Cách tiếp cận chuyển hóa ngược trong việc cai sữa: nhận diện vi khuẩn đường ruột trẻ sơ sinh có lợi cho miễn dịch, khai thác chuyển hóa của chúng để tìm nguồn thức ăn prebiotic trong không gian sản phẩm tự nhiên

Microbiome - Tập 6 - Trang 1-18 - 2018
Samanta Michelini1, Biju Balakrishnan2, Silvia Parolo1, Alice Matone1, Jane A. Mullaney3,4, Wayne Young3,4, Olivier Gasser5, Clare Wall6, Corrado Priami1,7, Rosario Lombardo1, Martin Kussmann2,8
1The Microsoft Research, University of Trento Centre for Computational and Systems Biology, Rovereto, Italy
2The Liggins Institute, The University of Auckland, Auckland, New Zealand
3AgResearch, Food & Bio-based Products, Palmerston North, New Zealand
4Riddet Institute, Palmerston North, New Zealand
5Malaghan Institute of Medical Research, Wellington, New Zealand
6Discipline of Nutrition, School of Medical Science, University of Auckland, Auckland, New Zealand
7Department of Computer Science, University of Pisa, Pisa, Italy
8National Science Challenge “High Value Nutrition”, Auckland, New Zealand

Tóm tắt

Cai sữa là một giai đoạn có sự thay đổi sinh lý rõ rệt. Việc giới thiệu thực phẩm đặc và sự thay đổi trong việc tiêu thụ sữa đi kèm với những điều chỉnh đáng kể trong hệ tiêu hóa, miễn dịch, phát triển và hệ vi sinh vật. Để xác định số lượng ít hơn của các bệnh nhiễm trùng như một lợi ích sức khỏe mong muốn cho trẻ sơ sinh trong giai đoạn cai sữa, chúng tôi đã xác định vi khuẩn đường ruột trẻ sơ sinh trong silico (tức là, bằng cách khai thác dữ liệu công khai tiên tiến) như là những tác nhân tiềm năng mang lại lợi ích này. Chúng tôi sau đó đã nghiên cứu các yêu cầu của những vi khuẩn này đối với các chất chuyển hóa ngoại sinh như là các nguồn thức ăn prebiotic tiềm năng, mà sau đó đã được tìm kiếm trong không gian sản phẩm tự nhiên. Bằng cách khai thác tài liệu từ lĩnh vực công cộng và một phương pháp chuyển hóa ngược trong silico, chúng tôi đã xây dựng các hiệp hội giữa thực phẩm probiotic-prebiotic, có thể hướng dẫn việc cung cấp mục tiêu các vi khuẩn có lợi cho sức khỏe miễn dịch qua thực phẩm cai sữa; phân tích sự cạnh tranh và cộng sinh cho (prebiotic) dinh dưỡng giữa các vi khuẩn được chọn lọc; và chuyển dịch thông tin này thành thiết kế một loại thức ăn bổ sung thí nghiệm cho trẻ sơ sinh tham gia vào một thử nghiệm lâm sàng ban đầu. Trong nghiên cứu này, chúng tôi đã áp dụng một chiến lược nghiên cứu vi sinh vật hướng đến lợi ích nhằm nâng cao sức khỏe miễn dịch trong giai đoạn đầu đời. Chúng tôi đã mở rộng từ lĩnh vực dinh dưỡng “cổ điển” sang dinh dưỡng phân tử với mục tiêu xác định những chất dinh dưỡng, vi khuẩn và cơ chế dẫn đến việc cung cấp mục tiêu nhằm cải thiện sức khỏe miễn dịch cho trẻ sơ sinh trong giai đoạn cai sữa. Tại đây, chúng tôi trình bày cách tiếp cận dựa trên sinh học hệ thống mà chúng tôi đã sử dụng để thông báo cho chúng tôi về những sự kết hợp prebiotic hứa hẹn nhất được biết đến hỗ trợ sự phát triển của các vi khuẩn đường ruột lợi khuẩn (“probiotics”) trong ruột trẻ sơ sinh, từ đó thúc đẩy sự phát triển của hệ thống miễn dịch một cách tích cực.

Từ khóa


Tài liệu tham khảo

Singh RK, Chang H-W, Yan D, Lee KM, Ucmak D, Wong K, et al. Influence of diet on the gut microbiome and implications for human health. J Transl Med. 2017;15:73. https://doi.org/10.1186/s12967-017-1175-y. Yang I, Corwin EJ, Brennan PA, Jordan S, Murphy JR, Dunlop A. The infant microbiome: implications for infant health and neurocognitive development. Nurs Res. 2016;65:76–88. https://doi.org/10.1097/NNR.0000000000000133. Gerritsen J, Smidt H, Rijkers GT, de Vos WM. Intestinal microbiota in human health and disease: the impact of probiotics. Genes Nutr. 2011;6:209. Tamburini S, Shen N, Wu HC, Clemente JC. The microbiome in early life: implications for health outcomes. Nat Med. 2016;22:713–22. https://doi.org/10.1038/nm.4142. Hill CJ, Lynch DB, Murphy K, Ulaszewska M, Jeffery IB, O’Shea CA, et al. Evolution of gut microbiota composition from birth to 24 weeks in the INFANTMET cohort. Microbiome. 2017;5:4. https://doi.org/10.1186/s40168-016-0213-y. Wu H, Tremaroli V, Bäckhed F. Linking microbiota to human diseases: a systems biology perspective. Trends Endocrinol Metab. 2015;26:758–70. Groer MW, Luciano AA, Dishaw LJ, Ashmeade TL, Miller E, Gilbert JA. Development of the preterm infant gut microbiome: a research priority. Microbiome. 2014;2:38. https://doi.org/10.1186/2049-2618-2-38. Belkaid Y, Hand TW. Role of the microbiota in immunity and inflammation. Cell. 2014;157:121–41. https://doi.org/10.1016/j.cell.2014.03.011. Fallani M, Amarri S, Uusijarvi A, Adam R, Khanna S, Aguilera M, et al. Determinants of the human infant intestinal microbiota after the introduction of first complementary foods in infant samples from five European centres. Microbiology. 2011;157:1385–92. https://doi.org/10.1099/mic.0.042143-0. Koenig JE, Spor A, Scalfone N, Fricker AD, Stombaugh J, Knight R, et al. Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci U S A. 2011;108(Suppl 1):4578–85. https://doi.org/10.1073/pnas.1000081107. Rodríguez JM, Murphy K, Stanton C, Ross RP, Kober OI, Juge N, et al. The composition of the gut microbiota throughout life, with an emphasis on early life. Microb Ecol Heal Dis. 2015;26. https://doi.org/10.3402/mehd.v26.26050. Bäckhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datchary P, et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe. 2015;17:690–703. https://doi.org/10.1016/j.chom.2015.04.004. Nagpal R, Kurakawa T, Tsuji H, Takahashi T, Kawashima K, Nagata S, et al. Evolution of gut Bifidobacterium population in healthy Japanese infants over the first three years of life: a quantitative assessment. Sci Rep. 2017;7:10097. https://doi.org/10.1038/s41598-017-10711-5. Haarman M, Knol J. Quantitative real-time PCR assays to identify and quantify fecal Bifidobacterium species in infants receiving a prebiotic infant formula. Appl Environ Microbiol. 2005;71:2318–24. https://doi.org/10.1128/AEM.71.5.2318-2324.2005. Duranti S, Lugli GA, Mancabelli L, Armanini F, Turroni F, James K, et al. Maternal inheritance of bifidobacterial communities and bifidophages in infants through vertical transmission. Microbiome. 2017;5:66. https://doi.org/10.1186/s40168-017-0282-6. Makino H, Kushiro A, Ishikawa E, Kubota H, Gawad A, Sakai T, et al. Mother-to-infant transmission of intestinal Bifidobacterial strains has an impact on the early development of vaginally delivered Infant’s microbiota. PLoS One. 2013;8:e78331. https://doi.org/10.1371/journal.pone.0078331. Pozo-Rubio T, Mujico JR, Marcos A, Puertollano E, Nadal I, Sanz Y, et al. Immunostimulatory effect of faecal Bifidobacterium species of breast-fed and formula-fed infants in a peripheral blood mononuclear cell/Caco-2 co-culture system; 2018. https://doi.org/10.1017/S0007114511001656. Gonzalez R, Blancas A, Santillana R, Azaola A, Wacher C. Growth and final product formation by Bifidobacterium infantis in aerated fermentations. Appl Microbiol Biotechnol. 2004;65:606–10. https://doi.org/10.1007/s00253-004-1603-9. Shimamura S, Abe F, Ishibashi N, Miyakawa H, Yaeshima T, Araya T, et al. Relationship between oxygen sensitivity and oxygen metabolism of Bifidobacterium species. J Dairy Sci. 1992;75:3296–306. https://doi.org/10.3168/jds.S0022-0302(92)78105-3. Mendes-Soares H, Chia N. Community metabolic modeling approaches to understanding the gut microbiome: bridging biochemistry and ecology. Free Radic Biol Med. 2017;105:102–9. https://doi.org/10.1016/J.FREERADBIOMED.2016.12.017. Levy R, Carr R, Kreimer A, Freilich S, Borenstein E. NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation. BMC Bioinformatics. 2015;16:164. https://doi.org/10.1186/s12859-015-0588-y. Adamberg S, Sumeri I, Uusna R, Ambalam P, Kondepudi KK, Adamberg K, et al. Survival and synergistic growth of mixed cultures of bifidobacteria and lactobacilli combined with prebiotic oligosaccharides in a gastrointestinal tract simulator. Microb Ecol Health Dis. 2014;25. https://doi.org/10.3402/MEHD.V25.23062. Rios-Covian D, Gueimonde M, Duncan SH, Flint HJ, de los Reyes-Gavilan CG. Enhanced butyrate formation by cross-feeding between Faecalibacterium prausnitzii and Bifidobacterium adolescentis. FEMS Microbiol Lett. 2015;362:fnv176. https://doi.org/10.1093/femsle/fnv176. Moens F, Verce M, De Vuyst L. Lactate- and acetate-based cross-feeding interactions between selected strains of lactobacilli, bifidobacteria and colon bacteria in the presence of inulin-type fructans. Int J Food Microbiol. 2017;241:225–36. https://doi.org/10.1016/J.IJFOODMICRO.2016.10.019. De Vuyst L, Leroy F. Cross-feeding between bifidobacteria and butyrate-producing colon bacteria explains bifdobacterial competitiveness, butyrate production, and gas production. Int J Food Microbiol. 2011;149:73–80. https://doi.org/10.1016/j.ijfoodmicro.2011.03.003. Moens F, Weckx S, De Vuyst L. Bifidobacterial inulin-type fructan degradation capacity determines cross-feeding interactions between bifidobacteria and Faecalibacterium prausnitzii. Int J Food Microbiol. 2016;231:76–85. https://doi.org/10.1016/J.IJFOODMICRO.2016.05.015. Zhang C, Yin A, Li H, Wang R, Wu G, Shen J, et al. Dietary modulation of gut microbiota contributes to alleviation of both genetic and simple obesity in children. EBioMedicine. 2015;2:968–84. Holzapfel WH, Wood BJB. Lactic acid bacteria in contemporary perspective. In: The genera of lactic acid Bacteria. Boston, MA: Springer US; 1995. p. 1–6. https://doi.org/10.1007/978-1-4615-5817-0_1. Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E, Noronha A, et al. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat Biotechnol. 2016;35:81–9. https://doi.org/10.1038/nbt.3703. Lombardo R, Priami C. Graphical modeling meets systems pharmacology. Gene Regul Syst Bio. 2017;11:1177625017691937. https://doi.org/10.1177/1177625017691937. Mattarelli P, Biavati B, Holzapfel WH, Wood BJ. The Bifidobacteria and related organisms: biology, taxonomy, applications. 2017. Holdeman LV, Kelley RW, Moore WEC, Krieg NR, Holt JH. Bergey’s manual of systematic bacteriology: Springer; 1984. https://doi.org/10.1007/978-0-387-68489-5. Levy R, Borenstein E. Metabolic modeling of species interaction in the human microbiome elucidates community-level assembly rules. Proc Natl Acad Sci. 2013;110:12804–9. https://doi.org/10.1073/pnas.1300926110. Chesson P, Kuang JJ. The interaction between predation and competition. Nature. 2008;456:235–8. https://doi.org/10.1038/nature07248. Umu ÖCO, Rudi K, Diep DB. Modulation of the gut microbiota by prebiotic fibres and bacteriocins. Microb Ecol Health Dis. 2017;28:1348886. https://doi.org/10.1080/16512235.2017.1348886. Dobson A, Cotter PD, Ross RP, Hill C. Bacteriocin production: a probiotic trait? Appl Environ Microbiol. 2012;78:1–6. https://doi.org/10.1128/AEM.05576-11. Zheng J, Gänzle MG, Lin XB, Ruan L, Sun M. Diversity and dynamics of bacteriocins from human microbiome. Environ Microbiol. 2015;17:2133–43. https://doi.org/10.1111/1462-2920.12662. Milani C, Turroni F, Duranti S, Lugli GA, Mancabelli L, Ferrario C, et al. Genomics of the genus Bifidobacterium reveals species-specific adaptation to the glycan-rich gut environment. Appl Environ Microbiol. 2015;82:980–91. https://doi.org/10.1128/AEM.03500-15. Takemoto K, Aie K. Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions. BMC Bioinformatics. 2017;18:278. https://doi.org/10.1186/s12859-017-1696-7. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29–34. https://doi.org/10.1093/nar/27.1.29. Parracho H, McCartney AL, Gibson GR. Probiotics and prebiotics in infant nutrition. Proc Nutr Soc. 2007;66:405–11. https://doi.org/10.1017/S0029665107005678. Rolim PM, Rolim PM. Development of prebiotic food products and health benefits. Food Sci Technol. 2015;35:3–10. https://doi.org/10.1590/1678-457X.6546. Abeshu MA, Lelisa A, Geleta B. Complementary feeding: review of recommendations, feeding practices, and adequacy of homemade complementary food preparations in developing countries – lessons from Ethiopia. Front Nutr. 2016;3:41. https://doi.org/10.3389/fnut.2016.00041. Leaman R, Islamaj Dogan R, Lu Z. DNorm: disease name normalization with pairwise learning to rank. Bioinformatics. 2013;29:2909–17. https://doi.org/10.1093/bioinformatics/btt474. Wei C-H, Harris BR, Kao H-Y, Lu Z. tmVar: a text mining approach for extracting sequence variants in biomedical literature. Bioinformatics. 2013;29:1433–9. https://doi.org/10.1093/bioinformatics/btt156. Leaman R, Wei C-H, Lu Z. tmChem: a high performance approach for chemical named entity recognition and normalization. J Cheminform 2015;7 Suppl 1 Text mining for chemistry and the CHEMDNER track:S3. doi:https://doi.org/10.1186/1758-2946-7-S1-S3. Wei C-H, Kao H-Y, Lu Z. GNormPlus: an integrative approach for tagging genes, gene families, and protein domains. Biomed Res Int. 2015;2015:1–7. https://doi.org/10.1155/2015/918710. Wei C-H, Kao H-Y, Lu Z. PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res. 2013;41(Web Server issue):W518–22. https://doi.org/10.1093/nar/gkt441. NIH HMP Working Group TNHW, Peterson J, Garges S, Giovanni M, McInnes P, Wang L, et al. The NIH human microbiome project. Genome Res. 2009;19:2317–23. https://doi.org/10.1101/gr.096651.109. Li J, Jia H, Cai X, Zhong H, Feng Q, Sunagawa S, et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol. 2014;32:834–41. https://doi.org/10.1038/nbt.2942. Yatsunenko T, Rey FE, Manary MJ, Trehan I, Dominguez-Bello MG, Contreras M, et al. Human gut microbiome viewed across age and geography. Nature. 2012;486:222–7. https://doi.org/10.1038/nature11053. Resource Coordinators NCBI. Database resources of the National Center for biotechnology information. Nucleic Acids Res. 2017;45:D12–7. https://doi.org/10.1093/nar/gkw1071. Manning C, Surdeanu M, Bauer J, Finkel J, Bethard S, McClosky D. The Stanford CoreNLP natural language processing toolkit. Proc 52nd Annu Meet Assoc Comput Linguist Syst Demonstr. 2014:55–60. https://doi.org/10.3115/v1/P14-5010. VMH. http://vmh.uni.lu/. Accessed 22 Dec 2017. Henry CS, DeJongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28:977–82. https://doi.org/10.1038/nbt.1672. King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, et al. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 2016;44:D515–22. https://doi.org/10.1093/nar/gkv1049. Carr R, Borenstein E. NetSeed: a network-based reverse-ecology tool for calculating the metabolic interface of an organism with its environment. Bioinformatics. 2012;28:734–5. https://doi.org/10.1093/bioinformatics/btr721. Cao Y, Wang Y, Zheng X, Li F, Bo X. RevEcoR: an R package for the reverse ecology analysis of microbiomes. BMC Bioinformatics. 2016;17:294. https://doi.org/10.1186/s12859-016-1088-4. Borenstein E, Feldman MW. Topological signatures of species interactions in metabolic networks. J Comput Biol. 2009;16:191–200. https://doi.org/10.1089/cmb.2008.06TT. Lopez-Siles M, Khan TM, Duncan SH, Harmsen HJM, Garcia-Gil LJ, Flint HJ. Cultured representatives of two major Phylogroups of human colonic Faecalibacterium prausnitzii can utilize pectin, Uronic acids, and host-derived substrates for growth. Appl Environ Microbiol. 2012;78:420–8. https://doi.org/10.1128/AEM.06858-11. Altmann F, Kosma P, O’Callaghan A, Leahy S, Bottacini F, Molloy E, et al. Genome analysis and characterisation of the exopolysaccharide produced by Bifidobacterium longum subsp. longum 35624™. PLoS One. 2016;11:e0162983. https://doi.org/10.1371/journal.pone.0162983. von Ehrenstein OS, Heck JE, Park AS, Cockburn M, Escobedo L, Ritz B. In utero and early-life exposure to ambient air toxics and childhood brain tumors: a population-based case-control study in California, USA Environ Health Perspect 2016;124:1093–1099. doi:https://doi.org/10.1289/ehp.1408582. Edmunds SM, Ajizian SJ, Liguori A. Acute obtundation in a 9-month-old patient: ethanol ingestion. Pediatr Emerg Care. 2014;30:739–41. https://doi.org/10.1097/PEC.0000000000000240.