Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis.

PURPOSE: Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients' prognosis remains poor due to tumor invasion and metastasis. MATERIALS AND METHODS: We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes. RESULT: By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS. CONCLUSION: In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

Comparison of critical biomarkers in 2 erectile dysfunction models based on GEO and NOS-cGMP-PDE5 pathway.

BACKGROUND: Erectile dysfunction is a disease commonly caused by diabetes mellitus (DMED) and cavernous nerve injury (CNIED). Bioinformatics analyses including differentially expressed genes (DEGs), enriched functions and pathways (EFPs), and protein-protein interaction (PPI) networks were carried out in DMED and CNIED rats in this study. The critical biomarkers that may intervene in nitric oxide synthase (NOS, predominantly nNOS, ancillary eNOS, and iNOS)-cyclic guanosine monophosphate (cGMP)-phosphodiesterase 5 enzyme (PDE5) pathway, an important mechanism in erectile dysfunction treatment, were then explored for potential clinical applications. METHODS: GSE2457 and GSE31247 were downloaded. Their DEGs with a |logFC (fold change)| > 0 were screened out. Database for Annotation, Visualization and Integrated Discovery (DAVID) online database was used to analyze the EFPs in Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes networks based on down-regulated and up-regulated DEGs respectively. PPI analysis of 2 datasets was performed in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. Interactions with an average score greater than 0.9 were chosen as the cutoff for statistical significance. RESULTS: From a total of 1710 DEGs in GSE2457, 772 were down-regulated and 938 were up-regulated, in contrast to the 836 DEGs in GSE31247, from which 508 were down-regulated and 328 were up-regulated. The 25 common EFPs such as aging and response to hormone were identified in both models. PPI results showed that the first 10 hub genes in DMED were all different from those in CNIED. CONCLUSIONS: The intervention of iNOS with the hub gene complement component 3 in DMED and the aging process in both DMED and CNIED deserves attention.

Gene Ontology representation for transcription factor functions.

Transcription plays a central role in defining the identity and functionalities of cells, as well as in their responses to changes in the cellular environment. The Gene Ontology (GO) provides a rigorously defined set of concepts that describe the functions of gene products. A GO annotation is a statement about the function of a particular gene product, represented as an association between a gene product and the biological concept a GO term defines. Critically, each GO annotation is based on traceable scientific evidence. Here, we describe the different GO terms that are associated with proteins involved in transcription and its regulation, focusing on the standard of evidence required to support these associations. This article is intended to help users of GO annotations understand how to interpret the annotations and can contribute to the consistency of GO annotations. We distinguish between three classes of activities involved in transcription or directly regulating it - general transcription factors, DNA-binding transcription factors, and transcription co-regulators.

Identification of Potential Key Genes Involved in the Carotid Atherosclerosis.

PURPOSE: Carotid atherosclerosis is a kind of systemic atherosclerosis in the carotid arteries. However, the efficiency of treatment is insufficient. Therefore, it is urgent to find therapeutic targets and deepen the understanding of carotid atherosclerosis. MATERIALS AND METHODS: In this study, we analyzed differentially expressed genes (DEGs) between atheroma plaque and macroscopically intact tissue (control samples). Furthermore, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analysis based on the DEGs. Four methods were used to identify the hub genes in the protein-protein interaction networks of the DEGs. Furthermore, we also performed network module analysis to reveal carotid atherosclerosis-related gene modules and biological functions. RESULTS: The enrichment results showed that the biological functions were related to inflammation, immunity, chemokine and cell adhesion molecule, such as PIK-Akt signaling pathway, Rap1 signaling pathway, MAPK signaling pathway, NOD-like receptor signaling pathway and B cell receptor signaling pathway. In addition, we screened the hub genes. A total of 16 up-regulated genes (C3AR1, CCR1, CCR2, CD33, CD53, CXCL10, CXCL8, CXCR4, CYBB, FCER1G, FPR2, ITGAL, ITGAM, ITGAX, ITGB2, and LILRB2) were identified as hub genes. A total of 5 gene modules were obtained. We found that biological functions obtained for each cluster were mostly related to immunity, chemokines and cell adhesion molecules. CONCLUSION: The present study identified key DEGs in atheroma plaque compared with control samples. The key genes involved in the development of carotid atherosclerosis may provide valuable therapeutic targets for carotid atherosclerosis.

Transcriptome Sequencing Identifies Potential Biomarker for White Matter Lesions Diagnosis in the Hypertension Population.

Hypertension is confirmed to be one of the major risk factors of leukoaraiosis (LA). However, the pathogenesis of LA is not completely understood and there is no reliable indicator for the early diagnosis of LA in the hypertensive population. This study was designed to explore the potential biomarker for LA diagnosis in patients with hypertension. And it serves as the basis for the further study of LA mechanism. In this study, This study included 110 subjects, including 50 in the LA group and 60 in the control group. First, we performed transcriptome sequencing and quantitative PCR (qPCR) in four samples from the LA group, and three from the control group (seven people) to identify relevant long non-coding RNAs (long ncRNAs or lncRNA). The 103 samples were used for qPCR validation of relevant lncRNAs and the results were consistent with the sequencing. In-depth bioinformatics analysis were performed on differentially expressed (DE) lncRNAs and mRNAs. Go-functional enrichment analysis was performed on DE mRNAs. Some DE mRNA were enriched to biological processes associated with LA, And some lncRNAs related to DE mRNAs were traceable through cis/trans analysis, suggesting that they might be regulated in some way. Additionally, potential biomarkers for LA diagnosis in the hypertension population were identified via RT-qPCR and receive operating characteristic curve (ROC) analysis of lncRNA. One lncRNA, AC020928.1, has been demonstrated to be potential biomarkers for LA diagnosis in the hypertension population. The results of the present study indicated that the lncRNA may have an important role in the pathogenesis of LA and may be a novel target for further research. As the relationship between lncRNAs and LA is just beginning to be unraveled, their specific mechanisms require further investigation.

Bayesian parameter estimation for automatic annotation of gene functions using observational data and phylogenetic trees.

Gene function annotation is important for a variety of downstream analyses of genetic data. But experimental characterization of function remains costly and slow, making computational prediction an important endeavor. Phylogenetic approaches to prediction have been developed, but implementation of a practical Bayesian framework for parameter estimation remains an outstanding challenge. We have developed a computationally efficient model of evolution of gene annotations using phylogenies based on a Bayesian framework using Markov Chain Monte Carlo for parameter estimation. Unlike previous approaches, our method is able to estimate parameters over many different phylogenetic trees and functions. The resulting parameters agree with biological intuition, such as the increased probability of function change following gene duplication. The method performs well on leave-one-out cross-validation, and we further validated some of the predictions in the experimental scientific literature.

Using Reactome to build an autophagy mechanism knowledgebase.

The 21st century has revealed much about the fundamental cellular process of autophagy. Autophagy controls the catabolism and recycling of various cellular components both as a constitutive process and as a response to stress and foreign material invasion. There is considerable knowledge of the molecular mechanisms of autophagy, and this is still growing as new modalities emerge. There is a need to investigate autophagy mechanisms reliably, comprehensively and conveniently. Reactome is a freely available knowledgebase that consists of manually curated molecular events (reactions) organized into cellular pathways (https://reactome.org/). Pathways/reactions in Reactome are hierarchically structured, graphically presented and extensively annotated. Data analysis tools, such as pathway enrichment, expression data overlay and species comparison, are also available. For customized analysis, information can also be programmatically queried. Here, we discuss the curation and annotation of the molecular mechanisms of autophagy in Reactome. We also demonstrate the value that Reactome adds to research by reanalyzing a previously published work on genome-wide CRISPR screening of autophagy components.Abbreviations: CMA: chaperone-mediated autophagy; GO: Gene Ontology; MA: macroautophagy; MI: microautophagy; MTOR: mechanistic target of rapamycin kinase; SQSTM1: sequestosome 1.

Efficient isolation of brain capillary from a single frozen mouse brain for protein expression analysis.

Isolated brain capillaries are essential for analyzing the changes of protein expressions at the blood-brain barrier (BBB) under pathological conditions. The standard brain capillary isolation methods require the use of at least five mouse brains in order to obtain a sufficient amount and purity of brain capillaries. The purpose of this study was to establish a brain capillary isolation method from a single mouse brain for protein expression analysis. We successfully isolated brain capillaries from a single frozen mouse brain by using a bead homogenizer in the brain homogenization step and combination of cell strainers and glass beads in the purification step. Western blot and proteomic analysis showed that proteins expressed at the BBB in mouse brain capillaries isolated by the developed method were more enriched than those isolated from a pool of five mouse brains by the standard method. By using the developed method, we further verified the changes in expression of BBB proteins in Glut1-deficient mouse. The developed method is useful for the analysis of various mice models with low numbers and enables us to understand, in more detail, the physiology and pathology of BBB.

DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.

Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype-phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene-disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases.

Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis.

OBJECTIVE: The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. METHODS: The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. RESULTS: A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. CONCLUSION: Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.

Influence of the go-based semantic similarity measures in multi-objective gene clustering algorithm performance.

Using a prior biological knowledge of relationships and genetic functions for gene similarity, from repository such as the Gene Ontology (GO), has shown good results in multi-objective gene clustering algorithms. In this scenario and to obtain useful clustering results, it would be helpful to know which measure of biological similarity between genes should be employed to yield meaningful clusters that have both similar expression patterns (co-expression) and biological homogeneity. In this paper, we studied the influence of the four most used GO-based semantic similarity measures in the performance of a multi-objective gene clustering algorithm. We used four publicly available datasets and carried out comparative studies based on performance metrics for the multi-objective optimization field and clustering performance indexes. In most of the cases, using Jiang-Conrath and Wang similarities stand in terms of multi-objective metrics. In clustering properties, Resnik similarity allows to achieve the best values of compactness and separation and therefore of co-expression of groups of genes. Meanwhile, in biological homogeneity, the Wang similarity reports greater number of significant GO terms. However, statistical, visual, and biological significance tests showed that none of the GO-based semantic similarity measures stand out above the rest in order to significantly improve the performance of the multi-objective gene clustering algorithm.

GOcats: A tool for categorizing Gene Ontology into subgraphs of user-defined concepts.

Gene Ontology is used extensively in scientific knowledgebases and repositories to organize a wealth of biological information. However, interpreting annotations derived from differential gene lists is often difficult without manually sorting into higher-order categories. To address these issues, we present GOcats, a novel tool that organizes the Gene Ontology (GO) into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. We tested GOcats performance using subcellular location categories to mine annotations from GO-utilizing knowledgebases and evaluated their accuracy against immunohistochemistry datasets in the Human Protein Atlas (HPA). In comparison to term categorizations generated from UniProt's controlled vocabulary and from GO slims via OWLTools' Map2Slim, GOcats outperformed these methods in its ability to mimic human-categorized GO term sets. Unlike the other methods, GOcats relies only on an input of basic keywords from the user (e.g. biologist), not a manually compiled or static set of top-level GO terms. Additionally, by identifying and properly defining relations with respect to semantic scope, GOcats can utilize the traditionally problematic relation, has_part, without encountering erroneous term mapping. We applied GOcats in the comparison of HPA-sourced knowledgebase annotations to experimentally-derived annotations provided by HPA directly. During the comparison, GOcats improved correspondence between the annotation sources by adjusting semantic granularity. GOcats enables the creation of custom, GO slim-like filters to map fine-grained gene annotations from gene annotation files to general subcellular compartments without needing to hand-select a set of GO terms for categorization. Moreover, GOcats can customize the level of semantic specificity for annotation categories. Furthermore, GOcats enables a safe and more comprehensive semantic scoping utilization of go-core, allowing for a more complete utilization of information available in GO. Together, these improvements can impact a variety of GO knowledgebase data mining use-cases as well as knowledgebase curation and quality control.

Bioinformatic identification of hub genes and key pathways in neutrophils of patients with acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is characterized as a neutrophil-dominant disorder without effective pharmacological interventions. Knowledge of neutrophils in ARDS patients at the transcriptome level is still limited. We aimed to identify the hub genes and key pathways in neutrophils of patients with ARDS. The transcriptional profiles of neutrophils from ARDS patients and healthy volunteers were obtained from the GSE76293 dataset. The differentially expressed genes (DEGs) between ARDS and healthy samples were screened using the limma R package. Subsequently, functional and pathway enrichment analyses were performed based on the database for annotation, visualization, and integrated discovery (DAVID). The construction of a protein-protein interaction network was carried out using the search tool for the retrieval of interacting genes (STRING) database and the network was visualized by Cytoscape software. The Cytoscape plugins cytoHubba and MCODE were used to identify hub genes and significant modules. Finally, 136 upregulated genes and 95 downregulated genes were identified. Gene ontology analyses revealed MHC class II plays a major role in functional annotations. SLC11A1, ARG1, CHI3L1, HP, LCN2, and MMP8 were identified as hub genes, and they were all involved in the neutrophil degranulation pathway. The MAPK and neutrophil degranulation pathways in neutrophils were considered as key pathways in the pathogenesis of ARDS. This study improves our understanding of the biological characteristics of neutrophils and the mechanisms underlying ARDS, and key pathways and hub genes identified in this work can serve as targets for novel ARDS treatment strategies.

Weighted gene co-expression network analysis reveals specific modules and biomarkers in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) ranks as the second most frequently occurring neurodegenerative disease. The precise pathogenic mechanism of this disease remains unknown. The aim of the present study was to identify the biomarkers in PD and classify the primary differentially expressed genes (DEGs). METHODS: The present study searched for and downloaded mRNA expression data from the Gene Expression Omnibus database to identify differences in mRNA expression in the substantia nigra (SN) and blood of patients with PD and healthy controls. In addition, in order to investigate the biological functions of the classified dysregulated genes, the present study utilized Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), reverse transcription-quantitative PCR (RT-qPCR), gene co-expression network analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A receiver operating characteristic (ROC) curve was applied to assay TMEM243 as a diagnostic marker. RESULTS: Between PD and controls in GSE20292, the present study identified 1862 DEGs. Using the weighted gene co-expression network analysis, the present study identified 15 modules in PD. The module preservation analysis revealed that the tan, blue and green-yellow modules were the most stable. KEGG pathway analysis revealed that five DEGs in the black module were significantly enriched in the ubiquitin-mediated proteolysis pathway, nucleotide excision repair pathway, mismatch repair pathway. The present study selected 303 genes with high connectivity in blue, green-yellow and tan modules as hub genes, where 58 were differentially expressed in both the GSE20292 and GSE54536 datasets. In the SN and blood, 11 genes exhibited the same trend of expression. Furthermore, in the blood samples of patients with PD, the results displayed a significant upregulation of TMEM243. The expression levels of CCR4, CAMK1D, ACTR1B and SPSB3 increased, while both the levels of INA and PSMD4 decreased. These findings are consistent with the bioinformatics analysis results but are not statistically significant. TMEM243 can be considered as a diagnostic biomarker (area under the curve = 0.694; sensitivity, 80 %; specificity, 56 %; P < 0.018). CONCLUSION: TMEM243 was distinctly upregulated in the blood samples of patients with PD, as validated via RT-qPCR, and was highly sensitive, revealing its potential as a biomarker for the future diagnosis of PD.

Comparative transcriptome analysis indicates that a core transcriptional network mediates isonuclear alloplasmic male sterility in wheat (Triticum aestivum L.).

BACKGROUND: Cytoplasmic male sterility (CMS) plays a crucial role in the utilization of heterosis and various types of CMS often have different abortion mechanisms. Therefore, it is important to understand the molecular mechanisms related to anther abortion in wheat, which remain unclear at present. RESULTS: In this study, five isonuclear alloplasmic male sterile lines (IAMSLs) and their maintainer were investigated. Cytological analysis indicated that the abortion type was identical in IAMSLs, typical and stainable abortion, and the key abortive period was in the binucleate stage. Most of the 1,281 core shared differentially expressed genes identified by transcriptome sequencing compared with the maintainer in the vital abortive stage were involved in the metabolism of sugars, oxidative phosphorylation, phenylpropane biosynthesis, and phosphatidylinositol signaling, and they were downregulated in the IAMSLs. Key candidate genes encoding chalcone--flavonone isomerase, pectinesterase, and UDP-glucose pyrophosphorylase were screened and identified. Moreover, further verification elucidated that due to the impact of downregulated genes in these pathways, the male sterile anthers were deficient in sugar and energy, with excessive accumulations of ROS, blocked sporopollenin synthesis, and abnormal tapetum degradation. CONCLUSIONS: Through comparative transcriptome analysis, an intriguing core transcriptome-mediated male-sterility network was proposed and constructed for wheat and inferred that the downregulation of genes in important pathways may ultimately stunt the formation of the pollen outer wall in IAMSLs. These findings provide insights for predicting the functions of the candidate genes, and the comprehensive analysis of our results was helpful for studying the abortive interaction mechanism in CMS wheat.

Analysis of lncRNA-mRNA networks after MEK1/2 inhibition based on WGCNA in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDA) responds poorly to treatment. Efforts have been exerted to prolong the survival time of PDA, but the 5-year survival rates remain disappointing. Understanding the molecular mechanisms of PDA development is significant. MEK/ERK pathway signaling has been proven to be important in PDA. lncRNA-mRNA networks have become a vital part of molecular mechanisms in the MEK/ERK pathway. Herein, weighted gene coexpression network analysis was used to investigate the coexpressed lncRNA-mRNA networks in the MEK/ERK pathway based on GSE45765. Differently expressed long noncoding RNA (lncRNA) and messenger RNA (mRNA) were found and 10 modules were identified based on coexpression profiles. Gene ontology and Kyoto Encyclopedia of Genes and Genomes were then performed to analyze the coexpressed lncRNA and mRNA in different modules. PDA cells and tissues were used to validate the analysis results. Finally, we found that NONHSAT185150.1 and B4GALT6 were negatively correlated with MEK1/2. By analyzing GSE45765, the genome-wide profiles of lncRNA-mRNA network after MEK1/2 was established, which might aid the development of drug-targeting MEK1/2 and the investigation of diagnostic markers.

Exploration of potential key pathways and genes in multiple ocular cancers through bioinformatics analysis.

PURPOSE: Primary cancers of the eye are common in ocular diseases. The objective of this study was to explore the underlying mechanisms and the potential target genes in multiple ocular cancers by bioinformatics approach. METHOD: These gene expression profiles of GSE24673 (Retinoblastoma), GSE44295 (Uveal melanoma), and GSE103439 (Basal cell carcinoma of the eyelid) were downloaded from Gene Expression Omniniub (GEO) database. The differentially expressed genes (DEGs) in the three gene chips were identified by limma package in R software and gene integration was performed by using "RobustRankAggreg" package. Gene set enrichment analysis (GSEA) and the Gene Ontology (GO) were performed to the selected genes. Moreover, survival analysis was used to estimate uveal melanoma dataset. RESULTS: In total, 509 DEGs were identified in GSE24673 (retinoblastoma), 305 DEGs were identified in GSE44295 (uveal melanoma), and 753 DEGs were identified in GSE103439 (basal cell carcinoma of the eyelid). Among those genes, only IGF2BP3 was shared for the three cancer types. A total of 20 DEGs were identified through gene integration (score < 0.05) and IGF2BP3 was ranked the top. Moreover, GO analysis results showed that the 20 DEGs were significantly enriched in WNT signaling pathway, DNA damage, and apoptotic process. GSEA showed that pathways related with cellular respiratory chain are differentially enriched in IGF2BP3 low expression phenotype. Finally, two genes (ID3 and SLC6A15) can predict the overall survival in uveal melanoma patients. CONCLUSIONS: This findings and results of study showed that the identification of DEGs and key pathways gives a promotion to understand the molecular mechanisms underlying the development of ocular cancers, which contribute to a more comprehensive understanding of cancers of the eye and provide new insights for these studies at gene level.

Computational de-orphanization of the olive oil biophenol oleacein: Discovery of new metabolic and epigenetic targets.

The health promoting effects of extra virgin olive oil (EVOO) relate to its unique repertoire of phenolic compounds. Here, we used a chemoinformatics approach to computationally identify endogenous ligands and assign putative biomolecular targets to oleacein, one of the most abundant secoiridoids in EVOO. Using a structure-based virtual profiling software tool and reference databases containing more than 9000 binding sites protein cavities, we identified 996 putative oleacein targets involving more than 700 proteins. We subsequently identified the high-level functions of oleacein in terms of biomolecular interactions, signaling pathways, and protein-protein interaction (PPI) networks. Delineation of the oleacein target landscape revealed that the most significant modules affected by oleacein were associated with metabolic processes (e.g., glucose and lipid metabolism) and chromatin-modifying enzymatic activities (i.e., histone post-translational modifications). We experimentally confirmed that, in a low-micromolar physiological range (<20 µmol/l), oleacein was capable of inhibiting the catalytic activities of predicted metabolic and epigenetic targets including nicotinamide N-methyltransferase, ATP-citrate lyase, lysine-specific demethylase 6A, and N-methyltransferase 4. Our computational de-orphanization of oleacein provides new mechanisms through which EVOO biophenols might operate as chemical prototypes capable of modulating the biologic machinery of healthy aging.

Integrating network topology, gene expression data and GO annotation information for protein complex prediction.

The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict protein complexes based on protein-protein interaction (PPI) networks in recent years. However, because the high throughput data obtained from experimental biotechnology are incomplete, and usually contain a large number of spurious interactions, most of the network-based protein complex identification methods are sensitive to the reliability of the PPI network. In this paper, we propose a new method, Identification of Protein Complex based on Refined Protein Interaction Network (IPC-RPIN), which integrates the topology, gene expression profiles and GO functional annotation information to predict protein complexes from the reconstructed networks. To demonstrate the performance of the IPC-RPIN method, we evaluated the IPC-RPIN on three PPI networks of Saccharomycescerevisiae and compared it with four state-of-the-art methods. The simulation results show that the IPC-RPIN achieved a better result than the other methods on most of the measurements and is able to discover small protein complexes which have traditionally been neglected.

WEADE: A workflow for enrichment analysis and data exploration.

Data analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and several stand-alone or web tools exist for that purpose. However, a comprehensive and consistent analysis downstream of the enrichment calculation is missing so far. With WEADE we present a free web application that offers an integrated workflow for the exploration of genomic data combining enrichment analysis with a versatile set of tools to directly compare and intersect experiments or candidate gene lists of any size or origin including cross-species data. Lastly, WEADE supports the graphical representation of output data in the form of functional interaction networks based on prior knowledge, allowing users to go from plain expression data to functionally relevant candidate sub-lists in an interactive and consistent manner.