Exploring Genetic Variability at PI, GSK3, HPA, and Glutamatergic Pathways in Lithium Response: Association With IMPA2, INPP1, and GSK3B Genes

Lithium response in bipolar disorder: Epigenome-wide DNA methylation signatures and epigenetic aging

Lithium (Li) is the first-line treatment for bipolar disorder (BD) even though only 30 % of BD patients are considered excellent responders. The mechanisms by which Li exerts its action are not clearly understood, but it has been suggested that specific epigenetic mechanisms, such as methylation processes, may play a role. In this regard, DNA methylation patterns can be used to estimate epigenetic age (EpiAge), which is accelerated in BD patients and reversed by Li treatment. Our first aim was to compare the DNA methylation profile in peripheral blood between BD patients categorized as excellent responders to Li (Ex-Rp) and non-responders (N-Rp). Secondly, EpiAge was estimated to detect differential age acceleration between the two groups. A total of 130 differentially methylated positions (DMPs) and 16 differentially methylated regions (DMRs) between Ex-Rp (n = 26) and N-Rp (n = 37) were identified (FDR adjusted p-value < 0.05). We found 122 genes mapping the DMPs and DMRs, nine of which (HOXB6, HOXB3, HOXB-AS3, TENM2, CACNA1B, ANK3, EEF2K, CYP1A1, and SORCS2) had previously been linked to Li response. We found genes related to the GSK3β pathway to be highly represented. Using FUMA, we found enrichment in Gene Ontology Cell Component for the synapse. Gene network analysis highlighted functions related to the cell cycle, nervous system development and function, and gene expression. No significant differences in age acceleration were found between Ex-Rp and N-Rp for any of the epigenetic clocks analysed. Our findings indicate that a specific methylation pattern could determine the response to Li in BD patients. We also found that a significant portion of the differentially methylated genes are closely associated with the GSK3β pathway, reinforcing the role of this system in Li response. Future longitudinal studies with larger samples will help to elucidate the epigenetic mechanisms underlying Li response.

Genetic and Epigenetic Markers of Lithium Response

The mood stabilizer lithium represents a cornerstone in the long term treatment of bipolar disorder (BD), although with substantial interindividual variability in clinical response. This variability appears to be modulated by genetics, which has been significantly investigated in the last two decades with some promising findings. In addition, recently, the interest in the role of epigenetics has grown significantly, since the exploration of these mechanisms might allow the elucidation of the gene–environment interactions and explanation of missing heritability. In this article, we provide an overview of the most relevant findings regarding the pharmacogenomics and pharmacoepigenomics of lithium response in BD. We describe the most replicated findings among candidate gene studies, results from genome-wide association studies (GWAS) as well as post-GWAS approaches supporting an association between high genetic load for schizophrenia, major depressive disorder or attention deficit/hyperactivity disorder and poor lithium response. Next, we describe results from studies investigating epigenetic mechanisms, such as changes in methylation or noncoding RNA levels, which play a relevant role as regulators of gene expression. Finally, we discuss challenges related to the search for the molecular determinants of lithium response and potential future research directions to pave the path towards a biomarker guided approach in lithium treatment.

The Genetics of Response to and Side Effects of Lithium Treatment in Bipolar Disorder: Future Research Perspectives

Although the mood stabilizer lithium is a first-line treatment in bipolar disorder, a substantial number of patients do not benefit from it and experience side effects. No clinical tool is available for predicting lithium response or the occurrence of side effects in everyday clinical practice. Multiple genetic research efforts have been performed in this field because lithium response and side effects are considered to be multifactorial endophenotypes. Available results from linkage and segregation, candidate-gene, and genome-wide association studies indicate a role of genetic factors in determining response and side effects. For example, candidate-gene studies often report GSK3β, brain-derived neurotrophic factor, and SLC6A4 as being involved in lithium response, and the latest genome-wide association study found a genome-wide significant association of treatment response with a locus on chromosome 21 coding for two long non-coding RNAs. Although research results are promising, they are limited mainly by a lack of replicability and, despite the collaboration of consortia, insufficient sample sizes. The need for larger sample sizes and “multi-omics” approaches is apparent, and such approaches are crucial for choosing the best treatment options for patients with bipolar disorder. In this article, we delineate the mechanisms of action of lithium and summarize the results of genetic research on lithium response and side effects.

Brain Volume-Related Polymorphisms of the Glycogen Synthase Kinase-3β Gene and Their Effect on Antidepressant Treatment in Major Depressive Disorder

BACKGROUND

Glycogen synthase kinase-3β (GSK-3β) polymorphisms are known to influence hippocampal brain tissue volume in individuals with major depressive disorder (MDD). However, the effects of the GSK-3β gene single nucleotide polymorphisms (SNPs) in those receiving antidepressant therapy are unknown.

OBJECTIVES

In the present study, we examined the relationship between brain volume-related SNPs of the GSK-3β gene and antidepressant treatment effects in patients with MDD.

METHODS

Paroxetine, fluvoxamine, or milnacipran was administered to 143 Japanese patients with MDD. Two SNPs of the GSK-3β gene (rs6438552 and rs12630592) that influence brain volume in the hippocampus were genotyped. For the primary outcome, the relationship between genetic variations in the SNPs and the percent change in the Hamilton Rating Scale for Depression (HAM-D) score at week 6 was examined. In addition, rs334558, which has been reported repeatedly, was also genotyped.

RESULTS

There was a significant correlation between the two SNPs and the percent change in the HAM-D scores at week 6 (rs6438552 A/A vs. A/G + G/G: p = 0.016; rs12630592 G/G vs. G/T + T/T: p = 0.016). There was high linkage disequilibrium between the rs6438552 and rs12630592 SNPs. The correlation between high therapeutic response over time and the two SNPs were also confirmed (rs6438552 A/A vs. others: p = 0.031; rs12630592 G/G vs. others: p = 0.031).

CONCLUSIONS

Our results suggest that two GSK-3β variants that influence brain volume were associated with changes in the HAM-D scores at week 6 in patients with MDD.

Twenty years of Lithium pharmacogenetics: A systematic review

Lithium is among the best proven treatments for patients diagnosed with Bipolar Disorder, however response to Lithium appears to be considerably variable among individuals and it has been suggested that this inconstancy in Lithium response could be genetically determined. Starting from this perspective, in the last few decades, a number of pharmacogenetic studies have attempted to identify genetic variants, which might be associated with response to Lithium in bipolar patients, in order to develop a pharmacogenetics test to tailor treatment on patients, identifying who will benefit the most from therapy with Lithium. Within this context, authors have critically reviewed pharmacogenetic studies of Lithium response in bipolar disorder, suggesting strategies for future work in this field. Computerized searches of PubMed and Embase databases, for studies published between 1998 and January 2018, was performed: 1162 studies were identified but only 37 relevant papers were selected for detailed review. Despite some interesting preliminary findings, the pharmacogenetics of Lithium and the development of a specific pharmacogenetics test in bipolar disorder appears to be a field still in its infancy, even though the advent of genome-wide association studies holds particular promise for future studies, which should include larger samples.

The Role of Pharmacogenomics in Bipolar Disorder: Moving Towards Precision Medicine

Bipolar disorder (BD) is a common and disabling psychiatric condition with a severe socioeconomic impact. BD is treated with mood stabilizers, among which lithium represents the first-line treatment. Lithium alone or in combination is effective in 60% of chronically treated patients, but response remains heterogenous and a large number of patients require a change in therapy after several weeks or months. Many studies have so far tried to identify molecular and genetic markers that could help us to predict response to mood stabilizers or the risk for adverse drug reactions. Pharmacogenetic studies in BD have been for the most part focused on lithium, but the complexity and variability of the response phenotype, together with the unclear mechanism of action of lithium, limited the power of these studies to identify robust biomarkers. Recent pharmacogenomic studies on lithium response have provided promising findings, suggesting that the integration of genome-wide investigations with deep phenotyping, in silico analyses and machine learning could lead us closer to personalized treatments for BD. Nevertheless, to date none of the genes suggested by pharmacogenetic studies on mood stabilizers have been included in any of the genetic tests approved by the Food and Drug Administration (FDA) for drug efficacy. On the other hand, genetic information has been included in drug labels to test for the safety of carbamazepine and valproate. In this review, we will outline available studies investigating the pharmacogenetics and pharmacogenomics of lithium and other mood stabilizers, with a specific focus on the limitations of these studies and potential strategies to overcome them. We will also discuss FDA-approved pharmacogenetic tests for treatments commonly used in the management of BD.

The pharmacoepigenomics informatics pipeline defines a pathway of novel and known warfarin pharmacogenomics variants

AIM

'Pharmacoepigenomics' methods informed by omics datasets and pre-existing knowledge have yielded discoveries in neuropsychiatric pharmacogenomics. Now we evaluate the generality of these methods by discovering an extended warfarin pharmacogenomics pathway.

MATERIALS & METHODS

We developed the pharmacoepigenomics informatics pipeline, a scalable multi-omics variant screening pipeline for pharmacogenomics, and conducted an experiment in the genomics of warfarin.

RESULTS

We discovered known and novel pharmacogenomics variants and genes, both coding and regulatory, for warfarin response, including adverse events. Such genes and variants cluster in a warfarin response pathway consolidating known and novel warfarin response variants and genes.

CONCLUSION

These results can inform a new warfarin test. The pharmacoepigenomics informatics pipeline may be able to discover new pharmacogenomics markers in other drug-disease systems.

Pharmacogenomic aspects of bipolar disorder: An update

The hopes for readily implementable precision medicine are high. For many complex disorders, such as bipolar disorder, these hopes critically hinge on tangible successes in pharmacogenetics of treatment response or susceptibility to adverse events. In this article, we review the current state of pharmacogenomics of bipolar disorder including latest results from candidate genes and genome-wide association studies. The majority of studies focus on response to lithium treatment. Although a host of genes has been studied, hardly any replicated findings have emerged so far. Very small samples sizes and heterogeneous phenotype definition may be considered the major impediments to success in this field. Drawing from current experiences and successes in studies on diagnostic psychiatric phenotypes, we suggest several approaches for our way forward.

The genetic overlap between mood disorders and cardiometabolic diseases: a systematic review of genome wide and candidate gene studies

Meta-analyses of genome-wide association studies (meta-GWASs) and candidate gene studies have identified genetic variants associated with cardiovascular diseases, metabolic diseases and mood disorders. Although previous efforts were successful for individual disease conditions (single disease), limited information exists on shared genetic risk between these disorders. This article presents a detailed review and analysis of cardiometabolic diseases risk (CMD-R) genes that are also associated with mood disorders. First, we reviewed meta-GWASs published until January 2016, for the diseases 'type 2 diabetes, coronary artery disease, hypertension' and/or for the risk factors 'blood pressure, obesity, plasma lipid levels, insulin and glucose related traits'. We then searched the literature for published associations of these CMD-R genes with mood disorders. We considered studies that reported a significant association of at least one of the CMD-R genes and 'depression' or 'depressive disorder' or 'depressive symptoms' or 'bipolar disorder' or 'lithium treatment response in bipolar disorder', or 'serotonin reuptake inhibitors treatment response in major depression'. Our review revealed 24 potential pleiotropic genes that are likely to be shared between mood disorders and CMD-Rs. These genes include MTHFR, CACNA1D, CACNB2, GNAS, ADRB1, NCAN, REST, FTO, POMC, BDNF, CREB, ITIH4, LEP, GSK3B, SLC18A1, TLR4, PPP1R1B, APOE, CRY2, HTR1A, ADRA2A, TCF7L2, MTNR1B and IGF1. A pathway analysis of these genes revealed significant pathways: corticotrophin-releasing hormone signaling, AMPK signaling, cAMP-mediated or G-protein coupled receptor signaling, axonal guidance signaling, serotonin or dopamine receptors signaling, dopamine-DARPP32 feedback in cAMP signaling, circadian rhythm signaling and leptin signaling. Our review provides insights into the shared biological mechanisms of mood disorders and cardiometabolic diseases.

Therapeutic Mechanisms of Lithium in Bipolar Disorder: Recent Advances and Current Understanding

Lithium is the most effective and well established treatment for bipolar disorder, and it has a broad array of effects within cellular pathways. However, the specific processes through which therapeutic effects occur and are maintained in bipolar disorder remain unclear. This paper provides a timely update to an authoritative review of pertinent findings that was published in CNS Drugs in 2013. A literature search was conducted using the Scopus database, and was limited by year (from 2012). There has been a resurgence of interest in lithium therapy mechanisms, perhaps driven by technical advancements in recent years that permit the examination of cellular mechanisms underpinning the effects of lithium-along with the reuptake of lithium in clinical practice. Recent research has further cemented glycogen synthase kinase 3β (GSK3β) inhibition as a key mechanism, and the inter-associations between GSK3β-mediated neuroprotective, anti-oxidative and neurotransmission mechanisms have been further elucidated. In addition to highly illustrative cellular research, studies examining higher-order biological systems, such as circadian rhythms, as well as employing innovative animal and human models, have increased our understanding of how lithium-induced changes at the cellular level possibly translate to changes at behavioural and clinical levels. Neural circuitry research is yet to identify clear mechanisms of change in bipolar disorder in response to treatment with lithium, but important structural findings have demonstrated links to the modulation of cellular mechanisms, and peripheral marker and pharmacogenetic studies are showing promising findings that will likely inform the exploration for predictors of lithium treatment response. With a deeper understanding of lithium's therapeutic mechanisms-from the cellular to clinical levels of investigation-comes the opportunity to develop predictive models of lithium treatment response and identify novel drug targets, and recent findings have provided important leads towards these goals.

Can a systems approach produce a better understanding of mood disorders?

BACKGROUND

One in twenty-five people suffer from a mood disorder. Current treatments are sub-optimal with poor patient response and uncertain modes-of-action. There is thus a need to better understand underlying mechanisms that determine mood, and how these go wrong in affective disorders. Systems biology approaches have yielded important biological discoveries for other complex diseases such as cancer, and their potential in affective disorders will be reviewed.

SCOPE OF REVIEW

This review will provide a general background to affective disorders, plus an outline of experimental and computational systems biology. The current application of these approaches in understanding affective disorders will be considered, and future recommendations made.

MAJOR CONCLUSIONS

Experimental systems biology has been applied to the study of affective disorders, especially at the genome and transcriptomic levels. However, data generation has been slowed by a lack of human tissue or suitable animal models. At present, computational systems biology has only be applied to understanding affective disorders on a few occasions. These studies provide sufficient novel biological insight to motivate further use of computational biology in this field.

GENERAL SIGNIFICANCE

In common with many complex diseases much time and money has been spent on the generation of large-scale experimental datasets. The next step is to use the emerging computational approaches, predominantly developed in the field of oncology, to leverage the most biological insight from these datasets. This will lead to the critical breakthroughs required for more effective diagnosis, stratification and treatment of affective disorders.