Identification of gene-related Korean patents and construction of a database server for Korean patent sequences

With the advent of high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly in Korea. However, there is little information on gene relatedness of the Korean patent sequences. The primary aims of this study are two-fold. First, we associated Korean patent applications with genes. Second, we have constructed a database server, named Patome@Korea, to provide the gene-patent map and the Korean patent data containing biological sequences. To associate the sequences with genes, we have received patent sequence data from the Korean Intellectual Property Office (KIPO) and annotated them with RefSeq and Entrez Gene. Through the association analysis, we found that nearly 14.7% of human genes were related to Korean patenting, compared to 25% of human genes in the US patent. We have consolidated the association results and the patent sequence data to a relational database and implemented a web-based user interface to provide search service. The database can be queried using application number, applicant, titles, gene ID/name, and RefSeq number. We also provide web-based BLAST facility to allow users to compare their sequences against patent sequences. Equal contribution.     

Inferred functions of "novel" genes identified in fibroblasts chondroinduced by demineralized bone

Little is known about the cellular mechanisms that control postnatal chondrocyte differentiation. As a first step towards identifying those mechanisms, gene expression shifts were characterized in an in vitro model of chondroinduction. In previous studies, several functional classes of genes (cytoskeletal and matrix elements, cell adhesion proteins, peptide growth factors, and signal transduction proteins) were found to be altered in human dermal fibroblasts (hDFs) cultured in porous collagen sponges with chondroinductive demineralized bone powder (DBP) for 3 days. In addition, a number of "novel" sequences were identified. In this study, molecular techniques were combined with computational methods to characterize those sequences. Gene expression of all 10 novel sequences tested was found in hDFs by RT-PCR. The sequences were compared to the human genome, and their cellular functions were inferred from genes that mapped to the same chromosomal coordinates. Only one of the novel sequences contained a protein-coding region (kinesin superfamily protein 26B). The others contained 3' untranslated (osteonectin, alpha-V integrin, RAP2B) or other untranslated regions (PTPN21, GAS6) of mRNAs. The cellular functions of the DBP-regulated genes described in this study fall into similar categories as those previously identified. These results provide new details on the cellular response of hDFs exposed to DBP.     

Gene expression analysis in LLC-PK1 renal tubular cells by atrial natriuretic peptide (ANP): correlation of homologous human genes with renal response

We used human DNA microarray to explore the differential gene expression profiling of atrial natriuretic peptide (ANP)-stimulated renal tubular epithelial kidney cells (LLC-PK1) in order to understand the biological effect of ANP on renal kidney cell's response. Gene expression profiling revealed 807 differentially expressed genes, consisting of 483 up-regulated and 324 down-regulated genes. The bioinformatics tool was used to gain a better understanding of differentially expressed genes in porcine genome homologous with human genome and to search the gene ontology and category classification, such as cellular component, molecular function and biological process. Four up-regulated genes of ATP1B1, H3F3A, ITGB1 and RHO that were typically validated by real-time quantitative PCR (RT-qPCR) analysis serve important roles in the alleviation of renal hypertrophy as well as other related effects. Therefore, the human array can be used for gene expression analysis in pig kidney cells and we believe that our findings of differentially expressed genes served as genetic markers and biological functions can lead to a better understanding of ANP action on the renal protective system and may be used for further therapeutic application.     

Biological Pathway‐Based Genome‐Wide Association Analysis Identified the Vasoactive Intestinal Peptide (VIP) Pathway Important for Obesity

Recent genome‐wide association (GWA) studies have identified a number of novel genes/variants predisposing to obesity. However, most GWA studies have focused on individual single‐nucleotide polymorphism (SNPs)/genes with a strong statistical association with a phenotypic trait without considering potential biological interplay of the tested genes. In this study, we performed biological pathway‐based GWA analysis for BMI and body fat mass. We used individual level genotype data generated from 1,000 unrelated US whites that were genotyped for ～500,000 SNPs. Statistical analysis of pathways was performed using a modification of the Gene Set Enrichment Algorithm. A total of 963 pathways extracted from the BioCarta, Kyoto Encyclopedia of Genes and Genomes (KEGG), Ambion GeneAssist, and Gene Ontology (GO) databases were analyzed. Among all of the pathways analyzed, the vasoactive intestinal peptide (VIP) pathway was most strongly associated with fat mass (nominal P = 0.0009) and was the third most strongly associated pathway with BMI (nominal P = 0.0006). After multiple testing correction, the VIP pathway achieved false‐discovery rate (FDR) q values of 0.042 and 0.120 for fat mass and BMI, respectively. Our study is the first to demonstrate that the VIP pathway may play an important role in development of obesity. The study also highlights the importance of pathway‐based GWA analysis in identification of additional genes/variants for complex human diseases.     

Gene functional similarity search tool (GFSST)

Background  

With the completion of the genome sequences of human, mouse, and other species and the advent of high throughput functional genomic research technologies such as biomicroarray chips, more and more genes and their products have been discovered and their functions have begun to be understood. Increasing amounts of data about genes, gene products and their functions have been stored in databases. To facilitate selection of candidate genes for gene-disease research, genetic association studies, biomarker and drug target selection, and animal models of human diseases, it is essential to have search engines that can retrieve genes by their functions from proteome databases. In recent years, the development of Gene Ontology (GO) has established structured, controlled vocabularies describing gene functions, which makes it possible to develop novel tools to search genes by functional similarity.     

Defining characteristics and conservation of poorly annotated genes in Caenorhabditis elegans using WormCat 2.0

Omics tools provide broad datasets for biological discovery. However, the computational tools for identifying important genes or pathways in RNA-seq, proteomics, or GWAS (Genome-Wide Association Study) data depend on Gene Ontogeny annotations and are biased toward well-described pathways. This limits their utility as poorly annotated genes, which could have novel functions, are often passed over. Recently, we developed an annotation and category enrichment tool for Caenorhabditis elegans genomic data, WormCat, which provides an intuitive visualization output. Unlike Gene Ontogeny-based enrichment tools, which exclude genes with no annotation information, WormCat 2.0 retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category enrichment exhibits tissue-specific expression patterns and can include genes with biological functions identified in published datasets. Poorly annotated genes are often considered to be potentially species-specific and thus, of reduced interest to the biomedical community. Instead, we find that around 3% of the UNASSIGNED genes have human orthologs, including some linked to human diseases. These human orthologs themselves have little annotation information. A recently developed method that incorporates lineage relationships (abSENSE) indicates that the failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes. This suggests that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows the association of UNASSIGNED genes with specific phenotypes and known pathways. Building these associations in C. elegans, with its robust genetic tools, provides a path to further functional study and insight into these understudied genes.     

Gene-set based genome-wide association analysis for the speed of sound in two skeletal sites of Korean women

The speed of sound (SOS) value is an indicator of bone mineral density (BMD). Previous genome-wide association (GWA) studies have identified a number of genes, whose variations may affect BMD levels. However, their biological implications have been elusive. We re-analyzed the GWA study dataset for the SOS values in skeletal sites of 4,659 Korean women, using a gene-set analysis software, GSA-SNP. We identified 10 common representative GO terms, and 17 candidate genes between these two traits (P_GS ＜ 0.05). Implication of these GO terms and genes in the bone mechanism is well supported by the literature survey. Interestingly, the significance levels of some member genes were inversely related, in several gene-sets that were shared between two skeletal sites. This implies that biological process, rather than SNP or gene, is the substantial unit of genetic association for SOS in bone. In conclusion, our findings may provide new insights into the biological mechanisms for BMD. [BMB Reports 2014; 47(6): 348-353]     

Comparisons of graph-structure clustering methods for gene expression data

Although many numerical clustering algorithms have been applied to gene expression dataanalysis,the essential step is still biological interpretation by manual inspection.The correlation betweengenetic co-regulation and affiliation to a common biological process is what biologists expect.Here,weintroduce some clustering algorithms that are based on graph structure constituted by biological knowledge.After applying a widely used dataset,we compared the result clusters of two of these algorithms in terms ofthe homogeneity of clusters and coherence of annotation and matching ratio.The results show that theclusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Clustersoftware,which contains the genes that are most expression correlative and most consistent with biologicalfunctions.Moreover,knowledge-guided analysis seems much more applicable than GO-Cluster in a largerdataset.     

Searching for coexpressed genes in three-color cDNA microarray data using a probabilistic model-based Hough Transform

The effects of a drug on the genomic scale can be assessed in a three-color cDNA microarray with the three color intensities represented through the so-called hexaMplot. In our recent study, we have shown that the Hough Transform (HT) applied to the hexaMplot can be used to detect groups of coexpressed genes in the normal-disease-drug samples. However, the standard HT is not well suited for the purpose because 1) the assayed genes need first to be hard-partitioned into equally and differentially expressed genes, with HT ignoring possible information in the former group; 2) the hexaMplot coordinates are negatively correlated and there is no direct way of expressing this in the standard HT and 3) it is not clear how to quantify the association of coexpressed genes with the line along which they cluster. We address these deficiencies by formulating a dedicated probabilistic model-based HT. The approach is demonstrated by assessing effects of the drug Rg1 on homocysteine-treated human umbilical vein endothetial cells. Compared with our previous study, we robustly detect stronger natural groupings of coexpressed genes. Moreover, the gene groups show coherent biological functions with high significance, as detected by the Gene Ontology analysis.     

From patenting genes to proteins: the search for utility via function

The debate regarding the patenting of genes has extended into the post-genome era. With only approximately 35000 genes deduced from the draft sequence of the human genome, there are fears that a few companies have already gained monopoly on the potential benefits from this knowledge. Nevertheless, it is accepted that proteins determine gene function and function is not readily predicted from gene sequence. Furthermore, genes can encode multiple proteins and a single protein can have multiple functions. Here, we argue that unraveling the intrinsic complexity of proteins and their functions is the key towards determining the utility requirement for patenting protein inventions and consider the possible socioeconomic impact.     

The effect of vorinostat on the development of resistance to doxorubicin in neuroblastoma

Histone deacetylase (HDAC) inhibitors, especially vorinostat, are currently under investigation as potential adjuncts in the treatment of neuroblastoma. The effect of vorinostat co-treatment on the development of resistance to other chemotherapeutic agents is unknown. In the present study, we treated two human neuroblastoma cell lines [SK-N-SH and SK-N-Be(2)C] with progressively increasing doses of doxorubicin under two conditions: with and without vorinsotat co-therapy. The resultant doxorubicin-resistant (DoxR) and vorinostat-treated doxorubicin resistant (DoxR-v) cells were equally resistant to doxorubicin despite significantly lower P-glycoprotein expression in the DoxR-v cells. Whole genome analysis was performed using the Ilumina Human HT-12 v4 Expression Beadchip to identify genes with differential expression unique to the DoxR-v cells. We uncovered a number of genes whose differential expression in the DoxR-v cells might contribute to their resistant phenotype, including hypoxia inducible factor-2. Finally, we used Gene Ontology to categorize the biological functions of the differentially expressed genes unique to the DoxR-v cells and found that genes involved in cellular metabolism were especially affected.     

Hypermutability of genes in Homo sapiens due to the hosting of long mono-SSR

Simple sequence repeats (SSRs) are very common short repeatsin eukaryotic genomes. "Long" SSRs are considered "hypermutable"sequences because they exhibit a high rate of expansion andcontraction. Because they are potentially deleterious, longSSRs tend to be uncommon in coding sequences. However, severalgenes contain long SSRs in their exonic sequences. Here, weidentify 1,291 human genes that host a mononucleotide SSR longenough to be prone to expansion or contraction, being calledhypermutable hereafter. On the basis of Gene Ontology annotations,we show that only a restricted number of functions are overrepresentedamong those hypermutable genes including cell cycle and maintenanceof DNA integrity. Using a probabilistic model, we show thatgenes involved in these functions are expected to host longSSRs because they tend to be long and/or are biased in nucleotidecomposition. Finally, we show that for almost all functionswe observe fewer hypermutable sequences than expected undera neutral model. There are however interesting exceptions, forexample, genes involved in protein and RNA transport, as wellas meiosis and mismatch repair functions that have as many hypermutablegenes as expected under neutrality. Conversely, there are functions(e.g., collagen-related genes) where hypermutable genes aremore often avoided than in other functions. Our results showthat, even though several functions harbor unusually long SSRin their exons, long SSRs are deleterious sequences in almostall functions and are removed by purifying selection. The strengthof this purifying selection however greatly varies from functionto function. We discuss possible explanations for this intriguingresult.     

Additional gene ontology structure for improved biological reasoning

MOTIVATION: The Gene Ontology (GO) is a widely used terminology for gene product characterization in, for example, interpretation of biology underlying microarray experiments. The current GO defines term relationships within each of the independent subontologies: molecular function, biological process and cellular component. However, it is evident that there also exist biological relationships between terms of different subontologies. Our aim was to connect the three subontologies to enable GO to cover more biological knowledge, enable a more consistent use of GO and provide new opportunities for biological reasoning. RESULTS: We propose a new structure, the Second Gene Ontology Layer, capturing biological relations not directly reflected in the present ontology structure. Given molecular functions, these paths identify biological processes where the molecular functions are involved and cellular components where they are active. The current Second Layer contains 6271 validated paths, covering 54% of the molecular functions of GO and can be used to render existing gene annotation sets more complete and consistent. Applying Second Layer paths to a set of 4223 human genes, increased biological process annotations by 24% compared to publicly available annotations and reproduced 30% of them. AVAILABILITY: The Second GO is publicly available through the GO Annotation Toolbox (GOAT.no): http://www.goat.no.