• Molecular & Cellular Toxicology
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• v.1 no.2
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• pp.73-77
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• 2005

Nickel is the one of potent environmental, the occupational pollutants and the classified human carcinogens. It is a serious hazard to human health, when the metal exposure. To prevent human diseases from the heavy metals, it is seemingly important that understanding of how nickel exerts their toxicity and carcinogenic effect at a molecular and a genomic level. The process of nickel absorption has been demonstrated as phagocytosis, iron channel and diffusion. Uptaked nickel has been suggested to induce carcinogenesis via two pathways, a direct DNA damaging pathway and an indirect DNA damaging pathway. The former was originated from the ability of metal to generate Reactive Oxygen Species (ROS) and the reactive intermediates to interact with DNA directly. Ni-generated ROS or Nickel itself, interacts with DNAs and histones to cause DNA damage and chromosomal abnormality. The latter was originated from an indirect DNA damage via inhibition of DNA repair, or condensation and methylation of DNA. Cells have ability to protect from the genotoxic stresses by changing gene expression. Microarray analysis of the cells treated with nickel or nickel compounds, show the specific altered gene expression profile. For example, HIF-I (Hypoxia-Inducible Factor I) and p53 were well known as transcription factors, which are upregulated in response to stress and activated by both soluble and insoluble nickel compounds. The induction of these important transcription factors exert potent selective pressure and leading to cell transformation. Genes of metallothionein and family of heat shock proteins which have been known to play role in protection and damage control, were also induced by nickel treatment. These gene expressions may give us a clue to understand of the carcinogenesis mechanism of nickel. Further discussions on molecular and genomic, are need in order to understand the specific mechanism of nickel toxicity and carcinogenicity.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.12
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• pp.1912-1921
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• 2020

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

• The Plant Pathology Journal
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• v.39 no.2
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• pp.191-206
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• 2023

Ground cherry (Physalis pubescens) is the most prominent species in the Solanaceae family due to its nutritional content, and prospective health advantages. It is grown all over the world, but notably in northern China. In 2019 firstly bacterial leaf spot (BLS) disease was identified on P. pubescens in China that caused by both BLS pathogens Xanthomonas euvesicatoria pv. euvesicatoria resulted in substantial monetary losses. Here, we compared whole genome sequences of X. euvesicatoria to other Xanthomonas species that caused BLS diseases for high similarities and dissimilarities in genomic sequences through average nucleotide identity (ANI) and BLAST comparison. Molecular techniques and phylogenetic trees were adopted to detect X. euvesicatoria on P. pubescens using recQ, hrpB1, and hrpB2 genes for efficient and precise identification. For rapid molecular detection of X. euvesicatoria, loop-mediated isothermal amplification, polymerase chain reaction (PCR), and real-time PCR techniques were used. Whole genome comparison results showed that the genome of X. euvesicatoria was more closely relative to X. perforans than X. vesicatoria, and X. gardneri with 98%, 84%, and 86% ANI, respectively. All infected leaves of P. pubescens found positive amplification, and negative controls did not show amplification. The findings of evolutionary history revealed that isolated strains XeC10RQ, XeH9RQ, XeA10RQ, and XeB10RQ that originated from China were closely relative and highly homologous to the X. euvesicatoria. This research provides information to researchers on genomic variation in BLS pathogens, and further molecular evolution and identification of X. euvesicatoria using the unique target recQ gene through advance molecular approaches.

• Communications for Statistical Applications and Methods
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• v.27 no.5
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• pp.535-546
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• 2020

In genetic association studies, pleiotropy is a phenomenon where a variant or a genetic region affects multiple traits or diseases. There have been many studies identifying cross-phenotype genetic associations. But, most of statistical approaches for detection of pleiotropy are based on individual tests where a single variant association with multiple traits is tested one at a time. These approaches fail to account for relations among correlated variants. Recently, multivariate regularization methods have been proposed to detect pleiotropy in analysis of high-dimensional genomic data. However, they suffer a problem of tuning parameter selection, which often results in either too many false positives or too small true positives. In this article, we applied selection probability to multivariate regularization methods in order to identify pleiotropic variants associated with multiple phenotypes. Selection probability was applied to individual elastic-net, unified elastic-net and multi-response elastic-net regularization methods. In simulation studies, selection performance of three multivariate regularization methods was evaluated when the total number of phenotypes, the number of phenotypes associated with a variant, and correlations among phenotypes are different. We also applied the regularization methods to a wild bean dataset consisting of 169,028 variants and 17 phenotypes.

• Molecules and Cells
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• v.43 no.8
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• pp.728-738
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• 2020

Time and cost-effective production of next-generation sequencing data has enabled the performance of population-scale comparative and evolutionary studies for various species, which are essential for obtaining the comprehensive insight into molecular mechanisms underlying species- or breed-specific traits. In this study, the evolutionary and functional analysis of Korean native pig (KNP) was performed using single nucleotide polymorphism (SNP) data by comparative and population genomic approaches with six different mammalian species and five pig breeds. We examined the evolutionary history of KNP SNPs, and the specific genes of KNP based on the uniqueness of non-synonymous SNPs among the used species and pig breeds. We discovered the evolutionary trajectory of KNP SNPs within the used mammalian species as well as pig breeds. We also found olfaction-associated functions that have been characterized and diversified during evolution, and quantitative trait loci associated with the unique traits of KNP. Our study provides new insight into the evolution of KNP and serves as a good example for a better understanding of domestic animals in terms of evolution and domestication using the combined approaches of comparative and population genomics.

• Journal of Plant Biotechnology
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• v.42 no.4
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• pp.336-341
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• 2015

Blueberry (Vaccinium spp.) is a bush that grows well at special cultural environments such as acid soil, high organic matter content, and a good drainage and aeration compared to other general crops. Blueberries are well known to contain high amounts of anthocyanins and phenolic compounds, resulting in high antioxidant activity that provides health benefits, and expanding the cultivation areas and consumer's demand in the worldwide. However, the full genome of blueberry has not been announced until now. Furthermore, the genomic analysis and transcriptome approaches are not so popular compare to major crops such as orange, apple, and grape. The aim of the review about blueberry genomic research is to establish the platform for setting blueberry breeding target, increasing proficiency of blueberry research, and making the practical cultivation techniques in Korea. The main topics in the blueberry genomic research including transcriptome, genetic mapping, and various markers are related with cold hardiness, chilling requirement, hot tolerance, anthocyanin content, and flavonoid synthesis pathway on various tissues like flower bud, leaf bud, shoot, root, and berry fruit. The review of the current status of blueberry genomic research will provide basic information to the breeders and researchers and will contribute to development of blueberry industry with sustainable productions and increase of blueberry consumption as new profitable crops in Korea.

• Journal of Life Science
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• v.24 no.12
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• pp.1269-1275
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• 2014

TALEN is a newly developed gene engineering method to knock out specific genes. It contains a DNA binding domain and a Fok1 nuclease domain in the TALEN plasmid. Therefore, the engineered TALEN construct can bind to any region of genomic DNA and cut the target nucleotide, thereby inducing mutation. In this study, we constructed two TALEN constructs targeted to a protein initiation codon (DBEX2) or the 25th upstream region (DBPR25) to enable mRNA synthesis of SETDB1 HMTase. We performed the TALEN cloning in two steps. The first step was from module vectors to pFUS array vectors. We confirmed successful cloning with a colony PCR experiment and Esp31 restriction enzyme digestion, which resulted in a smear band and a 1 Kb insert band, respectively The second step of the cloning was from a pFUS array vector to a mammalian TALEN expression vector. The engineered TALEN construct was sequenced with specific primers in an expression vector. As expected, a specific array from the module vectors was shown in the sequencing analysis. The specific module sequences were regularly arrayed in every 100 bp, and SETDB1 expression totally disappeared in the TALEN-DBEX2 transfection. PCR amplification targeting of DBEX2 was performed, and the PCR product was digested with a T7E1 restriction enzyme. The expression of SETDB1 was down-regulated in the TALEN-DBPR25 transfection. Morphological changes were also observed in the two TALEN constructs with transfected HeLa cells. These results suggest that the engineered TALEN constructs in two strategic approaches are very useful to knock-out of the SETDB1 gene and to study gene function.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2445-2452
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• 2012

RNA interference has created a breakthrough in gene silencing technology and there is now much debate on the successful usage of RNAi based methods in treating a number of debilitating diseases. Cancer is often regarded as a result of mutations in genomic DNA resulting in faulty gene expression. The occurrence of cancer can also be influenced by epigenetic irregularities in the chromatin structure which leads to alterations and mutations in DNA resulting in cancer cell formation. A number of therapeutic approaches have been put forth to treat cancer. Anti cancer therapy often involves chemotherapy targeting all the cells in common, whereby both cancer cells as well as normal cells get affected. Hence RNAi technology has potential to be a better therapeutic agent as it is possible to deactivate molecular targets like specific mutant genes. This review highlights the successful use of RNAi inducers against different types of cancer, thereby paving the way for specific therapeutic medicines.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.815-825
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• 2016

Marginalized random effects models (MREMs) are often used to analyze longitudinal categorical data. The models permit direct estimation of marginal mean parameters and specify the serial correlation of longitudinal categorical data via the random effects. However, it is not easy to estimate the random effects covariance matrix in the MREMs because the matrix is high-dimensional and must be positive-definite. To solve these restrictions, we introduce two modeling approaches of the random effects covariance matrix: partial autocorrelation and the modified Cholesky decomposition. These proposed methods are illustrated with the real data from Korean genomic epidemiology study.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.4
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• pp.587-596
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• 2001

In the last decade, rapid developments in molecular biotechnology and of genomic tools have enabled the creation of dense linkage maps across whole genomes of human, plant and animals. Successful development and implementation of interval mapping methodologies have allowed detection of the quantitative trait loci (QTL) responsible for economically important traits in experimental and commercial livestock populations. The candidate gene approach can be used in any general population with the availability of a large resource of candidate genes from the human or rodent genomes using comparative maps, and the validated candidate genes can be directly applied to commercial breeds. For the QTL detected from primary genome scans, two incipient fine mapping approaches are applied by generating new recombinants over several generations or utilizing historical recombinants with identity-by-descent (IBD) and linkage disequilibrium (LD) mapping. The high resolution definition of QTL position from fine mapping will allow the more efficient implementation of breeding programs such as marker-assisted selection (MAS) or marker-assisted introgression (MAI), and will provide a route toward cloning the QTL.