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Jeongmin Lim 2 Articles
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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  • 11 Download
  • 15 Web of Science
  • 16 Crossref
AbstractAbstract PDF
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.

Citations

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    Food Science and Biotechnology.2026; 35(8): 2345.     CrossRef
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    Frontiers in Microbiology.2026;[Epub]     CrossRef
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    Neurobiology of Disease.2024; 201: 106655.     CrossRef
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    Giuseppe Guido Maria Scarlata, Ludovico Abenavoli
    The International Journal of Gastroenterology and Hepatology Diseases.2024;[Epub]     CrossRef
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    Clinical and Translational Science.2023; 16(11): 2177.     CrossRef
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    Frontiers in Aging Neuroscience.2023;[Epub]     CrossRef
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    Frontiers in Microbiology.2022;[Epub]     CrossRef
Improved pipeline for reducing erroneous identification by 16S rRNA sequences using the Illumina MiSeq platform
Yoon-Seong Jeon , Sang-Cheol Park , Jeongmin Lim , Jongsik Chun , Bong-Soo Kim
J. Microbiol. 2015;53(1):60-69.   Published online January 4, 2015
DOI: https://doi.org/10.1007/s12275-015-4601-y
  • 1,083 View
  • 0 Download
  • 36 Crossref
AbstractAbstract PDF
The cost of DNA sequencing has decreased due to advancements in Next Generation Sequencing. The number of sequences obtained from the Illumina platform is large, use of this platform can reduce costs more than the 454 pyrosequencer. However, the Illumina platform has other challenges, including bioinformatics analysis of large numbers of sequences and the need to reduce erroneous nucleotides generated at the 3􍿁-ends of the sequences. These erroneous sequences can lead to errors in analysis of microbial communities. Therefore, correction of these erroneous sequences is necessary for accurate taxonomic identification. Several studies that have used the Illumina platform to perform metagenomic analyses proposed curating pipelines to increase accuracy. In this study, we evaluated the likelihood of obtaining an erroneous microbial composition using the MiSeq 250 bp paired sequence platform and improved the pipeline to reduce erroneous identifications. We compared different sequencing conditions by varying the percentage of control phiX added, the concentration of the sequencing library, and the 16S rRNA gene target region using a mock community sample composed of known sequences. Our recommended
method
corrected erroneous nucleotides and improved identification accuracy. Overall, 99.5% of the total reads shared 95% similarity with the corresponding template sequences and 93.6% of the total reads shared over 97% similarity. This indicated that the MiSeq platform can be used to analyze microbial communities at the genus level with high accuracy. The improved analysis method recommended in this study can be applied to amplicon studies in various environments using high-throughput reads generated on the MiSeq platform.

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