Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Alterations of the human gut microbiome in liver cirrhosis

Abstract

Liver cirrhosis occurs as a consequence of many chronic liver diseases that are prevalent worldwide. Here we characterize the gut microbiome in liver cirrhosis by comparing 98 patients and 83 healthy control individuals. We build a reference gene set for the cohort containing 2.69 million genes, 36.1% of which are novel. Quantitative metagenomics reveals 75,245 genes that differ in abundance between the patients and healthy individuals (false discovery rate < 0.0001) and can be grouped into 66 clusters representing cognate bacterial species; 28 are enriched in patients and 38 in control individuals. Most (54%) of the patient-enriched, taxonomically assigned species are of buccal origin, suggesting an invasion of the gut from the mouth in liver cirrhosis. Biomarkers specific to liver cirrhosis at gene and function levels are revealed by a comparison with those for type 2 diabetes and inflammatory bowel disease. On the basis of only 15 biomarkers, a highly accurate patient discrimination index is created and validated on an independent cohort. Thus microbiota-targeted biomarkers may be a powerful tool for diagnosis of different diseases.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Differentially abundant phyla in patients (n = 98) and healthy individuals (n = 83).
Figure 2: Differentially abundant MGS in patients (n = 123) and healthy individuals (n = 114).
Figure 3: PDI on the basis of gut microbial biomarkers.

Accession codes

Primary accessions

European Nucleotide Archive

Data deposits

The raw Illumina read data for all samples have been deposited in the European Bioinformatics Institute European Nucleotide Archive under accession number ERP005860.

References

  1. Fouts, D. E., Torralba, M., Nelson, K. E., Brenner, D. A. & Schnabl, B. Bacterial translocation and changes in the intestinal microbiome in mouse models of liver disease. J. Hepatol. 56, 1283–1292 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Cesaro, C. et al. Gut microbiota and probiotics in chronic liver diseases. Digest. Liver Dis. 43, 431–438 (2011)

    Article  Google Scholar 

  3. Wiest, R. & Garcia-Tsao, G. Bacterial translocation (BT) in cirrhosis. Hepatology 41, 422–433 (2005)

    CAS  Article  PubMed  Google Scholar 

  4. Nolan, J. P. The role of intestinal endotoxin in liver injury: a long and evolving history. Hepatology 52, 1829–1835 (2010)

    CAS  Article  PubMed  Google Scholar 

  5. Gill, S. R. et al. Metagenomic analysis of the human distal gut microbiome. Science 312, 1355–1359 (2006)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  6. Garcia-Tsao, G. & Wiest, R. Gut microflora in the pathogenesis of the complications of cirrhosis. Best Pract. Res. Clin. Gastroenterol. 18, 353–372 (2004)

    Article  PubMed  Google Scholar 

  7. Wiest, R., Krag, A. & Gerbes, A. Spontaneous bacterial peritonitis: recent guidelines and beyond. Gut 61, 297–310 (2012)

    CAS  Article  PubMed  Google Scholar 

  8. Bass, N. M. et al. Rifaximin treatment in hepatic encephalopathy. N. Engl. J. Med. 362, 1071–1081 (2010)

    CAS  Article  PubMed  Google Scholar 

  9. Benten, D. & Wiest, R. Gut microbiome and intestinal barrier failure–the “Achilles heel” in hepatology? J. Hepatol. 56, 1221–1223 (2012)

    Article  PubMed  Google Scholar 

  10. Yan, A. W. et al. Enteric dysbiosis associated with a mouse model of alcoholic liver disease. Hepatology 53, 96–105 (2011)

    CAS  Article  PubMed  Google Scholar 

  11. De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

  13. Chen, Y. et al. Characterization of fecal microbial communities in patients with liver cirrhosis. Hepatology 54, 562–572 (2011)

    Article  PubMed  Google Scholar 

  14. Nelson, K. E. et al. A catalog of reference genomes from the human microbiome. Science 328, 994–999 (2010)

    CAS  Article  PubMed  Google Scholar 

  15. Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)

    CAS  Article  ADS  PubMed  Google Scholar 

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

    Article  ADS  PubMed  Google Scholar 

  17. Turnbaugh, P. J. et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009)

    CAS  ADS  PubMed  Google Scholar 

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

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  19. Lepage, P. et al. Twin study indicates loss of interaction between microbiota and mucosa of patients with ulcerative colitis. Gastroenterology 141, 227–236 (2011)

    Article  PubMed  Google Scholar 

  20. Garrett, W. S. et al. Enterobacteriaceae act in concert with the gut microbiota to induce spontaneous and maternally transmitted colitis. Cell Host Microbe 8, 292–300 (2010)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Wen, L. et al. Innate immunity and intestinal microbiota in the development of type 1 diabetes. Nature 455, 1109–1113 (2008)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  ADS  PubMed  Google Scholar 

  23. Vijay-Kumar, M. et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 328, 228–231 (2010)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  24. Karlsson, F. H. et al. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nature Commun. 3, 1245 (2012)

    Article  ADS  CAS  Google Scholar 

  25. The Human Microbiome Project Consortium A framework for human microbiome research. Nature 486, 215–221 (2012)

    Article  ADS  CAS  Google Scholar 

  26. The Human Microbiome Project Consortium Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012)

    Article  ADS  CAS  PubMed Central  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nature Biotechnol http://dx.doi.org/10.1038/nbt.2939 (2014)

  31. Albertsen, M. et al. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nature Biotechnol. 31, 533–538 (2013)

    CAS  Article  Google Scholar 

  32. Zoetendal, E. G. et al. The human small intestinal microbiota is driven by rapid uptake and conversion of simple carbohydrates. ISME J. 6, 1415–1426 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Bauer, T. M. et al. Small intestinal bacterial overgrowth in human cirrhosis is associated with systemic endotoxemia. Am. J. Gastroenterol. 97, 2364–2370 (2002)

    Article  PubMed  Google Scholar 

  34. Chen, T. et al. The Human Oral Microbiome Database: a web accessible resource for investigating oral microbe taxonomic and genomic information. Database 2010, baq013 (2010)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Pagani, I. et al. The Genomes OnLine Database (GOLD) v.4: status of genomic and metagenomic projects and their associated metadata. Nucleic Acids Res. 40, D571–D579 (2012)

    CAS  Article  PubMed  Google Scholar 

  36. Saarela, M., Mogensen, G., Fonden, R., Matto, J. & Mattila-Sandholm, T. Probiotic bacteria: safety, functional and technological properties. J. Biotechnol. 84, 197–215 (2000)

    CAS  Article  PubMed  Google Scholar 

  37. Merritt, M. E. & Donaldson, J. R. Effect of bile salts on the DNA and membrane integrity of enteric bacteria. J. Med. Microbiol. 58, 1533–1541 (2009)

    CAS  Article  PubMed  Google Scholar 

  38. Marchandin, H. et al. Prosthetic joint infection due to Veillonella dispar. Eur. J Clin. Microbiol. Infect. Dis. 20, 340–342 (2001)

    CAS  Article  PubMed  Google Scholar 

  39. Hwang, J. J., Lau, Y. J., Hu, B. S., Shi, Z. Y. & Lin, Y. H. Haemophilus parainfluenzae and Fusobacterium necrophorum liver abscess: a case report. J. Microbiol. Immunol. Infect. 35, 65–67 (2002)

    PubMed  Google Scholar 

  40. Xu, M. et al. Changes of fecal Bifidobacterium species in adult patients with hepatitis B virus-induced chronic liver disease. Microb. Ecol. 63, 304–313 (2012)

    Article  PubMed  Google Scholar 

  41. Greenblum, S., Turnbaugh, P. J. & Borenstein, E. Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc. Natl Acad. Sci. USA 109, 594–599 (2012)

    CAS  Article  ADS  PubMed  Google Scholar 

  42. Krieger, D. et al. Manganese and chronic hepatic encephalopathy. Lancet 346, 270–274 (1995)

    CAS  Article  PubMed  Google Scholar 

  43. Ferenci, P., Schafer, D. F., Kleinberger, G., Hoofnagle, J. H. & Jones, E. A. Serum levels of gamma-aminobutyric-acid-like activity in acute and chronic hepatocellular disease. Lancet ii, 811–814 (1983)

    Article  Google Scholar 

  44. Minuk, G. Y., Winder, A., Burgess, E. D. & Sarjeant, E. J. Serum gamma-aminobutyric acid (GABA) levels in patients with hepatic encephalopathy. Hepatogastroenterology 32, 171–174 (1985)

    CAS  PubMed  Google Scholar 

  45. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  47. Li, M. et al. Symbiotic gut microbes modulate human metabolic phenotypes. Proc. Natl Acad. Sci. USA 105, 2117–2122 (2008)

    CAS  Article  ADS  PubMed  PubMed Central  Google Scholar 

  48. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Li, R. et al. De novo assembly of human genomes with massively parallel short read sequencing. Genome Res. 20, 265–272 (2010)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Li, R. et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25, 1966–1967 (2009)

    CAS  Article  PubMed  Google Scholar 

  51. Noguchi, H., Park, J. & Takagi, T. MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res. 34, 5623–5630 (2006)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. Powell, S. et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–D289 (2012)

    CAS  Article  PubMed  Google Scholar 

  53. Faust, K. et al. Microbial co-occurrence relationships in the human microbiome. PLOS Comput. Biol. 8, e1002606 (2012)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genet. 38, 904–909 (2006)

    CAS  Article  PubMed  Google Scholar 

  55. Ding, C. & Peng, H. Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3, 185–205 (2005)

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Program on Key Basic Research Project (2013CB531401), the National Natural Science Foundation of China (81301475 and 81330011), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (81121002), the Technology Group Project for Infectious Disease Control of Zhejiang Province (2009R50041) and the Metagenopolis grant ANR-11-DPBS-0001. We thank Q. Cao, K. Su, J. Shao and A. Ghozlane for help with data computation, and H. Zhang, H. Lu, Q. Bao, J. Ge, J. Jiang, Z. Ren and M. Ye for assistance with sample collection. We are thankful to the MetaHIT consortium for generating the gut gene set and the Human Microbiome Project for generating the reference genomes from human gut microbes.

Author information

Authors and Affiliations

Authors

Contributions

L.J.L., S.D.E., S.S.Z. and N.Q. designed the project. L.J.L., S.P.K. and N.Q. managed the project. F.L.Y., N.Q., Y.F.C., J.G., G.R.Q., X.J.H. and B.W.Z. collected samples and performed clinical study. J.G., Y.T.C. and W.X. performed DNA extraction experiments. Y.J., L.J.W., J.W.Z. and S.J.N. performed library construction and sequencing. L.J.L. and S.D.E. designed the analysis. N.Q., A.L., E.P., E.L.C., L.L., N.P., P.L., J.M.B., C.H.Y. and W.C.D. analysed the data. A.L. and N.Q. did the functional annotation analyses. L.S., E.P., E.L.C. and A.L. analysed the statistics. N.Q., F.L.Y., L.S. and E.P. wrote the paper. L.J.L. and S.D.E. revised the paper.

Corresponding authors

Correspondence to S. Dusko Ehrlich, Shusen Zheng or Lanjuan Li.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Venn diagram comparing the current major human microbiome gene set and the results of a principal component analysis of biomarkers distributed between patients with liver cirrhosis and healthy controls.

a, Venn diagram of the four currently available major human microbiome gene sets. The total gene number in each gene set and the overlapping areas are indicated. b, Venn diagram of the three major human gut gene sets (LC, liver cirrhosis gene set; T2D, type 2 diabetes gene set; MetaHIT, MetaHIT gene set; HMP, HMP gene set). c, Visualization of the principal component analysis results for the liver-cirrhosis-associated genes that differed significantly in the discovery cohort (FDR < 0.0001, Wilcoxon rank-sum test adjusted for multiple testing). The principal component analysis is built here using these genes in the validation cohort (25 patients with liver cirrhosis in red, 31 healthy controls in green).

Extended Data Figure 2 Phylogenetic abundance at the phylum, genus and species levels from liver cirrhosis and healthy control samples.

a, Phylogenetic abundance variation box plot at the phylum level and the 30 most abundant phylotypes at the genus and species levels in the healthy controls are shown. Red, green, blue, turquoise and purple represent Bacteroidetes, Firmicutes, Proteobacteria, Actinobacteria and other phyla, respectively. The colour of each genus and species corresponds with the colour of its respective phylum. b, Phylogenetic abundance variation box plot at the phylum level and the 30 most abundant phylotypes at the genus and species levels in the liver cirrhosis are shown (see Methods for the calculations). The boxes represent the interquartile range, from the first and third quartiles, and the inside line represents the median. The whiskers denote the lowest and highest values within an interquartile range of 1.53 from the first and third quartiles. The circles represent outliers beyond the whiskers.

Extended Data Figure 3 MGS enriched in healthy Chinese individuals (n = 114) are present in Danish individuals (n = 292).

Presence and abundance of 50 ‘tracer’ genes for each species; genes are in rows; abundance is indicated by colour gradient (white, not detected; red, most abundant). Individuals, ordered by increasing gene count, are in columns. Significance of correlation of species abundance (computed as mean abundance of the tracer genes) and gene count (q value, FDR adjusted) is given. Species in the Chinese cohort that were identical to those previously found, as correlated with the gene diversity in the Danish cohort27, are highlighted in red. Left, the Chinese healthy cohort. Right, the Danish obesity cohort.

Extended Data Figure 4 Massive changes in the gut microbiome in liver cirrhosis.

Top left, healthy individuals have more gut microbial genes than patients with liver cirrhosis. Gene count was computed after downsizing the mapped reads to a level of 6.2 million (ref. 27). The significance of the difference was computed using a Student’s t-test. Bottom, abundance of patient-enriched species (n = 28) in patients with liver cirrhosis (n = 98) and healthy controls (n = 83). The relative abundance of each patient-enriched species was computed as a sum of the abundances of all the genes assigned to it divided by the sum of the abundances of all gut microbial genes in each patient, which is equal to 1 in the normalized data set. Bar length indicates the relative abundance of a given species depicted by a different colour. Patients were ordered by the total patient-enriched species abundance; LPA and HPA quartiles (n = 24) are separated by red vertical lines. Top right, oral species are frequent in patients with liver cirrhosis. MGS enriched in healthy controls are largely not assigned to a species level, while those enriched in patients with liver cirrhosis are largely assigned to a species level and are mostly of oral origin (see Methods for species assignment).

Extended Data Figure 5 The distribution of eggNOG orthologue group and KEGG functional categories for liver-cirrhosis-related markers.

a, Comparison between the liver-cirrhosis-enriched and control-enriched eggNOG orthologue group markers for 24 eggNOG orthologue group functional categories shown by number. b, Comparison between the liver-cirrhosis-enriched and control-enriched eggNOG orthologue group markers for 24 eggNOG orthologue group functional categories shown by percentage. c, Comparison between the liver-cirrhosis-enriched and control-enriched KEGG orthologue group markers for each KEGG functional category shown by number. d, Comparison between the liver-cirrhosis-enriched and control-enriched KEGG orthologue group markers for each KEGG functional category shown by percentage.

Extended Data Figure 6 A comparison of the gene markers for the different groups.

a, Venn diagram showing a gene marker comparison of case-enriched gene markers from the liver cirrhosis, T2D and IBD studies. b, Venn diagram showing a gene marker comparison of control-enriched gene markers from the liver cirrhosis, T2D and IBD studies. c, The length of the bar (y axis) represents the number of genes; the P value in the related range is shown on the x axis. The pink and light green bars show genes involved in type 2 diabetes and liver cirrhosis, respectively. Inset, the log P value of the gene markers between the two studies.

Extended Data Figure 7 The distribution of eggNOG functional categories for case-enriched and control-enriched gene markers in liver cirrhosis only, T2D only and the liver cirrhosis/T2D groups.

a, Comparison of the eggNOG orthologue group functional categories for case-enriched gene markers shown by number. b, Comparison of the eggNOG orthologue group functional categories for case-enriched gene markers shown by percentage. c, Comparison of the eggNOG orthologue group functional categories for the control-enriched gene markers shown by number. d, Comparison of the eggNOG orthologue group functional categories for the control-enriched gene markers shown by percentage.

Extended Data Figure 8 The distribution of the KEGG functional categories for case-enriched and control-enriched gene markers in liver cirrhosis only, T2D only or the liver cirrhosis/T2D group.

a, Comparison of the KEGG pathway categories for the case-enriched gene markers shown by number. b, Comparison of the KEGG pathway categories for the case-enriched gene markers shown by percentage. c, Comparison of the KEGG pathway categories for the control-enriched gene markers shown by number. d, Comparison of the KEGG pathway categories for the control-enriched gene markers shown by percentage.

Extended Data Figure 9 Estimating the optimum number of markers and establishing the taxonomic assignment of MGS.

a, Comparison of the case-enriched gene markers. b, Comparison of the control-enriched gene markers. c, The mRMR method was used to identify the liver-cirrhosis-associated markers. Sequential subsets were generated at five-marker intervals. For each subset, the error rate was estimated using a leave-one-out cross-validation of a linear discrimination classifier. The optimum (highest value of the Matthews correlation coefficient) subset contains 15 gene markers. d, The study included a discovery and a validation phase. Volunteers for both phases were recruited in the same hospital. Both direct read mapping and de novo assembly were performed for each sample. A taxonomy profiling table was established for taxonomy analysis. A novel gut gene set was established, and annotated. Identification of the MGS, finding markers and validating markers is also shown. e, MGS enriched in Chinese patients with liver cirrhosis and healthy individuals. Species-level assignment was deduced from the best BlastN hits of genes from a given MGS at thresholds of the average of more than 95% identity and more than 90% overlap with genes from a sequenced genome. For MGS where these thresholds were not reached, an assignment was attributed at the lowest taxonomy level where at least 80% of the genes had the same best hit BlastP taxonomy; in all cases these criteria held true at higher taxonomic levels. f, Taxonomic assignments of 58 species related to gut gene richness in a Danish cohort27.

Extended Data Figure 10 Phylogenetic abundance of healthy controls in the discovery stage in the liver cirrhosis and T2D studies.

The relative abundance of top bacterial phylotypes at the phylum, genus and species levels, respectively, in the liver cirrhosis study (top three panels) and in the T2D study (bottom three panels).

Supplementary information

Supplementary Information

This zipped file contains Supplementary Tables 1-15 and a Supplementary Table guide. (ZIP 2789 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Qin, N., Yang, F., Li, A. et al. Alterations of the human gut microbiome in liver cirrhosis. Nature 513, 59–64 (2014). https://doi.org/10.1038/nature13568

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature13568

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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