The Genetics of MDD – A Review of Challenges and Opportunities

Heutink, Marianna R Peter

doi:10.4172/2167-1044.1000150

Cited by 5 publications

(2 citation statements)

References 57 publications

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“…Indeed, even though a clear biological function of the gene may exist, this information may not always be complete, leading to incorrect assumptions about these functions and therefore not always reflecting in an associated outcome. Moreover, these studies do not consider indirect associations as the interactions between gene and environment (Verbeek et al, 2014). The advent of genome-wide genotyping techniques with DNA microchip technology gave the opportunity to conduct genome-wide associations studies (GWASs) with the aim to identify risk factors of depression onset independently from the starting hypotheses and using large sets of samples.…”

Section: Introductionmentioning

confidence: 99%

The Emerging Role of SGK1 (Serum- and Glucocorticoid-Regulated Kinase 1) in Major Depressive Disorder: Hypothesis and Mechanisms

et al. 2020

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Major depressive disorder (MDD) is a heterogeneous psychiatric disease characterized by persistent low mood, diminished interests, and impaired cognitive and social functions. The multifactorial etiology of MDD is still largely unknown because of the complex genetic and environmental interactions involved. Therefore, no established mechanism can explain all the aspects of the disease. In this light, an extensive research about the pathophysiology of MDD has been carried out. Several pathogenic hypotheses, such as monoamines deficiency and neurobiological alterations in the stress-responsive system, including the hypothalamic-pituitary-adrenal (HPA) axis and the immune system, have been proposed for MDD. Over time, remarkable studies, mainly on preclinical rodent models, linked the serum-and glucocorticoid-regulated kinase 1 (SGK1) to the main features of MDD. SGK1 is a serine/threonine kinase belonging to the AGK Kinase family. SGK1 is ubiquitously expressed, which plays a pivotal role in the hormonal regulation of several ion channels, carriers, pumps, and transcription factors or regulators. SGK1 expression is modulated by cell stress and hormones, including gluco-and mineralocorticoids. Compelling evidence suggests that increased SGK1 expression or function is related to the pathogenic stress hypothesis of major depression. Therefore, the first part of the present review highlights the putative role of SGK1 as a critical mediator in the dysregulation of the HPA axis, observed under chronic stress conditions, and its controversial role in the neuroinflammation as well. The second part depicts the negative regulation exerted by SGK1 in the expression of both the brain-derived neurotrophic factor (BDNF) and the vascular endothelial growth factor (VEGF), resulting in an anti-neurogenic activity. Finally, the review focuses on the antidepressant-like effects of anti-oxidative nutraceuticals in several preclinical model of depression, resulting from the restoration of the physiological expression and/or activity of SGK1, which leads to an increase in neurogenesis. In summary, the purpose of this review is a systematic analysis of literature depicting SGK1 as molecular junction of the complex mechanisms underlying the MDD in an effort to suggest the kinase as a potential biomarker and strategic target in modern molecular antidepressant therapy.

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Section: Introductionmentioning

confidence: 99%

The Emerging Role of SGK1 (Serum- and Glucocorticoid-Regulated Kinase 1) in Major Depressive Disorder: Hypothesis and Mechanisms

et al. 2020

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“…With the rapidly growing number of people with depression worldwide, it is becoming one of the most severe disorders for humankind. In recent years, it has been established that depression affects a significant portion of the population, with a lifetime prevalence rate of 15% ( 13 ), endangering people's physical health. It has adversely affected at least 322 million people worldwide and ~4.4% of the world's population, with an incidence rate higher than the rate of global population growth ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

et al. 2023

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IntroductionDepression is a prevalent disorder worldwide, with potentially severe implications. It contributes significantly to an increased risk of diseases associated with multiple risk factors. Early accurate diagnosis of depressive symptoms is a critical first step toward management, intervention, and prevention. Various nutritional and dietary compounds have been suggested to be involved in the onset, maintenance, and severity of depressive disorders. Despite the challenges to better understanding the association between nutritional risk factors and the occurrence of depression, assessing the interplay of these markers through supervised machine learning remains to be fully explored.MethodsThis study aimed to determine the ability of machine learning-based decision support methods to identify the presence of depression using publicly available health data from the Korean National Health and Nutrition Examination Survey. Two exploration techniques, namely, uniform manifold approximation and projection and Pearson correlation, were performed for explanatory analysis among datasets. A grid search optimization with cross-validation was performed to fine-tune the models for classifying depression with the highest accuracy. Several performance measures, including accuracy, precision, recall, F1 score, confusion matrix, areas under the precision-recall and receiver operating characteristic curves, and calibration plot, were used to compare classifier performances. We further investigated the importance of the features provided: visualized interpretation using ELI5, partial dependence plots, and local interpretable using model-agnostic explanations and Shapley additive explanation for the prediction at both the population and individual levels.ResultsThe best model achieved an accuracy of 86.18% for XGBoost and an area under the curve of 84.96% for the random forest model in original dataset and the XGBoost algorithm with an accuracy of 86.02% and an area under the curve of 85.34% in the quantile-based dataset. The explainable results revealed a complementary observation of the relative changes in feature values, and, thus, the importance of emergent depression risks could be identified.DiscussionThe strength of our approach is the large sample size used for training with a fine-tuned model. The machine learning-based analysis showed that the hyper-tuned model has empirically higher accuracy in classifying patients with depressive disorder, as evidenced by the set of interpretable experiments, and can be an effective solution for disease control.

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Exploring the Hypothetical Role ofBacteroidesSpecies in Depression Progression: Insights from Metagenomic Analysis

Li,

Sun,

Yang

et al. 2024

Preprint

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Depression, a psychiatric disorder with significant morbidity and mortality, has a complex etiology. Recent advances in microbiome research have highlighted the potential role of gut microbiota in depression pathogenesis. This study utilized shotgun metagenomic sequencing to compare the fecal microbiota of 28 depression patients and 26 healthy individuals. Significant differences in gut microbiota composition were observed between the two groups. We generated 350 non-redundant high-quality metagenome-assembled Genomes (MAGs) by binning and conducted comparisons between the depression and control groups. Notably, we found that the MAGs enriched in people with depression mostly belonged toBactendicating a close link betweenBacteroidesabundance and the development of depression, suggesting thatBacteroidesmight be a potential culprit for deroides, ipression. In the depression group, we found that the module of nitric oxide synthesis was remarkably enriched, and allBacteroidesMAGs were annotated to nitric oxide synthase, suggesting that increased levels of Bacteroides may contribute to elevated nitric oxide synthesis. Specifically, the mean relative abundance about the genomes ofBacteroides xylanisolvens,Bacteroides caccae,Bacteroides fragilis,Bacteroides stercorisandBacteroides ovatusshowed strong discriminatory power in distinguishing depressed patients from healthy individuals (AUC=0.834). This research shed light on the potential role of gut microbiota in depression and highlights specific metabolic pathways and microbial markers for further investigation.

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The Genetics of MDD – A Review of Challenges and Opportunities

Cited by 5 publications

References 57 publications

The Emerging Role of SGK1 (Serum- and Glucocorticoid-Regulated Kinase 1) in Major Depressive Disorder: Hypothesis and Mechanisms

The Emerging Role of SGK1 (Serum- and Glucocorticoid-Regulated Kinase 1) in Major Depressive Disorder: Hypothesis and Mechanisms

Evaluation of nutritional status and clinical depression classification using an explainable machine learning method

Exploring the Hypothetical Role ofBacteroidesSpecies in Depression Progression: Insights from Metagenomic Analysis

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