved and colleagues is that the observations were based on cell lines representing healthy individuals instead of major depression patients’ peripheral blood lymphocytes.
4.4. Study by Belzeaux and colleagues (2012)
hypothesis that a convergent analysis of both mRNA and miRNA profiling can predict response to antidepressants with MDE patients in a transcriptome-based genome-wide approach (Belzeaux et al., 2012). They recruited 16 MDE patients and 13 matched controls and employed SurePrint G3 Human GE 8x60 K (Agilent Technologies, USA) for genome-wide expression analysis. In their report, a combination of four mRNAs was identified to be predictive of treatment response (including the palmitoyl-protein thioesterase 1 (PPT1), tumor necrosis factor (TNF), interleukin 1 beta (IL1B), and histone cluster 1 H1e (HIST1H1E) genes. Two miRNAs (such as miR-941 and miR-589) were also shown to be stable overexpression in MDE patients in comparison of miRNA expression between MDE patients and controls at 8 weeks.
The protein encoded by the PPT1 gene is a small glycoprotein in brain, and deficiency of this protein leads to infantile neuronal ceroid lipofuscinosis, a neurodegenerative storage disorder in childhood (Kim et al., 2008). To our knowledge, no other previous studies had implicated PPT1, TNF, IL1B, and HIST1H1E in antidepressant treatments or in MDD.
4.5. Study by Oved and colleagues (2013)
Similarly, in a genome-wide expression profiling study for the mode of action of antidepressants, Oved and colleagues conducted microarray expression profiling
experiments in human LCLs chronically treated with paroxetine from four unrelated adult male donors (Oved et al., 2013). Affymetrix GeneChip Human Gene 1.0 ST arrays or GeneChip miRNA 2.0 arrays (CA, USA) were employed for detecting the expression levels of genes and miRNAs, respectively (Oved et al., 2013). Their data revealed that the integrin beta 3 (ITGB3) gene exhibited 1.925-fold increased expression (P = 7.5 x 10 (-8)) for the four LCLs with chronic paroxetine exposure (Oved et al., 2013). In addition, they observed a decrease in the expression levels of two miRNAs, miR- 221 and miR-222, which were both predicted to target the ITGB3 gene (Oved et al., 2013).
A recent study suggested genetic interactions of the Itgb3 and serotonin transporter (Slc6a4) genes to modulate serotonin uptake in mouse brain (Whyte et al., 2014). To our knowledge, no other previous studies had implicated ITGB3 in antidepressant treatments or in MDD.
4.6. Study by Guilloux and colleagues (2014)
Very recently, Guilloux and colleagues tested the hypothesis that the gene expression profile can predict response to antidepressants prior to treatment initiation for MDD patients in a transcriptome-based genome-wide approach (Guilloux et al., 2014). They recruited 34 MDD patients with co-occurring anxiety and 33 matched controls and
employed Illumina HT12-v4.0 gene array (CA, USA) for genome-wide expression analysis. By using a machine learning method with support vector machines, a predictive 13-gene model was indicated to predict non-remission with 79.4% accuracy (sensitivity of 66.7% and specificity of 89.5%). The 13-gene predictive model includes the CD3d molecule delta (CD3D), CD97 molecule (CD97), interferon induced transmembrane protein 3 (IFITM3), ribosomal protein L5 (RPL5), granzyme A (GZMA), transgelin 2 (TAGLN2), TIMP metallopeptidase inhibitor 1 (TIMP1), ribosomal protein L24 (RPL24), proteasome subunit alpha type 4 (PSMA4), matrin 3 (MATR3), ribosomal protein L9 (RPL9), proteasome subunit alpha type 6 (PSMA6), ribosomal protein L17 (RPL17) genes. To our knowledge, these genes had not been indicated in antidepressant treatments or in MDD in other previous studies.
Their results were limited by the small size of the cohort and would need a much larger cohort of patients to better assess sensitivity and specificity of the proposed predictive model and biomarkers.
5. Limitations in current genome-wide gene expression microarray
studies
With respect to the aforementioned genome-wide gene expression profiling studies, there were several limitations. First, the small size of the cohort warrants no definite conclusions. Small cohort size can cause no biomarkers reaching genome-wide significance because of insufficient statistical power (Novianti et al., 2014). In future work, independent replication studies in much larger cohort sizes are needed to validate the role of the biomarkers discovered in these studies.
Further, many biomarkers did not replicate well across studies, making us to question whether the novel associations were valid. In addition, the co-medications may affect treatment response and should be tested for future personalized medicine in the treatment of MDD (Ulrich-Merzenich et al., 2012). And combined substance use (such as alcohol and smoking) should also be addressed as a response modifier.
Moreover, it could be possible that different antidepressants may be possessing different biomarkers due to different mechanisms of action as well as administered drug dose (Serretti et al., 2008). It is also important to examine the biomarkers between different ethnic groups because different populations could result in different findings (Serretti et al.,
2008). This comparison may implicate proper MDD treatment for different ethnic background.
6. Future outlook
In future work, it will be indispensably necessary to generate a panel of biomarkers that are highly reproducible as an indicator of MDD or antidepressant response. At this point, no biomarkers found in the previous genome-wide gene expression microarray studies would really qualify to be listed in the set because of the limitations as described above. We will also need to consider what kind of selection criteria for prioritizing biomarkers to be listed in such a panel.
As discussed in this review, an increasing amount of evidence supports the hypothesis that mRNA, miRNA, and lncRNA expression profiles have active roles in various biological processes in MDD or in antidepressant therapy. Therefore, it may make great contributions to the unveiling of the complex mechanisms underlying MDD or antidepressant therapy by considering systematic and integrative analysis of different RNA molecules, such as mRNAs, miRNAs, and lncRNAs, with potentially cooperative functions (Guo et al., 2014).
Furthermore, machine learning techniques such as the artificial neural network approach may provide a plausible way to predict drug efficacy in antidepressant therapy and establish models for predicting MDD (Lin et al., 2006). In future research, statistical
models will be established to predict the probability of disease status or drug efficacy to guide clinicians in choosing medications. Machine learning techniques such as the artificial neural network approach may also play a key role in assessing gene–environment interactions or RNA-RNA molecule correlations such as correlations between miRNA and mRNA (Lin et al., 2007; Lin and Hsu, 2009). The statistical modeling is essential to root out the false positive biomarkers discovered at the current association analyses of genome-wide gene expression microarray studies by using meta-analysis, pathway analysis, and gene-gene expression correlations (Rezola et al., 2014; Gaiteri et al., 2014).
In addition, it seems that MDD has a strong environmental cause as well as the genetic cause (Fass et al., 2014). Furthermore, other approaches such as epigenetics should be considered to obtain clinically meaningful prediction of antidepressant treatment if genome-wide gene expression microarray studies alone could not obtain replicable biomarkers (Fass et al., 2014). Epigenetic mechanisms are modulated by environmental factors and may be associated with therapeutic effects of antidepressant drugs (Menke and Binder, 2014). Ultimately, future studies may need to propose an integrative use of biomarkers, such as clinical, genetic, epigenetic, metabolomic, transcriptomic, proteomic, and imaging data, in order to precisely understand MDD pathogenesis as well as
antidepressant therapy (Breitenstein et al., 2014).
Finally, a major drawback for traditional antidepressants (such as serotonin re-uptake inhibitors) in MDD is that current therapies usually take weeks to reach efficacy (Autry et al., 2011). Therefore, faster-acting antidepressants such as ketamine are needed, especially for suicide-risk patients. Ketamine, an antagonist of N-methyl-D-aspartate (NMDA) receptor, demonstrates a rapid and sustained antidepressant effect up to two weeks (Krystal et al., 2013). Sarcosine also showed potential to improve depression promptly (Huang et al., 2013). Future studies in ketamine and sarcosine are needed to find biomarkers that are relevant to treatment outcome for MDD.
7. Summary
In summary, modeling tools based on biomarkers play a crucial role in distinguishing MDD from controls as well as in predicting the possible outcomes of antidepressant treatment. Future research using machine learning approaches is needed in order to model the interactions among biomarkers as well as to evaluate associations between antidepressant response and biomarkers in genome-wide gene expression microarray studies (Lin and Tsai, 2011; Lin and Tsai, 2012; Lin, 2012). These machine learning techniques may provide tools for clinical genome-wide transcriptional profiling studies and assist in finding biomarkers involved in responses to therapeutic drugs and adverse drug reactions (Lin and Tsai, 2011; Lin and Tsai, 2012; Lin, 2012). Over the next few years, novel machine learning methods could be employed to develop molecular diagnostic and prognostic tools with big data technology, which manages massive clinical datasets in genomics and personalized medicine (Lin and Tsai, 2011; Lin and Tsai, 2012; Lin, 2012). However, the results of genome-wide gene expression profiling studies can be integrated into routine clinical practice only after we overcome a number of major challenges (Lin and Tsai, 2011; Lin and Tsai, 2012; Lin, 2012). Personalized therapy for MDD will become a reality after larger prospective clinical trials have been conducted in more diverse human
populations to confirm biomarkers and clinical factors associated with MDD and antidepressant treatment response.
In this study, we reviewed several recent findings and relevant studies in genome-wide gene expression profiling for MDD as well for antidepressants. The work also underscores the importance of large-scale genome-wide studies to examine a greater diversity of populations in the clinical settings of mental diseases and their treatments. Now we obtain a major new piece in the puzzle after some pieces fitting the puzzle for gene expression profiling of MDD pathogenesis and antidepressant therapy have been investigated. To improve and personalize MDD treatment and prevention worldwide, the future effort will have to correlate these findings with other pieces until the picture of MDD treatment and prevention is sufficiently clear. Furthermore, these findings suggested that modeling tools based on clinical factors such as gene expression data may help patients and doctors make more informed and personalized decisions.
Acknowledgements
The authors extend their sincere thanks to Vita Genomics, Inc. for funding this research.
Role of the Funding Source
None.
Conflict of interest
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