scholarly journals To hum or not to hum: Neural transcriptome signature of courtship vocalization in a teleost fish

2020 ◽  
Author(s):  
Joel A. Tripp ◽  
Ni Y. Feng ◽  
Andrew H. Bass
Keyword(s):  
Gene Expression ◽  
Vocal Communication ◽  
Brain Regions ◽  
Vocal Behavior ◽  
Specific Gene ◽  
Motor Nucleus ◽  
Motor Behaviors ◽  
Behavioral States ◽  
Courtship Activity ◽  
Courtship Behaviors

AbstractFor many animal species, vocal communication is a critical social behavior, often a necessary component of reproductive success. In addition to the role of vocal behavior in social interactions, vocalizations are often demanding motor acts. Through understanding the genes involved in regulating and permitting vertebrate vocalization, we can better understand the mechanisms regulating vocal and, more broadly, motor behaviors. Here, we use RNA-sequencing to investigate neural gene expression underlying the performance of an extreme vocal behavior, the courtship hum of the plainfin midshipman fish (Porichthys notatus). Single hums can last up to two hours and may be repeated throughout an evening of courtship activity. We asked whether vocal behavioral states are associated with specific gene expression signatures in key brain regions that regulate vocalization by comparing transcript levels in humming versus non-humming males. We find that the circadian-related genes period3 and Clock are significantly upregulated in the vocal motor nucleus and preoptic area-anterior hypothalamus, respectively, in humming compared to non-humming males, indicating that internal circadian clocks may differ between these divergent behavioral states. In addition, we identify suites of differentially expressed genes related to synaptic transmission, ion channels and transport, hormone signaling, and metabolism and antioxidant activity that may permit or support humming behavior. These results underscore the importance of the known circadian control of midshipman humming and provide testable candidate genes for future studies of the neuroendocrine and motor control of energetically demanding courtship behaviors in midshipman fish and other vertebrate groups.

scholarly journals Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

2019 ◽  
Vol 36 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Empirical Bayes ◽  
Brain Regions ◽  
Tissue Level ◽  
Supplementary Information ◽  
Specific Gene ◽  
Expression Data ◽  
Cell Type ◽  
Multiple Measurements ◽  
Cell Type Specific

Abstract Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals What Do Machines Tell us About Dementia? Machine Learning Applied to Aging, Dementia and Traumatic Brain Injury Study

2021 ◽  
Author(s):  
Denis Arthur Pinheiro Moura ◽  
Joao Ricardo Mendes de Oliveira
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Expression Profiles ◽  
Brain Regions ◽  
Machine Learning Algorithms ◽  
Differentially Expressed ◽  
Classification Model ◽  
Specific Gene

Abstract Dementia, a syndrome characterized by the progressive deterioration of memory and cognition, arises from different pathologies, with Alzheimer's Disease (AD) its most common cause. Patterns of gene expression during dementia of different etiologies may function as generalist biomarkers of the condition. We used RNA-Seq data from the Allen Dementia and Traumatic Brain Injury Study (ADTBI) to identify differentially expressed genes in brains with dementia. Machine Learning algorithms Decision Trees (DT) and Random Forest (RF) were used to create models to identify dementia samples based on their gene expression profile. Importance analyses were conducted to identify the most relevant genes in each classification model. A total of 1629 differentially expressed (DE) genes were found in brains with the condition. Gene PAN3-AS1 was the only DE gene across more than three brain regions. The artificial intelligence models were capable of identifying correctly up to 92.85% of dementia samples. Our analyses provide interesting insights regarding using brain-specific gene expression profiles as biomarkers of dementia, identifying genes possibly involved with dementia, and guiding future studies in prediction and early identification of the syndrome.

scholarly journals Strategies for cellular deconvolution in human brain RNA sequencing data

2020 ◽  
Author(s):  
Olukayode A. Sosina ◽  
Matthew N Tran ◽  
Kristen R Maynard ◽  
Ran Tao ◽  
Margaret A. Taub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Size ◽  
Expression Profiles ◽  
Brain Regions ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Target User ◽  
Size Estimates

AbstractStatistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. Here we show that several existing deconvolution algorithms which estimate the RNA composition of homogenate tissue, relates to the amount of RNA attributable to each cell type, and not the cellular composition relating to the underlying fraction of cells. Incorporating “cell size” parameters into RNA-based deconvolution algorithms can successfully recover cellular fractions in homogenate brain RNA-seq data. We lastly show that using both cell sizes and cell type-specific gene expression profiles from brain regions other than the target/user-provided bulk tissue RNA-seq dataset consistently results in biased cell fractions. We report several independently constructed cell size estimates as a community resource and extend the MuSiC framework to accommodate these cell size estimates (https://github.com/xuranw/MuSiC/).

Epigenetic basis of psychiatric disorders: a narrative review

2021 ◽  
Vol 20 ◽  
Author(s):  
Fabio Panariello ◽  
Giuseppe Fanelli ◽  
Chiara Fabbri ◽  
Anna Rita Atti ◽  
Diana De Ronchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Environmental Factors ◽  
Psychiatric Disorders ◽  
Genetic Variants ◽  
Neural Circuit ◽  
Expression Patterns ◽  
Brain Regions ◽  
Epigenetic Modifications ◽  
Cochrane Library ◽  
Specific Gene

Background: Psychiatric disorders are complex, multifactorial illnesses with a demonstrated biological component in their etiopathogenesis. Epigenetic modifications, through the modulation of DNA methylation, histone modifications and RNA interference, tune tissue-specific gene expression patterns and play a relevant role in the etiology of psychiatric illnesses. Objective: This review aims to discuss the epigenetic mechanisms involved in psychiatric disorders, their modulation by environmental factors and their interactions with genetic variants, in order to provide a comprehensive picture of their mutual crosstalk. Methods: In accordance with the PRISMA guidelines, systematic searches of Medline, EMBASE, PsycINFO, Web of Science, Scopus, and the Cochrane Library were conducted. Results: Exposure to environmental factors, such as poor socio-economic status, obstetric complications, migration, and early life stressors, may lead to stable changes in gene expression and neural circuit function, playing a role in the risk of psychiatric diseases. The most replicated genes involved by studies using different techniques are discussed. Increasing evidence indicates that these sustained abnormalities are maintained by epigenetic modifications in specific brain regions and they interact with genetic variants in determining the risk of psychiatric disorders. Conclusion: An increasing amount of evidence suggests that epigenetics plays a pivotal role in the etiopathogenesis of psychiatric disorders. New therapeutic approaches may work by reversing detrimental epigenetic changes that occurred during the lifespan.

scholarly journals Yin Yang 1 sets up the stage for cerebellar astrocyte maturation

2021 ◽  
Author(s):  
Karli Mockenhaupt ◽  
Katarzyna M Tyc ◽  
Adam McQuiston ◽  
Avani Hariprashad ◽  
Debolina D Biswas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Brain Regions ◽  
Motor Deficits ◽  
Specific Gene ◽  
Yin Yang 1 ◽  
Yin Yang ◽  
Severe Motor ◽  
Cerebellar Astrocytes ◽  
Simultaneous Loss

Diverse subpopulations of astrocytes tile different brain regions to accommodate local requirements of neurons and associated neuronal circuits. Nevertheless, molecular mechanisms governing astrocyte diversity remain mostly unknown. We explored the role of a zinc finger transcription factor Yin Yang 1 (YY1) that is expressed in astrocytes. We found that specific deletion of YY1 from astrocytes causes severe motor deficits in mice, induces Bergmann gliosis, and results in simultaneous loss of GFAP expression in velate and fibrous cerebellar astrocytes. Single cell RNA-seq analysis showed that YY1 exerts specific effects on gene expression in subpopulations of cerebellar astrocytes. We found that although YY1 is dispensable for the initial stages of astrocyte development, it regulates subtype-specific gene expression during astrocyte maturation. Moreover, YY1 is continuously needed to maintain mature astrocytes in the adult cerebellum. Our findings suggest that YY1 plays critical roles regulating cerebellar astrocyte maturation during development and maintaining a mature phenotype of astrocytes in the adult cerebellum.

scholarly journals Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression

2018 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Empirical Bayes ◽  
Tissue Expression ◽  
Brain Regions ◽  
Tissue Level ◽  
Specific Gene ◽  
Expression Data ◽  
Cell Type ◽  
Multiple Measurements ◽  
Cell Type Specific

AbstractMotivationPatterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects.ResultsComplementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g., multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL).Availability and implementationWe implement this method as an R package MIND, hosted on https://github.com/randel/MIND.

scholarly journals Circuit-specific hippocampal ΔFosB underlies resilience to stress-induced social avoidance

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrew L. Eagle ◽  
Claire E. Manning ◽  
Elizabeth S. Williams ◽  
Ryan M. Bastle ◽  
Paula A. Gajewski ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Specific Activity ◽  
Ventral Hippocampus ◽  
Brain Regions ◽  
Depression Symptoms ◽  
Specific Gene ◽  
Social Avoidance ◽  
Novel Treatments ◽  
Experience Of Stress

Abstract Chronic stress is a key risk factor for mood disorders like depression, but the stress-induced changes in brain circuit function and gene expression underlying depression symptoms are not completely understood, hindering development of novel treatments. Because of its projections to brain regions regulating reward and anxiety, the ventral hippocampus is uniquely poised to translate the experience of stress into altered brain function and pathological mood, though the cellular and molecular mechanisms of this process are not fully understood. Here, we use a novel method of circuit-specific gene editing to show that the transcription factor ΔFosB drives projection-specific activity of ventral hippocampus glutamatergic neurons causing behaviorally diverse responses to stress. We establish molecular, cellular, and circuit-level mechanisms for depression- and anxiety-like behavior in response to stress and use circuit-specific gene expression profiling to uncover novel downstream targets as potential sites of therapeutic intervention in depression.

Sign in / Sign up

Export Citation Format

Share Document