• Journal of Veterinary Science
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• v.22 no.1
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• pp.4.1-4.13
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• 2021

Background: Microsporum canis is a zoonotic disease that can cause dermatophytosis in animals and humans. Objectives: In clinical practice, ketoconazole (KTZ) and other imidazole drugs are commonly used to treat M. canis infection, but its molecular mechanism is not completely understood. The antifungal mechanism of KTZ needs to be studied in detail. Methods: In this study, one strain of fungi was isolated from a canine suffering with clinical dermatosis and confirmed as M. canis by morphological observation and sequencing analysis. The clinically isolated M. canis was treated with KTZ and transcriptome sequencing was performed to identify differentially expressed genes in M. canis exposed to KTZ compared with those unexposed thereto. Results: At half-inhibitory concentration (½MIC), compared with the control group, 453 genes were significantly up-regulated and 326 genes were significantly down-regulated (p < 0.05). Quantitative reverse transcription polymerase chain reaction analysis verified the transcriptome results of RNA sequencing. Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that the 3 pathways of RNA polymerase, steroid biosynthesis, and ribosome biogenesis in eukaryotes are closely related to the antifungal mechanism of KTZ. Conclusions: The results indicated that KTZ may change cell membrane permeability, destroy the cell wall, and inhibit mitosis and transcriptional regulation through CYP51, SQL, ERG6, ATM, ABCB1, SC, KER33, RPA1, and RNP genes in the 3 pathways. This study provides a new theoretical basis for the effective control of M. canis infection and the effect of KTZ on fungi.

• Animal Bioscience
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• v.34 no.5
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• pp.922-930
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• 2021

Objective: Tibetan pigs, predominantly originating from the Tibetan Plateau, have been subjected to long-term natural selection in an extreme environment. To characterize the metabolic adaptations to hypoxic conditions, transcriptomic and proteomic expression patterns in the livers of Tibetan and Yorkshire pigs were compared. Methods: RNA and protein were extracted from liver tissue of Tibetan and Yorkshire pigs (n = 3, each). Differentially expressed genes and proteins were subjected to gene ontology and Kyoto encyclopedia of genes and genomes functional enrichment analyses. Results: In the RNA-Seq and isobaric tags for relative and absolute quantitation analyses, a total of 18,791 genes and 3,390 proteins were detected and compared. Of these, 273 and 257 differentially expressed genes and proteins were identified. Evidence from functional enrichment analysis showed that many genes were involved in metabolic processes. The combined transcriptomic and proteomic analyses revealed that small molecular biosynthesis, metabolic processes, and organic hydroxyl compound metabolic processes were the major processes operating differently in the two breeds. The important genes include retinol dehydrogenase 16, adenine phosphoribosyltransferase, prenylcysteine oxidase 1, sorbin and SH3 domain containing 2, ENSSSCG00000036224, perilipin 2, ladinin 1, kynurenine aminotransferase 1, and dimethylarginine dimethylaminohydrolase 1. Conclusion: The findings of this study provide novel insight into the high-altitude metabolic adaptation of Tibetan pigs.

• Genes and Genomics
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• v.40 no.11
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• pp.1157-1167
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• 2018

Sulfolobus species can grow on a variety of organic compounds as carbon and energy sources. These species degrade glucose to pyruvate by the modified branched Entner-Doudoroff pathway. We attempted to determine the differentially expressed genes (DEGs) under sugar-limited and sugar-rich conditions. RNA sequencing (RNA-seq) was used to quantify the expression of the genes and identify those DEGs between the S. acidocaldarius cells grown under sugar-rich (YT with glucose) and sugar-limited (YT only) conditions. The functions and pathways of the DEGs were examined using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Quantitative real-time PCR (qRT-PCR) was performed to validate the DEGs. Transcriptome analysis of the DSM 639 strain grown on sugar-limited and sugar-rich media revealed that 853 genes were differentially expressed, among which 481 were upregulated and 372 were downregulated under the glucose-supplemented condition. In particular, 70 genes showed significant changes in expression levels of ${\geq}$ twofold. GO and KEGG enrichment analyses revealed that the genes encoding components of central carbon metabolism, the respiratory chain, and protein and amino acid biosynthetic machinery were upregulated under the glucose condition. RNA-seq and qRT-PCR analyses indicated that the sulfur assimilation genes (Saci_2197-2204) including phosphoadenosine phosphosulfate reductase and sulfite reductase were significantly upregulated in the presence of glucose. The present study revealed metabolic networks in S. acidocaldarius that are induced in a glucose-dependent manner, improving our understanding of biomass production under sugar-rich conditions.

• Journal of Microbiology and Biotechnology
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• v.32 no.3
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• pp.365-377
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• 2022

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.697-709
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• 2022

Objective : The present study aimed to identify the function of ischemic stroke (IS) patients' peripheral blood and its role in IS, explore the pathogenesis, and provide direction for clinical research progress by comprehensive bioinformatics analysis. Methods : Two datasets, including GSE58294 and GSE22255, were downloaded from Gene Expression Omnibus database. GEO2R was utilized to obtain differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using the database annotation, visualization and integrated discovery database. The protein-protein interaction (PPI) network of DEGs was constructed by search tool of searching interactive gene and visualized by Cytoscape software, and then the Hub gene was identified by degree analysis. The microRNA (miRNA) and miRNA target genes closely related to the onset of stroke were obtained through the miRNA gene regulatory network. Results : In total, 36 DEGs, containing 27 up-regulated and nine down-regulated DEGs, were identified. GO functional analysis showed that these DEGs were involved in regulation of apoptotic process, cytoplasm, protein binding and other biological processes. KEGG enrichment analysis showed that these DEGs mediated signaling pathways, including human T-cell lymphotropic virus (HTLV)-I infection and microRNAs in cancer. The results of PPI network and cytohubba showed that there was a relationship between DEGs, and five hub genes related to stroke were obtained : SOCS3, KRAS, PTGS2, EGR1, and DUSP1. Combined with the visualization of DEG-miRNAs, hsa-mir-16-5p, hsa-mir-181a-5p and hsa-mir-124-3p were predicted to be the key miRNAs in stroke, and three miRNAs were related to hub gene. Conclusion : Thirty-six DEGs, five Hub genes, and three miRNA were obtained from bioinformatics analysis of IS microarray data, which might provide potential targets for diagnosis and treatment of IS.

• Journal of Plant Biotechnology
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• v.50
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• pp.127-136
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• 2023

Water scarcity decreases the rate of photosynthesis and, consequently, the yield of banana plants (Musa spp). In this study, transcriptome analysis was performed to identify photosynthesis-related genes in banana plants and determine their expression profiles under water stress conditions. Banana plantlets were in vitro cultured on Murashige and Skoog agar medium with and without 10% polyethylene glycol and marked as BP10 and BK. Chlorophyll contents in the plant shoots were determined spectrophotometrically. Two cDNA libraries generated from BK and BP10 plantlets, respectively, were used as the reference for transcriptome data. Gene ontology (GO) enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) and visualized using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway prediction. Morphological observations indicated that water deficiency caused chlorosis and reduced the shoot chlorophyll content of banana plantlets. GO enrichment identified 52 photosynthesis-related genes that were affected by water stress. KEGG visualization revealed the pathways related to the 52 photosynthesisr-elated genes and their allocations in four GO terms. Four, 12, 15, and 21 genes were related to chlorophyll biosynthesis, the Calvin cycle, the photosynthetic electron transfer chain, and the light-harvesting complex, respectively. Differentially expressed gene (DEG) analysis using DESeq revealed that 45 genes were down-regulated, whereas seven genes were up-regulated. Four of the down-regulated genes were responsible for chlorophyll biosynthesis and appeared to cause the decrease in the banana leaf chlorophyll content. Among the annotated DEGs, MaPNDO, MaPSAL, and MaFEDA were selected and validated using quantitative real-time PCR.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.137-147
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• 2015

This study proposes an algorithm for predicting breast cancer prognosis based on genetic network. We identify prognosis-specific network using gene expression data and PPI(protein-protein interaction) data. To acquire the network, we calculate Pearson's correlation coefficient(PCC) between genes in all PPI pairs using gene expression data. We develop a prediction model for breast cancer patients with estrogen-receptor-negative using the network as a classifier. We compare classification performance of our algorithm with existing algorithms on independent data and shows our algorithm is improved. In addition, we make an functionality analysis on the genes in the prognosis-specific network using GO(Gene Ontology) enrichment validation.

• Genomics & Informatics
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• v.5 no.1
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• pp.10-18
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• 2007

Numerous studies have reported that genes with similar expression patterns are co-regulated. From gene expression data, we have assumed that genes having similar expression pattern would share similar transcription factor binding sites (TFBSs). These function as the binding regions for transcription factors (TFs) and thereby regulate gene expression. In this context, various analysis tools have been developed. However, they have shortcomings in the combined analysis of expression patterns and significant TFBSs and in the functional analysis of target genes of significantly overrepresented putative regulators. In this study, we present a web-based A Functional Clustering Analysis Tool for Predicted Transcription Regulatory Elements and Gene Ontology Terms (FCAnalyzer). This system integrates microarray clustering data with similar expression patterns, and TFBS data in each cluster. FCAnalyzer is designed to perform two independent clustering procedures. The first process clusters gene expression profiles using the K-means clustering method, and the second process clusters predicted TFBSs in the upstream region of previously clustered genes using the hierarchical biclustering method for simultaneous grouping of genes and samples. This system offers retrieved information for predicted TFBSs in each cluster using $Match^{TM}$ in the TRANSFAC database. We used gene ontology term analysis for functional annotation of genes in the same cluster. We also provide the user with a combinatorial TFBS analysis of TFBS pairs. The enrichment of TFBS analysis and GO term analysis is statistically by the calculation of P values based on Fisher’s exact test, hypergeometric distribution and Bonferroni correction. FCAnalyzer is a web-based, user-friendly functional clustering analysis system that facilitates the transcriptional regulatory analysis of co-expressed genes. This system presents the analyses of clustered genes, significant TFBSs, significantly enriched TFBS combinations, their target genes and TFBS-TF pairs.

• The Plant Pathology Journal
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• v.32 no.3
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• pp.190-200
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• 2016

Xanthomonas oryzae pv. oryzae (Xoo) causes bacterial leaf blight (BLB) in rice (Oryza sativa L.). In this study, we investigated the effect of a mutation in opsX (XOO1056), which encodes a saccharide biosynthesis regulatory protein, on the virulence and bacterial chemotaxis of Xoo. We performed DNA micro-array analysis, which showed that 63 of 2,678 genes, including genes related to bacterial motility (flagellar and chemotaxis proteins) were significantly downregulated ($<\;-2\;log_2$ fold changes) by the mutation in opsX. Indeed, motility assays showed that the mutant strain was nonmotile on semisolid agar swarm plates. In addition, a mutant strain (opsX::Tn5) showed decreased virulence against the susceptible rice cultivar, IR24. Quantitative real-time RT-PCR reaction was performed to confirm the expression levels of these genes, including those related to flagella and chemotaxis, in the opsX mutant. Our findings revealed that mutation of opsX affects both virulence and bacterial motility. These results will help to improve our understanding of Xoo and provide insight into Xoo-rice interactions.

• Genomics & Informatics
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• v.8 no.3
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• pp.142-149
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• 2010

In recent years, genome-wide association (GWA) studies have successfully led to many discoveries of genetic variants affecting common complex traits, including height, blood pressure, and diabetes. Although GWA studies have made much progress in finding single nucleotide polymorphisms (SNPs) associated with many complex traits, such SNPs have been shown to explain only a very small proportion of the underlying genetic variance of complex traits. This is partly due to that fact that most current GWA studies have relied on single-marker approaches that identify single genetic factors individually and have limitations in considering the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and provide a better prediction of complex traits, since it utilizes combined information across variants. Recently, a new statistical method for joint identification of genetic variants for common complex traits via the elastic-net regularization method was proposed. In this study, we applied this joint identification approach to a large-scale GWA dataset (i.e., 8842 samples and 327,872 SNPs) in order to identify genetic variants of obesity for the Korean population. In addition, in order to test for the biological significance of the jointly identified SNPs, gene ontology and pathway enrichment analyses were further conducted.