Identifying Differentially Abundant Metabolic Pathways in Metagenomic Datasets

  • Bo Liu
  • Mihai Pop
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6053)

Abstract

Enabled by rapid advances in sequencing technology, metagenomic studies aim to characterize entire communities of microbes bypassing the need for culturing individual bacterial members. One major goal of such studies is to identify specific functional adaptations of microbial communities to their habitats. Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic data-sets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge. We show that MetaPath outperforms other common approaches when evaluated on simulated datasets. We also demonstrate the power of our methods in analyzing two, publicly available, metagenomic datasets: a comparison of the gut microbiome of obese and lean twins; and a comparison of the gut microbiome of infant and adult subjects. We demonstrate that the subpathways identified by our method provide valuable insights into the biological activities of the microbiome.

Keywords

Metagenomics Metabolic Pathway 

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References

  1. 1.
    Riesenfeld, C.S., Schloss, P.D., Handelsman, J.: Metagenomics: genomic analysis of microbial communities. Annual review of genetics 38, 525–552 (2004)CrossRefGoogle Scholar
  2. 2.
    Beja, O., Aravind, L., Koonin, E.V., Suzuki, M.T., Hadd, A., Nguyen, L.P., Jovanovich, S.B., Gates, C.M., Feldman, R.A., Spudich, J.L., Spudich, E.N., DeLong, E.F.: Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science 289, 1902–1906 (2000)CrossRefGoogle Scholar
  3. 3.
    Turnbaugh, P.J., Hamady, M., Yatsunenko, T., Cantarel, B.L., Duncan, A., Ley, R.E., Sogin, M.L., Jones, W.J., Roe, B.A., Affourtit, J.P., Egholm, M., Henrissat, B., Heath, A.C., Knight, R., Gordon, J.I.: A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009)CrossRefGoogle Scholar
  4. 4.
    Tatusov, R.L., Galperin, M.Y., Natale, D.A., Koonin, E.V.: The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic acids research 28, 33–36 (2000)CrossRefGoogle Scholar
  5. 5.
    Meyer, F., Paarmann, D., D’Souza, M., Olson, R., Glass, E.M., Kubal, M., Paczian, T., Rodriguez, A., Stevens, R., Wilke, A., Wilkening, J., Edwards, R.A.: The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC bioinformatics 9, 386 (2008)CrossRefGoogle Scholar
  6. 6.
    Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., Yamanishi, Y.: KEGG for linking genomes to life and the environment. Nucleic acids research 36, D480–D484 (2008)CrossRefGoogle Scholar
  7. 7.
    Kurokawa, K., Itoh, T., Kuwahara, T., Oshima, K., Toh, H., Toyoda, A., Takami, H., Morita, H., Sharma, V.K., Srivastava, T.P., Taylor, T.D., Noguchi, H., Mori, H., Ogura, Y., Ehrlich, D.S., Itoh, K., Takagi, T., Sakaki, Y., Hayashi, T., Hattori, M.: Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res. 14, 169–181 (2007)CrossRefGoogle Scholar
  8. 8.
    Rodriguez-Brito, B., Rohwer, F., Edwards, R.A.: An application of statistics to comparative metagenomics. BMC bioinformatics 7, 162 (2006)CrossRefGoogle Scholar
  9. 9.
    White, J.R., Nagarajan, N., Pop, M.: Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS computational biology 5, e1000352 (2009)CrossRefGoogle Scholar
  10. 10.
    Gianoulis, T.A., Raes, J., Patel, P.V., Bjornson, R., Korbel, J.O., Letunic, I., Yamada, T., Paccanaro, A., Jensen, L.J., Snyder, M., Bork, P., Gerstein, M.B.: Quantifying environmental adaptation of metabolic pathways in metagenomics. Proc. Natl. Acad. Sci. USA 106, 1374–1379 (2009)CrossRefGoogle Scholar
  11. 11.
    Tringe, S.G., von Mering, C., Kobayashi, A., Salamov, A.A., Chen, K., Chang, H.W., Podar, M., Short, J.M., Mathur, E.J., Detter, J.C., Bork, P., Hugenholtz, P., Rubin, E.M.: Comparative metagenomics of microbial communities. Science 308, 554–557 (2005)CrossRefGoogle Scholar
  12. 12.
    Sharon, I., Pati, I., Markowitz, V.M., Pinter, R.Y.: A Statistical Framework for the Functional Analysis of Metagenomes. In: Batzoglou, S. (ed.) RECOMB 2009. LNCS, vol. 5541, pp. 496–511. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Ideker, T., Ozier, O., Schwikowski, B., Siegel, A.F.: Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18(suppl. 1), S233–S240 (2002)Google Scholar
  14. 14.
    Dittrich, M.T., Klau, G.W., Rosenwald, A., Dandekar, T., Muller, T.: Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 24, i223–i231 (2008)CrossRefGoogle Scholar
  15. 15.
    Storey, J.D., Tibshirani, R.: Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. USA 100, 9440–9445 (2003)MATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Turnbaugh, P.J., Ley, R.E., Mahowald, M.A., Magrini, V., Mardis, E.R., Gordon, J.I.: An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)CrossRefGoogle Scholar
  17. 17.
    Gallistl, S., Sudi, K., Mangge, H., Erwa, W., Borkenstein, M.: Insulin is an independent correlate of plasma homocysteine levels in obese children and adolescents. Diabetes Care 23, 1348–1352 (2000)CrossRefGoogle Scholar
  18. 18.
    Eckel, R.H.: Obesity and heart disease: a statement for healthcare professionals from the Nutrition Committee, American Heart Association. Circulation 96, 3248–3250 (1997)Google Scholar
  19. 19.
    Borson-Chazot, F., Harthe, C., Teboul, F., Labrousse, F., Gaume, C., Guadagnino, L., Claustrat, B., Berthezene, F., Moulin, P.: Occurrence of hyperhomocysteinemia 1 year after gastroplasty for severe obesity. J. Clin. Endocrinol. Metab. 84, 541–545 (1999)CrossRefGoogle Scholar
  20. 20.
    Mojtabai, R.: Body mass index and serum folate in childbearing age women. Eur. J. Epidemiol. 19, 1029–1036 (2004)CrossRefGoogle Scholar
  21. 21.
    Tungtrongchitr, R., Pongpaew, P., Tongboonchoo, C., Vudhivai, N., Changbumrung, S., Tungtrongchitr, A., Phonrat, B., Viroonudomphol, D., Pooudong, S., Schelp, F.P.: Serum homocysteine, B12 and folic acid concentration in Thai overweight and obese subjects. Int. J. Vitam. Nutr. Res. 73, 8–14 (2003)CrossRefGoogle Scholar
  22. 22.
    Hirsch, S., Poniachick, J., Avendano, M., Csendes, A., Burdiles, P., Smok, G., Diaz, J.C., de la Maza, M.P.: Serum folate and homocysteine levels in obese females with non-alcoholic fatty liver. Nutrition 21, 137–141 (2005)CrossRefGoogle Scholar
  23. 23.
    Fokkema, M.R., Woltil, H.A., van Beusekom, C.M., Schaafsma, A., Dijck-Brouwer, D.A., Muskiet, F.A.: Plasma total homocysteine increases from day 20 to 40 in breastfed but not formula-fed low-birthweight infants. Acta Paediatr. 91, 507–511 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Bo Liu
    • 1
  • Mihai Pop
    • 1
  1. 1.Center for Bioinformatics and Computational Biology, Institute for Advanced Computer Studies, Department of Computer ScienceUniveristy of MarylandCollege ParkUSA

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