- Proposal of a health gut microbiome index based on a meta-analysis of Korean and global population datasets
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Hyun-Seok Oh , Uigi Min , Hyejin Jang , Namil Kim , Jeongmin Lim , Mauricio Chalita , Jongsik Chun
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J. Microbiol. 2022;60(5):533-549. Published online March 31, 2022
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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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Citations
Citations to this article as recorded by 
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Leigh A. Frame, Alison Warren, Abdelmohsen Al Qalam, Patrick G. Corr, Mina Farah, Michaela Karam, Katherine Rangoussis, Mehrshad Fahim Devin, Zeynep Celikkol, Lindsey Gordon, Daniel Villarreal, Elizabeth Catto, Yvonne Udam, Katherine Thompson, Owen Lubins Gut Microbes Reports.2026;[Epub] CrossRef - Impacts of dietary patterns on the gut microbiome: comparing a priori dietary indices and a posteriori dietary patterns
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- Improved pipeline for reducing erroneous identification by 16S rRNA sequences using the Illumina MiSeq platform
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Yoon-Seong Jeon , Sang-Cheol Park , Jeongmin Lim , Jongsik Chun , Bong-Soo Kim
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J. Microbiol. 2015;53(1):60-69. Published online January 4, 2015
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DOI: https://doi.org/10.1007/s12275-015-4601-y
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1,083
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0
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36
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Abstract
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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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