• Journal of Veterinary Science
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• v.21 no.4
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• pp.56.1-56.13
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• 2020

Background: The live bird market (LBM) plays an important role in the dynamic evolution of the avian influenza H5N1 virus. Objectives: The main objective of this study was to monitor the genetic diversity of the H5N1 viruses in LBMs in Indonesia. Methods: Therefore, the disease surveillance was conducted in the area of Banten, West Java, Central Java, East Java, and Jakarta Province, Indonesia from 2014 to 2019. Subsequently, the genetic characterization of the H5N1 viruses was performed by sequencing all 8 segments of the viral genome. Results: As a result, the H5N1 viruses were detected in most of LBMs in both bird' cloacal and environmental samples, in which about 35% of all samples were positive for influenza A and, subsequently, about 52% of these samples were positive for H5 subtyping. Based on the genetic analyses of 14 viruses isolated from LBMs, genetic diversities of the H5N1 viruses were identified including clades 2.1.3 and 2.3.2 as typical predominant groups as well as reassortant viruses between these 2 clades. Conclusions: As a consequence, zoonotic transmission to humans in the market could be occurred from the exposure of infected birds and/or contaminated environments. Moreover, new virus variants could emerge from the LBM environment. Therefore, improving pandemic preparedness raised great concerns related to the zoonotic aspect of new influenza variants because of its high adaptivity and efficiency for human infection.

• Animal Bioscience
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• v.36 no.11
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• pp.1727-1737
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• 2023

Objective: The poultry industry is a primary source of animal protein worldwide. The gut microbiota of poultry birds, such as chickens and ducks, is critical in maintaining their health, growth, and productivity. This study aimed to identify longitudinal changes in the gut microbiota of laying hens from birth to the pre-laying stage. Methods: From a total of 80 Hy-Line Brown laying hens, birds were selected based on weight at equal intervals to collect feces (n = 20 per growth) and ileal contents (n = 10 per growth) for each growth stage (days 10, 21, 58, and 101). The V4 regions of the 16S rRNA gene were amplified after extracting DNA from feces and ileal contents. Amplicon sequencing was performed using Illumina, followed by analysis. Results: Microbial diversity increased with growth stages, regardless of sampling sites. Microbial community analysis indicated that Firmicutes, Proteobacteria, and Bacteroidetes were the dominant phyla in the feces and ileal. The abundance of Lactobacillus was highest on day 10, and that of Escherichia-shigella was higher on day 21 than those at the other stages at the genus level (for the feces and ileal contents; p<0.05). Furthermore, Turicibacter was the most abundant genus after changing feed (for the feces and ileal contents; p<0.05). The fecal Ruminococcus torques and ileal Lysinibacillus were negatively correlated with the body weights of chickens (p<0.05). Conclusion: The gut microbiota of laying hens changes during the four growth stages, and interactions between microbiota and feed may be present. Our findings provide valuable data for understanding the gut microbiota of laying hens at various growth stages and future applied studies.

• Tuberculosis and Respiratory Diseases
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• v.86 no.4
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• pp.294-303
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• 2023

Background: The human lung serves as a niche for a unique and dynamic bacterial community related to the development and aggravation of multiple respiratory diseases. Therefore, identifying the microbiome status is crucial to maintaining the microecological balance and maximizing the therapeutic effect on lung diseases. Therefore, we investigated the histological type-based differences in the lung microbiomes of patients with lung cancer. Methods: We performed 16S rRNA sequencing to evaluate the respiratory tract microbiome present in bronchoalveolar lavage fluid. Patients with non-small cell lung cancer were stratified based on two main subtypes of lung cancer: adenocarcinoma and squamous cell carcinoma (SqCC). Results: Among the 84 patients analyzed, 64 (76.2%) had adenocarcinoma, and 20 (23.8%) had SqCC. The α- and β-diversities showed significant differences between the two groups (p=0.004 for Chao1, p=0.001 for Simpson index, and p=0.011 for PERMANOVA). Actinomyces graevenitzii was dominant in the SqCC group (linear discriminant analysis [LDA] score, 2.46); the populations of Haemophilus parainfluenza (LDA score, 4.08), Neisseria subflava (LDA score, 4.07), Porphyromonas endodontalis (LDA score, 3.88), and Fusobacterium nucleatum (LDA score, 3.72) were significantly higher in the adenocarcinoma group. Conclusion: Microbiome diversity is crucial for maintaining homeostasis in the lung environment, and dysbiosis may be related to the development and prognosis of lung cancer. The mortality rate was high, and the microbiome was not diverse in SqCC. Further large-scale studies are required to investigate the role of the microbiome in the development of different lung cancer types.

• Journal of Animal Reproduction and Biotechnology
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• v.39 no.2
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• pp.67-80
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• 2024

Background: Post-ovulatory aging (POA) of oocytes is related to a decrease in the quality and quantity of oocytes caused by aging. Previous studies on the characteristics of POA have investigated injury to early embryonic developmental ability, but no information is available on its effects on mitochondrial fission and mitophagy-related responses. In this study, we aimed to elucidate the molecular mechanisms underlying mitochondrial fission and mitophagy in in vitro maturation (IVM) oocytes and a POA model based on RNA sequencing analysis. Methods: The POA model was obtained through an additional 24 h culture following the IVM of matured oocytes. NMN treatment was administered at a concentration of 25 μM during the oocyte culture process. We conducted MitoTracker staining and Western blot experiments to confirm changes in mitochondrial function between the IVM and POA groups. Additionally, comparative transcriptome analysis was performed to identify differentially expressed genes and associated changes in mitochondrial dynamics between porcine IVM and POA model oocytes. Results: In total, 32 common genes of apoptosis and 42 mitochondrial fission and function uniquely expressed genes were detected (≥ 1.5-fold change) in POA and porcine metaphase II oocytes, respectively. Functional analyses of mitochondrial fission, oxidative stress, mitophagy, autophagy, and cellular apoptosis were observed as the major changes in regulated biological processes for oocyte quality and maturation ability compared with the POA model. Additionally, we revealed that the activation of NAD⁺ by nicotinamide mononucleotide not only partly improved oocyte quality but also mitochondrial fission and mitophagy activation in the POA porcine model. Conclusions: In summary, our data indicate that mitochondrial fission and function play roles in controlling oxidative stress, mitophagy, and apoptosis during maturation in POA porcine oocytes. Additionally, we found that NAD⁺ biosynthesis is an important pathway that mediates the effects of DRP1-derived mitochondrial morphology, dynamic balance, and mitophagy in the POA model.

• Reproductive and Developmental Biology
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• v.36 no.1
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• pp.7-12
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• 2012

DNA methyltransferase 1 (Dnmt1) gene contains three different isoform transcripts, Dnmt1s, Dnmt1o, and Dnmt1p, are produced by alternative usage of multiple first exons. Dnmt1o is specific to oocytes and preimplantation embryos, whereas Dnmt1s is expressed in somatic cells. Here we determined that porcine Dnmt1o gene had differentially methylated regions (DMRs) in 5'-flanking region, while those were not found in the Dnmt1s promoter region. The methylation patterns of the porcine Dnmt1o/Dnmt1s DMRs were investigated using bisulfite sequencing and pyrosequencing analysis through all preimplantation stages from one cell to blastocyst stage in in vivo or somatic cell nuclear transfer (SCNT). The Dnmt1o DMRs contained 8 CpG sites, which located in -640 bp to -30 bp upstream region from transcription start site of the Dnmt1o gene. The methylation status of 5 CpGs within the Dnmt1o DMRs were distinctively different at each stage from one-cell to blastocyst stage in the $in$ $vivo$ or SCNT, respectively. 55.62% methylation degree of the Dnmt1o DMRs in the $in$ $vivo$ was increased up to 84.38% in the SCNT embryo, moreover, $de$ $novo$ methylation and demethylation occurred during development of porcine embryos from the one-cell stage to the blastocyst stage. However, the DNA methylation states at CpG sites in the Dnmt1s promoter regions were hypomethylated, and dramatically not changed through one-cell to blastocyst stage in the $in$ $vivo$ or SCNT embryos. In the present study, we demonstrated that the DMRs in the promoter region of the porcine Dnmt1o was well conserved, contributing to establishment and maintenance of genome-wide patterns of DNA methylation in early embryonic development.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.2
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• pp.67-74
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• 2013

Approximate string matching problems have been studied in diverse fields. Recently, fast approximate string matching algorithms are being used to reduce the time and costs for the next generation sequencing. To measure the amounts of errors between two strings, we use a distance function such as the edit distance. Given two strings X(|X| = m) and Y(|Y| = n) over an alphabet ${\Sigma}$, the edit distance between X and Y is the minimum number of edit operations to convert X into Y. The edit distance between X and Y can be computed using the well-known dynamic programming technique in O(mn) time and space. The edit distance also can be computed using the Four-Russians' algorithm whose preprocessing step runs in $O((3{\mid}{\Sigma}{\mid})^{2t}t^2)$ time and $O((3{\mid}{\Sigma}{\mid})^{2t}t)$ space and the computation step runs in O(mn/t) time and O(mn) space where t represents the size of the block. In this paper, we present a parallelized version of the computation step of the Four-Russians' algorithm. Our algorithm computes the edit distance between X and Y in O(m+n) time using m/t threads. Then we implemented both the sequential version and our parallelized version of the Four-Russians' algorithm using CUDA to compare the execution times. When t = 1 and t = 2, our algorithm runs about 10 times and 3 times faster than the sequential algorithm, respectively.