Effect of Bovine Milk Fat Globule Membrane and Lactoferrin in Infant Formula on Gut Microbiome and Metabolome at 4 Months of Age

Maciej Chichlowski, Nicholas Bokulich, Cheryl L Harris, Jennifer L Wampler, Fei Li, Carol Lynn Berseth, Colin Rudolph, Steven S Wu, Maciej Chichlowski, Nicholas Bokulich, Cheryl L Harris, Jennifer L Wampler, Fei Li, Carol Lynn Berseth, Colin Rudolph, Steven S Wu

Abstract

Background: Milk fat globule membrane (MFGM) and lactoferrin (LF) are human-milk bioactive components demonstrated to support gastrointestinal and immune development. Significantly fewer diarrhea and respiratory-associated adverse events through 18 mo of age were previously reported in healthy term infants fed a cow-milk-based infant formula with an added source of bovine MFGM and bovine LF through 12 mo of age.

Objectives: The aim was to compare microbiota and metabolite profiles in a subset of study participants.

Methods: Stool samples were collected at baseline (10-14 d of age) and day 120. Bacterial community profiling was performed via 16S rRNA gene sequencing and alpha and beta diversity were analyzed (QIIME 2). Differentially abundant taxa were determined using linear discriminant analysis effect size (LefSE) and visualized (Metacoder). Untargeted stool metabolites were analyzed (HPLC/MS) and expressed as the fold-change between group means (control to MFGM+LF ratio).

Results: Alpha diversity increased significantly in both groups from baseline to 4 mo. Subtle group differences in beta diversity were demonstrated at 4 mo (Jaccard distance; R 2 = 0.01, P = 0.042). Specifically, Bacteroides uniformis and Bacteroides plebeius were more abundant in the MFGM+LF group at 4 mo. Metabolite profile differences for MFGM+LF versus control included lower fecal medium-chain fatty acids, deoxycarnitine, and glycochenodeoxycholate, and some higher fecal carbohydrates and steroids (P < 0.05). After applying multiple test correction, the differences in stool metabolomics were not significant.

Conclusions: Addition of bovine MFGM and LF in infant formula was associated with subtle differences in stool microbiome and metabolome by 4 mo of age, including increased prevalence of Bacteroides species. Stool metabolite profiles may be consistent with altered microbial metabolism. This trial was registered at https://ichgcp.net/clinical-trials-registry/NCT02274883" title="See in ClinicalTrials.gov">NCT02274883.

Keywords: formula feeding; infant microbiome; lactoferrin; metabolome; milk fat globule membrane (MFGM).

© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.

Figures

FIGURE 1
FIGURE 1
Degree of similarity between each sample at each study time point using beta diversity principal coordinate analysis plots. Each point represents a single sample, and the distance between each sample pair indicates the degree of dissimilarity of bacterial communities in those samples (proximal samples are more similar). Ellipses indicate the 95% CI. LF, lactoferrin; MFGM, milk fat globule membrane; PC, principal coordinate.
FIGURE 2
FIGURE 2
Differential heat trees display the mean proportion of bacterial components by study time point and group. Nodes represent each taxonomic rank from kingdom (bacteria, center) to species (tips of each branch). Node and edge (branch) width indicates the mean proportion of that taxon in samples belonging to that group. Size of nodes corresponds to the number of taxa and color intensity corresponds to proportions relative to bacterial samples overall. Only species detected at ≥0.01 mean proportion are displayed.
FIGURE 3
FIGURE 3
Identification of bacterial species that differentiated MFGM+LF vs. control feeding groups by LDA effect size (P < 0.05) at baseline (A) and day 120 (B). LDA, linear discriminant analysis; LF, lactoferrin; MFGM, milk fat globule membrane.
FIGURE 4
FIGURE 4
Differentially abundant metabolites distinguish MFGM+LF vs. control groups by day 120. Box and whisker plots show quartile values; center lines are medians, boxes are first and third quartiles, and whiskers are 95% CIs. Outliers are excluded for clarity. Only significant metabolites are shown (Kruskal-Wallis, P < 0.05). LF, lactoferrin; MFGM, milk fat globule membrane.
FIGURE 5
FIGURE 5
Microbe-metabolite vector analysis identifies co-occurrence probabilities between microbial ASVs and metabolites. Each row represents an individual bacterial ASV, labeled by its species ID, and each column represents an individual metabolite. The intersection of each row/column indicates their co-occurrence probability as an LCP. High LCPs (red) indicate strong co-occurrence probabilities between those features, and low LCPs (blue) indicate negative or null correlations. Margin vectors are colored according to bacterial family affiliation of taxa displayed along the y-axis and metabolite super family classification of metabolites displayed along the x-axis. Only the most significant correlations (features with at least 1 LCP >1.5) are shown. ASV, amplicon sequence variant; LCP, log conditional probability score.

References

    1. Stewart CJ, Ajami NJ, O'Brien JL, Hutchinson DS, Smith DP, Wong MC, Ross MC, Lloyd RE, Doddapaneni H, Metcalf GAet al. . Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature. 2018;562(7728):583–8.
    1. Brink LR, Mercer KE, Piccolo BD, Chintapalli SV, Elolimy A, Bowlin AK, Matazel KS, Pack L, Adams SH, Shankar Ket al. . Neonatal diet alters fecal microbiota and metabolome profiles at different ages in infants fed breast milk or formula. Am J Clin Nutr. 2020;111(6):1190–202.
    1. Bhinder G, Allaire JM, Garcia C, Lau JT, Chan JM, Ryz NR, Bosman ES, Graef FA, Crowley SM, Celiberto LSet al. . Milk fat globule membrane supplementation in formula modulates the neonatal gut microbiome and normalizes intestinal development. Sci Rep. 2017;7(1):45274.
    1. Rueda R. The role of dietary gangliosides on immunity and the prevention of infection. Br J Nutr. 2007;98(S1):S68–73.
    1. Peterson JA, Patton S, Hamosh M. Glycoproteins of the human milk fat globule in the protection of the breast-fed infant against infections. Neonatology. 1998;74(2):143–62.
    1. Spitsberg VL. Invited review: bovine milk fat globule membrane as a potential nutraceutical. J Dairy Sci. 2005;88(7):2289–94.
    1. Liao Y, Alvarado R, Phinney B, Lönnerdal B. Proteomic characterization of human milk fat globule membrane proteins during a 12 month lactation period. J Proteome Res. 2011;10(8):3530–41.
    1. Lonnerdal B. Infant formula and infant nutrition: bioactive proteins of human milk and implications for composition of infant formulas. Am J Clin Nutr. 2014;99(3):712S–7S.
    1. Delplanque B, Gibson R, Koletzko B, Lapillonne A, Strandvik B. Lipid quality in infant nutrition: current knowledge and future opportunities. J Pediatr Gastroenterol Nutr. 2015;61(1):8–17.
    1. Berding K, Wang M, Monaco MH, Alexander LS, Mudd AT, Chichlowski M, Waworuntu RV, Berg BM, Miller MJ, Dilger RNet al. . Prebiotics and bioactive milk fractions affect gut development, microbiota, and neurotransmitter expression in piglets. J Pediatr Gastroenterol Nutr. 2016;63(6):688–97.
    1. Le Huërou-Luron I, Bouzerzour K, Ferret-Bernard S, Ménard O, Le Normand L, Perrier C, Le Bourgot C, Jardin J, Bourlieu C, Carton Tet al. . A mixture of milk and vegetable lipids in infant formula changes gut digestion, mucosal immunity and microbiota composition in neonatal piglets. Eur J Nutr. 2018;57(2):463–76.
    1. Thompson RS, Roller R, Mika A, Greenwood BN, Knight R, Chichlowski M, Berg BM, Fleshner M. Dietary prebiotics and bioactive milk fractions improve NREM sleep, enhance REM sleep rebound and attenuate the stress-induced decrease in diurnal temperature and gut microbial alpha diversity. Front Behav Neurosci. 2017;10(240).
    1. Timby N, Domellöf M, Holgerson PL, West CE, Lönnerdal B, Hernell O, Johansson I. Oral microbiota in infants fed a formula supplemented with bovine milk fat globule membranes—a randomized controlled trial. PLoS One. 2017;12(1):e0169831.
    1. Timby N, Hernell O, Vaarala O, Melin M, Lonnerdal B, Domellof M. Infections in infants fed formula supplemented with bovine milk fat globule membranes. J Pediatr Gastroenterol Nutr. 2015;60(3):384–9.
    1. Li F, Wu SS, Berseth CL, Harris CL, Richards JD, Wampler JL, Zhuang W, Cleghorn G, Rudolph CD, Liu Bet al. . Improved neurodevelopmental outcomes associated with bovine milk fat globule membrane and lactoferrin in infant formula: a randomized, controlled trial. J Pediatr. 2019;215:24–31.e8.
    1. Zavaleta N, Kvistgaard AS, Graverholt G, Respicio G, Guija H, Valencia N, Lonnerdal B. Efficacy of an MFGM-enriched complementary food in diarrhea, anemia, and micronutrient status in infants. J Pediatr Gastroenterol Nutr. 2011;53(5):561–8.
    1. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer Met al. . Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6(8):1621–4.
    1. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar Fet al. . Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7.
    1. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581.
    1. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6(3):610–8.
    1. Janssen S, McDonald D, Gonzalez A, Navas-Molina JA, Jiang L, Xu ZZ, Winker K, Kado DM, Orwoll E, Manary Met al. . Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems. 2018;3(3):e00021–18.
    1. Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, Huttley GA, Gregory Caporaso J. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6(1):90.
    1. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71(12):8228–35.
    1. Bokulich NA, Dillon MR, Zhang Y, Rideout JR, Bolyen E, Li H, Albert PS, Caporaso JG. q2-Longitudinal: longitudinal and paired-sample analyses of microbiome data. mSystems. 2018;3(6):e00219–18.
    1. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26(1):32–46.
    1. Oksanen J, Blanchet F, Friendly M, Kindt R, Legendre P, McGlin D. Vegan: community ecology package. R package version 2.5-3. 2018.
    1. Foster ZSL, Sharpton TJ, Grünwald NJ. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput Biol. 2017;13(2):e1005404.
    1. Mandal S, Van Treuren W, White RA, Eggesbø M, Knight R, Peddada SD. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis. 2015;26(0):27663.
    1. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60.
    1. Evans AM, Bridgewater BR, Liu Q, Mitchell MW, Robinson RJ, Dai H. High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. Metabolomics: Open Access. 2014;4:1–7.
    1. Lee-Sarwar KA, Kelly RS, Lasky-Su J, Zeiger RS, O'Connor GT, Sandel MT, Bacharier LB, Beigelman A, Laranjo N, Gold DRet al. . Integrative analysis of the intestinal metabolome of childhood asthma. J Allergy Clin Immunol. 2019;144(2):442–54.
    1. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright Jet al. . SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72.
    1. Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with python. In: Proceedings of the 9th Python in Science Conference: Austin, TX; 2010:61.
    1. Morton JT, Aksenov AA, Nothias LF, Foulds JR, Quinn RA, Badri MH, Swenson TL, Van Goethem MW, Northen TR, Vazquez-Baeza Yet al. . Learning representations of microbe–metabolite interactions. Nat Methods. 2019;16(12):1306–14.
    1. Davis EC, Wang M, Donovan SM. The role of early life nutrition in the establishment of gastrointestinal microbial composition and function. Gut Microbes. 2017;8(2):143–71.
    1. He X, Parenti M, Grip T, Lönnerdal B, Timby N, Domellöf M, Hernell O, Slupsky CM. Fecal microbiome and metabolome of infants fed bovine MFGM supplemented formula or standard formula with breast-fed infants as reference: a randomized controlled trial. Sci Rep. 2019;9:11589.
    1. Timby N, Domellof M, Holgerson PL, West CE, Lonnerdal B, Hernell O, Johansson I. Oral microbiota in infants fed a formula supplemented with bovine milk fat globule membranes—a randomized controlled trial. PLoS One. 2017;12(1):e0169831.
    1. Sánchez E, De Palma G, Capilla A, Nova E, Pozo T, Castillejo G, Varea V, Marcos A, Garrote JA, Polanco Iet al. . Influence of environmental and genetic factors linked to celiac disease risk on infant gut colonization by Bacteroides species. Appl Environ Microbiol. 2011;77(15):5316–23.
    1. Yassour M, Jason E, Hogstrom LJ, Arthur TD, Tripathi S, Siljander H, Selvenius J, Oikarinen S, Hyöty H, Virtanen SMet al. . Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe. 2018;24(1):146–54.e4.
    1. Gauffin Cano P, Santacruz A, Moya Á, Sanz Y. Bacteroides uniformis CECT 7771 ameliorates metabolic and immunological dysfunction in mice with high-fat-diet induced obesity. PLoS One. 2012;7(7):e41079.
    1. Fernández-Murga ML, Sanz Y. Safety assessment of Bacteroides uniformis CECT 7771 isolated from stools of healthy breast-fed infants. PLoS One. 2016;11(1):e0145503–e.
    1. Backhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datchary P, Li Y, Xia Y, Xie H, Zhong Het al. . Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe. 2015;17(6):852.
    1. Gregory KE, LaPlante RD, Shan G, Kumar DV, Gregas M. Mode of birth influences preterm infant intestinal colonization with Bacteroides over the early neonatal period. Adv Neonatal Care. 2015;15(6):386–93.
    1. Azad MB, Konya T, Maughan H, Guttman DS, Field CJ, Chari RS, Sears MR, Becker AB, Scott JA, Kozyrskyj ALet al. . Gut microbiota of healthy Canadian infants: profiles by mode of delivery and infant diet at 4 months. Can Med Assoc J. 2013;185(5):385–94.
    1. Jakobsson HE, Abrahamsson TR, Jenmalm MC, Harris K, Quince C, Jernberg C, Bjorksten B, Engstrand L, Andersson AF. Decreased gut microbiota diversity, delayed Bacteroidetes colonisation and reduced Th1 responses in infants delivered by caesarean section. Gut. 2014;63(4):559–66.
    1. Yassour M, Vatanen T, Siljander H, Hamalainen AM, Harkonen T, Ryhanen SJ, Franzosa EA, Vlamakis H, Huttenhower C, Gevers Det al. . Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci Transl Med. 2016;8(343):343ra81.
    1. Bokulich NA, Chung J, Battaglia T, Henderson N, Jay M, Li H, DL A, Wu F, Perez-Perez GI, Chen Yet al. . Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci Transl Med. 2016;8(343):343ra82.
    1. Rueda R, Sabatel JL, Maldonado J, Molina-Font JA, Gil A. Addition of gangliosides to an adapted milk formula modifies levels of fecal Escherichia coli in preterm newborn infants. J Pediatr. 1998;133(1):90–94.
    1. Heinken A, Sahoo S, Fleming RMT, Thiele I. Systems-level characterization of a host-microbe metabolic symbiosis in the mammalian gut. Gut Microbes. 2013;4(1):28–40.
    1. Ogawa K, Ben RA, Pons S, Paolo MIL, Fernández LB. Volatile fatty acids, lactic acid, and ph in the stools of breast-fed and bottle-fed infants. J Pediatr Gastroenterol Nutr. 1992;15(3):248–52.

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться