The gut microbiome of extremely preterm infants randomized to the early progression of enteral feeding

Ariel A Salas, Kent A Willis, Waldemar A Carlo, Nengjun Yi, Li Zhang, William J Van Der Pol, Noelle E Younge, Elliot J Lefkowitz, Charitharth V Lal, Ariel A Salas, Kent A Willis, Waldemar A Carlo, Nengjun Yi, Li Zhang, William J Van Der Pol, Noelle E Younge, Elliot J Lefkowitz, Charitharth V Lal

Abstract

Background: Early progression of feeding could influence the development of the gut microbiome.

Methods: We collected fecal samples from extremely preterm infants randomized to receive either early (feeding day 2) or delayed (feeding day 5) feeding progression. After study completion, we compared samples obtained at three different time points (week 1, week 2, and week 3) to determine longitudinal differences in specific taxa between the study groups using unadjusted and adjusted negative binomial and zero-inflated mixed models. Analyses were adjusted for a mode of delivery, breastmilk intake, and exposure to antibiotics.

Results: We analyzed 137 fecal samples from 51 infants. In unadjusted and adjusted analyses, we did not observe an early transition to higher microbial diversity within samples (i.e., alpha diversity) or significant differences in microbial diversity between samples (i.e., beta diversity) in the early feeding group. Our longitudinal, single-taxon analysis found consistent differences in the genera Lactococcus, Veillonella, and Bilophila between groups.

Conclusions: Differences in single-taxon analyses independent of the mode of delivery, exposure to antibiotics, and breastmilk feeding suggest potential benefits of early progression of enteral feeding volumes. However, this dietary intervention does not appear to increase the diversity of the gut microbiome in the first 28 days after birth.

Trial registration: ClinicalTrials.gov identifier: NCT02915549.

Impact: Early progression of enteral feeding volumes with human milk reduces the duration of parenteral nutrition and the need for central venous access among extremely preterm infants. Early progression of enteral feeding leads to single-taxon differences in longitudinal analyses of the gut microbiome, but it does not appear to increase the diversity of the gut microbiome in the first 28 days after birth. Randomization in enteral feeding trials creates appealing opportunities to evaluate the effects of human milk diets on the gut microbiome.

Conflict of interest statement

The authors declare no competing interests.

© 2021. The Author(s).

Figures

Fig. 1. Feeding protocol to advance volumes…
Fig. 1. Feeding protocol to advance volumes according to feeding day.
Feeding volumes were advanced on either feeding day 2 or feeding day 5. Rates of feeding advancement were similar in both groups.
Fig. 2. Differences in microbial diversity between…
Fig. 2. Differences in microbial diversity between groups.
Differences in beta diversity according to advancing weeks of life were statistically significant in PERMANOVA and RDA analyses (a). Differences in beta diversity according to study group were not statistically significant by principal coordinate analysis (PCoA) of Bray–Curtis dissimilarity (PERMANOVA, R2 = 0.01, p = 0.09) or redundancy analysis (RDA, f = 1.41, p = 0.13). PERMANOVA permutational multivariate ANOVA, RDA redundancy analysis.
Fig. 3. Differences in microbial diversity between…
Fig. 3. Differences in microbial diversity between groups by week of life.
Significant differences in beta diversity between early and late feeding progression groups at three different time points (n = 57 at week 1, n = 42 at week 2, n = 38 at week 3) were not detected by principal coordinate analysis (PCoA) of Bray–Curtis dissimilarity. PERMANOVA permutational multivariate ANOVA.
Fig. 4. Taxonomic differences at the genus…
Fig. 4. Taxonomic differences at the genus level between groups according to week of life.
For the top 10 most common taxa at the genus level, there were no significant differences between groups at week 1 (n = 57). At week 2 (n = 42), the total read counts of Staphylococcus were higher in the late feeding group (p = 0.02). During week 3 (n = 38), no significant differences in the total read counts were found.
Fig. 5. Unadjusted and adjusted longitudinal analysis…
Fig. 5. Unadjusted and adjusted longitudinal analysis with negative binomial and zero-inflated mixed models to account for individual variability in the gut microbiome.
An unadjusted single-taxon analysis showed that Veillonella spp. counts were significantly higher in the early feeding group (a). An adjusted single-taxon analysis showed that Bilophila, Veillonella, and Lactococcus spp. were significantly higher in the early feeding group (b). The right labels denote statistically significant p values.

References

    1. Tamburini S, Shen N, Wu HC, Clemente JC. The microbiome in early life: implications for health outcomes. Nat. Med. 2016;22:713–722. doi: 10.1038/nm.4142.
    1. Cox LM, et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell. 2014;158:705–721. doi: 10.1016/j.cell.2014.05.052.
    1. Arrieta M-C, et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci. Transl. Med. 2015;307:52–62.
    1. La Rosa PS, et al. Patterned progression of bacterial populations in the premature infant gut. Proc. Natl Acad. Sci. USA. 2014;111:12522–12527. doi: 10.1073/pnas.1409497111.
    1. Grier A, et al. Impact of prematurity and nutrition on the developing gut microbiome and preterm infant growth. Microbiome. 2017;5:158. doi: 10.1186/s40168-017-0377-0.
    1. Pannaraj PS, et al. Association between breast milk bacterial communities and establishment and development of the infant gut microbiome. JAMA Pediatr. 2017;171:647–654. doi: 10.1001/jamapediatrics.2017.0378.
    1. Younge NE, et al. Disrupted maturation of the microbiota and metabolome among extremely preterm infants with postnatal growth failure. Sci. Rep. 2019;9:8167–12. doi: 10.1038/s41598-019-44547-y.
    1. Salas AA, et al. Early progressive feeding in extremely preterm infants: a randomized trial. Am. J. Clin. Nutr. 2018;107:365–370. doi: 10.1093/ajcn/nqy012.
    1. DeSantis TZ, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 2006;72:5069–5072. doi: 10.1128/AEM.03006-05.
    1. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217. doi: 10.1371/journal.pone.0061217.
    1. Zakrzewski M, et al. Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions. Bioinformatics. 2017;33:782–783.
    1. Pinheiro, J. C. & Bates, D. J. (eds). In Mixed-Effects Models in S and S-PLUS, 3–56 (Springer, 2000).
    1. Zhang X, et al. Negative binomial mixed models for analyzing microbiome count data. BMC Bioinform. 2017;18:4–10. doi: 10.1186/s12859-016-1441-7.
    1. Zhang X, et al. Negative binomial mixed models for analyzing longitudinal microbiome data. Front. Microbiol. 2018;9:1683. doi: 10.3389/fmicb.2018.01683.
    1. Zhang X, Yi N. NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis. BMC Bioinform. 2020;21:488–19. doi: 10.1186/s12859-020-03803-z.
    1. Zhang X, Yi N. Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data. Bioinformatics. 2020;36:2345–2351. doi: 10.1093/bioinformatics/btz973.
    1. MacDonald TT, Di, Sabatino A. The exposure of infants to Lactobacillus rhamnosus GG in Finland. J. Pediatr. Gastroenterol. Nutr. 2006;42:476–478. doi: 10.1097/.
    1. Levy R, et al. Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome. Proc. Natl Acad. Sci. USA. 2020;117:13839–13845. doi: 10.1073/pnas.1922498117.
    1. Gibson DL, et al. Maternal exposure to fish oil primes offspring to harbor intestinal pathobionts associated with altered immune cell balance. Gut Microbes. 2015;6:24–32. doi: 10.1080/19490976.2014.997610.
    1. Devkota S, et al. Dietary-fat-induced taurocholic acid promotes pathobiont expansion and colitis in Il10-/- mice. Nature. 2012;487:104–108. doi: 10.1038/nature11225.
    1. Bäckhed F, et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe. 2015;17:852. doi: 10.1016/j.chom.2015.05.012.
    1. Knapp S, et al. Natural competence is common among clinical isolates of Veillonella parvula and is useful for genetic manipulation of this key member of the oral microbiome. Front. Cell Infect. Microbiol. 2017;7:139. doi: 10.3389/fcimb.2017.00139.
    1. Sato-Suzuki Y, et al. Nitrite-producing oral microbiome in adults and children. Sci. Rep. 2020;10:16652. doi: 10.1038/s41598-020-73479-1.
    1. Frost F, et al. A structured weight loss program increases gut microbiota phylogenetic diversity and reduces levels of Collinsella in obese type 2 diabetics: a pilot study. PLoS ONE. 2019;14:e0219489. doi: 10.1371/journal.pone.0219489.
    1. Mashima I, Theodorea CF, Thaweboon B, Thaweboon S, Nakazawa F. Identification of Veillonella species in the tongue biofilm by using a novel one-step polymerase chain reaction method. PLoS ONE. 2016;11:e0157516. doi: 10.1371/journal.pone.0157516.
    1. Ayad EH, Verheul A, Engels WJ, Wouters JT, Smit G. Enhanced flavour formation by combination of selected lactococci from industrial and artisanal origin with focus on completion of a metabolic pathway. J. Appl. Microbiol. 2001;90:59–67. doi: 10.1046/j.1365-2672.2001.01219.x.
    1. Cavanagh D, Fitzgerald GF, McAuliffe O. From field to fermentation: the origins of Lactococcus lactis and its domestication to the dairy environment. Food Microbiol. 2015;47:45–61. doi: 10.1016/j.fm.2014.11.001.
    1. Gurien LA, Stallings-Archer K, Smith SD. Probiotic Lactococcus lactis decreases incidence and severity of necrotizing enterocolitis in a preterm animal model. J. Neonatal Perinat. Med. 2018;11:65–69. doi: 10.3233/NPM-181740.
    1. Willis KA, et al. Fungi form interkingdom microbial communities in the primordial human gut that develop with gestational age. FASEB J. 2019;33:12825–12837. doi: 10.1096/fj.201901436RR.
    1. Koo H, Crossman DK, Morrow CD. Strain tracking to identify individualized patterns of microbial strain stability in the developing infant gut ecosystem. Front. Pediatr. 2020;8:549844. doi: 10.3389/fped.2020.549844.

Source: PubMed

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