Social boldness correlates with brain gene expression in male green anoles

AbstractWithin populations, some individuals tend to exhibit a bold or shy social behavior phenotype relative to the mean. The neural underpinnings of these differing phenotypes – also described as syndromes, personalities, and coping styles – is an area of ongoing investigation. Although a social decision-making network has been described across vertebrate taxa, most studies examining activity within this network do so in relation to exhibited differences in behavioral expression. Our study instead focuses on constitutive gene expression in bold and shy individuals by isolating baseline gene expression profiles that influence social boldness predisposition, rather than those reflecting the results of social interaction and behavioral execution. We performed this study on male green anole lizards (Anolis carolinensis), an established model organism for behavioral research, which provides a crucial comparison group to investigations of birds and mammals. After identifying subjects as bold or shy through repeated reproductive and agonistic behavior testing, we used RNA sequencing to compare gene expression profiles between these groups within various forebrain, midbrain, and hindbrain regions. The ventromedial hypothalamus had the largest group differences in gene expression, with bold males having increased expression of calcium channels and neuroendocrine receptor genes compared to shy males. Conversely, shy males express more integrin alpha-10 in the majority of examined regions. There were no significant group differences in physiology or hormone levels. Our results highlight the ventromedial hypothalamus as an important center of behavioral differences across individuals and provide novel candidates for investigation into the regulation of individual variation in social behavior phenotype.

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ANISEED 2019: 4D exploration of genetic data for an extended range of tunicates

Nucleic Acids Research ◽

10.1093/nar/gkz955 ◽

2019 ◽

Cited By ~ 3

Author(s):

Justine Dardaillon ◽

Delphine Dauga ◽

Paul Simion ◽

Emmanuel Faure ◽

Takeshi A Onuma ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Model Organism ◽

Gene Expression Profiles ◽

Sister Group ◽

Model Organism Database ◽

Oikopleura Dioica ◽

Genetic Features ◽

Taxonomic Range ◽

Or Gene

Abstract ANISEED (https://www.aniseed.cnrs.fr) is the main model organism database for the worldwide community of scientists working on tunicates, the vertebrate sister-group. Information provided for each species includes functionally-annotated gene and transcript models with orthology relationships within tunicates, and with echinoderms, cephalochordates and vertebrates. Beyond genes the system describes other genetic elements, including repeated elements and cis-regulatory modules. Gene expression profiles for several thousand genes are formalized in both wild-type and experimentally-manipulated conditions, using formal anatomical ontologies. These data can be explored through three complementary types of browsers, each offering a different view-point. A developmental browser summarizes the information in a gene- or territory-centric manner. Advanced genomic browsers integrate the genetic features surrounding genes or gene sets within a species. A Genomicus synteny browser explores the conservation of local gene order across deuterostome. This new release covers an extended taxonomic range of 14 species, including for the first time a non-ascidian species, the appendicularian Oikopleura dioica. Functional annotations, provided for each species, were enhanced through a combination of manual curation of gene models and the development of an improved orthology detection pipeline. Finally, gene expression profiles and anatomical territories can be explored in 4D online through the newly developed Morphonet morphogenetic browser.

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Comparison of two different host plant genera responding to grapevine leafroll-associated virus 3 infection

Scientific Reports ◽

10.1038/s41598-020-64972-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Cecilia A. Prator ◽

Kar Mun Chooi ◽

Dan Jones ◽

Marcus W. Davy ◽

Robin M. MacDiarmid ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Expression Patterns ◽

Model Organism ◽

Gene Expression Profiles ◽

Gene Families ◽

Host Responses ◽

Narrow Host Range ◽

Host Interactions ◽

Physiological Processes

Abstract Grapevine leafroll-associated virus 3 (GLRaV-3) is one of the most important viruses of grapevine but, despite this, there remain several gaps in our understanding of its biology. Because of its narrow host range - limited to Vitis species - and because the virus is restricted to the phloem, most GLRaV-3 research has concentrated on epidemiology and the development of detection assays. The recent discovery that GLRaV-3 can infect Nicotiana benthamiana, a plant model organism, makes new opportunities available for research in this field. We used RNA-seq to compare both V. vinifera and P1/HC-Pro N. benthamiana host responses to GLRaV-3 infection. Our analysis revealed that the majority of DEGs observed between the two hosts were unique although responses between the two hosts also showed several shared gene expression results. When comparing gene expression patterns that were shared between the two hosts, we observed the downregulation of genes associated with stress chaperones, and the induction of gene families involved in primary plant physiological processes. This is the first analysis of gene expression profiles beyond Vitis to mealybug-transmitted GLRaV-3 and demonstrates that N. benthamiana could serve as a useful tool for future studies of GLRaV-3-host interactions.

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High throughput analysis reveals dissociable gene expression profiles in two independent neural systems involved in the regulation of social behavior

BMC Neuroscience ◽

10.1186/1471-2202-13-126 ◽

2012 ◽

Vol 13 (1) ◽

Cited By ~ 17

Author(s):

Tyler J Stevenson ◽

Kirstin Replogle ◽

Jenny Drnevich ◽

David F Clayton ◽

Gregory F Ball

Keyword(s):

Gene Expression ◽

Social Behavior ◽

High Throughput ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Neural Systems ◽

High Throughput Analysis ◽

Throughput Analysis

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Ecdysone-Related Biomarkers of Toxicity in the Model Organism Chironomus riparius: Stage and Sex-Dependent Variations in Gene Expression Profiles

PLoS ONE ◽

10.1371/journal.pone.0140239 ◽

2015 ◽

Vol 10 (10) ◽

pp. e0140239 ◽

Cited By ~ 10

Author(s):

Rosario Planelló ◽

Óscar Herrero ◽

Pablo Gómez-Sande ◽

Irene Ozáez ◽

Fernando Cobo ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Model Organism ◽

Gene Expression Profiles ◽

Chironomus Riparius

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Differential Protein Network Analysis of the Immune Cell Lineage

BioMed Research International ◽

10.1155/2014/363408 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Trevor Clancy ◽

Eivind Hovig

Keyword(s):

Gene Expression ◽

Protein Interaction ◽

Immune Cell ◽

Expression Profiles ◽

Cell Lineage ◽

Model Organism ◽

Gene Expression Profiles ◽

Interaction Network ◽

Genome Project ◽

Differential Protein

Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.

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Data mining of Gene expression profiles of Saccharomyces cerevisiae in response to mild heat stress response

10.1101/007468 ◽

2014 ◽

Cited By ~ 2

Author(s):

Kaiyuan Ji ◽

Wenli Ma ◽

Wenling Zheng

Keyword(s):

Gene Expression ◽

Heat Stress ◽

Expression Profiles ◽

Model Organism ◽

Gene Expression Profiles ◽

Time Frame ◽

Yeast Cells ◽

Nucleic Acid Metabolism ◽

Biological Processes ◽

Heat Stress Response

We chose yeast as a model organism to explore how eukaryotic cells respond to heat stress. This study provides details on the way yeast responds to temperature changes and is therefore an empirical reference for basic cell research and industrial fermentation of yeast. We use the Qlucore Omics Explorer (QOE) bioinformatics software to analyze the gene expression profiles of the heat stress from Gene Expression Omnibus (GEO). Genes and their expression are listed in heat maps, and the gene function is analyzed against the biological processes and pathways. We can find that the expression of genes changed over time after heat stress. Gene expression changed rapidly from 0 min to 60 min after heat shock, and gene expression stabilized between 60 min to 360 min. The yeast cells begin to adjust themselves to the high temperatures in terms of the level of gene expression at about 60 min. In all of the involved pathways and biological processes, those related to ribosome and nucleic acid metabolism declined in about 15?30 min and those related to starch and sucrose increased in the same time frame. Temperature can be a simple way to control the biological processes and pathways of cell.

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Gene Expression Profiles Underlying Aggressive Behavior in the Prefrontal Cortex of Cattle

10.21203/rs.3.rs-146648/v1 ◽

2021 ◽

Author(s):

Paulina G. Eusebi ◽

Natalia Sevane ◽

Thomas O´Rourke ◽

Manuel Pizarro ◽

Cedric Boeckx ◽

...

Keyword(s):

Gene Expression ◽

Prefrontal Cortex ◽

Aggressive Behavior ◽

Expression Profiles ◽

Model Organism ◽

Gene Expression Profiles ◽

Mental States ◽

Mapk Signaling ◽

Molecular Architecture ◽

Functional Interpretation

Abstract Background: Aggressive behavior is an ancient and conserved trait habitual for most animals in order to eat, protect themselves, compete for mating and defend their territories. Genetic factors have been shown to play an important role in the development of aggression both in animals and humans, displaying moderate to high heritability estimates. Although, such types of conducts have been studied in different animal models, the molecular architecture of aggressiveness remains poorly understood. This study compared gene expression profiles of 16 prefrontal cortex (PFC) samples from aggressive and non-aggressive cattle breeds: Lidia, selected for agonistic responses, and Wagyu, selected for tameness. Results: A total of 918 up-regulated and 278 down-regulated DEG were identified. The functional interpretation of the up-regulated genes in the aggressive cohort revealed enrichment of pathways such as the Alzheimer disease-presenilin, integrins or the ERK/MAPK signaling cascade, all implicated in the development of abnormal aggressive behaviors and neurophysiological disorders. Moreover, gonadotropins, are also up-regulated as natural mechanisms enhancing aggression. Concomitantly, heterotrimeric G-protein pathways, associated with low reactivity mental states, and the GAD2 gene, a repressor of agonistic reactions associated with PFC activity, are down-regulated, promoting the development of the aggressive responses selected for in Lidia cattle. We also identified six upstream regulators, whose functional activity fits with the etiology of abnormal behavioral responses associated with aggression. Conclusions: These transcriptional correlates of aggression, resulting, at least in part, from controlled artificial selection, can provide valuable insights into the complex architecture that underlies naturally developed agonistic behaviors.This analysis constitutes a first important step towards the identification of the genes and metabolic pathways that impulse aggression in cattle and, hence, we are providing a novel species as model organism for disentangling the mechanisms underlying variability in aggressive behavior.

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