• Animal Bioscience
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• v.34 no.9
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• pp.1466-1478
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• 2021

Objective: Ruminants are completely dependent on their microbiota for rumen fermentation, feed digestion, and consequently, their metabolism for productivity. This study aimed to evaluate the rumen bacteria of lactating yaks with different milk protein yields, using high-throughput sequencing technology, in order to understand the influence of these bacteria on milk production. Methods: Yaks with similar high milk protein yield (high milk yield and high milk protein content, HH; n = 12) and low milk protein yield (low milk yield and low milk protein content, LL; n = 12) were randomly selected from 57 mid-lactation yaks. Ruminal contents were collected using an oral stomach tube from the 24 yaks selected. High-throughput sequencing of bacterial 16S rRNA gene was used. Results: Ruminal ammonia N, total volatile fatty acids, acetate, propionate, and isobutyrate concentrations were found to be higher in HH than LL yaks. Community richness (Chao 1 index) and diversity indices (Shannon index) of rumen microbiota were higher in LL than HH yaks. Relative abundances of the Bacteroidetes and Tenericutes phyla in the rumen fluid were significantly increased in HH than LL yaks, but significantly decreased for Firmicutes. Relative abundances of the Succiniclasticum, Butyrivibrio 2, Prevotella 1, and Prevotellaceae UCG-001 genera in the rumen fluid of HH yaks was significantly increased, but significantly decreased for Christensenellaceae R-7 group and Coprococcus 1. Principal coordinates analysis on unweighted UniFrac distances revealed that the bacterial community structure of rumen differed between yaks with high and low milk protein yields. Furthermore, rumen microbiota were functionally enriched in relation to transporters, ABC transporters, ribosome, and urine metabolism, and also significantly altered in HH and LL yaks. Conclusion: We observed significant differences in the composition, diversity, fermentation product concentrations, and function of ruminal microorganisms between yaks with high and low milk protein yields, suggesting the potential influence of rumen microbiota on milk protein yield in yaks. A deeper understanding of this process may allow future modulation of the rumen microbiome for improved agricultural yield through bacterial community design.

• Journal of Microbiology and Biotechnology
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• v.30 no.6
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• pp.804-810
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• 2020

The profitability of the dairy and beef industries is largely affected by the actually achieved reproductive efficiency. Although a large proportion of cows worldwide are bred by artificial insemination (AI) services, many potential factors affecting the outcome of pregnancy by AI remain to be addressed. In the present study, we investigated the vaginal microbiota by high-throughput sequencing of 16S rRNA gene and analyzed their association with differential pregnancy outcomes (i.e., pregnant vs. nonpregnant) of multiple AI services in dairy cows. Sequencing of the V3-V4 region totally produced 512,046 high-quality sequences that were computationally clustered into 2,584 operational taxonomic units (OTUs). All OTUs were taxonomically assigned to 10 bacterial phyla. There were statistically significant differences among the three AI service times (T1, T2 and T3) with respect to the Shannon index and number of observed OTUs (p < 0.05). Bray-Curtis distance-based PCoA analysis also revealed that T2 group could be significantly distinguished from T1 and T3. However, no significant difference between the pregnant and nonpregnant cows was found in confidence regarding both alpha diversity and beta diversity. These results could help us better understand the possible influence of vaginal microbial community on pregnancy outcomes of AI service in cows.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.667-684
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• 2022

Gene expression data present the level of mRNA abundance of each gene, and analyses of gene expressions have provided key ideas for understanding the mechanism of diseases and developing new drugs and therapies. Nowadays high-throughput technologies such as DNA microarray and RNA-sequencing enabled the simultaneous measurement of thousands of gene expressions, giving rise to a characteristic of gene expression data known as high dimensionality. Due to the high-dimensionality, learning models to analyze gene expression data are prone to overfitting problems, and to solve this issue, dimension reduction or feature selection techniques are commonly used as a preprocessing step. In particular, we can remove irrelevant and redundant genes and identify important genes using gene selection methods in the preprocessing step. Various gene selection methods have been developed in the context of machine learning so far. In this paper, we intensively review recent works on gene selection methods using machine learning approaches. In addition, the underlying difficulties with current gene selection methods as well as future research directions are discussed.

• Molecules and Cells
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• v.40 no.8
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• pp.587-597
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• 2017

MicroRNAs (miRNAs) are essential small RNA molecules that regulate the expression of target mRNAs in plants and animals. Here, we aimed to identify miRNAs and their putative targets in Hibiscus syriacus, the national flower of South Korea. We employed high-throughput sequencing of small RNAs obtained from four different tissues (i.e., leaf, root, flower, and ovary) and identified 33 conserved and 30 novel miRNA families, many of which showed differential tissuespecific expressions. In addition, we computationally predicted novel targets of miRNAs and validated some of them using 5' rapid amplification of cDNA ends analysis. One of the validated novel targets of miR477 was a terpene synthase, the primary gene involved in the formation of disease-resistant terpene metabolites such as sterols and phytoalexins. In addition, a predicted target of conserved miRNAs, miR396, is SHORT VEGETATIVE PHASE, which is involved in flower initiation and is duplicated in H. syriacus. Collectively, this study provides the first reliable draft of the H. syriacus miRNA transcriptome that should constitute a basis for understanding the biological roles of miRNAs in H. syriacus.

• Journal of Veterinary Science
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• v.24 no.5
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• pp.73.1-73.16
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• 2023

Background: Highly pathogenic avian influenza virus (HPAIV) is considered a global threat to both human health and the poultry industry. MicroRNAs (miRNA) can modulate the immune system by affecting gene expression patterns in HPAIV-infected chickens. Objectives: To gain further insights into the role of miRNAs in immune responses against H5N1 infection, as well as the development of strategies for breeding disease-resistant chickens, we characterized miRNA expression patterns in tracheal tissues from H5N1-infected Ri chickens. Methods: miRNAs expression was analyzed from two H5N1-infected Ri chicken lines using small RNA sequencing. The target genes of differentially expressed (DE) miRNAs were predicted using miRDB. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were then conducted. Furthermore, using quantitative real-time polymerase chain reaction, we validated the expression levels of DE miRNAs (miR-22-3p, miR-146b-3p, miR27b-3p, miR-128-3p, miR-2188-5p, miR-451, miR-205a, miR-203a, miR-21-3p, and miR-200a3p) from all comparisons and their immune-related target genes. Results: A total of 53 miRNAs were significantly expressed in the infection samples of the resistant compared to the susceptible line. Network analyses between the DE miRNAs and target genes revealed that DE miRNAs may regulate the expression of target genes involved in the transforming growth factor-beta, mitogen-activated protein kinase, and Toll-like receptor signaling pathways, all of which are related to influenza A virus progression. Conclusions: Collectively, our results provided novel insights into the miRNA expression patterns of tracheal tissues from H5N1-infected Ri chickens. More importantly, our findings offer insights into the relationship between miRNA and immune-related target genes and the role of miRNA in HPAIV infections in chickens.

• Biomolecules & Therapeutics
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• v.26 no.3
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• pp.290-297
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• 2018

We aimed to understand the molecular changes in host cells that accompany infection by the seasonal influenza A H1N1 virus because the initial response rapidly changes owing to the fact that the virus has a robust initial propagation phase. Human epithelial alveolar A549 cells were infected and total RNA was extracted at 30 min, 1 h, 2 h, 4 h, 8 h, 24 h, and 48 h post infection (h.p.i.). The differentially expressed host genes were clustered into two distinct sets of genes as the infection progressed over time. The patterns of expression were significantly different at the early stages of infection. One of the responses showed roles similar to those associated with the enrichment gene sets to known 'gp120 pathway in HIV.' This gene set contains genes known to play roles in preventing the progress of apoptosis, which infected cells undergo as a response to viral infection. The other gene set showed enrichment of 'Drug Metabolism Enzymes (DMEs).' The identification of two distinct gene sets indicates that the virus regulates the cell's mechanisms to create a favorable environment for its stable replication and protection of gene metabolites within 8 h.

• Journal of Microbiology and Biotechnology
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• v.22 no.2
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• pp.159-165
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• 2012

The rhizobacterial composition varies according to the soil properties. To test if the effect of herbicides on the rhizobacterial communities of genetically modified NK603 glyphosate-tolerant maize varies according to different soil locations, a comparison was made between the effects of glyphosate (Roundup Plus), a post-emergence applied herbicide, and a pre-emergence applied herbicide (GTZ) versus untreated soil. The potential effect was monitored by direct amplification, cloning, and sequencing of the soil DNA encoding 16S rRNA, and high-throughput DNA pyrosequencing of the bacterial DNA coding for the 16S rRNA hypervariable V6 region. The results obtained using three different methods to analyze the herbicide effect on the rhizobacterial communities of genetically modified NK603 maize were comparable to those previously obtained when glyphosate-tolerant maize was grown in soil with different characteristics. Both herbicides decreased the bacterial diversity in the rhizosphere, with Actinobacteria being the taxonomic group most affected. The results suggest that both herbicides affected the structure of the maize rhizobacterial community, but glyphosate was environmentally less aggressive.

• Research in Plant Disease
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• v.28 no.4
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• pp.231-236
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• 2022

Oxypetalum coeruleum, commonly known as Tweedia, is a perennial herbaceous plant of the Apocynaceae family native to southern Brazil and Uruguay. Tweedia plants are grown as one of the most popular ornamental flowers for floral arrangement in Korea. In May 2021, several tweedia plants in a single greenhouse in Gimje, Jeollabuk-do were found to show virus-like symptoms including necrotic rings, vein-clearing, chlorotic mottle, and mosaic on the leaves, and necrosis on the stems. Here, we have identified tomato spotted wilt virus (TSWV) in symptomatic tweedia leaves by applying high-throughput RNA sequencing. In the result, a single infection by TSWV was verified without mixed infections of different virus species. To confirm the presence of TSWV, a reverse transcription polymerase chain reaction was performed with a specific primer set to the N gene of TSWV. The complete genomic sequence of L, M, and S segments of TSWV 'Oxy' isolate were determined and deposited in GenBank under accession numbers LC671525, LC671638, and LC671639, respectively. In the phylogenetic tree analysis by maximum likelihood method, 'Oxy' isolate showed a high relationship with TSWV 'Gumi' isolate from Gerbera jamesonii in Gyeongsangbuk-do, Korea; for all three RNA segments. To our knowledge, this is the first report of TSWV infection of O. coeruleum in Korea.

• Asian-Australasian Journal of Animal Sciences
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• v.14 no.6
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• pp.880-884
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• 2001

Rumen microbiology research has undergone several evolutionary steps: the isolation and nutritional characterization of readily cultivated microbes; followed by the cloning and sequence analysis of individual genes relevant to key digestive processes; through to the use of small subunit ribosomal RNA (SSU rRNA) sequences for a cultivation-independent examination of microbial diversity. Our knowledge of rumen microbiology has expanded as a result, but the translation of this information into productive alterations of ruminal function has been rather limited. For instance, the cloning and characterization of cellulase genes in Escherichia coli has yielded some valuable information about this complex enzyme system in ruminal bacteria. SSU rRNA analyses have also confirmed that a considerable amount of the microbial diversity in the rumen is not represented in existing culture collections. However, we still have little idea of whether the key, and potentially rate-limiting, gene products and (or) microbial interactions have been identified. Technologies allowing high throughput nucleotide and protein sequence analysis have led to the emergence of two new fields of investigation, genomics and proteomics. Both disciplines can be further subdivided into functional and comparative lines of investigation. The massive accumulation of microbial DNA and protein sequence data, including complete genome sequences, is revolutionizing the way we examine microbial physiology and diversity. We describe here some examples of our use of genomics- and proteomics-based methods, to analyze the cellulase system of Ruminococcus flavefaciens FD-1 and explore the genome of Ruminococcus albus 8. At Illinois, we are using bacterial artificial chromosome (BAC) vectors to create libraries containing large (>75 kbases), contiguous segments of DNA from R. flavefaciens FD-1. Considering that every bacterium is not a candidate for whole genome sequencing, BAC libraries offer an attractive, alternative method to perform physical and functional analyses of a bacterium's genome. Our first plan is to use these BAC clones to determine whether or not cellulases and accessory genes in R. flavefaciens exist in clusters of orthologous genes (COGs). Proteomics is also being used to complement the BAC library/DNA sequencing approach. Proteins differentially expressed in response to carbon source are being identified by 2-D SDS-PAGE, followed by in-gel-digests and peptide mass mapping by MALDI-TOF Mass Spectrometry, as well as peptide sequencing by Edman degradation. At Ohio State, we have used a combination of functional proteomics, mutational analysis and differential display RT-PCR to obtain evidence suggesting that in addition to a cellulosome-like mechanism, R. albus 8 possesses other mechanisms for adhesion to plant surfaces. Genome walking on either side of these differentially expressed transcripts has also resulted in two interesting observations: i) a relatively large number of genes with no matches in the current databases and; ii) the identification of genes with a high level of sequence identity to those identified, until now, in the archaebacteria. Genomics and proteomics will also accelerate our understanding of microbial interactions, and allow a greater degree of in situ analyses in the future. The challenge is to utilize genomics and proteomics to improve our fundamental understanding of microbial physiology, diversity and ecology, and overcome constraints to ruminal function.

• BMB Reports
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• v.51 no.9
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• pp.462-467
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• 2018

A previous study of ours indicated that Drosophila flightless-1 controls lipid metabolism, and that there is an accumulation of triglycerides in flightless-1 (fliI)-mutant flies, where this mutation triggers metabolic stress and an obesity phenotype. Here, with the aim of characterizing the function of FliI in metabolism, we analyzed the levels of gene expression and metabolites in fliI-mutant flies. The levels of enzymes related to glycolysis, lipogenesis, and the pentose phosphate pathway increased in fliI mutants; this result is consistent with the levels of metabolites corresponding to a metabolic pathway. Moreover, high-throughput RNA sequencing revealed that Drosophila FliI regulates the expression of genes related to biological processes such as chromosome organization, carbohydrate metabolism, and immune reactions. These results showed that Drosophila FliI regulates the expression of metabolic genes, and that dysregulation of the transcription controlled by FliI gives rise to metabolic stress and problems in the development and physiology of Drosophila.