Disease-gene Interactions Research Articles

Role of ribosomal pathways and comorbidity in COVID-19: Insight from SARS-CoV-2 proteins and host proteins interaction network analysis

The COVID-19 pandemic has become a significant global issue in terms of public health. While it is largely associated with respiratory complications, recent reports indicate that patients also experience neurological symptoms and other health issues. The objective of this study is to examine the network of protein-protein interactions (PPI) between SARS-CoV-2 proteins and human host proteins, pinpoint the central genes within this network implicated in disease pathology, and assess their viability as targets for drug development.The study adopts a network-based approach to construct a network of 29 SARS-CoV-2 proteins interacting with 2896 host proteins, with 176 host genes being identified as interacting genes with all the viral proteins. Gene ontology and pathway analysis of these host proteins revealed their role in biological processes such as translation, mRNA splicing, and ribosomal pathways. We further identified EEF2, RPS3, RPL9, RPS16, and RPL11 as the top 5 most connected hub genes in the disease-causing network, with significant interactions among each other. These hub genes were found to be involved in ribosomal pathways and cytoplasmic translation. Further a disease-gene interaction was also prepared to investigate the role of hub genes in other disorders and to understand the condition of comorbidity in COVID-19 patients. We also identified 13 drug molecules having interactions with all the hub genes, and estradiol emerged as the top potential drug target for the COVID-19 patients. Our study provides valuable insights using the protein-protein interaction network of SARS-CoV-2 proteins with host proteins and highlights the molecular basis of manifestation of COVID-19 and proposes drug for repurposing.As the pandemic continues to evolve, it is anticipated that investigating SARS-CoV-2 proteins will remain a critical area of focus for researchers globally, particularly in addressing potential challenges posed by specific SARS-CoV-2 variants in the future.

Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning

BackgroundDiscovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require a huge number of samples, while known DDIs are rare.MethodsIn this work, we present KnowDDI, a graph neural network-based method that addresses the above challenge. KnowDDI enhances drug representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns a knowledge subgraph for each drug-pair to interpret the predicted DDI, where each of the edges is associated with a connection strength indicating the importance of a known DDI or resembling strength between a drug-pair whose connection is unknown. Thus, the lack of DDIs is implicitly compensated by the enriched drug representations and propagated drug similarities.ResultsHere we show the evaluation results of KnowDDI on two benchmark DDI datasets. Results show that KnowDDI obtains the state-of-the-art prediction performance with better interpretability. We also find that KnowDDI suffers less than existing works given a sparser knowledge graph. This indicates that the propagated drug similarities play a more important role in compensating for the lack of DDIs when the drug representations are less enriched.ConclusionsKnowDDI nicely combines the efficiency of deep learning techniques and the rich prior knowledge in biomedical knowledge graphs. As an original open-source tool, KnowDDI can help detect possible interactions in a broad range of relevant interaction prediction tasks, such as protein-protein interactions, drug-target interactions and disease-gene interactions, eventually promoting the development of biomedicine and healthcare.

Potential mechanisms and drug prediction of Rheumatoid Arthritis and primary Sjögren's Syndrome: A public databases-based study.

Rheumatoid arthritis (RA) and primary Sjögren's syndrome (pSS) are the most common systemic autoimmune diseases, and they are increasingly being recognized as occurring in the same patient population. These two diseases share several clinical features and laboratory parameters, but the exact mechanism of their co-pathogenesis remains unclear. The intention of this study was to investigate the common molecular mechanisms involved in RA and pSS using integrated bioinformatic analysis. RNA-seq data for RA and pSS were picked up from the Gene Expression Omnibus (GEO) database. Co-expression genes linked with RA and pSS were recognized using weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. Then, we screened two public disease-gene interaction databases (GeneCards and Comparative Toxicogenomics Database) for common targets associated with RA and pSS. The DGIdb database was used to predict therapeutic drugs for RA and pSS. The Human microRNA Disease Database (HMDD) was used to screen out the common microRNAs associated with RA and pSS. Finally, a common miRNA-gene network was created using Cytoscape. Four hub genes (CXCL10, GZMA, ITGA4, and PSMB9) were obtained from the intersection of common genes from WGCNA, differential gene analysis and public databases. Twenty-four drugs corresponding to hub gene targets were predicted in the DGIdb database. Among the 24 drugs, five drugs had already been reported for the treatment of RA and pSS. Other drugs, such as bortezomib, carfilzomib, oprozomib, cyclosporine and zidovudine, may be ideal drugs for the future treatment of RA patients with pSS. According to the miRNA-gene network, hsa-mir-21 may play a significant role in the mechanisms shared by RA and pSS. In conclusion, we identified commom targets as potential biomarkers in RA and pSS from publicly available databases and predicted potential drugs based on the targets. A new understanding of the molecular mechanisms associated with RA and pSS is provided according to the miRNA-gene network.

CRISPR gene technology—the next best thing in medicine

CRISPR gene technology—the next best thing in medicine

Network-based approach for targeting human kinases commonly associated with amyotrophic lateral sclerosis and cancer.

Amyotrophic Lateral Sclerosis (ALS) is a rare progressive and chronic motor neuron degenerative disease for which at present no cure is available. In recent years, multiple genes encode kinases and other causative agents for ALS have been identified. Kinases are enzymes that show pleiotropic nature and regulate different signal transduction processes and pathways. The dysregulation of kinase activity results in dramatic changes in processes and causes many other human diseases including cancers. In this study, we have adopted a network-based system biology approach to investigate the kinase-based molecular interplay between ALS and other human disorders. A list of 62 ALS-associated-kinases was first identified and then we identified the disease associated with them by scanning multiple disease-gene interaction databases to understand the link between the ALS-associated kinases and other disorders. An interaction network with 36 kinases and 381 different disorders associated with them was prepared, which represents the complexity and the comorbidity associated with the kinases. Further, we have identified 5 miRNAs targeting the majority of the kinases in the disease-causing network. The gene ontology and pathways enrichment analysis of those miRNAs were performed to understand their biological and molecular functions along with to identify the important pathways. We also identified 3 drug molecules that can perturb the disease-causing network by drug repurposing. This network-based study presented hereby contributes to a better knowledge of the molecular underpinning of comorbidities associated with the kinases associated with the ALS disease and provides the potential therapeutic targets to disrupt the highly complex disease-causing network.

A Network-Based Analysis of Disease Modules From a Taxonomic Perspective.

Human-curated diseaseontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification principles underlying these ontologies are guided by the analysis of observable pathological similarities between disorders, often based on anatomical or histological principles. Although, thanks to recent advances in molecular biology, disease ontologies are slowly changing to integrate the etiological and genetic origins of diseases, nosology still reflects this "reductionist" perspective. Proximity relationships of disease modules (hereafter DMs) in the human interactome network are now increasingly used in diagnostics, to identify pathobiologically similar diseases and to support drug repurposing and discovery. On the other hand, similarity relations induced from structural proximity of DMs also have several limitations, such as incomplete knowledge of disease-gene relationships and reliability of clinical trials to assess their validity. The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related DMs in the interactome. We propose a method (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy. We demonstrate that the proposed method allows to systematically analyze commonalities and differences among structural and categorical similarity of human diseases, help refine and extend human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.

Potential Therapeutic Targets of Rehmannia Formulations on Diabetic Nephropathy: A Comparative Network Pharmacology Analysis.

Background: Previous retrospective cohorts showed that Rehmannia-6 (R-6, Liu-wei-di-huang-wan) formulations were associated with significant kidney function preservation and mortality reduction among chronic kidney disease patients with diabetes. This study aimed to investigate the potential mechanism of action of common R-6 variations in a clinical protocol for diabetic nephropathy (DN) from a system pharmacology approach. Study Design and Methods: Disease-related genes were retrieved from GeneCards and OMIM by searching “Diabetic Nephropathy” and “Macroalbuminuria”. Variations of R-6 were identified from a published existing clinical practice guideline developed from expert consensus and pilot clinical service program. The chemical compound IDs of each herb were retrieved from TCM-Mesh and PubChem. Drug targets were subsequently revealed via PharmaMapper and UniProtKB. The disease gene interactions were assessed through STRING, and disease–drug protein–protein interaction network was integrated and visualized by Cytoscape. Clusters of disease–drug protein–protein interaction were constructed by Molecular Complex Detection (MCODE) extension. Functional annotation of clusters was analyzed by DAVID and KEGG pathway enrichment. Differences among variations of R-6 were compared. Binding was verified by molecular docking with AutoDock. Results: Three hundred fifty-eight genes related to DN were identified, forming 11 clusters which corresponded to complement and coagulation cascades and signaling pathways of adipocytokine, TNF, HIF-1, and AMPK. Five variations of R-6 were analyzed. Common putative targets of the R-6 variations on DN included ACE, APOE, CCL2, CRP, EDN1, FN1, HGF, ICAM1, IL10, IL1B, IL6, INS, LEP, MMP9, PTGS2, SERPINE1, and TNF, which are related to regulation of nitric oxide biosynthesis, lipid storage, cellular response to lipopolysaccharide, inflammatory response, NF-kappa B transcription factor activity, smooth muscle cell proliferation, blood pressure, cellular response to interleukin-1, angiogenesis, cell proliferation, peptidyl-tyrosine phosphorylation, and protein kinase B signaling. TNF was identified as the seed for the most significant cluster of all R-6 variations. Targets specific to each formulation were identified. The key chemical compounds of R-6 have good binding ability to the putative protein targets. Conclusion: The mechanism of action of R-6 on DN is mostly related to the TNF signaling pathway as a core mechanism, involving amelioration of angiogenesis, fibrosis, inflammation, disease susceptibility, and oxidative stress. The putative targets identified could be validated through clinical trials.

Comprehensive Network Analysis Identified SIRT7, NTRK2, and CHI3L1 as New Potential Markers for Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IDD) is considered the basis of serious clinical symptoms, especially for low back pain (LBP). Therefore, it is essential to explore the regulatory role and diagnostic performance of dysregulated genes and potential drugs in IDD. Through WGCNA co-expression analysis, 36 co-expression modules were obtained. Among them, MidnightBlue and Red modules were the most related to IDD. Functional enrichment analysis showed that the Red module was mainly related to neutrophil activation and regulation of cytokine-mediated signaling pathway and apoptosis, whereas the MidnightBlue module was mainly related to extracellular matrix organization, bone development, extracellular matrix, extracellular matrix component, and other extracellular matrices. Furthermore, 356 genes highly related to the module were screened to construct a protein interaction network. Network degree distribution analysis showed that the known IDD-related genes had a higher degree of distribution. Enrichment analysis demonstrated that these genes were enriched in MAPK_SIGNALING_PATHWAY (FDR = 0.012), CHEMOKINE_SIGNALING_PATHWAY, and some other pathways. By constructing a disease-gene interaction network, three disease-specific genes were finally identified. Through combining with the drug-target gene interaction network, two potential therapeutic drugs, entrectinib and larotrectinib, were determined. Finally, based on these genes, the diagnostic model in the training dataset, test dataset, and verification dataset all showed a high diagnostic performance. The findings of this study contributed to the diagnosis of IDD and personalized treatment of IDD.

Integrating categorical and structural proximity in Disease Ontologies.

The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related disease module (DM) in the interactome. We propose a methodology (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy.Clinical relevance- Disease ontologies are extensively used for diagnostic evaluation and clinical decision support but still reflect the clinical reductionist perspective. We demonstrate that the proposed network-based methodology allows us to analyze commonalities and differences among structural and categorical similarity of human diseases, help refine human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.

Characterization of the bioactive compounds with efficacy against enteritis in Polygonum hydropiper L. by UHPLC‐Q‐Orbitrap HRMS combined with network pharmacological analysis**

AbstractPolygonum hydropiper L. (PL) is widely used in treating enteritis in China. This study interpreted the active ingredient and mechanism of PL against enteritis through UHPLC‐Q‐Orbitrap HRMS, network pharmacology and molecular docking. UHPLC‐Q‐Orbitrap HRMS of PL manifested 68 compounds. In addition, the primary candidate genes and potential active components were identified by topological analysis of the single‐component disease gene interaction network. The interaction between the active ingredient, whose drug‐likeness properties were confirmed by Lipinski's rule, and the therapeutic gene was confirmed by molecular docking analysis. AutoDock Vina in AutoDock Tools was used to conduct molecular docking between significant components and critical genes. The advantage of this experiment is that the UHPLC‐Q‐Orbitrap HRMS method increases the total chemical composition of PL. Secondly, we obtained active compounds through network pharmacology and found that PL mainly acts on multiple inflammatory pathways in the treatment of enteritis. 61 potential genes of PL for the treatment of enteritis were obtained. The genes were mainly involved in biological processes such as response to oxidative stress, inhibition of inflammatory factors and inflammatory pathways, and role in treating enteritis by participating in AR, NOS2, MMP9, MMP2, SRC, and other signaling pathways.

Evaluating disease similarity based on gene network reconstruction and representation.

Quantifying the associations between diseases is of great significance in increasing our understanding of disease biology, improving disease diagnosis, re-positioning and developing drugs. Therefore, in recent years, the research of disease similarity has received a lot of attention in the field of bioinformatics. Previous work has shown that the combination of the ontology (such as disease ontology and gene ontology) and disease-gene interactions are worthy to be regarded to elucidate diseases and disease associations. However, most of them are either based on the overlap between disease-related gene sets or distance within the ontology's hierarchy. The diseases in these methods are represented by discrete or sparse feature vectors, which cannot grasp the deep semantic information of diseases. Recently, deep representation learning has been widely studied and gradually applied to various fields of bioinformatics. Based on the hypothesis that disease representation depends on its related gene representations, we propose a disease representation model using two most representative gene resources HumanNet and Gene Ontology to construct a new gene network and learn gene (disease) representations. The similarity between two diseases is computed by the cosine similarity of their corresponding representations. We propose a novel approach to compute disease similarity, which integrates two important factors disease-related genes and gene ontology hierarchy to learn disease representation based on deep representation learning. Under the same experimental settings, the AUC value of our method is 0.8074, which improves the most competitive baseline method by 10.1%. The quantitative and qualitative experimental results show that our model can learn effective disease representations and improve the accuracy of disease similarity computation significantly. The research shows that this method has certain applicability in the prediction of gene-related diseases, the migration of disease treatment methods, drug development and so on. Supplementary data are available at Bioinformatics online.

Systematic analysis of Long non-coding RNAs reveals diagnostic biomarkers and potential therapeutic drugs for intervertebral disc degeneration

ABSTRACT Long non-coding RNAs (lncRNAs) are related to a variety of human diseases. However, little is known about the role of lncRNA in intervertebral disc degeneration (IDD). LncRNA expression profile of human IDD were downloaded from Gene Expression Omnibus (GEO) database. Potential biomarkers and therapeutic drugs for IDD were analyzed by weighted gene co-expression network analysis (WGCNA), R software package Limma, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 1455 differentially expressed genes and 423 differentially expressed lncRNAs. Twenty-six co-expression modules were obtained, among them, the tan, brown, and turquoise modules were most closely related to IDD. The turquoise module contained a large number of differential expressed lncRNAs and genes, these genes were mainly enriched in the MAPK signaling pathway, TGF-beta signaling pathway. Furthermore, we obtained 11,857 LmiRM-Degenerated, these lncRNAs and genes showed higher differential expression multiples and higher expression correlation. After constructing a disease-gene interaction network, 25 disease-specific genes and 9 disease-specific lncRNAs were identified. Combined with the drug-target gene interaction network, three drugs, namely, Calcium citrate, Calcium Phosphate, and Calcium phosphate dihydrate, which may have curative effects on IDD, were determined. Finally, a genetic diagnosis model and lncRNA diagnosis model with 100% diagnostic performance in both the training data set and the validation data set were established based on these genes and lncRNA. This study provided new diagnostic features for IDD and could help design personalized treatment of IDD.

Discovering metabolic disease gene interactions by correlated effects on cellular morphology

ObjectiveImpaired expansion of peripheral fat contributes to the pathogenesis of insulin resistance and Type 2 Diabetes (T2D). We aimed to identify novel disease–gene interactions during adipocyte differentiation. MethodsGenes in disease-associated loci for T2D, adiposity and insulin resistance were ranked according to expression in human adipocytes. The top 125 genes were ablated in human pre-adipocytes via CRISPR/CAS9 and the resulting cellular phenotypes quantified during adipocyte differentiation with high-content microscopy and automated image analysis. Morphometric measurements were extracted from all images and used to construct morphologic profiles for each gene. ResultsOver 107 morphometric measurements were obtained. Clustering of the morphologic profiles accross all genes revealed a group of 14 genes characterized by decreased lipid accumulation, and enriched for known lipodystrophy genes. For two lipodystrophy genes, BSCL2 and AGPAT2, sub-clusters with PLIN1 and CEBPA identifed by morphological similarity were validated by independent experiments as novel protein–protein and gene regulatory interactions. ConclusionsA morphometric approach in adipocytes can resolve multiple cellular mechanisms for metabolic disease loci; this approach enables mechanistic interrogation of the hundreds of metabolic disease loci whose function still remains unknown.

Relating Disease-Gene Interaction Network With Disease-Associated ncRNAs

NcRNAs (non-coding RNAs) are increasingly recognized as critical biomarkers for many diseases. Computational predictions of the most promising disease-associated ncRNAs for biomedical screening are therefore of great importance. The biggest challenge in developing computational approaches for such inference is the integration of data features because of the rich variety of ncRNAs in genome. Moreover, current methods might suffer from the cold-start problem. In order to address the problems, a resource-allocation-based method was presented in this paper to predict disease-associated ncRNAs by incorporating only experimentally supported disease-gene interactions. Potential ncRNAs for a disease of interest were then ranked according to final allocated resource values. When applied to collected datasets for leave-one-out cross-validation on 537 diseases, our approach improved previous methods and demonstrated excellent prediction performance. Case studies on three important diseases suggested most of the top predicted results were validated by independent sources, which showed the usefulness of our method in real situations. Comprehensive prediction results of disease-associated ncRNAs were finally released for future biomedical identification.

RPGeNet v2.0: expanding the universe of retinal disease gene interactions network.

RPGeNet offers researchers a user-friendly queriable tool to visualize the interactome network of visual disorder genes, thus enabling the identification of new potential causative genes and the assignment of novel candidates to specific retinal or cellular pathways. This can be highly relevant for clinical applications as retinal dystrophies affect 1:3000 people worldwide, and the causative genes are still unknown for 30% of the patients. RPGeNet is a refined interaction network interface that limits its skeleton network to the shortest paths between each and every known causative gene of inherited syndromic and non-syndromic retinal dystrophies. RPGeNet integrates interaction information from STRING, BioGRID and PPaxe, along with retina-specific expression data and associated genetic variants, over a Cytoscape.js web interface. For the new version, RPGeNet v2.0, the database engine was migrated to Neo4j graph database manager, which speeds up the initial queries and can handle whole interactome data for new ways to query the network. Further, user facilities have been introduced as the capability of saving and restoring a researcher customized network layout or as novel features to facilitate navigation and data projection on the network explorer interface. Responsiveness has been further improved by transferring some functionality to the client side.

Study on mechanism of Drynariae Rhizoma in treating osteoporosis with integrative pharmacology perspective

Drynariae Rhizoma has great significance in the clinical practice of osteoporosis treatment. Based on the perspective of integrative pharmacology, the study explored the mechanism of action of Drynariae Rhizoma in the treatment of osteoporosis. Six active components in Drynariae Rhizoma were obtained, mainly including glycosides and sterols. Taking the median of 2 times of "node connectivity" as the card value, the core node of the Chinese medicine target disease gene interaction network was selected. Based on this, three topological structural eigenvalues, such as "node connectivity" "node tightness" and "node connectivity" were calculated, thereby screening out four core targets of Drynariae Rhizoma treatment for osteoporosis, including thyroid parathyroid hormone 1 receptor (PTH1R), parathyroid hormone 2 receptor (PTH2R), calcitonin receptor gene (CALCR), and SPTBN1 gene (SPTBN1). Based on the gene ontology database GO and KEGG pathway database, the molecular function, intracellular localization, and biological reactions and pathways of proteins encoded by drug target genes were determined. Combined with enrichment calculation, it is predicted that osteoporosis may play a role in biosynthetic processes, such as circulatory system, nervous system, energy metabolism, prolactin signal pathway, GnRH signaling pathway, neurotrophic factor signaling pathway and other pathway. The conclusion of this study is certain with the existing research results, and the new target and new pathway could also be used as a theoretical basis for the further verification of osteoporosis.

Characterization of TCF21 Downstream Target Regions Identifies a Transcriptional Network Linking Multiple Independent Coronary Artery Disease Loci.

To functionally link coronary artery disease (CAD) causal genes identified by genome wide association studies (GWAS), and to investigate the cellular and molecular mechanisms of atherosclerosis, we have used chromatin immunoprecipitation sequencing (ChIP-Seq) with the CAD associated transcription factor TCF21 in human coronary artery smooth muscle cells (HCASMC). Analysis of identified TCF21 target genes for enrichment of molecular and cellular annotation terms identified processes relevant to CAD pathophysiology, including “growth factor binding,” “matrix interaction,” and “smooth muscle contraction.” We characterized the canonical binding sequence for TCF21 as CAGCTG, identified AP-1 binding sites in TCF21 peaks, and by conducting ChIP-Seq for JUN and JUND in HCASMC confirmed that there is significant overlap between TCF21 and AP-1 binding loci in this cell type. Expression quantitative trait variation mapped to target genes of TCF21 was significantly enriched among variants with low P-values in the GWAS analyses, suggesting a possible functional interaction between TCF21 binding and causal variants in other CAD disease loci. Separate enrichment analyses found over-representation of TCF21 target genes among CAD associated genes, and linkage disequilibrium between TCF21 peak variation and that found in GWAS loci, consistent with the hypothesis that TCF21 may affect disease risk through interaction with other disease associated loci. Interestingly, enrichment for TCF21 target genes was also found among other genome wide association phenotypes, including height and inflammatory bowel disease, suggesting a functional profile important for basic cellular processes in non-vascular tissues. Thus, data and analyses presented here suggest that study of GWAS transcription factors may be a highly useful approach to identifying disease gene interactions and thus pathways that may be relevant to complex disease etiology.

Matrix Factorization-Based Prediction of Novel Drug Indications by Integrating Genomic Space.

There has been rising interest in the discovery of novel drug indications because of high costs in introducing new drugs. Many computational techniques have been proposed to detect potential drug-disease associations based on the creation of explicit profiles of drugs and diseases, while seldom research takes advantage of the immense accumulation of interaction data. In this work, we propose a matrix factorization model based on known drug-disease associations to predict novel drug indications. In addition, genomic space is also integrated into our framework. The introduction of genomic space, which includes drug-gene interactions, disease-gene interactions, and gene-gene interactions, is aimed at providing molecular biological information for prediction of drug-disease associations. The rationality lies in our belief that association between drug and disease has its evidence in the interactome network of genes. Experiments show that the integration of genomic space is indeed effective. Drugs, diseases, and genes are described with feature vectors of the same dimension, which are retrieved from the interaction data. Then a matrix factorization model is set up to quantify the association between drugs and diseases. Finally, we use the matrix factorization model to predict novel indications for drugs.

Microarray gene expression profiling and bioinformatics analysis of premature ovarian failure in a rat model

Premature ovarian failure (POF) remains one of the major gynecological problems worldwide which affected 1% of women. Even though tremendous achievements had been acquired as opposed to years past, molecular pathogenesis associated with POF is still unclear and needs to be well-defined.The aim of this study was to analyze the gene expression profiles in the POF rat model. To predict potential regulating factors, we firstly treated female Sprague Dawley (SD) rat with 4-vinylcyclohexene diepoxide (VCD). Total RNA from ovarian tissue was converted to cDNA and hybridized to mRNA Chip array. The differentially expressed genes (DEGs) were identified by two-sample t test and assessed using hierarchical clustering and Principal Component Analysis methods. Potential regulatory targets associated with these DEGs were constructed using BisoGenet in Cytoscape. Gene Ontology (GO) and functional enrichment analysis were performed using BiNGO and DAVID, respectively.As the results, 25 DEGs were found to be closely associated with POF initiation. Hierarchical clustering and Principal Component Analysis on the transcriptional profiles revealed an excellent separation of the vehicle and POF compartments. Pathway enrichment analysis based on the disease-gene interaction network analysis led to the identification of two core signaling pathways that were strongly affected during POF initiation and progression: immune response and cardiovascular disorders.In conclusion, we constructed a gene regulatory network associated with POF using the microarray gene expression profiling, and screened out some genes or transcription factors that may be used as potential molecular therapeutic targets for POF.

Studying synthetic lethal interactions in the zebrafish system: insight into disease genes and mechanisms

The post-genomic era is marked by a pressing need to functionally characterize genes through understanding gene-gene interactions, as well as interactions between biological pathways. Exploiting a phenomenon known as synthetic lethality, in which simultaneous loss of two interacting genes leads to loss of viability, aids in the investigation of these interactions. Although synthetic lethal screening is a powerful technique that has been used with great success in many model organisms, including Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans, this approach has not yet been applied in the zebrafish, Danio rerio. Recently, the zebrafish has emerged as a valuable system to model many human disease conditions; thus, the ability to conduct synthetic lethal screening using zebrafish should help to uncover many unknown disease-gene interactions. In this article, we discuss the concept of synthetic lethality and provide examples of its use in other model systems. We further discuss experimental approaches by which the concept of synthetic lethality can be applied to the zebrafish to understand the functions of specific genes.