scholarly journals Ketamine’s pharmacogenomic network in human brain contains sub-networks associated with glutamate neurotransmission and with neuroplasticity

2020 ◽  
Author(s):  
Gerald A. Higgins ◽  
Samuel A. Handelman ◽  
Ari Allyn-Feuer ◽  
Alex S. Ade ◽  
James S. Burns ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Neuroimaging ◽  
Association Studies ◽  
Expression Patterns ◽  
Biological Data ◽  
Stress Disorders ◽  
Chromatin Interaction ◽  
Postmortem Human Brain ◽  
Regulatory Rnas ◽  
Glutamate Neurotransmission

AbstractThe pharmacogenomic network responsible for the rapid antidepressant action of ketamine and concomitant adverse events in patients has been poorly defined. Integrative, multi-scale biological data analytics helps explain ketamine’s action. Using a validated computational pipeline, candidate ketamine-response genes and regulatory RNAs from published literature, binding affinity studies, and single nucleotide polymorphisms (SNPs) from genomewide association studies (GWAS), we identified 108 SNPs associated with 110 genes and regulatory RNAs. All of these SNPs are classified as enhancers, and additional chromatin interaction mapping in human neural cell lines and tissue shows enhancer-promoter interactions involving other network members. Pathway analysis and gene set optimization identified three composite sub-networks within the broader ketamine pharmacogenomic network. Expression patterns of ketamine network genes within the postmortem human brain are concordant with ketamine neurocircuitry based on the results of 24 published functional neuroimaging studies. The ketamine pharmacogenomic network is enriched in forebrain regions known to be rapidly activated by ketamine, including cingulate cortex and frontal cortex, and is significantly regulated by ketamine (p=6.26E-33; Fisher’s exact test). The ketamine pharmacogenomic network can be partitioned into distinct enhancer sub-networks associated with: (1) glutamate neurotransmission, chromatin remodeling, smoking behavior, schizophrenia, pain, nausea, vomiting, and post-operative delirium; (2) neuroplasticity, depression, and alcohol consumption; and (3) pharmacokinetics. The component sub-networks explain the diverse action mechanisms of ketamine and its analogs. These results may be useful for optimizing pharmacotherapy in patients diagnosed with depression, pain or related stress disorders.One Sentence SummaryThe ketamine network in the human brain consists of sub-networks associated with glutamate neurotransmission, neuroplasticity, and pharmacokinetics.

scholarly journals Integrative genomics approach identifies conserved transcriptomic networks in Alzheimer’s disease

2019 ◽  
Author(s):  
Samuel Morabito ◽  
Emily Miyoshi ◽  
Neethu Michael ◽  
Vivek Swarup
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Association Studies ◽  
Brain Regions ◽  
Genome Wide Association Studies ◽  
Integrative Genomics ◽  
Cell Type ◽  
Postmortem Human Brain ◽  
Cell Transcriptome

AbstractAlzheimer’s disease (AD) is a devastating neurological disorder characterized by changes in cell-type proportions and consequently marked alterations of the transcriptome. Here we use a data-driven systems biology approach across multiple cohorts of human AD, encompassing different brain regions, and integrate with multi-scale datasets comprising of DNA methylation, histone acetylation, transcriptome- and genome-wide association studies as well as quantitative trait loci to define the genetic architecture of AD. We perform co-expression network analysis across more than twelve hundred human brain samples, identifying robust AD-associated dysregulation of the transcriptome, unaltered in normal human aging. We further integrate co-expression modules with single-cell transcriptome generated from 27,321 nuclei from postmortem human brain to identify AD-specific transcriptional changes and assess cell-type proportion changes in the human AD brain. We also show that genetic variants of AD are enriched in a glial AD-associated module and identify key transcription factors regulating co-expressed modules. Additionally, we validate our results in multiple published human AD datasets which are easily accessible using our online resource (https://swaruplab.bio.uci.edu/consensusAD).

Phenylalanine Metabolism is Dysregulated in Human Hippocampus with Alzheimer’s Disease Related Pathological Changes

2021 ◽  
pp. 1-14
Author(s):  
Pan Liu ◽  
Qian Yang ◽  
Ning Yu ◽  
Yan Cao ◽  
Xue Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Human Brain ◽  
Pathological Examination ◽  
Pathological Changes ◽  
Targeted Metabolomics ◽  
Phenylpyruvic Acid ◽  
Postmortem Human Brain ◽  
Metabolic Dysregulation ◽  
Phenylalanine Metabolism

Background: Alzheimer’s disease (AD) is one of the most challenging diseases causing an increasing burden worldwide. Although the neuropathologic diagnosis of AD has been established for many years, the metabolic changes in neuropathologic diagnosed AD samples have not been fully investigated. Objective: To elucidate the potential metabolism dysregulation in the postmortem human brain samples assessed by AD related pathological examination. Methods: We performed untargeted and targeted metabolomics in 44 postmortem human brain tissues. The metabolic differences in the hippocampus between AD group and control (NC) group were compared. Results: The results show that a pervasive metabolic dysregulation including phenylalanine metabolism, valine, leucine, and isoleucine biosynthesis, biotin metabolism, and purine metabolism are associated with AD pathology. Targeted metabolomics reveal that phenylalanine, phenylpyruvic acid, and N-acetyl-L-phenylalanine are upregulated in AD samples. In addition, the enzyme IL-4I1 catalyzing transformation from phenylalanine to phenylpyruvic acid is also upregulated in AD samples. Conclusion: There is a pervasive metabolic dysregulation in hippocampus with AD-related pathological changes. Our study suggests that the dysregulation of phenylalanine metabolism in hippocampus may be an important pathogenesis for AD pathology formation.

scholarly journals “Guilt by association” is not competitive with genetic association for identifying autism risk genes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margot Gunning ◽  
Paul Pavlidis
Keyword(s):  
Machine Learning ◽  
Genetic Association ◽  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Disorders ◽  
Autism Spectrum ◽  
Biological Data ◽  
Disease Genes ◽  
Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

Phenylethylamine Metabolism by Postmortem Human Brain and Liver Preparations

1985 ◽  
pp. 487-498 ◽  
Author(s):  
A. D. Mosnaim ◽  
M. E. Wolf ◽  
J. Chevesich ◽  
O. H. Callaghan ◽  
P. Szanto
Keyword(s):  
Human Brain ◽  
Postmortem Human Brain

scholarly journals A pH-eQTL interaction at the RIT2-SYT4 Parkinson's disease risk locus in the substantia nigra

2020 ◽  
Author(s):  
Sejal Patel ◽  
Derek Howard ◽  
Leon French
Keyword(s):  
Gene Expression ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Human Brain ◽  
Disease Risk ◽  
Brain Regions ◽  
Dopamine Neurons ◽  
Postmortem Human Brain ◽  
Increased Risk

BACKGROUND: Parkinson's disease (PD) causes severe motor and cognitive disabilities that result from the progressive loss of dopamine neurons in the substantia nigra. The rs12456492 variant in the RIT2 gene has been repeatedly associated with increased risk for Parkinson's disease. From a transcriptomic perspective, a meta-analysis found that RIT2 gene expression is correlated with pH in the human brain. OBJECTIVE: To assess pH associations at the RIT2-SYT4 locus. METHODS: Linear models to examine two datasets that assayed rs12456492, gene expression, and pH in the postmortem human brain. RESULTS: Using the BrainEAC dataset, we replicate the positive correlation between RIT2 gene expression and pH in the human brain. Furthermore, we found that the relationship between expression and pH is influenced by rs12456492. When tested across ten brain regions, this interaction is specifically found in the substantia nigra. A similar association was found for the co-localized SYT4 gene. In addition, SYT4 associations are stronger in a combined model with both genes, and the SYT4 interaction appears to be specific to males. In the GTEx dataset, the pH associations involving rs12456492 and expression of either SYT4 and RIT2 was not seen. This null finding may be due to the short postmortem intervals (PMI) of the GTEx tissue samples. In the BrainEAC data, we tested the effect of PMI and only observed the interactions in the longer PMI samples. CONCLUSIONS: These previously unknown associations suggest novel mechanistic roles for rs12456492, RIT2, and SYT4 in the regulation of pH in the substantia nigra.

scholarly journals Transcribed enhancers in the human brain identify novel disease risk mechanisms

2021 ◽  
Author(s):  
Pengfei Dong ◽  
Gabriel E. Hoffman ◽  
Pasha Apontes ◽  
Jaroslav Bendl ◽  
Samir Rahman ◽  
...  
Keyword(s):  
Human Brain ◽  
Disease Risk ◽  
Association Studies ◽  
Population Level ◽  
Cell Type ◽  
Level Variation ◽  
Functional Interpretation ◽  
Neuropsychiatric Diseases ◽  
Order Of Magnitude ◽  
Cell Type Specific

Enhancer RNAs (eRNAs) constitute an important tissue- and cell-type-specific layer of the regulome. Identification of risk variants for neuropsychiatric diseases within enhancers underscores the importance of understanding the population-level variation of eRNAs in the human brain. We jointly analyzed cell type-specific transcriptome and regulome data to identify 30,795 neuronal and 23,265 non-neuronal eRNAs, expanding the catalog of known human brain eRNAs by an order of magnitude. Examination of the population-level variation of the transcriptome and regulome in 1,382 brain samples identified reproducible changes affecting cis- and trans-co-regulation of eRNA-gene modules in schizophrenia. We show that 13% of schizophrenia heritability is jointly mediated in cis by brain gene and eRNA expression. Inclusion of eRNAs in transcriptome-wide association studies facilitated fine-mapping and functional interpretation of disease loci. Overall, our study characterizes the eRNA-gene regulome and genetic mechanisms in the human cortex in both healthy and disease states.

scholarly journals The neural representational space of social memory

2017 ◽  
Author(s):  
Sarah L. Dziura ◽  
James C. Thompson
Keyword(s):  
Social Networks ◽  
Human Brain ◽  
Incidental Learning ◽  
Functional Neuroimaging ◽  
Brain Activity ◽  
Temporoparietal Junction ◽  
Representational Space ◽  
Analysis Technique ◽  
The Social ◽  
Multivariate Pattern

AbstractSocial functioning involves learning about the social networks in which we live and interact; knowing not just our friends, but also who is friends with our friends. Here we utilized a novel incidental learning paradigm and representational similarity analysis (RSA), a functional MRI multivariate pattern analysis technique, to examine the relationship between learning social networks and the brain's response to the faces within the networks. We found that accuracy of learning face pair relationships through observation is correlated with neural similarity patterns to those pairs in the left temporoparietal junction (TPJ), the left fusiform gyrus, and the subcallosal ventromedial prefrontal cortex (vmPFC), all areas previously implicated in social cognition. This model was also significant in portions of the cerebellum and thalamus. These results show that the similarity of neural patterns represent how accurately we understand the closeness of any two faces within a network, regardless of their true relationship. Our findings indicate that these areas of the brain not only process knowledge and understanding of others, but also support learning relations between individuals in groups.Significance StatementKnowledge of the relationships between people is an important skill that helps us interact in a highly social world. While much is known about how the human brain represents the identity, goals, and intentions of others, less is known about how we represent knowledge about social relationships between others. In this study, we used functional neuroimaging to demonstrate that patterns in human brain activity represent memory for recently learned social connections.

scholarly journals Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

2021 ◽  
Author(s):  
Milton Pividori ◽  
Sumei Lu ◽  
Binglan Li ◽  
Chun Su ◽  
Matthew E. Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Expression Patterns ◽  
Cell Types ◽  
Joint Analysis ◽  
Genetic Associations ◽  
Disease Etiology ◽  
Mediating Role ◽  
Gene Modules ◽  
Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

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