Investigation of tryptophan hydroxylase 2 (TPH2) in schizophrenia and in the response to antipsychotics

Early antidepressant treatment response prediction in major depression using clinical and TPH2 DNA methylation features based on machine learning approaches

OBJECTIVE

To identify DNA methylation and clinical features, and to construct machine learning classifiers to assign the patients with major depressive disorder (MDD) into responders and non-responders after a 2-week treatment into responders and non-responders.

METHOD

Han Chinese patients (291 in total) with MDD comprised the study population. Datasets contained demographic information, environment stress factors, and the methylation levels of 38 methylated sites of tryptophan hydroxylase 2 (TPH2) genes in peripheral blood samples. Recursive Feature Elimination (RFE) was employed to select features. Five classification algorithms (logistic regression, classification and regression trees, support vector machine, logitboost and random forests) were used to establish the models. Performance metrics (AUC, F-Measure, G-Mean, accuracy, sensitivity, specificity, positive predictive value and negative predictive value) were computed with 5-fold-cross-validation. Variable importance was evaluated by random forest algorithm.

RESULT

RF with RFE outperformed the other models in our samples based on the demographic information and clinical features (AUC = 61.2%, 95%CI: 60.1-62.4%) / TPH2 CpGs features (AUC = 66.6%, 95%CI: 65.4-67.8%) / both clinical and TPH2 CpGs features (AUC = 72.9%, 95%CI: 71.8-74.0%).

CONCLUSION

The effects of TPH2 on the early-stage antidepressant response were explored by machine learning algorithms. On the basis of the baseline depression severity and TPH2 CpG sites, machine learning approaches can enhance our ability to predict the early-stage antidepressant response. Some potentially important predictors (e.g., TPH2-10-60 (rs2129575), TPH2-2-163 (rs11178998), age of first onset, age) in early-stage treatment response could be utilized in future fundamental research, drug development and clinical practice.

Identifying patients with major depressive disorder based on tryptophan hydroxylase-2 methylation using machine learning algorithms

OBJECTIVES

This study aimed to identify patients with major depressive disorder (MDD) by developing different machine learning (ML) models based on tryptophan hydroxylase-2 (TPH2) methylation and environmental stress.

METHODS

The data were collected from 291 patients with MDD and 100 healthy control participants: individual basic information, the Negative Life Events Scale (NLES) scores, the Childhood Trauma Questionnaire (CTQ) scores and the methylation level at 38 CpG sites in TPH2. Information gain was used to select critical input variables. Support vector machine (SVM), back propagation neural network (BPNN) and random forest (RF) algorithms were used to build recognition models, which were evaluated by the 10-fold cross-validation. SHapley Additive exPlanations (SHAP) method was used to evaluate features importance.

RESULTS

Gender, NLES scores, CTQ scores and 13 CpG sites in TPH2 gene were considered as predictors in the models. Three ML algorithms showed satisfactory performance in predicting MDD and the BPNN model indicated best prediction effects.

CONCLUSION

ML models with TPH2 methylation and environmental stress were identified to possess great performance in identifying patients with MDD, which provided precious experience for artificial intelligence to assist traditional diagnostic methods in the future.

Genetic Determinants of Gating Functions: Do We Get Closer to Understanding Schizophrenia Etiopathogenesis?

Deficits in the gating of sensory stimuli, i.e., the ability to suppress the processing of irrelevant sensory input, are considered to play an important role in the pathogenesis of several neuropsychiatric disorders, in particular schizophrenia. Gating is disrupted both in schizophrenia patients and their unaffected relatives, suggesting that gating deficit may represent a biomarker associated with a genetic liability to the disorder. To assess the strength of the evidence for the etiopathogenetic links between genetic variation, gating efficiency, and schizophrenia, we carried out a systematic review of human genetic association studies of sensory gating (suppression of the P50 component of the auditory event-related brain potential) and sensorimotor gating (prepulse inhibition of the acoustic startle response). Sixty-three full-text articles met the eligibility criteria for inclusion in the review. In total, 117 genetic variants were reported to be associated with gating functions: 33 variants for sensory gating, 80 variants for sensorimotor gating, and four variants for both sensory and sensorimotor gating. However, only five of these associations (four for prepulse inhibition-CHRNA3 rs1317286, COMT rs4680, HTR2A rs6311, and TCF4 rs9960767, and one for P50 suppression-CHRNA7 rs67158670) were consistently replicated in independent samples. Although these variants and genes were all implicated in schizophrenia in research studies, only two polymorphisms (HTR2A rs6311 and TCF4 rs9960767) were also reported to be associated with schizophrenia at a meta-analytic or genome-wide level of evidence. Thus, although gating is widely considered as an important endophenotype of schizophrenia, these findings demonstrate that evidence for a common genetic etiology of impaired gating functions and schizophrenia is yet unsatisfactory, warranting further studies in this field.

Pharmacogenomics of Cognitive Dysfunction and Neuropsychiatric Disorders in Dementia

Symptomatic interventions for patients with dementia involve anti-dementia drugs to improve cognition, psychotropic drugs for the treatment of behavioral disorders (BDs), and different categories of drugs for concomitant disorders. Demented patients may take >6–10 drugs/day with the consequent risk for drug–drug interactions and adverse drug reactions (ADRs >80%) which accelerate cognitive decline. The pharmacoepigenetic machinery is integrated by pathogenic, mechanistic, metabolic, transporter, and pleiotropic genes redundantly and promiscuously regulated by epigenetic mechanisms. CYP2D6, CYP2C9, CYP2C19, and CYP3A4/5 geno-phenotypes are involved in the metabolism of over 90% of drugs currently used in patients with dementia, and only 20% of the population is an extensive metabolizer for this tetragenic cluster. ADRs associated with anti-dementia drugs, antipsychotics, antidepressants, anxiolytics, hypnotics, sedatives, and antiepileptic drugs can be minimized by means of pharmacogenetic screening prior to treatment. These drugs are substrates, inhibitors, or inducers of 58, 37, and 42 enzyme/protein gene products, respectively, and are transported by 40 different protein transporters. APOE is the reference gene in most pharmacogenetic studies. APOE-3 carriers are the best responders and APOE-4 carriers are the worst responders; likewise, CYP2D6-normal metabolizers are the best responders and CYP2D6-poor metabolizers are the worst responders. The incorporation of pharmacogenomic strategies for a personalized treatment in dementia is an effective option to optimize limited therapeutic resources and to reduce unwanted side-effects.

TPH2 polymorphisms across the spectrum of psychiatric morbidity: A systematic review and meta-analysis

Tryptophan hydroxylase 2 (TPH2) is the rate-limiting enzyme in brain serotonin synthesis. The TPH2 gene has frequently been investigated in relation to psychiatric morbidity. The aim of the present review is to integrate results from association studies between TPH2 single nucleotide polymorphisms (SNPs) and various psychiatric disorders, which we furthermore quantified with meta-analysis. We reviewed 166 studies investigating 69 TPH2 SNPs in a broad range of psychiatric disorders, including over 30,000 patients. According to our meta-analysis, TPH2 polymorphisms show strongest associations with mood disorders, suicide (attempt) and schizophrenia. Despite small effect sizes, we conclude that TPH2 SNPs in the coding and non-coding areas (rs4570625, rs11178997, rs11178998, rs10748185, rs1843809, rs4290270, rs17110747) are each associated with one or more psychopathological conditions. Our findings highlight the possible common serotonergic mechanisms of the investigated psychiatric disorders. Yet, the functional relevance of most TPH2 polymorphisms is unclear. Characterizing how exactly the different TPH2 variants influence the serotonergic neurotransmission is a next necessary step in understanding the psychiatric disorders where serotonin is implicated.

Serotonin and the Brain's Rich Club-Association Between Molecular Genetic Variation on the TPH2 Gene and the Structural Connectome

The rich club comprises a densely mutually connected set of hub regions in the brain, thought to serve as a processing and integration core. We assessed the impact of normal variation of the tryptophane hydroxylase 2 gene's promotor region (TPH2 rs4570625) on structural connectivity of the rich club pathways by means of a candidate gene association design. Tryptophane hydroxylase 2 (TPH2) is a rate-limiting enzyme in the biosynthesis of serotonin and is known to inhibit, in addition to its role as a trans-synaptic messenger, axonal and dendritic growth. The TPH2 T-variant has been associated with reduced mRNA expression and reduced serotonin levels, which may particularly influence the development of macroscale anatomical connectivity. Here, we show larger mean connectivity in the rich club in carriers of the T-variant, suggesting potential effects of upregulation of neural connectivity growth in this central core system. In addition, by edge-removal statistics, we show that the TPH2-associated higher levels of rich club connectivity are of importance for the functioning of the total structural network. The observed association is speculated to result from an effect of serotonin levels on brain development, potentially leading to stronger structural connectivity in heavily interconnected hubs.

A review of genetic alterations in the serotonin pathway and their correlation with psychotic diseases and response to atypical antipsychotics

Serotonin is a neurotransmitter that plays a predominant role in mood regulation. The importance of the serotonin pathway in controlling behavior and mental status is well recognized. All the serotonin elements - serotonin receptors, serotonin transporter, tryptophan hydroxylase and monoamine oxidase proteins - can show alterations in terms of mRNA or protein levels and protein sequence, in schizophrenia and bipolar disorder. Additionally, when examining the genes sequences of all serotonin elements, several single nucleotide polymorphisms (SNPs) have been found to be more prevalent in schizophrenic or bipolar patients than in healthy individuals. Several of these alterations have been associated either with different phenotypes between patients and healthy individuals or with the response of psychiatric patients to the treatment with atypical antipsychotics. The complex pattern of genetic diversity within the serotonin pathway hampers efforts to identify the key variations contributing to an individual's susceptibility to the disease. In this review article, we summarize all genetic alterations found across the serotonin pathway, we provide information on whether and how they affect schizophrenia or bipolar disorder phenotypes, and, on the contribution of familial relationships on their detection frequencies. Furthermore, we provide evidence on whether and how specific gene polymorphisms affect the outcome of schizophrenic or bipolar patients of different ethnic groups, in response to treatment with atypical antipsychotics. All data are discussed thoroughly, providing prospective for future studies.

Polymorphisms of serotonin neurotransmission and their effects on antipsychotic drug action

The receptor pharmacology of many antipsychotic drugs includes actions at various serotonin (5-hydroxytryptamine [5-HT]) receptors. The 5-HT neurotransmitter system is thought to be involved in many of the consequences of treatment with antipsychotic drugs, including both symptom response, primarily of negative and depressive symptoms, and adverse effects, notably extrapyramidal side effects and weight gain. There is substantial interindividual variability in these drug effects, to which genetic variability contributes. We review here the influence of functional polymorphisms in genes associated with 5-HT function, including the various processes of neurotransmitter synthesis, receptors, transporters and metabolism, on the clinical response to, and adverse effects of, antipsychotic drugs. The relatively young field of epigenetics also contributes to the variability of 5-HT-related genes in influencing drug response. Several of these findings inform our understanding of the mechanisms of antipsychotic drug action, and also provide the opportunity for the development of genetic testing for personalized medicine.

Personalized medicine in psychiatry: problems and promises

The central theme of personalized medicine is the premise that an individual's unique physiologic characteristics play a significant role in both disease vulnerability and in response to specific therapies. The major goals of personalized medicine are therefore to predict an individual's susceptibility to developing an illness, achieve accurate diagnosis, and optimize the most efficient and favorable response to treatment. The goal of achieving personalized medicine in psychiatry is a laudable one, because its attainment should be associated with a marked reduction in morbidity and mortality. In this review, we summarize an illustrative selection of studies that are laying the foundation towards personalizing medicine in major depressive disorder, bipolar disorder, and schizophrenia. In addition, we present emerging applications that are likely to advance personalized medicine in psychiatry, with an emphasis on novel biomarkers and neuroimaging.