Biology subjects

Turecki, G.

Publications and source records attributed to Turecki, G..

47 records · Page 3Linked to original sources

bioRxiv2020

Increased functional coupling of the mu opioid receptor in the anterior insula of depressed individuals

The mu opioid receptor (MOR) is a G protein-coupled receptor that plays an essential role in reward and hedonic processes, and that has been implicated in disorders such as depression and addiction. Over the last decade, several brain imaging studies in depressed patients have consistently found that dysregulation of MOR function occurs in particular in the anterior insular cortex, an important brain site for the perception of internal states and emotional regulation. To investigate molecular mechanisms that may underlie these effects, here we assessed genetic polymorphisms, expression, and functional G-protein coupling of MOR in a large post-mortem cohort (N=95) composed of depressed individuals who died by suicide, and healthy controls. Results indicated that depression, but not comorbid substance use disorder or acute opiate consumption, was associated with increased MOR activity. This effect was partly explained by a specific increase in expression of the inhibitory alpha G-protein subunit GNAI2. Consistent with previous neuroimaging studies, our findings support the notion that enhanced endogenous opioidergic tone in the anterior insula may buffer negative affective states in depressed individuals, a mechanism that could potentially contribute to the antidepressant efficacy of emerging opioid-based medications.

neuroscience↗

bioRxiv2020

Cocaine-induced neuron subtype mitochondrial dynamics through Egr3 transcriptional regulation

Mitochondrial function is required for brain energy homeostasis and neuroadaptation. Recent studies demonstrate that cocaine affects mitochondrial dynamics and morphological characteristics within the nucleus accumbens (NAc). Further, mitochondria are differentially regulated by cocaine in dopamine receptor-1 containing medium spiny neurons (D1-MSNs) vs dopamine receptor-2 (D2)-MSNs. However, there is little understanding into cocaine-induced transcriptional mechanisms and their role in regulating mitochondrial processes. Here, we demonstrate that cocaine enhances binding of the transcription factor, early growth response factor 3 (Egr3), to nuclear genes involved in mitochondrial function and dynamics. Moreover, cocaine exposure regulates mRNA of these mitochondria-associated nuclear genes in both contingent or noncontingent cocaine administration and in both rodent models and human postmortem tissue. Interestingly, several mitochondrial nuclear genes showed distinct profiles of expression in D1-MSNs vs D2-MSNs, with cocaine exposure generally increasing mitochondrial-associated nuclear gene expression in D1-MSNs vs suppression in D2-MSNs. We further show that blunting Egr3 expression in D1-MSNs blocks cocaine-enhancement of the mitochondrial-associated transcriptional coactivator, peroxisome proliferator-activated receptor gamma coactivator (PGC1), and the mitochondrial fission molecule, dynamin related protein 1 (Drp1). Finally, reduction of D1-MSN Egr3 expression attenuates cocaine-induced enhancement of small-sized mitochondria, causally demonstrating that Egr3 regulates mitochondrial morphological adaptations. Collectively, these studies demonstrate cocaine exposure impacts mitochondrial dynamics and morphology by Egr3 transcriptional regulation of mitochondria-related nuclear gene transcripts; indicating roles for these molecular mechanisms in neuronal function and plasticity occurring with cocaine exposure.

neuroscience↗

bioRxiv2020

Deep feature extraction of single-cell transcriptomes by generative adversarial network

MotivationSingle-cell RNA-sequencing (scRNA-seq) has opened the opportunities to dissect the heterogeneous cellular composition and interrogate the cell-type-specific gene expression patterns across diverse conditions. However, batch effects such as laboratory conditions and individual-variability hinder their usage in cross-condition design. ResultsWe present single-cell Generative Adversarial Network (scGAN). Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network, which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects. We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder. AvailabilityThe code is available at https://github.com/li-lab-mcgill/singlecell-deepfeature Contactyueli@cs.mcgill.ca

bioinformatics↗

bioRxiv2019

Restriction Enzyme Based Enriched L1Hs sequencing (REBELseq)

Long interspersed element-1 retrotransposons (LINE-1 or L1) are ~6 kb mobile DNA elements implicated in the origins of many Mendelian and complex diseases. The actively retrotransposing L1s are mostly limited to the L1 human specific Ta subfamily. In this manuscript, we present REBELseq as a method for the construction of differentially amplified next-generation sequencing libraries and bioinformatic identification of Ta subfamily long interspersed element-1 human specific elements. REBELseq was performed on DNA isolated from NeuN+ neuronal nuclei from postmortem brain samples of 177 individuals and empirically-driven bioinformatic and experimental cutoffs were established. REBELseq reliably identified both known and novel Ta subfamily L1 insertions distributed throughout the genome. Differences in the proportion of individuals possessing a given reference or non-reference retrotransposon insertion were identified. We conclude that REBELseq is an unbiased, whole genome approach to the amplification and detection of Ta subfamily L1 retrotransposons.

genetics↗

bioRxiv2019

Differential Treatment Benefit Prediction For Treatment Selection in Depression: A Deep Learning Analysis of STAR*D and CO-MED Data

BackgroundDepression affects one in nine people, but treatment response rates remain low. There is significant potential in the use of computational modelling techniques to predict individual patient responses and thus provide more personalized treatment. Deep learning is a promising computational technique that can be used for differential treatment selection based on predicted remission probability.\n\nMethodsUsing STAR*D and CO-MED trial data, we employed deep neural networks to predict remission after feature selection. Differential treatment benefit was estimated in terms of improvement of population remission rates after application of the model for treatment selection using both naive and conservative approaches. The naive approach assessed population remission rate in five sets of 200 patients held apart from the training set; the conservative approach used bootstrapping for sample generation and focused on population remission rate for patients who actually received the drug predicted by the model compared to the general population.\n\nResultsOur deep learning model predicted remission in a pooled CO-MED/STAR*D dataset (including four treatments) with an AUC of 0.69 using 17 input features. Our naive analysis showed an improvement of remission of over 30% (from a 34.33% population remission rate to 46.12%). Our conservative analysis showed a 7.2% improvement in population remission rate (p= 0.01, C.I. 2.48% {+/-} .5%).\n\nConclusionOur model serves as proof-of-concept that deep learning has utility in differential prediction of antidepressant response when selecting from a number of treatment options. These models may have significant real-world clinical implications.

neuroscience↗

bioRxiv2019

Evidence of decreased gap junction coupling between astrocytes and oligodendrocytes in the anterior cingulate cortex of depressed suicides

Glial dysfunction is a major feature in the pathophysiology of mood disorders. While altered astrocyte (AS) and oligodendrocyte-lineage (OL) cell functions have been associated with depression, the crosstalk between these two major glial cell types has never been assessed in that context. AS are potent regulators of OL cells and myelination, in part through gap junction-mediated intercellular communication made possible by the heterotypic coupling of AS-specific (Cx30 and Cx43) and OL-specific (Cx32 and Cx47) connexins, allowing cytosolic transport and metabolic support to OL cells. Because changes in the expression of AS-specific connexins have been previously reported in the brain of depressed individuals, this study aimed at addressing the integrity of AS-OL coupling in the anterior cingulate cortex (ACC) of depressed suicides. Using immunofluorescence and confocal imaging, we characterized the distribution of the AS-specific Cx30 in the ACC, and mapped its expression onto oligodendrocyte somas and myelinated axons as well as brain vasculature in post-mortem brain samples from depressed suicides (N=48) and matched controls (N=23). The differential gene expression of key components of the gap junction nexus was also screened through RNA-sequencing dataset previously generated by our group, and validated by quantitative real-time PCR. Our results indicate that Cx30 expression mapping to OL cells is selectively decreased in depressed suicides, an effect that was associated with decreased expression of OL-specific connexins Cx32 and Cx47, as well as the downregulation of major connexin-interacting proteins essential for the scaffolding, trafficking and function of gap junction channels. These results provide a first evidence of impaired gap junction mediated communication between astrocytes and oligodendrocytes in the ACC of individuals with mood disorders. These changes in glial coupling are likely to have significant impact on brain function, and may contribute to the altered OL function previously reported in this brain region.

neuroscience↗

bioRxiv2018

Non-CG methylation and multiple epigenetic layers associate child abuse with immune and small GTPase dysregulation

Early-life adversity (ELA) is a major predictor of psychopathology, and is thought to increase lifetime risk by epigenetically regulating the genome. Here, focusing on the lateral amygdala, a major brain site for emotional homeostasis, we described molecular cross-talk among multiple epigenetic mechanisms, including 6 histone marks, DNA methylation and the transcriptome, in subjects with a history of ELA and controls. We first uncovered, in the healthy brain, previously unknown interactions among epigenetic layers, in particular related to non-CG methylation in the CAC context. We then showed that ELA associates with methylomic changes that are as frequent in the CAC as in the canonical CG context, while these two forms of plasticity occur in sharply distinct genomic regions, features, and chromatin states. Combining these multiple data indicated that immune-related and small GTPase signaling pathways are most consistently impaired in the amygdala of ELA individuals. Overall, this work provides new insights into epigenetic brain regulation as a function of early-life experience.

neuroscience↗

bioRxiv2018

Cell-type-specific methylome-wide association studies implicate neurodegenerative processes and neuroimmune communication in major depressive disorder

We studied the methylome in three collections of human postmortem brain (N=206) and blood samples (N=1,132) of subjects with major depressive disorder (MDD) and controls. Using an epigenomic deconvolution approach we performed cell-type-specific methylome-wide association studies (MWAS) within sub-populations of neurons/glia and granulocytes/T-cells/B-cells/monocytes for bulk brain and blood data, respectively. Multiple MWAS findings in neurons/glia replicated across brain collections (ORs=509-538, P-values<1x10-5) and were reproducible in an array-based MWAS of sorted neurons/glia from a fourth brain collection (N=58). Pathway analyses implicated p75NTR/VEGF signaling, neurodegeneration, and blood-brain barrier perturbation. Cell-type-specific analysis in blood identified associations in CD14+ monocytes -- a cell type strongly linked to neuroimmune processes and stress. Top results in neurons/glia/bulk and monocytes were enriched for genes supported by GWAS for MDD (ORs=2.02-2.87, P-values=0.003 to <1x10-5), neurodegeneration and other psychiatric disorders. In summary, we identified novel MDD-methylation associations by using epigenomic deconvolution that provided important mechanistic insights for the disease.

genomics↗

bioRxiv2018

Zfp189 Mediates Stress Resilience Through a CREB-Regulated Transcriptional Network in Prefrontal Cortex

Stress resilience involves numerous brain-wide transcriptional changes. Determining the organization and orchestration of these transcriptional events may reveal novel antidepressant targets, but this remains unexplored. Here, we characterize the resilient transcriptome with co-expression analysis and identify a single transcriptionally-active uniquely-resilient gene network. Zfp189, a previously unstudied zinc finger protein, is the top network key driver and its overexpression in prefrontal cortical (PFC) neurons preferentially activates this network, alters neuronal activity and promotes behavioral resilience. CREB, which binds Zfp189, is the top upstream regulator of this network. To probe CREB-Zfp189 interactions as a network regulatory mechanism, we employ CRISPR-mediated locus-specific transcriptional reprogramming to direct CREB selectively to the Zfp189 promoter. This single molecular interaction in PFC neurons recapitulates the pro-resilient Zfp189-dependent downstream effects on gene network activity, electrophysiology and behavior. These findings reveal an essential role for Zfp189 and a CREB-Zfp189 regulatory axis in mediating a central transcriptional network of resilience.

neuroscience↗

bioRxiv2018

Single-nucleus RNA sequencing shows convergent evidence from different cell types for altered synaptic plasticity in major depressive disorder.

Major depressive disorder (MDD) is a complex illness that involves the interaction of different brain systems, pathways, and cell types. Past molecular studies of MDD relied on cellular homogenates of post-mortem brain tissue, making it impossible to determine gene expression changes within individual cells. Using single-cell transcriptomics, we examined almost 80,000 nuclei from the dorsolateral prefrontal cortex of individuals with MDD and healthy controls. Our analyses identified 26 distinct cellular clusters, and over 60% of these showed transcriptional differences between groups. Specifically, 96 genes were differentially expressed, the majority of which were downregulated. Convergent evidence from our analyses, including gene expression, differential correlation, and gene ontology implicated dysregulation of synaptic plasticity in the etiopathogenesis of MDD. Our results show that this high-resolution approach can reveal previously undetectable changes in specific cell types in the context of complex phenotypes and heterogeneous tissues.

neuroscience↗

bioRxiv2017

BECon: A tool for interpreting DNA methylation findings from blood in the context of brain

Tissue differences are one of the largest contributors to variability in the human DNA methy-lome. Despite the tissue specific nature of DNA methylation, the inaccessibility of human brain samples necessitates the frequent use of surrogate tissues such as blood, in studies of associations between DNA methylation and brain function and health. Results from studies of surrogate tissues in humans are difficult to interpret in this context, as the connection between blood-brain DNA methylation is tenuous and not well documented. Here we aimed to provide a resource to the community to aid interpretation of blood based DNA methylation results in the context of brain tissue. We used paired samples from 16 individuals from three brain regions and whole blood, run on the Illumina 450K Human Methylation Array to quantify the concordance of DNA methylation between tissues. From these data we have made available metrics on: the variability of CpGs in our blood and brain samples, the concordance of CpGs between blood and brain, and estimations of how strongly a CpG is affected by cell composition in both blood and brain through the web application BECon (Blood-Brain Epigenetic Concordance; https://redgar598.shinyapps.io/BECon/). We anticipate that BECon will enable biological interpretation of blood based human DNA methylation results, in the context of brain.

genetics↗