A catalog of the mouse gut metagenome

Subjects

An Author Correction to this article was published on 03 January 2019

This article has been updated

Abstract

We established a catalog of the mouse gut metagenome comprising 2.6 million nonredundant genes by sequencing DNA from fecal samples of 184 mice. To secure high microbiome diversity, we used mouse strains of diverse genetic backgrounds, from different providers, kept in different housing laboratories and fed either a low-fat or high-fat diet. Similar to the human gut microbiome, >99% of the cataloged genes are bacterial. We identified 541 metagenomic species and defined a core set of 26 metagenomic species found in 95% of the mice. The mouse gut microbiome is functionally similar to its human counterpart, with 95.2% of its Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologous groups in common. However, only 4.0% of the mouse gut microbial genes were shared (95% identity, 90% coverage) with those of the human gut microbiome. This catalog provides a useful reference for future studies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Rarefaction curves based on gene profiles of the total set of 184 samples and the individual subgroups.
Figure 2: Top 20 core bacterial genera in the mouse and human gut microbiota.
Figure 3: Comparison of the human and mice gut catalog at the gene and KO levels.
Figure 4: PCoA analysis.

Accession codes

Primary accessions

European Nucleotide Archive

Change history

  • 18 December 2018

    In the version of this article initially published, the y-axis numbering in Figure 1 was high by a factor of 10; the correct range is 0.5 to 2.5 million nonredundant genes. The error has been corrected in the HTML and PDF versions of the article.

References

  1. 1

    Cho, I. & Blaser, M.J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  2. 2

    Clemente, J.C., Ursell, L.K., Parfrey, L.W. & Knight, R. The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3

    Tremaroli, V. & Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 489, 242–249 (2012).

    CAS  PubMed  Article  Google Scholar 

  4. 4

    Ridaura, V.K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013).

    Article  CAS  Google Scholar 

  5. 5

    Kamada, N., Seo, S.-U., Chen, G.Y. & Núñez, G. Role of the gut microbiota in immunity and inflammatory disease. Nat. Rev. Immunol. 13, 321–335 (2013).

    CAS  PubMed  Article  Google Scholar 

  6. 6

    Cotillard, A. et al. ANR MicroObes consortium. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).

    CAS  PubMed  Article  Google Scholar 

  7. 7

    Le Chatelier, E. et al. MetaHIT consortium. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

    CAS  PubMed  Article  Google Scholar 

  8. 8

    Ahern, P.P., Faith, J.J. & Gordon, J.I. Mining the human gut microbiota for effector strains that shape the immune system. Immunity 40, 815–823 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9

    Huttenhower, C., Kostic, A.D. & Xavier, R.J. Inflammatory bowel disease as a model for translating the microbiome. Immunity 40, 843–854 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. 10

    Faith, J.J., Ahern, P.P., Ridaura, V.K., Cheng, J. & Gordon, J.I. Identifying gut microbe-host phenotype relationships using combinatorial communities in gnotobiotic mice. Sci. Transl. Med. 6, 220ra11 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  11. 11

    Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12

    Thaiss, C.A. et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell 159, 514–529 (2014).

    CAS  PubMed  Article  Google Scholar 

  13. 13

    Zhang, C. et al. Structural modulation of gut microbiota in life-long calorie-restricted mice. Nat. Commun. 4, 2163 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. 14

    Qin, J. et al. MetaHIT Consortium. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. 15

    Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    CAS  PubMed  Article  Google Scholar 

  16. 16

    Gerlach, W. & Stoye, J. Taxonomic classification of metagenomic shotgun sequences with CARMA3. Nucleic Acids Res. 39, e91 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. 17

    Zhang, X. et al. Structural changes of gut microbiota during berberine-mediated prevention of obesity and insulin resistance in high-fat diet-fed rats. PLoS One 7, e42529 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18

    Everard, A. et al. Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity. ISME J. 8, 2116–2130 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19

    Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. 20

    Nielsen, H.B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).

    CAS  PubMed  Article  Google Scholar 

  21. 21

    Jensen, L.J. et al. eggNOG: automated construction and annotation of orthologous groups of genes. Nucleic Acids Res. 36, D250–D254 (2008).

    CAS  PubMed  Article  Google Scholar 

  22. 22

    Ogata, H. et al. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 27, 29–34 (1999).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. 23

    Cantarel, B.L. et al. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 37, D233–D238 (2009).

    CAS  PubMed  Article  Google Scholar 

  24. 24

    Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P.M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25

    Anderson, M.J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).

    Google Scholar 

  26. 26

    McArdle, B.H. & Anderson, M.J. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001).

    Article  Google Scholar 

  27. 27

    Zhao, L. The gut microbiota and obesity: from correlation to causality. Nat. Rev. Microbiol. 11, 639–647 (2013).

    CAS  PubMed  Article  Google Scholar 

  28. 28

    Zhang, C. et al. Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice. ISME J. 4, 232–241 (2010).

    CAS  PubMed  Article  Google Scholar 

  29. 29

    Zhang, C. et al. Structural resilience of the gut microbiota in adult mice under high-fat dietary perturbations. ISME J. 6, 1848–1857 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30

    Suez, J. et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514, 181–186 (2014).

    CAS  PubMed  Article  Google Scholar 

  31. 31

    Schwab, C. et al. Longitudinal study of murine microbiota activity and interactions with the host during acute inflammation and recovery. ISME J. 8, 1101–1114 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32

    Lacombe, A. et al. Lowbush wild blueberries have the potential to modify gut microbiota and xenobiotic metabolism in the rat colon. PLoS One 8, e67497 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33

    Friswell, M.K. et al. Site and strain-specific variation in gut microbiota profiles and metabolism in experimental mice. PLoS One 5, e8584 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34

    Ley, R.E. et al. Obesity alters gut microbial ecology. Proc. Natl. Acad. Sci. USA 102, 11070–11075 (2005).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35

    Turnbaugh, P.J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36

    Hildebrand, F. et al. Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biol. 14, R4 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  37. 37

    Turnbaugh, P.J., Bäckhed, F., Fulton, L. & Gordon, J.I. Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38

    Turnbaugh, P.J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39

    Wu, G.D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40

    Patrone, V. et al. Short-term modifications in the distal gut microbiota of weaning mice induced by a high-fat diet. Microbiology 158, 983–992 (2012).

    CAS  PubMed  Article  Google Scholar 

  41. 41

    Parks, B.W. et al. Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Cell Metab. 17, 141–152 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42

    David, L.A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS  Article  Google Scholar 

  43. 43

    Luo, R. et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience 1, 18–23 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  44. 44

    Kent, W.J. BLAT--the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  45. 45

    Altschul, S.F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46

    Moriya, Y., Itoh, M., Okuda, S., Yoshizawa, A.C. & Kanehisa, M. KAAS: an automatic genome annotation and pathway reconstruction server. Nucleic Acids Res. 35, W182–W185 (2007).

    PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank the sequencing and bioinformatics staff at BGI-Shenzhen for help and advice. This study was supported by the Danish Natural Science Research Foundation, the Carlsberg Foundation, The Danish Council for Strategic Research (grant 11-116163), the National Basic Research Program of China (973 Program, 2013CB531400, 2011CB504004 and 2010CB945500), the Shenzhen Municipal Government of China (the research and development of the novel personalized gut microbiota probiotic production, CXZZ20150330171521403), Theme-based Research Scheme of the Hong Kong Research Grants Council (T12-403-11), the Metagenopolis grant (ANR-11-DPBS-0001), Knut and Alice Wallenberg Foundation, Swedish Research Council, the Novo Nordisk foundation, Torsten Söderberg's foundation. F.B. is a recipient of ERC Consolidator Grant (European Research Council, Consolidator grant 615362-METABASE).

Author information

Affiliations

Authors

Contributions

S.D.E., J.C.L., J.W., L.M. and K.K. conceived and designed the project. K.K., L.M., J.W., L.X. and Q.F. monitored the project. X.L., H.L., J.Z., D.Z., C.L., Z.F., J.C., J.G., Q.H., D.K., C.C., T.R.L., D.W., J.Y., J.J.Y.S, Q.L., V.T. and J.D. collected samples and performed experiments. K.K., L.M., J.W., M.A., S.L., X.Q., H.B.N., P.D., S.B.S., J.C.L., S.D.E., J.D., F.B., Z.X., J.L., Z.L., Q.F. and L.X. analyzed and interpreted the data. K.K., L.M., J.W., M.A., H.J., E.L.C., Q.F. and L.X. wrote the paper. All authors commented on the manuscript.

Corresponding authors

Correspondence to Jun Wang or Lise Madsen or Karsten Kristiansen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Taxonomic annotation of the mouse catalogue.

(a) Taxonomic annotation of the 2.6M mouse catalogue to the superkingdom, phylum, genus and species levels. 60.31 % of the genes in the catalogue were annotated to the phylum level, whereas only 9.84% of these genes could be annotated to the genus level, and further only 1.40% of these could be annotated at the species level. (b) Taxonomic annotation of bacterial genus profiles of the mouse catalogue based on alignments against the NR and HMP databases, respectively. The mice from different providers exhibit significantly different taxonomic structures, especially prominent for mice from Taconic Denmark (TDK). Of note significantly more genes in the microbiomes of mice from TDK (unit 1-9) map to the HMP database, contrasting the microbiomes of mice from other providers (units 10-25). See supplementary table S1 for further information on the units. (c) The taxonomical distribution of the top 6 phyla and genera in the mouse catalogue compared with the 4.3M human catalogue. The percentage of the genes assigned to the Firmicutes phylum is about 20% higher in the mouse microbiome compared with the human microbiome, whereas the percentage of genes assigned to the Bacteroidetes in mouse microbiome is almost 50% lower than in the human microbiome. Thus, the ratio of Firmicutes to Bacteroidetes is markedly different in mice and humans.

Supplementary Figure 2 Gene content and GC composition of the MGS.

(a) Histogram of co-abundance gene group (CAG) size distribution in terms of gene content. The scale is logarithmic as indicated by the bar widths. The dashed line marks the 700-gene threshold separating small CAGs from MGS. There are 4334 small CAGs and 541 MGS. (b) Box-plot showing the distribution of gene-wise GC percentages of the 541 MGS. Each box shows the distribution for a single MGS. The MGS are sorted according to their median GC gene percentage. On average the inter-quintile range (IQR) of the MGS GC gene percentages is 5.6 % and only 3 % of the MGS have an IQR of 10% or more. On average randomly sampled gene sets of matched sizes have an IQR of 13.9 % (± 0.4)

Supplementary Figure 3 Percentage of the genes shared between mice and humans in relation to stringency of alignment.

The term “identical gene“ is defined by the “coverage“ (the percentage of covered gene length) and the “identity“ after the alignment. The number of the genes shared by mice and humans changes in relation to the stringency of “coverage“ and “identity“. When coverage is above 50%, the number of shared gene changes very little, and the “0.9 identity” curve overlaps with the “0.85 identity” curve, demonstrating that the number of shared gene would not change significantly if “identity“ is lower than 90%.

Supplementary Figure 4 Functional classification of the mouse microbiome.

The predicted genes of the mouse catalogue mapped to the (a) eggNOG functional classification and (b) KEGG pathway classification. Most of the functions belong to pathways involved in housekeeping functions such as metabolism, genetic information processing and environmental information processing. (c), Comparison of the distribution of KOs at the KEGG functional level and (d) pathways in the mouse and the human catalogue. The black bars and lines denote KOs and pathways, respectively, shared between the mouse and human catalogue. Red denotes KOs and pathways uniquely found in the human catalogue and green denotes KOs and pathways uniquely found in the mouse catalogue. Most of the KOs are shared between the mouse and the human metagenomes, reflecting the common needs for maintenance in the digestive tract. The KOs unique for the human microbiota are enriched for pathways involved in nucleotide metabolism, energy metabolism and translation, while KOs unique for the mouse microbiota are enriched for pathways involved in membrane transport, metabolism of amino acid, and degradation of xenobiotics.

Supplementary Figure 5 Distribution of genes annotated against CAZy database in the mouse and human gut bacterial gene catalogue.

(a) Venn diagram of the CAZy families in human and mouse. A total of 304 enzyme families were annotated in the mouse catalogue and 301 of them are shared with human, further demonstrating the functional similarity between the mouse and the human gut microbiomes. (b) The distribution of genes annotated against the CAZy database. All the functional genes were divided into 6 enzyme classes/associated modules, CBM = carbohydrate-binding modules, CE = carbohydrate esterases, CL = cellulases, GH = glycoside hydrolases, GT = glycosyl transferases, PL = polysaccharide lyases. The distribution of the CAZy enzyme classes in the mouse gut bacterial gene catalogue is very similar to that of human gut bacterial catalogue reflecting the conservation of pathways involved in the breakdown, biosynthesis or modification of carbohydrates and glycoconjugates in the mouse and human gut microbiomes.

Supplementary Figure 6 Comparison of the mouse and human microbiome α- and β-diversity.

(a) Comparison of α- and β-diversity at the gene and KO levels in the human and mouse microbiome using Shannon and Whittaker indices. The mouse microbiota exhibits higher α-diversity than that of the human microbiota at the gene level, but lower α-diversity at the KO level, indicating that more genes in the mouse microbiome are associated with a given function/pathway. (b), Comparison of the human microbiome and the individual mouse samples in relation to provider, housing laboratory, strain, diet and gender (See supplementary table S1) at the functional level. The heat map is based on the Whittaker index calculated from the human catalogue and 184 individual mouse samples. Of note, the microbiomes of mice supplied by TDK and kept at NIFES exhibited significantly higher similarity with the human microbiota than the microbiomes of all other mice.

Supplementary Figure 7 Comparison of α-diversity.

Comparison of α-diversity at the genus, gene, KO and MGS levels in the mouse microbiome in relation to provider, housing laboratory, strain, diet and gender using the Shannon index. See supplementary table S1. Comparison of relevant units provides information on how provider, housing laboratory, strain, diet, and gender affect α-diversity at the genus, gene, KO and MGS levels. At the provider levels we noted significant differences between JUS and TUS in mice fed the high fat diet at all levels. Interestingly, at the KO level, mice from TUS exhibited higher α-diversity than mice from JUS, whereas the opposite was found comparing TUS and JUS at the genus level. At the housing laboratory level, the most conspicuous differences were observed between Sv129 mice from TDK housed at DTU and NIFES. Considering mouse strains, we observed as expected marked differences at the level of genes, KOs and genera. In relation to diets, we observed a remarkable robust increase in α-diversity in the high fat fed mice at the level of genes and genera, but less pronounced inverse effects at the KO level. We observed no significant effects on α-diversity in relation to gender. The results obtained using MGS were consistent with those obtained based on analyses at the gene level.

Supplementary Figure 8 Comparison of β-diversity.

Comparison of beta diversity at the genus, gene, KO and MGS levels in the mouse microbiome in relation to provider, housing laboratory, strain, diet and gender using the Whittaker index. See supplementary table S1. At the provider level, we noted significant differences between JUS and TUS at the gene, KO and genus level, but not at the MGS level, with mice from TUS showing higher β-diversity than mice from JUS. At the housing laboratory level, we also observed marked differences, with TDK mice housed at DTU exhibiting the highest β-diversity. As expected we also observed significant difference between mouse strains, with C57BL/J mice obtained from TDK and housed at DTU standing out. In addition, the NOD mice also exhibit significantly higher β-diversity at the KO level comparable to that of the C57BL/6 mice obtained from TDK and housed at DTU. In response to diet we observed a rather robust decrease in β-diversity at the gene and genera level contrasting the increase in α-diversity observed in high fat diet fed mice. Finally, female mice seemed to have higher β-diversity than C57BL/6 male mice.

Supplementary Figure 9 Principal coordinates analyses at the gene and KO levels.

Principal coordinates analyses (PCoA) of gene and KO profiles in individual mouse samples in relation to (a) provider (b) housing laboratory (c), strain (d), diet and (e), gender. The mice samples in different subgroups (see supplementary table S1) were used to illustrate the influence of these confounding factors on the mouse gut microbiome at the levels of genes and function. Consistent with the results of a PERMANOVA test, all of these factors, provider, housing laboratory, strain, diet and gender, impact markedly on the mice gut microbiome at both gene and KO levels.

Supplementary Figure 10 Relative abundance of bacterial genera and functional pathways in relation to provider, housing laboratory, mouse strain, diet and gender.

(a) Enrichment of selected bacterial genera in relation to provider, housing laboratory, mouse strain, diet and gender. The distribution of the bacterial genera is very different in the factor-related subgroups. Especially Alistipes, Bacteroides and Akkermansia exhibit marked differences in relation to all five factors affecting the relative abundance of genera. (b) Heatmap based on KO profiles demonstrates that the relative abundance especially of pathways involved in biotin biosynthesis, pyridoxal biosynthesis, zinc transport, cellobiose-specific and mannose-spcific PTS systems differs significantly in relation to provider, housing laboratory, strain, diet and gender.

Supplementary Figure 11 Relative abundances of 14 genera exhibiting significant differences.

The bacterial genera whose relative abundances are above 1% in any sample were used in a Wilcoxon test in relation to (a) provider (b) housing laboratory (c) strain (d) diet and (e) gender. See supplementary table S1. The test results were adjusted for FDR. “***”indicates p-value ≤ 0.001, “**”indicates p-value ≤ 0.01, “*”indicates p-value ≤ 0.05, “NS” indicates p-value ≥ 0.05. Besides the significant influence of the provider, housing laboratory, strain, diet, and gender on the gut bacterial genera, different effect sizes of these factors were also observed. Thus, “provider” markedly affects Bacteriodes and Alistipes to different degrees comparing G8 and G9; “strain” differentially affects Akkermansia in comparing G2, G3 and G4; and diet significantly affects Bacteriodes comparing G13, G14 and G15.

Supplementary Figure 12 Relative abundances of 12 species exhibiting significant differences.

The bacterial species whose relative abundances are above 0.1% in any sample were used in a Wilcoxon test to demonstrate the impact of (a) provider (b) housing laboratory (c) strain (d) diet and (e) gender (see supplementary table S1) on the mouse gut microbiome. The test results were adjusted for FDR. “***” indicates p-value 0.001, “**”indicates p-value 0.01, “*”indicates p-value 0.05, “NS” indicates p-value 0.05. The “provider” markedly affects Lactobacillus johnsoni to different degrees comparing G8 and G9; “strain” differentially affects Bacteroides intestinalis and Bacteroides vulgatus comparing G2, G3 and G4; and diet significantly affects Akkermansia muciniphila, Bacteroides intestinalis, Bacteroides vulgatus and Lactobacillus johnsoni comparing G13, G14 and G15.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12 and Supplementary Results and Discussion (PDF 7796 kb)

Supplementary Table 1

The background information and the subgroups of all the 184 samples (XLSX 41 kb)

Supplementary Table 2

The assembly results of the 184 samples (XLSX 50 kb)

Supplementary Table 3

The assembly results of the 2 deep sequenced samples (XLSX 45 kb)

Supplementary Table 4

The distribution, frequency and the annotation of the MGS (XLSX 97 kb)

Supplementary Table 5

The distribution of the KOs and Modules in the human and mouse (XLSX 40 kb)

Supplementary Table 6

The PERMANOVA test results of all the factors based on gene, KO and genera levels (XLSX 50 kb)

Supplementary Table 7

The enrichement of the KOs in the samples from different suppliers (XLSX 29 kb)

Supplementary Table 8

The enrichment of the KOs in the samples from different housing-labs (XLSX 28 kb)

Supplementary Table 9

The enrichment of the KOs in the samples from different strains (XLSX 30 kb)

Supplementary Table 10

The enrichment of the KOs in the samples under different diets (XLSX 28 kb)

Supplementary Table 11

The enrichment of the function pathways in different subgroups (XLSX 31 kb)

Supplementary Table 12

The enrichment of the KOs in the samples under different genders (XLSX 19 kb)

Supplementary Table 13

The mapping rate of the published metatranscriptomic data to the 2.6M catalogue (XLSX 40 kb)

Supplementary Table 14

The mapping rate of the published metagenomics data to the 2.6M gene set (XLSX 34 kb)

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xiao, L., Feng, Q., Liang, S. et al. A catalog of the mouse gut metagenome. Nat Biotechnol 33, 1103–1108 (2015). https://doi.org/10.1038/nbt.3353

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing