scholarly journals Behavior-related gene regulatory networks: A new level of organization in the brain

2020 ◽  
Vol 117 (38) ◽  
pp. 23270-23279 ◽  
Author(s):  
Saurabh Sinha ◽  
Beryl M. Jones ◽  
Ian M. Traniello ◽  
Syed A. Bukhari ◽  
Marc S. Halfon ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Neuronal Networks ◽  
Multiple Scales ◽  
Brain Activity ◽  
Affirmative Answer ◽  
Review Literature ◽  
Gene Regulatory ◽  
The Rich ◽  
Temporal Dimensions ◽  
The Brain

Neuronal networks are the standard heuristic model today for describing brain activity associated with animal behavior. Recent studies have revealed an extensive role for a completely distinct layer of networked activities in the brain—the gene regulatory network (GRN)—that orchestrates expression levels of hundreds to thousands of genes in a behavior-related manner. We examine emerging insights into the relationships between these two types of networks and discuss their interplay in spatial as well as temporal dimensions, across multiple scales of organization. We discuss properties expected of behavior-related GRNs by drawing inspiration from the rich literature on GRNs related to animal development, comparing and contrasting these two broad classes of GRNs as they relate to their respective phenotypic manifestations. Developmental GRNs also represent a third layer of network biology, playing out over a third timescale, which is believed to play a crucial mediatory role between neuronal networks and behavioral GRNs. We end with a special emphasis on social behavior, discuss whether unique GRN organization andcis-regulatory architecture underlies this special class of behavior, and review literature that suggests an affirmative answer.

scholarly journals Recent advances in functional neuroimaging analysis for cognitive neuroscience

2018 ◽  
Vol 2 ◽  
pp. 239821281775272 ◽  
Author(s):  
Nitin Williams ◽  
Richard N. Henson
Keyword(s):  
Functional Neuroimaging ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Recent Analysis ◽  
Temporal Data ◽  
New Methods ◽  
Neuroimaging Data ◽  
The Rich ◽  
Scientific Questions ◽  
The Brain

Functional magnetic resonance imaging and electro-/magneto-encephalography are some of the main neuroimaging technologies used by cognitive neuroscientists to study how the brain works. However, the methods for analysing the rich spatial and temporal data they provide are constantly evolving, and these new methods in turn allow new scientific questions to be asked about the brain. In this brief review, we highlight a handful of recent analysis developments that promise to further advance our knowledge about the working of the brain. These include (1) multivariate approaches to decoding the content of brain activity, (2) time-varying approaches to characterising states of brain connectivity, (3) neurobiological modelling of neuroimaging data, and (4) standardisation and big data initiatives.

Abstract 312: Transcriptional and Posttranscriptional Circuitry in the Brain After Transient and Permanent Ischemia

2014 ◽  
Vol 34 (suppl_1) ◽  
Author(s):  
Fatiha Tabet ◽  
Sandy Lee ◽  
Luisa F Cuesta Torres ◽  
Michael G Levin ◽  
Grant R Drummond ◽  
...  
Keyword(s):  
Gene Expression ◽  
Validation Studies ◽  
Regulatory Networks ◽  
Functional Roles ◽  
Sham Procedure ◽  
Gene Regulatory ◽  
Neurological Processes ◽  
The Brain

Background: Stroke is a major neurovascular disease and a leading cause of mortality and long-term disability. Within cells of the brain, short non-encoding microRNAs (miRNAs) serve to modulate gene expression and likely contribute to most, neurological processes. However, miRNA changes in the brain tissue in response to stroke have not been reported. Aim: To investigate the functional roles of brain miRNAs and gene regulatory networks in stroke injury. Methods: Adult (8-12 weeks old) male C57Bl/6 mice underwent intraluminal filament-induced middle cerebral artery (MCA) occlusion. Permanent ischemia (ischemia no reperfusion, InoR; n=8) was achieved by occlusion for 24 h, and ischemia with reperfusion (IR; n=8) was completed after 30 min of MCA followed by 23.5 h of reperfusion. Sham-operated mice (n=8) were used as controls. Total RNA was isolated from mouse brains and gene arrays (Affymetrix) and miRNA arrays (TaqMan OpenArray microRNA) were performed. Validation studies were performed using RT-PCR and TaqMan Individual Assays. Results: Relative to the sham-operated mice, InoR significantly altered (p≤0.05; fold-change≥1.5) the levels of 471 genes (mRNA) in the brain. By contrast, IR resulted in only 114 significant changes in gene expression after 24 h. Brain miRNAs were also very sensitive to both ischemia and reperfusion. 28 miRNAs (11 down, 17 up) were significantly altered by InoR compared to the sham procedure. Likewise, 12 miRNAs (3 down, 9 up) were significantly altered with reperfusion compared to the sham procedure. Interestingly, we found 10 miRNAs to be significantly altered (5 up, 5 down) with ischemia (InoR/Sham), but were also significantly corrected towards normal Sham levels by 23.5 h reperfusion (IR/InoR). Validation studies confirmed that levels of multiple miRNAs were significantly altered with InoR. Reperfusion increased the levels of all these miRNAs. 48% (327/680) of the mRNAs that were altered were predicted targets of significantly altered miRNAs, and our results showed inverse directional changes. Conclusion: Results from our study show the role of miRNAs and post-transcriptional circuits in both adaptive and maladaptive responses to ischemic stroke and reperfusion.

scholarly journals Gene regulatory networks underlying human microglia maturation

2021 ◽  
Author(s):  
Nicole G Coufal ◽  
Christopher Glass ◽  
Claudia Han ◽  
Rick Z. Li ◽  
Emily Hansen ◽  
...  
Keyword(s):  
Brain Development ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Neurodevelopmental Disorders ◽  
Regulatory Networks ◽  
Critical Time ◽  
Open Chromatin ◽  
Potential Contribution ◽  
Gene Regulatory ◽  
The Brain

The fetal period is a critical time for brain development, characterized by neurogenesis, neural migration, and synaptogenesis(1-3). Microglia, the tissue resident macrophages of the brain, are observed as early as the fourth week of gestation4 and are thought to engage in a variety of processes essential for brain development and homeostasis(5-11). Conversely, microglia phenotypes are highly regulated by the brain environment(12-14). Mechanisms by which human brain development influences the maturation of microglia and microglia potential contribution to neurodevelopmental disorders remain poorly understood. Here, we performed transcriptomic analysis of human fetal and postnatal microglia and corresponding cortical tissue to define age-specific brain environmental factors that may drive microglia phenotypes. Comparative analysis of open chromatin profiles using bulk and single-cell methods in conjunction with a new computational approach that integrates epigenomic and single-cell RNA-seq data allowed decoding of cellular heterogeneity with inference of subtype- and development stage-specific transcriptional regulators. Interrogation of in vivo and in vitro iPSC-derived microglia models provides evidence for roles of putative instructive signals and downstream gene regulatory networks which establish human-specific fetal and postnatal microglia gene expression programs and potentially contribute to neurodevelopmental disorders.

scholarly journals The interpersonal neuroscience of social learning

2021 ◽  
Author(s):  
Yafeng Pan ◽  
Giacomo Novembre ◽  
Andreas Olsson
Keyword(s):  
Social Learning ◽  
Learning Outcomes ◽  
Paradigm Shift ◽  
Brain Activity ◽  
Mechanistic Models ◽  
Communicative Behaviors ◽  
Time Interaction ◽  
Transmission Of Information ◽  
The Rich ◽  
The Brain

The study of the brain mechanisms underpinning social behavior is currently undergoing a paradigm shift, moving its focus from single individuals to the real-time interaction among groups of individuals. Although this development opens unprecedented opportunities to study how interpersonal brain activity shapes behaviors through learning, there have been few direct connections to the rich field of learning science. Our paper examines how the rapidly developing field of interpersonal neuroscience is (and could be) contributing to our understanding of social learning. To this end, we first review recent research extracting indices of brain-to-brain coupling (BtBC) in the context of social behaviors, and in particular social learning. We then discuss how studying communicative behaviors during learning can aid the interpretation of BtBC, and how studying BtBC can inform our understanding of such behaviors. Importantly, we then discuss how BtBC and communicative behaviors collectively can predict learning outcomes, suggesting several causative and mechanistic models. Finally, we highlight key methodological and interpretational challenges, as well as exciting opportunities for integrating research in interpersonal neuroscience with social learning, and propose a multi-person framework for understanding how interpersonal transmission of information between individual brains shapes social learning.

scholarly journals A single-cell catalogue of regulatory states in the ageing Drosophila brain

2017 ◽  
Author(s):  
Kristofer Davie ◽  
Jasper Janssens ◽  
Duygu Koldere ◽  
Uli Pech ◽  
Sara Aibar ◽  
...  
Keyword(s):  
Single Cell ◽  
Regulatory Networks ◽  
Neuronal Cell ◽  
Cell Types ◽  
Disease Mutations ◽  
Drosophila Brain ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
The Brain

SummaryThe diversity of cell types and regulatory states in the brain, and how these change during ageing, remains largely unknown. Here, we present a single-cell transcriptome catalogue of the entire adult Drosophila melanogaster brain sampled across its lifespan. Both neurons and glia age through a process of “regulatory erosion”, characterized by a strong decline of RNA content, and accompanied by increasing transcriptional and chromatin noise. We identify more than 50 cell types by specific transcription factors and their downstream gene regulatory networks. In addition to neurotransmitter types and neuroblast lineages, we find a novel neuronal cell state driven by datilografo and prospero. This state relates to neuronal birth order, the metabolic profile, and the activity of a neuron. Our single-cell brain catalogue reveals extensive regulatory heterogeneity linked to ageing and brain function and will serve as a reference for future studies of genetic variation and disease mutations.

scholarly journals Bi-directional Modeling Between Cross-Scale Neural Activity

2020 ◽  
Author(s):  
Yin-Jui Chang ◽  
Yuan-I Chen ◽  
Hsin-Chih Yeh ◽  
Jose M. Carmena ◽  
Samantha R. Santacruz
Keyword(s):  
Multiple Scales ◽  
Brain Activity ◽  
Head Model ◽  
Brain Areas ◽  
Explanatory Theory ◽  
Neural Data ◽  
Multi Scale ◽  
Learning Techniques ◽  
Low Dimensional ◽  
The Brain

AbstractFundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas the population brain activity has been studied in the micro-to meso-scale in building the connections between the dynamical patterns and the behaviors, such studies were often done at a single length scale and lacked an explanatory theory that identifies the neuronal origin across multiple scales. Here we introduce the NeuroBondGraph Network, a dynamical system incorporating both biological-inspired components and deep learning techniques to capture cross-scale dynamics that can infer and map the neural data from multiple scales. We demonstrated our model is not only 3.5 times more accurate than the popular sphere head model but also extracts more synchronized phase and correlated low-dimensional latent dynamics. We also showed that we can extend our methods to robustly predict held-out data across 16 days. Accordingly, the NeuroBondGraph Network opens the door to revealing comprehensive understanding of the brain computation, where network mechanisms of multi-scale communications are critical.

scholarly journals Reverse Engineering Gene Networks Using Global-Local Shrinkage Rules

2019 ◽  
Author(s):  
Viral Panchal ◽  
Daniel Linder
Keyword(s):  
T Cell Activation ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Activation ◽  
Network Inference ◽  
Structure Learning ◽  
Multiple Scales ◽  
Real Data ◽  
Gene Regulatory

AbstractInferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

scholarly journals Reverse engineering gene networks using global–local shrinkage rules

2019 ◽  
Vol 10 (1) ◽  
pp. 20190049 ◽  
Author(s):  
Viral Panchal ◽  
Daniel F. Linder
Keyword(s):  
T Cell Activation ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Activation ◽  
Network Inference ◽  
Structure Learning ◽  
Multiple Scales ◽  
Real Data ◽  
Gene Regulatory

Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global–local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

scholarly journals GR-regulated enhancers play a central role in the gene regulatory networks underlying drug addiction

2022 ◽  
Author(s):  
Sascha Duttke ◽  
Patricia Montilla-Perez ◽  
Max W Chang ◽  
Hairi Li ◽  
Hao Chen ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Drug Exposure ◽  
Public Health Problem ◽  
Specific Gene ◽  
Major Public Health Problem ◽  
Self Administration ◽  
Deconvolution Analysis ◽  
Gene Regulatory ◽  
The Brain

Substance abuse and addiction represent a major public health problem that impacts multiple dimensions of society, including healthcare, economy, and workforce. In 2021, over 100,000 drug overdose deaths have been reported in the US with an alarming increase in fatalities related to opioids and psychostimulants. Understanding of the fundamental gene regulatory mechanisms underlying addiction and related behaviors could facilitate more effective treatments. To explore how repeated drug exposure alters gene regulatory networks in the brain, we combined capped small (cs)RNA-seq, which accurately captures nascent-like initiating transcripts from total RNA, with Hi-C and single nuclei (sn)ATAC-seq. We profiled initiating transcripts in two addiction-related brain regions, the prefrontal cortex (PFC) and the nucleus accumbens (NAc), from rats that were never exposed to drugs or were subjected to prolonged abstinence after oxycodone or cocaine intravenous self-administration (IVSA). Interrogating over 100,000 active transcription start regions (TSRs) revealed that most TSRs had hallmarks of bona-fide enhancers and highlighted the KLF/SP1, RFX and AP1 transcription factors families as central to establish brain-specific gene regulatory programs. Analysis of rats with addiction-like behaviors versus controls identified addiction-associated repression of transcription at regulatory enhancers recognized by nuclear receptor subfamily 3 group C (NR3C) factors, which include glucocorticoid receptors. Cell-type deconvolution analysis using snATAC-seq uncovered a potential role of glial cells in driving the gene regulatory programs associated with addiction-related phenotypes. These findings highlight the power of advanced transcriptomics methods to provide insight into how addiction perturbs gene regulatory programs in the brain.

scholarly journals The Interpersonal Neuroscience of Social Learning

2021 ◽  
pp. 174569162110084
Author(s):  
Yafeng Pan ◽  
Giacomo Novembre ◽  
Andreas Olsson
Keyword(s):  
Social Learning ◽  
Learning Outcomes ◽  
Paradigm Shift ◽  
Brain Activity ◽  
Mechanistic Models ◽  
Communicative Behaviors ◽  
Time Interaction ◽  
Transmission Of Information ◽  
The Rich ◽  
The Brain

The study of the brain mechanisms underpinning social behavior is currently undergoing a paradigm shift, moving its focus from single individuals to the real-time interaction among groups of individuals. Although this development opens unprecedented opportunities to study how interpersonal brain activity shapes behaviors through learning, there have been few direct connections to the rich field of learning science. Our article examines how the rapidly developing field of interpersonal neuroscience is (and could be) contributing to our understanding of social learning. To this end, we first review recent research extracting indices of brain-to-brain coupling (BtBC) in the context of social behaviors and, in particular, social learning. We then discuss how studying communicative behaviors during learning can aid the interpretation of BtBC and how studying BtBC can inform our understanding of such behaviors. We then discuss how BtBC and communicative behaviors collectively can predict learning outcomes, and we suggest several causative and mechanistic models. Finally, we highlight key methodological and interpretational challenges as well as exciting opportunities for integrating research in interpersonal neuroscience with social learning, and we propose a multiperson framework for understanding how interpersonal transmission of information between individual brains shapes social learning.

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