Association of Novel ALX4 Gene Polymorphisms with Antidepressant Treatment Response: Findings from the CO-MED Trial

Prenatal and postnatal challenges affect the hypothalamic molecular pathways that regulate hormonal levels

This study aimed to improve our understanding of how the hypothalamus mediates the effects of prenatal and postnatal challenges on behavior and sensitivity to stimuli. A pig model of virally initiated maternal immune activation (MIA) was used to investigate potential interactions of the prenatal challenge both with sex and with postnatal nursing withdrawal. The hypothalami of 72 females and males were profiled for the effects of MIA and nursing withdrawal using RNA-sequencing. Significant differential expression (FDR-adjusted p value < 0.05) was detected in the profile of 222 genes. Genes involved in the Gene Ontology biological process of regulation of hormone levels tended to be over-expressed in individuals exposed to both challenges relative to individuals exposed to either one challenge, and most of these genes were over-expressed in MIA females relative to males across nursing levels. Differentially expressed genes included Fshb, Ttr, Agrp, Gata3, Foxa2, Tfap2b, Gh1, En2, Cga, Msx1, and Npy. The study also found that prenatal and postnatal challenges, as well as sex, impacted the regulation of neurotransmitter activity and immune effector processes in the hypothalamus. In particular, the olfactory transduction pathway genes were over-expressed in weaned MIA males, and several transcription factors were potentially found to target the differentially expressed genes. Overall, these results highlight how multiple environmental challenges can interact and affect the molecular mechanisms of the hypothalamus, including hormonal, immune response, and neurotransmitter processes.

Precision Medicine in Antidepressants Treatment

Precision medicine uses innovative approaches to improve disease prevention and treatment outcomes by taking into account people's genetic backgrounds, environments, and lifestyles. Treatment of depression is particularly challenging, given that 30-50% of patients do not respond adequately to antidepressants, while those who respond may experience unpleasant adverse drug reactions (ADRs) that decrease their quality of life and compliance. This chapter aims to present the available scientific data that focus on the impact of genetic variants on the efficacy and toxicity of antidepressants. We compiled data from candidate gene and genome-wide association studies that investigated associations between pharmacodynamic and pharmacokinetic genes and response to antidepressants regarding symptom improvement and ADRs. We also summarized the existing pharmacogenetic-based treatment guidelines for antidepressants, used to guide the selection of the right antidepressant and its dose based on the patient's genetic profile, aiming to achieve maximum efficacy and minimum toxicity. Finally, we reviewed the clinical implementation of pharmacogenomics studies focusing on patients on antidepressants. The available data demonstrate that precision medicine can increase the efficacy of antidepressants and reduce the occurrence of ADRs and ultimately improve patients' quality of life.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional “omic” measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (<age 18) and adult-onset depression. The most significant variant (p = 8.77 × 10−8) localized to an intron of SAMD3. In silico functional annotation of top signals (p < 1 × 10−5) demonstrated gene expression enrichment in the brain and during embryonic development. Network analysis identified differential associations between four variants (in/near INTU, FAT1, CNTN6, and TM9SF2) and plasma metabolites (phosphatidylcholines, carnitines, biogenic amines, and amino acids) in early- compared with adult-onset MDD. Multi-omics integration identified differential biosignatures of early- and adult-onset MDD. These biosignatures call for future studies to follow participants from childhood through adulthood and collect repeated -omics and neuroimaging measures to validate and deeply characterize the biomarkers of susceptibility and/or resistance to MDD development.

Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS' and CO-MED's escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS' escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.

Prospects for personalization of depression treatment with genome sequencing

The effectiveness of antidepressants in the treatment of major depressive disorder varies considerably between patients. With these interindividual differences and a number of antidepressants to choose from, the first choice of treatment often fails to produce improvement in the patient's condition. A substantial part of the variation in response to antidepressants can be explained by genetic factors. Accordingly, variants related to drug metabolism in two pharmacogenes, CYP2D6 and CYP2C19, have already been translated into guidelines for antidepressant prescriptions. The role of variants in other genes that influence antidepressant responses is not yet understood. Furthermore, rare and individual variants account for a substantial part of genetic differences in antidepressant efficacy. Recent years have brought a tremendous increase in the accessibility of genome sequencing in terms of data availability and its clinical use. In this review, we summarize recent developments and current issues in the personalization of major depressive disorder treatment through pharmacogenomics.

Early pregnancy dyslipidemia is associated with placental DNA methylation at loci relevant for cardiometabolic diseases

Aim: To identify placental DNA methylation changes that are associated with early pregnancy maternal dyslipidemia. Materials & methods: We analyzed placental genome-wide DNA methylation (n = 262). Genes annotating differentially methylated CpGs were evaluated for gene expression in placenta (n = 64). Results: We found 11 novel significant differentially methylated CpGs associated with high total cholesterol, low-density lipoprotein cholesterol and triglycerides, and low high-density lipoprotein cholesterol. High triglycerides were associated with decreased methylation of cg02785814 (ALX4) and decreased expression of ALX4 in placenta. Genes annotating the differentially methylated CpGs play key roles in lipid metabolism and were enriched in dyslipidemia pathways. Functional annotation found cis-methylation quantitative trait loci for genetic loci in ALX4 and EXT2. Conclusion: Our findings lend novel insights into potential placental epigenetic mechanisms linked with maternal dyslipidemia. Trial Registration: ClinicalTrials.gov, NCT00912132.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.

On the utilization of polygenic risk scores for therapeutic targeting

The promise of personalized genomic medicine is that knowledge of a person's gene sequences and activity will facilitate more appropriate medical interventions, particularly drug prescriptions, to reduce the burden of disease. Early successes in oncology and pediatrics have affirmed the power of positive diagnosis and are mostly based on detection of one or a few mutations that drive the specific pathology. However, genetically more complex diseases require the development of polygenic risk scores (PRSs) that have variable accuracy. The rarity of events often means that they have necessarily low precision: many called positives are actually not at risk, and only a fraction of cases are prevented by targeted therapy. In some situations, negative prediction may better define the population at low risk. Here, I review five conditions across a broad spectrum of chronic disease (opioid pain medication, hypertension, type 2 diabetes, major depression, and osteoporotic bone fracture), considering in each case how genetic prediction might be used to target drug prescription. This leads to a call for more research designed to evaluate genetic likelihood of response to therapy and a call for evaluation of PRS, not just in terms of sensitivity and specificity but also with respect to potential clinical efficacy.

Moving pharmacoepigenetics tools for depression toward clinical use

BACKGROUND

Major depressive disorder (MDD) is a leading cause of disability worldwide, and over half of patients do not achieve symptom remission following an initial antidepressant course. Despite evidence implicating a strong genetic basis for the pathophysiology of MDD, there are no adequately validated biomarkers of treatment response routinely used in clinical practice. Pharmacoepigenetics is an emerging field that has the potential to combine both genetic and environmental information into treatment selection and further the goal of precision psychiatry. However, this field is in its infancy compared to the more established pharmacogenetics approaches.

METHODS

We prepared a narrative review using literature searches of studies in English pertaining to pharmacoepigenetics and treatment of depressive disorders conducted in PubMed, Google Scholar, PsychINFO, and Ovid Medicine from inception through January 2019. We reviewed studies of DNA methylation and histone modifications in both humans and animal models of depression.

RESULTS

Emerging evidence from human and animal work suggests a key role for epigenetic marks, including DNA methylation and histone modifications, in the prediction of antidepressant response. The challenges of heterogeneity of patient characteristics and loci studied as well as lack of replication that have impacted the field of pharmacogenetics also pose challenges to the development of pharmacoepigenetic tools. Additionally, given the tissue specific nature of epigenetic marks as well as their susceptibility to change in response to environmental factors and aging, pharmacoepigenetic tools face additional challenges to their development.

LIMITATIONS

This is a narrative and not systematic review of the literature on the pharmacoepigenetics of antidepressant response. We highlight key studies pertaining to pharmacoepigenetics and treatment of depressive disorders in humans and depressive-like behaviors in animal models, regardless of sample size or methodology. While we discuss DNA methylation and histone modifications, we do not cover microRNAs, which have been reviewed elsewhere recently.

CONCLUSIONS

Utilization of genome-wide approaches and reproducible epigenetic assays, careful selection of the tissue assessed, and integration of genetic and clinical information into pharmacoepigenetic tools will improve the likelihood of developing clinically useful tests.

Pharmacogenomics and Biomarkers of Depression

The standard of care for antidepressant treatment in major depressive disorder (MDD) is a trial-and-error approach. Patients often have to undergo multiple medication trials for weeks to months before finding an effective treatment. Clinical factors such as severity of baseline symptoms and the presence of specific individual (anhedonia or insomnia) or cluster (atypical, melancholic, or anxious) of symptoms are commonly used without any evidence of their utility in selecting among currently available antidepressants. Genomic and proteomic biomarker have gained recent attention for their potential in informing antidepressant medication selection. In this report, we have reviewed some of the major pharmacogenomics studies along with individual genetic and proteomic biomarker of antidepressant response. Additionally, we have reviewed the blood-based protein biomarkers that can inform selection of one antidepressant over another. Among all currently available biomarkers, C-reactive protein (CRP) appears to be the most promising and pragmatic choice. Low CRP (<1 mg/L) in patients with MDD predicts better response to escitalopram while higher levels are associated with better response to noradrenergic/dopaminergic antidepressants. Future studies are needed to demonstrate the superiority of a CRP-based treatment assignment over high-quality measurement-based care in real-world clinical practices.