Journal of Microbiology

Meta-Analysis

Proposal of a health gut microbiome index based on a meta-analysis of Korean and global population datasets: Hyun-Seok Oh , Uigi Min , Hyejin Jang , Namil Kim , Jeongmin Lim , Mauricio Chalita , Jongsik Chun; J. Microbiol. 2022;60(5):533-549. Published online March 31, 2022; DOI: https://doi.org/10.1007/s12275-022-1526-0

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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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Characterization of pathogenic microbiome on removable prostheses with different levels of cleanliness using 2bRAD-M metagenomic sequencing
Tong Wah Lim, Shi Huang, Yuesong Jiang, Yufeng Zhang, Michael Francis Burrow, Colman McGrath
Journal of Oral Microbiology.2024;[Epub] CrossRef
Gut microbial signatures in clinically stable ulcerative colitis according to the mucosal state and associated symptoms
Soyoung Kim, Yeonjae Jung, Seung Bum Lee, Hyun‐Seok Oh, Sung Noh Hong
Journal of Gastroenterology and Hepatology.2024; 39(2): 319. CrossRef
Difference in gut microbial dysbiotic patterns between body-first and brain-first Parkinson's disease
Don Gueu Park, Woorim Kang, In-Ja Shin, Mauricio Chalita, Hyun-Seok Oh, Dong-Wook Hyun, Hyun Kim, Jongsik Chun, Young-Sil An, Eun Jeong Lee, Jung Han Yoon
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Giuseppe Guido Maria Scarlata, Ludovico Abenavoli
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Ki Young Huh, Woo Kyung Chung, Jiyeon Park, SeungHwan Lee, Min‐Gul Kim, Jaeseong Oh, Kyung‐Sang Yu
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Car Reen Kok, Devin Rose, Robert Hutkins
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Journal Articles

Comparative genomic analysis of selenium utilization traits in different marine environments: Muhammad Farukh; J. Microbiol. 2020;58(2):113-122. Published online January 29, 2020; DOI: https://doi.org/10.1007/s12275-020-9250-0

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Abstract

Selenium (Se) is an essential trace element for many organisms, which is required in the biosynthesis of proteins with selenocysteine, tRNAs with selenouridine, and certain enzymes with Se as a cofactor. Recent large-scale metagenomics projects provide a unique opportunity for studying the global trends of Se utilization in marine environments. Here, we analyzed samples from different marine microbial communities, revealed by the Tara Oceans project, to characterize the Se utilization traits. We found that the selenophosphate synthetase gene, which defines the overall Se utilization, and Se utilization traits are present in all samples. Regions with samples rich and poor in Se utilization traits were categorized. From the analysis of environmental factors, the mesopelagic zone and high temperature (> 15°C) of water are favorable, while geographical location has little influence on Se utilization. All Se utilization traits showed a relatively independent occurrence. The taxonomic classification of Se traits shows that most of the sequences corresponding to Se utilization traits belong to the phylum Proteobacteria. Overall, our study provides useful insights into the general features of Se utilization in ocean samples and may help to understand the evolutionary dynamics of Se utilization in different marine environments.

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The selenophosphate synthetase family: A review
Bruno Manta, Nadezhda E Makarova, Marco Mariotti
Free Radical Biology and Medicine.2022; 192: 63. CrossRef
Selenium Metabolism and Selenoproteins in Prokaryotes: A Bioinformatics Perspective
Yan Zhang, Jiao Jin, Biyan Huang, Huimin Ying, Jie He, Liang Jiang
Biomolecules.2022; 12(7): 917. CrossRef
Uses of Selenium Nanoparticles in the Plant Production
Iqra Bano, Sylvie Skalickova, Hira Sajjad, Jiri Skladanka, Pavel Horky
Agronomy.2021; 11(11): 2229. CrossRef

Development of a Method Based on Surface Enhanced Laser Desorption and Ionization Time of Flight Mass Spectrometry for Rapid Identification of Klebsiella pneumoniae: Daiwen Xiao , Yongchang Yang , Hua Liu , Hua Yu , Yingjun Yan , Wenfang Huang , Wei Jiang , Weijin Liao , Qi Hu , Bo Huang; J. Microbiol. 2009;47(5):646-650. Published online October 24, 2009; DOI: https://doi.org/10.1007/s12275-009-0092-z

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Abstract: A method based on surface enhanced laser desorption and ionization time of flight mass spectrometry (SELDI-TOF MS) was developed for the rapid identification of Klebsiella pneumoniae by directly applying bacterial colonies without further protein extraction. A total of 40 K. pneumoniae and 114 other related microorganisms isolated clinically were analyzed by SELDI-TOF MS. An identification model for K. pneumoniae was established by artificial neural networks (ANNs) with classification accuracy of 100%. The model was blindly tested with 43 K. pneumoniae and 53 control bacteria again. The results showed that the model was successful with accuracy of 96.9%, sensitivity of 100% and specificity of 94.3%. This strategy is potential for rapid identification of K. pneumoniae.

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