Multidimensional predictors of antidepressant responses: Integrating mitochondrial, genetic, metabolic and environmental factors with clinical outcomes

The neuropsychopharmacology of acetyl-L-carnitine (LAC): basic, translational and therapeutic implications

Mitochondrial metabolism can contribute to nuclear histone acetylation among other epigenetic mechanisms. A central aspect of this signaling pathway is acetyl-L-carnitine (LAC), a pivotal mitochondrial metabolite best known for its role in fatty acid oxidation. Work from our and other groups suggested LAC as a novel epigenetic modulator of brain plasticity and a therapeutic target for clinical phenotypes of depression linked to childhood trauma. Aberrant mitochondrial metabolism of LAC has also been implicated in the pathophysiology of Alzheimer's disease. Furthermore, mitochondrial dysfunction is linked to other processes implicated in the pathophysiology of both major depressive disorders and Alzheimer's disease, such as oxidative stress, inflammation, and insulin resistance. In addition to the rapid epigenetic modulation of glutamatergic function, preclinical studies showed that boosting mitochondrial metabolism of LAC protects against oxidative stress, rapidly ameliorates insulin resistance, and reduces neuroinflammation by decreasing proinflammatory pathways such as NFkB in hippocampal and cortical neurons. These basic and translational neuroscience findings point to this mitochondrial signaling pathway as a potential target to identify novel mechanisms of brain plasticity and potential unique targets for therapeutic intervention targeted to specific clinical phenotypes.

Brain insulin signaling as a potential mediator of early life adversity effects on physical and mental health

In numerous brain structures, insulin signaling modulates the homeostatic processes, sensitivity to reward pathways, executive function, memory, and cognition. Through human studies and animal models, mounting evidence implicates central insulin signaling in the metabolic, physiological, and psychological consequences of early life adversity. In this review, we describe the consequences of early life adversity in the brain where insulin signaling is a key factor and how insulin may moderate the effects of adversity on psychiatric and cardio-metabolic health outcomes. Further understanding of how early life adversity and insulin signaling impact specific brain regions and mental and physical health outcomes will assist in prevention, diagnosis, and potential intervention following early life adversity.

Single Cell Transcriptome of Stress Vulnerability Network in mouse Prefrontal Cortex

Increased vulnerability to stress is a major risk factor for the manifestation of several mood disorders, including major depressive disorder (MDD). Despite the status of MDD as a significant donor to global disability, the complex integration of genetic and environmental factors that contribute to the behavioral display of such disorders has made a thorough understanding of related etiology elusive. Recent developments suggest that a brain-wide network approach is needed, taking into account the complex interplay of cell types spanning multiple brain regions. Single cell RNA-sequencing technologies can provide transcriptomic profiling at the single-cell level across heterogenous samples. Furthermore, we have previously used local field potential oscillations and machine learning to identify an electrical brain network that is indicative of a predisposed vulnerability state. Thus, this study combined single cell RNA-sequencing (scRNA-Seq) with electrical brain network measures of the stress-vulnerable state, providing a unique opportunity to access the relationship between stress network activity and transcriptomic changes within individual cell types. We found especially high numbers of differentially expressed genes between animals with high and low stress vulnerability brain network activity in astrocytes and glutamatergic neurons but we estimated that vulnerability network activity depends most on GABAergic neurons. High vulnerability network activity included upregulation of microglia and mitochondrial and metabolic pathways, while lower vulnerability involved synaptic regulation. Genes that were differentially regulated with vulnerability network activity significantly overlapped with genes identified as having significant SNPs by human GWAS for depression. Taken together, these data provide the gene expression architecture of a previously uncharacterized stress vulnerability brain state, enabling new understanding and intervention of predisposition to stress susceptibility.

Epigenetic embedding of childhood adversity: mitochondrial metabolism and neurobiology of stress-related CNS diseases

This invited article ad memoriam of Bruce McEwen discusses emerging epigenetic mechanisms underlying the long and winding road from adverse childhood experiences to adult physiology and brain functions. The conceptual framework that we pursue suggest multidimensional biological pathways for the rapid regulation of neuroplasticity that utilize rapid non-genomic mechanisms of epigenetic programming of gene expression and modulation of metabolic function via mitochondrial metabolism. The current article also highlights how applying computational tools can foster the translation of basic neuroscience discoveries for the development of novel treatment models for mental illnesses, such as depression to slow the clinical manifestation of Alzheimer's disease. Citing an expression that many of us heard from Bruce, while "It is not possible to roll back the clock," deeper understanding of the biological pathways and mechanisms through which stress produces a lifelong vulnerability to altered mitochondrial metabolism can provide a path for compensatory neuroplasticity. The newest findings emerging from this mechanistic framework are among the latest topics we had the good fortune to discuss with Bruce the day before his sudden illness when walking to a restaurant in a surprisingly warm evening that preluded the snowstorm on December 18th, 2019. With this article, we wish to celebrate Bruce's untouched love for Neuroscience.

Sodium-Glucose Cotransporter-2 Inhibitors in Depression

ABSTRACT

Novel treatment strategies that refract existing treatment algorithms for depressive disorders are being sought. Abnormal brain bioenergetic metabolism may represent an alternative, therapeutically targetable neurobiological basis for depression. A growing body of research points to endogenous ketones as candidate neuroprotective metabolites with the potential to enhance brain bioenergetics and improve mood. Sodium-glucose cotransporter-2 (SGLT2) inhibitors, originally approved for the treatment of diabetes, induce ketogenesis and are associated with mood improvement in population-based studies. In this column, we highlight the rationale for the hypothesis that ketogenesis induced by SGLT2 inhibitors may be an effective treatment for depressive disorders.

Explanatory model of symptoms of stress, anxiety and depression in the general population: Cross-sectional study during the COVID-19 pandemic

COVID-19 pandemic has had a great impact worldwide, specially affecting mental health and has undoubtedly taken part in human behaviour modification, increasing global health burden and with stress, anxiety and depression being the main contributors to this load. Because of the importance of this issue, the objective of this study was the creation of an explanatory model for the causal relationship of the main psychological variables: stress, anxiety and depression in the COVID-19 pandemic context. A cross-sectional study was carried out with a sample of 709 volunteers, sociodemographic variables and psychological symptoms were measured through a virtual DASS-21 questionnaire, during the COVID-19 pandemic, dated from November 2 to 6, 2020. A structural equation model using the weighted least squares means and the adjusted variance was employed for the creation and adjustment of the explanatory relational model. The results showed the presence of stress, anxiety and depression symptoms among the general population. The model showed an adequate fit (CFI = 0.94; TLI = 0.94; RMSEA = 0.06; P = 0.000) and was able to explain more than 80% of depressive symptoms (R²  = 0.86) and more than 70% of anxiety symptoms (R²  = 0.72), in addition to showing a unidirectional causal relationship of long-term stress on anxiety, and anxiety on depressive symptoms, showing a linked behaviour of the same, in the adjusted model. It was also outlined that this model was characterized by being expressed mainly in women, with lower quality of sleep and at a younger age.

BDNF as a Mediator of Antidepressant Response: Recent Advances and Lifestyle Interactions

Conventional antidepressants are widely employed in several psychiatric and neurologic disorders, yet the mechanisms underlying their delayed and partial therapeutic effects are only gradually being understood. This narrative review provides an up-to-date overview of the interplay between antidepressant treatment and Brain-Derived Neurotrophic Factor (BDNF) signaling. In addition, the impact of nutritional, environmental and physiological factors on BDNF and the antidepressant response is outlined. This review underlines the necessity to include information on lifestyle choices in testing and developing antidepressant treatments in the future.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.