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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Many methods for rare variant association studies require permutations to assess the significance of tests. Standard permutations assume that all individuals are exchangeable and do not take population stratification (PS), a known confounding factor in genetic studies, into account. We propose a novel strategy, LocPerm, in which individual phenotypes are permuted only with their closest ancestry-based neighbors. We performed a simulation study, focusing on small samples, to evaluate and compare LocPerm with standard permutations and classical adjustment on first principal components. Under the null hypothesis, LocPerm was the only method providing an acceptable type I error, regardless of sample size and level of stratification. The power of LocPerm was similar to that of standard permutation in the absence of PS, and remained stable in different PS scenarios. We conclude that LocPerm is a method of choice for taking PS and/or small sample size into account in rare variant association studies.

Autosomal dominant (AD) NFKB1 deficiency is thought to be the most common genetic etiology of common variable immunodeficiency (CVID). However, the causal link between NFKB1 variants and CVID has not been demonstrated experimentally and genetically, and there has been insufficient biochemical characterization and enrichment analysis. We show that the cotransfection of NFKB1-deficient HEK293T cells (lacking both p105 and its cleaved form p50) with a κB reporter, NFKB1/p105, and a homodimerization-defective RELA/p65 mutant results in p50:p65 heterodimer-dependent and p65:p65 homodimer-independent transcriptional activation. We found that 59 of the 90 variants in patients with CVID or related conditions were loss of function or hypomorphic. By contrast, 258 of 260 variants in the general population or patients with unrelated conditions were neutral. None of the deleterious variants displayed negative dominance. The enrichment in deleterious NFKB1 variants of patients with CVID was selective and highly significant (P = 2.78 × 10-15). NFKB1 variants disrupting NFKB1/p50 transcriptional activity thus underlie AD CVID by haploinsufficiency, whereas neutral variants in this assay should not be considered causal.

Population stratification is a confounder of genetic association studies. In analyses of rare variants, corrections based on principal components (PCs) and linear mixed models (LMMs) yield conflicting conclusions. Studies evaluating these approaches generally focused on limited types of structure and large sample sizes. We investigated the properties of several correction methods through a large simulation study using real exome data, and several within- and between-continent stratification scenarios. We considered different sample sizes, with situations including as few as 50 cases, to account for the analysis of rare disorders. Large samples showed that accounting for stratification was more difficult with a continental than with a worldwide structure. When considering a sample of 50 cases, an inflation of type-I-errors was observed with PCs for small numbers of controls (≤ 100), and with LMMs for large numbers of controls (≥ 1000). We also tested a novel local permutation method (LocPerm), which maintained a correct type-I-error in all situations. Powers were equivalent for all approaches pointing out that the key issue is to properly control type-I-errors. Finally, we found that power of analyses including small numbers of cases can be increased, by adding a large panel of external controls, provided an appropriate stratification correction was used.

Autosomal inborn errors of type I IFN immunity and autoantibodies against these cytokines underlie at least 10% of critical COVID-19 pneumonia cases. We report very rare, biochemically deleterious X-linked TLR7 variants in 16 unrelated male individuals aged 7 to 71 years (mean: 36.7 years) from a cohort of 1,202 male patients aged 0.5 to 99 years (mean: 52.9 years) with unexplained critical COVID-19 pneumonia. None of the 331 asymptomatically or mildly infected male individuals aged 1.3 to 102 years (mean: 38.7 years) tested carry such TLR7 variants (p = 3.5 × 10^-5). The phenotypes of five hemizygous relatives of index cases infected with SARS-CoV-2 include asymptomatic or mild infection (n=2, 5 and 38 years), or moderate (n=1, 5 years), severe (n=1, 27 years), or critical (n=1, 29 years) pneumonia. Two boys (aged 7 and 12 years) from a cohort of 262 male patients with severe COVID-19 pneumonia (mean: 51.0 years) are hemizygous for a deleterious TLR7 variant. The cumulative allele frequency for deleterious TLR7 variants in the male general population is < 6.5x10^-4 We also show that blood B cell lines and myeloid cell subsets from the patients do not respond to TLR7 stimulation, a phenotype rescued by wild-type TLR7 The patients' blood plasmacytoid dendritic cells (pDCs) produce low levels of type I IFNs in response to SARS-CoV-2. Overall, X-linked recessive TLR7 deficiency is a highly penetrant genetic etiology of critical COVID-19 pneumonia, in about 1.8% of male patients below the age of 60 years. Human TLR7 and pDCs are essential for protective type I IFN immunity against SARS-CoV-2 in the respiratory tract.

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.