Faculty Bibliography

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.

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.

Background: The advent of new protocols to isolate specific exosomes enriched for markers expressed in the brain opened up the possibility to study in-vivo molecular mechanisms in the human brain. Prior studies showed an essential role of mitochondrial metabolism of acetyl-L-carnitine (LAC) in the rapid regulation of brain plasticity; and as a promising therapeutic target for mood and cognitive disorders. Here, we used exosomes to identify molecular mechanisms of mitochondrial metabolism in major depressive disorder (MDD), and how this emerging pathway is affected by childhood trauma.
Method(s): Samples from a well-characterized cohort of subjects with MDD(n=58) and age-/sex-matched healthy controls (HC,n=29) (Nasca et al,PNAS2018;Molecular Psychiatric2020). Participants completed the Childhood Trauma Questionnaire. We isolated brain-enriched exosomes using precipitation technology and magnetic beads conjuncted with L1-CAM, a marker highly expressed in the brain.
Result(s): Our new data show a decreased in-vivo expression of the main enzyme responsible for the synthesis of LAC in LCAM+ exosomes in subjects with MDD versus HC (p=0.02), independently of psychotropic drugs. Furthermore, we found the lowest expression of this enzyme in subjects with the highest rates of childhood trauma (emotional abuse; p=0.04;r=0.51). Lastly, using computational approaches we show how signatures of brain mitochondrial metabolism and peripheral LAC levels can define specific endophenotypes of MDD.
Conclusion(s): We report in-vivo evidence for brain mitochondrial metabolism as a possible biological determinant of MDD with a specific relationship with emotional abuse. This study suggests that advancing exosome applications can lead to develop novel mechanistic frameworks of regulation of brain plasticity in a variety of CNS disorders characterized by mitochondrial dysfunction. Funding Source: Hope for Depression Research Foundation Keywords: Exosomes, Mitochondria, Epigenetics, Early Life Stress, Acetylcarnitine
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Background: There is a need for new mechanistic models to develop objective tools that can aid the diagnosis of depression and monitor antidepressant responses. We and others showed that, in rodent models, administration of acetylcarnitine (LAC) leads to rapid and sustained antidepressant response by epigenetic regulation of glutamatergic function in key brain areas, such as the ventral dentate gyrus. Using exosomes, our group also showed decreased levels of the pivotal mitochondrial metabolite LAC in clinical endophenotypes of depression characterized by brain insulin resistance.
Method(s): Here, we used computational approaches and efficiently leveraged biological samples from a previous randomized, placebo-controlled trial with pioglitazone to determine the role of mitochondrial metabolism in relation to previously described individual predictors of antidepressant responses spanning cellular aging, metabolic function and childhood trauma.
Result(s): Our new findings showed the lowest LAC levels in those subjects with depression characterized by elevated BMI, high reported rates of emotional abuse and decreased leucocytes telomere length(n=47). Using prediction profilers, we also showed that decreased baseline LAC levels, elevated BMI and high reported rates of emotional abuse predict lack of antidepressant response to pioglitazone(p=0.02,r=0.86) with a stronger ability than each individual measure alone.
Conclusion(s): The findings of multidimensional signatures involved in the pathophysiology of depression and their role in predicting antidepressant outcomes provide a starting point for development of a mechanistic framework linking biological networks and environmental factors to clinical outcomes in pursuit of more effective personalized medicine strategies. Supported By: Supported by the NARSAD Young Investigator Award, Robertson Therapeutic Development Foundation and the Hope for Depression Research Foundation. Keywords: Epigenetics, Glutamate, Hippocampus, Mitochondria, Endophenotypes
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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.

OBJECTIVE:Major depressive disorder is the leading cause of disability worldwide. Yet, there remain significant challenges in predicting new cases of major depression and devising strategies to prevent the disorder. An important first step in this process is identifying risk factors for the incidence of major depression. There is accumulating biological evidence linking insulin resistance, another highly prevalent condition, and depressive disorders. The objectives of this study were to examine whether three surrogate measures of insulin resistance (high triglyceride-HDL [high-density lipoprotein] ratio; prediabetes, as indicated by fasting plasma glucose level; and high central adiposity, as measured by waist circumference) at the time of study enrollment were associated with an increased rate of incident major depressive disorder over a 9-year follow-up period and to assess whether the new onset of these surrogate measures during the first 2 years after study enrollment was predictive of incident major depressive disorder during the subsequent follow-up period. METHODS:The Netherlands Study of Depression and Anxiety (NESDA) is a multisite longitudinal study of the course and consequences of depressive and anxiety disorders in adults. The study population comprised 601 NESDA participants (18-65 years old) without a lifetime history of depression or anxiety disorders. The study's outcome was incident major depressive disorder, defined using DSM-IV criteria. Exposure measures included triglyceride-HDL ratio, fasting plasma glucose level, and waist circumference. RESULTS:Fourteen percent of the sample developed major depressive disorder during follow-up. Cox proportional hazards models indicated that higher triglyceride-HDL ratio was positively associated with an increased risk for incident major depression (hazard ratio=1.89, 95% CI=1.15, 3.11), as were higher fasting plasma glucose levels (hazard ratio=1.37, 95% CI=1.05, 1.77) and higher waist circumference (hazard ratio=1.11 95% CI=1.01, 1.21). The development of prediabetes in the 2-year period after study enrollment was positively associated with incident major depressive disorder (hazard ratio=2.66, 95% CI=1.13, 6.27). The development of high triglyceride-HDL ratio and high central adiposity (cut-point ≥100 cm) in the same period was not associated with incident major depression. CONCLUSIONS:Three surrogate measures of insulin resistance positively predicted incident major depressive disorder in a 9-year follow-up period among adults with no history of depression or anxiety disorder. In addition, the development of prediabetes between enrollment and the 2-year study visit was positively associated with incident major depressive disorder. These findings may have utility for evaluating the risk for the development of major depression among patients with insulin resistance or metabolic pathology.

OBJECTIVE:Endocannabinoids have been implicated in the pathophysiology of Major Depressive Disorder (MDD) and might represent potential targets for therapeutic intervention. Objectives of the study were: (1) to measure plasma levels of endocannabinoids in a group of antidepressant-free depressed outpatients; (2) to explore their relationship with the severity of depressive symptoms as subjectively perceived by the patients; and (3) to investigate the effect of the selective serotonin reuptake inhibitor escitalopram on endocannabinoid levels. METHODS:We measured plasma levels of the two major endocannabinoids, 2-arachidonoylglycerol (2-AG) and N-arachidonoylethanolamine (anadamide), in 12 drug-free outpatients diagnosed with MDD and in 12 matched healthy controls. In the patient group, endocannabinoids plasma levels were assessed at baseline and after 2 months of treatment with escitalopram. RESULTS:Baseline plasma levels of the two endocannabinoids did not differ between depressed patients and healthy controls. However, there was a significant inverse correlation between 2-arachidonoylglycerol levels and the severity of subjectively perceived depressive symptoms. Treatment with escitalopram did not change endocannabinoid levels in depressed patients, although it caused the expected improvement of depressive symptoms. CONCLUSIONS:Our results suggest that 2-arachidonylglycerol, the most abundant endocannabinoid in the central nervous system, might act to mitigate depressive symptoms, and raise the interesting possibility that 2-arachidonylglycerol and anandamide are differentially regulated in patients affected by MDD. Also, our data suggest but do not prove that the endocannabinoid system is not regulated by serotonergic transmission, at least in depressed patients.

The detection of copy number variations (CNVs) in whole-exome sequencing (WES) data is important, as CNVs may underlie a number of human genetic disorders. The recently developed HMZDelFinder algorithm can detect rare homozygous and hemizygous (HMZ) deletions in WES data more effectively than other widely used tools. Here, we present HMZDelFinder_opt, an approach that outperforms HMZDelFinder for the detection of HMZ deletions, including partial exon deletions in particular, in WES data from laboratory patient collections that were generated over time in different experimental conditions. We show that using an optimized reference control set of WES data, based on a PCA-derived Euclidean distance for coverage, strongly improves the detection of HMZ complete exon deletions both in real patients carrying validated disease-causing deletions and in simulated data. Furthermore, we develop a sliding window approach enabling HMZDelFinder_opt to identify HMZ partial deletions of exons that are undiscovered by HMZDelFinder. HMZDelFinder_opt is a timely and powerful approach for detecting HMZ deletions, particularly partial exon deletions, in WES data from inherently heterogeneous laboratory patient collections.

Emerging evidence suggests that hierarchical status provides vulnerability to develop stress-induced depression. Energy metabolic changes in the nucleus accumbens (NAc) were recently related to hierarchical status and vulnerability to develop depression-like behavior. Acetyl-L-carnitine (LAC), a mitochondria-boosting supplement, has shown promising antidepressant-like effects opening therapeutic opportunities for restoring energy balance in depressed patients. We investigated the metabolic impact in the NAc of antidepressant LAC treatment in chronically-stressed mice using ¹H-magnetic resonance spectroscopy (¹H-MRS). High rank, but not low rank, mice, as assessed with the tube test, showed behavioral vulnerability to stress, supporting a higher susceptibility of high social rank mice to develop depressive-like behaviors. High rank mice also showed reduced levels of several energy-related metabolites in the NAc that were counteracted by LAC treatment. Therefore, we reveal a metabolic signature in the NAc for antidepressant-like effects of LAC in vulnerable mice characterized by restoration of stress-induced neuroenergetics alterations and lipid function.

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.