Chronic restraint stress induces depression-like behaviors and alterations in the afferent projections of medial prefrontal cortex from multiple brain regions in mice.

INTRODUCTION: The medial prefrontal cortex (mPFC) forms output pathways through projection neurons, inversely receiving adjacent and long-range inputs from other brain regions. However, how afferent neurons of mPFC are affected by chronic stress needs to be clarified. In this study, the effects of chronic restraint stress (CRS) on the distribution density of mPFC dendrites/dendritic spines and the projections from the cortex and subcortical brain regions to the mPFC were investigated. METHODS: In the present study, C57BL/6 J transgenic (Thy1-YFP-H) mice were subjected to CRS to establish an animal model of depression. The infralimbic (IL) of mPFC was selected as the injection site of retrograde AAV using stereotactic technique. The effects of CRS on dendrites/dendritic spines and afferent neurons of the mPFC IL were investigaed by quantitatively assessing the distribution density of green fluorescent (YFP) positive dendrites/dendritic spines and red fluorescent (retrograde AAV recombinant protein) positive neurons, respectively. RESULTS: The results revealed that retrograde tracing virus labeled neurons were widely distributed in ipsilateral and contralateral cingulate cortex (Cg1), second cingulate cortex (Cg2), prelimbic cortex (PrL), infralimbic cortex, medial orbital cortex (MO), and dorsal peduncular cortex (DP). The effects of CRS on the distribution density of mPFC red fluorescence positive neurons exhibited regional differences, ranging from rostral to caudal or from top to bottom. Simultaneously, CRS resulted a decrease in the distribution density of basal, proximal and distal dendrites, as well as an increase in the loss of dendritic spines of the distal dendrites in the IL of mPFC. Furthermore, varying degrees of red retrograde tracing virus fluorescence signals were observed in other cortices, amygdala, hippocampus, septum/basal forebrain, hypothalamus, thalamus, mesencephalon, and brainstem in both ipsilateral and contralateral brain. CRS significantly reduced the distribution density of red fluorescence positive neurons in other cortices, hippocampus, septum/basal forebrain, hypothalamus, and thalamus. Conversely, CRS significantly increased the distribution density of red fluorescence positive neurons in amygdala. CONCLUSION: Our results suggest a possible mechanism that CRS leads to disturbances in synaptic plasticity by affecting multiple inputs to the mPFC, which is characterized by a decrease in the distribution density of dendrites/dendritic spines in the IL of mPFC and a reduction in input neurons of multiple cortices to the IL of mPFC as well as an increase in input neurons of amygdala to the IL of mPFC, ultimately causing depression-like behaviors.

GETdb: A comprehensive database for genetic and evolutionary features of drug targets.

The development of an innovative drug is complex and time-consuming, and the drug target identification is one of the critical steps in drug discovery process. Effective and accurate identification of drug targets can accelerate the drug development process. According to previous research, evolutionary and genetic information of genes has been found to facilitate the identification of approved drug targets. In addition, allosteric proteins have great potential as targets due to their structural diversity. However, this information that could facilitate target identification has not been collated in existing drug target databases. Here, we construct a comprehensive drug target database named Genetic and Evolutionary features of drug Targets database (GETdb, http://zhanglab.hzau.edu.cn/GETdb/page/index.jsp). This database not only integrates and standardizes data from dozens of commonly used drug and target databases, but also innovatively includes the genetic and evolutionary information of targets. Moreover, this database features an effective allosteric protein prediction model. GETdb contains approximately 4000 targets and over 29,000 drugs, and is a user-friendly database for searching, browsing and downloading data to facilitate the development of novel targets.

GBDT_KgluSite: An improved computational prediction model for lysine glutarylation sites based on feature fusion and GBDT classifier.

BACKGROUND: Lysine glutarylation (Kglu) is one of the most important Post-translational modifications (PTMs), which plays significant roles in various cellular functions, including metabolism, mitochondrial processes, and translation. Therefore, accurate identification of the Kglu site is important for elucidating protein molecular function. Due to the time-consuming and expensive limitations of traditional biological experiments, computational-based Kglu site prediction research is gaining more and more attention. RESULTS: In this paper, we proposed GBDT_KgluSite, a novel Kglu site prediction model based on GBDT and appropriate feature combinations, which achieved satisfactory performance. Specifically, seven features including sequence-based features, physicochemical property-based features, structural-based features, and evolutionary-derived features were used to characterize proteins. NearMiss-3 and Elastic Net were applied to address data imbalance and feature redundancy issues, respectively. The experimental results show that GBDT_KgluSite has good robustness and generalization ability, with accuracy and AUC values of 93.73%, and 98.14% on five-fold cross-validation as well as 90.11%, and 96.75% on the independent test dataset, respectively. CONCLUSION: GBDT_KgluSite is an effective computational method for identifying Kglu sites in protein sequences. It has good stability and generalization ability and could be useful for the identification of new Kglu sites in the future. The relevant code and dataset are available at https://github.com/flyinsky6/GBDT_KgluSite .

Deep_KsuccSite: A novel deep learning method for the identification of lysine succinylation sites.

Identification of lysine (symbol Lys or K) succinylation (Ksucc) sites centralizes the basis for disclosing the mechanism and function of lysine succinylation modifications. Traditional experimental methods for Ksucc site ientification are often costly and time-consuming. Therefore, it is necessary to construct an efficient computational method to prediction the presence of Ksucc sites in protein sequences. In this study, we proposed a novel and effective predictor for the identification of Ksucc sites based on deep learning algorithms that was termed as Deep_KsuccSite. The predictor adopted Composition, Transition, and Distribution (CTD) Composition (CTDC), Enhanced Grouped Amino Acid Composition (EGAAC), Amphiphilic Pseudo-Amino Acid Composition (APAAC), and Embedding Encoding methods to encode peptides, then constructed three base classifiers using one-dimensional (1D) convolutional neural network (CNN) and 2D-CNN, and finally utilized voting method to get the final results. K-fold cross-validation and independent testing showed that Deep_KsuccSite could serve as an effective tool to identify Ksucc sites in protein sequences. In addition, the ablation experiment results based on voting, feature combination, and model architecture showed that Deep_KsuccSite could make full use of the information of different features to construct an effective classifier. Taken together, we developed Deep_KsuccSite in this study, which was based on deep learning algorithm and could achieved better prediction accuracy than current methods for lysine succinylation sites. The code and dataset involved in this methodological study are permanently available at the URL https://github.com/flyinsky6/Deep_KsuccSite.

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms.

Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo amino acid compositions, which were then carried out at two levels: categories and features. In brief, extra-tree was initially used for feature selection while SVM was then used to build the classification model. After that, the most suitable models for each category and each feature were selected by comparing with the three ensemble learning algorithms, that is, Random Forest, Adaboost, and Xgboost. According to our results, profile-based features were more suitable for building predictive models among the four categories. AUC value of the model constructed by Xgboost algorithm on independent data set could reach 92.66%. Moreover, Distance-based Residue, Physicochemical Distance Transformation and Profile-based Physicochemical Distance Transformation performed much better among the 17 features. AUC value of the Adaboost classifier constructed by Profile-based Physicochemical Distance Transformation on the independent dataset achieved 93.74%. Taken together, we proposed a better model with improved prediction capacity for protein-protein interactions between human and HCV in this study, which could provide practical reference for further experimental investigation into HCV-related diseases in future.Communicated by Ramaswamy H. Sarma.

Estradiol attenuates chronic restraint stress-induced dendrite and dendritic spine loss and cofilin1 activation in ovariectomized mice.

Ovarian hormone deprivation is associated with mood disorders, such as depression, and estradiol therapy is significantly more effective than placebos in treating major depression associated with menopause onset. However, the effect of estradiol on neuronal plasticity and its mechanisms remain to be further elucidated. In this study, behavioral assessments were used to examine the antidepressant effect of estradiol in ovariectomized (OVX) B6.Cg-TgN (Thy-YFP-H)-2Jrs transgenic mice on chronic restraint stress (CRS)-induced dendrite and dendritic spine loss; Yellow fluorescent protein (YFP) is characteristically expressed in excitatory neurons in transgenic mice, and its three-dimensional images were used to evaluate the effect of estradiol on the density of different types of dendritic spines. Quantification and distribution of cofilin1 and p-cofilin1 were determined by qPCR, Western blots, and immunohistochemistry, respectively. The results revealed that treatment with estradiol or clomipramine significantly improved depression-like behaviors. Estradiol treatment also significantly upregulated the dendritic density in all areas examined and increased the density of filopodia-type, thin-type and mushroom-type spines in the hippocampal CA1 and elevated the thin-type and mushroom-type spine density in the PFC. Consistent with these changes, estradiol treatment significantly increased the density of p-cofilin1 immunopositive dendritic spines. Thus, these data reveal a possible estradiol antidepressant mechanism, in that estradiol promoted the phosphorylation of cofilin1 and reduced the loss of dendrites and dendritic spines, which of these dendritic spines include not only immature spines such as filopodia-type, but also mature spines such as mushroom-type, and attenuated the depression-like behavior.

Endothelium-derived Cdk5 deficit aggravates air pollution-induced peripheral vasoconstriction through AT1R upregulation.

PM2.5 infiltrates into circulation and increases the risk of systemic vascular dysfunction. As the first-line barrier against external stimuli, the molecular mechanism of the biological response of vascular endothelial cells to PM2.5 exposure remains unclear. In this study, 4-week-old mice were exposed to Hangzhou 'real' airborne PM2.5 for 2 months and were found to display bronchial and alveolar damage. Importantly, in the present study, we have demonstrated that Cdk5 deficit induced peripheral vasoconstriction through angiotensin II type 1 receptor under angiotensin II stimulation in Cdh5-cre;Cdk5f/n mice. In the brain, Cdk5 deficit increased the myogenic activity in the medullary arterioles under external pressure. On the other hand, no changes in cerebral blood flow and behavior patterns were observed in the Cdh5-cre;Cdk5f/n mice exposed to PM2.5. Therefore, our current findings indicate that CDK5 plays an important role in endothelium cell growth, migration, and molecular transduction, which is also a sensor for the response of vascular endothelial cells to PM2.5.

The Antidepressant Effects of Resveratrol are Accompanied by the Attenuation of Dendrite/Dendritic Spine Loss and the Upregulation of BDNF/p-cofilin1 Levels in Chronic Restraint Mice.

Depression afflicts more than 300 million people worldwide, but there is currently no universally effective drug in clinical practice. In this study, chronic restraint stress (CRS)-induced mice depression model was used to study the antidepressant effects of resveratrol and its mechanism. Our results showed that resveratrol significantly attenuated depression-like behavior in mice. Consistent with behavioral changes, resveratrol significantly attenuated CRS-induced reduction in the density of dendrites and dendritic spines in both hippocampus and medial prefrontal cortex (mPFC). Meanwhile, in hippocampus and mPFC, resveratrol consistently alleviated CRS-induced cofilin1 activation by increasing its ser3 phosphorylation. In addition, cofilin1 immunofluorescence distribution in neuronal inner peri-membrane in controls, and cofilin1 diffusely distribution in the cytoplasm in CRS group were common in hippocampus. However, the distribution of cofilin1 in mPFC was reversed. Pearson's correlation analysis revealed that there was a significant positive correlation found between the sucrose consumption in sucrose preference test and the dendrite density in multiple sub-regions of hippocampus and mPFC, and a significant negative correlation between the immobility time in tail suspension test and the dendrite/dendritic spine density in several different areas of hippocampus and mPFC. P-cofilin1 was significantly positively correlated with the overall dendritic spine density in mPFC as well as with the overall dendrite density or BDNF in the hippocampus. Our results suggest that the BDNF/cofilin1 pathway, in which cofilin1 may be activated in a brain-specific manner, was involved in resveratrol's attenuating the dendrite and dendritic spine loss and behavioral abnormality.

Exploring the Genetic Association of the ABAT Gene with Alzheimer's Disease.

Accumulating evidence demonstrated that GABAergic dysfunction contributes to the pathogenesis of Alzheimer's disease (AD). The GABA aminotransferase (ABAT) gene encodes a mitochondrial GABA transaminase and plays key roles in the biogenesis and metabolism of gamma-aminobutyric acid (GABA), which is a major inhibitory neurotransmitter. In this study, we performed an integrative study at the genetic and expression levels to investigate the potential genetic association between the ABAT gene and AD. Through re-analyzing data from the currently largest meta-analysis of AD genome-wide association study (GWAS), we identified genetic variants in the 3'-UTR of ABAT as the top AD-associated SNPs (P < 1 × 10-4) in this gene. Functional annotation of these AD-associated SNPs indicated that these SNPs are located in the regulatory regions of transcription factors or/and microRNAs. Expression quantitative trait loci (eQTL) analysis and luciferase reporter assay showed that the AD risk alleles of these SNPs were associated with a reduced expression level of ABAT. Further analysis of mRNA expression data and single-cell transcriptome data of AD patients showed that ABAT reduction in the neuron is an early event during AD development. Overall, our results indicated that ABAT genetic variants may be associated with AD through affecting its mRNA expression. An abnormal level of ABAT will lead to a disturbance of the GABAergic signal pathway in AD brains.