Uncovering associations between mental illness diagnosis, nitric oxide synthase gene variation, and peripheral nitric oxide concentration.

Nitric oxide (NO) signalling has been implicated in the pathogenesis of several mental illnesses; however, its specific contribution remains unclear. We investigated whether peripheral NO concentration is associated with specific diagnoses, and whether there is a correlation with genetic variation in NO synthase (NOS) genes. We included 185 participants in the study; 52 healthy controls, 43 major depressive disorder (MDD) patients, 41 bipolar disorder (BPD) patients, and 49 schizophrenia (SCZ) patients. Clinical, genetic, and biochemical data were collected at admission to a psychiatric hospital and at discharge. Serum was used to quantify concentration of the stable NO metabolites nitrite and nitrate. Individuals were genotyped for the NOS1 exon 1f variable number of tandem repeats 1 (VNTR1) polymorphism, and single nucleotide polymorphisms (SNPs) in the NOS1, NOS1AP and NOS3 genes. At admission, SCZ patients were found to have significantly higher peripheral NO metabolite (NOx-) concentrations compared to healthy controls, MDD and BPD patients. NOS1 exon 1f VNTR1 short allele carriers were found to have significantly increased NOx- concentration. Moreover, this result was still significant in patients even at discharge. The data also revealed that patients who did not remit in their depressive symptoms had significantly increased NOx- concentration compared to remitters at discharge, supported by the finding of a significant positive correlation between depression symptom severity and NOx- concentration. Taken together, it is possible that elevated peripheral NOx- concentration is associated with increased severity of psychopathology, potentially due to NOS1 exon1f VNTR1 genotype. Our results further implicate NO signalling in mental illness pathogenesis, supporting its possible use as a peripheral biomarker, and imply that NOS genotype may play a significant role in regulating peripheral NOx- concentration.

Longitudinal multi-level biomarker analysis of BDNF in major depression and bipolar disorder.

Preliminary evidence suggests that BDNF (brain derived neurotrophic factor) rs6265 genetic polymorphism, BDNF gene promotor methylation and BDNF serum levels might play an important role in the pathogenesis of affective disorders. As studies testing the BDNF system across molecular levels are sparse, this study aimed at investigating the BDNF val66met genotype, BDNF DNA methylation changes and peripheral BDNF serum levels in acute and remitted phases of MDD (major depressive disorder) and BD (bipolar disorder) and healthy controls. We found a significant difference of methylation levels at CpG site 1-1-1 and 3-1-1 between MDD and healthy controls (p < 0.003) with MDD patients showing significantly higher methylation levels. CpG 5-2-1 revealed a statistically significant difference between MDD and healthy controls and MDD and BD (p = 0.00003). Similar to the results of the methylation analysis a significant difference between MDD and healthy controls was found in BDNF serum levels (p = 0.002) with significantly lower BDNF serum levels in MDD compared to healthy controls. A difference between the samples from admission and discharge from hospital of both BDNF gene methylation and serum levels could not be detected in the present study and no influence of the BDNF val66met genotype on neither methylation nor BDNF serum level.

Phase-and disorder-specific differences in peripheral metabolites of the kynurenine pathway in major depression, bipolar affective disorder and schizophrenia.

OBJECTIVES: Kynurenine, kynurenic and quinolinic acid are important metabolites in tryptophan metabolism. Due to an involvement in glutamatergic neurotransmission and immune response, previous studies have investigated this pathway in mental disorders such as major depressive disorder (MDD), bipolar disorder (BD) or schizophrenia (SCZ). Tryptophan and kynurenine have been shown to be decreased across disorders, hinting at the missing link how inflammation causes neurotoxicity and psychiatric symptoms. The main aim of our study was to investigate if individual catabolites could serve as diagnostic biomarkers for MDD, BD and SCZ. METHODS: We measured plasma levels of tryptophan, kynurenine, kynurenic acid, quinolinic acid and ratio of quinolinic acid/kynurenic acid using mass spectrometry in n = 175 participants with acute episodes and after remission, compared with controls. RESULTS: Decreased levels of all tryptophan catabolites were found in the whole patient group, driven by the difference between BD and HC. Manic and mixed phase BD individuals displayed significantly lower kynurenine and kynurenic acid levels. We could not find significant differences between disorders. Upon reaching remission, changes in catabolite levels partially normalised. CONCLUSIONS: Our data suggests an involvement of the kynurenine pathway in mental disorders, especially BD but disqualifying those metabolites as biomarkers for differential diagnosis.

Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology.

Bipolar disorder is a heritable mental illness with complex etiology. We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus. Significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6, MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci. Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.

Risk Stratification for Bipolar Disorder Using Polygenic Risk Scores Among Young High-Risk Adults.

Objective: Identifying high-risk groups with an increased genetic liability for bipolar disorder (BD) will provide insights into the etiology of BD and contribute to early detection of BD. We used the BD polygenic risk score (PRS) derived from BD genome-wide association studies (GWAS) to explore how such genetic risk manifests in young, high-risk adults. We postulated that BD-PRS would be associated with risk factors for BD. Methods: A final sample of 185 young, high-risk German adults (aged 18-35 years) were grouped into three risk groups and compared to a healthy control group (n = 1,100). The risk groups comprised 117 cases with attention deficit hyperactivity disorder (ADHD), 45 with major depressive disorder (MDD), and 23 help-seeking adults with early recognition symptoms [ER: positive family history for BD, (sub)threshold affective symptomatology and/or mood swings, sleeping disorder]. BD-PRS was computed for each participant. Logistic regression models (controlling for sex, age, and the first five ancestry principal components) were used to assess associations of BD-PRS and the high-risk phenotypes. Results: We observed an association between BD-PRS and combined risk group status (OR = 1.48, p < 0.001), ADHD diagnosis (OR = 1.32, p = 0.009), MDD diagnosis (OR = 1.96, p < 0.001), and ER group status (OR = 1.7, p = 0.025; not significant after correction for multiple testing) compared to healthy controls. Conclusions: In the present study, increased genetic risk for BD was a significant predictor for MDD and ADHD status, but not for ER. These findings support an underlying shared risk for both MDD and BD as well as ADHD and BD. Improving our understanding of the underlying genetic architecture of these phenotypes may aid in early identification and risk stratification.