Early Diet Impacts Infant Rhesus Gut Microbiome ... - ACS Publications

Subscriber access provided by Georgetown University | Lauinger and Blommer Libraries

Article

Early Diet Impacts Infant Rhesus Gut Microbiome, Immunity and Metabolism Aifric O'Sullivan, Xuan He, Elizabeth M.S. McNiven, Neill W. Haggarty, Bo Lönnerdal, and Carolyn Marie Slupsky J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr4001702 • Publication Date (Web): 07 May 2013 Downloaded from http://pubs.acs.org on May 12, 2013

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

Early Diet Impacts Infant Rhesus Gut Microbiome, Immunity and Metabolism Aifric O’Sullivan1,2†, Xuan He1, Elizabeth M. S. McNiven1, Neill W. Haggarty3, Bo Lönnerdal1, Carolyn M. Slupsky1,2*

1

Department of Nutrition, 2Department of Food Science and Technology, One Shields Avenue,

University of California, Davis. Davis, California, USA 95616; 3

Fonterra Ingredients Innovation, Fonterra Co-operative Group, Private Bag 11029, Fitzherbert

Dairy Farm Road, Palmerston North, New Zealand (NWH).

Keywords: Infant, pediatric, nutrition, breast feeding, formula feeding, metabolism, metabolomics, NMR spectroscopy, gut microbiota, microbiome, immune system

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 49

ABSTRACT Epidemiological research has indicated a relationship between infant formula feeding and increased risk of chronic diseases later in life including obesity, type-2 diabetes, and cardiovascular disease. The present study used an infant rhesus monkey model to compare the comprehensive metabolic implications of formula- and breast-feeding practices using NMR spectroscopy to characterize metabolite fingerprints from urine and serum, in combination with anthropometric measurements, fecal microbial profiling and cytokine measurements. Here we show that formula-fed infants are larger than their breast-fed counterparts and have a different gut microbiome that includes higher levels of bacteria from the Ruminococcus genus, and lower levels of bacteria from the Lactobacillus genus. In addition, formula-fed infants have higher serum insulin coupled with higher amino acid levels, while amino acid degradation products were higher in breast-fed infants. Increases in serum and urine galactose and urine galactitol were observed in the second month of life in formula-fed infants, along with higher levels of TNFα, IFN-γ, IL-1β, IL-4 and other cytokines and growth factors at week 4. These results demonstrate that metabolic and gut microbiome development of formula-fed infants is different from breast-fed infants, and the choice of infant feeding may hold future health consequences.


2

Page 3 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INTRODUCTION Feeding strategy (breast-fed vs. formula-fed) has been shown to both directly and indirectly influence human infant growth by impacting body composition1, intestinal maturation2, gut microbial succession3, as well as postnatal endocrine4 and immune responses5. However, the underlying mechanisms and metabolic states that define these outcomes are poorly understood. This can partly be attributed to limitations imposed when designing human infant studies, such as the inability to perform randomized controlled studies, the allocation of infants to specific diets, and the challenge of eliminating confounding factors such as socioeconomic conditions. Consequently, studies investigating infant feeding practices and their implications can benefit from the use of appropriate animal models6. Rhesus monkeys (Macaca mulatta) are an ideal animal model for human nutrition research due to the similar genetic make-up7, physiology, and behavior compared to humans8-10. The microbiota of adult monkeys is dominated by Firmicutes and Bacteroidetes, much like humans; however, taxa within these phyla reveal differences between humans and rhesus monkeys11. Nevertheless, shifts in the microbiota of rhesus adults with disease occur similarly to shifts in microbiota of humans with disease, emphasizing the importance of the rhesus model in understanding human disease11. Given that the nutritional needs of rhesus infants are similar to those of human infants, such that regular human infant formula can be fed to rhesus infants from birth to 3-4 months of age without any nutritional modifications8, 9, rhesus infants are an ideal model for human dietary studies. Indeed, a recent metabolomics investigation confirmed the relevance and importance of this model for infant nutrition and developmental research, and revealed similarities in the milk microbiome between humans and rhesus monkeys10.


3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 49

A number of factors have been shown to affect the succession of microbiota in the infant gastrointestinal tract including gestational age, mode of birth, host genetics, antibacterial drugs, lifestyle, and diet12, 13. Infant diet, through exclusive milk feeding, is the most influential factor to shape the configuration and function of the gut microbiome14. Unlike adult fecal microbiota, which has been shown to be highly stable and functionally uniform over time, human infants appear to have heterogeneous, diverse, and dynamic microbial communities due to variations in temporal patterns and environmental exposure15, 16. It has been repeatedly shown that diet strikingly affects the gut microbial community in the neonate17, 18. To more fully understand the interactions between diet, the gut microbiome, and human health, the synergistic effect of both diet and the gut microbiome on host metabolism and immunity is an important area of investigation. Since the neonatal infant has a diet that is limited to either breast milk or formula, this represents an ideal system with which to explore human health and the interaction with diet. The present study used an infant rhesus monkey model to examine the impact of diet on gut microbiome development, metabolism, and immunity. The metabolic implications of formulaand breast-feeding practices were compared using NMR spectroscopy to characterize metabolite fingerprints from urine and serum. These results were combined with differences in anthropometric measurements, immune biomarker patterns, and comparative analysis of fecal microbiota. Together, results from this investigation provide a comprehensive examination of the system-wide metabolic consequences of different infant feeding regimens, and support the contention that infant feeding practice profoundly influences metabolism in developing infants.


4

Page 5 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


MATERIALS AND METHODS Subjects. Ten infant rhesus monkeys (Macaca mulatta) were assigned to this study at birth from the California national Primate Research Center (CNPRC) at the University of California, Davis. All monkeys were maintained indoors at the CNPRC under the constant care of nursery and veterinary staff who are experienced in the handling of Old World non-human primates. Infants were randomly assigned to two diet treatment groups and were either exclusively breastfed or bottle-fed a standard infant formula from birth until 3 months of age (n=5, 2 males and 3 females per group). Breast-fed infants were housed with their mothers, while formula-fed nursery infants were housed individually in polycarbonate isolates with a surrogate mother (a terrycloth dummy) for the first month of life, and matched with other formula-fed infants and housed in stainless steel cages for the remainder of the study. Protocols were approved by the University of California, Davis Institutional Animal Care and Use Committee and conducted in accordance with the Department of Agriculture Animal Welfare Act. Weight (kg) and crown-rump length (cm) were recorded at birth and every 2 weeks thereafter. Animal health was evaluated daily by the animal care staff. Hematological parameters were measured at birth and microbiology and hematology reports were generated every 4 weeks after birth (Figure 1). Diets. Infant formula was provided by Fonterra Ingredients Innovation (Fonterra Co-operative Group, Palmerston North, New Zealand), and was based on a 60% whey, 40% casein dry blended formula containing 26.2 g fat, 13.1 g protein, and 55.51 g carbohydrate per 100 g of dry formula, and was appropriately mixed with water to the final concentration as shown in Table 1. For comparison, rhesus milk contains approximately 48.0 g/L fat, 79.0 g/L carbohydrate19 and 11.6 g/L protein20. Amino acid compositions of the experimental formula and rhesus milk are presented in Table 1. Formula was prepared fresh by animal care staff. Infants were fed


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 49

according to an animal husbandry protocol that started with hand feeding every 2 h from a nursing bottle with a nipple for up to 5 days, and progressed to self-feeding from a self-feeder for ad libitum access. Beginning at 2 days of age, infant rhesus macaques are taught how to self-feed by being placed next to the feeder nipple with their surrogate and gently held in place while they self-fed. All formula-fed infants graduated to self-feeding by 5 days, and consumed similar amounts of formula. Breast-fed infants were exclusively breast-fed, and the amount of milk obtained could not be recorded. Lactating mothers received a commercial monkey chow and were provided with fruit and vegetables twice a week and water was available ad libitum. Urine, Fecal, and Serum Sample collection. Urine samples were collected at birth and every week up to 12 weeks of age by staff at the Primate Center using a specially designed metabolic unit as previously described10. Urine samples were collected for most time points except the birth time point from breast-fed monkeys #1 and #4, and from breast-fed monkey #3 at week 8, and #5 at weeks 2 and 11. Fecal samples were collected at birth, 4, 8 and 12 weeks of age using the same system. Where investigators failed to collect a whole sample, specimens were collected on cotton tipped swabs. Once collected, urine and fecal samples were frozen immediately at -20 ºC, followed by long-term storage at -80 ºC. Blood samples were drawn in the morning via femoral venipuncture at birth and every 2 weeks thereafter for metabolomic analysis. Staff at the Primate Center collected additional serum samples at 1 week of life and every 2 weeks thereafter up until week 11 for insulin measurements. Animals were not specifically fasted prior to serum collection. A summary of sample collection times is provided in Figure 1. Samples were allowed to clot at room temperature for 30 minutes. Following centrifugation, serum aliquots were stored at -80 ºC until analysis. All individuals handling serum, urine, and fecal samples were aware of


6

Page 7 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


all hazards associated with working with samples obtained from Old World non-human primates, and as such adhered to standard biological safety level 2 procedures when handling. DNA Extraction and Purification. DNA was extracted from monkey stool samples (average wet weight: 0.167g ± 0.044) of each group and each time point according to the modified protocol of Martinez and coworkers21. Weighed fecal samples were diluted in ice cold PBS to the final volume 2 mL, thoroughly mixed, and centrifuged at 8,000 x g for 5 min. The resulting pellet was washed 3 times by suspension in 2 mL ice-cold PBS and centrifuged at 8,000 x g (5 min), discarding the supernatant. The pellet was then suspended in 200 µL of Lysis buffer (20 mM Tris-HCL [pH 8.0], 2 mM EDTA, 1.2% Triton X-100) supplemented with 40 mg/mL lysozyme (Sigma, St. Louis, MO) and incubated at 37 °C for 30 min. The next steps of fecal DNA extraction were performed using the QIAamp DNA Stool Mini Kit (Qiagen, Inc., Valencia, CA), according to the manufacturer’s standard protocol with the following modification: after addition of ASL buffer, samples were subjected to bead beating for 2 min using Mini-Beadbeater -16 (Biospec Products Inc, Bartlesville, OK) and subsequent heating for 5 minutes at 95 ºC. DNA extracts were stored at -20 ºC until further analysis. One fecal sample for breast-fed infant #2 was not included in analysis of fecal samples at birth due to the inability of this infant to produce a sample at that time point. Library construction and 16S rRNA sequencing. The V4 region of the 16S rRNA gene from extracted fecal DNA samples was amplified using the primer pair F515 and R806 modified according to Bokulich et al.22. An 8 bp Hamming error-correcting barcode was attached to the forward primer to enable sample multiplexing. PCR reactions and the following amplicon purifications were performed according to the protocol described22 with 5 pmol of primer added for each PCR reaction. Purified DNA libraries were submitted to the UC Davis Genome Center


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 49

DNA Technologies Core for a 150 bp paired-end sequencing on Illumina GAIIx platform. Paired-end Illumina sequencing of the large V4 region has previously been successfully used to construct phylogenetic trees23. The resulting sequences were analyzed in Quantitative Insights Into Microbial Ecology (QIIME) pipeline v1.5.024 according to the following preprocessing criteria: (1) Truncation of reads if more than three sequential bases with minimum Phred quality score below 3 were found; (2) exact match between primer sequences and barcode tags was observed; and (3) the number of consecutive low quality base calls were not shorter than 75% of the input length. OTU selection was performed using QIIME implemented in UCLUST against the most recent version of the Greengenes core database25, and clustered with a threshold of 97% identity excluding reads that did not match the database. This OTU selection step was performed using the parameters as previously described16. One breast-fed sample at week 12 was removed due to very low sequence reads. To evaluate bacterial diversity, representative taxonomies of the most abundant sequences associated with each barcode combination were chosen using PyNAST26, 27 with a relaxed neighbor-joining tree built using fasttree28. OTUs were classified taxonomically using the Ribosomal Database Project classifier with a 0.8 confidence threshold. A retrained RDP classifier using taxa from the Greengenes reference at the genus level was used to obtain a better classification resolution down to the genus level. Taxonomic groups observed with fewer than 10 counts were omitted. Possible differences in the microbial community structures among the two study groups were explored using unweighted UniFrac matrices29 followed by Principal Coordinate Analysis (PCoA) using the “beta_diversity_through_plots.py” workflow script. Samples were also clustered based on between-sample distances using the Unweighted Pair


8

Page 9 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Group Method with Arithmetric mean (UPGMA) clustering method (“upgma.clust.py”) and viewed by using FigTree v1.3.1. Biochemical assays. Hemoglobin, hematocrit, white blood cell (WBC) count, and red blood cell (RBC) count were quantified with an automated electronic cell counter (Baker 9010 Analyzer; Serono-Baker, Allentown, PA). Serum insulin was analyzed using a commercially available kit (Linco, St Louis) that was validated for cross-reactivity with nonhuman primate insulin. Serum cytokines were measured using a multiplex bead immunoassay kit, the Cytokine Monkey Magnetic 28-Plex Panel, and a Luminex® platform (Invitrogen, CA, USA). Invitrogen’s Cytokine Monkey Magnetic 28-Plex Panel is designed for the quantitative determination of epidermal growth factor (EGF), eotaxin, fibroblast growth factor basic (FGF-basic), granulocyte colony-stimulating factor (G-CSF), granulocyte/macrophage colony stimulating factor (GMCSF), hepatocyte growth factor (HGF), interferon-gamma (IFN- γ), interleukin-1 beta (IL-1β), interleukin-1 receptor antagonist (IL-1RA), IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-15, IL17, interferon–inducible T cell alpha chemoattractant (I-TAC), monocyte chemotactic protein-1 (MCP-1), macrophage-derived chemokine (MDC), macrophage migration inhibitory factor (MIF), monokine induced by gamma interferon (MIG), macrophage inflammatory protein-1 alpha (MIP-1α), macrophage inflammatory protein-1 beta (MIP-1β), regulated upon activation, normal T-cell expressed, and secreted (RANTES), tumor necrosis factor-alpha (TNF-α), and vascular endothelial growth factor (VEGF). Samples were prepared and measurements performed according to manufacturer’s instructions. Measurement of eight growth factors and chemokines (GM-CSF, IL-5, IL-6, IL-10, IL-15, IL-17, I-TAC, and VEGF) were outside the limits of detection of the assay and were not considered for further analysis or interpretation.


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Page 10 of 49

H NMR spectroscopy sample preparation: Urine and serum samples were removed from -80 ºC

storage and allowed to thaw. Urine samples were prepared for analysis by the addition of an internal standard containing 5 mM 3-(trimethylsilyl)-1-propanesulfonic acid-d6 (DSS-d6) and 0.2% NaN3 in 99.8% D2O; 65 µL was added to 585 µL of urine. The pH of each sample was subsequently adjusted to 6.8 ± 0.1 by adding small amounts of NaOH or HCl after adding internal standard to minimize pH-based peak movement and ensure easier compound identification. Serum samples were prepared by filtering through a 3,000 MW cut-off filter (Pall, Ann Arbor, MI) to remove insoluble lipid particles and proteins, and adjusting the filtrate volume to 585 µL with ultrapure water (Millipore Synergy UV system, Millipore, Billerica, MI) when sample volume was limited. 65 µL of internal standard was added and pH adjusted as described above. 600 µL aliquots of urine or serum were subsequently transferred to a 5 mm Bruker NMR tubes, and stored at 4 ºC until NMR acquisition (within 24 h of sample preparation). NMR Data acquisition: NMR spectra were acquired as previously described30 using a Bruker Avance 600 MHz NMR equipped with a SampleJet autosampler using a NOESY-presaturation pulse sequence (noesypr) at 25 ºC. Water saturation was achieved during the prescan delay (2.5 s) and mixing time (100 ms). Spectra were acquired with 8 dummy scans and 32 transients over a spectral width of 12 ppm with a total acquisition time of 2.5 s. NMR spectral processing and metabolite identification. Once acquired, all spectra were zerofilled to 128k data points, Fourier transformed with a 0.5-Hz line broadening applied and manually phased and baseline corrected using Chenomx NMR Suite v6.1 Processor (Chenomx Inc., Edmonton, Canada). Metabolite quantification was achieved using the 600-MHz library from Chenomx NMR Suite v6.1 Profiler, which uses the concentration of a known reference signal (in this case DSS) to determine the concentration of individual compounds as previously


10

Page 11 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


described31. Metabolites were quantified in micromolar (µM) units and exported from Chenomx for analysis. Corrections for sample dilution were made where appropriate using a dilution factor. The dilution factor was calculated by dividing the final volume of the sample by the initial volume of serum or urine. Outlier Elimination. Initial PCA of NMR data detected 3 distinct serum samples (breast-fed monkey #2 at week 2, and #5 at weeks 2 and 6) and 6 distinct urine samples (formula-fed monkeys #2 at weeks 2 and 9, and #3 at week 7; as well as breast-fed monkeys #2 at week 2, #3 at week3, and #5 at week 11) that were outliers based on Hotelling’s T2-ellipse. The NMR spectra and annotated data for these samples were inspected. All nine outliers were prepared from low volume samples; they were therefore removed from subsequent analysis to avoid potential bias. Statistical analysis. To approximate normality, OTU relative abundance was arcsine transformed and all anthropometric, metabolomic, hematologic and serum cytokine data were log10 transformed. Body weight, crown-rump length, insulin, OTU relative abundance and metabolite concentrations were compared using Mixed-Effect Models repeated measures ANOVA to identify age effects, diet effects, and age by diet interactions (lme function in nlme package, R). Principal component analysis (PCA) (unsupervised multivariate analysis) was applied to explore the NMR metabolomic data set (serum and urine) and scores plots were visually inspected for trends or outliers in the data by using SIMCA-P software (version 11.0; Umetrics, Umeå, Sweden). Data were mean centered and unit variance scaled. The quality of all models was judged by the goodness-of-fit parameter (R2X) and the predictive ability parameter (Q2), which is calculated by an internal cross-validation of the data and the predictability calculated on a leaveout basis. For ANOVA (as well as PCA), urine metabolites were expressed as micromole of


11


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 49

metabolite per millimole of creatinine. Differences in hematologic parameters, cytokine concentrations, and metabolite concentrations at specific time points between groups were validated using a multiple independent samples t-tests in R. All p-values from multiple comparison tests were adjusted by false discovery rate (FDR). Significance was assumed at p < 0.05. Data are expressed as mean ± SEM.


12

Page 13 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


RESULTS Impact on growth and health markers: To investigate the association between diet, immune development, the gut microbiome, and host metabolism, ten healthy term infant rhesus monkeys were randomly assigned to two dietary treatments: exclusively breast-fed or bottle-fed a standard infant formula from birth until 3 months of age (n=5, 2 males and 3 females per group). At birth, there was no difference in mean body weight (breast-fed: 0.535 ± 0.044 kg; formula-fed: 0.546 ± 0.018 kg) or length (breast-fed: 19.8 ± 0.6 cm; formula-fed: 20.1 ± 0.9 cm) between the groups. Overall, body size was significantly affected by feeding strategy (Fig. 2A,B). Formula-fed infants weighed significantly more and were longer than their breast-fed counterparts at all experimental time points starting from week 2 and continuing to 12 weeks of age (repeated measures ANOVA, weight: p = 0.053; length: p = 0.030). Similarly, analysis of serum insulin (Fig. 2C) also revealed a significant effect of diet (repeated measures ANOVA, p = 0.008). At week 1, two of the formula-fed monkeys had very high insulin (> 60 µIU/mL), while the remaining 3 monkeys had an average insulin concentration of 13 ± 4 µIU/mL. It is unclear why the two monkeys would have such high serum insulin at this time point, and one explanation may be due to a recent intake of formula. In general, more variability was observed in the formula-fed group, and this may be due to the infants drinking slightly more formula less often resulting in more variability compared with the breast-fed infants who suckle frequently. Hemoglobin concentrations were slightly lower in formula-fed infants (12 g/dL vs 13 g/dL) at week 8, but not significant after FDR correction (Table 2). Illness was not reported for any of the monkeys, supporting that all monkeys in the study were clinically healthy throughout. To


13


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 49

determine whether the differences in growth, body composition and insulin concentrations between breast-fed and formula-fed infants were associated with other health-related parameters, the gut microbiome, serum cytokines, and both urine and serum metabolic profiles were investigated. Impact of feeding strategy on gut microbial colonization: DNA extracted from stool samples collected at birth, 4, 8, and 12 weeks of age was subjected to multiplex Illumina sequencing of the V4 region of bacterial 16S rRNA genes. Sequence reads were filtered to remove low-quality sequences and analyzed using Qiime24. OTUs were taxonomically assigned at 97% sequence similarity, and revealed a taxonomic distribution with a clear compositional change from 4 weeks of age onward regardless of feeding strategy. From week 4 onward, the overall community profiles for both breast-fed and formula-fed infants included representatives of the Firmicutes, Actinobacteria, and Tenericutes phyla. The most dominant genus was Bifidobacterium (Actinobacteria phylum), followed by genera belonging to the Lactobacillales and Clostridales orders of the Firmicutes phylum including members of the Lactobacillaceae, Lachnospiraceae and Ruminococcacea families. After Bifidobacterium, the most abundant genus was Lactobacillus in breast-fed infants and Ruminococcus in formula-fed infants. Bacterial community structures were compared between formula-fed and breast-fed infants across all four time points starting at birth using PCoA of unweighted UniFrac distance matrices29. Strong clustering by feeding strategy and experimental time were observed (Fig. 3A). Moreover, a UPGMA hierarchical clustering based on the same UniFrac distances (Fig. 3B) at week 4, 8 and 12 confirmed the result from the previous PCoA plot (Fig. 3A). Together, the


14

Page 15 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


results suggested significantly different microbial communities based on feeding strategy. Multiple independent repeated measures ANOVA was applied to compare the dynamic changes in gut microbial composition. Differences were identified in the top two abundant genera in formula-fed and breast-fed infants (Fig. 3C). Bifidobacterium levels increased in the breast-fed group over time, while the formula-fed group decreased (repeated measures ANOVA, p = 0.060). A trend for increasing abundance of Lactobacillus in the breast-fed group (repeated measures ANOVA, p < 0.001) and increasing numbers of Ruminococcus in the formula-fed group (repeated measures ANOVA, p < 0.001) was also observed over time. Impact of feeding strategy on cytokine profiles: Using a solid phase multiplex protein assay, complete results for 20 serum cytokines at birth, 4, 8, and 12 weeks of age were compared between formula-fed and breast-fed infants (Table 3). As expected, samples collected at birth showed no significant differences between the two groups. At 4 weeks, between-group comparisons revealed that many of the cytokines, chemokines, and growth factors were significantly higher in the formula-fed group, including EGF, FGF-basic, GCSF, HGF, IFN-γ, IL-1β, IL-1RA, IL-2, IL-4, IL-8, MIG, MIP-1α, RANTES, and TNFα (multiple independent sample t-tests, p < 0.05 with FDR correction). However, at 8 and 12 weeks, few significant differences were observed, with eotaxin significantly higher, and IL-4 significantly lower in formula-fed infants at week 8 only (multiple independent sample t-tests, p < 0.05 with FDR correction). These findings suggested a trend toward convergence of immune marker profiles after an initial increase in some pro-inflammatory immune biomarkers in formula-fed infants in the first month of life. Impact of feeding strategy on infant urine and serum metabolic profiles:


15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 49

To explore infant metabolism based on diet, serum and urine metabolite profiles were measured using 1H NMR spectroscopy, and 69 serum and 96 urine metabolites were identified. Initial analysis of both urine and serum NMR data indicated that samples collected prior to any dietary treatment (week 0) were similar between the breast-fed and formula-fed groups, and therefore were not considered in subsequent comparisons (Fig. 4). PCA revealed distinct separation between breast-fed and formula-fed infants for serum samples collected from week 2, and urine samples collected from week 1 onward (Fig. 4). To examine these metabolic changes in more detail, repeated measures ANOVA were computed. In serum, metabolites differentiating breastfed from formula-fed infants included amino acids, ketones and other compounds (Fig. 5). Urinary metabolite differences included those associated with the gut microbiome, as well as sugar, amino acid, and protein metabolism (Fig. 6). High urinary lactose excretion in formula-fed infants was observed, which was in contrast to the consistently low excretion in the breast-fed group (Fig. 6) (multiple samples t-test p < 0.05 at weeks 4, 5, and 12). Both breast milk and infant formula provide similar amounts of lactose (Table 3). Galactitol concentration was also significantly higher in the urine of formula-fed infants (120.9 ± 9.8 mM/M creatinine) compared with breast-fed infants (62.1 ± 2.7 mM/M creatinine), particularly in month 2 with a tendency to normalize by the end of month 3. For some of the formula-fed infants, galactose concentration was also high in serum (multiple samples t-test, p < 0.05 at weeks 4, 8, and 12), and urine (multiple samples t-test, p < 0.05 at weeks 5 and 6); however, this was not significant across all weeks (Figs 5, 6). Total circulating essential amino acids (the sum of lysine, histidine, valine, leucine, isoleucine, phenylalanine, methionine, tryptophan, and threonine) (repeated measures ANOVA, p < 0.001) and nonessential amino acids (the sum of alanine, arginine, asparagine, aspartate, glutamate,


16

Page 17 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


glutamine, glycine, ornithine, proline, serine, taurine, tyrosine) (repeated measures ANOVA, p = 0.035) were higher in the formula-fed group. In particular, alanine, arginine, asparagine, aspartate, glutamate, lysine, methionine, phenylalanine, serine, taurine, threonine and branchedchain amino acids (BCAAs: isoleucine, leucine and valine) were significantly higher in formulafed infants throughout the feeding period (multiple repeated measures ANOVA, p < 0.001 with FDR correction) (Fig. 5). The only amino acid significantly higher in breast-fed infant serum was glutamine (Fig. 5). Additionally, our results showed that in contrast to breast-fed infants, formula-fed infants exhibited rapid increases in serum BCAAs in the first month of life, while acetoacetate and glucose remained low and showed stable concentrations from week 2 until 3 months of age. Other differences between formula-fed and breast-fed infants included higher urinary concentrations of lysine, carnitine, allantoin, taurine, and TMAO (multiple repeated measures ANOVA, p < 0.05 with FDR correction), and lower concentrations of myo-inositol in the urine of formula-fed infant rhesus monkeys (multiple independent samples t-tests, p < 0.05 at weeks 3 – 5, and 9 - 12) (Fig. 6). In addition, higher concentrations of allantoin in the serum of formulafed monkeys (76.3 ± 5.7 µM) compared with breast-fed monkeys (57.9 ± 5.2 µM), which approached significance (multiple repeated measures ANOVA, p = 0.097 with FDR correction) was observed. Moreover, a significantly lower urinary pH was observed for formula-fed infants (formula-fed: 6.35 ± 0.13; breast-fed: 7.02 ± 0.13; p < 0.001).


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 49

DISCUSSION The immediate postnatal period is a vulnerable time when infants are undergoing rapid maturation and should be considered a critical developmental window with implications for future health. In this study, an infant rhesus macaque model was employed to investigate how diet (formula or breast milk) impacted the dynamic changes of early infant metabolic development by comparing growth, gut microbial community establishment, as well as host cytokine and metabolic profiles of formula-fed and breast-fed infants. Formula-fed infants gained weight and grew more rapidly than breast-fed infants during the first 3 months of life (Fig. 2). Several others studies have described similar growth trajectories for formula-fed infants, characterized by an increased growth velocity 1, 32, 33. Rapid weight gain in infancy has been correlated with later development of obesity, dyslipidemia, insulin resistance and cardiovascular disease34-37. Interestingly, results from this study also show that serum insulin concentrations were higher at all measured time points in formula-fed infants after the introduction of formula, potentially setting the stage for insulin resistance later in life. The combined effects of insulin as a growth promoting hormone38 and an inhibitor of lipolysis may help explain the rapid growth rate and increased adiposity reported in formula-fed infants39, 40. These results provide further evidence that early infant diet is a critical factor that is likely to ultimately shape long-term health. UPGMA cluster analysis starting from the first measurement after dietary intervention identified substantial differences in the gut microbiome of formula-fed and breast-fed infants suggesting that dietary composition has an important influence on gut microbiota succession, although exposure to other environmental bacteria, for example on the mother’s skin, may also play a role. Indeed, Lactobacillus is one of the major bacterial genera in monkey milk10, 41, and was observed


18

Page 19 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


here as having the second highest abundance in the feces of the breast-fed rhesus infants in contrast to its low abundance in formula-fed rhesus infants. Interestingly, some species of Ruminococcus have been shown to be pro-inflammatory42, 43 while some species of Lactobacillus and Bifidobacterium are anti-inflammatory43, 44, which may explain the differences in immune function between formula-fed and breast-fed infants as described below. Furthermore, microbial colonization was dynamic at all time points, with the breast-fed group having the greatest variation between infants at each time point. This may be explained by the fact that in contrast to formula, which is the same for every infant, milk will vary from dam to dam with slight differences in carbohydrate, fat, protein, metabolite, and microbial content45-47. The observed differences in the gut microbiota composition of formula-fed and breast-fed infants will also likely contribute to gut maturation and permeability as well as the other metabolic differences. It has been shown that breast-fed infants have better immune function than formula-fed infants48, 49

. Our results indicate an elevated inflammatory state in the formula-fed group in the first month

compared with breast-fed rhesus infants (Table 3). These findings suggested a trend toward convergence of immune marker profiles after an initial increase in some pro-inflammatory immune biomarkers in the first month of life. Indeed, breast-fed infants did have higher levels of Lactobacillus species in their feces, and as mentioned above, species of Lactobacillus (specifically L. johnsonii, which is present in monkey milk41), are known to reduce levels of proinflammatory chemokines50, 51. Additionally, the beneficial role of breast-feeding on immune function in early infancy has been well-established52, 53, as immunological properties of breast milk provide immediate protection against infection and facilitate immune development and maturation49, 52. Furthermore, it has previously been shown that intestinal permeability decreases faster in breast-fed infants than in formula-fed infants54. L. reuteri, which is also present in


19


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 49

monkey milk41, has been shown to reduce intestinal permeability55. Although a change in gut permeability was not directly measured in the present study, the higher urinary lactose excretion of formula-fed infants (Fig.6) is indicative of higher intestinal permeability. Thus the diet induced pro-inflammatory response of the formula-fed group may be due to increased gut permeability as a result of a different gut microbial profile. Nevertheless, we cannot rule out the possibility that the difference in immune function in the first month of life may be due to a difference in stress between the nursery-reared formula-fed monkeys as opposed to the motherreared breast-fed monkeys. Our results showed that in contrast to breast-fed infants, formula-fed infants exhibited rapid increases in serum BCAAs in the first month of life, while acetoacetate and glucose remained low, and showed stable concentrations from 2 weeks until 3 months of age. Previous studies have reported higher serum BCAAs in formula-fed human infants4, 56, 57, both postprandially58 and fasting59, as well as lower glucose56 when compared with breast-fed infants. The higher concentrations of BCAAs are particularly interesting given the evidence for their contribution to insulin secretion4, 56, 57, as well as the recent reports linking BCAA-related metabolic profiles with an insulin resistant phenotype60, 61. Higher concentrations of serum glutamine in breast-fed infants are interesting, considering glutamine concentrations of formula are higher than rhesus milk (Table 1). Glutamine can be consumed in the diet or endogenously formed from glutamate and ammonia by glutamine synthetase that is found in the liver, brain, and muscle tissue. The urea cycle and glutamine synthesis are the two main ammonia-detoxifying systems in the body, where glutamine synthetase serves as a nitrogen scavenger for any ammonia escaping urea synthesis62. Notably, serum and urine urea concentrations were not significantly different between formula-fed and


20

Page 21 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


breast-fed infants, suggesting that despite the higher protein content of infant formula (Table 1), the breakdown products of protein metabolism are lower in formula-fed infants. These findings, combined with known amino acid degradation pathways (Fig. 7), suggest the possibility that formula-fed infants have a reduced ability to metabolize amino acids compared with their breastfed counterparts. Previous studies have reported similar differences in serum amino acid levels from formula-fed and breast-fed human infants63. Other differences between formula-fed and breast-fed infants included higher concentrations of urinary carnitine in formula-fed infants after birth, which could be due to endogenous synthesis from excess lysine and methionine in the diet64, 65, or to its presence in bovine-based infant formula. Interestingly, allantoin levels were higher in the urine of formula-fed infant rhesus monkeys after birth (Fig. 6). Allantoin is derived from uric acid by the enzyme uricase, which is functional in most mammals (including rhesus monkeys) except humans and some non-human primates66. Uric acid is the product of nucleic acid metabolism. It has previously been shown that higher levels of uric acid in adult human serum combined with lower urinary pH is associated with an increased odds ratio of developing metabolic syndrome67, 68. It is therefore interesting to speculate that the slightly higher serum allantoin concentration combined with a decreased urine pH may indicate increased odds of the formula-fed rhesus infants developing metabolic syndrome later in life. Indeed, the combination of increased growth rate, higher serum insulin, higher allantoin, and higher BCAAs evident in formula-fed infants are suggestive of metabolic complications in the long-term. However, further studies are needed to confirm whether these short-term effects translate into long-term outcomes. The larger body size, higher blood insulin level, different inflammatory and metabolic biomarker profiles in formula-fed infants support the hypothesis that postnatal nutritional manipulation


21


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 49

induces changes in metabolism. Together with altered metabolism, and inflammatory biomarker profiles, a strong systematic and multi-compartmental effect on infant development was observed which was reflected in the serum and urine as distinct metabolic phenotypes. The main assumption in this study is that infant monkeys will regulate milk or formula consumption to meet the needs for growth and development, as both groups of monkeys were able to feed ad libitum. Potential confounding factors include activity levels, as well as environmental and psychological stress, and impacts of these variables on the measurements performed in this study remain to be investigated. A limitation of this study was the fact that infants were not strictly fasted prior to serum sample collection. This presents a problem for all infant studies as clinical practice dictates that infants are fed frequently. In spite of this, based on results from this study, we speculate that formula-fed infants experience metabolic stress that could play a part in the commonly reported relationship between formula-feeding and increased risk of obesity and related metabolic conditions. Our findings support the contention that infant feeding practice profoundly influences metabolism in developing infants and may be the link between early feeding practices and the development of metabolic disease later in life. With the current focus on the contribution of early life exposures in the prevention of chronic disease, the potential to improve nutritional interventions in the absence of breast-feeding during this critical developmental period must be explored. Our data suggest that reducing protein levels in infant formula may reduce the level of metabolic stress experienced by the infant. This study demonstrates that metabolomics is a powerful tool for rapid evaluation of the effect of dietary treatment on infants, and should play an important role in defining the consequences of, and improvements to, formula-based nutrition for non-breastfed infants.


22

Page 23 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


AUTHOR INFORMATION Corresponding Author *Carolyn M. Slupsky, Department of Nutrition, One Shields Avenue, University of California, Davis, CA 95616. Ph: (530) 752 – 6804; Fax: (530) 752 – 8966 e-mail: [email protected] †Current address: UCD Institute of Food and Health, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland. Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding Sources This publication was made possible by support from Fonterra Research and Development Centre.

ACKNOWLEDGMENT We would like to thank Sarah Davis and Toni Traill at the UC Davis primate center for taking care of, and collecting samples from, the monkeys in our study. We are indebted to Dr. Darya Mishchuk for help with collecting the NMR data and purifying DNA from fecal material, and Ms. Tina Du for cytokine assays. We are particularly grateful to Dr. David Mills for providing us with DNA extraction and 16S rDNA amplification protocols. We would also thank Dr. Karen Kalanetra and Nicholas Bokulich for their technical advice, Drs. Maria Marco and James Dekker for critical reading of the manuscript, and the UC Davis CA&ES Computing Cluster Center


23


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 49

(Farm) for providing access to computing resources. All authors claim no competing financial interests. Supporting Information Available: This material is available free of charge via the Internet at http://pubs.acs.org.

ABBREVIATIONS PCA, principal components analysis; DSS-d6, 3-(trimethylsilyl)-1-propanesulfonic acid-d6; EGF, Epidermal growth factor; FGF-basic, Fibroblast growth factor basic; G-CSF, Granulocyte colony-stimulating factor; HGF, Hepatocyte growth factor; IFN-γ, Interferon-gamma; IL-1β, Interleukin-1 beta; IL-1RA, Interleukin-1 receptor antagonist; MCP-1, Monocyte chemotactic protein-1; MDC, Macrophage-derived chemokine; MIF, Macrophage migration inhibitory factor; MIG, monokine induced by gamma interferon; MIP-1α, Macrophage inflammatory protein-1 alpha; MIP-1β, Macrophage inflammatory protein-1 beta; RANTES, Regulated upon Activation, Normal T-cell Expressed, and Secreted; TNF-α, Tumor necrosis factor-alpha; WBC, white blood cell; RBC, red blood cell.


24

Page 25 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


REFERENCES 1.

Butte, N. F.; Wong, W. W.; Hopkinson, J. M.; Smith, E. O.; Ellis, K. J., Infant feeding mode affects early growth and body composition. Pediatrics 2000, 106, 1355-1366.

2.

Le Huërou-Luron, I.; Blat, S.; Boudry, G., Breast- vs. formula-feeding: impacts on the digestive tract and immediate and long-term health effects. Nutr Res Rev 2010, 23, 23-36.

3.

Schwartz, S.; Friedberg, I.; Ivanov, I. V.; Davidson, L. A.; Goldsby, J. S.; Dahl, D. B.; Herman, D.; Wang, M.; Donovan, S. M.; Chapkin, R., A metagenomic study of dietdependent interaction between gut microbiota and host in infants reveals differences in immune response. Genome Biol 2012, 13, R32.

4.

Lucas, A.; Sarson, D. L.; Blackburn, A. M.; Adrian, T. E.; Aynsley-Green, A.; Bloom, S. R., Breast vs bottle: endocrine responses are different with formula feeding. Lancet 1980, 1, 1267-1269.

5.

Andersson, Y.; Hammarström, M.-L.; Lönnerdal, B.; Graverholt, G.; Fält, H.; Hernell, O., Formula feeding skews immune cell composition toward adaptive immunity compared to breastfeeding. J Immunol 2009, 183, 4322-4328.

6.

Lönnerdal, B., Preclinical assessment of infant formula. Ann Nutr Metab 2012, 60, 196199.

7.

Gibbs, R. A.; Rogers, J.; Katze, M. G.; Bumgarner, R.; Weinstock, G. M.; Mardis, E. R.; Remington, K. A.; Strausberg, R. L.; Venter, J. C.; Wilson, R. K.; Batzer, M. A.; Bustamante, C. D.; Eichler, E. E.; Hahn, M. W.; Hardison, R. C.; Makova, K. D.; Miller, W.; Milosavljevic, A.; Palermo, R. E.; Siepel, A.; Sikela, J. M.; Attaway, T.; Bell, S.; Bernard, K. E.; Buhay, C. J.; Chandrabose, M. N.; Dao, M.; Davis, C.; Delehaunty, K. D.; Ding, Y.; Dinh, H. H.; Dugan-Rocha, S.; Fulton, L. A.; Gabisi, R. A.; Garner, T. T.;


25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 49

Godfrey, J.; Hawes, A. C.; Hernandez, J.; Hines, S.; Holder, M.; Hume, J.; Jhangiani, S. N.; Joshi, V.; Khan, Z. M.; Kirkness, E. F.; Cree, A.; Fowler, R. G.; Lee, S.; Lewis, L. R.; Li, Z.; Liu, Y.-S.; Moore, S. M.; Muzny, D.; Nazareth, L. V.; Ngo, D. N.; Okwuonu, G. O.; Pai, G.; Parker, D.; Paul, H. A.; Pfannkoch, C.; Pohl, C. S.; Rogers, Y.-H.; Ruiz, S. J.; Sabo, A.; Santibanez, J.; Schneider, B. W.; Smith, S. M.; Sodergren, E.; Svatek, A. F.; Utterback, T. R.; Vattathil, S.; Warren, W.; White, C. S.; Chinwalla, A. T.; Feng, Y.; Halpern, A. L.; Hillier, L. W.; Huang, X.; Minx, P.; Nelson, J. O.; Pepin, K. H.; Qin, X.; Sutton, G. G.; Venter, E.; Walenz, B. P.; Wallis, J. W.; Worley, K. C.; Yang, S.-P.; Jones, S. M.; Marra, M. A.; Rocchi, M.; Schein, J. E.; Baertsch, R.; Clarke, L.; Csürös, M.; Glasscock, J.; Harris, R. A.; Havlak, P.; Jackson, A. R.; Jiang, H.; Liu, Y.; Messina, D. N.; Shen, Y.; Song, H. X.-Z.; Wylie, T.; Zhang, L.; Birney, E.; Han, K.; Konkel, M. K.; Lee, J.; Smit, A. F. A.; Ullmer, B.; Wang, H.; Xing, J.; Burhans, R.; Cheng, Z.; Karro, J. E.; Ma, J.; Raney, B.; She, X.; Cox, M. J.; Demuth, J. P.; Dumas, L. J.; Han, S.-G.; Hopkins, J.; Karimpour-Fard, A.; Kim, Y. H.; Pollack, J. R.; Vinar, T.; Addo-Quaye, C.; Degenhardt, J.; Denby, A.; Hubisz, M. J.; Indap, A.; Kosiol, C.; Lahn, B. T.; Lawson, H. A.; Marklein, A.; Nielsen, R.; Vallender, E. J.; Clark, A. G.; Ferguson, B.; Hernandez, R. D.; Hirani, K.; Kehrer-Sawatzki, H.; Kolb, J.; Patil, S.; Pu, L.-L.; Ren, Y.; Smith, D. G.; Wheeler, D. A.; Schenck, I.; Ball, E. V.; Chen, R.; Cooper, D. N.; Giardine, B.; Hsu, F.; Kent, W. J.; Lesk, A.; Nelson, D. L.; O'brien, W. E.; Prüfer, K.; Stenson, P. D.; Wallace, J. C.; Ke, H.; Liu, X.-M.; Wang, P.; Xiang, A. P.; Yang, F.; Barber, G. P.; Haussler, D.; Karolchik, D.; Kern, A. D.; Kuhn, R. M.; Smith, K. E.; Zwieg, A. S., Evolutionary and biomedical insights from the rhesus macaque genome. Science 2007, 316, 222-234.


26

Page 27 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


8.

Kelleher, S. L.; Casas, I.; Carbajal, N.; Lonnerdal, B., Supplementation of infant formula with the probiotic lactobacillus reuteri and zinc: impact on enteric infection and nutrition in infant rhesus monkeys. J Pediatr Gastroenterol Nutr 2002, 35, 162-168.

9.

Kelleher, S. L.; Chatterton, D.; Nielsen, K.; Lonnerdal, B., Glycomacropeptide and alphalactalbumin supplementation of infant formula affects growth and nutritional status in infant rhesus monkeys. Am J Clin Nutr 2003, 77, 1261-1268.

10.

O'Sullivan, A.; He, X.; McNiven, E. M. S.; Hinde, K.; Haggarty, N. W.; Lonnerdal, B.; Slupsky, C. M., Metabolomic phenotyping validates the infant rhesus monkey as a model of human infant metabolism. J Pediatr Gastroenterol Nutr 2013, 56, 355-363.

11.

Mckenna, P.; Hoffmann, C.; Minkah, N.; Aye, P. P.; Lackner, A.; Liu, Z.; Lozupone, C. A.; Hamady, M.; Knight, R.; Bushman, F. D., The macaque gut microbiome in health, lentiviral infection, and chronic enterocolitis. PLoS Pathogens 2008, 4, e20.

12.

Adlerberth, I.; Wold, A. E., Establishment of the gut microbiota in Western infants. Acta Paediatrica 2009, 98, 229-238.

13.

Isolauri, E., Development of healthy gut microbiota early in life. J Pediatr Child Health 2012, 48, 1-6.

14.

Chichlowski, M.; German, J. B.; Lebrilla, C. B.; Mills, D. A., The Influence of Milk Oligosaccharides on Microbiota of Infants: Opportunities for Formulas. Ann Rev Food Sci Technol 2011, 2, 331-351.

15.

Koenig, J. E.; Spor, A.; Scalfone, N.; Fricker, A. D.; Stombaugh, J.; Knight, R.; Angenent, L. T.; Ley, R. E., Succession of microbial consortia in the developing infant gut microbiome. Proc Natl Acad Sci USA 2010, 108, 4578-4585.


27


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

16.

Page 28 of 49

Yatsunenko, T.; Rey, F. E.; Manary, M. J.; Trehan, I.; Dominguez-Bello, M. G.; Contreras, M.; Magris, M.; Hidalgo, G.; Baldassano, R. N.; Anokhin, A. P.; Heath, A. C.; Warner, B.; Reeder, J.; Kuczynski, J.; Caporaso, J. G.; Lozupone, C. A.; Lauber, C.; Clemente, J. C.; Knights, D.; Knight, R.; Gordon, J. I., Human gut microbiome viewed across age and geography. Nature 2012, 486, 222-227.

17.

Harmsen, H. J.; Wildeboer-Veloo, A. C.; Raangs, G. C.; Wagendorp, A. A.; Klijn, N.; Bindels, J. G.; Welling, G. W., Analysis of intestinal flora development in breast-fed and formula-fed infants by using molecular identification and detection methods. J Pediatr Gastroenterol Nutr 2000, 30, 61-67.

18.

Mackie, R. I.; Sghir, A.; Gaskins, H. R., Developmental microbial ecology of the neonatal gastrointestinal tract. Am J Clin Nutr 1999, 69, 1035S-1045S.

19.

Lönnerdal, B.; Keen, C. L.; Glazier, C. E.; Anderson, J., A longitudinal study of rhesus monkey (Macaca mulatta) milk composition: trace elements, minerals, protein, carbohydrate, and fat. Pediatr Res 1984, 18, 911-914.

20.

Davis, T. A.; Nguyen, H. V.; Garcia-Bravo, R.; Fiorotto, M. L.; Jackson, E. M.; Lewis, D. S.; Lee, D. R.; Reeds, P. J., Amino acid composition of human milk is not unique. J Nutr 1994, 124, 1126-1132.

21.

Martínez, I.; Kim, J.; Duffy, P. R.; Schlegel, V. L.; Walter, J., Resistant starches types 2 and 4 have differential effects on the composition of the fecal microbiota in human subjects. PLoS ONE 2010, 5, e15046.

22.

Bokulich, N. A.; Bamforth, C. W.; Mills, D. A., Brewhouse-resident microbiota are responsible for multi-stage fermentation of American coolship ale. PLoS ONE 2012, 7, e35507.


28

Page 29 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


23.

Werner, J. J.; Zhou, D.; Caporaso, J. G.; Knight, R.; Angenent, L. T., Comparison of Illumina paired-end and single-direction sequencing for microbial 16S rRNA gene amplicon surveys. ISME J 2012, 6, 1273-1276.

24.

Caporaso, J. G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F. D.; Costello, E. K.; Fierer, N.; Peña, A. G.; Goodrich, J. K.; Gordon, J. I.; Huttley, G. A.; Kelley, S. T.; Knights, D.; Koenig, J. E.; Ley, R. E.; Lozupone, C. A.; McDonald, D.; Muegge, B. D.; Pirrung, M.; Reeder, J.; Sevinsky, J. R.; Turnbaugh, P. J.; Walters, W. A.; Widmann, J.; Yatsunenko, T.; Zaneveld, J.; Knight, R., QIIME allows analysis of high-throughput community sequencing data. Nature Meth 2010, 7, 335-336.

25.

Edgar, R. C., Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010, 26, 2460-2461.

26.

Caporaso, J. G.; Bittinger, K.; Bushman, F. D.; DeSantis, T. Z.; Andersen, G. L.; Knight, R., PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 2010, 26, 266-267.

27.

DeSantis, T. Z.; Hugenholtz, P.; Keller, K.; Brodie, E. L.; Larsen, N.; Piceno, Y. M.; Phan, R.; Andersen, G. L., NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Res 2006, 34, W394-W399.

28.

Price, M. N.; Dehal, P. S.; Arkin, A. P., FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol 2009, 26, 1641-1650.

29.

Lozupone, C.; Knight, R., UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005, 71, 8228-8235.

30.

Slupsky, C. M.; Rankin, K. N.; Wagner, J.; Fu, H.; Chang, D.; Weljie, A. M.; Saude, E. J.; Lix, B.; Adamko, D. J.; Shah, S.; Greiner, R.; Sykes, B. D.; Marrie, T. J.,


29


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 49

Investigations of the effects of gender, diurnal variation, and age in human urinary metabolomic profiles. Anal Chem 2007, 79, 6995-7004. 31.

Weljie, A. M.; Newton, J.; Mercier, P.; Carlson, E.; Slupsky, C. M., Targeted profiling: quantitative analysis of 1H NMR metabolomics data. Anal Chem 2006, 78, 4430-4442.

32.

Escribano, J.; Luque, V.; Ferre, N.; Mendez-Riera, G.; Koletzko, B.; Grote, V.; Demmelmair, H.; Bluck, L.; Wright, A.; Closa-Monasterolo, R.; Group, E. C. O. T. S., Effect of protein intake and weight gain velocity on body fat mass at 6 months of age: the EU Childhood Obesity Programme. Int J Obes 2012, 36, 548-553.

33.

Karaolis-Danckert, N.; Günther, A. L. B.; Kroke, A.; Hornberg, C.; Buyken, A. E., How early dietary factors modify the effect of rapid weight gain in infancy on subsequent body-composition development in term children whose birth weight was appropriate for gestational age. Am J Clin Nutr 2007, 86, 1700-1708.

34.

Chomtho, S.; Wells, J. C.; Williams, J. E.; Davies, P. S.; Lucas, A.; Fewtrell, M. S., Infant growth and later body composition: evidence from the 4-component model. Am J Clin Nutr 2008, 87, 1776-1784.

35.

Monteiro, P. O. A.; Victora, C. G., Rapid growth in infancy and childhood and obesity in later life--a systematic review. Obes Rev 2005, 6, 143-154.

36.

Owen, C. G.; Martin, R. M.; Whincup, P. H.; Smith, G. D.; Cook, D. G., Does breastfeeding influence risk of type 2 diabetes in later life? A quantitative analysis of published evidence. Am J Clin Nutr 2006, 84, 1043-1054.

37.

Singhal, A., Early nutrition and long-term cardiovascular health. Nutr Rev 2006, 64, S44S49.

38.

Hill, D. J.; Milner, R. D., Insulin as a growth factor. Pediatr Res 1985, 19, (9), 879-886.


30

Page 31 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


39.

Dewey, K. G., Growth characteristics of breast-fed compared to formula-fed infants. Biol Neonate 1998, 74, 94-105.

40.

Dewey, K. G.; Heinig, M. J.; Nommsen, L. A.; Peerson, J. M.; Lönnerdal, B., Growth of breast-fed and formula-fed infants from 0 to 18 months: the DARLING Study. Pediatrics 1992, 89, 1035-1041.

41.

Jin, L.; Hinde, K.; Tao, L., Species diversity and relative abundance of lactic acid bacteria in the milk of rhesus monkeys (Macaca mulatta). J Med Primatol 2011, 40, 52 - 58.

42.

Png, C. W.; Linden, S. K.; Gilshenan, K. S.; Zoetendal, E. G.; McSweeney, C. S.; Sly, L. I.; McGuckin, M. A.; Florin, T. H. J., Mucolytic bacteria with increased prevalence in IBD mucosa augment in vitro utilization of mucin by other bacteria. Am J Gastroenterol 2010, 105, 2420 - 2428.

43.

Sartor, R. B., Key questions to guide a better understanding of host-commensal microbiota interactions in intestinal inflammation. Mucosal Immun 2010, 4, 127 - 132.

44.

Fernandez, E. M.; Pot, B.; Grangette, C., Beneficial effect of probiotics in IBD. Gut Microbes 2011, 2, 280 - 286.

45.

DeLeoz, M. L. A.; Gaerlan, S. C.; Strum, J. S.; Dimapasoc, L. M.; Mirmiran, M.; Tancredi, D. J.; Smilowitz, J. T.; Kalenetra, K. M.; Mills, D. A.; German, J. B.; Lebrilla, C. B.; Underwood, M. A., Lacto-N-tetraose, fucosylation, and secretor status are highly variable in human milk oligosaccharides from women delivering preterm. J Proteome Res 2012, 11, 4662-4672.

46.

Hinde, K.; Power, M. L.; Oftedal, O. T., Rhesus macaque milk: Magnitude, sources, and consequences of individual variation over lactation. Am J Phys Anthropol 2009, 138, 148157.


31


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

47.

Page 32 of 49

Thum, C.; Cookson, A. L.; Otter, D. E.; McNabb, W. C.; Hodgkinson, A. J.; Dyer, J.; Roy, N. C., Can nutritional modulation of the maternal intestinal microbiota influence the development of the infant gastrointestinal tract? J Nutr 2012, (in press).

48.

Eidelman, A. I., Breastfeeding and the use of human milk: An analysis of the American Academy of Pediatrics 2012 Breastfeeding Policy Statement. Breastfeeding Med 2012, 7, 323-324.

49.

Field, C. J., The immunological components of human milk and their effect on immune development in infants. J Nutr 2005, 135, 1-4.

50.

Bindels, L. B.; Beck, R.; Schakman, O.; Martin, J. C.; DeBacker, F.; Sohet, F. M.; Dewulf, E. M.; Pachikian, B. D.; Neyrinck, A. M.; Thissen, J.-P.; Verrax, J.; Calderon, P. B.; Pot, B.; Grangette, C.; Cani, P. D.; Scott, K. P.; Delzenne, N. M., Restoring specific Lactobacilli levels decreases inflammation and muscle atrophy markers in an acute leukemia mouse model. PLoS One 2012, 7, e37971.

51.

Sgouras, D. N.; Panayotopoulou, E. G.; Martinez-Gonzalez, B.; Petraki, K.; Michopoulos, S.; Mentis, A., Lactobacillus johnsonii La1 attenuates Helicobacter pyloriassociated gastritis and reduces levels of proinflammatory chemokines in C57BL/6 mice. Clin Diagn Lab Immun 2005, 12, 1378 - 1386.

52.

Kainonen, E.; Rautava, S.; Isolauri, E., Immunological programming by breast milk creates an anti-inflammatory cytokine milieu in breast-fed infants compared to formulafed infants. Br J Nutr 2012, (in press).

53.

Pabst, H. F.; Spady, D. W.; Pilarski, L. M.; Carson, M. M.; Beeler, J. A.; Krezolek, M. P., Differential modulation of the immune response by breast- or formula-feeding of infants. Acta Paediatr 1997, 86, 1291-1297.


32

Page 33 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


54.

Catassi, C.; Bonucci, A.; Coppa, G. V.; Carlucci, A.; Giorgi, P. L., Intestinal permeability changes during the first month: effect of natural versus artificial feeding. J Pediatr Gastroenterol Nutr 1995, 21, 383-386.

55.

Ahrne, S.; Hagslatt, M.-L. J., Effect of Lactobacilli on paracellular permeability in the gut. Nutrients 2011, 3, 104 - 117.

56.

Socha, P.; Grote, V.; Gruszfeld, D.; Janas, R.; Demmelmair, H.; Closa-Monasterolo, R.; Subias, J. E.; Scaglioni, S.; Verduci, E.; Dain, E.; Langhendries, J. P.; Perrin, E.; Koletzko, B.; Group, f. t. E. C. O. T. S., Milk protein intake, the metabolic-endocrine response, and growth in infancy: data from a randomized clinical trial. Am J Clin Nutr 2011, 94, 1776S-1784S.

57.

Wallensteen, M.; Lindblad, B. S.; Zetterström, R.; Persson, B., Acute C-peptide, insulin and branched chain amino acid response to feeding in formula and breast fed infants. Acta Paediatr Scand 1991, 80, 143-148.

58.

Tikanoja, T.; Simell, O.; Viikari, M.; Järvenpää, A. L., Plasma amino acids in term neonates after a feed of human milk or formula. Acta Paediatr 1982, 71, 391-397.

59.

Janas, L. M.; Picciano, M. F.; Hatch, T. F., Indices of protein metabolism in term infants fed human milk, whey-predominant formula, or cow's milk formula. Pediatrics 1985, 75, 775-784.

60.

Newgard, C. B.; An, J.; Bain, J. R.; Muehlbauer, M. J.; Stevens, R. D.; Lien, L. F.; Haqq, A. M.; Shah, S. H.; Arlotto, M.; Slentz, C. A.; Rochon, J.; Gallup, D.; Ilkayeva, O.; Wenner, B. R.; Yancy, W. S.; Eisenson, H.; Musante, G.; Surwit, R. S.; Millington, D. S.; Butler, M. D.; Svetkey, L. P., A branched-chain amino acid-related metabolic signature


33


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 49

that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 2009, 9, 311-326. 61.

Wang, T. J.; Larson, M. G.; Vasan, R. S.; Cheng, S.; Rhee, E. P.; McCabe, E.; Lewis, G. D.; Fox, C. S.; Jacques, P. F.; Fernandez, C.; O'Donnell, C. J.; Carr, S. A.; Mootha, V. K.; Florez, J. C.; Souza, A.; Melander, O.; Clish, C. B.; Gerszten, R. E., Metabolite profiles and the risk of developing diabetes. Nature 2011, 17, 448-453.

62.

Haberle, J.; Gorg, B.; Toutain, A.; Rutsch, F.; Benoist, J.-F.; Gelot, A.; Suc, A.-L.; Koch, H. G.; Schliess, F.; Haussinger, D., Inborn error of amino acid synthesis: Human glutamine synthetase deficiency. J Inherit Metab Dis 2006, 29, 352-358.

63.

Hernell, O.; Lönnerdal, B., Nutritional evaluation of protein hydrolysate formulas in healthy term infants: plasma amino acids, hematology, and trace elements. Am J Clin Nutr 2003, 78, 296-301.

64.

Krajcovicová-Kudlácková, M.; Simoncic, R.; Béderová, A.; Babinská, K.; Béder, I., Correlation of carnitine levels to methionine and lysine intake. Physiol Res 2000, 49, 399-402.

65.

Vaz, F. M.; Wanders, R. J. A., Carnitine biosynthesis in mammals. Biochem J 2002, 361, 417-429.

66.

Fanelli Jr, G M; Beyer Jr, K. H., Uric acid in nonhuman primates with special reference to Its renal transport. Ann Rev Pharmacol 1974, 14, 355-364.

67.

Hara, S.; Tsuji, H.; Ohmoto, Y.; Amakawa, K.; Hsieh, S. D.; Arase, Y.; Nakajima, H., High serum uric acid level and low urine pH as predictors of metabolic syndroms: a retrospective cohort study in a Japanese urban population. Metabolism 2012, 61, 281-288.


34

Page 35 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


68.

Onat, A.; Uyarel, H.; Hergenç, G.; Karabulut, A.; Albayrak, S.; Sari, I.; Yazici, M.; Keleş, I., Serum uric acid is a determinant of metabolic syndrome in a population-based study. Am J Hypertens 2006, 19, 1055-1062.


35


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 49

FIGURE LEGENDS

Figure 1. Summary of sample collection times. Figure 2. Characterization of growth trajectories and circulating insulin of formula-fed (closed circles, n = 5) and breast-fed (open circles, n = 5) infant rhesus monkeys. (A) Weight (p = 0.053), (B) length (measured from crown to rump) (p = 0.030), and (C) serum insulin concentrations (p = 0.008) are significantly higher in the formula-fed group. Data presented are mean ± SEM. P-values were calculated using repeated measures ANOVA. Figure 3. 16S rRNA gene surveys reveal differences in microbial composition by feeding strategies. (A) Principal coordinates analysis (PCoA) of unweighted Unifrac distances of 16S rRNA sequences demonstrates distinct clustering according to feeding strategy and experimental time point. The plot on the left is highlighting changes in the formula-fed group over time, and on the right, changes in the breast-fed group over time. The inset shows the similarities of the monkeys at birth, prior to assignment to the formula-fed or breast-fed group. (B) Arithmetic mean (UPGMA) clustering of monkey intestinal microbial communities in breast-fed (BF) and formula-fed (FF) groups based on the unweighted UniFrac distance. Subject color-coding: red, formula-fed; green, breast-fed; blue, samples collected at birth from all monkeys. (C) Plot of the relative abundance (after transformation by an arcsine function) of Bifidobacterium, Lactobacillus, and Ruminococcus (multiple repeated measures ANOVA, p = 0.06, p < 0.001, p < 0.001, respectively). Data are presented as mean ± SEM. Figure 4. Principal Component Analysis (PCA) reveals distinct separation between serum and urine metabolomic profiles from breast-fed (green), formula-fed (red) infant rhesus monkeys, as


36

Page 37 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


well as measurements collected at birth (blue, assigned to formula-fed, yellow assigned to breastfed) infant rhesus monkeys. (A) Serum NMR data collected from infants from week 2 of life onwards for breast-fed (n=27), formula-fed (n = 30), as well as serum collected from birth infants (n=9) (R2X = 0.67; Q2 = 0.29). (B) Urine NMR data collected from infants from week 1 of life onwards for breast-fed (n = 54) and formula-fed (n = 57) infants; as well as urine collected from birth infants (n=8) (R2X = 0.48; Q2 = 0.22). In both score plots, the confidence interval is defined by the Hotelling’s T2 ellipse (95% confidence interval). Figure 5. Comparison of serum metabolite concentrations in formula-fed (closed circles) and breast-fed (open circles) infant rhesus monkeys from birth to 3 months of age. Data are presented as mean ± SEM. After exclusion of the birth time point, and with FDR adjustment, the following metabolites are significantly different (repeated measures ANOVA, p < 0.05): leucine, isoleucine, valine, lysine, phenylalanine, methionine, threonine, alanine, arginine, asparagine, aspartate, glutamate, glutamine, serine, taurine, glucose, acetoacetate, creatinine, and myoinositol. The remaining metabolites: pyruvate (p = 0.29), citrate (p = 0.14), succinate (p = 0.14), fumarate (p = 0.10), galactose (p=0.08), allantoin (p = 0.10) were not significantly different using repeated measures ANOVA. Specific time points where these metabolites are significantly different were determined using independent samples t-tests, and are indicated as: * (p < 0.05); ** (p < 0.01) (Citrate, weeks 4-8; succinate, weeks 4, and 8; fumarate, weeks 4 and 6; galactose, weeks 4, 8, and 12; and allantoin weeks 0 and 6. No significant differences were observed for pyruvate). Figure 6. Comparison of urine metabolite concentrations in formula-fed (closed circles) and breast-fed (open circles) infant rhesus monkeys from birth to 3 months of age. Data are presented as mean ± SEM. After FDR adjustment, the following metabolites were significantly different


37


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 49

(repeated measures ANOVA, p < 0.05): allantoin, lysine, carnitine, taurine, galactitol, and TMAO. The remaining metabolites: lactose (p = 0.30), galactose (p = 0.20), and myo-inositol (p = 0.20) were not significantly different using multiple repeated measures ANOVA. Specific time points where these metabolites are significantly different were determined using independent samples t-tests, and are indicated as: * (p < 0.05); ** (p < 0.01); *** (p < 0.001). (Lactose, weeks 4, 5, and 12; myo-inositol, weeks 3 – 5, and 9 – 12; galactose, weeks 5, 6, and 12.) Figure 7: Pathway of amino acid degradation. Those metabolites that are higher in formula-fed are indicated in red, those higher in breast-fed are indicated in green, those that are not significantly different are highlighted in cyan, and those that were not measured are in black.


38

Page 39 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 1. Macronutrient and amino acid composition of standard infant formula used in the study and rhesus milk. Protein (g/L) Fat (g/L) Carbohydrate (g/L) Amino acids (µM) Alanine Arginine Aspartic acid / Asparagine Cysteine Glutamic acid / Glutamine Glycine Histidine Isoleucine Leucine Lysine Methionine Phenylalanine Proline Serine Threonine Tryptophan Tyrosine Valine

Formula 18.3 36.7 77.7

Rhesus Milk 11.6 48.0 79.0

9,260 3,474 13,883 1,526 25,222 4,886 2,837 9,089 15,100 11,684 3,319 4,774 13,859 9,952 9,700 1,527 4,454 10,192

5,890 2,790 8,213 1,103 15,456 3,383 2,526 6,595 11,153 6,172 1,575 3,608 13,160 5,426 5,289 ND 2,539 6,934

ND, Not determined.


39


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 40 of 49

Table 2: Hematologic data for formula-fed (n = 5) and breast-fed (n = 5) infant monkeys at birth, 4, 8, and 12 weeks of age. Birth

Week 4

Week 8

Week 12

Mean ± SEM

Mean ± SEM

Mean ± SEM

Mean ± SEM

WBC (x10^3 /uL) Formula-fed Breast-fed

13.3 ± 2.4 11.7 ± 2.3

5.7 ± 1.6 8.1 ± 1.3

8.8 ± 1.0 9.9 ± 1.4

7.9 ± 0.9 9.3 ± 2.5

RBC (x10^6 /uL) Formula-fed Breast-fed

5.8 ± 0.4 6.2 ± 0.4

4.8 ± 0.1 5.2 ± 0.3

5.0 ± 0.1 5.5 ± 0.2

5.3 ± 0.1 5.3 ± 0.3

Hemoglobin (gm/dL) Formula-fed Breast-fed

17.0 ± 0.8 18.3 ± 1.2

12.0 ± 0.1 13.1 ± 0.6

12.0 ± 0.2 13.0 ± 0.3

12.4 ± 0.2 12.1 ± 0.6

Hematocrit (%) Formula-fed Breast-fed

52.6 ± 2.1 55.9 ± 3.5

38.1 ± 0.3 39.9 ± 1.8

37.9 ± 0.9 40.4 ± 1.1

38.9 ± 0.6 38.0 ± 1.7

p-values are computed by independent samples t-test and adjusted by FDR. * indicates significant difference (p < 0.05) between groups


40

Page 41 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 3: Serum cytokine ratios of formula-fed (FF) and breast-fed (BF) rhesus infants at each month. In FF

Month 1 FF:BF

IL-1β IL-1RA IL-2 IL-4 IL-8 IL-12 Eotaxin G-CSF IFN-γ MCP MDC MIF MIG MIP-1α MIP-1β RANTES

+ + + + + + + + + + + + + + +

1.16 1.69 1.95 1.12 2.42 1.34 1.50 1.23 3.52 1.16 1.73 2.36 1.48 1.27 BLD-BF 1.59

EGF FGF-basic HGF TNF α

+ + + +

4.07 1.86 1.66 1.17

Month 2 In FF FF:BF Cytokines and Chemokines 0.97 ** * 0.90 * 0.73 ** 0.79 * 0.79 + 1.19 + 3.88 + 1.01 *** + 1.06 * + 1.10 + 1.22 + 2.68 *** 0.75 0.93 *** 0.89 ** 0.91 Growth Factors + 1.92 *** 0.80 ** 0.64 *** ** 0.92

In FF

*

*

+ + + + + + + + + + + + + + + + + +

Month 3 FF:BF 1.07 1.00 1.19 1.04 1.37 1.19 1.29 1.00 1.30 1.24 1.69 2.42 1.08 1.04 0.99 2.03 2.14 1.46 1.04 1.03

Significant differences between diets for each month were calculated by independent samples t-test with FDR correction. Column 1 for each month “In FF” indicates the directional difference in the cytokine or growth factor concentration “+” = higher in formula-fed infants; “-“ = lower in formula-fed infants. Column 2 for each month “FF:BF” presents the fold difference of formula-fed cytokine or growth factor concentration. “4.07” = 4.07 times higher in formula-fed compared to breast-fed. Fold difference is based on mean concentrations. BLD-BF, below limit of detection in breast-fed; BLD-FF, Below limit of detection in formula-fed. Column 3 for each month presents the adjusted p-value. * indicates significant difference (p < 0.05) between groups; ** indicates significant difference (p < 0.01) between groups; *** indicates significant difference (p < 0.001) between groups.


41


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 42 of 49

TOC Graphic:


42

Page 43 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 1. Summary of sample collection times. 104x75mm (600 x 600 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 2. Characterization of growth trajectories and circulating insulin of formula-fed (closed circles, n = 5) and breast-fed (open circles, n = 5) infant rhesus monkeys. (A) Weight (p = 0.053), (B) length (measured from crown to rump) (p = 0.030), and (C) serum insulin concentrations (p = 0.008) are significantly higher in the formula-fed group. Data presented are mean ± SEM, and p-values represent analysis using repeated measures ANOVA. 40x11mm (600 x 600 DPI)


Page 44 of 49

Page 45 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 3. 16S rRNA gene surveys reveal differences in microbial composition by feeding strategies. (A) Principal coordinates analysis (PCoA) of unweighted Unifrac distances of 16S rRNA sequences demonstrates distinct clustering according to feeding strategy and experimental time point. The plot on the left is highlighting changes in the formula-fed group over time, and on the right, changes in the breast-fed group over time. The inset shows the similarities of the monkeys assigned to both groups at week 0. (B) Arithmetic mean (UPGMA) clustering of monkey intestinal microbial communities in breast-fed (BF) and formula-fed (FF) groups based on the unweighted UniFrac distance. Subject color-coding: red, formula-fed; green, breast-fed; blue, samples collected at birth from all monkeys. (C) Plot of the relative abundance (after transformation by an arcsine function) of Bifidobacterium, Lactobacillus, and Ruminococcus (p = 0.06, p < 0.001, p < 0.001, respectively, repeated measures ANOVA) in breast-fed (green) and formula-fed (red). Data are presented as mean ± SEM. 114x138mm (600 x 600 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ACS Paragon Plus Environment

Page 46 of 49

Page 47 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 5. Comparison of serum metabolite concentrations in formula-fed (closed circles) and breast-fed (open circles) infant rhesus monkeys from birth to 3 months of age. Data are presented as mean ± SEM. After exclusion of the birth time point, and with FDR adjustment, the following metabolites are significantly different (repeated measures ANOVA, p < 0.05): leucine, isoleucine, valine, lysine, phenylalanine, methionine, threonine, alanine, arginine, asparagine, aspartate, glutamate, glutamine, serine, taurine, glucose, acetoacetate, creatinine, and myo-inositol. The remaining metabolites: pyruvate (p = 0.29), citrate (p = 0.14), succinate (p = 0.14), fumarate (p = 0.10), galactose (p=0.08), allantoin (p = 0.10) were not significantly different using repeated measures ANOVA. Specific time points where these metabolites are significantly different were determined using independent samples t-tests, and are indicated as: * (p < 0.05); ** (p < 0.01) (Citrate, weeks 4-8; succinate, weeks 4, and 8; fumarate, weeks 4 and 6; galactose, weeks 4, 8, and 12; and allantoin weeks 0 and 6. No significant differences were observed for pyruvate). 221x313mm (300 x 300 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ACS Paragon Plus Environment

Page 48 of 49

Page 49 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 6. Comparison of urine metabolite concentrations in formula-fed (closed circles) and breast-fed (open circles) infant rhesus monkeys from birth to 3 months of age. Data are presented as mean ± SEM. After FDR adjustment, the following metabolites were significantly different (repeated measures ANOVA, p < 0.05): allantoin, lysine, carnitine, taurine, galactitol, and TMAO. The remaining metabolites: lactose (p = 0.30), galactose (p = 0.20), and myo-inositol (p = 0.20) were not significantly different using multiple repeated measures ANOVA. Specific time points where these metabolites are significantly different were determined using independent samples t-tests, and are indicated as: * (p < 0.05); ** (p < 0.01); *** (p < 0.001). (Lactose, weeks 4, 5, and 12; myo-inositol, weeks 3 – 5, and 9 – 12; galactose, weeks 5, 6, and 12.) 144x119mm (600 x 600 DPI)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Figure 7: Pathway of amino acid degradation. Those metabolites that are higher in formula-fed are indicated in red, those higher in breast-fed are indicated in green, those that are not significantly different are highlighted in cyan, and those that were not measured are in black. 55x34mm (600 x 600 DPI)


Page 50 of 49

Page 51 of 49

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Figure 7: Pathway of amino acid degradation. Those metabolites that are higher in formula-fed are indicated in red, those higher in breast-fed are indicated in green, those that are not significantly different are highlighted in cyan, and those that were not measured are in black. 55x34mm (600 x 600 DPI)


Early Diet Impacts Infant Rhesus Gut Microbiome ... - ACS Publications

Recommend Documents