GScluster: Network-weighted gene-set clustering analysis

Background: Gene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets. Results: Here, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks. Conclusions: Network-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis

Patome: a database server for biological sequence annotation and analysis in issued patents and published patent applications

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene–patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene–patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at ; the information is updated bimonthly

Antiasthmatic Effects of Herbal Complex MA and Its Fermented Product MA128

This study was conducted to determine if oral administration of the novel herbal medicine, MA, and its Lactobacillus acidophilus fermented product, MA128, have therapeutic properties for the treatment of asthma. Asthma was induced in BALB/c mice by systemic sensitization to ovalbumin (OVA) followed by intratracheal, intraperitoneal, and aerosol allergen challenges. MA and MA128 were orally administered 6 times a week for 4 weeks. At 1 day after the last ovalbumin exposure, airway hyperresponsiveness was assessed and samples of bronchoalveolar lavage fluid, lung cells, and serum were collected for further analysis. We investigated the effect of MA and MA128 on airway hyperresponsiveness, pulmonary eosinophilic infiltration, various immune cell phenotypes, Th2 cytokine production, OVA-specific IgE production, and Th1/Th2 cytokine production in this mouse model of asthma. In BALB/c mice, we found that MA and MA128 treatment suppressed eosinophil infiltration into airways and blood, allergic airway inflammation and AHR by suppressing the production of IL-5, IL-13, IL-17, Eotaxin, and OVA-specific IgE, by upregulating the production of OVA-specific Th1 cytokine (IFN-γ), and by downregulating OVA-specific Th2 cytokine (IL-4) in the culture supernatant of spleen cells. The effectiveness of MA was increased by fermentation with Lactobacillus acidophilus

Identification of Gene Expression Signature Modulated by Nicotinamide in a Mouse Bladder Cancer Model

BACKGROUND: Urinary bladder cancer is often a result of exposure to chemical carcinogens such as cigarette smoking. Because of histological similarity, chemically-induced rodent cancer model was largely used for human bladder cancer studies. Previous investigations have suggested that nicotinamide, water-soluble vitamin B3, may play a key role in cancer prevention through its activities in cellular repair. However, to date, evidence towards identifying the genetic alterations of nicotinamide in cancer prevention has not been provided. Here, we search for the molecular signatures of cancer prevention by nicotinamide using a N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN)-induced urinary bladder cancer model in mice. METHODOLOGY/PRINCIPAL FINDINGS: Via microarray gene expression profiling of 20 mice and 233 human bladder samples, we performed various statistical analyses and immunohistochemical staining for validation. The expression patterns of 893 genes associated with nicotinamide activity in cancer prevention were identified by microarray data analysis. Gene network analyses of these 893 genes revealed that the Myc and its associated genes may be the most important regulator of bladder cancer prevention, and the gene expression signature correlated well with protein expression data. Comparison of gene expression between human and mouse revealed that BBN-induced mouse bladder cancers exhibited gene expression profiles that were more similar to those of invasive human bladder cancers than to those of non-invasive human bladder cancers. CONCLUSIONS/SIGNIFICANCE: This study demonstrates that nicotinamide plays an important role as a chemo-preventive and therapeutic agent in bladder cancer through the regulation of the Myc oncogenic signature. Nicotinamide may represent a promising therapeutic modality in patients with muscle-invasive bladder cancer

REGNET: Mining context-specific human transcription networks using composite genomic information

Background: Genome-wide expression profiles reflect the transcriptional networks specific to the given cell context. However, most statistical models try to estimate the average connectivity of the networks from a collection of gene expression data, and are unable to characterize the context-specific transcriptional regulations. We propose an approach for mining context-specific transcription networks from a large collection of gene expression fold-change profiles and composite gene-set information.Results: Using a composite gene-set analysis method, we combine the information of transcription factor binding sites, Gene Ontology or pathway gene sets and gene expression fold-change profiles for a variety of cell conditions. We then collected all the significant patterns and constructed a database of context-specific transcription networks for human (REGNET). As a result, context-specific roles of transcription factors as well as their functional targets are readily explored. To validate the approach, nine predicted targets of E2F1 in HeLa cells were tested using chromatin immunoprecipitation assay. Among them, five (Gadd45b, Dusp6, Mll5, Bmp2 and E2f3) were successfully bound by E2F1. c-JUN and the EMT transcription networks were also validated from literature.Conclusions: REGNET is a useful tool for exploring the ternary relationships among the transcription factors, their functional targets and the corresponding cell conditions. It is able to provide useful clues for novel cell-specific transcriptional regulations. The REGNET database is available at http://mgrc.kribb.re.kr/regnet.open0

Generation of Whole-Genome Sequencing Data for Comparing Primary and Castration-Resistant Prostate Cancer

Because castration-resistant prostate cancer (CRPC) does not respond to androgen deprivation therapy and has a very poor prognosis, it is critical to identify a prognostic indicator for predicting high-risk patients who will develop CRPC. Here, we report a dataset of whole genomes from four pairs of primary prostate cancer (PC) and CRPC samples. The analysis of the paired PC and CRPC samples in the whole-genome data showed that the average number of somatic mutations per patients was 7,927 in CRPC tissues compared with primary PC tissues (range, 1,691 to 21,705). Our whole-genome sequencing data of primary PC and CRPC may be useful for understanding the genomic changes and molecular mechanisms that occur during the progression from PC to CRPC

Interglial Crosstalk in Obesity-Induced Hypothalamic Inflammation

Glial cells have recently gained particular attention for their close involvement in neuroinflammation and metabolic disorders including obesity and diabetes. In the central nervous system (CNS), different types of resident glial cells have been documented to express several signaling molecules and related receptors, and their crosstalks have been implicated in physiology and pathology of the CNS. Emerging evidence illustrates that malfunctioning glia and their products are an important component of hypothalamic inflammation. Recent studies have suggested that glia–glia crosstalk is a pivotal mechanism of overnutrition-induced chronic hypothalamic inflammation, which might be intrinsically associated with obesity/diabetes and their pathological consequences. This review covers the recent advances in the molecular aspects of interglial crosstalk in hypothalamic inflammation, proposing a central role of such a crosstalk in the development of obesity, diabetes, and related complications. Finally, we discuss the possibilities and challenges of targeting glial cells and their crosstalk for a better understanding of hypothalamic inflammation and related metabolic dysfunctions