Antidepressant actions of the exercise-regulated gene VGF

Exercise has many health benefits, including antidepressant actions in depressed human subjects, but the mechanisms underlying these effects have not been elucidated. We used a custom microarray to identify a previously undescribed profile of exercise-regulated genes in the mouse hippocampus, a brain region implicated in mood and antidepressant response. Pathway analysis of the regulated genes shows that exercise upregulates a neurotrophic factor signaling cascade that has been implicated in the actions of antidepressants. One of the most highly regulated target genes of exercise and of the growth factor pathway is the gene encoding the VGF nerve growth factor, a peptide precursor previously shown to influence synaptic plasticity and metabolism. We show that administration of a synthetic VGF-derived peptide produces a robust antidepressant response in mice and, conversely, that mutation of VGF in mice produces the opposite effects. The results suggest a new role for VGF and identify VGF signaling as a potential therapeutic target for antidepressant drug development.     

A Pathway-Based View of Human Diseases and Disease Relationships

It is increasingly evident that human diseases are not isolated from each other. Understanding how different diseases are related to each other based on the underlying biology could provide new insights into disease etiology, classification, and shared biological mechanisms. We have taken a computational approach to studying disease relationships through 1) systematic identification of disease associated genes by literature mining, 2) associating diseases to biological pathways where disease genes are enriched, and 3) linking diseases together based on shared pathways. We identified 4,195 candidate disease associated genes for 1028 diseases. On average, about 50% of disease associated genes of a disease are statistically mapped to pathways. We generated a disease network which consists of 591 diseases and 6,931 disease relationships. We examined properties of this network and provided examples of novel disease relationships which cannot be readily captured through simple literature search or gene overlap analysis. Our results could potentially provide insights into the design of novel, pathway-guided therapeutic interventions for diseases.     

Antidepressant treatment effects on hippocampal protein turnover: Molecular and spatial insights from mass spectrometry

A major challenge in managing depression is that antidepressant drugs take a long time to exert their therapeutic effects. For the development of faster-acting treatments, it is crucial to get an improved understanding of the molecular mechanisms underlying antidepressant mode of action. Here, we used a novel mass spectrometry-based workflow to investigate how antidepressant treatment affects brain protein turnover at single-cell and subcellular resolution. We combined stable isotope metabolic labeling, quantitative Tandem Mass Spectrometry (qTMS) and Multi-isotope Imaging Mass Spectrometry (MIMS) to simultaneously quantify and image protein synthesis and turnover in hippocampi of mice treated with the antidepressant paroxetine. We identified changes in turnover of individual hippocampal proteins that reveal altered metabolism-mitochondrial processes and report on subregion-specific turnover patterns upon paroxetine treatment. This workflow can be used to investigate brain protein turnover changes in vivo upon pharmacological interventions at a resolution and precision that has not been possible with other methods to date. Our results reveal acute paroxetine effects on brain protein turnover and shed light on antidepressant mode of action.     

Microarray analysis of ageing-related signatures and their expression in tumors based on a computational biology approach

Ageing and cancer have been associated with genetic and genomic changes.The identification of common signatures between ageing and cancer can reveal shared molecular mechanisms underlying them.In this study,we collected ageing-related gene signatures from ten published studies involved in six different human tissues and an online resource.We found that most of these gene signatures were tissuespecific and a few were related to multiple tissues.We performed a genome-wide examination of the expression of these signatures in various human tumor types,and found that a large proportion of these signatures were universally differentially expressed among normal vs.tumor phenotypes.Functional analyses of the highly-overlapping genes between ageing and cancer using DAVID tools have identified important functional categories and pathways linking ageing with cancer.The convergent and divergent mechanisms between ageing and cancer are discussed.This study provides insights into the biology of ageing and cancer,suggesting the possibility of potential interventions aimed at postponing ageing and preventing cancer.     

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution.     

Genes and (common) pathways underlying drug addiction

Drug addiction is a serious worldwide problem with strong genetic and environmental influences. Different technologies have revealed a variety of genes and pathways underlying addiction; however, each individual technology can be biased and incomplete. We integrated 2,343 items of evidence from peer-reviewed publications between 1976 and 2006 linking genes and chromosome regions to addiction by single-gene strategies, microrray, proteomics, or genetic studies. We identified 1,500 human addiction-related genes and developed KARG (http://karg.cbi.pku.edu.cn), the first molecular database for addiction-related genes with extensive annotations and a friendly Web interface. We then performed a meta-analysis of 396 genes that were supported by two or more independent items of evidence to identify 18 molecular pathways that were statistically significantly enriched, covering both upstream signaling events and downstream effects. Five molecular pathways significantly enriched for all four different types of addictive drugs were identified as common pathways which may underlie shared rewarding and addictive actions, including two new ones, GnRH signaling pathway and gap junction. We connected the common pathways into a hypothetical common molecular network for addiction. We observed that fast and slow positive feedback loops were interlinked through CAMKII, which may provide clues to explain some of the irreversible features of addiction.     

Time-dependent network analysis reveals molecular targets underlying the development of diet-induced obesity and non-alcoholic steatohepatitis

Prolonged high-fat diet leads to the development of obesity and multiple comorbidities including non-alcoholic steatohepatitis (NASH), but the underlying molecular basis is not fully understood. We combine molecular networks and time course gene expression profiles to reveal the dynamic changes in molecular networks underlying diet-induced obesity and NASH. We also identify hub genes associated with the development of NASH. Core diet-induced obesity networks were constructed using Ingenuity pathway analysis (IPA) based on 332 high-fat diet responsive genes identified in liver by time course microarray analysis (8 time points over 24 weeks) of high-fat diet-fed mice compared to normal diet-fed mice. IPA identified five core diet-induced obesity networks with time-dependent gene expression changes in liver. These networks were associated with cell-to-cell signaling and interaction (Network 1), lipid metabolism (Network 2), hepatic system disease (Network 3 and 5), and inflammatory response (Network 4). When we merged these core diet-induced obesity networks, Tlr2, Cd14, and Ccnd1 emerged as hub genes associated with both liver steatosis and inflammation and were altered in a time-dependent manner. Further, protein–protein interaction network analysis revealed Tlr2, Cd14, and Ccnd1 were interrelated through the ErbB/insulin signaling pathway. Dynamic changes occur in molecular networks underlying diet-induced obesity. Tlr2, Cd14, and Ccnd1 appear to be hub genes integrating molecular interactions associated with the development of NASH. Therapeutics targeting hub genes and core diet-induced obesity networks may help ameliorate diet-induced obesity and NASH.     

Identification of New Candidate Genes and Chemicals Related to Esophageal Cancer Using a Hybrid Interaction Network of Chemicals and Proteins

Cancer is a serious disease responsible for many deaths every year in both developed and developing countries. One reason is that the mechanisms underlying most types of cancer are still mysterious, creating a great block for the design of effective treatments. In this study, we attempted to clarify the mechanism underlying esophageal cancer by searching for novel genes and chemicals. To this end, we constructed a hybrid network containing both proteins and chemicals, and generalized an existing computational method previously used to identify disease genes to identify new candidate genes and chemicals simultaneously. Based on jackknife test, our generalized method outperforms or at least performs at the same level as those obtained by a widely used method - the Random Walk with Restart (RWR). The analysis results of the final obtained genes and chemicals demonstrated that they highly shared gene ontology (GO) terms and KEGG pathways with direct and indirect associations with esophageal cancer. In addition, we also discussed the likelihood of selected candidate genes and chemicals being novel genes and chemicals related to esophageal cancer.     

Global State Measures of the Dentate Gyrus Gene Expression System Predict Antidepressant-Sensitive Behaviors

Background

Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common form of medication treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems respond to treatments may be critical for understanding antidepressant resistance.

Methods

We take a novel approach to this problem by demonstrating that the gene expression system of the dentate gyrus responds to fluoxetine (FLX), a commonly used antidepressant medication, in a stereotyped-manner involving changes in the expression levels of thousands of genes. The aggregate behavior of this large-scale systemic response was quantified with principal components analysis (PCA) yielding a single quantitative measure of the global gene expression system state.

Results

Quantitative measures of system state were highly correlated with variability in levels of antidepressant-sensitive behaviors in a mouse model of depression treated with fluoxetine. Analysis of dorsal and ventral dentate samples in the same mice indicated that system state co-varied across these regions despite their reported functional differences. Aggregate measures of gene expression system state were very robust and remained unchanged when different microarray data processing algorithms were used and even when completely different sets of gene expression levels were used for their calculation.

Conclusions

System state measures provide a robust method to quantify and relate global gene expression system state variability to behavior and treatment. State variability also suggests that the diversity of reported changes in gene expression levels in response to treatments such as fluoxetine may represent different perspectives on unified but noisy global gene expression system state level responses. Studying regulation of gene expression systems at the state level may be useful in guiding new approaches to augmentation of traditional antidepressant treatments.     

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease(CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism(SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium(WTCCC) SNP datasets of CAD and control samples were used to assess the jointeffect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction(PPI)knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease.These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.     

Genomic profiling of cortical neurons following exposure to beta-amyloid

In vitro and in vivo studies have shown that beta-amyloid peptide induces neuronal cell death. To explore the molecular basis underlying beta-amyloid-induced toxicity, we analyzed gene expression profiles of cultured rat cortical neurons treated for 24 and 48 h with synthetic beta-amyloid peptide. From the 8740 genes interrogated by oligonucleotide microarray analysis, 241 genes were found to be differentially expressed and segregated into distinct clusters. Functional clustering based on gene ontologies showed coordinated expression of genes with common biological functions and metabolic pathways. The comparison with genes differentially expressed in cerebellar granule neurons following serum and potassium deprivation indicates the existence of common regulatory mechanisms underlying neuronal cell death. Our results offer a genomic view of the changes that accompany beta-amyloid-induced neurodegeneration.