Early Life Stress Alters Gene Expression and Cytoarchitecture in the Prefrontal Cortex Leading to Social Impairment and Increased Anxiety.
TL;DR: In this article, the authors performed social isolation on weaned pre-adolescent mice until adolescence and investigated these behaviors and PFC characteristics in adolescent mice and found that early life stress (ELS) induced social impairments in social novelty, social interaction, and social preference.
Abstract: Early life stress (ELS), such as abuse, neglect, and maltreatment, exhibits a strong impact on the brain and mental development of children. However, it is not fully understood how ELS affects social behaviors and social-associated behaviors as well as developing prefrontal cortex (PFC). In this study, we performed social isolation on weaned pre-adolescent mice until adolescence and investigated these behaviors and PFC characteristics in adolescent mice. We found the ELS induced social impairments in social novelty, social interaction, and social preference in adolescent mice. We also observed increases of anxiety-like behaviors in ELS mice. In histological analysis, we found a reduced number of neurons and an increased number of microglia in the PFC of ELS mice. To identify the gene associated with behavioral and histological features, we analyzed transcriptome in the PFC of ELS mice and identified 15 differentially expressed genes involved in transcriptional regulation, stress, and synaptic signaling. Our study demonstrates that ELS influences social behaviors, anxiety-like behaviors through cytoarchitectural and transcriptomic alterations in the PFC of adolescent mice.
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TL;DR: The types of ELA that may be driving different neuropsychiatric outcomes and brain changes in humans are investigated and whether rodent models of Ela can provide translationally relevant information regarding links between specific types of experience and changes in neural circuits underlying dysfunction is evaluated.
Abstract: It is now well-established that early life adversity (ELA) predisposes individuals to develop several neuropsychiatric conditions, including anxiety disorders, and major depressive disorder. However, ELA is a very broad term, encompassing multiple types of negative childhood experiences, including physical, sexual and emotional abuse, physical and emotional neglect, as well as trauma associated with chronic illness, family separation, natural disasters, accidents, and witnessing a violent crime. Emerging literature suggests that in humans, different types of adverse experiences are more or less likely to produce susceptibilities to certain conditions that involve affective dysfunction. To investigate the driving mechanisms underlying the connection between experience and subsequent disease, neuroscientists have developed several rodent models of ELA, including pain exposure, maternal deprivation, and limited resources. These studies have also shown that different types of ELA paradigms produce different but somewhat overlapping behavioral phenotypes. In this review, we first investigate the types of ELA that may be driving different neuropsychiatric outcomes and brain changes in humans. We next evaluate whether rodent models of ELA can provide translationally relevant information regarding links between specific types of experience and changes in neural circuits underlying dysfunction.
13 citations
TL;DR: S-ketamine is a promising drug in alleviating mechanical allodynia, anxiety-like behaviors, and pro-inflammatory responses in discrete brain regions in a model of PTSD.
Abstract: This study aims to explore the regulatory effect of S-ketamine on the mechanical allodynia, anxiety-like behaviors and microglia activation in adult male rats exposed to an animal model of post-traumatic stress disorder (PTSD). The rat PTSD model was established by the exposure to single-prolonged stress (SPS), and 1 day later, rats were intraperitoneally injected with 5 mg/kg S-ketamine or normal saline, respectively. Paw withdrawal mechanical threshold was measured 2 days before, and 1, 3, 5, 7, 10, 14, 21 and 28 days after injection to assess mechanical allodynia in the SPS-exposed rats. For anxiety-like behaviors, the open field test and elevated plus maze test were performed at 7 and 14 days after S-ketamine treatment in the SPS-exposed rats, respectively. SPS-induced rats presented pronounced mechanical allodynia and anxiety-like behaviors, which were alleviated by S-ketamine treatment. After behavioral tests, rats were sacrificed for collecting the anterior cingulate cortex (ACC), prefrontal cortex (PFC), dorsal striatum, and periaqueductal gray (PAG). Protein levels of TNF-α, IL-1β, p-NF-κB, and NF-κB in brain regions were examined by Western blot. In addition, microglia activation in each brain region was determined by immunofluorescence staining of the microglia-specific biomarker Iba-1. Interestingly, pro-inflammatory cytokines were significantly upregulated in the dorsal striatum and PAG, rather than ACC and PFC. Activated microglia was observed in the dorsal striatum and PAG as well, and upregulated p-NF-κB was detected in the dorsal striatum. Inflammatory response, phosphorylation of NF-κB and microglia activation in certain brain regions were significantly alleviated by S-ketamine treatment. Collectively, S-ketamine is a promising drug in alleviating mechanical allodynia, anxiety-like behaviors, and pro-inflammatory responses in discrete brain regions in a model of PTSD.
6 citations
TL;DR: It is demonstrated that the Si-based agent is an effective prophylactic agent against MIA during pregnancy, suggesting that this silicon (Si)-based hydrogen-producing antioxidant agent may be a preventative or therapeutic agent for ASD and other disease risks in child health suppressing MIA damage.
Abstract: Maternal immune activation (MIA) is triggered by infection or autoimmune predisposition during pregnancy, and cytokines produced by MIA are transmitted through the placenta to the fetal brain, implicating at the onset risks and vulnerability for developmental and psychiatric disorders, such as autism spectrum disorder (ASD) and schizophrenia. To address these kinds of problem in child health, we have developed a silicon (Si)-based hydrogen-producing antioxidant (Si-based agent) that continuously and effectively produces hydrogen in the body. Medical hydrogen is known to have antioxidative, anti-inflammatory, and antiapoptotic effects, therefore we applied our Si-based agent as a potential therapeutic agent to MIA. Using a MIA mouse model, we found that the Si-based agent improved the social communication of MIA offspring mice. We also found that the Si-based agent suppressed the expressions of inflammation-associated genes Ifna1 and Il-6 in the mouse brain. These results demonstrate that the Si-based agent is an effective prophylactic agent against MIA during pregnancy, suggesting that our Si-based agent may be a preventative or therapeutic agent for ASD and other disease risks in child health suppressing MIA damage.
4 citations
TL;DR: The evidence that animal models of early adversity impinge on fundamental mechanisms of brain aging is discussed, setting up a substratum that can accelerate and compromise the time-line and nature of brain Aging, and increase risk for aging-associated neuropathologies.
Abstract: Early adversity is an important risk factor that influences brain aging. Diverse animal models of early adversity, including gestational stress and postnatal paradigms disrupting dam-pup interactions evoke not only persistent neuroendocrine dysfunction and anxio-depressive behaviors, but also perturb the trajectory of healthy brain aging. The process of brain aging is thought to involve hallmark features such as mitochondrial dysfunction and oxidative stress, evoking impairments in neuronal bioenergetics. Furthermore, brain aging is associated with disrupted proteostasis, progressively defective epigenetic and DNA repair mechanisms, the build-up of neuroinflammatory states, thus cumulatively driving cellular senescence, neuronal and cognitive decline. Early adversity is hypothesized to evoke an “allostatic load” via an influence on several of the key physiological processes that define the trajectory of healthy brain aging. In this review we discuss the evidence that animal models of early adversity impinge on fundamental mechanisms of brain aging, setting up a substratum that can accelerate and compromise the time-line and nature of brain aging, and increase risk for aging-associated neuropathologies.
3 citations
TL;DR: In this article , the contribution of ceramides metabolism as a modulator of major psychiatric disorders such as depression, anxiety, bipolar disorder, schizophrenia, and attention deficit hyperactivity disorder was addressed.
Abstract: Psychiatric disorders affect 970 million people worldwide, representing a significant source of disability. Although the underlying neurobiological traits for these disorders are not fully understood, a complex interplay between psychological, environmental, and biological factors contributes to their outcomes. Recent advances in lipidomic analysis and artificial intelligence algorithms have improved the identification of selective lipid species modulating the susceptibility to mental disorders. Sphingolipids (SLs) and ceramides‐related SLs are among the most abundant lipids species in the brain that support major key pathways during neurodevelopment and brain plasticity. High levels of ceramides in plasma and brain contribute to psychiatric illness susceptibility in humans and animal models. However, the neuropathological mechanism regarding the involvement of ceramides in these disorders remain inconclusive. The brain is highly susceptible to nutritional insults, which could lead to functional impairment and influence the development and progression of psychiatric disorders. While the brain relies on glucose metabolism to support its physiological needs, a selective nutrient formula appears to have greater effects on brain health than others. For instance, consumption of high‐energy diets is associated with brain anatomical, physiological, and metabolic changes, including ceramides metabolism. Herein, we will address the contribution of ceramides metabolism as a modulator of major psychiatric disorders such as depression, anxiety, bipolar disorder, schizophrenia, and attention deficit‐hyperactivity disorder. We will also describe molecular and cellular targets of ceramides metabolism assisting the maintenance and progression of psychiatric disorders and their modulation by dietary formulas as non‐pharmacologic treatments.
3 citations
References
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TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
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47,038 citations
TL;DR: Timmomatic is developed as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data and is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.
Abstract: Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data.
Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested.
Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic
Contact: ed.nehcaa-htwr.1oib@ledasu
Supplementary information: Supplementary data are available at Bioinformatics online.
39,291 citations
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
30,684 citations
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
29,413 citations
TL;DR: This work presents HTSeq, a Python library to facilitate the rapid development of custom scripts for high-throughput sequencing data analysis, and presents htseq-count, a tool developed with HTSequ that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes.
Abstract: Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an opensource software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
15,744 citations