The interaction of P11 methylation and early-life stress impacts the antidepressant response in patients with major depressive disorder

Leveraging DNA methylation to predict treatment response in major depressive disorder: A critical review

Major depressive disorder (MDD) is a debilitating and prevalent mental disorder with a high disease burden. Despite a wide array of different treatment options, many patients do not respond to initial treatment attempts. Selection of the most appropriate treatment remains a significant clinical challenge in psychiatry, highlighting the need for the development of biomarkers with predictive utility. Recently, the epigenetic modification DNA methylation (DNAm) has emerged to be of great interest as a potential predictor of MDD treatment outcomes. Here, we review efforts to date that seek to identify DNAm signatures associated with treatment response in individuals with MDD. Searches were conducted in the databases PubMed, Scopus, and Web of Science with the concepts and keywords MDD, DNAm, antidepressants, psychotherapy, cognitive behavior therapy, electroconvulsive therapy, transcranial magnetic stimulation, and brain stimulation therapies. We identified 32 studies implicating DNAm patterns associated with MDD treatment outcomes. The majority of studies (N = 25) are focused on selected target genes exploring treatment outcomes in pharmacological treatments (N = 22) with a few studies assessing treatment response to electroconvulsive therapy (N = 3). Additionally, there are few genome-scale efforts (N = 7) to characterize DNAm patterns associated with treatment outcomes. There is a relative dearth of studies investigating DNAm patterns in relation to psychotherapy, electroconvulsive therapy, or transcranial magnetic stimulation; importantly, most existing studies have limited sample sizes. Given the heterogeneity in both methods and results of studies to date, there is a need for additional studies before existing findings can inform clinical decisions.

Epigenetic mechanisms in depression: Implications for pathogenesis and treatment

The risk of depression is influenced by both genetic and environmental factors. It has been suggested that epigenetic mechanisms may mediate the risk of depression following exposure to adverse life events. Epigenetics encompasses stable alterations in gene expression that are controlled through transcriptional, post-transcriptional, translational, or post-translational processes, including DNA modifications, chromatin remodeling, histone modifications, RNA modifications, and non-coding RNA (ncRNA) regulation, without any changes in the DNA sequence. In this review, we explore recent research advancements in the realm of epigenetics concerning depression. Furthermore, we evaluate the potential of epigenetic changes as diagnostic and therapeutic biomarkers for depression.

Prediction of Early Antidepressant Efficacy in Patients with Major Depressive Disorder Based on Multidimensional Features of rs-fMRI and P11 Gene DNA Methylation: Prédiction de l'efficacité précoce d'un antidépresseur chez des patients souffrant du trouble dépressif majeur d'après les caractéristiques multidimensionnelles de la méthylation de l'ADN du gène P11 et de la IRMf-rs

OBJECTIVE

This study established a machine learning model based on the multidimensional data of resting-state functional activity of the brain and P11 gene DNA methylation to predict the early efficacy of antidepressant treatment in patients with major depressive disorder (MDD).

METHODS

A total of 98 Han Chinese MDD were analysed in this study. Patients were divided into 51 responders and 47 nonresponders according to whether the Hamilton Depression Rating Scale-17 items (HAMD-17) reduction rate was ≥50% after 2 weeks of antidepressant treatment. At baseline, the Illumina HiSeq Platform was used to detect the methylation of 74 CpG sites of the P11 gene in peripheral blood samples. Resting-state functional magnetic resonance imaging (rs-fMRI) scan detected the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in 116 brain regions. The least absolute shrinkage and selection operator analysis method was used to perform feature reduction and feature selection. Four typical machine learning methods were used to establish support vector machine (SVM), random forest (RF), Naïve Bayes (NB), and logistic regression (LR) prediction models based on different combinations of functional activity of the brain, P11 gene DNA methylation and clinical/demographic features after screening.

RESULTS

The SVM model based on ALFF, ReHo, FC, P11 methylation, and clinical/demographic features showed the best performance, with 95.92% predictive accuracy and 0.9967 area under the receiver operating characteristic curve, which was better than RF, NB, and LR models.

CONCLUSION

The multidimensional data features combining rs-fMRI, DNA methylation, and clinical/demographic features can predict the early antidepressant efficacy in MDD.

Alterations of BDNF, mGluR5, Homer1a, p11 and excitatory/inhibitory balance in corticolimbic brain regions of suicide decedents

BACKGROUND

Developing biological based approaches for preventing suicide has become a priority. In recent years, there has been a surge in studies investigating the role of the glutamatergic system in suicide, although it remains unclear.

METHODS

We evaluated changes in the gene expression of the metabotropic glutamate receptor 5 (mGluR5) and its scaffolding proteins Homer1a and p11 in the dorsolateral prefrontal cortex (DLPFC), amygdala (AMY), and hippocampus (HIP) of 28 suicide decedents (S) (with no clinical psychiatric history or treatment with anxiolytics or antidepressants) and 26 controls (C) by real-time PCR (qPCR). Indeed, we measured BDNF gene expression and VGluT1 and VGAT immunoreactivities in the HIP by qPCR and immunohistochemistry, respectively. Cases and controls matched for age (C: 48.6 ± 11.6 years; S: 46.9 ± 14.5 years) and postmortem interval (PMI; C: 20.1 ± 13h; S: 16.9 ± 5h).

RESULTS

In DLPFC, S had lower p11 gene expression levels, but no differences were found in mGluR5 or Homer1a. In the AMY and HIP, mGluR5 and Homer1a were increased, p11 and BDNF were reduced. In the HIP, there were less VGAT-ir and more VGluT1-ir.

LIMITATIONS

Future studies are necessary to evaluate protein levels, and determine the cell types and potential compensatory mechanisms in a larger sample including S diagnosed with psychiatric disorders, females and different ethnicities.

CONCLUSIONS

This study identified significant alterations in mGluR5, Homer1a, p11, BDNF and excitatory/inhibitory balance in corticolimbic brain areas of S. These results further characterize the biological basis of suicide, contributing to the identification of potential biomarkers for suicide prevention.

Treatment-resistant depression: definition, prevalence, detection, management, and investigational interventions

Treatment-resistant depression (TRD) is common and associated with multiple serious public health implications. A consensus definition of TRD with demonstrated predictive utility in terms of clinical decision-making and health outcomes does not currently exist. Instead, a plethora of definitions have been proposed, which vary significantly in their conceptual framework. The absence of a consensus definition hampers precise estimates of the prevalence of TRD, and also belies efforts to identify risk factors, prevention opportunities, and effective interventions. In addition, it results in heterogeneity in clinical practice decision-making, adversely affecting quality of care. The US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) have adopted the most used definition of TRD (i.e., inadequate response to a minimum of two antidepressants despite adequacy of the treatment trial and adherence to treatment). It is currently estimated that at least 30% of persons with depression meet this definition. A significant percentage of persons with TRD are actually pseudo-resistant (e.g., due to inadequacy of treatment trials or non-adherence to treatment). Although multiple sociodemographic, clinical, treatment and contextual factors are known to negatively moderate response in persons with depression, very few factors are regarded as predictive of non-response across multiple modalities of treatment. Intravenous ketamine and intranasal esketamine (co-administered with an antidepressant) are established as efficacious in the management of TRD. Some second-generation antipsychotics (e.g., aripiprazole, brexpiprazole, cariprazine, quetiapine XR) are proven effective as adjunctive treatments to antidepressants in partial responders, but only the olanzapine-fluoxetine combination has been studied in FDA-defined TRD. Repetitive transcranial magnetic stimulation (TMS) is established as effective and FDA-approved for individuals with TRD, with accelerated theta-burst TMS also recently showing efficacy. Electroconvulsive therapy is regarded as an effective acute and maintenance intervention in TRD, with preliminary evidence suggesting non-inferiority to acute intravenous ketamine. Evidence for extending antidepressant trial, medication switching and combining antidepressants is mixed. Manual-based psychotherapies are not established as efficacious on their own in TRD, but offer significant symptomatic relief when added to conventional antidepressants. Digital therapeutics are under study and represent a potential future clinical vista in this population.