박테리오파아지 T7 의 기능에 관한 연구;복제단백질간의 단백질 상호작용

김학준,김영태 한국생명과학회 1996 생명과학회지 Vol.6 No.3

박테리오파지 T7 gene 2.5 단백질은 single-stranded DNA 결합 단백질로 박태리오파지 T7의 DNA복제, 재조합, 및 수선에 필수적으로 요구된다. Gene 2.5 protein은 T7의 DNA 합성과 성장에 필수적인 단백질이다. Gene 2.5 Protein이 중요시 되는 이유는 이 단백질이 T7의 다른 복제 필수단백질인 T7의 다른 복제 필수단백질인 T7 DNA polymerase 와 gene 4 protein(helicase/primase)와 서로 상호작용할 것으로 제안되었기 때문이다. (Kim and Richardson, J. Biol. Chem., 1992;1994). 이 단백질의 단백질 상호작용을 가능하게 하는 domain은 carboxyl-terminal domain일 것으로 여러 실험에서 대두되었기에, 이 domain의 특성을 파악하기 위해 야생형과 변이체 gene 2.5 단백질들을 각각 GST에 융합한후 fusion 단백질을 정제하였다. 정제된 이 융합 단백질들의 carboxyl-terminal domain이 T7 복제 단백질들과 상호작용을 조사하는지를 조사하기 위해 affinity chromatography로 이용하였다. 실험 결과, 아생형 GST-gene 2.5 융합단잭질(GST-2.5 (WT))는 T7 DNA polymerase 와 상호작용을 하였지만. 변이형 융합단백질(GST-2.5$\Delta$21C)는 interaction을 하지 못했다. 이 결과는 carbohyl-terminal domain이 단백질-단백질 상호작용을 하는데 직접적으로 관여하는 것을 증명하였다. 또한,GST2.5(WT)는 gene 4 protein(helicase/primase)와 직접 상호작용을 하나. GST2.5$\Delta$21C는 상호작용을 하지 못하는 것으로 나타났다. 따라서 gene 4 proteins와의 상호작용에도 gene 2.5 protein의 carboxyl-terminal domain이 직접 관여 한다는 것이 증명되었다. 이상의 결과에서 gene 2.5 protein은 박테리오파지 T7 의 유전자 목제 시 단백질-단백질 상호작용에 관혀아며, 특히 gene 2.5 protein의 carboxyl-terminal domain이 이러한 상호작용에 직접적으로 관여하는 domain이라는 것을 알 수가 있었다. Bacteriophage T7 gene 2.5 protein, a single-stranded DNA binding protein, is required for T7 DNA replication, recombination, and repair. T7 gene 2.5 protein has two distinctive domains, DNA binding and C-terminal domain, directly involved in protein-protein interaction. Gene 2.5 protein participates in the DNA replication of Bacteriophage T7, which makes this protein essential for the T7 growth and DNA replication. What gene 2.5 protein makes important at T7 growth and DNA replication is its binding affinity to single-stranded DNA and the protein-protein important at T7 DNA replication proteins which are essential for the T7 DNA synthesis. We have constructed pGST2.5(WT) encoding the wild-type gene 2.5 protein and pGST2.5$\Delta $21C lacking C-terminal 21 amino acid residues. The purified GST-fusion proteins, GST2.5(WT) and GST2.5(WT)$\Delta$21C, were used for whether the carboxyl-terminal domain participates in the protein-protein interactions or not. GST2.5(WT) and GST2.5$\Delta$21C showed the difference in the protein-protein interaction. GST2.5(WT) interacted with T7 DNA polymerase and gene 4 protein, but GST2.5$\Delta$21C did not interact with either protein. Secondly, GST2.5(WT) interacts with gene 4 proteins (helicase/primase) but not GST2.5$\Delta$21C. these results proved the involvement of the carboxyl-terminal domain of gene 2.5 protein in the protein-protein interaction. We clearly conclude that carboxy-terminal domain of gene 2.5 protein is firmly involved in protein-protein interactions in T7 replication proteins.

Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes

Li, Donghe,Wo, Sungho Korea Genome Organization 2016 Genomics & informatics Vol.14 No.4

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.

Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes

Donghe Li,원성호 한국유전체학회 2016 Genomics & informatics Vol.14 No.4

Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.

Drug repositioning using drug-disease vectors based on an integrated network

Lee, Taekeon,Yoon, Youngmi BioMed Central 2018 BMC bioinformatics Vol.19 No.1

<P><B>Background</B></P><P>Diverse interactions occur between biomolecules, such as activation, inhibition, expression, or repression. However, previous network-based studies of drug repositioning have employed interaction on the binary protein-protein interaction (PPI) network without considering the characteristics of the interactions. Recently, some studies of drug repositioning using gene expression data found that associations between drug and disease genes are useful information for identifying novel drugs to treat diseases. However, the gene expression profiles for drugs and diseases are not always available. Although gene expression profiles of drugs and diseases are available, existing methods cannot use the drugs or diseases, when differentially expressed genes in the profiles are not included in their network.</P><P><B>Results</B></P><P>We developed a novel method for identifying candidate indications of existing drugs considering types of interactions between biomolecules based on known drug-disease associations. To obtain associations between drug and disease genes, we constructed a directed network using protein interaction and gene regulation data obtained from various public databases providing diverse biological pathways. The network includes three types of edges depending on relationships between biomolecules. To quantify the association between a target gene and a disease gene, we explored the shortest paths from the target gene to the disease gene and calculated the types and weights of the shortest paths. For each drug-disease pair, we built a vector consisting of values for each disease gene influenced by the drug. Using the vectors and known drug-disease associations, we constructed classifiers to identify novel drugs for each disease.</P><P><B>Conclusion</B></P><P>We propose a method for exploring candidate drugs of diseases using associations between drugs and disease genes derived from a directed gene network instead of gene regulation data obtained from gene expression profiles. Compared to existing methods that require information on gene relationships and gene expression data, our method can be applied to a greater number of drugs and diseases. Furthermore, to validate our predictions, we compared the predictions with drug-disease pairs in clinical trials using the hypergeometric test, which showed significant results. Our method also showed better performance compared to existing methods for the area under the receiver operating characteristic curve (AUC).</P><P><B>Electronic supplementary material</B></P><P>The online version of this article (10.1186/s12859-018-2490-x) contains supplementary material, which is available to authorized users.</P>

Investigation of gene-gene interactions of clock genes for chronotype in a healthy Korean population

Park, Mira,Kim, Soon Ae,Shin, Jieun,Joo, Eun-Jeong Korea Genome Organization 2020 Genomics & informatics Vol.18 No.4

Chronotype is an important moderator of psychiatric illnesses, which seems to be controlled in some part by genetic factors. Clock genes are the most relevant genes for chronotype. In addition to the roles of individual genes, gene-gene interactions of clock genes substantially contribute to chronotype. We investigated genetic associations and gene-gene interactions of the clock genes BHLHB2, CLOCK, CSNK1E, NR1D1, PER1, PER2, PER3, and TIMELESS for chronotype in 1,293 healthy Korean individuals. Regression analysis was conducted to find associations between single nucleotide polymorphism (SNP) and chronotype. For gene-gene interaction analyses, the quantitative multifactor dimensionality reduction (QMDR) method, a nonparametric model-free method for quantitative phenotypes, were performed. No individual SNP or haplotype showed a significant association with chronotype by both regression analysis and single-locus model of QMDR. QMDR analysis identified NR1D1 rs2314339 and TIMELESS rs4630333 as the best SNP pairs among two-locus interaction models associated with chronotype (cross-validation consistency [CVC] = 8/10, p = 0.041). For the three-locus interaction model, the SNP combination of NR1D1 rs2314339, TIMELESS rs4630333, and PER3 rs228669 showed the best results (CVC = 4/10, p < 0.001). However, because the mean differences between genotype combinations were minor, the clinical roles of clock gene interactions are unlikely to be critical.

Construction of Synaptic Neural Network for Genetic Interaction Analysis

Jaeyong Yee,Mira Park 한국자료분석학회 2021 Journal of the Korean Data Analysis Society Vol.23 No.4

Contribution by a single gene to the association with trait may be either independent or through interactions with other genes. Examining all available genes for the main effect should be carried out without the time constraint. However the number of possible interacting combinations would soon become formidably large with the growing number of genes. Therefore it is often coerced to identify a group of candidate genes for the interaction and investigate only within it. Such an identification process should be able to select the group of genes having possibilities to interact with each other. Main effect of each gene should not necessarily be the criterion for the selection. We devised a neural network process that was quite sensitive to the interaction of a particular gene to the remaining ones. Contribution of each gene to the association by the genes as a whole was estimated. Selection was made based on the statistical significance for the existence of such contribution. It was demonstrated that this process might perform reliable candidate gene selection for the interaction even when the selected genes did not show significant main effect, through single scan of each individual gene.

Gene-Diet Interaction on Cancer Risk in Epidemiological Studies

Lee, Sang-Ah The Korean Society for Preventive Medicine 2009 예방의학회지 Vol.42 No.6

Genetic factors clearly play a role in carcinogenesis, but migrant studies provide unequivocal evidence that environmental factors are critical in defining cancer risk. Therefore, one may expect that the lower availability of substrate for biochemical reactions leads to more genetic changes in enzyme function; for example, most studies have indicated the variant MTHFR genotype 677TT is related to biomarkers, such as homocysteine concentrations or global DNA methylation particularly in a low folate diet. The modification of a phenotype related to a genotype, particularly by dietary habits, could support the notion that some of inconsistencies in findings from molecular epidemiologic studies could be due to differences in the populations studied and unaccounted underlying characteristics mediating the relationship between genetic polymorphisms and the actual phenotypes. Given the evidence that diet can modify cancer risk, gene-diet interactions in cancer etiology would be anticipated. However, much of the evidence in this area comes from observational epidemiology, which limits the causal inference. Thus, the investigation of these interactions is essential to gain a full understanding of the impact of genetic variation on health outcomes. This report reviews current approaches to gene-diet interactions in epidemiological studies. Characteristics of gene and dietary factors are divided into four categories: one carbon metabolism-related gene polymorphisms and dietary factors including folate, vitamin B group and methionines; oxidative stress-related gene polymorphisms and antioxidant nutrients including vegetable and fruit intake; carcinogen-metabolizing gene polymorphisms and meat intake including heterocyclic amins and polycyclic aromatic hydrocarbon; and other gene-diet interactive effect on cancer.

gsGator: an integrated web platform for cross-species gene set analysis

Kang, Hyunjung,Choi, Ikjung,Cho, Sooyoung,Ryu, Daeun,Lee, Sanghyuk,Kim, Wankyu BioMed Central 2014 BMC bioinformatics Vol.15 No.-

<P><B>Background</B></P><P>Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. Often, GSA needs to be done highly interactively by combining or modifying gene lists or inspecting gene-gene interactions in a molecular network.</P><P><B>Description</B></P><P>We developed <I>gsGator</I>, a web-based platform for functional interpretation of gene sets with useful features such as cross-species GSA, simultaneous analysis of multiple gene sets, and a fully integrated network viewer for visualizing both GSA results and molecular networks. An extensive set of gene annotation information is amassed including GO & pathways, genomic annotations, protein-protein interaction, transcription factor-target (TF-target), miRNA targeting, and phenotype information for various model organisms. By combining the functionalities of <I>Set Creator, Set Operator and Network Navigator</I>, user can perform highly flexible and interactive GSA by creating a new gene list by any combination of existing gene sets (intersection, union and difference) or expanding genes interactively along the molecular networks such as protein-protein interaction and TF-target. We also demonstrate the utility of our interactive and cross-species GSA implemented in gsGator by several usage examples for interpreting genome-wide association study (GWAS) results. gsGator is freely available at http://gsGator.ewha.ac.kr.</P><P><B>Conclusions</B></P><P>Interactive and cross-species GSA in gsGator greatly extends the scope and utility of GSA, leading to novel insights via conserved functional gene modules across different species.</P>

Bacteriophage T7의 유전자 복제기작에 관한 생화학적, 분자생물학적 특성 연구

KIM Young Tae The Korean Society of Fisheries and Aquatic Scienc 1995 한국수산과학회지 Vol.28 No.2

본 연구에서는 유전자 복제기작을 생화학적, 분자생물학적 방법을 사용하여 bacteriphage T7을 대상으로 연구하였다. Bacteriophage T7의 유전자 복제, 재조합, 수선시 필수 단백질로 작용하는 gene 2.5 단백질의 생체내 기능에 대한 유전학적 연구와 단백질을 분리 정제하여 복제 단백질들과의 상호작용에 대한 연구를 수행하였다. 연구결과 gene 2.5 단백질은 DNA복제시 필수 구성단백질로 작용하며, 복제과정에서 유전자 복제에 관여하는 핵심 단백질들인 DNA polymerase, helicase/primase와 직접 단백질-단백질 상호 협동 작용을 하는 r것을 증명하였다. 특히 gene 2.5 단백질의 C-terminal domain이 절편된 변이체의 경우 복제 단백질들과 상호작용이 결여되었다. 따라서 C-terminal domain이 gene 2.5 단백질의 기능에 필수적으로 관여함을 입증하였다. Bacteriophage T7 gene 2.5 protein, a single-stranded DNA binding protein, has been implicated in T7 DNA replication, recombination, and repair. Purified gene 2.5 protein has been shown to interact with the phage encoded gene 5 protein (DNA polymerase) and gene 4 proteins (helicase and primase) and stimulates their activities. Genetic analysis of T7 phage defective in gene 2.5 shows that the gene 2.5 protein is essential for T7 DNA replication and growth. T7 phage that contain null mutants of gene 2.5 were constructed by homologous recombination. These mutant phage $(T7\Delta2.5)$ cannot grow in Escherichia coli. After infection of E. coli with $T7\Delta2.5$, host DNA synthesis is shut off, and $T7\Delta2.5$ DNA synthesis is reduced to less than $1\%$ of wild-type phage DNA synthesis (Kim and Richardson, 1993, Proc. Natl. Aca. Sci. USA, 90, 10173-10177). A truncated gene 2.5 protein $(GP2.5-\Delta21C)$ deleted the 21 carboxyl terminal amino acids was constructed by in vitro mutagenesis. $GP2.5-\Delta21C$ cannot substitute for wild-type gene 2.5 protein in vivo; the phage are not viable and exhibit less than $1\%$ of the DNA synthesis observed in wild-type phage-infected cells. $GP2.5-\Delta21C$ has been purified to apparent homogeneity from cells overexpressing its cloned gene. Purified $GP2.5-\Delta21C$ does not physically into「act with T1 gene 4 protein as measured by affinity chromatography and immunoblot analysis. The mutant protein cannot stimulate T7 gene 4 protein activity on RNA-primed DNA synthesis and primer synthesis. These results suggest that C-terminal domain of gene 2.5 protein is essential for protein-protein interactions.

Application of Crossover Analysis-logistic Regression in the Assessment of Gene- environmental Interactions for Colorectal Cancer

Wu, Ya-Zhou,Yang, Huan,Zhang, Ling,Zhang, Yan-Qi,Liu, Ling,Yi, Dong,Cao, Jia Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.5

Background: Analysis of gene-gene and gene-environment interactions for complex multifactorial human disease faces challenges regarding statistical methodology. One major difficulty is partly due to the limitations of parametric-statistical methods for detection of gene effects that are dependent solely or partially on interactions with other genes or environmental exposures. Based on our previous case-control study in Chongqing of China, we have found increased risk of colorectal cancer exists in individuals carrying a novel homozygous TT at locus rs1329149 and known homozygous AA at locus rs671. Methods: In this study, we proposed statistical method-crossover analysis in combination with logistic regression model, to further analyze our data and focus on assessing gene-environmental interactions for colorectal cancer. Results: The results of the crossover analysis showed that there are possible multiplicative interactions between loci rs671 and rs1329149 with alcohol consumption. Multifactorial logistic regression analysis also validated that loci rs671 and rs1329149 both exhibited a multiplicative interaction with alcohol consumption. Moreover, we also found additive interactions between any pair of two factors (among the four risk factors: gene loci rs671, rs1329149, age and alcohol consumption) through the crossover analysis, which was not evident on logistic regression. Conclusions: In conclusion, the method based on crossover analysis-logistic regression is successful in assessing additive and multiplicative gene-environment interactions, and in revealing synergistic effects of gene loci rs671 and rs1329149 with alcohol consumption in the pathogenesis and development of colorectal cancer.