scholarly journals Dynamic brain network changes in resting-state reflect neuroplasticity: molecular and cognitive evidence

2019 ◽  
Author(s):  
Zhaowen Liu ◽  
Xiao Xiao ◽  
Kai Zhang ◽  
Qi Zhao ◽  
Xinyi Cao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Functional Connectivity ◽  
Resting State ◽  
Temporal Variability ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Brain Network ◽  
Brain Gene Expression ◽  
Functional Brain Networks

AbstractResting-state functional brain networks demonstrate dynamic changes on the scale of seconds. However, their genetic mechanisms and profound cognitive relevance remain less explored. We identified 459 Bonferroni-corrected genes, by associating temporal variability of regional functional connectivity patterns with Allen Brain gene expression profiles across the whole brain. These genes are partially verified in developing human brain gene expression in the BrainSpan Atlas, and are found to be involved in the enrichment of short- and long-term plasticity processes. The former process depends on synaptic plasticity, involving ion transmembrane transport, action potential propagation, and modulation. The latter process depends on structural plasticity, including axonal genesis, development, and guidance. Results from a longitudinal cognitive training study further revealed that baseline variability of the hippocampal network predicted cognitive ability changes after three months of training. Our genetic association results suggest that the short-term plasticity processes may account for the rapid changes of regional functional connectivity, while the underlying long-term plasticity processes explain why temporal variability can predict long-term learning outcomes. To our knowledge, this is the first demonstration that measuring the dynamic brain network can lead to a non-invasive quantification of neuroplasticity in humans.

scholarly journals Transcriptome-wide gene expression plasticity in Stipa grandis in response to grazing intensity differences

2020 ◽  
Author(s):  
Zhenhua Dang ◽  
Yuanyuan Jia ◽  
Yunyun Tian ◽  
Jiabin Li ◽  
Yanan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metabolic Pathways ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Grazing Intensity ◽  
Glycolate Oxidase ◽  
Bisphosphate Carboxylase ◽  
Grazing Intensities

Organisms have evolved effective and distinct adaptive strategies to survive. Stipa grandis is one of the widespread dominant species on the typical steppe of the Inner Mongolian Plateau, and is regarded as a suitable species for studying the effects of grazing in this region. Although phenotypic (morphological and physiological) variations in S. grandis in response to long-term grazing have been identified, the molecular mechanisms underlying adaptations and plastic responses remain largely unknown. Accordingly, we performed a transcriptomic analysis to investigate changes in gene expression of S. grandis under four different grazing intensities. A total of 2,357 differentially expressed genes (DEGs) were identified among the tested grazing intensities, suggesting long-term grazing resulted in gene expression plasticity that affected diverse biological processes and metabolic pathways in S. grandis. DEGs were identified that indicated modulation of Calvin–Benson cycle and photorespiration metabolic pathways. The key gene´expression profiles encoding various proteins (e.g., Ribulose-1,5-bisphosphate carboxylase/oxygenase, fructose-1,6-bisphosphate aldolase, glycolate oxidase etc.) involved in these pathways suggest that they may synergistically respond to grazing to increase the resilience and stress tolerance of S. grandis. Our findings provide scientific clues for improving grassland use and protection, and identify important questions to address in future transcriptome studies.

scholarly journals Brain network dynamics correlate with personality traits

2019 ◽  
Author(s):  
Aya Kabbara ◽  
Veronique Paban ◽  
Arnaud Weill ◽  
Julien Modolo ◽  
Mahmoud Hassan
Keyword(s):  
Functional Connectivity ◽  
Personality Traits ◽  
Resting State ◽  
Brain Networks ◽  
Neural Substrates ◽  
Brain Network ◽  
Functional Brain ◽  
Human Connectome Project ◽  
Functional Brain Networks ◽  
The Brain

AbstractIntroductionIdentifying the neural substrates underlying the personality traits is a topic of great interest. On the other hand, it is now established that the brain is a dynamic networked system which can be studied using functional connectivity techniques. However, much of the current understanding of personality-related differences in functional connectivity has been obtained through the stationary analysis, which does not capture the complex dynamical properties of brain networks.ObjectiveIn this study, we aimed to evaluate the feasibility of using dynamic network measures to predict personality traits.MethodUsing the EEG/MEG source connectivity method combined with a sliding window approach, dynamic functional brain networks were reconstructed from two datasets: 1) Resting state EEG data acquired from 56 subjects. 2) Resting state MEG data provided from the Human Connectome Project. Then, several dynamic functional connectivity metrics were evaluated.ResultsSimilar observations were obtained by the two modalities (EEG and MEG) according to the neuroticism, which showed a negative correlation with the dynamic variability of resting state brain networks. In particular, a significant relationship between this personality trait and the dynamic variability of the temporal lobe regions was observed. Results also revealed that extraversion and openness are positively correlated with the dynamics of the brain networks.ConclusionThese findings highlight the importance of tracking the dynamics of functional brain networks to improve our understanding about the neural substrates of personality.

scholarly journals Meta-analysis of gene expression profiles in long-term non-progressors infected with HIV-1

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Sun Young Lee ◽  
Yong Kwang Park ◽  
Cheol-Hee Yoon ◽  
Kisoon Kim ◽  
Kyung-Chang Kim
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Gene Expression Profiles ◽  
Hiv 1

scholarly journals Integrated analysis of the aging brain transcriptome and proteome in tauopathy

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Carl Grant Mangleburg ◽  
Timothy Wu ◽  
Hari K. Yalamanchili ◽  
Caiwei Guo ◽  
Yi-Chen Hsieh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Brain ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Mutant Form ◽  
Brain Gene Expression ◽  
Brain Transcriptome ◽  
Age Dependent

Abstract Background Tau neurofibrillary tangle pathology characterizes Alzheimer’s disease and other neurodegenerative tauopathies. Brain gene expression profiles can reveal mechanisms; however, few studies have systematically examined both the transcriptome and proteome or differentiated Tau- versus age-dependent changes. Methods Paired, longitudinal RNA-sequencing and mass-spectrometry were performed in a Drosophila model of tauopathy, based on pan-neuronal expression of human wildtype Tau (TauWT) or a mutant form causing frontotemporal dementia (TauR406W). Tau-induced, differentially expressed transcripts and proteins were examined cross-sectionally or using linear regression and adjusting for age. Hierarchical clustering was performed to highlight network perturbations, and we examined overlaps with human brain gene expression profiles in tauopathy. Results TauWT induced 1514 and 213 differentially expressed transcripts and proteins, respectively. TauR406W had a substantially greater impact, causing changes in 5494 transcripts and 697 proteins. There was a ~ 70% overlap between age- and Tau-induced changes and our analyses reveal pervasive bi-directional interactions. Strikingly, 42% of Tau-induced transcripts were discordant in the proteome, showing opposite direction of change. Tau-responsive gene expression networks strongly implicate innate immune activation. Cross-species analyses pinpoint human brain gene perturbations specifically triggered by Tau pathology and/or aging, and further differentiate between disease amplifying and protective changes. Conclusions Our results comprise a powerful, cross-species functional genomics resource for tauopathy, revealing Tau-mediated disruption of gene expression, including dynamic, age-dependent interactions between the brain transcriptome and proteome.

scholarly journals Identification of Monotonically Differentially Expressed Genes for IFN-β-Treated Multiple Sclerosis Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Suyan Tian ◽  
Lei Zhang
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Biologically Relevant ◽  
Therapeutic Mechanism ◽  
The Central Nervous System ◽  
Multiple Sclerosis Patients ◽  
Term Response

Multiple sclerosis (MS) is a common neurological disability of the central nervous system. Immune-modulatory therapy with interferon-β (IFN-β) has been used as a first-line treatment to prevent relapses in MS patients. While the therapeutic mechanism of IFN-β has not been fully elucidated, the data of microarray experiments that collected longitudinal gene expression profiles to evaluate the long-term response of IFN-β treatment have been analyzed using statistical methods that were incapable of dealing with such data. In this study, the GeneRank method was applied to generate weighted gene expression values and the monotonically expressed genes (MEGs) for both IFN-β treatment responders and nonresponders were identified. The proposed procedure identified 13 MEGs for the responders and 2 MEGs for the nonresponders, most of which are biologically relevant to MS. Our work here provides some useful insight into the mechanism of IFN-β treatment for MS patients. A full understanding of the therapeutic mechanism will enable a more personalized treatment strategy possible.

Divergent brain gene expression profiles between alternative behavioural helper types in a cooperative breeder

2018 ◽  
Vol 27 (20) ◽  
pp. 4136-4151 ◽  
Author(s):  
Claudia Kasper ◽  
Francois Olivier Hebert ◽  
Nadia Aubin-Horth ◽  
Barbara Taborsky
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Brain Gene Expression
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