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Failure to respond to treatment in epithelial ovarian cancer can often be attributed to platinum-based chemotherapy resistance. However, the possible mechanisms or candidate biomarkers associated with platinum resistance are yet to be elucidated, even though many researchers have performed related studies.We performed RNA sequencing of clinical specimens obtained from patients with platinum-sensitive or resistant epithelial ovarian cancer (EOC). Furthermore, various bioinformatics approaches, including spatial analysis of functional enrichment, were used to identify key regulators and associated underlying mechanisms of platinum resistance in EOC.Through RNA-sequencing, we identified 263 differentially expressed genes (98 upregulated and 165 downregulated) and subjected them to Gene Oncology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, which were characterized to the traditional platinum-resistant characteristics. Subsequently, the gene interaction network and module analysis by spatial analysis of functional enrichment software demonstrated protein kinase C and casein kinase substrate in neurons 3 (PACSIN3) as the only upregulated hub gene, and neurotensin (NTS) and KIAA0319 as downregulated hub genes in platinum-resistant EOC. We selected PACSIN3 for further analysis because it has not been studied in relation to response to platinum-based chemotherapy. PACSIN3 was significantly upregulated in ovarian cancer cells compared to immortalized human ovarian surface epithelial cells. In addition, cisplatin-induced apoptosis was measured in PACSIN3 knockout OVCA433 and BRCA-mutated EOC cell line, SNU251, by a fluorescence-activated cell sorting-based Annexin-V/propium iodide double staining assay, which revealed a significant increase in apoptosis.Taken together, the present study presents PACSIN3 as a promising predictive biomarker associated with platinum resistance, especially in BRCA-mutated epithelial ovarian cancers.

We analyzed the relationship between egg consumption, body composition, and serum cholesterol levels. We obtained data on egg consumption by using a food frequency questionnaire (FFQ) (13,132 adults) and the 24-h dietary recall (24HR) (13,366 adults) from the fourth and fifth Korea National Health and Nutrition Examination Surveys (2008–2011). In men, consuming 2–3 eggs/week was associated with higher fat mass (FM), percentage body fat (PBF), and fat-to-muscle ratio (FtoM), compared to consuming

Background Keloid scarring is a fibroproliferative disease caused by aberrant genetic activation with an unclear underlying mechanism. Genetic predisposition, aberrant cellular responses to environmental factors, increased inflammatory cytokines and epithelial–mesenchymal transition (EMT) phenomena are known as major contributors. In this study, we aimed to identify the molecular drivers that initiate keloid pathogenesis. Methods Bulk tissue RNA sequencing analyses of keloid and normal tissues along with ex vivo and in vitro tests were performed to identify the contributing genes to keloid pathogenesis. An animal model of inflammatory keloid scarring was reproduced by replication of a skin fibrosis model with intradermal bleomycin injection in C57BL/6 mice. Results Gene set enrichment analysis revealed upregulation of Wnt family member 5A (WNT5A) expression and genes associated with EMT in keloid tissues. Consistently, human keloid tissues and the bleomycin-induced skin fibrosis animal model showed significantly increased expression of WNT5A and EMT markers. Increased activation of the interleukin (IL)-6/Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway and subsequent elevation of EMT markers was also observed in keratinocytes co-cultured with WNT5A-activated fibroblasts or keloid fibroblasts. Furthermore, WNT5A silencing and the blockage of IL-6 secretion via neutralizing IL-6 antibody reversed hyperactivation of the STAT pathway and EMT markers in keratinocytes. Lastly, STAT3 silencing significantly reduced the EMT-like phenotypes in both keratinocytes and IL-6-stimulated keratinocytes. Conclusions Intercellular communication via the WNT5A and STAT pathways possibly underlies a partial mechanism of EMT-like phenomena in keloid pathogenesis. IL-6 secreted from WNT5A-activated fibroblasts or keloid fibroblasts activates the JAK/STAT signaling pathway in adjacent keratinocytes which in turn express EMT markers. A better understanding of keloid development and the role of WNT5A in EMT will promote the development of next-generation targeted treatments for keloid scars.

The goal of our study was to demonstrate the spectrum of genomic alterations present in the residual disease of patients with advanced high-grade serous ovarian cancer (HGSOC) after neoadjuvant chemotherapy (NAC), including matched pretreatment biopsies. During the study period between 2006 and 2017, we collected pre-NAC and post-NAC tumor tissue samples from patients with advanced HGSOC. We performed combined next-generation sequencing and immunohistochemistry to identify actionable targets and pathway activation in post-NAC residual tumors. We also examined whether post-NAC profiling of residual HGSOC identified targetable molecular lesions in the chemotherapy-resistant component of tumors. Among 102 post-NAC samples, 41 (40%) of patients had mutations in homologous recombination repair (HRR) genes (HRR deficiency). Patients with HRR mutations had higher tumor mutation burdens (p < 0.001) and higher alterations in the PI3K-AKT-mTOR pathway (p = 0.004) than patients without these HRR mutations. Nevertheless, we found no significant differences in progression-free survival (p = 0.662) and overall survival (OS; p = 0.828) between the two groups. Most patients (91%) had alterations in at least one of the targetable pathways, and those patients with cell cycle (p = 0.004) and PI3K-AKT-mTOR signaling (p = 0.005) pathway alterations had poorer OS (Bonferroni-corrected threshold = 0.0083, 0.05/6). We showed the genomic landscape of tumor cells remaining in advanced HGSOC after NAC. Once validated, these data can help inform biomarker-driven adjuvant studies in targeting residual tumors to improve the outcomes of patients with advanced HGSOC after NAC.

Proteins are major functional molecules that physically and functionally interact to carry out cellular processes. The physical interactions are generally mediated by domain-level interactions. Thus, novel protein-protein interactions can be predicted using various computational methods based on domain-domain interactions, using resolved structures of protein complexes. Functional protein interactions can be inferred based on shared domains between proteins, since proteins involved in the same biological processes tend to harbor common domains. We recently developed a method of inferring functional interactions between proteins using associations between their domain compositions, which can be represented as domain profiles. Since the method requires only protein domain annotations, it can be easily applied to any species with a sequenced genome. Here, we describe in detail the method of generating domain profiles for proteins and measuring the association between them to infer functional interactions between proteins. We also demonstrate that domain profile association can be used to successfully construct a large-scale functional network of human proteins.

Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes., Author summary Protein domains are basic functional units of proteins, yet domain-based pathway annotations for proteins are challenging tasks because many domains are pervasive among diverse pathways. Therefore, we developed a network-based scoring scheme to measure pathway specificity of domains, and then used it to identify pathway-specific domains. Surprisingly, we observed substantially more disease mutations in pathway-specific domains than non-specific domains. We found evidences that mutations of pathway-specific domains tend to perturb pathway integrity via disrupting within-pathway protein-protein interactions. We also demonstrated prediction capacity of pathway-specific domains for complex diseases with experimental validations. Our study demonstrated the usefulness of pathway information for protein domains in interpreting non-random distribution of disease mutations among domains and identification of disease genes and variants.

Advanced high-throughput sequencing technology accumulated massive amount of genomics and transcriptomics data in the public databases. Due to the high technical accessibility, DNA and RNA sequencing have huge potential for the study of gene functions in most species including animals and crops. A proven analytic platform to convert sequencing data to gene functional information is co-functional network. Because all genes exert their functions through interactions with others, network analysis is a legitimate way to study gene functions. The workflow of network-based functional study is composed of three steps: (i) inferencing co-functional links, (ii) evaluating and integrating the links into genome-scale networks, and (iii) generating functional hypotheses from the networks. Co-functional links can be inferred from DNA sequencing data by using phylogenetic profiling, gene neighborhood, domain profiling, associalogs, and co-expression analysis from RNA sequencing data. The inferred links are then evaluated and integrated into a genome-scale network with aid from gold-standard co-functional links. Functional hypotheses can be generated from the network based on (i) network connectivity, (ii) network propagation, and (iii) subnetwork analysis. The functional analysis pipeline described here requires only sequencing data which can be readily available for most species by next-generation sequencing technology. Therefore, co-functional networks will greatly potentiate the use of the sequencing data for the study of genetics in any cellular organism.

Multiple genes and their interactions are involved in most human diseases. This pathway-centric view of human pathology is beginning to guide our approaches to disease research. Analytical algorithms describing human gene networks have been developed for three major tasks in disease research: (i) disease gene prioritization, (ii) disease module discovery, and (iii) stratification of complex diseases. To understand the underlying biology of human diseases, identification of disease genes and disease pathways is crucial. The functional interdependence between genes for disease progression has been identified by their connections in gene networks, which enables prediction of novel disease genes based on their connections to known disease genes. Disease modules can be identified by subnetworks that are enriched for patient-specific activated or mutated genes. Network biology also facilitates the subtyping of complex diseases such as cancer, which is a prerequisite for developing personalized medicinal therapi...

During the last decade, genome-wide association studies (GWAS) have represented a major approach to dissect complex human genetic diseases. Due in part to limited statistical power, most studies identify only small numbers of candidate genes that pass the conventional significance thresholds (e.g. P ≤ 5 × 10−8). This limitation can be partly overcome by increasing the sample size, but this comes at a higher cost. Alternatively, weak association signals can be boosted by incorporating independent data. Previously, we demonstrated the feasibility of boosting GWAS disease associations using gene networks. Here, we present a web server, GWAB (www.inetbio.org/gwab), for the network-based boosting of human GWAS data. Using GWAS summary statistics (P-values) for SNPs along with reference genes for a disease of interest, GWAB reprioritizes candidate disease genes by integrating the GWAS and network data. We found that GWAB could more effectively retrieve disease-associated reference genes than GWAS could alone. As an example, we describe GWAB-boosted candidate genes for coronary artery disease and supporting data in the literature. These results highlight the inherent value in sub-threshold GWAS associations, which are often not publicly released. GWAB offers a feasible general approach to boost such associations for human disease genetics.

Network “guilt by association” (GBA) is a proven approach for identifying novel disease genes based on the observation that similar mutational phenotypes arise from functionally related genes. In principle, this approach could account even for nonadditive genetic interactions, which underlie the synergistic combinations of mutations often linked to complex diseases. Here, we analyze a large-scale, human gene functional interaction network (dubbed HumanNet). We show that candidate disease genes can be effectively identified by GBA in cross-validated tests using label propagation algorithms related to Google's PageRank. However, GBA has been shown to work poorly in genome-wide association studies (GWAS), where many genes are somewhat implicated, but few are known with very high certainty. Here, we resolve this by explicitly modeling the uncertainty of the associations and incorporating the uncertainty for the seed set into the GBA framework. We observe a significant boost in the power to detect validated candidate genes for Crohn's disease and type 2 diabetes by comparing our predictions to results from follow-up meta-analyses, with incorporation of the network serving to highlight the JAK–STAT pathway and associated adaptors GRB2/SHC1 in Crohn's disease and BACH2 in type 2 diabetes. Consideration of the network during GWAS thus conveys some of the benefits of enrolling more participants in the GWAS study. More generally, we demonstrate that a functional network of human genes provides a valuable statistical framework for prioritizing candidate disease genes, both for candidate gene-based and GWAS-based studies.

In proteomics research, the identification of differentially expressed proteins observed under specific conditions is one of key issues. There are several ways to detect the change of a specific protein's expression level such as statistical analysis and graphical visualization. However, it is quiet difficult to handle the spot information of an individual protein manually by these methods, because there are a considerable number of proteins in a tissue sample. In this paper, using database and data mining techniques, the application plan of OLAP data cube and Discovery-driven exploration is proposed. By using data cubes, it is possible to analyze the relationship between proteins and relevant clinical information as well as analyzing the differentially expressed proteins by disease. We propose the measure and exception indicators which are suitable to analyzing protein expression level changes are proposed. In addition, we proposed the reducing method of calculating InExp in Discovery-driven exploration. We also evaluate the utility and effectiveness of the data cube and Discovery-driven exploration in the lung cancer 2-DE gel image.Keywords:Proteome Informatics, Data Mining, On-Line Analytical Processing, Two-Dimensional Electrophoresis

The reconstruction of transcriptional regulatory networks (TRNs) is a long-standing challenge in human genetics. Numerous computational methods have been developed to infer regulatory interactions between human transcriptional factors (TFs) and target genes from high-throughput data and their performance evaluation requires gold-standard interactions. Here we present a database of literature-curated human TF-target interactions, TRRUST (transcriptional regulatory relationships unravelled by sentence-based text-mining, http://www.grnpedia.org/trrust), which currently contains 8,015 interactions between 748 TF genes and 1,975 non-TF genes. A sentence-based text-mining approach was employed for efficient manual curation of regulatory interactions from approximately 20 million Medline abstracts. To the best of our knowledge, TRRUST is the largest publicly available database of literature-curated human TF-target interactions to date. TRRUST also has several useful features: i) information about the mode-of-regulation; ii) tests for target modularity of a query TF; iii) tests for TF cooperativity of a query target; iv) inferences about cooperating TFs of a query TF; and v) prioritizing associated pathways and diseases with a query TF. We observed high enrichment of TF-target pairs in TRRUST for top-scored interactions inferred from high-throughput data, which suggests that TRRUST provides a reliable benchmark for the computational reconstruction of human TRNs.

2-dimensional electrophoresis(2DE) is a separation technique to identify proteins contained in a sample. However, the image is very sensitive to its experimental conditions as well as the quality of scanning. In order to adjust the possible variation of spots in a particular image, a user should manually annotate landmark spots on each gel image to analyze the spots of different images together. However, this operation is an error-prone and tedious job. This thesis develops an automated method of extracting the landmark spots of an image based on landmark profile. The landmark profile is created by clustering the previously identified landmarks of sample images of the same type. The profile contains the various properties of clusters identified for each landmark. When the landmarks of a new image need to be fount all the candidate spots of each landmark are first identified by examining the properties of its clusters. Subsequently, all the landmark spots of the new image are collectively found by the well-known optimization algorithm . The performance of this method is illustrated by various experiments on real 2DE images of mouse`s brain-tissues.

To address the role of gap junction hemichannels in apoptosis, the cell death induced by staurosporine (ST) was evaluated in wild type HeLa cells (HeLa-WT) and transfectants expressing either full-length connexin43 (HeLa-Cx43) or a C-terminal truncation of Cx43 (HeLa-DeltaCT). Cell death was measured with fluorescence-activated cell sorting (FACS), both DNA and nuclear fragmentation methods and assays for PARP and caspase 3. The ST-mediated cell death was accelerated in HeLa-Cx43 cells compared to HeLa-WT and HeLa-DeltaCT. To determine why HeLa-Cx43 cells were more susceptible to ST, the phosphorylation state and the localization of Cx43 protein within cells were examined using specific Cx43 antibodies. The phosphorylated forms of Cx43 were sharply reduced in HeLa-Cx43 cells treated with ST. Moreover, in ST-treated HeLa-Cx43 cells, Cx43 was mainly observed at the cell surface. In contrast, the truncated form of Cx43 found in HeLa-DeltaCT cells, which lacks many of the normal phosphorylation sites, was observed in the cytosol with ST treatment. To examine the hemichannels in the plasma membranes of ST-treated HeLa-Cx43 cells, several dye uptake methods using carboxyfluorescein and propidium iodide were employed. While the number of fluorescent cells did not change in HeLa-WT and HeLa-DeltaCT cells with ST treatment, the number of fluorescent HeLa-Cx43 cells increased more than ten-fold. These results indicate that the increases in cell surface Cx43 seen with immunofluorescence and the elevated hemichannel activities detected with dye uptake could help explain the accelerated cell death observed in ST-treated HeLa-Cx43 cells.

Cryptococcus neoformans is an opportunistic human pathogenic fungus that causes meningoencephalitis. Due to the increasing global risk of cryptococcosis and the emergence of drug-resistant strains, the development of predictive genetics platforms for the rapid identification of novel genes governing pathogenicity and drug resistance of C. neoformans is imperative. The analysis of functional genomics data and genome-scale mutant libraries may facilitate the genetic dissection of such complex phenotypes but with limited efficiency. Here, we present a genome-scale co-functional network for C. neoformans, CryptoNet, which covers ~81% of the coding genome and provides an efficient intermediary between functional genomics data and reverse-genetics resources for the genetic dissection of C. neoformans phenotypes. CryptoNet is the first genome-scale co-functional network for any fungal pathogen. CryptoNet effectively identified novel genes for pathogenicity and drug resistance using guilt-by-association and context-associated hub algorithms. CryptoNet is also the first genome-scale co-functional network for fungi in the basidiomycota phylum, as Saccharomyces cerevisiae belongs to the ascomycota phylum. CryptoNet may therefore provide insights into pathway evolution between two distinct phyla of the fungal kingdom. The CryptoNet web server (www.inetbio.org/cryptonet) is a public resource that provides an interactive environment of network-assisted predictive genetics for C. neoformans.

A network-based approach has proven useful for the identification of novel genes associated with complex phenotypes, including human diseases. Because network-based gene prioritization algorithms are based on propagating information of known phenotype-associated genes through networks, the pathway structure of each phenotype might significantly affect the effectiveness of algorithms. We systematically compared two popular network algorithms with distinct mechanisms – direct neighborhood which propagates information to only direct network neighbors, and network diffusion which diffuses information throughout the entire network – in prioritization of genes for worm and human phenotypes. Previous studies reported that network diffusion generally outperforms direct neighborhood for human diseases. Although prioritization power is generally measured for all ranked genes, only the top candidates are significant for subsequent functional analysis. We found that high prioritizing power of a network algorithm for all genes cannot guarantee successful prioritization of top ranked candidates for a given phenotype. Indeed, the majority of the phenotypes that were more efficiently prioritized by network diffusion showed higher prioritizing power for top candidates by direct neighborhood. We also found that connectivity among pathway genes for each phenotype largely determines which network algorithm is more effective, suggesting that the network algorithm used for each phenotype should be chosen with consideration of pathway gene connectivity.

Rice is the most important staple food crop and a model grass for studies of bioenergy crops. We previously published a genome-scale functional network server called RiceNet, constructed by integrating diverse genomics data and demonstrated the use of the network in genetic dissection of rice biotic stress responses and its usefulness for other grass species. Since the initial construction of the network, there has been a significant increase in the amount of publicly available rice genomics data. Here, we present an updated network prioritization server for Oryza sativa ssp. japonica, RiceNet v2 (http://www.inetbio.org/ricenet), which provides a network of 25 765 genes (70.1% of the coding genome) and 1 775 000 co-functional links. Ricenet v2 also provides two complementary methods for network prioritization based on: (i) network direct neighborhood and (ii) context-associated hubs. RiceNet v2 can use genes of the related subspecies O. sativa ssp. indica and the reference plant Arabidopsis for versatility in generating hypotheses. We demonstrate that RiceNet v2 effectively identifies candidate genes involved in rice root/shoot development and defense responses, demonstrating its usefulness for the grass research community.

We describe an integrated proteome database, termed Yonsei Proteome Research Center Proteome Database (YPRC-PDB) which can store, retrieve and analyze various information including two-dimensional electrophoresis (2-DE) images and associated spot information that were obtained during studies of hepatocellular carcinoma (HCC). YPRC-PDB is also designed to perform as a laboratory information management system that manages sample information, clinical background, conditions of both sample preparation and 2-DE, and entire sets of experimental results. It also features query system and data-mining applications, which are amenable to automatically analyze expression level changes of a specific protein and directly link to clinical information. The user interface is web-based, so that the results from other laboratories can be shared effectively. In particular, the master gel image query is equipped with a graphic tool that can easily identify the relationship between the specific pathological stage of HCC and expression levels of a potential marker protein on the master gel image. Thus, YPRC-PDB is a versatile integrated database suitable for subsequent analyses. The information in YPRC-PDB is updated easily and it is available to authorized users on the World Wide Web (http://yprcpdb.proteomix.org/ approximately damduck/).

Saccharomyces cerevisiae, i.e. baker's yeast, is a widely studied model organism in eukaryote genetics because of its simple protocols for genetic manipulation and phenotype profiling. The high abundance of publicly available data that has been generated through diverse 'omics' approaches has led to the use of yeast for many systems biology studies, including large-scale gene network modeling to better understand the molecular basis of the cellular phenotype. We have previously developed a genome-scale gene network for yeast, YeastNet v2, which has been used for various genetics and systems biology studies. Here, we present an updated version, YeastNet v3 (available at http://www.inetbio.org/yeastnet/), that significantly improves the prediction of gene-phenotype associations. The extended genome in YeastNet v3 covers up to 5818 genes (similar to 99% of the coding genome) wired by 362 512 functional links. YeastNet v3 provides a new web interface to run the tools for network-guided hypothesis generations. YeastNet v3 also provides edge information for all data-specific networks (similar to 2 million functional links) as well as the integrated networks. Therefore, users can construct alternative versions of the integrated network by applying their own data integration algorithm to the same data-specific links.