Advances in biomarkers of major depressive disorder.

CHAPTER SEVEN

Advances in Biomarkers of Major Depressive Disorder Tiao-Lai Huang1, Chin-Chuen Lin Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan 1 Corresponding author: e-mail address: [email protected]

Contents 1. Introduction 2. Previous Research (Lipids and Proteins) 2.1 Lipid profiles 2.2 Immune/inflammation 2.3 Brain-derived neurotrophic factor 3. Genetic Studies 3.1 Serotonin system 3.2 Monoamine metabolic enzymes 3.3 Brain-derived neurotrophic factor 3.4 HPA axis (FK506-binding protein 5) 4. Proteomics, Metabolomics, and Beyond 4.1 Our experience with proteomics 4.2 Proteomics 4.3 Metabolomics 4.4 Protein interactomics 5. Conclusion References

178 179 179 180 180 181 181 183 185 186 188 188 188 191 193 194 195

Abstract Major depressive disorder (MDD) is characterized by mood, vegetative, cognitive, and even psychotic symptoms and signs that can cause substantial impairments in quality of life and functioning. Biomarkers are measurable indicators that could help diagnosing MDD or predicting treatment response. In this chapter, lipid profiles, immune/inflammation, and neurotrophic factor pathways that have long been implicated in the pathogenesis of MDD are discussed. Then, pharmacogenetics and epigenetics of serotonin transport and its metabolism pathway, brain-derived neurotrophic factor, and abnormality of hypothalamo– pituitary–adrenocortical axis also revealed new biomarkers. Lastly, new techniques, such as proteomics and metabolomics, which allow researchers to approach the studying of MDD with new directions and make new discoveries are addressed.

Advances in Clinical Chemistry, Volume 68 ISSN 0065-2423 http://dx.doi.org/10.1016/bs.acc.2014.11.003

#

2015 Elsevier Inc. All rights reserved.

177

178

Tiao-Lai Huang and Chin-Chuen Lin

In the future, more data are needed regarding pathophysiology of MDD, including protein levels, single nucleotide polymorphism, epigenetic regulation, and clinical data in order to better identify reliable and consistent biomarkers for diagnosis, treatment choice, and outcome prediction.

ABBREVIATIONS 5-HTTLPR the serotonin transporter gene-linked polymorphic region BDNF brain-derived neurotrophic factor COMT catechol-O-methyl transferase CpG cytosine-phosphate-guanosine CRH corticotropin-releasing hormone CRP C reactive protein CSF cerebrospinal fluid GC gas chromatography HPA hypothalamo–pituitary–adrenocortical HTR2A serotonin receptor 2A IL interleukin LC liquid chromatography MAO-A monoamine oxidase A MDD major depressive disorder MS mass spectrometry PI3K-mTOR phosphatidylinositol 3-kinase and the mammalian target of rapamycin SNP single nucleotide polymorphism SSRI selective serotonin reuptake inhibitor STAR*D sequenced treatment alternatives to relieve depression TPH tryptophan hydroxylase TNF-α tumor necrosis factor-α TrkB tropomyosin-related kinase B VNTR variable number of tandem repeats Y2H yeast two-hybrid screening

1. INTRODUCTION Major depressive disorder (MDD), or major depression, is characterized by mood, vegetative, cognitive, and even psychotic symptoms and signs that can cause substantial impairment in quality of life and function at levels comparable to that observed with chronically physically ill patients [1]. According to the World Health Organization (WHO), MDD will be the leading cause of global disability by the year 2030. Clinically, diagnosis of MDD has been based on patient interviews and supplemental information provided by family and/or friends, if available. In some cases, clinicians enlist

Advances in Biomarkers of Major Depressive Disorder

179

the aid of tools, i.e., checklists and self-report questionnaires. Basically, this process relies on a list of symptoms and signs derived from the Diagnostic and Statistical Manual of Mental Disorders or the International Statistical Classification of Diseases and Related Health Problems. As can be appreciated, the objectivity of such a symptom-based assessment process is questionable. Biomarkers are measurable indicators of normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic intervention [2]. It is likely that appropriately chosen biomarkers would be more objective and as such significantly enhance the traditional symptom-based assessment of psychiatric disorders. In psychiatry, biomarkers could be used to support the presence or absence of specific diseases (diagnostic biomarkers), provide individualized treatment (treatment biomarkers), measure treatment progress (treatment response biomarkers), and predict the onset of future disease (predictive biomarkers) [3–5]. Another challenge following MDD diagnosis is treatment. The predominant strategy, use of antidepressant drugs, shows only modest efficacy. In fact, up to 40% of patients do not respond to current treatment and typically experience undesirable side effects [6]. Furthermore, antidepressants often take weeks to effect, if they work at all. Even then, MDD has high rates of relapse and treatment resistance [7]. Therefore, identification of biomarkers that could predict individual response is highly valued in clinical practice.

2. PREVIOUS RESEARCH (LIPIDS AND PROTEINS) 2.1. Lipid profiles Low serum total cholesterol has been associated with increased violence and suicidal death [8]. Interestingly, MDD patients have lower serum total cholesterol [9–11]. Mechanistically, low serum cholesterol could decrease neuromembrane cholesterol content thus leading to failure of presynaptic serotonin reuptake [12]. Other circulating lipids or subtypes of cholesterol subtypes have been studied in MDD [13,14]. In an earlier study, no significant differences were found in serum total cholesterol in patients with mania versus MDD [15]. Men and women with MDD showed differential lipid profiles [16]. Patients with MDD with melancholic feature were compared to those with atypical feature [17]. Interestingly, the serum concentration of triglyceride and very low-density lipoprotein cholesterol were different in men, whereas high-density lipoprotein cholesterol was different in women. In MDD, no differences in lipid

180


profile were found between those with melancholic and atypical features with or without suicide attempt, or between single depressive episode, or recurrent episodes [18]. In MDD, cholesterol and lipids appear associated with stress, hypothalamo–pituitary–adrenocortical (HPA) axis, and inflammation/immunity [19].

2.2. Immune/inflammation Depression is accompanied by activation of the immune/inflammatory system that include changes in serum acute-phase proteins [20,21] and cytokines [22–24]. In fact, cytokine imbalance may play a role in the pathogenesis of major depression [25,26]. Multiple pathways (such as upregulation of serotonin and dopamine transporters) and the activation of indoleamine dioxygenase (which leads to decreased serotonin and increased excitotoxicity) have also been reported thus linking inflammation with major depression [27,28]. Meta-analysis has shown that increased serum tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) are the most consistent markers associated MDD [29]. Meta-analysis of longitudinal studies on C reactive protein (CRP) or IL-6 and subsequent depressive symptoms found an association between increased CRP and depressive symptoms [30]. Another meta-analysis of antidepressant effects on TNFα, IL-6, and IL-1β, found that IL-1β was related to antidepressant treatment response, though stratified subgroup analysis by antidepressant class indicated that serotonin reuptake inhibitors reduced IL-6 significantly [31]. Our research experience in Taiwan supported the relationship between immune/inflammatory pathway and MDD. Patients with MDD had lower albumin [32]. Chronic hemodialysis patients with comorbid MDD had decreased albumin and increased ferritin [33]. Patients with MDD had increased TNF-α versus controls, and patients with MDD and melancholic features had increased serum IL-1β and IL-1β/IL-10 ratio compared to those without melancholic features [34]. Patients with MDD, however, did not have increased high-sensitivity CRP versus healthy individuals [35].

2.3. Brain-derived neurotrophic factor Brain-derived neurotrophic factor (BDNF) has been chosen as a candidate molecule for MDD [36–39] based on previous studies that supported this relationship [40–45]. The role of BDNF and its receptor tropomyosin-related kinase B (TrkB) in the pathophysiology of MDD has been investigated. Lower serum BDNF


181

was found in MDD versus healthy controls in women, but not men. After antidepressant treatment, serum BDNF was increased, especially in women [46]. Subsequently, no difference in serum BDNF was noted in MDD (vs. controls), whereas serum TrkB was increased in MDD [47]. In subgroups, we found no difference in serum BDNF in obese versus nonobese individuals with MDD or between MDD patients with or without suicide history [48]. Serum TrkB was increased in obese subjects with MDD. However, no difference was found between patients with MDD with and without suicide history. These earlier studies have shown that lipid profiles, immune/inflammation factors, and BDNF were associated with MDD, its phenotypes, and/or treatment response. Unfortunately, findings were often inconsistent. Genetic studies have, however, revealed much insight over the past few decades. As such, an overview of molecules associated with several known pathways will be discussed.

3. GENETIC STUDIES Two popular fields of genetic studies are pharmacogenetics and epigenetics. Pharmacogenetics involves the genetic variations that affect individual responses to drugs to better predict clinical outcome. Epigenetics involves the heritable changes in gene activity not caused by changes in DNA sequence, such as DNA methylation.

3.1. Serotonin system Two of the most studied components of serotonin system are the serotonin transporter gene-linked polymorphic region (5-HTTLPR) and the serotonin receptor 2A (HTR2A). 3.1.1 The serotonin transporter gene-linked polymorphic region The serotonin transporter 5-HTT is the product of SLC6A4 gene. Various polymorphisms of SLC6A4 gene can affect the number and function of the gene product, thus influencing serotonin reuptake. For example, the long allele results in increased SLC6A4 gene activity versus the short one [49]. The relationship between the presence of long allele and treatment response has been investigated. Among 102 inpatients with MDD with psychotic features, carriers of long allele of 5-HTT promoter showed a better response to fluvoxamine [50]. However, ethnic groups have widely different frequencies of long allele. The long allele is present in 29–43% of Caucasians, but

182


only 1–12% of Asians [51]. The short allele is present in 42% of Caucasians and 79% of Asians [52]. In white non-Hispanic depressed adults treated with citalopram, multiple variations in the SLC6A4 gene were associated with remission, but not white Hispanic or African Americans [53]. A metaanalysis suggested that the long allele of 5-HTTLPR may be a predictor of antidepressant response in Caucasians, but not Asians [54]. Two recent studies also showed that the l/l allele for 5-HTTLPR was associated with a better treatment response to escitalopram, but in Caucasians only [55,56]. In some Asian studies, favorable treatment outcome was associated with the short allele [57,58]. A study of 84 monozygotic twins found intrapair DNA methylation variation at 10 of the 20 studied cytosine-phosphate-guanosine (CpG) sites of SLC6A4 promoter region that correlated to depression scores [59]. In a Japanese study, DNA methylation profiles at the CpG island of SLC6A4 could not distinguish healthy controls, unmedicated patients with MDD, and medicated patients with MDD. The 5-HTTLPR allele had no effect on methylation profile. However, the methylation rates for several CpG differed significantly after treatment [60]. A study on Caucasian patients with MDD found that DNA hypomethylation of the 5-HTT transcriptional control region was associated with impaired escitalopram response [61].

3.1.2 Serotonin receptor 2A The HTR2A gene, which encodes the serotonin 2A receptor, is downregulated by selective serotonin reuptake inhibitors (SSRIs). In the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study of 1953 patients with MDD, a single nucleotide polymorphism (SNP) rs7997012 was significantly associated with response to citalopram, an SSRIs [62]. In a large German study of 637 subjects, rs7997012 was associated with remission, but with an inverse allelic association when compared to STAR*D [63]. In another large study of 760 adult patients with moderate-to-severe depression, several variants in HTR2A-predicted response to SSRIs escitalopram with one marker rs9316233 and explained 1.1% of variance [64]. Unfortunately, HTR2A polymorphism association with treatment response has not been replicated [65–68]. Meta-analysis found that HTR2A SNP rs6311 did not confer increased risk to MDD [69]. To our knowledge, there have been no published epigenetic studies on HTR2A.


183

3.2. Monoamine metabolic enzymes Tryptophan hydroxylase (TPH), monoamine oxidase A (MAO-A), and catechol-O-methyl transferase (COMT) are the most commonly studied enzymes in monoamine metabolic pathway. 3.2.1 Tryptophan hydroxylase TPH is an enzyme involved in the synthesis of the neurotransmitter serotonin. In humans, two distinct TPH genes, located on chromosomes 11 and 12, encode different homologous enzymes TPH1 and TPH2. A218C polymorphism (rs1800532) of the TPH1 gene in patients with MDD has been associated with treatment response in some studies. TPH A/A was associated with a slower response to fluvoxamine [70]. TPH A/A and A/C variants were associated with a poorer response to paroxetine treatment [71]. TPH1 A/A and A/C variants were associated with poorer remission to citalopram [72]. However, subsequent larger studies found no association of TPH1 A218C polymorphism and treatment response [64,73,74]. Most recent studies also continued this trend of inconsistency. The A allele at TPH1 A218C was associated with citalopram efficacy only in melancholic and psychotic MDD [67]. Another study found that marked interaction between CC genotype and remission status was associated with endpoint harm avoidance score [75]. A Korean study found that TPH1 218A/C polymorphisms did not predict treatment response in MDD [68]. To our knowledge, epigenetic studies on TPH1 have not been published. In TPH2, three SNPs in TPH2 were positively associated with treatment response [76]. TPH2 rs10879346 polymorphism was found to be an independent predictor of antidepressant response [77]. TPH2 rs17110747-G homozygote polymorphism was more frequently present in MDD (vs. controls) [78]. In addition, the proportion of TPH2 rs2171363 heterozygote carriers was higher in responders versus nonresponders. Interestingly, a large cohort study found no significant association between TPH2 gene variants and treatment response [64]. In more recent studies, no association was found between MDD and TPH2 microdeletion [79] as well as gene polymorphisms [80]. However, an additive effect was observed when 5-HTTLPR was considered along with TPH2 polymorphism [81]. Patients with combined TPH2-703G and 5-HTTLPR long alleles were the most likely to respond to citalopram treatment. To our knowledge, there have been no published epigenetic studies on TPH2.

184


3.2.2 Monoamine oxidase A MAO-A, encoded by the MAOA gene, is an isozyme of monoamine oxidase that deaminates norepinephrine (noradrenaline), epinephrine (adrenaline), serotonin, and dopamine. The variable number of tandem repeats (VNTR) in the promoter region of MAO-A gene could influence transcription activity. Longer alleles (3.5 or 4 copies of the repeat sequence) are transcribed 2–10 more efficiently than shorter alleles (3 or 5 copies of the repeat) [76]. Patients, especially females with MDD, had increased frequency of 4-repeat (4R) MAO-A allele. MDD females with 3R homozygosity had significantly better response when compared to 4R carriers [82]. Long MAO-A alleles (3a, 4, 5) were associated with slower and less efficient overall response of antidepressant treatment in MDD females [83]. MDD patients with short-form VNTR had greater response to mirtazapine [84]. Other studies, however, found no association between MAO-A and antidepressant treatment response [76,85]. In terms of other loci, MDD females homozygous for the T-allele of MAO-A T941G polymorphism had a faster and better treatment response than TG/GG-patients [86]. Another study found no association between MAO-A T941G polymorphism and treatment response [87]. In a remission study, MAO-A polymorphism rs6609257 appeared to be involved in placebo response following treatment with bupropion or placebo [88]. In females with MDD, lower methylation at two individual CpG sites in the MAO-A promoter region was associated with impaired response to escitalopram, but not after correction for multiple testing [89]. An interesting finding was that the effect of MAO-A on treatment response appeared more prominent in females with MDD. A possible explanation may involve the location of MAO-A on the X chromosome. Thus far, MAO-A appeared more effective in predicting clinical outcome in females only. 3.2.3 Catechol-O-methyl transferase COMT, encoded by COMT gene in humans, is an enzyme that degrades catecholamines, such as dopamine, epinephrine, and norepinephrine. A study of the COMT Val(108/158)Met polymorphism (rs4680) found that Val/Val and Val/Met genotypes were associated with treatment response of mirtazapine, but not paroxetine [90]. Another study found that Met/Met genotype was associated with nonremission of citalopram, but not fluvoxamine or paroxetine [91]. Other genotype studies were, however,


185

contradictory. The Met/Met variant of the COMT gene was the best genetic predictor of treatment outcome [92]. Met carriers of rs4680 were associated with treatment response of fluvoxamine [93]. In a study investigating seven SNPs including rs4680, GG genotype of the rs2075507 was less common in treatment resistant patients with MDD, while the C-C-A haplotype of rs4633, rs4818, and rs4680 was related to treatment response [94]. The G-T-G-G haplotype for rs6269, rs4633, rs4818, and rs4680 was only present in the MDD group. COMT rs165737 and a diplotype containing COMT rs165599 and COMT rs165737 were associated with clinical improvement [95]. A more recent study, however, found no association between rs4680 and antidepressant response [66]. A Japanese study found that three SNPs located in the 50 region of COMT were associated with remission in fluvoxamine-treated outpatients with moderate-to-severe depression [96]. In the Chinese Han population, the COMT Met/Val genotype was more commonly found in MDD, but no association was found between COMT polymorphisms and treatment response or remission [97]. In a sample of Korean patients with MDD, COMT rs4680 was not associated with diagnosis or antidepressant response [98]. Studies on a single SNP (e.g., rs4680) were inconsistent and restricted to certain antidepressants. Haplotype studies appeared more promising. To our knowledge, there have been no published epigenetic studies on COMT.

3.3. Brain-derived neurotrophic factor BDNF supported the survival of existing neurons and encouraged growth and differentiation of new neurons and synapses. The most commonly studied polymorphism of BDNF is Val66Met (rs6265). Some studies found an association between rs6265 and antidepressant response. In Chinese, rs6265 was associated with early antidepressant response [99]. In elderly MDD, Met66 allele carriers were more likely to be remitted at 6 months [100]. Interestingly, other studies found no association between this polymorphism and treatment response. A Japanese study found that serum BDNF, but not the BDNF Val66Met polymorphism, was related to mirtazapine response in MDD [101]. In a Korean study, Val66Met did not influence mirtazapine response but resulted in increased plasma BDNF [102]. A 50 untranslated region SNP, rs61888800, was associated with antidepressant response [103]. The rs908867 A allele and TAT haplotype of rs12273363, rs908867, and rs1491850 were associated with remitter status [104]. Although the BDNF rs7124442 TT genotype was associated with

186


worse treatment outcome in a German sample, which finding was not replicated in the STAR*D trial [105]. Another large cohort study (GENDEP) also could not find an association between BDNF polymorphism and treatment response [64]. Unfortunately, the role of the BDNF polymorphism has remained unclear. Genotypes rs11030086 AA, rs6265, rs988712 CC, and rs988748 CC predicted antidepressant response in geriatric depression [106]. In a combined study, seven BDNF SNPs and nine TrkB SNPs were associated with treatment response [107]. In another study, two BDNF gene polymorphisms (rs11030101 and rs61888800) were not associated with response or remission [108]. The epigenetic mechanisms at the BDNF promoter have been investigated in animal studies of depression, especially the epigenetic mechanisms related to BDNF exon I, exon IV, and exon IX [109–111]. The relationship of epigenetic methylation of BDNF and its receptor TrkB with the antidepressant response and suicide attempts in the human brain were investigated previously [103,112,113]. Promoter methylation of BDNF exons I and IV was studied in MDD [39,114–119]. DNA methylation of the BDNF exon IV promoter was associated with antidepressant response. Interestingly, baseline methylation status at CpG position 87 could predict response. Nonresponders had a significantly decreased methylated C-fraction than responders. Patients without methylation at CpG site 87 had increased risk of nonresponse than those with any methylation. We investigated the role of BDNF promoter exon IX gene methylation by recruiting 51 patients with MDD and 62 healthy controls. Patients with MDD had a increased methylation at CpG site 217, and a decreased methylation at CpG site 327 and CpG site 362. Antidepressant responders had increased methylation at CpG site 24 and CpG site 324 (T.L. Huang, R.F. Chen, C.T. Lee, Y.T. Lo, unpublished data).

3.4. HPA axis (FK506-binding protein 5) Dysregulation of HPA axis has been associated with symptoms of MDD. Altered regulation of corticotropin and cortisol secretory activity as well as impaired corticosteroid receptor signaling resulted in increased production and secretion of corticotropin-releasing hormone (CRH) in brain regions involved in causality of depression [120]. FK506-binding protein 5 (FKBP5) is a cochaperone of heat shock protein 90 that regulates glucocorticoid receptor (GR) sensitivity [121]. Three


187

SNPs (rs1360780 TT, rs4713916 AA, rs3800373 CC) were strongly associated with antidepressant response and remission [122]. In another study, the rs1360780 T and rs3800373 C alleles were associated with antidepressant response [123]. Two other studies found no association between rs1360780 with antidepressant response [124,125]. Although rs1360780, rs4713916, and rs3800373 were investigated in the large STAR*D trial, only rs4713916 A allele was associated with antidepressant response and remission [126]. An explanation to inconsistency among these studies could be the limited sensitivity of scale-based assessment of antidepressant response [127]. In addition to depression rating scale, normalization of dexamethasone (Dex)/CRH test was also used an objective measurement of clinical improvement. This study found that rs4713916 and rs3800373 were associated with reduction of cortisol secretion in the Dex/CRH test. Meta-analysis found that FKBP5 gene rs4713916 polymorphism, but not rs1360780 and rs3800373, was associated with antidepressant response in MDD [128]. Recently, the rs352428 GG genotype was associated with 8 weeks (Mayo study) and 6 weeks antidepressant response in the STAR*D replication study [129]. FKBP5 mRNA in peripheral blood cells, decreased plasma cortisol and ACTH were associated with significant GR resistance in patients with depression carrying the rs1360780 allele [130]. In a seven FKBP5 polymorphisms haplotype block (rs3800373, rs755658, rs9296158, rs7748266, rs1360780, rs9394309, and rs9470080), two haplotype combinations (ACATTGT and CCACTAT) were more frequently found in MDD [131]. To our knowledge, epigenetic studies on FKBP5 have not been published. It should be noted that single target studies, i.e., protein, SNP, or epigenetic modifications, have yielded the most conflicting results. MDD is a heterogeneous disorder and a large number of pathways have been speculated in its pathogenesis. As such, combination of biomarkers, such as haplotype studies, would likely yield better results. In 2013, the MDDScore was established [132]. Measurement of nine serum biomarkers (alpha1 antitrypsin, apolipoprotein CIII, BDNF, cortisol, epidermal growth factor, myeloperoxidase, prolactin, resistin, and soluble TNF-α receptor type II) demonstrated a sensitivity of 91.7% and specificity of 81.3% (36 MDD patients and 43 controls). In the replication study, a sensitivity of 91.1% and a specificity of 81% were obtained in 34 MDD patients. Due to its test performance, the MDDScore became commercialized. A similar tool was also developed for schizophrenia. In 2010, Bahn’s research team identified a disease signature comprised of 51 analytes that

188


distinguished schizophrenia with a sensitivity of 83% and a specificity of 83% [133]. The receiver operating characteristic area under the curve (ROCAUC) was 89%. Although initially commercialized as a blood test, VeriPsych was later withdrawn [134].

4. PROTEOMICS, METABOLOMICS, AND BEYOND Research has advanced with the discovery of new techniques or technologies, such as proteomics and metabolomics.

4.1. Our experience with proteomics Our team combined acid hydrolysis with matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF MS) to rapidly study changes in positive and negative acute-phase proteins in the serum of MDD patients [135]. This study found two proteins, transferrin (negative acute-phase protein) and fibrinogen (positive acute-phase protein), were noteworthy. Preliminary data showed that the average ion signal ratio derived from transferrin/fibrinogen was 3.58 (1.93) in healthy controls versus 1.02 (0.52) for MDD. In another study, high-resolution two-dimensional differential gel electrophoresis (2D-DIGE), MALDI-TOF MS, Western blot, and bioinformatics were combined to study platelet proteins in MDD (n ¼ 10) versus healthy controls (n ¼ 10) [136]. Protein disulfide isomerase A3 (PDIA3) and F-actin-capping protein subunit beta (CAPZB) were increased in MDD. In contrast, fibrinogen beta chain, fibrinogen gamma chain, retinoic acid receptor beta, glutathione peroxidase 1, SH3 domain-containing protein 19, and T-complex protein 1 subunit beta were decreased. Interestingly, these involve inflammation/immunity, oxidative stress, and neurogenesis.

4.2. Proteomics Proteomics, the study of proteome, the set of expressed proteins by a cell, tissue, or organism, in a given moment, under a determined condition [137], can also be used to study MDD. 4.2.1 Brain tissue Martins-de-Souza et al. [138], used shotgun proteomics in the analysis of postmortem dorsolateral prefrontal cortex brain tissue in MDD (n ¼ 24) versus matched controls (n ¼ 12). Major pathways associated with MDD were


189

metabolic/energy pathways (32%), transport of molecules (22%), and cell communication and signaling. In MDD, oxidative phosphorylation appeared to be the most affected energy pathway, while in schizophrenia, glycolysis appeared most affected [139]. Interestingly, MDD patients with psychosis showed similarities in proteins that were differentially expressed in schizophrenia, including glycolysis enzymes [138]. Another important finding was the increased histidine triad nucleotide-binding protein 1 (HINT1) in MDD brain. Although hypothesized to interfere with the HPA axis, studies using HINT1 knockout mice were inconsistent [140,141]. Liquid chromatography–mass spectrometry (LC–MS) was employed to investigate phosphorylation of the proteome, i.e., the phosphoproteome [142]. Analysis of postmortem dorsolateral prefrontal cortex brain tissue in MDD (n ¼ 24) and matched controls (n ¼ 12) revealed 90 proteins with differential phosphorylation level. The majority of these phosphorylated proteins were associated with synaptic transmission and cellular architecture. Two-dimensional gel electrophoresis combined with MS sequencing was used to evaluate postmortem frontal cortices in schizophrenia, bipolar disorder, MDD versus nonpsychiatric controls [143]. Glial fibrillary acidic protein, dihydropyrimidinase-related protein 2, ubiquinone cytochrome c reductase core protein 1, carbonic anhydrase 1, and fructose biphosphate aldolase C were differentially expressed in psychiatric disorders. These proteins involve regulation of axonal guidance, neuronal growth cone collapse, cell migration as well as energy metabolism. In a similar study involving the anterior cingulate cortex, 19 distinct proteins were identified [144]. These included aconitate hydratase, malate dehydrogenase, fructose bisphosphate aldolase A, ATP synthase, succinyl CoA ketoacid transferase, carbonic anhydrase, alpha- and beta-tubulin, dihydropyrimidinase-related protein-1 and -2, neuronal protein 25, trypsin precursor, glutamate dehydrogenase, glutamine synthetase, sorcin, vacuolar ATPase, creatine kinase, albumin, and guanine nucleotide-binding protein beta subunit. These proteins mainly involve cytoskeletal architecture and mitochondrial energy metabolism. 4.2.2 Cerebrospinal fluid Cerebrospinal fluid (CSF) was evaluated by surface-enhanced laser desorption ionization MS in a group of patients with schizophrenia (n ¼ 58), depression (n ¼ 16), obsessive compulsive disorder (n ¼ 5), and Alzheimer’s disease (n ¼ 10) [145]. Results showed that depression was associated with a

190


distinct decrease of secretogranin II 529–566 peptide, but depression and schizophrenia shared increased VGF23–62 peptide expression. VGF is a protein and neuropeptide that may play a role in regulating energy homeostasis, metabolism, and synaptic plasticity. In another study, CSF (12 depressed patients and 12 controls) was analyzed by two-dimensional polyacrylamide gel electrophoresis and TOF MS [146]. Biomarkers of interests included 11 proteins and 144 peptide features that differed significantly in depression versus controls. Differences in phosphorylation pattern of several proteins were also detected. These proteins involve neuroprotection and neuronal development, sleep regulation, and amyloid plaque deposition in the aging brain. 4.2.3 Peripheral blood It is surprising that not many studies have focused on proteomic investigation of peripheral blood given its ease of collection. Multidimensional LC–MS was used to evaluate plasma proteins in first onset, treatment-naive depressed patients (n ¼ 21) versus controls (n ¼ 21) [147]. Specimens were immunodepleted of seven high-abundance proteins and labeled with isobaric tags to assess relative and absolute quantitation. Proteins identified in this study involve lipid metabolism and immunoregulation. Multiplex immunoassay and label-free LC–MS was used to evaluate serum in first onset, antidepressant drug-naive MDD (n ¼ 38) [148]. A number of proteins including angiotensin-converting enzyme, acutephase proteins (ferritin and serotransferrin), BDNF, complement component C4-B, cortisol, cytokines (macrophage migration inhibitory factor and IL-16), extracellular receptor for advanced glycosylation end products-binding protein, growth hormone, and superoxide dismutase-1 were found to change or were correlated with severity of symptoms. Increased proinflammatory and oxidative stress response, hyperactivated HPA axis, and dysregulated growth factor pathways could contribute to acute stages of first onset MDD. Multiplex human MAP(R) immunoassay and LC–MS were used to profile serum in MDD patients treated by electroconvulsive therapy [149]. A number of proteins, implicated in earlier studies, were identified. These included BDNF, CD40L, IL-8, IL-13, EGF, IGF-1, pancreatic polypeptide, SCF, and sortilin-1. At admission, proteomes of peripheral blood mononuclear cells were used to predict a successful antidepressant treatment, i.e., responders versus


191

nonresponders [149]. This study found a number of proteins that were differentially expressed in responders versus nonresponders including proteins of the integrin signaling pathway, ubiquitin proteasome system, and Rasrelated pathway. It is noteworthy that integrins control synaptic plasticity and transmission as well as play important roles in glutamatergic transmission. The ubiquitin proteasome system involves learning and memory. Ras-related proteins involve intracellular membrane trafficking and cellular proliferation. Proteomes of responders and nonresponders before and after 6 weeks of antidepressant treatment were also compared [150]. Interestingly, twothirds of differentially expressed proteins were downregulated in responders, whereas most differentially expressed proteins were upregulated in nonresponders. Proteomics has high potential to discover biomarkers in MDD and predict antidepressant response. Peripheral blood findings facilitate translation to clinical practice. Unfortunately, approximately 1 million proteins could derive from less than 25,000 genes [151]. As mentioned earlier, these proteins could have very different function based on their posttranslational modification. As such, metabolomics could provide a complementary approach to proteomics.

4.3. Metabolomics Metabolomics is the omics science of biochemistry. It utilizes a global approach to understanding regulation of metabolic pathways and metabolic networks of a biological system. Metabolomics can complement data derived from genomics and proteomics to assist in providing a systemic approach to the study of human health and disease [152].

4.3.1 Cerebrospinal fluid To date, there have been no metabolomic studies of MDD brain tissue. In fact, there has only been one article using CSF [153]. In this study, tryptophan, tyrosine, purine, and related pathways were analyzed in CSF from nonmedicated patients with MDD (n ¼ 14), remitted patients with MDD (n ¼ 14) versus controls (n ¼ 18). Increased methionine and methionine/ glutathione ratio was found in remitted patients suggesting involvement of methylation and oxidative stress pathways. Nonmedicated MDD samples showed no significant differences several metabolic pathways.

192


4.3.2 Peripheral blood Plasma from 30 elderly subjects (9 MDD, 11 remitted, and 10 controls) was analyzed by gas chromatography (GC)–MS [154]. In MDD, lipid metabolites and neurotransmitters (dicarboxylic fatty acids, glutamate, and aspartate) were increased. In remitted patients, the metabolic profile was similar to controls suggesting that antidepressant treatment normalized aberrant pathways. Plasma from patients with MDD and heart failure and nondepressed heart failure patients were analyzed using GC–MS and LC–MS metabolomics [155]. The depressed group had increased concentration of several amino acids and dicarboxylic fatty acids. These findings were consistent with the aforementioned report. Plasma from 58 first-episode drug-naive patients with MDD and 42 controls were analyzed by nuclear magnetic resonance (NMR) and orthogonal partial least-squares discriminant analysis [156]. A panel of metabolites were identified that could effectively distinguished depression with a sensitivity and specificity of 92.8 and 83.3%, respectively. Metabolomics could also be used to predict antidepressant response. Using LC electrochemical array (LCECA), serum was evaluated from 43 outpatients with MDD receiving sertraline and 46 outpatients with MDD receiving placebo [157]. In this double-blind study, multivariate analysis showed that metabolic profiles partially differentiated responders versus nonresponders. Markers included phenylalanine, tryptophan, purine, and tocopherol. Dihydroxyphenylacetic acid, tocopherols, and serotonin were common metabolites in differentiating responders and nonresponders to drug and placebo. In the follow-up study, serum (baseline, 1 week, and 4 weeks) from patients with MDD receiving sertraline or placebo were analyzed using a GC–TOF MS [158]. Sertraline- and placebo-induced differences in metabolites were related to tricarboxylic acid cycle, urea cycle, fatty acids, and intermediates of lipid biosynthesis, amino acids, sugars, and gut-derived metabolites. More extensive changes were noted after 4 weeks, a finding consistent with delayed antidepressant clinical effect. Pathway analysis in the sertraline group suggested an effect of drug on ATP-binding cassette and solute transporters, fatty acid receptors and transporters, and G signaling molecules and lipid metabolism. Serum of outpatients with MDD randomly assigned to sertraline (n ¼ 35) or placebo (n ¼ 40) were analyzed using a LCECA [159]. This study focused on changes within methoxyindole and kynurenine (KYN) branches of the


193

tryptophan pathway. Sertraline responders had increased pretreatment 5-methoxytryptamine (5-MTPM), greater reduced 5-MTPM after treatment, increased 5-methoxytryptophol (5-MTPOL) and melatonin (MEL), and decreased KYN/MEL and 3-hydroxykynurenine 3-OHKY/MEL ratio posttreatment. Placebo responders had increased 5-MTPOL and MEL and significantly decreased KYN/MEL and 3-OHKY/MEL. These findings suggested that treatment response, due to antidepressant or placebo, could be associated with preferential utilization of serotonin for production of MEL and 5-MTPOL. Plasma from 25 healthy adults and 46 patients with MDD, including 23 patients with early life stress (ELS) and 23 patients without ELS were analyzed with GC–MS coupled with multivariate statistical analysis [160]. Metabolic profiles distinguished patients with MDD from controls as well as MDD patients with ELS from those without ELS. A total of 16 metabolites distinguished MDD with ELS from controls and 13 metabolites distinguished MDD with ELS from MDD without ELS. Common metabolites included amino acids (alanine, glycine), carbohydrate (galactose), fatty acids (linoleic acid), and cholesterol. 4.3.3 Urine Urine samples of 82 first-episode drug-naive patients with MDD and 82 healthy controls were analyzed using a NMR spectroscopy-based metabonomics [161]. Five metabolites, malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine, distinguished MDD from controls. This panel effectively distinguished another sample set consisting of 44 patients with MDD and 52 controls with an area under the receiver operating characteristic curve (AUC) of 0.89.

4.4. Protein interactomics Interactome could be considered a biological network wherein molecular interactions of a particular protein are functionally mapped. The study of the interactome could help reveal dysfunctional pathways and possibly accelerate biomarker and therapeutic discovery [162]. Protein interactomics often employ yeast two-hybrid screening (Y2H) [163], tandem affinity purification [164], and coimmunoprecipitation (coIP) [165]. The phosphatidylinositol 3-kinase and the mammalian target of rapamycin (PI3K–mTOR) pathway may play a central role in MDD therapeutics through immune cell activation by inflammatory cytokines [166].

194


In an Y2H interactome analysis, 31 components of the PI3K–mTOR pathway were evaluated with respect to MDD [167]. A total of 802 interactions within the PI3K–mTOR pathway were mapped, including 67 new interactions. More importantly, the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor was identified as an interactor and in vitro substrate of GSK3A and GSK3B. GSK3 inhibitors, such as lithium, increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. As such, DEAF1 may represent a therapeutic target of lithium. Interactomics may be useful to integrate known datasets. A comprehensive analysis framework at the systemic level was proposed using a set of MDD candidate genes [168]. This study was based on multiple lines of evidence including association, linkage, gene expression (both human and animal studies), regulatory pathway, and literature search. The resulting pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules significantly enriched with MDD genes: neurotransmission and immune system related, supporting the neuropathologic hypothesis of MDD.

5. CONCLUSION MDD is a heterogeneous disease. Biomarkers to effectively diagnose MDD and reliably predict treatment response are highly needed in clinical practice. Thus far, no single molecule or a single pathway has been identified. Proteomics, metabolomics, and interactomics allow large-scale analyses to elucidate molecules or pathways involved in underlying pathophysiology. Several computer programs have been developed to assist in this integration. The web-based tool STRING (Search Tool for the Retrieval of INteracting Genes/proteins) aims to provide a global view of protein interaction via automated mining of scientific publications, transferring interactions from one model organism to the other, and providing statistical information on any functional enrichment observed in their networks [169]. Computergenerated interactions must be interpreted with caution. These, however, could provide the basis of new hypotheses. In conclusion, more data are clearly needed to fully assess the pathophysiology of MDD. The data pool is tremendous and includes protein assessment, SNPs, epigenetic regulation, and clinical data to better identify reliable and consistent biomarkers for diagnosis and treatment [170,171] as well as predict future outcome.


195

REFERENCES [1] J.M. Pyne, T.L. Patterson, R.M. Kaplan, S. Ho, J.C. Gillin, S. Golshan, et al., Preliminary longitudinal assessment of quality of life in patients with major depression, Psychopharmacol. Bull. 33 (1) (1997) 23–29. [2] Biomarkers Definitions Working Group, Biomarkers and surrogate endpoints: preferred definitions and conceptual framework, Clin. Pharmacol. Ther. 69 (3) (2001) 89–95. [3] P. Boksa, A way forward for research on biomarkers for psychiatric disorders, J. Psychiatry Neurosci. 38 (2) (2013) 75–77. [4] W. Kluge, M. Alsaif, P.C. Guest, E. Schwarz, S. Bahn, Translating potential biomarker candidates for schizophrenia and depression to animal models of psychiatric disorders, Expert. Rev. Mol. Diagn. 11 (7) (2011) 721–733. [5] H.D. Schmidt, R.C. Shelton, R.S. Duman, Functional biomarkers of depression: diagnosis, treatment, and pathophysiology, Neuropsychopharmacology 36 (12) (2011) 2375–2394. [6] J.W. Murrough, D.S. Charney, Is there anything really novel on the antidepressant horizon? Curr. Psychiatry Rep. 14 (6) (2012) 643–649. [7] M.R. DiMatteo, H.S. Lepper, T.W. Croghan, Depression is a risk factor for noncompliance with medical treatment: meta-analysis of the effects of anxiety and depression on patient adherence, Arch. Intern. Med. 160 (14) (2000) 2101–2107. [8] M.F. Muldoon, S.B. Manuck, K.A. Matthews, Lowering cholesterol concentrations and mortality: a quantitative review of primary prevention trials, Br. Med. J. 301 (6747) (1990) 309–314. [9] C.J. Glueck, M. Tieger, R. Kunkel, T. Hamer, T. Tracy, J. Speirs, Hypocholesterolemia and affective disorders, Am. J. Med. Sci. 308 (4) (1994) 218–225. [10] S.O. Olusi, A.A. Fido, Serum lipid concentrations in patients with major depressive disorder, Biol. Psychiatry 40 (11) (1996) 1128–1131. [11] T. Partonen, J. Haukka, J. Virtamo, P.R. Taylor, J. Lonnqvist, Association of low serum total cholesterol with major depression and suicide, Br. J. Psychiatry 175 (1999) 259–262. [12] B. Wallner, I.H. Machatschke, The evolution of violence in men: the function of central cholesterol and serotonin, Prog. Neuropsychopharmacol. Biol. Psychiatry 33 (3) (2009) 391–397. [13] C.C. Chen, F.H. Lu, J.S. Wu, C.J. Chang, Correlation between serum lipid concentrations and psychological distress, Psychiatry Res. 102 (2) (2001) 153–162. [14] M. Maes, R. Smith, A. Christophe, E. Vandoolaeghe, A. Van Gastel, H. Neels, et al., Lower serum high-density lipoprotein cholesterol (HDL-C) in major depression and in depressed men with serious suicidal attempts: relationship with immuneinflammatory markers, Acta Psychiatr. Scand. 95 (3) (1997) 212–221. [15] T.L. Huang, Serum cholesterol levels in mood disorders associated with physical violence or suicide attempts in Taiwanese, Chang Gung Med. J. 24 (9) (2001) 563–568. [16] T.L. Huang, S.C. Wu, Y.S. Chiang, J.F. Chen, Correlation between serum lipid, lipoprotein concentrations and anxious state, depressive state or major depressive disorder, Psychiatry Res. 118 (2) (2003) 147–153. [17] T.L. Huang, J.F. Chen, Lipid and lipoprotein levels in depressive disorders with melancholic feature or atypical feature and dysthymia, Psychiatry Clin. Neurosci. 58 (3) (2004) 295–299. [18] T.L. Huang, Serum lipid profiles in major depression with clinical subtypes, suicide attempts and episodes, J. Affect. Disord. 86 (1) (2005) 75–79. [19] T.L. Huang, J.F. Chen, Cholesterol and lipids in depression: stress, hypothalamo– pituitary–adrenocortical axis, and inflammation/immunit, Adv. Clin. Chem. 39 (2005) 81–105.

196


[20] M. Maes, J. Delange, R. Ranjan, H.Y. Meltzer, R. Desnyder, W. Cooremans, et al., Acute phase proteins in schizophrenia, mania and major depression: modulation by psychotropic drugs, Psychiatry Res. 66 (1) (1997) 1–11. [21] C. Song, T. Dinan, B.E. Leonard, Changes in immunoglobulin, complement and acute phase protein levels in the depressed patients and normal controls, J. Affect. Disord. 30 (4) (1994) 283–288. [22] M. Maes, E. Bosmans, E. Suy, C. Vandervorst, C. DeJonckheere, J. Raus, Depressionrelated disturbances in mitogen-induced lymphocyte responses and interleukin-1 beta and soluble interleukin-2 receptor production, Acta Psychiatr. Scand. 84 (4) (1991) 379–386. [23] A. Seidel, V. Arolt, M. Hunstiger, L. Rink, A. Behnisch, H. Kirchner, Cytokine production and serum proteins in depression, Scand. J. Immunol. 41 (6) (1995) 534–538. [24] T.T. Chang, Y.C. Yen, Cytokines and major psychiatric disorders, Taiwan. J. Psychiatry (Taipei) 24 (2010) 257–268. [25] A.M. Myint, B.E. Leonard, H.W. Steinbusch, Y.K. Kim, Th1, Th2, and Th3 cytokine alterations in major depression, J. Affect. Disord. 88 (2) (2005) 167–173. [26] O.J.G. Schiepers, M.C. Wichers, M. Maes, Cytokines and major depression, Prog. Neuropsychopharmacol. Biol. Psychiatry 29 (2) (2005) 201–217. [27] R. Krishnadas, J. Cavanagh, Depression: an inflammatory illness? J. Neurol. Neurosurg. Psychiatry 83 (5) (2012) 495–502. [28] B.E. Leonard, The concept of depression as a dysfunction of the immune system, Curr. Immunol. Rev. 6 (3) (2010) 205–212. [29] Y. Dowlati, N. Herrmann, W. Swardfager, H. Liu, L. Sham, E.K. Reim, et al., A metaanalysis of cytokines in major depression, Biol. Psychiatry 67 (5) (2010) 446–457. [30] V. Valkanova, K.P. Ebmeier, C.L. Allan, CRP, IL-6 and depression: a systematic review and meta-analysis of longitudinal studies, J. Affect. Disord. 150 (3) (2013) 736–744. [31] J. Hannestad, N. DellaGioia, M. Bloch, The effect of antidepressant medication treatment on serum levels of inflammatory cytokines: a meta-analysis, Neuropsychopharmacology 36 (12) (2011) 2452–2459. [32] T.L. Huang, Lower serum albumin levels in patients with mood disorders, Chang Gung Med. J. 25 (8) (2002) 509–513. [33] T.L. Huang, C.T. Lee, Low serum albumin and high ferritin levels in chronic hemodialysis patients with major depression, Psychiatry Res. 152 (2–3) (2007) 277–280. [34] T.L. Huang, C.T. Lee, T-helper 1/T-helper 2 cytokine imbalance and clinical phenotypes of acute-phase major depression, Psychiatry Clin. Neurosci. 61 (4) (2007) 415–420. [35] T.L. Huang, F.C. Lin, High-sensitivity C-reactive protein levels in patients with major depressive disorder and bipolar mania, Prog. Neuropsychopharmacol. Biol. Psychiatry 31 (2) (2007) 370–372. [36] A.R. Brunoni, M. Lopes, F. Fregni, A systematic review and meta-analysis of clinical studies on major depression and BDNF levels: implications for the role of neuroplasticity in depression, Int. J. Neuropsychopharmacol. 11 (8) (2008) 1169–1180. [37] V. Krishnan, E.J. Nestler, The molecular neurobiology of depression, Nature 455 (7215) (2008) 894–902. [38] Y. Dwivedi, Brain-derived neurotrophic factor: role in depression and suicide, Neuropsychiatr. Dis. Treat. 5 (2009) 433–449. [39] H.J. Kang, J.M. Kim, J.Y. Lee, S.Y. Kim, K.Y. Bae, S.W. Kim, et al., BDNF promoter methylation and suicidal behavior in depressive patients, J. Affect. Disord. 151 (2) (2013) 679–685. [40] R.S. Duman, G.R. Heninger, E.J. Nestler, A molecular and cellular theory of depression, Arch. Gen. Psychiatry 54 (7) (1997) 597–606.


197

[41] C.A. Altar, Neurotrophins and depression, Trends Pharmacol. Sci. 20 (2) (1999) 59–61. [42] E.J. Nestler, M. Barrot, R.J. DiLeone, A.J. Eisch, S.J. Gold, L.M. Monteggia, Neurobiology of depression, Neuron 34 (1) (2002) 13–25. [43] Y. Dwivedi, H.S. Rizavi, R.R. Conley, R.C. Roberts, C.A. Tamminga, G.N. Pandey, Altered gene expression of brain-derived neurotrophic factor and receptor tyrosine kinase B in postmortem brain of suicide subjects, Arch. Gen. Psychiatry 60 (8) (2003) 804–815. [44] A.S. Gonul, F. Akdeniz, F. Taneli, O. Donat, C. Eker, S. Vahip, Effect of treatment on serum brain-derived neurotrophic factor levels in depressed patients, Eur. Arch. Psychiatry Clin. Neurosci. 255 (6) (2005) 381–386. [45] F. Karege, G. Bondolfi, N. Gervasoni, M. Schwald, J.M. Aubry, G. Bertschy, Low brain-derived neurotrophic factor (BDNF) levels in serum of depressed patients probably results from lowered platelet BDNF release unrelated to platelet reactivity, Biol. Psychiatry 57 (9) (2005) 1068–1072. [46] T.L. Huang, C.T. Lee, Y.L. Liu, Serum brain-derived neurotrophic factor levels in patients with major depression: effects of antidepressants, J. Psychiatr. Res. 42 (7) (2008) 521–525. [47] Y.Y. Hung, C.J. Lin, T.L. Huang, Higher serum tropomyosin-related kinase B protein level in major depression, Prog. Neuropsychopharmacol. Biol. Psychiatry 34 (4) (2010) 610–612. [48] C.C. Lin, T.L. Huang, Increased serum tropomyosin-related kinase B protein level in obese patients with major depressive disorder, Taiwan. J. Psychiatry 24 (4) (2010) 301–306. [49] K.P. Lesch, J. Meyer, K. Glatz, G. Flugge, A. Hinney, J. Hebebrand, et al., The 5-HT transporter gene-linked polymorphic region (5-HTTLPR) in evolutionary perspective: alternative biallelic variation in rhesus monkeys. Rapid communication, J. Neural Transm. 104 (11–12) (1997) 1259–1266. [50] E. Smeraldi, R. Zanardi, F. Benedetti, D. Di Bella, J. Perez, M. Catalano, Polymorphism within the promoter of the serotonin transporter gene and antidepressant efficacy of fluvoxamine, Mol. Psychiatry 3 (6) (1998) 508–511. [51] N. Goldman, D.A. Glei, Y.H. Lin, M. Weinstein, The serotonin transporter polymorphism (5-HTTLPR): allelic variation and links with depressive symptoms, Depress. Anxiety 27 (3) (2010) 260–269. [52] H. Kunugi, M. Hattori, T. Kato, M. Tatsumi, T. Sakai, T. Sasaki, et al., Serotonin transporter gene polymorphisms: ethnic difference and possible association with bipolar affective disorder, Mol. Psychiatry 2 (6) (1997) 457–462. [53] D.A. Mrazek, A.J. Rush, J.M. Biernacka, D.J. O’Kane, J.M. Cunningham, E.D. Wieben, et al., SLC6A4 variation and citalopram response, Am. J. Med. Genet. B Neuropsychiatr. Genet. 150B (3) (2009) 341–351. [54] S. Porcelli, C. Fabbri, A. Serretti, Meta-analysis of serotonin transporter gene promoter polymorphism (5-HTTLPR) association with antidepressant efficacy, Eur. Neuropsychopharmacol. 22 (4) (2012) 239–258. [55] C.A. Bousman, J. Sarris, E.S. Won, H.S. Chang, A. Singh, H.Y. Lee, et al., Escitalopram efficacy in depression: a cross-ethnicity examination of the serotonin transporter promoter polymorphism, J. Clin. Psychopharmacol. 34 (5) (2014) 645–648. [56] C. Ng, J. Sarris, A. Singh, C. Bousman, K. Byron, L.H. Peh, et al., Pharmacogenetic polymorphisms and response to escitalopram and venlafaxine over 8 weeks in major depression, Hum. Psychopharmacol. 28 (5) (2013) 516–522. [57] W. Myung, S.W. Lim, S. Kim, H. Kim, J.W. Chung, M.Y. Seo, et al., Serotonin transporter genotype and function in relation to antidepressant response in Koreans, Psychopharmacology (Berl.) 225 (2) (2013) 283–290.

198


[58] E.S. Won, H.S. Chang, H.Y. Lee, B.J. Ham, M.S. Lee, Association between serotonin transporter-linked polymorphic region and escitalopram antidepressant treatment response in Korean patients with major depressive disorder, Neuropsychobiology 66 (4) (2012) 221–229. [59] J. Zhao, J. Goldberg, J.D. Bremner, V. Vaccarino, Association between promoter methylation of serotonin transporter gene and depressive symptoms: a monozygotic twin study, Psychosom. Med. 75 (6) (2013) 523–529. [60] S. Okada, S. Morinobu, M. Fuchikami, M. Segawa, K. Yokomaku, T. Kataoka, et al., The potential of SLC6A4 gene methylation analysis for the diagnosis and treatment of major depression, J. Psychiatr. Res. 53 (2014) 47–53. [61] K. Domschke, N. Tidow, K. Schwarte, J. Deckert, K.P. Lesch, V. Arolt, et al., Serotonin transporter gene hypomethylation predicts impaired antidepressant treatment response, Int. J. Neuropsychopharmacol. 17 (8) (2014) 1167–1176. [62] F.J. McMahon, S. Buervenich, D. Charney, R. Lipsky, A.J. Rush, A.F. Wilson, et al., Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment, Am. J. Hum. Genet. 78 (5) (2006) 804–814. [63] S. Lucae, M. Ising, S. Horstmann, B.T. Baune, V. Arolt, B. Muller-Myhsok, et al., HTR2A gene variation is involved in antidepressant treatment response, Eur. Neuropsychopharmacol. 20 (1) (2010) 65–68. [64] R. Uher, P. Huezo-Diaz, N. Perroud, R. Smith, M. Rietschel, O. Mors, et al., Genetic predictors of response to antidepressants in the GENDEP project, Pharmacogenomics J. 9 (4) (2009) 225–233. [65] J. Staeker, S. Leucht, B. Laika, W. Steimer, Polymorphisms in serotonergic pathways influence the outcome of antidepressant therapy in psychiatric inpatients, Genet. Test. Mol. Biomarkers 18 (1) (2014) 20–31. [66] A. Serretti, C. Fabbri, S. Pellegrini, S. Porcelli, P. Politi, S. Bellino, et al., No effect of serotoninergic gene variants on response to interpersonal counseling and antidepressants in major depression, Psychiatry Investig. 10 (2) (2013) 180–189. [67] B. Arias, C. Fabbri, F. Gressier, A. Serretti, M. Mitjans, C. Gasto, et al., TPH1, MAOA, serotonin receptor 2A and 2C genes in citalopram response: possible effect in melancholic and psychotic depression, Neuropsychobiology 67 (1) (2013) 41–47. [68] Y.G. Kim, H.S. Chang, E.S. Won, B.J. Ham, M.S. Lee, Serotonin-related polymorphisms in TPH1 and HTR5A genes are not associated with escitalopram treatment response in Korean patients with major depression, Neuropsychobiology 69 (4) (2014) 210–219. [69] C. Jin, W. Xu, J. Yuan, G. Wang, Z. Cheng, Meta-analysis of association between the -1438A/G (rs6311) polymorphism of the serotonin 2A receptor gene and major depressive disorder, Neurol. Res. 35 (1) (2013) 7–14. [70] A. Serretti, R. Zanardi, D. Rossini, C. Cusin, R. Lilli, E. Smeraldi, Influence of tryptophan hydroxylase and serotonin transporter genes on fluvoxamine antidepressant activity, Mol. Psychiatry 6 (5) (2001) 586–592. [71] A. Serretti, R. Zanardi, C. Cusin, D. Rossini, C. Lorenzi, E. Smeraldi, Tryptophan hydroxylase gene associated with paroxetine antidepressant activity, Eur. Neuropsychopharmacol. 11 (5) (2001) 375–380. [72] B.J. Ham, B.C. Lee, J.W. Paik, R.H. Kang, M.J. Choi, I.G. Choi, et al., Association between the tryptophan hydroxylase-1 gene A218C polymorphism and citalopram antidepressant response in a Korean population, Prog. Neuropsychopharmacol. Biol. Psychiatry 31 (1) (2007) 104–107. [73] C.J. Hong, T.J. Chen, Y.W. Yu, S.J. Tsai, Response to fluoxetine and serotonin 1A receptor (C-1019G) polymorphism in Taiwan Chinese major depressive disorder, Pharmacogenomics J. 6 (1) (2006) 27–33.


199

[74] E.J. Peters, S.L. Slager, G.D. Jenkins, M.S. Reinalda, H.A. Garriock, S.I. Shyn, et al., Resequencing of serotonin-related genes and association of tagging SNPs to citalopram response, Pharmacogenet. Genomics 19 (1) (2009) 1–10. [75] K. Andre, O. Kampman, M. Viikki, A. Illi, E. Setala-Soikkeli, O. Poutanen, et al., TPH1 A218C polymorphism and temperament in major depression, BMC Psychiatry 13 (2013) 118. [76] E.J. Peters, S.L. Slager, P.J. McGrath, J.A. Knowles, S.P. Hamilton, Investigation of serotonin-related genes in antidepressant response, Mol. Psychiatry 9 (9) (2004) 879–889. [77] M.V. Tzvetkov, J. Brockmoller, I. Roots, J. Kirchheiner, Common genetic variations in human brain-specific tryptophan hydroxylase-2 and response to antidepressant treatment, Pharmacogenet. Genomics 18 (6) (2008) 495–506. [78] S.J. Tsai, C.J. Hong, Y.J. Liou, Y.W. Yu, T.J. Chen, S.J. Hou, et al., Tryptophan hydroxylase 2 gene is associated with major depression and antidepressant treatment response, Prog. Neuropsychopharmacol. Biol. Psychiatry 33 (4) (2009) 637–641. [79] C. Hammer, F. Degenhardt, L. Priebe, A.M. Stutz, S. Heilmann, S.M. Waszak, et al., A common microdeletion affecting a hippocampus- and amygdala-specific isoform of tryptophan hydroxylase 2 is not associated with affective disorders, Bipolar Disord. 16 (7) (2014) 764–768. [80] N.E. Nazree, A.C. Loke, N.Z. Zainal, Z. Mohamed, Lack of association between TPH2 gene polymorphisms with major depressive disorder in multiethnic Malaysian population, Asia Pac. Psychiatry (2013) [Epub ahead of print]. [81] B. Rotberg, S. Kronenberg, M. Carmel, A. Frisch, D. Brent, G. Zalsman, et al., Additive effects of 5-HTTLPR (serotonin transporter) and tryptophan hydroxylase 2G-703T gene polymorphisms on the clinical response to citalopram among children and adolescents with depression and anxiety disorders, J. Child Adolesc. Psychopharmacol. 23 (2) (2013) 117–122. [82] Y.W. Yu, S.J. Tsai, C.J. Hong, T.J. Chen, M.C. Chen, C.W. Yang, Association study of a monoamine oxidase A gene promoter polymorphism with major depressive disorder and antidepressant response, Neuropsychopharmacology 30 (9) (2005) 1719–1723. [83] K. Domschke, C. Hohoff, L.S. Mortensen, T. Roehrs, J. Deckert, V. Arolt, et al., Monoamine oxidase A variant influences antidepressant treatment response in female patients with major depression, Prog. Neuropsychopharmacol. Biol. Psychiatry 32 (1) (2008) 224–228. [84] D.S. Tzeng, C.C. Chien, F.W. Lung, C.Y. Yang, MAOA gene polymorphisms and response to mirtazapine in major depression, Hum. Psychopharmacol. 24 (4) (2009) 293–300. [85] A. Serretti, R. Zanardi, L. Franchini, P. Artioli, D. Dotoli, A. Pirovano, et al., Pharmacogenetics of selective serotonin reuptake inhibitor response: a 6-month follow-up, Pharmacogenetics 14 (9) (2004) 607–613. [86] A. Tadic, M.J. Muller, D. Rujescu, R. Kohnen, H.H. Stassen, N. Dahmen, et al., The MAOA T941G polymorphism and short-term treatment response to mirtazapine and paroxetine in major depression, Am. J. Med. Genet. B Neuropsychiatr. Genet. 144B (3) (2007) 325–331. [87] A.F. Leuchter, J.T. McCracken, A.M. Hunter, I.A. Cook, J.E. Alpert, Monoamine oxidase A and catechol-O-methyltransferase functional polymorphisms and the placebo response in major depressive disorder, J. Clin. Psychopharmacol. 29 (4) (2009) 372–377. [88] A.K. Tiwari, C.C. Zai, G. Sajeev, T. Arenovich, D.J. Muller, J.L. Kennedy, Analysis of 34 candidate genes in bupropion and placebo remission, Int. J. Neuropsychopharmacol. 16 (4) (2013) 771–781.

200


[89] K. Domschke, N. Tidow, K. Schwarte, C. Ziegler, K.P. Lesch, J. Deckert, et al., Pharmacoepigenetics of depression: no major influence of MAO-A DNA methylation on treatment response, J. Neural Transm. (2014). [90] A. Szegedi, D. Rujescu, A. Tadic, M.J. Muller, R. Kohnen, H.H. Stassen, et al., The catechol-O-methyltransferase Val108/158Met polymorphism affects short-term treatment response to mirtazapine, but not to paroxetine in major depression, Pharmacogenomics J. 5 (1) (2005) 49–53. [91] B. Arias, A. Serretti, C. Lorenzi, C. Gasto, R. Catalan, L. Fananas, Analysis of COMT gene (Val 158 Met polymorphism) in the clinical response to SSRIs in depressive patients of European origin, J. Affect. Disord. 90 (2–3) (2006) 251–256. [92] D. Spronk, M. Arns, K.J. Barnett, N.J. Cooper, E. Gordon, An investigation of EEG, genetic and cognitive markers of treatment response to antidepressant medication in patients with major depressive disorder: a pilot study, J. Affect. Disord. 128 (1–2) (2011) 41–48. [93] F. Benedetti, S. Dallaspezia, C. Colombo, C. Lorenzi, A. Pirovano, E. Smeraldi, Effect of catechol-O-methyltransferase Val(108/158)Met polymorphism on antidepressant efficacy of fluvoxamine, Eur. Psychiatry 25 (8) (2010) 476–478. [94] N.A. Kocabas, C. Faghel, M. Barreto, S. Kasper, S. Linotte, J. Mendlewicz, et al., The impact of catechol-O-methyltransferase SNPs and haplotypes on treatment response phenotypes in major depressive disorder: a case–control association study, Int. Clin. Psychopharmacol. 25 (4) (2010) 218–227. [95] J.P. Houston, J. Kohler, K.M. Ostbye, A. Heinloth, R.H. Perlis, Association of catechol-O-methyltransferase variants with duloxetine response in major depressive disorder, Psychiatry Res. 189 (3) (2011) 475–477. [96] N. Fukui, Y. Suzuki, T. Sugai, J. Watanabe, S. Ono, N. Tsuneyama, et al., Promoter variation in the catechol-O-methyltransferase gene is associated with remission of symptoms during fluvoxamine treatment for major depression, Psychiatry Res. 218 (3) (2014) 353–355. [97] X. Shen, Y. Wu, T. Guan, X. Wang, M. Qian, M. Lin, et al., Association analysis of COMT/MTHFR polymorphisms and major depressive disorder in Chinese Han population, J. Affect. Disord. 161 (2014) 73–78. [98] A. Chiesa, L. Lia, S. Alberti, S.J. Lee, C. Han, A.A. Patkar, et al., Lack of influence of rs4680 (COMT) and rs6276 (DRD2) on diagnosis and clinical outcomes in patients with major depression, Int. J. Psychiatry Clin. Pract. 18 (2) (2014) 97–102. [99] G. Xu, K. Lin, D. Rao, Y. Dang, H. Ouyang, G. Mai, et al., Brain-derived neurotrophic factor gene polymorphism (Val66Met) and the early response to antidepressant in Chinese Han population, Psychiatr. Genet. 22 (4) (2012) 214–215. [100] W.D. Taylor, D.R. McQuoid, A. Ashley-Koch, J.R. MacFall, J. Bridgers, R.R. Krishnan, et al., BDNF Val66Met genotype and 6-month remission rates in late-life depression, Pharmacogenomics J. 11 (2) (2011) 146–154. [101] A. Katsuki, R. Yoshimura, T. Kishi, H. Hori, W. Umene-Nakano, A. IkenouchiSugita, et al., Serum levels of brain-derived neurotrophic factor (BDNF), BDNF gene Val66Met polymorphism, or plasma catecholamine metabolites, and response to mirtazapine in Japanese patients with major depressive disorder (MDD), CNS Spectr. 17 (3) (2012) 155–163. [102] R.H. Kang, H.S. Chang, M.L. Wong, M.J. Choi, J.Y. Park, H.Y. Lee, et al., Brainderived neurotrophic factor gene polymorphisms and mirtazapine responses in Koreans with major depression, J. Psychopharmacol. 24 (12) (2010) 1755–1763. [103] J. Licinio, C. Dong, M.L. Wong, Novel sequence variations in the brain-derived neurotrophic factor gene and association with major depression and antidepressant treatment response, Arch. Gen. Psychiatry 66 (5) (2009) 488–497.


201

[104] M. Gratacos, V. Soria, M. Urretavizcaya, J.R. Gonzalez, J.M. Crespo, M. Bayes, et al., A brain-derived neurotrophic factor (BDNF) haplotype is associated with antidepressant treatment outcome in mood disorders, Pharmacogenomics J. 8 (2) (2008) 101–112. [105] K. Domschke, B. Lawford, G. Laje, K. Berger, R. Young, P. Morris, et al., Brainderived neurotrophic factor (BDNF) gene: no major impact on antidepressant treatment response, Int. J. Neuropsychopharmacol. 13 (1) (2010) 93–101. [106] G.M. Murphy Jr., J.E. Sarginson, H.S. Ryan, R. O’Hara, A.F. Schatzberg, L.C. Lazzeroni, BDNF and CREB1 genetic variants interact to affect antidepressant treatment outcomes in geriatric depression, Pharmacogenet. Genomics 23 (6) (2013) 301–313. [107] J.M. Hennings, M.A. Kohli, D. Czamara, M. Giese, A. Eckert, C. Wolf, et al., Possible associations of NTRK2 polymorphisms with antidepressant treatment outcome: findings from an extended tag SNP approach, PLoS One 8 (6) (2013) e64947. [108] A. Illi, M. Viikki, O. Poutanen, E. Setala-Soikkeli, T. Nuolivirta, O. Kampman, et al., No support for a role for BDNF gene polymorphisms rs11030101 and rs61888800 in major depressive disorder or antidepressant response in patients of Finnish origin, Psychiatr. Genet. 23 (1) (2013) 33–35. [109] O. Berton, C.A. McClung, R.J. Dileone, V. Krishnan, W. Renthal, S.J. Russo, et al., Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress, Science 311 (5762) (2006) 864–868. [110] N.M. Tsankova, O. Berton, W. Renthal, A. Kumar, R.L. Neve, E.J. Nestler, Sustained hippocampal chromatin regulation in a mouse model of depression and antidepressant action, Nat. Neurosci. 9 (4) (2006) 519–525. [111] N. Tsankova, W. Renthal, A. Kumar, E.J. Nestler, Epigenetic regulation in psychiatric disorders, Nat. Rev. Neurosci. 8 (5) (2007) 355–367. [112] C. Ernst, V. Deleva, X. Deng, A. Sequeira, A. Pomarenski, T. Klempan, et al., Alternative splicing, methylation state, and expression profile of tropomyosin-related kinase B in the frontal cortex of suicide completers, Arch. Gen. Psychiatry 66 (1) (2009) 22–32. [113] N. Mellios, H.S. Huang, S.P. Baker, M. Galdzicka, E. Ginns, S. Akbarian, Molecular determinants of dysregulated GABAergic gene expression in the prefrontal cortex of subjects with schizophrenia, Biol. Psychiatry 65 (12) (2009) 1006–1014. [114] T.L. Roth, F.D. Lubin, A.J. Funk, J.D. Sweatt, Lasting epigenetic influence of earlylife adversity on the BDNF gene, Biol. Psychiatry 65 (9) (2009) 760–769. [115] S. Keller, M. Sarchiapone, F. Zarrilli, A. Videtic, A. Ferraro, V. Carli, et al., Increased BDNF promoter methylation in the Wernicke area of suicide subjects, Arch. Gen. Psychiatry 67 (3) (2010) 258–267. [116] M. Fuchikami, S. Morinobu, M. Segawa, Y. Okamoto, S. Yamawaki, N. Ozaki, et al., DNA methylation profiles of the brain-derived neurotrophic factor (BDNF) gene as a potent diagnostic biomarker in major depression, PLoS One 6 (8) (2011) e23881. [117] C. D’Addario, B. Dell’Osso, M.C. Palazzo, B. Benatti, L. Lietti, E. Cattaneo, et al., Selective DNA methylation of BDNF promoter in bipolar disorder: differences among patients with BDI and BDII, Neuropsychopharmacology 37 (7) (2012) 1647–1655. [118] T. Ikegame, M. Bundo, Y. Murata, K. Kasai, T. Kato, K. Iwamoto, DNA methylation of the BDNF gene and its relevance to psychiatric disorders, J. Hum. Genet. 58 (7) (2013) 434–438. [119] A. Tadic, L. Muller-Engling, K.F. Schlicht, A. Kotsiari, N. Dreimuller, A. Kleimann, et al., Methylation of the promoter of brain-derived neurotrophic factor exon IV and antidepressant response in major depression, Mol. Psychiatry 19 (3) (2014) 281–283. [120] F. Holsboer, The corticosteroid receptor hypothesis of depression, Neuropsychopharmacology 23 (5) (2000) 477–501.

202


[121] T. Jaaskelainen, H. Makkonen, J.J. Palvimo, Steroid up-regulation of FKBP51 and its role in hormone signaling, Curr. Opin. Pharmacol. 11 (4) (2011) 326–331. [122] E.B. Binder, D. Salyakina, P. Lichtner, G.M. Wochnik, M. Ising, B. Putz, et al., Polymorphisms in FKBP5 are associated with increased recurrence of depressive episodes and rapid response to antidepressant treatment, Nat. Genet. 36 (12) (2004) 1319–1325. [123] J. Kirchheiner, R. Lorch, E. Lebedeva, A. Seeringer, I. Roots, J. Sasse, et al., Genetic variants in FKBP5 affecting response to antidepressant drug treatment, Pharmacogenomics 9 (7) (2008) 841–846. [124] S. Papiol, B. Arias, C. Gasto, B. Gutierrez, R. Catalan, L. Fananas, Genetic variability at HPA axis in major depression and clinical response to antidepressant treatment, J. Affect. Disord. 104 (1–3) (2007) 83–90. [125] S.J. Tsai, C.J. Hong, T.J. Chen, Y.W. Yu, Lack of supporting evidence for a genetic association of the FKBP5 polymorphism and response to antidepressant treatment, Am. J. Med. Genet. B Neuropsychiatr. Genet. 144B (8) (2007) 1097–1098. [126] M. Lekman, G. Laje, D. Charney, A.J. Rush, A.F. Wilson, A.J. Sorant, et al., The FKBP5-gene in depression and treatment response—an association study in the sequenced treatment alternatives to relieve depression (STAR*D) cohort, Biol. Psychiatry 63 (12) (2008) 1103–1110. [127] A. Zobel, A. Schuhmacher, F. Jessen, S. Hofels, O. von Widdern, M. Metten, et al., DNA sequence variants of the FKBP5 gene are associated with unipolar depression, Int. J. Neuropsychopharmacol. 13 (5) (2010) 649–660. [128] Y.F. Zou, F. Wang, X.L. Feng, W.F. Li, J.H. Tao, F.M. Pan, et al., Meta-analysis of FKBP5 gene polymorphisms association with treatment response in patients with mood disorders, Neurosci. Lett. 484 (1) (2010) 56–61. [129] K.A. Ellsworth, I. Moon, B.W. Eckloff, B.L. Fridley, G.D. Jenkins, A. Batzler, et al., FKBP5 genetic variation: association with selective serotonin reuptake inhibitor treatment outcomes in major depressive disorder, Pharmacogenet. Genomics 23 (3) (2013) 156–166. [130] A. Menke, T. Klengel, J. Rubel, T. Bruckl, H. Pfister, S. Lucae, et al., Genetic variation in FKBP5 associated with the extent of stress hormone dysregulation in major depression, Genes Brain Behav. 12 (3) (2013) 289–296. [131] A. Szczepankiewicz, A. Leszczynska-Rodziewicz, J. Pawlak, B. Narozna, A. Rajewska-Rager, M. Wilkosc, et al., FKBP5 polymorphism is associated with major depression but not with bipolar disorder, J. Affect. Disord. 164 (2014) 33–37. [132] G.I. Papakostas, R.C. Shelton, G. Kinrys, M.E. Henry, B.R. Bakow, S.H. Lipkin, et al., Assessment of a multi-assay, serum-based biological diagnostic test for major depressive disorder: a pilot and replication study, Mol. Psychiatry 18 (3) (2013) 332–339. [133] E. Schwarz, R. Izmailov, M. Spain, A. Barnes, J.P. Mapes, P.C. Guest, et al., Validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia, Biomark. Insights 5 (2010) 39–47. [134] S.F. Kingsmore, Comment on “validation of a blood-based laboratory test to aid in the confirmation of a diagnosis of schizophrenia”, Biomark. Insights 9 (2014) 7. [135] L.H. Lo, T.L. Huang, J. Shiea, Acid hydrolysis followed by matrix-assisted laser desorption/ionization mass spectrometry for the rapid diagnosis of serum protein biomarkers in patients with major depression, Rapid Commun. Mass Spectrom. 23 (5) (2009) 589–598. [136] T.L. Huang, M.L. Sung, T.Y. Chen, 2D-DIGE proteome analysis on the platelet proteins of patients with major depression, Proteome Sci. 12 (1) (2014) 1. [137] M.R. Wilkins, C. Pasquali, R.D. Appel, K. Ou, O. Golaz, J.C. Sanchez, et al., From proteins to proteomes: large scale protein identification by two-dimensional electrophoresis and amino acid analysis, Biotechnology (N Y) 14 (1) (1996) 61–65.


203

[138] D. Martins-de-Souza, P.C. Guest, L.W. Harris, N. Vanattou-Saifoudine, M.J. Webster, H. Rahmoune, et al., Identification of proteomic signatures associated with depression and psychotic depression in post-mortem brains from major depression patients, Transl. Psychiatry 2 (2012) e87. [139] D. Martins-de-Souza, L.W. Harris, P.C. Guest, S. Bahn, The role of energy metabolism dysfunction and oxidative stress in schizophrenia revealed by proteomics, Antioxid. Redox Signal. 15 (7) (2011) 2067–2079. [140] E. Barbier, J.B. Wang, Anti-depressant and anxiolytic like behaviors in PKCI/HINT1 knockout mice associated with elevated plasma corticosterone level, BMC Neurosci. 10 (2009) 132. [141] J. Varadarajulu, M. Lebar, G. Krishnamoorthy, S. Habelt, J. Lu, I. Bernard Weinstein, et al., Increased anxiety-related behaviour in Hint1 knockout mice, Behav. Brain Res. 220 (2) (2011) 305–311. [142] D. Martins-de-Souza, P.C. Guest, N. Vanattou-Saifoudine, H. Rahmoune, S. Bahn, Phosphoproteomic differences in major depressive disorder postmortem brains indicate effects on synaptic function, Eur. Arch. Psychiatry Clin. Neurosci. 262 (8) (2012) 657–666. [143] N.L. Johnston-Wilson, C.D. Sims, J.P. Hofmann, L. Anderson, A.D. Shore, E.F. Torrey, et al., Disease-specific alterations in frontal cortex brain proteins in schizophrenia, bipolar disorder, and major depressive disorder. The Stanley Neuropathology Consortium, Mol. Psychiatry 5 (2) (2000) 142–149. [144] C.L. Beasley, K. Pennington, A. Behan, R. Wait, M.J. Dunn, D. Cotter, Proteomic analysis of the anterior cingulate cortex in the major psychiatric disorders: evidence for disease-associated changes, Proteomics 6 (11) (2006) 3414–3425. [145] J.T. Huang, F.M. Leweke, D. Oxley, L. Wang, N. Harris, D. Koethe, et al., Disease biomarkers in cerebrospinal fluid of patients with first-onset psychosis, PLoS Med. 3 (11) (2006) e428. [146] C. Ditzen, N. Tang, A.M. Jastorff, L. Teplytska, A. Yassouridis, G. Maccarrone, et al., Cerebrospinal fluid biomarkers for major depression confirm relevance of associated pathophysiology, Neuropsychopharmacology 37 (4) (2012) 1013–1025. [147] H.B. Xu, R.F. Zhang, D. Luo, Y. Zhou, Y. Wang, L. Fang, et al., Comparative proteomic analysis of plasma from major depressive patients: identification of proteins associated with lipid metabolism and immunoregulation, Int. J. Neuropsychopharmacol. 15 (10) (2012) 1413–1425. [148] V. Stelzhammer, F. Haenisch, M.K. Chan, J.D. Cooper, J. Steiner, H. Steeb, et al., Proteomic changes in serum of first onset, antidepressant drug-naive major depression patients, Int. J. Neuropsychopharmacol. 17 (10) (2014) 1599–1608. [149] V. Stelzhammer, P.C. Guest, M. Rothermundt, C. Sondermann, N. Michael, E. Schwarz, et al., Electroconvulsive therapy exerts mainly acute molecular changes in serum of major depressive disorder patients, Eur. Neuropsychopharmacol. 23 (10) (2013) 1199–1207. [150] D. Martins-de-Souza, G. Maccarrone, M. Ising, S. Kloiber, S. Lucae, F. Holsboer, et al., Blood mononuclear cell proteome suggests integrin and Ras signaling as critical pathways for antidepressant treatment response, Biol. Psychiatry 76 (2014) e15–e17. [151] B. Breitenstein, S. Scheuer, F. Holsboer, Are there meaningful biomarkers of treatment response for depression? Drug Discov. Today 19 (2014) 539–561. [152] R. Kaddurah-Daouk, K.R. Krishnan, Metabolomics: a global biochemical approach to the study of central nervous system diseases, Neuropsychopharmacology 34 (1) (2009) 173–186. [153] R. Kaddurah-Daouk, P. Yuan, S.H. Boyle, W. Matson, Z. Wang, Z.B. Zeng, et al., Cerebrospinal fluid metabolome in mood disorders-remission state has a unique metabolic profile, Sci. Rep. 2 (2012) 667.

204


[154] L.A. Paige, M.W. Mitchell, K.R. Krishnan, R. Kaddurah-Daouk, D.C. Steffens, A preliminary metabolomic analysis of older adults with and without depression, Int. J. Geriatr. Psychiatry 22 (5) (2007) 418–423. [155] D.C. Steffens, J. Wei, K.R. Krishnan, E.D. Karoly, M.W. Mitchell, C.M. O’Connor, et al., Metabolomic differences in heart failure patients with and without major depression, J. Geriatr. Psychiatry Neurol. 23 (2) (2010) 138–146. [156] P. Zheng, H.C. Gao, Q. Li, W.H. Shao, M.L. Zhang, K. Cheng, et al., Plasma metabonomics as a novel diagnostic approach for major depressive disorder, J. Proteome Res. 11 (3) (2012) 1741–1748. [157] R. Kaddurah-Daouk, S.H. Boyle, W. Matson, S. Sharma, S. Matson, H. Zhu, et al., Pretreatment metabotype as a predictor of response to sertraline or placebo in depressed outpatients: a proof of concept, Transl. Psychiatry 1 (2011) e26. [158] R. Kaddurah-Daouk, M.B. Bogdanov, W.R. Wikoff, H. Zhu, S.H. Boyle, E. Churchill, et al., Pharmacometabolomic mapping of early biochemical changes induced by sertraline and placebo, Transl. Psychiatry 3 (2013) e223. [159] H. Zhu, M.B. Bogdanov, S.H. Boyle, W. Matson, S. Sharma, S. Matson, et al., Pharmacometabolomics of response to sertraline and to placebo in major depressive disorder—possible role for methoxyindole pathway, PLoS One 8 (7) (2013) e68283. [160] X. Ding, S. Yang, W. Li, Y. Liu, Z. Li, Y. Zhang, et al., The potential biomarker panels for identification of major depressive disorder (MDD) patients with and without early life stress (ELS) by metabonomic analysis, PLoS One 9 (5) (2014) e97479. [161] P. Zheng, Y. Wang, L. Chen, D. Yang, H. Meng, D. Zhou, et al., Identification and validation of urinary metabolite biomarkers for major depressive disorder, Mol. Cell. Proteomics 12 (1) (2013) 207–214. [162] B. Coulombe, Mapping the disease protein interactome: toward a molecular medicine GPS to accelerate drug and biomarker discovery, J. Proteome Res. 10 (1) (2011) 120–125. [163] J. Chen, J. Zhou, C.K. Sanders, J.P. Nolan, H. Cai, A surface display yeast two-hybrid screening system for high-throughput protein interactome mapping, Anal. Biochem. 390 (1) (2009) 29–37. [164] E. Scifo, A. Szwajda, J. Debski, K. Uusi-Rauva, T. Kesti, M. Dadlez, et al., Drafting the CLN3 protein interactome in SH-SY5Y human neuroblastoma cells: a label-free quantitative proteomics approach, J. Proteome Res. 12 (5) (2013) 2101–2115. [165] J.J. Bonfiglio, G. Maccarrone, C. Rewerts, F. Holsboer, E. Arzt, C.W. Turck, et al., Characterization of the B-Raf interactome in mouse hippocampal neuronal cells, J. Proteomics 74 (2) (2011) 186–198. [166] T. Weichhart, M.D. Saemann, The PI3K/Akt/mTOR pathway in innate immune cells: emerging therapeutic applications, Ann. Rheum. Dis. 67 (Suppl. 3) (2008) iii70–iii74. [167] F. Pilot-Storck, E. Chopin, J.F. Rual, A. Baudot, P. Dobrokhotov, M. RobinsonRechavi, et al., Interactome mapping of the phosphatidylinositol 3-kinase-mammalian target of rapamycin pathway identifies deformed epidermal autoregulatory factor-1 as a new glycogen synthase kinase-3 interactor, Mol. Cell. Proteomics 9 (7) (2010) 1578–1593. [168] P. Jia, C.F. Kao, P.H. Kuo, Z. Zhao, A comprehensive network and pathway analysis of candidate genes in major depressive disorder, BMC Syst. Biol. 5 (Suppl. 3) (2011) S12. [169] A. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic, A. Roth, et al., STRING v9.1: protein–protein interaction networks, with increased coverage and integration, Nucleic Acids Res. 41 (Database issue) (2013) D808–D815. [170] C.E. Huang, T.L. Huang, Intramuscular lorazepam in catatonia in patients with acute renal failure: a report of two cases, Chang Gung Med. J. 33 (1) (2010) 106–109. [171] Y.C. Huang, C.C. Lin, Y.Y. Hung, T.L. Huang, Rapid relief of catatonia in mood disorder by lorazepam and diazepam, Biomed. J. 36 (1) (2013) 35–39.

Peripheral biomarkers in animal models of major depressive disorder.

Neuroimaging-based biomarkers for treatment selection in major depressive disorder.

Urinary peptidomics identifies potential biomarkers for major depressive disorder.

The inflammatory cytokines: molecular biomarkers for major depressive disorder?

Major depressive disorder.

Neurobiology of major depressive disorder.

Hyperthermia for Major Depressive Disorder?

Seasonal variation of depressive symptoms in unipolar major depressive disorder.

Intermediate phenotypes and biomarkers of treatment outcome in major depressive disorder.

EEG biomarkers in major depressive disorder: discriminative power and prediction of treatment response.

Gene expression in major depressive disorder.

Hippocampal neuroplasticity in major depressive disorder.

Risks for suicidality in major depressive disorder.

Patient-reported functioning in major depressive disorder.

Temporal discounting in major depressive disorder.

Autobiographical episodic memory in major depressive disorder.

Prodromal symptoms in primary major depressive disorder.

Resting state networks in major depressive disorder.

Delayed mood transitions in major depressive disorder.

Blood transcriptomic biomarkers in adult primary care patients with major depressive disorder undergoing cognitive behavioral therapy.

Depressive rumination alters cortisol decline in Major Depressive Disorder.

Glutamate metabolism in major depressive disorder.

Physical anhedonia in major depressive disorder.

Fluoxetine in tricyclic refractory major depressive disorder.