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RISS 인기검색어
- 검색키워드
- 저자 : Chalita Mauricio (검색결과 4 건)
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UACG: Up-to-Date Archaeal Core Genes and Software for Phylogenomic Tree Reconstruction
Na Seong-In,Bailey Michael James,Chalita Mauricio,Cho Jae Hyoung,Chun Jongsik 한국미생물학회 2023 The journal of microbiology Vol.61 No.7
In the post-genomic era, phylogenomics is a powerful and routinely-used tool to discover evolutionary relationships between microorganisms. Inferring phylogenomic trees by concatenating core gene sequences into a supermatrix is the standard method. The previously released up-to-date bacterial core gene (UBCG) tool provides a pipeline to infer phylogenomic trees using single-copy core genes for the Bacteria domain. In this study, we established up-to-date archaeal core gene (UACG), comprising 128 genes suitable for inferring archaeal phylogenomic trees. To test the gene set, we selected the Haloarcula genus and scrutinized its phylogeny. The phylogeny inferred using the UACG tool was consistent with the orthoANIu dendrogram, whereas the 16S rRNA gene phylogeny showed high intragenomic heterogeneity resulting in phylogenetic discrepancies. The software tool using the UACG set is available at https:// www. ezbio cloud. net/ tools/ uacg.
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Oh Hyun-Seok,Min Uigi,Jang Hyejin,Kim Namil,Lim Jeongmin,Chalita Mauricio,Chun Jongsik 한국미생물학회 2022 The journal of microbiology Vol.60 No.5
The disruption of the human gut microbiota has been linked to host health conditions, including various diseases. However, no reliable index for measuring and predicting a healthy microbiome is currently available. Here, the sequencing data of 1,663 Koreans were obtained from three independent studies. Furthermore, we pooled 3,490 samples from public databases and analyzed a total of 5,153 fecal samples. First, we analyzed Korean gut microbiome covariates to determine the influence of lifestyle on variation in the gut microbiota. Next, patterns of microbiota variations across geographical locations and disease statuses were confirmed using a global cohort and disease data. Based on comprehensive comparative analysis, we were able to define three enterotypes among Korean cohorts, namely, Prevotella type, Bacteroides type, and outlier type. By a thorough categorization of dysbiosis and the evaluation of microbial characteristics using multiple datasets, we identified a wide spectrum of accuracy levels in classifying health and disease states. Using the observed microbiome patterns, we devised an index named the gut microbiome index (GMI) that could consistently predict health conditions from human gut microbiome data. Compared to ecological metrics, the microbial marker index, and machine learning approaches, GMI distinguished between healthy and non-healthy individuals with a higher accuracy across various datasets. Thus, this study proposes a potential index to measure health status of gut microbiome that is verified from multiethnic data of various diseases, and we expect this model to facilitate further clinical application of gut microbiota data in future.
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Park Kuenyoul,Lee Jaewoong,Lee Kihyun,Jung Jiwon,Kim Sung-Han,Lee Jina,Chalita Mauricio,Yoon Seok-Hwan,Chun Jongsik,Hur Kyu-Hwa,Sung Heungsup,Kim Mi-Na,Lee Hae Kyung 대한의학회 2021 Journal of Korean medical science Vol.36 No.4
Background: Coronavirus disease 2019 (COVID-19) outbreaks emerged at two university- affiliated hospitals in Seoul (hospital A) and Uijeongbu City (hospital S) in the metropolitan Seoul area in March 2020. The aim of this study was to investigate epidemiological links between the outbreaks using whole genome sequencing (WGS) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods: Fifteen patients were enrolled in the study, including four non-outbreak (A1–A4) and three outbreak cases (A5–A7) in hospital A and eight cases (S1–S8) in hospital S. Patients' hospital stays, COVID-19 symptoms, and transfer history were reviewed. RNA samples were submitted for WGS and genome-wide single nucleotide variants and phylogenetic relationships were analyzed. Results: The index patient (A5) in hospital A was transferred from hospital S on 26 March. Patients A6 and A7 were the family caregiver and sister, respectively, of the patient who shared a room with A5 for 4 days. Prior to transfer, A5 was at the next bed to S8 in the emergency room on 25 March. Patient S6, a professional caregiver, took care of the patient in the room next to S8's room for 5 days until 22 March and then S5 for another 3 days. WGS revealed that SARS-CoV-2 in A2, A3, and A4 belong to clades V/B.2, S/A, and G/B.1, respectively, whereas that of A5–A7 and S1-S5 are of the V/B.2.1 clade and closely clustered. In particular, SARS-CoV-2 in patients A5 and S5 showed perfect identity. Conclusion: WGS is a useful tool to understand epidemiology of SARS-CoV-2. It is the first study to elucidate the role of patient transfer and caregivers as links of nosocomial outbreaks of COVID-19 in multiple hospitals.
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