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A high-quality genome compendium of the human gut microbiome of Inner Mongolians

Nature Microbiology volume 8, pages 150–161 (2023)Cite this article

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Abstract

Metagenome-based resources have revealed the diversity and function of the human gut microbiome, but further understanding is limited by insufficient genome quality and a lack of samples from typically understudied populations. Here we used hybrid long-read PromethION and short-read HiSeq sequencing to characterize the faecal microbiota of 60 Inner Mongolian individuals (n = 180 samples over three time points) who were part of a probiotic yogurt intervention trial. We present the Inner Mongolian Gut Genome catalogue, comprising 802 closed and 5,927 high-quality metagenome-assembled genomes. This approach achieved high genome continuity and substantially increased the resolution of genomic elements, including ribosomal RNA operons, metabolic gene clusters, prophages and insertion sequences. Particularly, we report the ribosomal RNA operon copy numbers for uncultured species, over 12,000 previously undescribed gut prophages and the distribution of insertion sequence elements across gut bacteria. Overall, these data provide a high-quality, large-scale resource for studying the human gut microbiota.

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Fig. 1: Effective assembly of a high number of species-level CMAGs.
Fig. 2: The IMGG catalogue as an expanded genomic resource.
Fig. 3: Enhanced genomic resolution of genetic elements in IMGG.
Fig. 4: Overview of the MGC pool in the human gut microbiome.
Fig. 5: A glance at the undescribed gut prophages and IS elements.

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Data Availability

All sequencing data (Illumina and Nanopore) generated in this study and the high-quality genomes in the IMGG dataset can be found under NCBI BioProject PRJNA763692. The 6,729 high-quality IMGGs are available at https://doi.org/10.6084/m9.figshare.19661523. Source data are provided with this paper.

Code availability

The in-house scripts for performing bioinformatics analyses in this work can be found in GitHub at https://github.com/jinhao94/nanopore_script.git and https://github.com/jinhao94/binning_script.git.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant numbers 31622043 (Z.S.), 31720103911 (H.Z.), 31972083 (L.-Y.K.), 32001711 (Q.H.)), the earmarked fund for China Agriculture Research System (CARS-36, H.Z.), the Inner Mongolia Science and Technology Major Projects (2021ZD0014, Z.S.), and the Natural Science Foundation of Inner Mongolia Autonomous Region (2020ZD12, Z.S.). We thank Jiachao Zhang (Hainan University) and Shenghui Li for their suggestions; all volunteers for their participation; and the Inner Mongolia Tongfang Discovery Tech. Co., Ltd. for providing storage space and computing resources.

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Author notes

  1. These authors contributed equally: Hao Jin, Keyu Quan, Qiuwen He.

Authors and Affiliations

  1. Inner Mongolia Key Laboratory of Dairy Biotechnology and Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China

    Hao Jin, Keyu Quan, Qiuwen He, Lai-Yu Kwok, Teng Ma, Yalin Li, Feiyan Zhao, Lijun You, Heping Zhang & Zhihong Sun

  2. Key Laboratory of Dairy Products Processing, Ministry of Agriculture and Rural Affairs, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China

    Hao Jin, Keyu Quan, Qiuwen He, Lai-Yu Kwok, Teng Ma, Yalin Li, Feiyan Zhao, Lijun You, Heping Zhang & Zhihong Sun

  3. Key Laboratory of Dairy Biotechnology and Engineering, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China

    Hao Jin, Keyu Quan, Qiuwen He, Lai-Yu Kwok, Teng Ma, Yalin Li, Feiyan Zhao, Lijun You, Heping Zhang & Zhihong Sun

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Contributions

Z.S. and H.Z. conceived and designed the study. Q.H., H.J., F.Z. and Y.L. performed the probiotic intervention trial and experimental work. H.J. and K.Q. performed bioinformatic analyses. H.J., K.Q., T.M. and L.Y. performed statistical analyses. Z.S. and H.Z. supervised all data analysis. H.J. drafted the manuscript. L.-Y.K. reviewed and revised the paper critically. All authors contributed to data interpretation, read and approved the final manuscript.

Corresponding authors

Correspondence to Heping Zhang or Zhihong Sun.

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Nature Microbiology thanks Ami Bhatt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Enhanced accuracy of 16S rRNA gene copy number in Inner Mongolian Gut Genomes.

Comparison of ribosomal RNA operon (rrn) gene copy number between Inner Mongolian Gut Genomes (IMGGs) and their counterpart complete genomes identified in the National Center for Biotechnology Information (NCBI) database.

Source data

Extended Data Fig. 2 Functional distribution of complete metabolic gene clusters across the Inner Mongolian Gut Genomes dataset.

Functional distribution of complete metabolic gene clusters across the Inner Mongolian Gut Genomes dataset. The prediction was performed by gutSMASH, which categorized metabolic gene clusters (MGCs) into different gene cluster classes based on their products: [npAA] non-proteinogenic amino acids; [Aromatic] derivatives of benzene; [SCFA-other] a SCFA is produced in combination with another molecule; [Putative] gene clusters of unknown function; [SCFA] fatty acids with 5 carbon atoms maximum; [Other] unclassified pathways; [Aliphatic_amine] ammonia derivatives where at least one H has been replaced by alkyl substituents; [E-MGC] related to energy-capturing mechanisms.

Source data

Extended Data Fig. 3 Principal coordinates analysis showing phylum-based clustering trends of metabolic gene clusters.

Permutational analysis of variance (Adonis test; R = 0.38, P < 0.001; n = 15,476) was performed using the adonis function in the vegan package based on the Bray-Curtis distance with 9999 permutations.

Source data

Extended Data Fig. 4 Size and frequency of hybrid metabolic gene clusters.

(a) Comparison between the length of hybrid (containing multiple functional domains; n = 11,693) and single-functional-domain (n = 85,654) metabolic gene clusters (MGCs). The boxes represent the interquartile range, the lines inside the boxes represent the medians, and the whiskers denote the lowest and highest values within 1.5 times the interquartile range. (b) The most frequently observed hybrid MGC combination pair. Statistical difference was tested by Wilcoxon rank-sum test (two-sided).

Source data

Extended Data Fig. 5 The uneven intra-species distribution of insertion sequence elements.

Distribution of insertion sequence (IS) elements across the 15 most represented metagenome-assembled genomes (MAGs) in the dataset.

Source data

Extended Data Fig. 6 The most frequently involved Kyoto Encyclopedia of Genes and Genomes (KEGG) brites and pathways (3rd level) of neighboring genes of insertion sequence elements.

BR and PATH represent Kyoto Encyclopedia of Genes and Genomes (KEGG) brites and pathways, respectively, and codes are not given to components that are ‘not included in pathway or brite’ based on KEGG orthology (KO). The color key shows the 2nd level KEGG pathways, of which the brites and pathways (3rd level) in the horizontal bar chart belong to.

Source data

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Jin, H., Quan, K., He, Q. et al. A high-quality genome compendium of the human gut microbiome of Inner Mongolians. Nat Microbiol 8, 150–161 (2023). https://doi.org/10.1038/s41564-022-01270-1

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