Skip to main content
SpringerLink
Log in

Human milk and infant formula differentially alters the microbiota composition and functional gene relative abundance in the small and large intestines in weanling rats

  • Original Contribution
  • Published:
European Journal of Nutrition Aims and scope Submit manuscript

Abstract

Purpose

Human breast milk is the optimal source of nutrients for growing infants. However, many circumstances can arise which preclude breast milk feeding, leading to the use of infant formula, including during the weaning period. Many diet-related effects are modulated by the gut microbiome. Therefore, we investigated the effect of human milk (HM) or infant formula (IF) on the gut microbiota in weanling rats.

Methods

The gut microbiota of weanling male Sprague–Dawley rats fed HM or IF for 28 days was analysed by shotgun metagenome sequencing. Caecal contents were analysed by liquid chromatography–mass spectrometry metabolomics.

Results

Numerous genera within the Proteobacteria phylum were relatively more abundant in the ileum, caecum, and colon of rats fed HM, including ileal Escherichia (HM = 9.6% ± 4.3 SEM; IF = 0.9% ± 0.3 SEM; P = 0.03). Other taxa that differed between HM- and IF-fed rats included Prevotella and Ruminococcus. Overall, more differences were observed in the ileum than the caecum and colon between rats fed HM and IF. For the rats fed IF, in the ileum, the relative abundance of Bifidobacterium was higher (HM = 1.7% ± 0.7 SEM; IF = 5.0% ± 1.5 SEM; P = 0.04) with gene functions related to carbohydrate and amino acid metabolism also decreased. In the caecum, metabolic features such as bile acids were elevated while amino sugars were also decreased.

Conclusion

Our results show that HM and IF composition differences are reflected in the gut microbiome composition and function in both the small and large intestines.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Garrido D, Dallas DC, Mills DA (2013) Consumption of human milk glycoconjugates by infant-associated bifidobacteria: mechanisms and implications. Microbiology (Reading, England) 159(Pt 4):649–664. https://doi.org/10.1099/mic.0.064113-0

    Article  CAS  Google Scholar 

  2. Maga EA, Desai PT, Weimer BC, Dao N, Kultz D, Murray JD (2012) Consumption of lysozyme-rich milk can alter microbial fecal populations. Appl Environ Microbiol 78(17):6153–6160. https://doi.org/10.1128/AEM.00956-12

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Maga EA, Weimer BC, Murray JD (2013) Dissecting the role of milk components on gut microbiota composition. Gut Microbes 4(2):136–139. https://doi.org/10.4161/gmic.23188

    Article  PubMed  PubMed Central  Google Scholar 

  4. Macpherson AJ, Uhr T (2004) Induction of protective IgA by intestinal dendritic cells carrying commensal bacteria. Science (New York, NY) 303(5664):1662–1665

    Article  CAS  Google Scholar 

  5. Gale C, Logan KM, Santhakumaran S, Parkinson JRC, Hyde MJ, Modi N (2012) Effect of breastfeeding compared with formula feeding on infant body composition: a systematic review and meta-analysis. Am J Clin Nutr 95(3):656–669. https://doi.org/10.3945/ajcn.111.027284

    Article  CAS  PubMed  Google Scholar 

  6. Stuebe A (2009) The risks of not breastfeeding for mothers and infants. Rev Obstet Gynecol 2(4):222–231

    PubMed  PubMed Central  Google Scholar 

  7. Timmerman HM, Rutten NBMM, Boekhorst J, Saulnier DM, Kortman GAM, Contractor N, Kullen M, Floris E, Harmsen HJM, Vlieger AM, Kleerebezem M, Rijkers GT (2017) Intestinal colonisation patterns in breastfed and formula-fed infants during the first 12 weeks of life reveal sequential microbiota signatures. Sci Rep 7(1):8327. https://doi.org/10.1038/s41598-017-08268-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Liu Z, Roy NC, Guo Y, Jia H, Ryan L, Samuelsson L, Thomas A, Plowman J, Clerens S, Day L, Young W (2016) Human breast milk and infant formulas differentially modify the intestinal microbiota in human infants and host physiology in rats. J Nutr 146(2):191–199. https://doi.org/10.3945/jn.115.223552

    Article  CAS  PubMed  Google Scholar 

  9. Bengtsson-Palme J, Hartmann M, Eriksson Karl M, Pal C, Thorell K, Larsson Dan Göran J, Nilsson Rolf H (2015) metaxa2: improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Mol Ecol Resour 15(6):1403–1414. https://doi.org/10.1111/1755-0998.12399

    Article  CAS  PubMed  Google Scholar 

  10. Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA (2008) The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform 9:386. https://doi.org/10.1186/1471-2105-9-386

    Article  CAS  Google Scholar 

  11. Glass EM, Wilkening J, Wilke A, Antonopoulos D (2010) Meyer F (2010) Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes. Cold Spring Harbor protocols 1:pdb prot5368. https://doi.org/10.1101/pdb.prot5368

    Article  Google Scholar 

  12. Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang H-Y, Cohoon M, de Crécy-Lagard V, Diaz N, Disz T, Edwards R, Fonstein M, Frank ED, Gerdes S, Glass EM, Goesmann A, Hanson A, Iwata-Reuyl D, Jensen R, Jamshidi N, Krause L, Kubal M, Larsen N, Linke B, McHardy AC, Meyer F, Neuweger H, Olsen G, Olson R, Osterman A, Portnoy V, Pusch GD, Rodionov DA, Rückert C, Steiner J, Stevens R, Thiele I, Vassieva O, Ye Y, Zagnitko O, Vonstein V (2005) The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res 33(17):5691–5702. https://doi.org/10.1093/nar/gki866

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Dixon P (2003) VEGAN, a package of R functions for community ecology. J Veg Sci 14(6):927–930. https://doi.org/10.1111/j.1654-1103.2003.tb02228.x

    Article  Google Scholar 

  14. Wheeler B, Torchiano M (2016) lmPerm: permutation tests for linear models. R package version 2.1.0.https://CRAN.Rproject.org/package=lmPerm

  15. Subbaraj AK, Kim YHB, Fraser K, Farouk MM (2016) A hydrophilic interaction liquid chromatography–mass spectrometry (HILIC–MS) based metabolomics study on colour stability of ovine meat. Meat Sci 117:163–172

    Article  CAS  PubMed  Google Scholar 

  16. Fraser K, Lane GA, Otter DE, Hemar Y, Quek S-Y, Harrison SJ, Rasmussen S (2013) Analysis of metabolic markers of tea origin by UHPLC and high resolution mass spectrometry. Food Res Int 53(2):827–835. https://doi.org/10.1016/j.foodres.2012.10.015

    Article  CAS  Google Scholar 

  17. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3):779–787

    Article  CAS  PubMed  Google Scholar 

  18. Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5(3):299–314

    Google Scholar 

  19. Dunn WB, Wilson ID, Nicholls AW, Broadhurst D (2012) The importance of experimental design and QC samples in large-scale and MS-driven untargeted metabolomic studies of humans. Bioanalysis 4(18):2249–64

    Article  CAS  PubMed  Google Scholar 

  20. Parsons HM, Ekman DR, Collette TW, Viant MR (2009) Spectral relative standard deviation: a practical benchmark in metabolomics. Analyst 134(3):478–485

    Article  CAS  PubMed  Google Scholar 

  21. Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic acids research 43(W1):W251–W257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Fan TW-M, Fiehn O, Goodacre R, Griffin JL (2007) Proposed minimum reporting standards for chemical analysis. Metabolomics 3(3):211–221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, Custodio DE, Abagyan R, Siuzdak G (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27(6):747–751

    Article  CAS  PubMed  Google Scholar 

  24. Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, Ojima Y, Tanaka K, Tanaka S, Aoshima K (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45(7):703–714

    Article  CAS  PubMed  Google Scholar 

  25. Romano KA, Vivas EI, Amador-Noguez D, Rey FE (2015) Intestinal microbiota composition modulates choline bioavailability from diet and accumulation of the proatherogenic metabolite trimethylamine-N-oxide. MBio 6(2):e02481. https://doi.org/10.1128/mBio.02481-14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Joyce SA, MacSharry J, Casey PG, Kinsella M, Murphy EF, Shanahan F, Hill C, Gahan CG (2014) Regulation of host weight gain and lipid metabolism by bacterial bile acid modification in the gut. Proc Natl Acad Sci USA 111(20):7421–7426. https://doi.org/10.1073/pnas.1323599111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hagey LR, Krasowski MD (2013) Microbial biotransformations of bile acids as detected by electrospray mass spectrometry. Adv Nutr Int Rev J 4(1):29–35

    Article  CAS  Google Scholar 

  28. Barroso LA, Dean J, Holzle U (2003) Web search for a planet: the Google cluster architecture. IEEE Micro 23(2):22–28

    Article  Google Scholar 

  29. Masarwi M, Solnik HI, Phillip M, Yaron S, Shamir R, Pasmanic-Chor M, Gat-Yablonski G (2018) Food restriction followed by refeeding with a casein- or whey-based diet differentially affects the gut microbiota of pre-pubertal male rats. J Nutr Biochem 51:27–39. https://doi.org/10.1016/j.jnutbio.2017.08.014

    Article  CAS  PubMed  Google Scholar 

  30. Pokusaeva K, Fitzgerald GF, van Sinderen D (2011) Carbohydrate metabolism in Bifidobacteria. Genes Nutr 6(3):285–306. https://doi.org/10.1007/s12263-010-0206-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mengheri E, Nobili F, Vignolini F, Pesenti M, Brandi G, Biavati B (1999) Bifidobacterium animalis protects intestine from damage induced by zinc deficiency in rats. J Nutr 129(12):2251–2257. https://doi.org/10.1093/jn/129.12.2251

    Article  CAS  PubMed  Google Scholar 

  32. Aoki R, Tsuchida S, Arai Y, Ohno K, Nishijima T, Mawatari T, Mikami Y, Ushida K (2016) Effect of Bifidobacterium animalis subsp. lactis GCL2505 on the physiological function of intestine in a rat model. Food Sci Nutr 4(6):782–790. https://doi.org/10.1002/fsn3.344

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Chen MF, Weng KF, Huang SY, Liu YC, Tseng SN, Ojcius DM, Shih SR (2016) Pretreatment with a heat-killed probiotic modulates monocyte chemoattractant protein-1 and reduces the pathogenicity of influenza and enterovirus 71 infections. Mucosal Immunol 10:215. https://doi.org/10.1038/mi.2016.31

    Article  CAS  PubMed  Google Scholar 

  34. Ulluwishewa D, Anderson RC, Young W, McNabb WC, van Baarlen P, Moughan PJ, Wells JM, Roy NC (2015) Live Faecalibacterium prausnitzii in an apical anaerobic model of the intestinal epithelial barrier. Cell Microbiol 17(2):226–240. https://doi.org/10.1111/cmi.12360

    Article  CAS  PubMed  Google Scholar 

  35. Bode L (2012) Human Milk Oligosaccharides: every baby needs a sugar mama. Glycobiology. https://doi.org/10.1093/glycob/cws074

    Article  PubMed  PubMed Central  Google Scholar 

  36. Bode L (2018) Human milk oligosaccharides in the prevention of necrotizing enterocolitis: a journey from in vitro and in vivo models to mother-infant cohort studies. Front Pediatr 6(385):10. https://doi.org/10.3389/fped.2018.00385

    Article  Google Scholar 

  37. Luchansky SJ, Yarema KJ, Takahashi S, Bertozzi CR (2003) GlcNAc 2-epimerase can serve a catabolic role in sialic acid metabolism. J Biol Chem 278(10):8035–8042

    Article  CAS  PubMed  Google Scholar 

  38. Ridlon JM, Kang DJ, Hylemon PB (2006) Bile salt biotransformations by human intestinal bacteria. J Lipid Res 47(2):241–259. https://doi.org/10.1194/jlr.R500013-JLR200

    Article  CAS  PubMed  Google Scholar 

  39. Hammons J, Jordan W, Stewart R, Taulbee J, Berg R (1988) Age and diet effects on fecal bile acids in infants. J Pediatr Gastroenterol Nutr 7(1):30–38

    Article  CAS  PubMed  Google Scholar 

  40. Mercer KE, Diaz-Rubio ME, Bhattacharyya S, Sharma N, Yeruva L (2016) Programming effects of infant diet on cholesterol/bile acid synthesis and absorption in piglets. FASEB J 30(1_supplement):267.266. https://doi.org/10.1096/fasebj.30.1_supplement.267.6

    Article  Google Scholar 

  41. Halpern MD, Dvorak B (2008) Does abnormal bile acid metabolism contribute to NEC? Seminars in perinatology, vol 2. Elsevier, Amsterdam, pp 114–121

    Google Scholar 

  42. Garrido D, Ruiz-Moyano S, Mills DA (2012) Release and utilization of N-acetyl-d-glucosamine from human milk oligosaccharides by Bifidobacterium longum subsp. infantis. Anaerobe 18(4):430–435. https://doi.org/10.1016/j.anaerobe.2012.04.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chen T, Long W, Zhang C, Liu S, Zhao L, Hamaker BR (2017) Fiber-utilizing capacity varies in Prevotella- versus Bacteroides-dominated gut microbiota. Sci Rep 7(1):2594. https://doi.org/10.1038/s41598-017-02995-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. De Filippo C, Cavalieri D, Di Paola M, Ramazzotti M, Poullet JB, Massart S, Collini S, Pieraccini G, Lionetti P (2010) 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(33):14691–14696. https://doi.org/10.1073/pnas.1005963107

    Article  PubMed  PubMed Central  Google Scholar 

  45. Ze X, Duncan SH, Louis P, Flint HJ (2012) Ruminococcus bromii is a keystone species for the degradation of resistant starch in the human colon. ISME J 6(8):1535–1543. https://doi.org/10.1038/ismej.2012.4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Coppa GV, Gabrielli O, Zampini L, Galeazzi T, Ficcadenti A, Padella L, Santoro L, Soldi S, Carlucci A, Bertino E, Morelli L (2011) Oligosaccharides in 4 different milk groups, Bifidobacteria, and Ruminococcus obeum. J Pediatr Gastroenterol Nutr 53(1):80–87. https://doi.org/10.1097/MPG.0b013e3182073103

    Article  CAS  PubMed  Google Scholar 

  47. Fuhrer A, Sprenger N, Kurakevich E, Borsig L, Chassard C, Hennet T (2010) Milk sialyllactose influences colitis in mice through selective intestinal bacterial colonization. J Exp Med 207(13):2843–2854. https://doi.org/10.1084/jem.20101098

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the technical assistance of Hedley Stirrat for the metabolomics analysis and Paul Maclean for bioinformatics support. All authors designed the study; WY wrote the manuscript with input from all authors; ZL, AS, KF, HJ, WC, LD, NCR, and WY conducted the research and analysed the data; WY, NCR, LD had primary responsibility for the final content. All authors read and approved the final manuscript.

Author information

Author notes

  1. Zhenmin Liu and Arvind Subbaraj contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Dairy Biotechnology, Dairy Research Institute, Bright Dairy and Food Co. Ltd, Shanghai, China

    Zhenmin Liu, Hongxin Jia & Wenliang Chen

  2. AgResearch Ltd., Grasslands Research Centre, Tennent Drive, Palmerston North, 4410, New Zealand

    Arvind Subbaraj, Karl Fraser, Li Day, Nicole C. Roy & Wayne Young

  3. Riddet Institute, Massey University, Palmerston North, New Zealand

    Karl Fraser, Nicole C. Roy & Wayne Young

  4. High-Value Nutrition National Science Challenge, University of Auckland, Auckland, New Zealand

    Karl Fraser, Nicole C. Roy & Wayne Young

Authors
  1. Zhenmin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arvind Subbaraj
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karl Fraser
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongxin Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenliang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Li Day
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nicole C. Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wayne Young
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wayne Young.

Ethics declarations

Conflict of interest

This study was funded by Bright Dairy & Food Co. Ltd. Zhenmin Liu, Hongxin Jia, and Wenliang Chen are employees of Bright Dairy & Food Co. Ltd. Bright Dairy & Food Co. Ltd is a producer of infant formulae; Arvind Subbaraj, Karl Fraser, Li Day, Nicole, and Wayne Young declare no conflict of interests.

Ethics

Collection of HM was performed with approved consent by participants, Approval no. XHEC-C-2012-024, Xinhua Hospital Ethics Committee Affiliated to Shanghai Jiào tong University School of Medicine. The animal study was conducted under the oversight of the AgResearch Grasslands Animal Ethics Committee (approval number AEC13099; Palmerston North, New Zealand) in accordance with the New Zealand Animal Welfare Act 1999.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 26 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, Z., Subbaraj, A., Fraser, K. et al. Human milk and infant formula differentially alters the microbiota composition and functional gene relative abundance in the small and large intestines in weanling rats. Eur J Nutr 59, 2131–2143 (2020). https://doi.org/10.1007/s00394-019-02062-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00394-019-02062-w

Keywords

Search

Navigation