Stimulated gene expression profiles as a blood marker of major depressive disorder

Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy

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.

The promise of biomarkers in diagnosing major depression in primary care: the present and future

Major depressive disorder (MDD) is the most prevalent psychiatric disorder, but it can be underdiagnosed or misdiagnosed. Most people with depression are seen in primary care settings, where there are limited resources to diagnose and treat the patient. There is a lack of clinically validated objective laboratory-based diagnostic tests to diagnose MDD; however, it is clear that these tests could greatly improve the correct and timely diagnosis. This review aims to give a cross-sectional view of current efforts of DNA methylomic, transcriptomic, and proteomic approaches to identify biomarkers. We outline our view of the biomarker developmental steps from discovery to clinical application. We then propose that better cooperation will lead us closer to the common goal of identifying biological biomarkers for major depression. "The important thing is not to stop questioning. Curiosity has its own reason for existing." Albert Einstein.

Biological tests for major depressive disorder that involve leukocyte gene expression assays

BACKGROUND

Development of easy-to-use biological diagnostic tests for major depressive disorder (MDD) may facilitate MDD diagnosis and delivery of optimal treatment. Here, we examined leukocyte gene expression to develop a biological diagnostic test for MDD.

METHODS

25 drug-naive MDD patients (MDDs) and 25 age- and sex-matched healthy subjects (Controls) participated in a pilot study. A subsequent replication study involved 20 MDDs and 18 Controls. We used custom-made PCR array plates to examine mRNA levels of 40 candidate genes in leukocyte samples to assess whether any combination of these genes could be used to differentiate MDDs from Controls based on expression profiles.

RESULTS

Among 40 candidate genes, we identified a set of seven genes (PDGFC, SLC6A4, PDLIM5, ARHGAP24, PRNP, HDAC5, and IL1R2), each of which had expression levels that differed significantly between MDD and Control samples in the pilot study. To identify genes whose expression best differentiated between MDDs and Controls, a linear discriminant function was developed to discriminate between MDDs and Controls based on the standardized values of gene expression after Z-score transformation. Ultimately, five genes (PDGFC, SLC6A4, ARHGAP24, PRNP, and HDAC5) were selected for a multi-assay diagnostic test. In the pilot study, this diagnostic test demonstrated sensitivity and specificity of 80% and 92%, respectively. The replication study yielded nearly identical results, sensitivity of 85% and specificity of 89%.

CONCLUSIONS

Using leukocyte gene expression profiles, we could differentiate MDDs from Controls with adequate sensitivity and specificity. Additional markers not yet identified might further improve the performance of this test.

Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression

Cognitive impairment is highly prevalent among individuals with late-life depression (LLD) and tends to persist even after successful treatment. The biological mechanisms underlying cognitive impairment in LLD are complex and likely involve abnormalities in multiple pathways, or 'cascades,' reflected in specific biomarkers. Our aim was to evaluate peripheral (blood-based) evidence for biological pathways associated with cognitive impairment in older adults with LLD. To this end, we used a data-driven comprehensive proteomic analysis (multiplex immunoassay including 242 proteins), along with measures of structural brain abnormalities (gray matter atrophy and white matter hyperintensity volume via magnetic resonance imaging), and brain amyloid-β (Aβ) deposition (PiB-positron emission tomography). We analyzed data from 80 older adults with remitted major depression (36 with mild cognitive impairment (LLD+MCI) and 44 with normal cognitive (LLD+NC)) function. LLD+MCI was associated with differential expression of 24 proteins (P<0.05 and q-value <0.30) related mainly to the regulation of immune-inflammatory activity, intracellular signaling, cell survival and protein and lipid homeostasis. Individuals with LLD+MCI also showed greater white matter hyperintensity burden compared with LLD+NC (P=0.015). We observed no differences in gray matter volume or brain Aβ deposition between groups. Machine learning analysis showed that a group of three proteins (Apo AI, IL-12 and stem cell factor) yielded accuracy of 81.3%, sensitivity of 75% and specificity of 86.4% in discriminating participants with MCI from those with NC function (with an averaged cross-validation accuracy of 76.3%, sensitivity of 69.4% and specificity of 81.8% with nested cross-validation considering the model selection bias). Cognitive impairment in LLD seems to be related to greater cerebrovascular disease along with abnormalities in immune-inflammatory control, cell survival, intracellular signaling, protein and lipid homeostasis, and clotting processes. These results suggest that individuals with LLD and cognitive impairment may be more vulnerable to accelerated brain aging and shed light on possible mediators of their elevated risk for progression to dementia.

Proteomic analysis of serum from patients with major depressive disorder to compare their depressive and remission statuses

OBJECTIVE

Currently, there are a few biological markers to aid in the diagnosis and treatment of depression. However, it is not sufficient for diagnosis. We attempted to identify differentially expressed proteins during depressive moods as putative diagnostic biomarkers by using quantitative proteomic analysis of serum.

METHODS

Blood samples were collected twice from five patients with major depressive disorder (MDD) at depressive status before treatment and at remission status during treatment. Samples were individually analyzed by liquid chromatography-tandem mass spectrometry for protein profiling. Differentially expressed proteins were analyzed by label-free quantification. Enzyme-linked immunosorbent assay (ELISA) results and receiver-operating characteristic (ROC) curves were used to validate the differentially expressed proteins. For validation, 8 patients with MDD including 3 additional patients and 8 matched normal controls were analyzed.

RESULTS

The quantitative proteomic studies identified 10 proteins that were consistently upregulated or downregulated in 5 MDD patients. ELISA yielded results consistent with the proteomic analysis for 3 proteins. Expression levels were significantly different between normal controls and MDD patients. The 3 proteins were ceruloplasmin, inter-alpha-trypsin inhibitor heavy chain H4 and complement component 1qC, which were upregulated during the depressive status. The depressive status could be distinguished from the euthymic status from the ROC curves for these proteins, and this discrimination was enhanced when all 3 proteins were analyzed together.

CONCLUSION

This is the first proteomic study in MDD patients to compare intra-individual differences dependent on mood. This technique could be a useful approach to identify MDD biomarkers, but requires additional proteomic studies for validation.

Biomarker approaches in major depressive disorder evaluated in the context of current hypotheses

Major depressive disorder is a heterogeneous disorder, mostly diagnosed on the basis of symptomatic criteria alone. It would be of great help when specific biomarkers for various subtypes and symptom clusters of depression become available to assist in diagnosis and subtyping of depression, and to enable monitoring and prognosis of treatment response. However, currently known biomarkers do not reach sufficient sensitivity and specificity, and often the relation to underlying pathophysiology is unclear. In this review, we evaluate various biomarker approaches in terms of scientific merit and clinical applicability. Finally, we discuss how combined biomarker approaches in both preclinical and clinical studies can help to make the connection between the clinical manifestations of depression and the underlying pathophysiology.

Clinical characteristics of inflammation-associated depression: Monocyte gene expression is age-related in major depressive disorder

Increased inflammatory activation might only be present in a subgroup of depressed individuals in which immune processes are especially relevant to disease development. We aimed to analyze demographic, depression, and trauma characteristics of major depressive disorder (MDD) patients with regard to inflammatory monocyte gene expression. Fifty-six naturalistically treated MDD patients (32 ± 12 years) and 57 healthy controls (HC; 31 ± 11 years) were analyzed by the Inventory of Depressive Symptomatology (IDS) and by the Childhood Trauma Questionnaire (CTQ). We determined the expression of 38 inflammatory and immune activation genes including the glucocorticoid receptor (GR)α and GRβ genes in purified CD14(+) monocytes using quantitative-polymerase chain reaction (RT-qPCR). Monocyte gene expression was age-dependent, particularly in MDD patients. Increased monocyte gene expression and decreased GRα/β ratio were only present in MDD patients aged ⩾ 28 years. Post hoc analyses of monocyte immune activation in patients <28 years showed two subgroups: a subgroup with a severe course of depression (recurrent type, onset <15 years) - additionally characterized by panic/arousal symptoms and childhood trauma - that had a monocyte gene expression similar to HC, and a second subgroup with a milder course of the disorder (73% first episode depression, onset ⩾15 years) - additionally characterized by the absence of panic symptoms - that exhibited a strongly reduced inflammatory monocyte activation compared to HC. In conclusion, monocyte immune activation was not uniformly raised in MDD patients but was increased only in patients of 28 years and older.

Blood diagnostic biomarkers for major depressive disorder using multiplex DNA methylation profiles: discovery and validation

Aberrant DNA methylation in the blood of patients with major depressive disorder (MDD) has been reported in several previous studies. However, no comprehensive studies using medication-free subjects with MDD have been conducted. Furthermore, the majority of these previous studies has been limited to the analysis of the CpG sites in CpG islands (CGIs) in the gene promoter regions. The main aim of the present study is to identify DNA methylation markers that distinguish patients with MDD from non-psychiatric controls. Genome-wide DNA methylation profiling of peripheral leukocytes was conducted in two set of samples, a discovery set (20 medication-free patients with MDD and 19 controls) and a replication set (12 medication-free patients with MDD and 12 controls), using Infinium HumanMethylation450 BeadChips. Significant diagnostic differences in DNA methylation were observed at 363 CpG sites in the discovery set. All of these loci demonstrated lower DNA methylation in patients with MDD than in the controls, and most of them (85.7%) were located in the CGIs in the gene promoter regions. We were able to distinguish patients with MDD from the control subjects with high accuracy in the discriminant analysis using the top DNA methylation markers. We also validated these selected DNA methylation markers in the replication set. Our results indicate that multiplex DNA methylation markers may be useful for distinguishing patients with MDD from non-psychiatric controls.

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

An objective, laboratory-based diagnostic tool could increase the diagnostic accuracy of major depressive disorders (MDDs), identify factors that characterize patients and promote individualized therapy. The goal of this study was to assess a blood-based biomarker panel, which showed promise in adolescents with MDD, in adult primary care patients with MDD and age-, gender- and race-matched nondepressed (ND) controls. Patients with MDD received cognitive behavioral therapy (CBT) and clinical assessment using self-reported depression with the Patient Health Questionnaire-9 (PHQ-9). The measures, including blood RNA collection, were obtained before and after 18 weeks of CBT. Blood transcript levels of nine markers of ADCY3, DGKA, FAM46A, IGSF4A/CADM1, KIAA1539, MARCKS, PSME1, RAPH1 and TLR7, differed significantly between participants with MDD (N=32) and ND controls (N=32) at baseline (q< 0.05). Abundance of the DGKA, KIAA1539 and RAPH1 transcripts remained significantly different between subjects with MDD and ND controls even after post-CBT remission (defined as PHQ-9 <5). The ROC area under the curve for these transcripts demonstrated high discriminative ability between MDD and ND participants, regardless of their current clinical status. Before CBT, significant co-expression network of specific transcripts existed in MDD subjects who subsequently remitted in response to CBT, but not in those who remained depressed. Thus, blood levels of different transcript panels may identify the depressed from the nondepressed among primary care patients, during a depressive episode or in remission, or follow and predict response to CBT in depressed individuals.

Monitoring candidate gene expression variations before, during and after a first major depressive episode in a 51-year-old man

BACKGROUND

Although psychiatric disorders are frequently characterized by clinical heterogeneity, high recurrence, and unpredictable prognosis, studies of mRNA expression variations in blood cells from psychiatric patients constitute a promising avenue to establish clinical biomarkers. We report here, to our knowledge, the first genetic monitoring of a major depressive episode (MDE).

CASE PRESENTATION

The subject is a 51-year-old male, who was healthy at baseline and whose blood mRNA was monitored over 67 weeks for expression variations of 9 candidate genes. At week 20 the subject experienced a mild to moderate unexpected MDE, and oral antidepressant treatment was initiated at week 29. At week 36, the patient recovered from his MDE. After 6 months, antidepressant treatment was discontinued and the subject remained free of depressive symptoms. Genetic monitoring revealed that mRNA expression of SLC6A4/5HTT increased with the emergence of a depressive state, which later returned to basal levels after antidepressant treatment and during MDE recovery. PDLIM5, S100A10 and TNF mRNA showed also an interesting pattern of expression with regards to MDE evolution.

CONCLUSION

This case demonstrated the applicability of peripheral mRNA expression as a way to monitor the natural history of MDE.

Are there meaningful biomarkers of treatment response for depression?

During the past decades, the prevalence of affective disorders has been on the rise globally, with only one out of three patients achieving remission in acute treatment with antidepressants. The identification of physiological markers that predict treatment course proves useful in increasing therapeutic success. On the basis of well-documented, recent findings in depression research, we highlight and discuss the most promising biomarkers for antidepressant therapy response. These include genetic variants and gene expression profiles, proteomic and metabolomic markers, neuroendocrine function tests, electrophysiology and imaging techniques. Ultimately, this review proposes an integrative use of biomarkers for antidepressant treatment outcome.

Clinical, neuroimaging, and genetic characteristics of megalencephalic leukoencephalopathy with subcortical cysts in Egyptian patients

BACKGROUND

Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is a rare and genetically heterogeneous cerebral white matter disease. Clinically, it is characterized by macrocephaly, developmental delay, and seizures. We explore the clinical spectrum, neuroimaging characteristics, and gene involvement in the first patients with megalencephalic leukoencephalopathy with subcortical cysts described from Egypt.

PATIENTS

Six patients were enrolled from three unrelated families. Patient inclusion criteria were macrocephaly, developmental delay, normal urinary organic acids, and brain imaging of diffuse cerebral white matter involvement. Direct sequencing of the MLC1 gene in patients' families and GliaCAM in one questionable case was performed using BigDye Terminator cycle sequencing.

RESULTS

Clinical heterogeneity, both intra- and interfamilial, was clearly evident. Developmental delays ranged from globally severe or moderate to mild delay in achieving walking or speech. Head circumference above the ninety-seventh percentile was a constant feature. Neuroimaging featured variability in white matter involvement and subcortical cysts. However, findings of posterior fossa changes and brain stem atrophy were frequently (66.6%) identified in these Egyptian patients. Discrepancy between severe brain involvement and normal mental functions was evident, particularly in patients from the third family. MLC1 mutations were confirmed in all patients. Deletion/insertion mutation in exon 11 (c.908-918delinsGCA, p.Val303 Gly fsX96) was recurrent in two families, whereas a missense mutation in exon 10 (c.880 C > T, p.Pro294Ser) was identified in the third family.

CONCLUSIONS

This report extends our knowledge of the clinical and neuroimaging features of megalencephalic leukoencephalopathy with subcortical cysts. It confirms the apparent lack of selective disadvantage of MLC1 mutations on gamete conception and transmission as supported by the presence of multiple affected siblings in Egyptian families.

Inflammatory activation is associated with a reduced glucocorticoid receptor alpha/beta expression ratio in monocytes of inpatients with melancholic major depressive disorder

In this study, we used new technology to investigate whether a coherent pattern of enhanced expression of inflammatory and other immune activation genes in circulating monocytes is found in patients with major depression. Since a high inflammatory state of monocytes might be related to glucocorticoid resistance, we also included the genes for the two isoforms of the glucocorticoid receptor. For this study, we aimed at finding a similar coherent pattern of inflammatory and immune activation genes in monocytes of patients with MDD and recruited 47 medication-free melancholic MDD inpatients and 42 healthy controls. A quantitative-polymerase chain reaction (Q-PCR) monocyte gene expression analysis was performed using a panel of inflammatory-related genes previously identified as abnormally regulated in mood disorder patients. Selected serum cytokines/chemokines were assessed using a cytometric bead array. Depressive symptoms were analysed using Hamilton depression scores (HAMD). Thirty-four of the 47 monocyte inflammatory-related genes were significantly upregulated and 2 were significantly downregulated as compared to controls, the latter including the gene for the active GRα in particular in those with a high HAMD score. The reduced GRα expression correlated strongly to the upregulation of the inflammatory genes in monocytes. Serum levels of IL6, IL8, CCL2 and VEGF were significantly increased in patients compared to controls. Our data show the deregulation of two interrelated homoeostatic systems, that is, the immune system and the glucocorticoid system, co-occurring in major depression.

A novel urinary metabolite signature for diagnosing major depressive disorder

Major depressive disorder (MDD) is a prevalent and debilitating mental disorder. Yet, there are no objective biomarkers available to support diagnostic laboratory testing for this disease. Here, gas chromatography-mass spectrometry was applied to urine metabolic profiling of 126 MDD and 134 control subjects. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to identify the differential metabolites in MDD subjects relative to healthy controls. The OPLS-DA analysis of data from training samples (82 first-episode, drug-naïve MDD subjects and 82 well-matched healthy controls) showed that the depressed group was significantly distinguishable from the control group. Totally, 23 differential urinary metabolites responsible for the discrimination between the two groups were identified. Postanalysis, 6 of the 23 metabolites (sorbitol, uric acid, azelaic acid, quinolinic acid, hippuric acid, and tyrosine) were defined as candidate diagnostic biomarkers for MDD. Receiver operating characteristic analysis of combined levels of these six biomarkers yielded an area under the receiver operating characteristic curve (AUC) of 0.905 in distinguishing training samples; this simplified metabolite signature classified blinded test samples (44 MDD subjects and 52 healthy controls) with an AUC of 0.837. Furthermore, a composite panel by the addition of previously identified urine biomarker (N-methylnicotinamide) to this biomarker panel achieved a more satisfactory accuracy, yielding an AUC of 0.909 in the training samples and 0.917 in the test samples. Taken together, these results suggest this composite urinary metabolite signature should facilitate development of a urine-based diagnostic test for MDD.

Correlative gene expression pattern linking RNF123 to cellular stress-senescence genes in patients with depressive disorder: implication of DRD1 in the cerebral cortex

BACKGROUND

The expression level of the RNF1213 gene in blood cells has been identified as a disease risk marker, more than ten years before the diagnosis of depression (Glahn et al., 2012). To explore the status of this gene in the acute depressive state we have quantified the expression of RNF123 in the blood leukocytes (N=17), dorsolateral prefrontal and cingulate cortex (N=24) of patients with diagnosed depression and of matched controls. We have measured the expression of the DRD1 gene as a "neuronal probe". We have also quantified the mRNA of six genes previously identified as markers of the biopsychological stress associated with major depression: FOS, DUSP1, OGG1, STMN1, p16(INK4a) and TERT.

METHODS

The steady state of mRNA has been quantified by the real-time quantitative PCR technique.

RESULTS

RNF123 was overexpressed by 45% in the cingulate cortex of patients with psychotic depression. There were distinct co-expression patterns of RNF123 and stress-related genes in the blood cells and brain cortex of patients, demonstrating a transcriptional regulatory shift. In both the prefrontal and cingulate cortex of these patients a strong correlation interlinked STMN1, TERT and DRD1 pointing to a role of these genes in dopamine signaling.

LIMITATIONS

The two groups of patients were clinically heterogeneous. All the patients had received antidepressant treatment, details of which were not available.

CONCLUSION

We did not identify RNF123 as a clinically relevant, peripheral state marker of depression, but our study probably lacked statistical power to detect small effect size. It is likely to be involved in distinct pleiotropic molecular pathways at peripheral (blood) and central (brain) level.

Gene expression: biomarker of antidepressant therapy?

While antidepressant therapy is an essential treatment of major depression, a substantial group of treated patients do not respond to therapy, or suffer from severe side effects. Moreover, the time of onset of the clinical improvement is often delayed. Antidepressants as currently available usually enhance serotonergic, noradrenergic and dopaminergic neurotransmission and may contribute to the inadequate remission rates for major depression. Therefore biomarkers enabling the identification of subgroups of patients and also finding unprecedented targets would provide the basis for personalized medication and thus improve treatment efficacy and reduce side effects. Several pharmacogenetic studies on antidepressant treatment response using single nucleotide polymorphism (SNPs) mapping have been performed but provided only modest findings. Therefore the analysis of gene expression to integrate genomic activity and environmental effects promises a new approach to cope with the complexity of factors influencing antidepressant treatment. Here gene expression studies focusing on candidate genes and genome-wide approaches using RNA derived from peripheral blood cells are reviewed. The most promising findings exist for hypothalamic-pituitary-adrenal (HPA) axis, inflammation and neuroplasticity related genes. However, straightforward translation into tailored treatment is still unlikely. Contradictory results limit the clinical use of the findings. Future studies are necessary, which could include functional analysis and consider gene-environment interactions.

Coding and noncoding gene expression biomarkers in mood disorders and schizophrenia

Mood disorders and schizophrenia are common and complex disorders with consistent evidence of genetic and environmental influences on predisposition. It is generally believed that the consequences of disease, gene expression, and allelic heterogeneity may be partly the explanation for the variability observed in treatment response. Correspondingly, while effective treatments are available for some patients, approximately half of the patients fail to respond to current neuropsychiatric treatments. A number of peripheral gene expression studies have been conducted to understand these brain-based disorders and mechanisms of treatment response with the aim of identifying suitable biomarkers and perhaps subgroups of patients based upon molecular fingerprint. In this review, we summarize the results from blood-derived gene expression studies implemented with the aim of discovering biomarkers for treatment response and classification of disorders. We include data from a biomarker study conducted in first-episode subjects with schizophrenia, where the results provide insight into possible individual biological differences that predict antipsychotic response. It is concluded that, while peripheral studies of expression are generating valuable results in pathways involving immune regulation and response, larger studies are required which hopefully will lead to robust biomarkers for treatment response and perhaps underlying variations relevant to these complex disorders.

Abnormalities of neurotransmitter and neuropeptide systems in human neuroepithelioma cells infected by three Toxoplasma strains

Since Toxoplasma gondii can establish a persistent infection in the central nervous system in humans, we studied its effects on a host's neurotransmitter and neuropeptide systems (NNS). Using microarray technology, we have screened the expression of genes coding for NNS in human neuroepithelioma cells in response to representative strains of Toxoplasma to identify potential target genes. Transcripts that displayed expression levels distinct from uninfected controls were examined by RT-PCR and Western blot. Our results indicate the presence of disturbed NNS upon Toxoplasma infection and the extent of this disturbance varies considerably among the three strains. In cells infected by type I strain, three neurotransmitter systems (dopamine, glutamate and serotonin) and two neuropeptides (PROK2 and TAC1) displayed abnormalities relative to controls. Type III infection led to the change of a critical enzyme, TDO2, in the kynurenine pathway. No significant effects of type II infection were found in the NNS. These data may have implications for understanding the pathogenesis and heterogeneity of neurologic disturbances in toxoplasmosis.

Utilization of never-medicated bipolar disorder patients towards development and validation of a peripheral biomarker profile

There are currently no biological tests that differentiate patients with bipolar disorder (BPD) from healthy controls. While there is evidence that peripheral gene expression differences between patients and controls can be utilized as biomarkers for psychiatric illness, it is unclear whether current use or residual effects of antipsychotic and mood stabilizer medication drives much of the differential transcription. We therefore tested whether expression changes in first-episode, never-medicated BPD patients, can contribute to a biological classifier that is less influenced by medication and could potentially form a practicable biomarker assay for BPD. We employed microarray technology to measure global leukocyte gene expression in first-episode (n=3) and currently medicated BPD patients (n=26), and matched healthy controls (n=25). Following an initial feature selection of the microarray data, we developed a cross-validated 10-gene model that was able to correctly predict the diagnostic group of the training sample (26 medicated patients and 12 controls), with 89% sensitivity and 75% specificity (p<0.001). The 10-gene predictor was further explored via testing on an independent cohort consisting of three pairs of monozygotic twins discordant for BPD, plus the original enrichment sample cohort (the three never-medicated BPD patients and 13 matched control subjects), and a sample of experimental replicates (n=34). 83% of the independent test sample was correctly predicted, with a sensitivity of 67% and specificity of 100% (although this result did not reach statistical significance). Additionally, 88% of sample diagnostic classes were classified correctly for both the enrichment (p=0.015) and the replicate samples (p<0.001). We have developed a peripheral gene expression biomarker profile, that can classify healthy controls from patients with BPD receiving antipsychotic or mood stabilizing medication, which has both high sensitivity and specificity. Moreover, assay of three first-episode patients who had never received such medications, to first enrich the expression dataset for disease-related genes independent of medication effects, and then to test the 10-gene predictor, validates the peripheral biomarker approach for BPD.

Cortical-striatal gene expression in neonatal hippocampal lesion (NVHL)-amplified cocaine sensitization

Cortical-striatal circuit dysfunction in mental illness may enhance addiction vulnerability. Neonatal ventral hippocampal lesions (NVHL) model this dual diagnosis causality by producing a schizophrenia syndrome with enhanced responsiveness to addictive drugs. Rat genome-wide microarrays containing >24 000 probesets were used to examine separate and co-occurring effects of NVHLs and cocaine sensitization (15 mg/kg/day × 5 days) on gene expression within medial prefrontal cortex (MPFC), nucleus accumbens (NAC), and caudate-putamen (CAPU). Two weeks after NVHLs robustly amplified cocaine behavioral sensitization, brains were harvested for genes of interest defined as those altered at P < 0.001 by NVHL or cocaine effects or interactions. Among 135 genes so impacted, NVHLs altered twofold more than cocaine, with half of all changes in the NAC. Although no genes were changed in the same direction by both NVHL and cocaine history, the anatomy and directionality of significant changes suggested synergy on the neural circuit level generative of compounded behavioral phenotypes: NVHL predominantly downregulated expression in MPFC and NAC while NVHL and cocaine history mostly upregulated CAPU expression. From 75 named genes altered by NVHL or cocaine, 27 had expression levels that correlated significantly with degree of behavioral sensitization, including 11 downregulated by NVHL in MPFC/NAC, and 10 upregulated by NVHL or cocaine in CAPU. These findings suggest that structural and functional impoverishment of prefrontal-cortical-accumbens circuits in mental illness is associated with abnormal striatal plasticity compounding with that in addictive disease. Polygenetic interactions impacting neuronal signaling and morphology within these networks likely contribute to addiction vulnerability in mental illness.

The diagnosis of depression: current and emerging methods

Depression is one of the leading causes of disability in adolescents and adults, particularly starting from age 15 years and older. Diagnosis of depression has traditionally been made based on clinical criteria, including patient current symptoms and history. This process is widely used but relies on subjective interpretation. To standardize both the data obtained and data interpretation, various interview-based instruments and noninterview methods exist for screening and testing for depression in various clinical settings. This article evaluates the technical basis for and clinical performance of these various instruments and methods to diagnosis depression in clinical settings. Traditional tools include physician-administered or patient self-administered interview tools that have reasonable clinical accuracy depending on the threshold score and may lead to a full diagnostic evaluation for high-risk patients. In addition, older laboratory methods such as the dexamethasone test have contributed to the diagnosis of depression over a long period. Newer diagnostic methods such as genomics, proteomics, and metabolomics are technically sophisticated and objective and are beginning to emerge in psychiatry. Although promising, further evaluation of these methods is needed to fully demonstrate their clinical value and accuracy.

Megalencephalic leukoencephalopathy with subcortical cysts: chronic white matter oedema due to a defect in brain ion and water homoeostasis

Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is characterised by chronic white matter oedema. The disease has an infantile onset and leads to slow neurological deterioration in most cases, but, surprisingly, some patients recover. The first disease gene, MLC1, identified in 2001, is mutated in 75% of patients. At that time, nothing was known about MLC1 protein function and the pathophysiology of MLC. More recently, HEPACAM (also called GLIALCAM) has been identified as a second disease gene. GlialCAM serves as an escort for MLC1 and the chloride channel CLC2. The defect in MLC1 has been shown to hamper the cell volume regulation of astrocytes. One of the most important consequences involves the potassium siphoning process, which is essential in brain ion and water homoeostasis. An understanding of the mechanisms of white matter oedema in MLC is emerging. Further insight into the specific function of MLC1 is necessary to find treatment targets.

Responder and nonresponder patients exhibit different peripheral transcriptional signatures during major depressive episode

To date, it remains impossible to guarantee that short-term treatment given to a patient suffering from a major depressive episode (MDE) will improve long-term efficacy. Objective biological measurements and biomarkers that could help in predicting the clinical evolution of MDE are still warranted. To better understand the reason nearly half of MDE patients respond poorly to current antidepressive treatments, we examined the gene expression profile of peripheral blood samples collected from 16 severe MDE patients and 13 matched controls. Using a naturalistic and longitudinal design, we ascertained mRNA and microRNA (miRNA) expression at baseline, 2 and 8 weeks later. On a genome-wide scale, we detected transcripts with roles in various biological processes as significantly dysregulated between MDE patients and controls, notably those involved in nucleotide binding and chromatin assembly. We also established putative interactions between dysregulated mRNAs and miRNAs that may contribute to MDE physiopathology. We selected a set of mRNA candidates for quantitative reverse transcriptase PCR (RT-qPCR) to validate that the transcriptional signatures observed in responders is different from nonresponders. Furthermore, we identified a combination of four mRNAs (PPT1, TNF, IL1B and HIST1H1E) that could be predictive of treatment response. Altogether, these results highlight the importance of studies investigating the tight relationship between peripheral transcriptional changes and the dynamic clinical progression of MDE patients to provide biomarkers of MDE evolution and prognosis.

MK4MDD: a multi-level knowledge base and analysis platform for major depressive disorder

Background

Major depressive disorder (MDD) is a complex neuropsychiatric syndrome with high heterogeneity. There are different levels of biological components that underlie MDD and interact with each other. To uncover the disease mechanism, large numbers of studies at different levels have been conducted. There is a growing need to integrate data from multiple levels of research into a database to provide a systematic review of current research results. The cross level integration will also help bridge gaps of different research levels for further understanding on MDD. So far, there has been no such effort for MDD.

Descriptions

We offer researchers a Multi-level Knowledge base for MDD (MK4MDD) to study the interesting interplay of components in the pathophysiological cascade of MDD from genetic variations to diagnostic syndrome. MK4MDD contains 2,341 components and 5,206 relationships between components based on reported experimental results obtained by diligent literature reading with manual curation. All components were well classified with careful curation and supplementary annotation. The powerful search and visualization tools make all data in MK4MDD form a cross-linked network to be applied to a broad range of both basic and applied research.

Conclusions

MK4MDD aims to provide researchers with a central knowledge base and analysis platform for MDD etiological and pathophysiological mechanisms research. MK4MDD is freely available at http://mdd.psych.ac.cn.

Comparison of brain and blood gene expression in an animal model of negative symptoms in schizophrenia

OBJECTIVES

To investigate the potential of white blood cells as probes for central processes we have measured gene expression in both the anterior cingulate cortex and white blood cells using a putative animal model of negative symptoms in schizophrenia.

METHODS

The model is based on the capability of ketamine to induce psychotic symptoms in healthy volunteers and to worsen such symptoms in schizophrenic patients. Classical fear conditioning is used to assess emotional processing and cognitive function in animals exposed to sub-chronic ketamine vs. controls. Gene expression was measured using a commercially sourced whole genome rat gene array. Data analyses were performed using ANOVA (Systat 11).

RESULTS

In both anterior cingulate cortex and white blood cells a significant interaction between ketamine and fear conditioning could be observed. The outcome is largely supported by our subsequent metagene analysis. Moreover, the correlation between gene expression in brain and blood is about constant when no ketamine is present (r~0.4). With ketamine, however, the correlation becomes very low (r~0.2) when there is no fear, but it increases to ~0.6 when fear and ketamine are both present. Our results show that under normal conditions ketamine lowers gene expression in the brain, but this effect is completely reversed in combination with fear conditioning, indicating a stimulatory action.

CONCLUSION

This paradoxical outcome indicates that extreme care must be taken when using gene expression data from white blood cells as marker for psychiatric disorders, especially when pharmacological and environmental interactions are at play.

Dexamethasone stimulated gene expression in peripheral blood is a sensitive marker for glucocorticoid receptor resistance in depressed patients

Although gene expression profiles in peripheral blood in major depression are not likely to identify genes directly involved in the pathomechanism of affective disorders, they may serve as biomarkers for this disorder. As previous studies using baseline gene expression profiles have provided mixed results, our approach was to use an in vivo dexamethasone challenge test and to compare glucocorticoid receptor (GR)-mediated changes in gene expression between depressed patients and healthy controls. Whole genome gene expression data (baseline and following GR-stimulation with 1.5 mg dexamethasone p.o.) from two independent cohorts were analyzed to identify gene expression pattern that would predict case and control status using a training (N=18 cases/18 controls) and a test cohort (N=11/13). Dexamethasone led to reproducible regulation of 2670 genes in controls and 1151 transcripts in cases. Several genes, including FKBP5 and DUSP1, previously associated with the pathophysiology of major depression, were found to be reliable markers of GR-activation. Using random forest analyses for classification, GR-stimulated gene expression outperformed baseline gene expression as a classifier for case and control status with a correct classification of 79.1 vs 41.6% in the test cohort. GR-stimulated gene expression performed best in dexamethasone non-suppressor patients (88.7% correctly classified with 100% sensitivity), but also correctly classified 77.3% of the suppressor patients (76.7% sensitivity), when using a refined set of 19 genes. Our study suggests that in vivo stimulated gene expression in peripheral blood cells could be a promising molecular marker of altered GR-functioning, an important component of the underlying pathology, in patients suffering from depressive episodes.

Blood-based gene expression profiles models for classification of subsyndromal symptomatic depression and major depressive disorder

Subsyndromal symptomatic depression (SSD) is a subtype of subthreshold depressive and also lead to significant psychosocial functional impairment as same as major depressive disorder (MDD). Several studies have suggested that SSD is a transitory phenomena in the depression spectrum and is thus considered a subtype of depression. However, the pathophysioloy of depression remain largely obscure and studies on SSD are limited. The present study compared the expression profile and made the classification with the leukocytes by using whole-genome cRNA microarrays among drug-free first-episode subjects with SSD, MDD, and matched controls (8 subjects in each group). Support vector machines (SVMs) were utilized for training and testing on candidate signature expression profiles from signature selection step. Firstly, we identified 63 differentially expressed SSD signatures in contrast to control (P< = 5.0E-4) and 30 differentially expressed MDD signatures in contrast to control, respectively. Then, 123 gene signatures were identified with significantly differential expression level between SSD and MDD. Secondly, in order to conduct priority selection for biomarkers for SSD and MDD together, we selected top gene signatures from each group of pair-wise comparison results, and merged the signatures together to generate better profiles used for clearly classify SSD and MDD sets in the same time. In details, we tried different combination of signatures from the three pair-wise compartmental results and finally determined 48 gene expression signatures with 100% accuracy. Our finding suggested that SSD and MDD did not exhibit the same expressed genome signature with peripheral blood leukocyte, and blood cell–derived RNA of these 48 gene models may have significant value for performing diagnostic functions and classifying SSD, MDD, and healthy controls.

Plasma metabonomics as a novel diagnostic approach for major depressive disorder

Major depressive disorder (MDD) is a socially detrimental psychiatric disorder, contributing to increased healthcare expenditures and suicide rates. However, no empirical laboratory-based tests are available to support the diagnosis of MDD. In this study, a NMR-based plasma metabonomic method for the diagnosis of MDD was tested. Proton nuclear magnetic resonance ((1)H NMR) spectra of plasma sampled from first-episode drug-naı̈ve depressed patients (n = 58) and healthy controls (n = 42) were recorded and analyzed by orthogonal partial least-squares discriminant analysis (OPLS-DA). The OPLS-DA score plots of the spectra demonstrated that the depressed patient group was significantly distinguishable from the healthy control group. Moreover, the method accurately diagnosed blinded samples (n = 26) in an independent replication cohort with a sensitivity and specificity of 92.8% and 83.3%, respectively. Taken together, NMR-based plasma metabonomics may offer an accurate empirical laboratory-based method applicable to the diagnosis of MDD.

Analysis of the specific pathways and networks of prostate cancer for gene expression profiles in the Chinese population

The global physiological function of specifically expressed genes of prostate cancer in Chinese patients is unclear. This study aims to determine the genome-wide expression of genes related to prostate cancer in the Chinese population. Genes that were differentially expressed in prostate cancer were identified using DNA microarray technology. Expressions were validated by using real-time PCR. The identified genes were analyzed using the ingenuity pathway analysis (IPA) to investigate the gene ontology, functional pathway and network. A total of 1,444 genes (Fold time ≥ 1.5; P ≤ 0.05) were differentially expressed in prostate primary tumor tissue compared with benign tissue. IPA revealed a unique landscape where inductions of certain pathways were involved in Cell Cycle Regulation and proliferation. Network analysis not only confirmed that protein interactions lead to the deregulation of DNA Replication, Recombination and Repair, Cellular Compromise and Cell Cycle, Genetic Disorders and Connective Tissue Disorders, but it was also observed that many of the genes regulated by Myc contributed to the modulation of lipid Metabolism and Nucleic Acid Metabolism. Both pathway and network analysis exhibited some remarkable characteristics of prostate cancer for Chinese patients, which showed profound differences from that of other non-Chinese populations. These differences may provide new insights into the molecular cascade of prostate cancer that occurs in Chinese patients.

Gene expression biomarkers of response to citalopram treatment in major depressive disorder

There is significant variability in antidepressant treatment outcome, with ∼30-40% of patients with major depressive disorder (MDD) not presenting with adequate response even following several trials. To identify potential biomarkers of response, we investigated peripheral gene expression patterns of response to antidepressant treatment in MDD. We did this using Affymetrix HG-U133 Plus2 microarrays in blood samples, from untreated individuals with MDD (N=63) ascertained at a community outpatient clinic, pre and post 8-week treatment with citalopram, and used a regression model to assess the impact of gene expression differences on antidepressant response. We carried out technical validation of significant probesets by quantitative reverse transcriptase PCR and conducted central nervous system follow-up of the most significant result in post-mortem brain samples from 15 subjects who died during a current MDD episode and 11 sudden-death controls. A total of 32 probesets were differentially expressed according to response to citalopram treatment following false discovery rate correction. Interferon regulatory factor 7 (IRF7) was the most significant differentially expressed gene and its expression was upregulated by citalopram treatment in individuals who responded to treatment. We found these results to be concordant with our observation of decreased expression of IRF7 in the prefrontal cortex of MDDs with negative toxicological evidence for antidepressant treatment at the time of death. These findings point to IRF7 as a gene of interest in studies investigating genomic factors associated with antidepressant response.

The utility of gene expression in blood cells for diagnosing neuropsychiatric disorders

Objective diagnostic tools are required for neuropsychiatric disorders. Gene expression in blood cells may provide such a tool and has already been used to construct classifiers capable of diagnosing many human diseases. This chapter discusses the use of microarray gene expression data to construct diagnostic classifiers for neuropsychiatric disorders. The potential pitfalls of microarray gene expression analysis and the experimental design and methods suitable for classifier construction are described in detail. A review of studies that have analyzed gene expression in blood cells from patients with neuropsychiatric disorders is presented with an emphasis on the feasibility of generating a diagnostic classifier for schizophrenia. Finally, the future directions of the field are discussed with respect to using blood gene expression to tailor antipsychotic medications to individual patients, applying microRNA expression for diagnostic purposes, as well as the implications of next-generation sequencing technologies for gene expression analysis.

Is there a personalized medicine for mood disorders?

Major Depressive Disorder (MDD) and antidepressant therapy response are largely based on behavioral criteria, which are known to correlate at best modestly with biological measures. Therefore, it is not surprising that the search for peripheral biological markers (biomarkers) being assessed in distant biological systems such as body fluids has not yet resulted in clinically convincing measures for MDD diagnostics or treatment evaluation. Imaging genetics studies, however, have been successful in the search for intermediate imaging phenotypes of MDD and treatment response that are directly related to the neurobiological underpinnings of MDD, but are not suitable for a broad clinical use today. Hence, we argue that intermediate phenotypes derived from imaging genetics studies should be utilized as substitutes of behaviorally assessed psychiatric diagnoses or therapy response in the search for easily accessible peripheral biomarkers. This article will further cover the current state of peripheral and neural biomarker research.