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Major depressive disorder and energy metabolism: diving deep with omics technology

Omics technologies have revealed molecular insights into the complex relationship between impaired energy metabolism and major depressive disorder.

Major Depressive Disorder (MDD) is a severe psychiatric disorder associated with intense sadness, lack of motivation and early mortality. Despite a rise in MDD incidence worldwide, its complex pathogenesis remains unclear. Currently, growing evidence supports the central role of impaired energy metabolism in MDD occurrence and progression. Patients generally exhibit imbalances in metabolic homeostasis in the mitochondria and brain. As mitochondrial dysfunction typically results in reduced ATP levels, this may contribute to the increased fatigue seen in patients.

As reviewed recently, omics technologies have enhanced our understanding of MDD pathogenesis on multiple levels. In the past 5 years, omics approaches have confirmed that energy metabolism is impaired during MDD. Numerous MDD biomarkers have now been identified at the gene, protein and metabolite levels. Applying multiple omics technologies facilitates biomedical research, as it generates a more detailed and complete picture of complex disorders. Ultimately, these findings may aid in therapeutic discovery.

Impaired energy metabolism in MDD

Since multiple genes are thought to influence MDD onset and development, genomics and transcriptomics have provided DNA-level insights into disease mechanisms. Whole-genome sequencing has revealed elevated mitochondrial genome copy numbers and sequences in MDD patients. This supports the role of mitochondrial metabolic dysfunction in MDD. Meanwhile, altered glycolytic gene expression has been observed in the brains of mouse models, implicating perturbations in glucose metabolism in MDD.

Beyond the genome, proteomics and metabolomics have generated functional insights into MDD progression. Proteomic studies have reported abnormal expression of enzymes, such as GAPDH and VGLUT1, in the prefrontal cortex and hippocampus of MDD rat models compared to controls. These enzymes are related to glycolysis, the tricarboxylic acid (TCA) cycle and oxidative phosphorylation metabolic pathways. Similarly, metabolomics has revealed MDD-associated changes in the metabolite profiles of both animal models and human patients. Many of the altered metabolites, especially branched-chain amino acids and fatty acids, are involved in the TCA cycle.  

Prospects of omics technologies in MDD research

Omics technologies have revealed changes in genes, proteins and metabolites involved in energy metabolism during MDD. These may represent informative biomarkers for novel treatment strategies. However, the specific mechanisms underlying the relationship between energy metabolism and MDD remain poorly understood. These mechanisms could potentially be revealed by multi-omics approaches. By combining transcriptomics and metabolomics, one study revealed altered gene expression and metabolite profiles in MDD mice. These changes were tightly linked to mitochondrial lipid metabolism and oxidative phosphorylation. In the future, integrating omics technologies may enable comprehensive analyses of the interplay between the genome, transcriptome, proteome and metabolome in MDD.

Currently, the use of omics technology for MDD biomarker discovery faces different challenges. For one, the metabolome is highly susceptible to genetic, physiological and environmental perturbations. Care must be taken in removing confounding variables during data analysis to ensure biomarkers are identified accurately and specifically. Additionally, putative biomarkers require clinical validation before therapeutic application, particularly if they were first identified in animal models.

Developments in omics technology can be expected to continue at a rapid pace. It is hope that this will enrich our understanding of energy metabolism in MDD and illuminate potential therapeutic biomarkers. For patients, the wider use of omics technologies may represent a light at the end of the tunnel.

Image credit: FreePik pch.vector

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Depression / Multi-omics

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