Human milk oligosaccharide DSLNT and gut microbiome in preterm infants predicts necrotising enterocolitis

Abstract

Objective: Necrotising enterocolitis (NEC) is a devastating intestinal disease primarily affecting preterm infants. The underlying mechanisms are poorly understood: mother's own breast milk (MOM) is protective, possibly relating to human milk oligosaccharide (HMO) and infant gut microbiome interplay. We investigated the interaction between HMO profiles and infant gut microbiome development and its association with NEC.

Design: We performed HMO profiling of MOM in a large cohort of infants with NEC (n=33) with matched controls (n=37). In a subset of 48 infants (14 with NEC), we also performed longitudinal metagenomic sequencing of infant stool (n=644).

Results: Concentration of a single HMO, disialyllacto-N-tetraose (DSLNT), was significantly lower in MOM received by infants with NEC compared with controls. A MOM threshold level of 241 nmol/mL had a sensitivity and specificity of 0.9 for NEC. Metagenomic sequencing before NEC onset showed significantly lower relative abundance of Bifidobacterium longum and higher relative abundance of Enterobacter cloacae in infants with NEC. Longitudinal development of the microbiome was also impacted by low MOM DSLNT associated with reduced transition into preterm gut community types dominated by Bifidobacterium spp and typically observed in older infants. Random forest analysis combining HMO and metagenome data before disease accurately classified 87.5% of infants as healthy or having NEC.

Conclusion: These results demonstrate the importance of HMOs and gut microbiome in preterm infant health and disease. The findings offer potential targets for biomarker development, disease risk stratification and novel avenues for supplements that may prevent life-threatening disease.

Keywords: molecular biology; oligosaccharides; prebiotic.

Figure 1

Analysis of HMO profiles and DSLNT concentration in NEC and CTRLs. (A) Orthogonal partial least squares discriminant analysis of maternal HMO profiles fed to infants diagnosed with NEC and CTRLs. The p value was calculated based on 2000 permutations. (B) Visual representation of p values obtained from comparison of individual HMOs between NEC and CTRL groups. Wilcoxon rank-sum test was applied, and p values were adjusted with the false discovery rate algorithm. The line indicates p value=0.05. (C) Univariate receiver operating characteristic curve generated on DSLNT concentration identified 241 nmol/mL as the best threshold for NEC prediction. The performance of the classification is defined by the AUC, specificity (false-positive rate) and sensitivity (true-positive rate). (D), Box plot showing the concentration of DSLNT between NEC and controls. Blue line represents the 241 nmol/mL threshold. 2′-FL, 2′-fucosyllactose; 3′-FL, 3′-Fucosyllactose; 6′SL, 6′-Sialyllactose; adj, adjusted; AUC, area under the curve; CTRL, control; DFLac, difucosyllactose; DFLNH, difucosyllacto-N-hexaose; DFLNT, difucosyllacto-N-tetraose; DSLNH, disialyllacto-n-hexaose; DSLNT, disialyllacto-N-tetraose; FDSLNH, fucodisialyllacto-N-hexaose; FLNH, fucosyllacto-N-hexaose; HMO, human milk oligosaccharide; lacto-N- neotetraose, 3′-SL, 3′-sialyllactose; LNFP, lacto-N-fucopentaose; LNH, lacto-N-hexaose; LNnT; LNnT, lacto-N-neotetraose; LNT, lacto-N-tetraose; LST, sialyllacto-N-tetraose; NEC, necrotising enterocolitis.

Figure 2

Analysis of HMO profiles with stratification of NEC-M and NEC-S. (A) Partial least squares discriminant analysis of HMO profiles from CTRL, NEC-M and NEC-S infants. NEC-M and NEC-S cluster together and separately from CTRLs (p<0.001). P values were calculated based on 2000 permutations. Box plots of (B) DSLNT and (C) LNnT concentration between CTRL, NEC-M and NEC-S infants. Kruskal-Wallis followed by Dunn’s test using Bonferroni adjustment was applied. (D) Adjusted linear regression model for DSLNT and LNnT including potential clinical confounders. P values were corrected by false discovery rate (FDR). Significant variables are indicated by asterisks: *** denotes FDR p<0.001; ** denotes FDR p>0.01. CTRL, control; DOL, day of life; DSLNT, disialyllacto-N-tetraose; GA, gestational age; HMO, human milk oligosaccharide; LNnT, lacto-N-neotetraose; NEC, necrotising enterocolitis; NEC-M, medically managed NEC; NEC-S, ninfants with NEC that underwent surgery; PMA postmenstrual age.

Figure 3

Cross-sectional analysis of preterm stool metagenome profiles between NEC and matched controls. Analysis includes the sample closest NEC onset (median of 3 days prior to NEC) and a corresponding control sample matched by day of life. (A) Alpha diversity based on observed species (richness) and Shannon diversity. (B) Bray-Curtis principal coordinate analysis. (C) Box plots showing the relative abundance of significant phyla. (D) Box plots showing the relative abundance of significant species. NEC, necrotising enterocolitis.

Figure 4

Analysis of PGCTs by infants receiving maternal milk above or below the 241 nmol/mL DSLNT threshold. The entire dataset of 644 samples formed five distinct clusters based on lowest Laplace approximation following Dirichlet multinomial clustering. (A) Heatmap showing the relative abundance of dominant bacterial species within each PGCT cluster. The phyla for each species are also shown. (B) Transition model showing the progression of samples through each PGCT, from day of life 0–60 across eight distinct time points. Plots are separated based on whether the concentration of DSLNT in maternal milk was above or below the 241 nmol/mL threshold. Nodes and edges are sized based on the total counts. Nodes are coloured according to Dirichlet Multinomial Mixtures (DMM) cluster number and edges are coloured by the transition frequency. Transitions with less than 5% frequency are not shown. DSLNT, disialyllacto-N-tetraose; PGCT, preterm gut community type.

Figure 5

Modelling of cross-sectional HMO and infant stool metagenomic profiles using Adonis and random forest. (A) Horizontal bar plots showing the variance (r2) in maternal HMO and infant stool metagenomic profiles explained by clinical covariates as modelled by univariate Adonis. Variables with a false discovery rate p value of <0.05 are shown in red. (B) Feature importance from combined HMO and metagenome random forest classification model. Mean decrease accuracy value defines the contribution given by a certain feature to classification process. CTRL, control; DOL, day of life; HMO, human milk oligosaccharide; NEC, necrotising enterocolitis; PMA, postmenstrual age.