Mus musculus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA320640 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Transcriptional regulatory dynamics drive coordinated metabolic and neural response to social challenge in mice.
| PRJNA318634 | ENA
Project description:Transcriptional regulatory dynamics drive coordinated metabolic and neural response to social challenge in mice. [RNA-Seq data set]
| PRJNA318643 | ENA
Project description:Agonistic encounters with conspecifics are powerful effectors of future behavior that evoke strong and durable neurobiological responses. We recently identified a deeply conserved “toolkit” of transcription factors (TFs) that respond to social challenge across diverse species in coordination with distinct conserved signatures of energy metabolism and developmental signaling. To further characterize this response and its transcriptional drivers in mice, we examined gene expression and chromatin landscape in the hypothalamus, frontal cortex, and amygdala of socially challenged and control animals over time. The data revealed a complex spatiotemporal pattern of metabolic, neural, and developmental transcriptomic signatures coordinated with significant shifts in the accessibility of distally located regulatory elements. Transcriptional regulatory network and motif analyses revealed an interacting network of TFs correlated with differential gene expression across the tissues and time points we assayed, including the early-acting and conserved regulator of energy metabolism and development, ESRRA. Cell-type deconvolution analysis attributed the early metabolic activity implicated by our transcriptomic analysis primarily to oligodendrocytes and the developmental signal to neurons, and we confirmed the presence of ESRRA in both oligodendrocytes and neurons throughout the brain. To assess the role of this nuclear receptor as an early trigger of this coordinated response, we used chromatin immunoprecipitation to map ESRRA binding sites to a set of genes involved in metabolic regulation and enriched in challenge-associated differentially expressed genes. Together, these data support a rich model linking metabolic and neural responses to social challenge, and identify regulatory drivers with unprecedented tissue and temporal resolution.
2017-03-24 | GSE80346 | GEO
Project description:Agonistic encounters with conspecifics are powerful effectors of future behavior that evoke strong and durable neurobiological responses. We recently identified a deeply conserved “toolkit” of transcription factors (TFs) that respond to social challenge across diverse species in coordination with distinct conserved signatures of energy metabolism and developmental signaling. To further characterize this response and its transcriptional drivers in mice, we examined gene expression and chromatin landscape in the hypothalamus, frontal cortex, and amygdala of socially challenged and control animals over time. The data revealed a complex spatiotemporal pattern of metabolic, neural, and developmental transcriptomic signatures coordinated with significant shifts in the accessibility of distally located regulatory elements. Transcriptional regulatory network and motif analyses revealed an interacting network of TFs correlated with differential gene expression across the tissues and time points we assayed, including the early-acting and conserved regulator of energy metabolism and development, ESRRA. Cell-type deconvolution analysis attributed the early metabolic activity implicated by our transcriptomic analysis primarily to oligodendrocytes and the developmental signal to neurons, and we confirmed the presence of ESRRA in both oligodendrocytes and neurons throughout the brain. To assess the role of this nuclear receptor as an early trigger of this coordinated response, we used chromatin immunoprecipitation to map ESRRA binding sites to a set of genes involved in metabolic regulation and enriched in challenge-associated differentially expressed genes. Together, these data support a rich model linking metabolic and neural responses to social challenge, and identify regulatory drivers with unprecedented tissue and temporal resolution.
2017-03-24 | GSE81122 | GEO
Project description:Neural transcriptomic responses to endotoxin challenge across social treatments
| PRJNA841225 | ENA
Project description:The aim of this experiment was to test whether the social setting changed neural transcriptomic responses to an endotoxin challenge in zebra finches
2023-03-06 | GSE203528 | GEO
Project description:Social interactions can drive distinct gene expression profiles which may vary by social context. Here we use female sailfin molly fish (Poecilia latipinna) to identify genomic profiles associated with preference behavior in distinct social contexts: male-interactions (mate choice) versus female-interactions (shoaling partner preference). We measured behavior of 15 females interacting in a non-contact environment with either two males or two females for 30 minutes followed by whole brain transcriptomic profiling by RNA sequencing. We profiled females that exhibited high levels of social affiliation and great variation in preference behavior to identify an order of magnitude more differentially expressed genes associated with behavioral variation than by differences in social context. Using linear modeling (limma), we took advantage of the individual variation in preference behavior to identify unique gene sets that exhibited distinct correlational patterns of expression with preference behavior in each social context. By combining limma and weighted gene co-expression network analyses (WGCNA) approaches we identify a refined set of 401 genes robustly associated with mate preference that is independent of shoaling partner preference or general social affiliation. While our refined gene set confirmed neural plasticity pathways involved in moderating female preference behavior, we also identified a significant proportion of discovered that our preference-associated genes were enriched for ‘immune system’ gene ontology categories. We hypothesize that the association between mate preference and transcriptomic immune function is driven by the less well-known role of these genes in neural plasticity which is likely involved in higher-order learning and processing during mate choice decisions.
2023-07-01 | GSE233523 | GEO
Project description:Massively parallel characterization of regulatory dynamics during neural induction [ChIP-seq]
| PRJNA473881 | ENA
Project description:Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
2023-07-06 | GSE236518 | GEO
Project description:Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
2023-07-06 | GSE236517 | GEO