A catalog of the mouse gut metagenome

Journal name:
Nature Biotechnology
Volume:
33,
Pages:
1103–1108
Year published:
DOI:
doi:10.1038/nbt.3353
Received
Accepted
Published online

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.

At a glance

Figures

  1. Rarefaction curves based on gene profiles of the total set of 184 samples and the individual subgroups.
    Figure 1: Rarefaction curves based on gene profiles of the total set of 184 samples and the individual subgroups.

    The gene number of the samples in different subgroups was calculated after 100 random samplings with replacement (we randomly chose specific numbers of samples and calculated the gene numbers, and repeated this 100 times), and the median was plotted. Completeness of the total sample set according to incidence-based coverage estimator and Chao1 index was 99.9%, demonstrating the high coverage of the catalog in the current sample set. The rarefaction curves also illustrate the importance of combining data from different providers, housing laboratories, strains and feed. The impact of these confounding factors is illustrated by the gene count of the subgroups.

  2. Top 20 core bacterial genera in the mouse and human gut microbiota.
    Figure 2: Top 20 core bacterial genera in the mouse and human gut microbiota.

    (a,b) The top 20 most abundant core bacterial genera in mouse (a) and human (b) gut microbiota are depicted with their interquartile ranges (IQRs; boxes), median values (dark lines in the boxes), lowest and highest values within 1.5 times IQR from the first and third quartiles (whiskers above and below the boxes), and outliers beyond the whiskers (dots). The genera in green and blue font exhibit higher abundances in mouse and human samples, respectively.

  3. Comparison of the human and mice gut catalog at the gene and KO levels.
    Figure 3: Comparison of the human and mice gut catalog at the gene and KO levels.

    (a) Unique nonredundant genes in humans and mice, with only 102,830 (4.0%) genes overlapping, which emphasizes marked differences between the human and mouse gut microbiome at the gene level. (b) Comparison of human (blue) and mouse (green) data based on KEGG annotation, which emphasizes functional similarity of the mouse and human gut microbiota despite the marked differences at the gene level shown in a. (c) PCoA analysis based on KOs of the human (blue) and mouse (green) gut microbiota. Although 80% of the KOs are shared by the human and mouse gut microbiota, PCoA still clearly separates the two gut microbiomes. The top 10 KOs driving the separation are indicated in the figure. The percentage contributions to the variance of the data from principal components 1 and 2 (PC1 and PC2) are listed along the axes representing them.

  4. PCoA analysis.
    Figure 4: PCoA analysis.

    (a,b) Analysis of the 184 mouse samples based on gene profiles (a) and KO profiles (b). Symbol color denotes providers, symbol shape denotes housing laboratory, and hollow and solid symbols denote low-fat (LF) and high fat (HF) diet, respectively. The PCoA demonstrates how provider is a main driver in separation at both the gene and KO levels followed by housing laboratory and diet. Notably, mice from different providers are clearly separated along PC2, and low-fat and high-fat diets are clearly separated along PC1 at the KO level. The percentage contributions to the variance of the data from principal components 1 and 2 (PC1 and PC2) are listed along the axes representing them.

  5. Taxonomic annotation of the mouse catalogue.
    Supplementary Fig. 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.

  6. Gene content and GC composition of the MGS.
    Supplementary Fig. 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)

  7. Percentage of the genes shared between mice and humans in relation to stringency of alignment.
    Supplementary Fig. 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%.

  8. Functional classification of the mouse microbiome.
    Supplementary Fig. 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.

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

  10. Comparison of the mouse and human microbiome [alpha]- and [beta]-diversity.
    Supplementary Fig. 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.

  11. Comparison of [alpha]-diversity.
    Supplementary Fig. 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.

  12. Comparison of [beta]-diversity.
    Supplementary Fig. 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.

  13. Principal coordinates analyses at the gene and KO levels.
    Supplementary Fig. 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.

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

  15. Relative abundances of 14 genera exhibiting significant differences.
    Supplementary Fig. 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.

  16. Relative abundances of 12 species exhibiting significant differences.
    Supplementary Fig. 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.

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Author information

  1. These authors contributed equally to this work.

    • Liang Xiao,
    • Qiang Feng &
    • Suisha Liang

Affiliations

  1. Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China.

    • Liang Xiao,
    • Qiang Feng,
    • Suisha Liang,
    • Zhongkui Xia,
    • Xinmin Qiu,
    • Xiaoping Li,
    • Jianfeng Zhang,
    • Dongya Zhang,
    • Chuan Liu,
    • Zhiwei Fang,
    • Junhua Li,
    • Huijue Jia,
    • Zhou Lan,
    • Manimozhiyan Arumugam,
    • Jun Wang,
    • Lise Madsen &
    • Karsten Kristiansen
  2. Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

    • Qiang Feng,
    • Si Brask Sonne,
    • Qin Hao,
    • Dorota Kotowska,
    • Camilla Colding,
    • Jun Wang,
    • Lise Madsen &
    • Karsten Kristiansen
  3. Pfizer Experimental Medicine, Pfizer Inc., South San Francisco, California, USA.

    • Hua Long,
    • Joyce Chou,
    • Jacob Glanville &
    • John C Lin
  4. National Food Institute, Technical University of Denmark, Søborg, Denmark.

    • Tine Rask Licht
  5. Key Laboratory of Regenerative Biology, Guangzhou Institute of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.

    • Donghai Wu
  6. Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences (Chinese University Hong Kong), Shenzhen Research Institute, The Chinese University of Hong Kong, Hong Kong, China.

    • Jun Yu,
    • Joseph Jao Yiu Sung &
    • Qiaoyi Liang
  7. The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden.

    • Valentina Tremaroli &
    • Fredrik Bäckhed
  8. Department of Systems Biology, Technical University of Denmark, Kongens Lyngby, Denmark.

    • Piotr Dworzynski &
    • H Bjørn Nielsen
  9. The Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.

    • Fredrik Bäckhed &
    • Manimozhiyan Arumugam
  10. Institut National de la Recherche Agronomique (Microbiologie de l'Alimentation au Service de la Santé), Jouy en Josas, France.

    • Joël Doré
  11. Institut National de la Recherche Agronomique, Metagenopolis, Jouy en Josas, France.

    • Joël Doré,
    • Emmanuelle Le Chatelier &
    • S Dusko Ehrlich
  12. King's College London, Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy's Hospital, London Bridge, UK.

    • S Dusko Ehrlich
  13. Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia.

    • Jun Wang
  14. Macau University of Science and Technology, Avenida Wai long, Taipa, Macau, China.

    • Jun Wang
  15. National Institute of Nutrition and Seafood Research, Bergen, Norway.

    • Lise Madsen

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.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

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Supplementary information

Supplementary Figures

  1. Supplementary Figure 1: Taxonomic annotation of the mouse catalogue. (280 KB)

    (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.

  2. Supplementary Figure 2: Gene content and GC composition of the MGS. (221 KB)

    (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)

  3. Supplementary Figure 3: Percentage of the genes shared between mice and humans in relation to stringency of alignment. (117 KB)

    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%.

  4. Supplementary Figure 4: Functional classification of the mouse microbiome. (674 KB)

    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.

  5. Supplementary Figure 5: Distribution of genes annotated against CAZy database in the mouse and human gut bacterial gene catalogue. (112 KB)

    (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.

  6. Supplementary Figure 6: Comparison of the mouse and human microbiome α- and β-diversity. (567 KB)

    (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.

  7. Supplementary Figure 7: Comparison of α-diversity. (432 KB)

    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.

  8. Supplementary Figure 8: Comparison of β-diversity. (404 KB)

    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.

  9. Supplementary Figure 9: Principal coordinates analyses at the gene and KO levels. (356 KB)

    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.

  10. Supplementary Figure 10: Relative abundance of bacterial genera and functional pathways in relation to provider, housing laboratory, mouse strain, diet and gender. (758 KB)

    (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.

  11. Supplementary Figure 11: Relative abundances of 14 genera exhibiting significant differences. (1,137 KB)

    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.

  12. Supplementary Figure 12: Relative abundances of 12 species exhibiting significant differences. (365 KB)

    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.

PDF files

  1. Supplementary Text and Figures (7,983 KB)

    Supplementary Figures 1–12 and Supplementary Results and Discussion

Excel files

  1. Supplementary Table 1 (41,989 KB)

    The background information and the subgroups of all the 184 samples

  2. Supplementary Table 2 (41,989 KB)

    The assembly results of the 184 samples

  3. Supplementary Table 3 (41,989 KB)

    The assembly results of the 2 deep sequenced samples

  4. Supplementary Table 4 (99,348 KB)

    The distribution, frequency and the annotation of the MGS

  5. Supplementary Table 5 (41,971 KB)

    The distribution of the KOs and Modules in the human and mouse

  6. Supplementary Table 6 (52,144 KB)

    The PERMANOVA test results of all the factors based on gene, KO and genera levels

  7. Supplementary Table 7 (29,811 KB)

    The enrichement of the KOs in the samples from different suppliers

  8. Supplementary Table 8 (28,983 KB)

    The enrichment of the KOs in the samples from different housing-labs

  9. Supplementary Table 9 (31,299 KB)

    The enrichment of the KOs in the samples from different strains

  10. Supplementary Table 10 (29,313 KB)

    The enrichment of the KOs in the samples under different diets

  11. Supplementary Table 11 (32,437 KB)

    The enrichment of the function pathways in different subgroups

  12. Supplementary Table 12 (19,604 KB)

    The enrichment of the KOs in the samples under different genders

  13. Supplementary Table 13 (41,964 KB)

    The mapping rate of the published metatranscriptomic data to the 2.6M catalogue

  14. Supplementary Table 14 (35,576 KB)

    The mapping rate of the published metagenomics data to the 2.6M gene set

Additional data