• Asian-Australasian Journal of Animal Sciences
• /
• 제19권7호
• /
• pp.953-957
• /
• 2006

The eukaryotic elongation factor 1 A (EEF1A) participates in protein synthesis by forming the eEF1A GTP tRNA complex to deliver aminoacyl-tRNA to the A site of ribosomes. This study described cDNA sequences and partial genomic structure of porcine EEF1A1. The porcine EEF1A1 gene encoded a protein with 462 amino acids, which shared complete homology with human, chimpanzee and dog. The temporal expression pattern showed the diversity of EEF1A1 level in mRNA was relatively minor in prenatal embryo skeletal muscle, however, the expression decreased during aging after birth in skeletal muscle of the Chinese Tongcheng pig. The spatial expression patterns indicated that the gene expressed in skeletal muscle, heart, lung, liver, kidney, fat and spleen. In addition, we assigned the gene to porcine chromosome 1 using a radiation hybrid panel.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
• /
• pp.623-626
• /
• 2006

Group of genes controls the functioning of a cell by complex interactions. These interacting gene groups are called Gene Regulatory Networks (GRNs). Two previous data mining approaches, clustering and classification have been used to analyze gene expression data. While these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rule. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and detect gene expression patterns applying FP-growth algorithm. And then, we construct gene regulatory network from frequent gene patterns using chain rule. Finally, we validated our proposed method by showing that our experimental results are consistent with published results.

• Journal of Animal Science and Technology
• /
• 제58권4호
• /
• pp.17.1-17.7
• /
• 2016

Background: The Sapsaree (Canis familiaris) is a Korean native dog that is very friendly, protective, and loyal to its owner, and is registered as a natural monument in Korea (number: 368). To investigate large-scale gene expression profiles and identify the genes related to exercise-induced stress in the Sapsaree, we performed whole-transcriptome RNA sequencing and analyzed gene expression patterns before and after exercise performance. Results: We identified 525 differentially expressed genes in ten dogs before and after exercise. Gene Ontology classification and KEGG pathway analysis revealed that the genes were mainly involved in metabolic processes, such as programmed cell death, protein metabolic process, phosphatidylinositol signaling system, and cation binding in cytoplasm. The ten Sapsarees could be divided into two groups based on the gene expression patterns before and after exercise. The two groups were significantly different in terms of their basic body type ($p{\leq}0.05$). Seven representative genes with significantly different expression patterns before and after exercise between the two groups were chosen and characterized. Conclusions: Body type had a significant effect on the patterns of differential gene expression induced by exercise. Whole-transcriptome sequencing is a useful method for investigating the biological characteristics of the Sapsaree and the large-scale genomic differences of canines in general.

• 정보과학회 논문지
• /
• 제43권1호
• /
• pp.54-60
• /
• 2016

뇌의 유전자 발현 정보는 영역별 기능과 밀접한 관련이 있어 이를 분석하기 위해 다수의 유전자들 간의 발현 정도 및 발현 위치 정보와의 관계에 대한 연구가 이루어지고 있다. 본 논문에서는 컴퓨터 기술을 통해 알렌 뇌과학연구소에서 제공하는 약 2만여개의 쥐 뇌 유전자 발현 정보 중 뇌의 해마 영역을 중점적으로 분석하여 유전자들을 자동으로 분류해내고 발현 위치 정보를 기반으로 군집화하여 가시화하는 방법을 제안한다. 이를 통해 해마 내 전체적으로 발현되는 유전자들과 특정 영역에만 발현되는 유전자들을 분류할 수 있었고 그 중 특정 영역에 발현되는 유전자들의 위치정보 기반으로 군집화된 데이터를 뇌 지도와 함께 관찰 할 수 있었다. 본 연구는 뇌의 기능과 영역과의 관계성 관련 생물학적 연구를 위한 실험군 선정작업에 이용되어 실험설계시간을 줄일 수 있고 기존에 알려진 뇌의 해부학적 구조보다 더욱 세분화된 구조를 발견할 수 있는 가능성을 제시할 것으로 기대된다.

• Asian-Australasian Journal of Animal Sciences
• /
• 제18권12호
• /
• pp.1684-1690
• /
• 2005

Using mRNA differential display technique, we investigated differential gene expression in hybrids relative to their parents in a diallel cross involving four chicken breeds in order to provide an insight into the molecular basis of heterosis in chicken. The results indicated that there was extensive differential gene expression between chicken F1 hybrids and their parents which was classified into four kinds of patterns as following: (1) bands only detected in hybrid F1; (2) bands only absent in hybrid F1; (3) bands only detected in parent P1 or P2; (4) bands absent in parent P1 or P2. Forty-two differentially expressed cDNAs were cloned and sequenced, and their expression patterns were confirmed by Reverse-Northern dot blot. Sequence analysis and database searches revealed that genes showed differential expression between hybrid and parents were regulatory and functional genes involved in metabolism, mRNA splicing, transcriptional regulation, cell cycles and protein modification. These results indicated that hybridization between two parents can cause changes in expression of a variety of genes. In conclusion, that the altered pattern of gene expression in hybrids may be responsible for heterosis in chickens.

• Asian-Australasian Journal of Animal Sciences
• /
• 제18권5호
• /
• pp.682-685
• /
• 2005

Large White, an introduced European pig breed, and Meishan, a Chinese indigenous pig breed, were hybridized directly and reciprocally and a total of 260 pigs, including purebreds, Large White and Meishan, and their hybrids, White${\times}$Meishan (LM) and Meishan${\times}$Large White (ML) pigs, were bred in our laboratory. The mRNA differential display PCR (DD-PCR) was used to detect the age-dependent changes of differential gene expression in backfat tissue between hybrids and parents. Some measures were taken to reduce the false positives in our experiment. Among the total of 2,686 bands obtained, 1,952 bands (about 72.67%) were reproducible and eight patterns (fifteen kinds) of gene expression were observed. The percentage of differentially expressed genes between hybrids and parents is 56.86% at the age of four months and 57.71% at the age of six months. This indicated that the differences of gene expression between hybrids and their parents were very obvious. U-test was used to compare the patterns of gene expression between the age of four and six months, and results showed that bands occurring in only one hybrid and bands displayed in one hybrid and one parent were significantly different at p<0.05, and bands visualized in only two hybrids were significantly different at p<0.01. These indicated that differential gene expression between hybrids and parents changed at different ages.

• 정보처리학회논문지D
• /
• 제14D권1호
• /
• pp.9-20
• /
• 2007

유전자들의 그룹은 복잡한 상호작용들을 통해 세포의 기능이 조절되며 이러한 상호작용을 하는 유전자 그룹들을 유전자 조절 네트워크 (GRNs: Gene Regulatory Networks)라고 한다. 이전의 유전자 발현 분석 기법인 군집화와 분류는 단지 상동성에 의한 유전자들 사이의 소속을 결정하는 데에는 유용하나 분자 활동에서의 같은 클래스에서 발견되어지는 유전자들 사이의 조절 관계를 식별할 수 없다. 더욱이 유전자들이 어떻게 연관되는 지와 유전자들이 서로 어떻게 조절하는지에 대한 매커니즘의 이해가 필요하다. 따라서 이 논문에서는 시계열 마이크로어레이 데이터로부터의 유전자들의 조절 관계를 발견하기 위해서 빈발 패턴 마이닝과 연쇄 규칙을 이용한 새로운 접근법을 제안하였다. 이 기법에서는 먼저, 빈발 패턴 마이닝 적용을 위한 적절한 데이터 변환 방법을 제안하였고 FP-growth을 이용하여 유전자 발현 패턴들을 발견한다. 그런 다음, 연쇄 규칙을 이용하여 빈발한 유전자 패턴들로부터 유전자 조절 네트워크를 구축하였다. 마지막으로 제안된 기법의 검증은 공개된 유전자들의 조절 관계와 실험 결과의 일치함을 보임으로써 평가하였다.

• Molecular & Cellular Toxicology
• /
• 제1권4호
• /
• pp.248-255
• /
• 2005

To understand the response of cancer cells to anticancer drugs at the gene expression level, we examined the gene expression changes in response to five anticancer drugs, 5-fluorouracil, cytarabine, cisplatin, paclitaxel, and cytochalasin D in NCI-H460 human lung cancer cells. Of the five drugs, 5-fluorouracil had the most distinctive gene expression signature. By clustering genes whose expression changed significantly, we identified three clusters with unique gene expression patterns. The first cluster reflected the up-regulation of gene expression by cisplatin, and included genes involved in cell death and DNA repair. The second cluster pointed to a general reduction of gene expression by most of the anticancer drugs tested. A number of genes in this cluster are involved in signal transduction that is important for communication between cells and reception of extracellular signals. The last cluster represented reduced gene expression in response to 5-fluorouracil, the genes involved being implicated in DNA metabolism, the cell cycle, and RNA processing. Since the gene expression signature of 5-fluorouracil was unique, we investigated it in more detail. Significance analysis of microarray data (SAM) identified 808 genes whose expression was significantly altered by 5-fluorouracil. Among the up-regulated genes, those affecting apoptosis were the most noteworthy. The down-regulated genes were mainly associated with transcription-and translation-related processes which are known targets of 5-fluorouracil. These results suggest that the gene expression signature of an anticancer drug is closely related to its physiological action and the response of caner cells.

• Genomics & Informatics
• /
• 제17권3호
• /
• pp.30.1-30.4
• /
• 2019

Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.

• The Plant Pathology Journal
• /
• 제30권1호
• /
• pp.33-42
• /
• 2014

Nivalenol (NIV) and deoxynivalenol (DON) are predominant Fusarium-producing mycotoxins found in grains, which are mainly produced by Fusarium asiaticum and F. graminearum. NIV is found in most of cereals grown in Korea, but the genetic basis for NIV production by F. asiaticum has not been extensively explored. In this study, 12 genes belonging to the trichothecene biosynthetic gene cluster were compared at the transcriptional level between two NIV-producing F. asiaticum and four DON-producing F. graminearum strains. Chemical analysis revealed that time-course toxin production patterns over 14 days did not differ between NIV and DON strains, excluding F. asiaticum R308, which was a low NIV producer. Both quantitative real-time polymerase chain reaction and Northern analysis revealed that the majority of TRI gene transcripts peaked at day 2 in both NIV and DON producers, which is 2 days earlier than trichothecene accumulation in liquid medium. Comparison of the gene expression profiles identified an NIV-specific pattern in two transcription factor-encoding TRI genes (TRI6 and TRI10) and TRI101, which showed two gene expression peaks during both the early and late incubation periods. In addition, the amount of trichothecenes produced by both DON and NIV producers were correlated with the expression levels of TRI genes, regardless of the trichothecene chemotypes. Therefore, the reduced production of NIV by R308 compared to NIV or DON by the other strains may be attributable to the significantly lower expression levels of the TRI genes, which showed early expression patterns.