An emerging epigenetic framework of systemic and central mechanisms underlying stress-related disorders

L-carnitine and Acetyl-L Carnitine: A Possibility for Treating Alterations Induced by Obesity in the Central Nervous System

Excessive consumption of nutrients, as well as obesity, leads to an inflammatory process, especially in adipose tissue. This inflammation reaches the systemic level and, subsequently, the central nervous system (CNS), which can lead to oxidative stress and mitochondrial dysfunction, resulting in brain damage. Thus, adequate treatment for obesity is necessary, including lifestyle changes (diet adequation and physical activity) and pharmacotherapy. However, these drugs can adversely affect the individual's health. In this sense, searching for new therapeutic alternatives for reestablishing metabolic homeostasis is necessary. L-carnitine (LC) and acetyl-L-carnitine (LAC) have neuroprotective effects against oxidative stress and mitochondrial dysfunction in several conditions, including obesity. Therefore, this study aimed to conduct a narrative review of the literature on the effect of LC and LAC on brain damage caused by obesity, in particular, on mitochondrial dysfunction and oxidative stress. Overall, these findings highlight that LC and LAC may be a promising treatment for recovering REDOX status and mitochondrial dysfunction in the CNS in obesity. Future work should focus on better elucidating the molecular mechanisms behind this treatment.

Identifying circulating biomarkers for major depressive disorder

Objective

To date, the current diagnosis of major depressive disorder (MDD) still depends on clinical symptomatologic criteria, misdiagnosis and ineffective treatment are common. The study aimed to explore circulating biomarkers for MDD diagnosis.

Methods

A high-throughput antibody array technology was utilized to detect 440 circulating cytokines in eight MDD patients and eight age-and gender-matched healthy controls. LASSO regression was conducted for MDD-related characteristic proteins selection. Enzyme-linked immunosorbent assay (ELISA) was used to validate the characteristic proteins in 40 MDD patients and 40 healthy controls. Receiver operating characteristic (ROC) curve was employed to evaluate the diagnostic values of characteristic proteins for discriminating MDD patients from healthy controls. Correlations between the levels of characteristic proteins and depression severity (HAMD-17 scores) were evaluated using linear regression.

Results

The levels of 59 proteins were found aberrant in MDD patients compared with healthy controls. LASSO regression found six MDD-related characteristic proteins including insulin, CD40L, CD155, Lipocalin-2, HGF and LIGHT. ROC curve analysis showed that the area under curve (AUC) values of six characteristic proteins were more than 0.85 in discriminating patients with MDD from healthy controls. Furthermore, significant relationship was found between the levels of insulin, CD155, Lipocalin-2, HGF, LIGHT and HAMD-17 scores in MDD group.

Conclusion

These results suggested that six characteristic proteins screened from 59 proteins differential in MDD may hold promise as diagnostic biomarkers in discriminating patients with MDD. Among six characteristic proteins, insulin, CD155, Lipocalin-2, HGF and LIGHT might be useful to estimate the severity of depressive symptoms.

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.

Regional gene expression signatures are associated with sex-specific functional connectivity changes in depression

The neural substrates of depression may differ in men and women, but the underlying mechanisms are incompletely understood. Here, we show that depression is associated with sex-specific patterns of abnormal functional connectivity in the default mode network and in five regions of interest with sexually dimorphic transcriptional effects. Regional differences in gene expression in two independent datasets explained the neuroanatomical distribution of abnormal connectivity. These gene sets varied by sex and were strongly enriched for genes implicated in depression, synapse function, immune signaling, and neurodevelopment. In an independent sample, we confirmed the prediction that individual differences in default mode network connectivity are explained by inferred brain expression levels for six depression-related genes, including PCDH8, a brain-specific protocadherin integral membrane protein implicated in activity-related synaptic reorganization. Together, our results delineate both shared and sex-specific changes in the organization of depression-related functional networks, with implications for biomarker development and fMRI-guided therapeutic neuromodulation.

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

Major depressive disorder (MDD) is a primary psychiatric illness worldwide; there is a dearth of new mechanistic models for the development of better therapeutic strategies. Although we continue to discover individual biological factors, a major challenge is the identification of integrated, multidimensional traits underlying the complex heterogeneity of depression and treatment outcomes. Here, we set out to ascertain the emergence of the novel mitochondrial mediator of epigenetic function acetyl-L-carnitine (LAC) in relation to previously described individual predictors of antidepressant responses to the insulin-sensitizing agent pioglitazone. Herein, we report that i) subjects with MDD and shorter leukocyte telomere length (LTL) show decreased levels of LAC, increased BMI, and a history of specific types of childhood trauma; and that ii) these multidimensional factors spanning mitochondrial metabolism, cellular aging, metabolic function, and childhood trauma provide more detailed signatures to predict longitudinal changes in depression severity in response to pioglitazone than individual factors. The findings of multidimensional signatures involved in the pathophysiology of depression and their role in predicting treatment outcomes provide a starting point for the development of a mechanistic framework linking biological networks and environmental factors to clinical outcomes in pursuit of personalized medicine strategies to effectively treat MDD.

Insulin receptor substrate in brain-enriched exosomes in subjects with major depression: on the path of creation of biosignatures of central insulin resistance

Insulin signaling is critical for neuroplasticity, cerebral metabolism as well as for systemic energy metabolism. In rodent studies, impaired brain insulin signaling with resultant insulin resistance (IR) modulates synaptic plasticity and the corresponding behavioral functions. Despite discoveries of central actions of insulin, in vivo molecular mechanisms of brain IR until recently have proven difficult to study in the human brain. In the current study, we leveraged recent technological advances in molecular biology and herein report an increased number of exosomes enriched for L1CAM, a marker predominantly expressed in the brain, in subjects with major depressive disorder (MDD) as compared with age- and sex-matched healthy controls (HC). We also report increased concentration of the insulin receptor substrate-1 (IRS-1) in L1CAM⁺ exosomes in subjects with MDD as compared with age- and sex-matched HC. We found a relationship between expression of IRS-1 in L1CAM⁺ exosomes and systemic IR as assessed by homeostatic model assessment of IR in HC, but not in subjects with MDD. The increased IRS-1 levels in L1CAM⁺ exosomes were greater in subjects with MDD and were associated with suicidality and anhedonia. Finally, our data suggested sex differences in serine-312 phosphorylation of IRS-1 in L1CAM⁺ exosomes in subjects with MDD. These findings provide a starting point for creating mechanistic framework of brain IR in further development of personalized medicine strategies to effectively treat MDD.

Secreted Extracellular Vesicle Molecular Cargo as a Novel Liquid Biopsy Diagnostics of Central Nervous System Diseases

Secreted extracellular vesicles (EVs) are heterogeneous cell-derived membranous granules which carry a large diversity of molecules and participate in intercellular communication by transferring these molecules to target cells by endocytosis. In the last decade, EVs’ role in several pathological conditions, from etiology to disease progression or therapy evasion, has been consolidated, including in central nervous system (CNS)-related disorders. For this review, we performed a systematic search of original works published, reporting the presence of molecular components expressed in the CNS via EVs, which have been purified from plasma, serum or cerebrospinal fluid. Our aim is to provide a list of molecular EV components that have been identified from both nonpathological conditions and the most common CNS-related disorders. We discuss the methods used to isolate and enrich EVs from specific CNS-cells and the relevance of its components in each disease context.

Childhood trauma and insulin resistance in patients suffering from depressive disorders

OBJECTIVE

Insulin resistance (IR) is a metabolic dysfunction often co-morbid with major depressive disorder (MDD). The paths to development of MDD remain largely unspecified, highlighting a need for identification of risk factors. Here, we tested whether specific subscales of childhood trauma as well as family history of type-2 diabetes (Fam-Hx-Dm2) are risk factors for development of metabolic dysfunction and severity of depressive symptoms.

RESEARCH DESIGN AND METHODS

We used a sample of 45 adults suffering from MDD that was well-characterized for insulin resistance and sensitivity as assessed by measures of fasting plasma glucose (FPG) plasma insulin (FPI) levels, body mass index (BMI), weight, homeostasis model assessment of insulin sensitivity (HOMA), Matsuda index as well as both glucose and insulin responses to oral glucose challenges. Severity of depressive symptoms was assessed with the Hamilton Depression Rating Scale (HDRS-21). Physical, sexual and emotional abuse as well as physical and emotional neglect were assessed with the Childhood Trauma Questionnaire. First- or second-degree relatives with type-2 diabetes defined fam-Hx-DM2.

RESULTS

Individuals reporting higher rates of emotional abuse were more likely to have greater IR as showed by elevated FPI levels and HOMA. No association was found with any of the other subscales of childhood trauma (e.g., physical abuse). Similarly, Fam-Hx-DM2 was associated with greater degree of IR as shown by elevated FPI, HOMA, but also FPG, weight and BMI. Moreover, we report a relationship and interaction between Fam-Hx-DM2 and emotional abuse on severity of depressive symptoms. Specifically, emotional abuse and Fam-HX-DM2 predicted severity of depressive symptoms at HDRS-21. Also, severity of depressive symptoms was greater with higher reported rates of emotional abuse but only in patients with negative Fam-Hx-Dm2. Individuals reporting higher emotional abuse and negative Fam-Hx-Dm2 also showed higher FPG levels. Conversely, individuals reporting higher emotional abuse and positive Fam-Hx-Dm2 showed higher FPI levels. This data suggest that Fam-Hx-Dm2 may define two different metabolic endophenotypes.

CONCLUSIONS

Our findings suggest that Fam-HX-DM2 and emotional abuse represent separate risk factors for developing metabolic dysfunction (i.e.: IR) in patients suffering from MDD, and that the effects of emotional abuse on psychiatric illness may depend upon the personal characteristics, including Fam-Hx-DM2.