Understanding the representative gut microbiota dysbiosis in metformin-treated Type 2 diabetes patients using genome-scale metabolic modeling

Frontiers in Physiology

Volumn 9, Issue JUN, 2018, Pages

  Rosario, Dorines ^a   Benfeitas, Rui ^a   Bidkhori, Gholamreza ^a   Zhang, Cheng ^a   Uhlen, Mathias ^a   Shoaie, Saeed ^b,c   Mardinoglu, Adil ^a,d  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b KING'S COLLEGE LONDON   (United Kingdom)

^c KAROLINSKA INSTITUTE   (Sweden)

^d CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

Dysbiosis; Genome scale metabolic models; Gut microbiota; Host microbiome interactions; Systems biology

Indexed keywords

ACETIC ACID; AMINO ACID; BUTYRIC ACID; HISTIDINE; IRON; LIPOPOLYSACCHARIDE; METFORMIN; OXYGEN; PHENYLALANINE; PYRIDOXINE; SHORT CHAIN FATTY ACID; SQUALENE; TYROSINE;

AEROBIC METABOLISM; AKKERMANSIA MUCINIPHILA; AMINO ACID METABOLISM; ANAEROBIC METABOLISM; ARTICLE; BACTERIAL GROWTH; BACTERIAL METABOLISM; BIOMASS PRODUCTION; CELL METABOLISM; CHOLESTEROL SYNTHESIS; CLOSTRIDIUM; CONTROLLED STUDY; DYSBIOSIS; ESCHERICHIA COLI; FATTY ACID SYNTHESIS; GLUCOSE METABOLISM; GROWTH INHIBITION; HUMAN; INTESTINE FLORA; INTESTINIBACTER BARTLETTII; MACRONUTRIENT; NON INSULIN DEPENDENT DIABETES MELLITUS; NONHUMAN; NUTRIENT AVAILABILITY; NUTRIENT UPTAKE; OXIDATIVE PHOSPHORYLATION; PREVALENCE; SUBDOLIGRANULUM VARIABILE;

EID: 85048971156     PISSN: None     EISSN: 1664042X     Source Type: Journal    
DOI: 10.3389/fphys.2018.00775     Document Type: Article

References (50)

1. Agren, R., Liu, L., Shoaie, S., Vongsangnak, W., Nookaew, I., and Nielsen, J. (2013). The RAVEN toolbox and its use for generating a genome-scale metabolic model for Penicillium chrysogenum. PLoS Comput. Biol. 9:e1002980. doi: 10.1371/journal.pcbi.1002980
2. Al Khodor, S., Reichert, B., and Shatat, I. F. (2017). The microbiome and blood pressure: can microbes regulate our blood pressure? Front. Pediatr. 5:138. doi: 10.3389/fped.2017.00138
3. Baek, J. M., Mazumdar, S., Lee, S. W., Jung, M. Y., Lim, J. H., Seo, S. W., et al. (2013). Butyrate production in engineered Escherichia coli with synthetic scaffolds. Biotechnol. Bioeng. 110, 2790-2794. doi: 10.1002/bit.24925
4. Benfeitas, R., Uhlen, M., Nielsen, J., and Mardinoglu, A. (2017). New challenges to study heterogeneity in cancer redox metabolism. Front. Cell Develop. Biol. 5:65. doi: 10.3389/fcell.2017.00065
5. Berríos-Rivera, S. J., Bennett, G. N., and San, K.-Y. (2002). The effect of increasing NADH availability on the redistribution of metabolic fluxes in Escherichia coli chemostat cultures. Metab. Eng. 4, 230-237. doi: 10.1006/mben.2002.0228
6. Braun, V., and Braun, M. (2002). Iron transport and signaling in Escherichia coli. FEBS Lett. 529, 78-85
7. Brillat-Savarin, A. (1826). You are what you eat. Physiol. Gout 32, 243-245. doi: 10.1002/cind.786-16.x
8. Bueno, E., Mesa, S., Bedmar, E. J., Richardson, D. J., and Delgado, M. J. (2012). Bacterial adaptation of respiration from oxic to microoxic and anoxic conditions: redox control. Antioxid. Redox Signal. 16, 819-852. doi: 10.1089/ars.2011.4051
9. de la Cuesta-Zuluaga, J., Mueller, N. T., Corrales-Agudelo, V., Velasquez-Mejia, E. P., Carmona, J. A., Abad, J. M., et al. (2017). Metformin is associated with higher relative abundance of mucin-degrading Akkermansia muciniphila and several short-chain fatty acid-producing microbiota in the gut. Diabetes Care 40, 54-62. doi: 10.2337/dc16-1324
10. Derrien, M., Vaughan, E. E., Plugge, C. M., and De Vos, W. M. (2004). Akkermansia muciniphila gen. nov., sp. nov., a human intestinal mucin-degrading bacterium. Int. J. Syst. Evol. Microbiol. 54, 1469-1476. doi: 10.1099/ijs.0.02873-0
11. Díaz, E., Ferrández, A., Prieto, M. A., and García, J. L. (2001). Biodegradation of aromatic compounds by Escherichia coli. Microbiol. Mol. Biol. Rev. 65, 523-569. doi: 10.1128/MMBR.65.4.523
12. Donaldson, G. P., Lee, S. M., and Mazmanian, S. K. (2015). Gut biogeography of the bacterial microbiota. Nat. Rev. Microbiol. 14, 20-32. doi: 10.1038/nrmicro3552
13. Edwards, J. S., Ibarra, R. U., and Palsson, B. O. (2001). In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 19, 125-130. doi: 10.1038/84379
14. Eker, S., Krummenacker, M., Shearer, A. G., Tiwari, A., Keseler, I. M., Talcott, C., et al. (2013). Computing minimal nutrient sets from metabolic networks via linear constraint solving. BMC Bioinformatics 14:114. doi: 10.1186/1471-2105-14-114
15. Feist, A. M., Henry, C. S., Reed, J. L., Krummenacker, M., Joyce, A. R., Karp, P. D., et al. (2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3:121. doi: 10.1038/msb4100155
16. Forslund, K., Hildebrand, F., Nielsen, T., Falony, G., Le Chatelier, E., Sunagawa, S., et al. (2015). Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262-266. doi: 10.1038/nature15766
17. Fuchs, G., Boll, M., and Heider, J. (2011). Microbial degradation of aromatic compounds-from one strategy to four. Nat. Rev. Microbiol. 9, 803-816. doi: 10.1038/nrmicro2652
18. Henry, C. S., Dejongh, M., Best, A. A., Frybarger, P. M., Linsay, B., and Stevens, R. L. (2010). High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977-982. doi: 10.1038/nbt.1672
19. Hyotylainen, T., Jerby, L., Petaja, E. M., Mattila, I., Jantti, S., Auvinen, P., et al. (2016). Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease. Nat. Commun. 7:8994. doi: 10.1038/ncomms9994
20. Imielinski, M., Belta, C., Rubin, H., and Halasz, A. (2006). Systematic analysis of conservation relations in Escherichia coli genome-scale metabolic network reveals novel growth media. Biophys. J. 90, 2659-2672. doi: 10.1529/biophysj.105.069278
21. Jahng, J., Jung, I. S., Choi, E. J., Conklin, J. L., and Park, H. (2012). The effects of methane and hydrogen gases produced by enteric bacteria on ileal motility and colonic transit time. Neurogastroenterol. Motil. 24, 185-191. doi: 10.1111/j.1365-2982.2011.01819.x
22. Ji, B., and Nielsen, J. (2015). From next-generation sequencing to systematic modeling of the gut microbiome. Front. Genet. 6:219. doi: 10.3389/fgene.2015.00219
23. Kaelin, W. G. Jr. (2005). The concept of synthetic lethality in the context of anticancer therapy. Nat. Rev. Cancer 5, 689-698. doi: 10.1038/nrc1691
24. Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergstrom, G., Behre, C. J., Fagerberg, B., et al. (2013). Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99-103. doi: 10.1038/nature12198
25. Keseler, I. M., Mackie, A., Peralta-Gil, M., Santos-Zavaleta, A., Gama-Castro, S., Bonavides-Martínez, C., et al. (2013). EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 41, 605-612. doi: 10.1093/nar/gks1027
26. Koppenol, W. H. (1993). The centennial of the Fenton reaction. Free Radic. Biol. Med. 15, 645-651
27. Lee, S., Zhang, C., Arif, M., Liu, Z., Benfeitas, R., Bidkhori, G., et al. (2018). TCSBN: a database of tissue and cancer specific biological networks. Nucleic Acids Res. 46, D595-D600. doi: 10.1093/nar/gkx994
28. Lee, S., Zhang, C., Liu, Z., Klevstig, M., Mukhopadhyay, B., Bergentall, M., et al. (2017). Network analyses identify liver-specific targets for treating liver diseases. Mol. Syst. Biol. 13:938. doi: 10.15252/msb.20177703
29. Lewis, S., and Cochrane, S. (2007). Alteration of sulfate and hydrogen metabolism in the human colon by changing intestinal transit rate. Am. J. Gastroenterol. 102, 624-633. doi: 10.1111/j.1572-0241.2006.01020.x
30. Louis, P., and Flint, H. J. (2009). Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol. Lett. 294, 1-8. doi: 10.1111/j.1574-6968.2009.01514.x
31. Lovley, D. R., and Phillips, E. J. (1986). Organic matter mineralization with reduction of ferric iron in anaerobic sediments. Appl. Environ. Microbiol. 51, 683-689
32. Magnusdottir, S., Heinken, A., Kutt, L., Ravcheev, D. A., Bauer, E., Noronha, A., et al. (2017). Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35, 81-89. doi: 10.1038/nbt.3703
33. Mardinoglu, A., Boren, J., and Smith, U. (2016). Confounding effects of metformin on the human gut microbiome in type 2 diabetes. Cell Metab. 23, 10-12. doi: 10.1016/j.cmet.2015.12.012
34. Mardinoglu, A., Boren, J., Smith, U., Uhlen, M., and Nielsen, J. (2018). Systems biology in hepatology: approaches and applications. Nat. Rev. Gastroenterol. Hepatol. 15, 365-377. doi: 10.1038/s41575-018-0007-8
35. Mardinoglu, A., and Nielsen, J. (2012). Systems medicine and metabolic modelling. J. Intern. Med. 271, 142-154. doi: 10.1111/j.1365-2796.2011.02493.x
36. Mardinoglu, A., Shoaie, S., Bergentall, M., Ghaffari, P., Zhang, C., Larsson, E., et al. (2015). The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol. Syst. Biol. 11:834. doi: 10.15252/msb.20156487
37. Moreno-Vivian, C., Cabello, P., Martinez-Luque, M., Blasco, R., and Castillo, F. (1999). Prokaryotic nitrate reduction: molecular properties and functional distinction among bacterial nitrate reductases. J. Bacteriol. 181, 6573-6584
38. O'Neil, N. J., Bailey, M. L., and Hieter, P. (2017). Synthetic lethality and cancer. Nat. Rev. Genet. 18, 613-623. doi: 10.1038/nrg.2017.47
39. Pratapa, A., Balachandran, S., and Raman, K. (2015). Fast-SL: an efficient algorithm to identify synthetic lethal sets in metabolic networks. Bioinformatics 31, 3299-3305. doi: 10.1093/bioinformatics/btv352
40. Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., et al. (2012). A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55-60. doi: 10.1038/nature11450
41. Schellenberger, J., Thiele, I., and Orth, J. D. (2012). Quantitative prediction of cellular metabolism with constraint-based models. Nat. Prot. 6, 1290-1307. doi: 10.1038/nprot.2011.308
42. Shoaie, S., Ghaffari, P., Kovatcheva-Datchary, P., Mardinoglu, A., Sen, P., Pujos-Guillot, E., et al. (2015). Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab. 22, 320-331. doi: 10.1016/j.cmet.2015.07.001
43. Shoaie, S., Karlsson, F., Mardinoglu, A., Nookaew, I., Bordel, S., and Nielsen, J. (2013). Understanding the interactions between bacteria in the human gut through metabolic modeling. Sci. Rep. 3:2532. doi: 10.1038/srep02532
44. Tremaroli, V., and Bäckhed, F. (2012). Functional interactions between the gut microbiota and host metabolism. Nature 489, 242-249. doi: 10.1038/nature11552
45. Turanli, B., Grøötli, M., Borén, J., Nielsen, J., Uhlen, M., Arga, K. Y., et al. (2018). Drug repositioning for effective prostate cancer treatment. Front. Physiol. 9:500. doi: 10.3389/fphys.2018.00500
46. Uhlen, M., Zhang, C., Lee, S., Sjostedt, E., Fagerberg, L., Bidkhori, G., et al. (2017). A pathology atlas of the human cancer transcriptome. Science 357:eaan2507. doi: 10.1126/science.aan2507
47. Varemo, L., Scheele, C., Broholm, C., Mardinoglu, A., Kampf, C., Asplund, A., et al. (2015). Proteome-and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes. Cell Rep. 11, 921-933. doi: 10.1016/j.celrep.2015.04.010
48. Wattam, A. R., Davis, J. J., Assaf, R., Boisvert, S., Brettin, T., Bun, C., et al. (2017). Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res. 45, D535-D542. doi: 10.1093/nar/gkw1017
49. Wu, H., Esteve, E., Tremaroli, V., Khan, M. T., Caesar, R., Mannerås-Holm, L., et al. (2017). Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med. 23, 850-858. doi: 10.1038/nm.4345
50. Zhang, C., and Hua, Q. (2016). Applications of genome-scale metabolic models in biotechnology and systems medicine. Front. Physiol. 6:413. doi: 10.3389/fphys.2015.00413