Integrating Gene Ontology Research Articles

Integrated Analysis of Transcriptome Profiles and lncRNA-miRNA-mRNA Competing Endogenous RNA Regulatory Network to Identify Biological Functional Effects of Genes and Pathways Associated with Johne's Disease in Dairy Cattle.

Paratuberculosis or Johne's disease (JD), a chronic granulomatous gastroenteritis caused by Mycobacterium avium subsp. paratuberculosis (MAP), causes huge economic losses and reduces animal welfare in dairy cattle herds worldwide. At present, molecular mechanisms and biological functions involved in immune responses to MAP infection of dairy cattle are not clearly understood. Our purpose was to integrate transcriptomic profiles and competing endogenous RNA (ceRNA) network analyses to identify key messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of peripheral blood mononuclear cells (PBMCs) for MAP infection in dairy cattle. In total, 28 lncRNAs, 42 miRNAs, and 370 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In this regard, we identified 21 hub genes (CCL20, CCL5, CD40, CSF2, CXCL8, EIF2AK2, FOS, IL10, IL17A, IL1A, IL1B, IRF1, MX2, NFKB1, NFKBIA, PTGS2, SOCS3, TLR4, TNF, TNFAIP3, and VCAM1) involved in MAP infection. Furthermore, eight candidate subnets with eight lncRNAs, 29 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 510, 22, and 11 significantly enriched GO terms related to MAP infection in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways related to MAP infection that were enriched included the immune system process, defense response, response to cytokine, leukocyte migration, regulation of T cell activation, defense response to bacterium, NOD-like receptor, B cell receptor, TNF, NF-kappa B, IL-17, and T cell receptor signaling pathways. Contributions of transcriptome profiles from MAP-positive and MAP-negative sample groups plus a ceRNA regulatory network underlying phenotypic differences in the intensity of pathogenicity of JD provided novel insights into molecular mechanisms associated with immune system responses to MAP infection in dairy cattle.

Implementing link prediction in protein networks via feature fusion models based on graph neural networks

MotivationProtein-protein interactions serve as the cornerstone for various biochemical processes within biological organisms. Existing research methodologies predominantly employ link prediction techniques to analyze these interaction networks. However, traditional approaches often fall short in delivering satisfactory predictive performance when applied to multi-species datasets. Current computational methods largely focus on analyzing the network topology, resulting in a somewhat monolithic feature set. The integration of diverse features in the model could potentially yield superior performance and broader applicability. To this end, we propose an autoencoder model built on graph neural networks, designed to enhance both predictive performance and generalizability by leveraging the integration of gene ontology. ResultsIn this research, we developed AGraphSAGE, a model specifically designed for analyzing protein-protein interaction network data. By seamlessly integrating gene ontology into the graph structure, we employed a dual-channel graph sampling and aggregation network that capitalizes on topological information to process high-dimensional features. Feature fusion is achieved through the implementation of graph attention mechanisms, and we adopted a link prediction framework as the experimental training model. Performance was evaluated on real-world datasets using key metrics, such as Area Under the Curve (AUC). A hyperparameter search space was established, and a Bayesian optimization strategy was applied to iteratively fine-tune the model, assessing the impact of various parameters on predictive efficacy. The experimental results validate that our proposed model is capable of effectively predicting protein-protein interactions across diverse biological species.

Integrated Comparative Transcriptome and circRNA-lncRNA-miRNA-mRNA ceRNA Regulatory Network Analyses Identify Molecular Mechanisms Associated with Intramuscular Fat Content in Beef Cattle.

Intramuscular fat content (IMF), one of the most important carcass traits in beef cattle, is controlled by complex regulatory factors. At present, molecular mechanisms involved in regulating IMF and fat metabolism in beef cattle are not well understood. Our objective was to integrate comparative transcriptomic and competing endogenous RNA (ceRNA) network analyses to identify candidate messenger RNAs (mRNAs) and regulatory RNAs involved in molecular regulation of longissimus dorsi muscle (LDM) tissue for IMF and fat metabolism of 5 beef cattle breeds (Angus, Chinese Simmental, Luxi, Nanyang, and Shandong Black). In total, 34 circRNAs, 57 lncRNAs, 15 miRNAs, and 374 mRNAs were identified by integrating gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Furthermore, 7 key subnets with 16 circRNAs, 43 lncRNAs, 7 miRNAs, and 237 mRNAs were detected through clustering analyses, whereas GO enrichment analysis of identified RNAs revealed 48, 13, and 28 significantly enriched GO terms related to IMF in biological process, molecular function, and cellular component categories, respectively. The main metabolic-signaling pathways associated with IMF and fat metabolism that were enriched included metabolic, calcium, cGMP-PKG, thyroid hormone, and oxytocin signaling pathways. Moreover, MCU, CYB5R1, and BAG3 genes were common among the 10 comparative groups defined as important candidate marker genes for fat metabolism in beef cattle. Contributions of transcriptome profiles from various beef breeds and a competing endogenous RNA (ceRNA) regulatory network underlying phenotypic differences in IMF provided novel insights into molecular mechanisms associated with meat quality.

PFresGO: an attention mechanism-based deep-learning approach for protein annotation by integrating gene ontology inter-relationships.

The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations. Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with 'state-of-the-art' methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins. PFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO. Supplementary data are available at Bioinformatics online.

Integrative Studies of Human Cord Blood Derived Mononuclear Cells and Umbilical Cord Derived Mesenchyme Stem Cells in Ameliorating Bronchopulmonary Dysplasia.

Bronchopulmonary dysplasia (BPD) is a common pulmonary complication observed in preterm infants that is composed of multifactorial pathogenesis. Current strategies, albeit successful in moderately reducing morbidity and mortality of BPD, failed to draw overall satisfactory conclusion. Here, using a typical mouse model mimicking hallmarks of BPD, we revealed that both cord blood-derived mononuclear cells (CB-MNCs) and umbilical cord-derived mesenchymal stem cells (UC-MSCs) are efficient in alleviating BPD. Notably, infusion of CB-MNCs has more prominent effects in preventing alveolar simplification and pulmonary vessel loss, restoring pulmonary respiratory functions and balancing inflammatory responses. To further elucidate the underlying mechanisms within the divergent therapeutic effects of UC-MSC and CB-MNC, we systematically investigated the long noncoding RNA (lncRNA)–microRNA (miRNA)–messenger RNA (mRNA) and circular RNA (circRNA)–miRNA–mRNA networks by whole-transcriptome sequencing. Importantly, pathway analysis integrating Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)/gene set enrichment analysis (GSEA) method indicates that the competing endogenous RNA (ceRNA) network is mainly related to the regulation of GTPase activity (GO: 0043087), extracellular signal-regulated kinase 1 (ERK1) and ERK2 signal cascade (GO: 0070371), chromosome regulation (GO: 0007059), and cell cycle control (GO: 0044770). Through rigorous selection of the lncRNA/circRNA-based ceRNA network, we demonstrated that the hub genes reside in UC-MSC- and CB-MNC-infused networks directed to the function of cell adhesion, motor transportation (Cdk13, Lrrn2), immune homeostasis balance, and autophagy (Homer3, Prkcd) relatively. Our studies illustrate the first comprehensive mRNA–miRNA–lncRNA and mRNA–miRNA–circRNA networks in stem cell-infused BPD model, which will be valuable in identifying reliable biomarkers or therapeutic targets for BPD pathogenesis and shed new light in the priming and conditioning of UC-MSCs or CB-MNCs in the treatment of neonatal lung injury.

Circ2GO: A Database Linking Circular RNAs to Gene Function

Simple SummaryRibonucleic acids (RNAs) are generally linear chains of nucleotides which function in many cellular processes, best known in protein biosynthesis. In the last decade, circular RNAs have been discovered which are circularized after their synthesis and differ in important features from linear RNAs. These circular RNAs have meanwhile been implicated in important cellular processes in health and disease. Here, we present a comprehensive database, circ2GO, compiling and analyzing circular RNAs found in lung cancer cell lines providing the data in tables as well as visualizing it in transcript maps and in heatmaps. Importantly, we also provide easy-to-use online tools to find circular forms of genes associated with specific molecular functions, biological processes or cellular components or predict their targeted microRNAs. This resource will enable researchers to rapidly identify circular RNAs relevant for their specific research question.Circular RNAs (circRNAs) play critical roles in a broad spectrum of physiological and pathological processes, including cancer. Here, we provide a comprehensive database—circ2GO—systematically linking circRNAs to the functions and processes of their linear counterparts. circ2GO contains 148,811 circular human RNAs originating from 12,251 genes, which we derived from deep transcriptomics after rRNA depletion in a panel of 60 lung cancer and non-transformed cell lines. The broad circRNA expression dataset is mapped to all isoforms of the respective gene. The data are visualized in transcript maps and in heatmaps, to intuitively display a comprehensive portrait for the abundance of circRNAs across transcripts and cell lines. By integrating gene ontology (GO) information for all genes in our dataset, circ2GO builds a connection between circRNAs and their host genes’ biological functions and molecular mechanisms. Additionally, circ2GO offers target predictions for circRNA—microRNA (miRNA) pairs for 25,166 highly abundant circRNAs from 6578 genes and 897 high-confidence human miRNAs. Visualization, user-friendliness, intuitive and advanced forward and reverse search options, batch processing and download options make circ2GO a comprehensive source for circRNA information to build hypotheses on their function, processes, and miRNA targets.

Construction and analysis of the protein-protein interaction network for the olfactory system of the silkworm Bombyx mori.

Olfaction plays an essential role in feeding and information exchange in insects. Previous studies on the olfaction of silkworms have provided a wealth of information about genes and proteins, yet,most studies have only focused on a single gene or protein related to the insect's olfaction. The aim of the current study isto determine key proteins in the olfactory system of the silkworm, and further understand protein-protein interactions (PPIs) in the olfactory system of Lepidoptera. To achieve this goal, we integrated Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses. Furthermore, we selected 585 olfactory-related proteins and constructed a (PPI) network for the olfactory system of the silkworm. Network analysis led to the identification of several key proteins, including GSTz1, LOC733095, BGIBMGA002169-TA, BGIBMGA010939-TA, GSTs2, GSTd2, Or-2, and BGIBMGA013255-TA. A comprehensive evaluation of the proteins showed that glutathione S-transferases (GSTs) had the highest ranking. GSTs also had the highest enrichment levels in GO and KEGG. In conclusion, our analysis showed that key nodes in the biological network had a significant impact on the network, and the key proteins identified via network analysis could serve as new research targets to determine their functions in olfaction.

Coexpression enrichment analysis at the single-cell level reveals convergent defects in neural progenitor cells and their cell-type transitions in neurodevelopmental disorders.

A large number of genes have been implicated in neurodevelopmental disorders (NDDs), but their contributions to NDD pathology are difficult to decipher without understanding their diverse roles in different brain cell types. Here, we integrated NDD genetics with single-cell RNA sequencing data to assess coexpression enrichment patterns of various NDD gene sets. We identified midfetal cortical neural progenitor cell development—more specifically, the ventricular radial glia-to-intermediate progenitor cell transition at gestational week 10—as a key point of convergence in autism spectrum disorder (ASD) and epilepsy. Integrated Gene Ontology–based analysis further revealed that ASD genes activate neural differentiation and inhibit cell cycle during the transition, whereas epilepsy genes function as downstream effectors in the same processes, offering one possible explanation for the high comorbidity rate of the two disorders. This approach provides a framework for investigating the cell-type-specific pathophysiology of NDDs.

Archiving of Somatic Novel Mutations Using Whole-Exome Sequencing in Pakistani Myeloid Leukemic Patients

Introduction Acute myeloid leukemia (AML) is a highly malignant cancer of the bone marrow, clinically and genetically heterogenous clonal disease illustrated by the accumulation of acquired somatic genetic alterations in hematopoietic progenitor cells that modify normal mechanisms of self-renewal, proliferation and differentiation. AML cytogenetic studies provide important diagnostic and prognostic information for AML patients. However, approximately 50% of AML patients have a normal karyotype (NK- AML). Large number of causal mutations of AML has not yet been uncovered. Although disease etiology is still unknown after multiple OMIC's study were published. In this review an attempt is made to approach the subject in the light of currently available literature. Material and Methods Material and information of diagnosed cases of de novo AML (n=14) used after approval of Institutional Ethics Review Committee. Morphological subtypes of AML were classified according to WHO classification. A researcher used retrospective medical record review to obtain clinical, diagnostic data including disease status and blood chemistry and as well as archived genomic DNA of untreated cases. Mutational analysis was done to identify AML somatic mutations using the whole-exome sequencing. The library preparation along with the capture used the illumina TruSeq DNA Exome kit. NGS HS Kit -Proposed sequencing platform - illumina® NovaSeq 6000, 300 cycles -100X coverage - approx. 6Gb per sample. We explored the functional impact of the genes identified in the mutational analyses through an integrated Gene Ontology (GO) and pathway analysis. Results Majority of patients were 10 male with median age 40 range (23-60 years). AML with normal karyo type AML (NK-AML) were five included with maturation, without maturation and monocytic lineage. Significant somatic single nucleotide variants (SNVs) were identified and pathway analysis performed to determine frequently affected signaling pathways. We identified significant, novel recurrent mutations in MAML3 gene (8 patients). No significant novel gene identified in three abnormal karyo type AML (AK-AML). Five out of the 30 novel genes have previously been reported to be associated with other diseases. MAML3 showed statistical significance exclusively in NK-AML patients(n=5). A total of 700 genes identified 500 missense, 25 nonsense, 90 frameshift indels, and/or three stop codon deletions. Using the IntOGen platform, we identified MAP kinase, cell cycle, actin cytoskeleton regulation, PI3K-Akt signaling and other pathways in cancer as affected in the samples. Conclusion This data is the first of its kind from the Pakistani population.Specific patterns of genomic alterations may play an important role in sub types of morphological AML. Future studies to evaluate the usefulness of these genes in genetic testing for the early diagnosis and prognostic prediction of AML patients would be worthwhile. Keywords: Acute myeloid leukemia, Gene ontology, Pathway analysis, Somatic mutation, Subtype-specific mutation, Whole-exome sequencing Disclosures No relevant conflicts of interest to declare.

Integrative network and brain expression analysis reveals mechanistic modules in ataxia

BackgroundGenetic forms of ataxia are a heterogenous group of degenerative diseases of the cerebellum. Many causative genes have been identified. We aimed to systematically investigate these genes to better understand...

MTGO: PPI Network Analysis Via Topological and Functional Module Identification

Protein-protein interaction (PPI) networks are viable tools to understand cell functions, disease machinery, and drug design/repositioning. Interpreting a PPI, however, it is a particularly challenging task because of network complexity. Several algorithms have been proposed for an automatic PPI interpretation, at first by solely considering the network topology, and later by integrating Gene Ontology (GO) terms as node similarity attributes. Here we present MTGO - Module detection via Topological information and GO knowledge, a novel functional module identification approach. MTGO let emerge the bimolecular machinery underpinning PPI networks by leveraging on both biological knowledge and topological properties. In particular, it directly exploits GO terms during the module assembling process, and labels each module with its best fit GO term, easing its functional interpretation. MTGO shows largely better results than other state of the art algorithms (including recent GO-based ones) when searching for small or sparse functional modules, while providing comparable or better results all other cases. MTGO correctly identifies molecular complexes and literature-consistent processes in an experimentally derived PPI network of Myocardial infarction. A software version of MTGO is available freely for non-commercial purposes at https://gitlab.com/d1vella/MTGO.

The Novel Secreted Meloidogyne incognita Effector MiISE6 Targets the Host Nucleus and Facilitates Parasitism in Arabidopsis.

Meloidogyne incognita is highly specialized parasite that interacts with host plants using a range of strategies. The effectors are synthesized in the esophageal glands and secreted into plant cells through a needle-like stylet during parasitism. In this study, based on RNA-seq and bioinformatics analysis, we predicted 110 putative Meloidogyne incognita effectors that contain nuclear localization signals (NLSs). Combining the Burkholderia glumae–pEDV based screening system with subcellular localization, from 20 randomly selected NLS effector candidates, we identified an effector MiISE6 that can effectively suppress B. glumae-induced cell death in Nicotiana benthamiana, targets to the nuclei of plant cells, and is highly expressed in early parasitic J2 stage. Sequence analysis showed that MiISE6 is a 157-amino acid peptide, with an OGFr_N domain and two NLS motifs. Hybridization in situ verified that MiISE6 is expressed in the subventral esophageal glands. Yeast invertase secretion assay validated the function of the signal peptide harbored in MiISE6. Transgenic Arabidopsis thaliana plants expressing MiISE6 become more susceptible to M. incognita. Inversely, the host-derived RNAi of MiISE6 of the nematode can decrease its parasitism on host. Based on transcriptome analysis of the MiISE6 transgenic Arabidopsis samples and the wild-type samples, we obtained 852 differentially expressed genes (DEGs). Integrating Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, we found that expression of MiISE6 in Arabidopsis can suppress jasmonate signaling pathway. In addition, the expression of genes related to cell wall modification and the ubiquitination proteasome pathway also have detectable changes in the transgenic plants. Results from the present study suggest that MiISE6 is involved in interaction between nematode-plant, and plays an important role during the early stages of parasitism by interfering multiple signaling pathways of plant. Moreover, we found homologs of MiISE6 in other sedentary nematodes, Meloidogyne hapla and Globodera pallida. Our experimental results provide evidence to decipher the molecular mechanisms underlying the manipulation of host immune defense responses by plant parasitic nematodes, and transcriptome data also provide useful information for further study nematode–plant interactions.

Identification of somatic mutations using whole-exome sequencing in Korean patients with acute myeloid leukemia

BackgroundAcute myeloid leukemia (AML) is a biologically and clinically heterogeneous cancer of the bone marrow that is characterized by the rapid growth of abnormal myeloid cells.MethodsWe performed a mutational analysis to identify AML somatic mutations using the whole-exome sequencing data of 36 tumor-normal sample pairs from Korean patients with de novo AML. We explored the functional impact of the genes identified in the mutational analyses through an integrated Gene Ontology (GO) and pathway analysis.ResultsA total of 11 genes, including NEFH (p = 6.27 × 10−13 and q = 1.18 × 10−8) and TMPRSS13 (p = 1.40 × 10−10 and q = 1.32 × 10−6), also demonstrated q values less than 0.1 in 36 Korean AML patients. Five out of the 11 novel genes have previously been reported to be associated with other cancers. Two gene mutations, CEBPA (p = 5.22 × 10−5) and ATXN3 (p = 9.75 × 10−4), showed statistical significance exclusively in the M2 and M3 subtypes of the French-American-British classifications, respectively. A total of 501 genes harbored 478 missense, 22 nonsense, 93 frameshift indels, and/or three stop codon deletions and these gene mutations significantly enriched GO terms for signal transduction (GO:0007165, p = 1.77 × 10−3), plasma membrane (GO:0005886, p = 3.07 × 10−4), and scaffold protein binding (GO:0097110, p = 8.65 × 10−4). The mitogen-activated protein kinase (hsa04010, 7.67 × 10−4) was the most enriched Kyoto Encyclopedia of Genes and Genomes pathway.ConclusionsMorphological AML subtypes may in part reflect subtype specific patterns of genomic alterations. Following validation, future studies to evaluate the usefulness of these genes in genetic testing for the early diagnosis and prognostic prediction of AML patients would be worthwhile.

Identification of similarities and differences between myeloid and lymphoid acute leukemias using a gene-gene interaction network

Acute myeloid leukemia (AML) is a cancer that affects the myeloid line of blood cells. Acute lymphoid leukemia (ALL) is a type of leukemia that targets the lymphoid line of blood cells. As the comparison of these two types facilitates in the understanding of their molecular pathology, exploring the similarities and differences in the mechanisms of them is worthwhile. We identified 28 novel AML- and ALL-related genes shared in both of them using a short path algorithm. By integrating gene ontology (GO) and KEGG pathway annotations, we revealed the underlying molecular features of AML and ALL. We finally obtained 160 optimal GO terms that could satisfactorily distinguish two types. Further analysis revealed that the results agree well with previous knowledge. Determining the common and different features between AML and ALL facilitates the classification of leukemia and is thus clinically relevant for exploring the molecular markers.

Interactome mapping reveals important pathways in skeletal muscle development of pigs.

The regulatory relationship and connectivity among genes involved in myogenesis and hypertrophy of skeletal muscle in pigs still remain large challenges. Presentation of gene interactions is a potential way to understand the mechanisms of developmental events in skeletal muscle. In this study, genome-wide transcripts and miRNA profiling was determined for Landrace pigs at four time points using microarray chips. A comprehensive method integrating gene ontology annotation and interactome network mapping was conducted to analyze the biological patterns and interaction modules of muscle development events based on differentially expressed genes and miRNAs. Our results showed that in total 484 genes and 34 miRNAs were detected for the duration from embryonic stage to adult in pigs, which composed two linear expression patterns with consensus changes. Moreover, the gene ontology analysis also disclosed that there were three typical biological events i.e., microstructure assembly of sarcomere at early embryonic stage, myofibril formation at later embryonic stage and function establishments of myoblast cells at postnatal stage. The interactome mappings of different time points also found the down-regulated trend of gene expression existed across the whole duration, which brought a possibility to introduce the myogenesis related miRNAs into the interactome regulatory networks of skeletal muscle in pigs.

Integrating GO and KEGG terms to characterize and predict acute myeloid leukemia-related genes

Background/objectiveAcute myeloid leukemia (AML) is a progressive and malignant cancer of myelogenous blood cells, which disturbs the production of normal blood cells. Although several risk and genetic factors (AML-related genes) have been investigated, the concrete mechanism underlying the development of AML remains unclear. In view of this, it is crucial to develop an effective computational method for meaningfully characterizing AML genes and accurately predicting novel AML genes.MethodsIn this study, we integrated gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations as features to characterize AML genes. We also provided an optimal set of features for predicting AML-related genes by using the minimum redundancy maximum relevance (mRMR) algorithm and dagging metaclassifier.ResultsWe obtained 26 optimal GO terms that characterized AML genes well. Finally, we predicted 464 novel genes to provide clinical researchers with additional candidates and useful insights for further analysis of AML.DiscussionAn in-depth feature analysis indicated that the results are quite consistent with previous knowledge. We developed a systematic method to identify the possible underlying mechanism of AML by analyzing the related genes. Our method has the ability to identify the types of features that are optimal to meaningfully interpret AML and accurately predict more AML genes for further clinical researches.

Predicting False Positives of Protein-Protein Interaction Data by Semantic Similarity Measures<sup>§</sup>

Recent technical advances in identifying protein-protein interactions (PPIs) have generated the genomic-wide interaction data, collectively collectively referred to as the interactome. These interaction data give an insight into the underlying mechanisms of biological processes. However, the PPI data determined by experimental and computational methods include an extremely large number of false positives which are not confirmed to occur in vivo. Filtering PPI data is thus a critical preprocessing step to improve analysis accuracy. Integrating Gene Ontology (GO) data is proposed in this article to assess reliability of the PPIs. We evaluate the performance of various semantic similarity measures in terms of functional consistency. Protein pairs with high semantic similarity are considered highly likely to share common functions, and therefore, are more likely to interact. We also propose a combined method of semantic similarity to apply to predicting false positive PPIs. The experimental results show that the combined hybrid method has better performance than the individual semantic similarity classifiers. The proposed classifier predicted that 58.6% of the S. cerevisiae PPIs from the BioGRID database are false positives.

Linkers of Cell Polarity and Cell Cycle Regulation in the Fission Yeast Protein Interaction Network

The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify ‘linker’ proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key role in controlling the temporal changes in the localization of the polarity machinery. Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation. Detailed bibliographic inspection of other predicted ‘linkers’ also confirms the predictive power of the method. As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.

Identification of genes involved in radioresistance of nasopharyngeal carcinoma by integrating gene ontology and protein-protein interaction networks

Radioresistance remains one of the important factors in relapse and metastasis of nasopharyngeal carcinoma. Thus, it is imperative to identify genes involved in radioresistance and explore the underlying biological processes in the development of radioresistance. In this study, we used cDNA microarrays to select differential genes between radioresistant CNE-2R and parental CNE-2 cell lines. One hundred and eighty-three significantly differentially expressed genes (p<0.05) were identified, of which 138 genes were upregulated and 45 genes were downregulated in CNE-2R. We further employed publicly available bioinformatics related software, such as GOEAST and STRING to examine the relationship among differentially expressed genes. The results show that these genes were involved in type I interferon-mediated signaling pathway biological processes; the nodes tended to have high connectivity with the EGFR pathway, IFN-related pathways, NF-κB. The node STAT1 has high connectivity with other nodes in the protein-protein interaction (PPI) networks. Finally, the reliability of microarray data was validated for selected genes by semi-quantitative RT-PCR and Western blotting. The results were consistent with the microarray data. Our study suggests that microarrays combined with gene ontology and protein interaction networks have great value in the identification of genes of radioresistance in nasopharyngeal carcinoma; genes involved in several biological processes and protein interaction networks may be relevant to NPC radioresistance; in particular, the verified genes CCL5, STAT1-α, STAT2 and GSTP1 may become potential biomarkers for predicting NPC response to radiotherapy.

Finding New Genes for Non-Syndromic Hearing Loss through an In Silico Prioritization Study

At present, 51 genes are already known to be responsible for Non-Syndromic hereditary Hearing Loss (NSHL), but the knowledge of 121 NSHL-linked chromosomal regions brings to the hypothesis that a number of disease genes have still to be uncovered. To help scientists to find new NSHL genes, we built a gene-scoring system, integrating Gene Ontology, NCBI Gene and Map Viewer databases, which prioritizes the candidate genes according to their probability to cause NSHL. We defined a set of candidates and measured their functional similarity with respect to the disease gene set, computing a score () that relies on the assumption that functionally related genes might contribute to the same (disease) phenotype. A Kolmogorov-Smirnov test, comparing the pair-wise distribution on the disease gene set with the distribution on the remaining human genes, provided a statistical assessment of this assumption. We found at a p-value that the former pair-wise is greater than the latter, justifying a prioritization strategy based on the functional similarity of candidate genes respect to the disease gene set. A cross-validation test measured to what extent the ranking for NSHL is different from a random ordering: adding 15% of the disease genes to the candidate gene set, the ranking of the disease genes in the first eight positions resulted statistically different from a hypergeometric distribution with a p-value and a power . The twenty top-scored genes were finally examined to evaluate their possible involvement in NSHL. We found that half of them are known to be expressed in human inner ear or cochlea and are mainly involved in remodeling and organization of actin formation and maintenance of the cilia and the endocochlear potential. These findings strongly indicate that our metric was able to suggest excellent NSHL candidates to be screened in patients and controls for causative mutations.