. 2025 Dec 10;33(12):2130-2147.e7.

doi: 10.1016/j.chom.2025.11.002. Epub 2025 Nov 26.

Environmental and maternal imprints on infant gut metabolic development

Kine Eide Kvitne¹, Celeste Allaband², Jennifer C Onuora³, Daniela Perry⁴, Simone Zuffa⁵, Lucas Patel⁶, Vincent Charron-Lamoureux⁵, Ipsita Mohanty⁵, Kristija Sejane⁷, Abubaker Patan⁵, Abdullah Al Mahmud⁸, Tahmeed Ahmed⁹, Diego G Bassani¹⁰, Antonio González², Davidson H Hamer¹¹, Rashidul Haque¹², Benjamin Ho¹, Md Iqbal Hossain⁹, Mohammad Shahidul Islam¹³, Daniel McDonald², Lisa G Pell¹⁴, Huma Qamar¹⁴, Daniel E Roth¹⁵, Samir Saha¹³, Prakesh S Shah¹⁶, Md Muniruzzaman Siddiqui¹⁷, Shafiqul Alam Sarker⁹, Shamima Sultana⁹, Sydney Thomas¹, Lindsey A Burnett¹⁸, Shirley M Tsunoda¹, Lars Bode⁷, Pieter C Dorrestein¹⁹, Rob Knight²⁰

Affiliations

¹ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.
² Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
³ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
⁴ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
⁵ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Medical Scientist Training Program, University of California, San Diego, La Jolla, CA, USA.
⁷ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Larsson-Rosenquist Foundation Mother-Milk-Infant Center of Research Excellence (MOMI CORE), University of California, San Diego, La Jolla, CA, USA; Human Milk Institute (HMI), University of California, San Diego, La Jolla, CA, USA.
⁸ Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh; Shield Pharmaceuticals Corp., Ronkonkoma, NY, USA.
⁹ Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
¹⁰ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
¹¹ Department of Global Health, Boston University School of Public Health, Boston, MA, USA; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
¹² Infectious Disease Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
¹³ Child Health Research Foundation, Dhaka, Bangladesh.
¹⁴ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada.
¹⁵ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
¹⁶ Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, Mt. Sinai Hospital, Toronto, ON, Canada.
¹⁷ Mohammadpur Fertility Services and Training Centre, Dhaka, Bangladesh.
¹⁸ Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA.
¹⁹ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, CA, USA. Electronic address: pdorrestein@health.ucsd.edu.
²⁰ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA; Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA. Electronic address: rknight@health.ucsd.edu.

PMID: 41308639
PMCID: PMC12875530
DOI: 10.1016/j.chom.2025.11.002

Environmental and maternal imprints on infant gut metabolic development

Kine Eide Kvitne et al. Cell Host Microbe. 2025.

. 2025 Dec 10;33(12):2130-2147.e7.

doi: 10.1016/j.chom.2025.11.002. Epub 2025 Nov 26.

Authors

Affiliations

¹ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA.
² Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
³ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
⁴ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA.
⁵ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, CA, USA.
⁶ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA; Medical Scientist Training Program, University of California, San Diego, La Jolla, CA, USA.
⁷ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Larsson-Rosenquist Foundation Mother-Milk-Infant Center of Research Excellence (MOMI CORE), University of California, San Diego, La Jolla, CA, USA; Human Milk Institute (HMI), University of California, San Diego, La Jolla, CA, USA.
⁸ Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh; Shield Pharmaceuticals Corp., Ronkonkoma, NY, USA.
⁹ Nutrition Research Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
¹⁰ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
¹¹ Department of Global Health, Boston University School of Public Health, Boston, MA, USA; Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
¹² Infectious Disease Division, International Centre for Diarrhoeal Disease Research, Bangladesh, Dhaka, Bangladesh.
¹³ Child Health Research Foundation, Dhaka, Bangladesh.
¹⁴ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada.
¹⁵ Centre for Global Child Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
¹⁶ Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Department of Pediatrics, Mt. Sinai Hospital, Toronto, ON, Canada.
¹⁷ Mohammadpur Fertility Services and Training Centre, Dhaka, Bangladesh.
¹⁸ Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Diego, La Jolla, CA, USA.
¹⁹ Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, CA, USA. Electronic address: pdorrestein@health.ucsd.edu.
²⁰ Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA; Center for Microbiome Innovation, University of California, San Diego, La Jolla, CA, USA; Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA; Shu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA. Electronic address: rknight@health.ucsd.edu.

PMID: 41308639
PMCID: PMC12875530
DOI: 10.1016/j.chom.2025.11.002

Abstract

Early life is a critical period for immune and metabolic development, but these patterns remain underexplored in populations from low- and middle-income countries. Here, we profile the microbiome and metabolome of 55 Bangladeshi mother-infant dyads over the first 6 months of life. Importantly, we observe an increase in microbially derived bile amidates and N-acyl lipids with age in conjunction with reads matching the bile salt hydrolase/transferase (bsh) gene. Although microbial source tracking confirms maternal fecal seeding, a substantial environmental contribution is also highlighted. Differences in infant fecal metabolic profiles are associated with delivery mode, maternal milk composition, household assets, and household-level water treatment. Cesarean section (C-section) delivery and untreated drinking water are linked to transient metabolic differences, including increases in bile amidates, N-acyl lipids, and other host-microbe co-metabolic products, including acylcarnitines. Multi-omics analysis reveals specific microbial-metabolite relationships, highlighting how early environmental and maternal living circumstances influence gut metabolic development through the microbiome.

Keywords: acylcarnitines; bile acids; bile salt hydrolase/transferase; early-life development; human milk oligosaccharides; infant; low- and middle-income countries; metabolomics; microbial metabolites; microbiome.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests P.C.D. is an advisor and holds equity in Cybele, Sirenas, and BileOmix, and he is a scientific co-founder, advisor, and holds equity and/or receives income from Ometa, Enveda, and Arome with prior approval by UC San Diego. P.C.D. consulted for DSM Animal Health in 2023. R.K. is a scientific advisory board member and consultant for BiomeSense, Inc., has equity, and receives income. R.K. is a scientific advisory board member and has equity in GenCirq. R.K. is a consultant and scientific advisory board member for DayTwo and receives income. R.K. has equity in and acts as a consultant for Cybele. R.K. is a co-founder of Biota, Inc., and has equity. R.K. is a co-founder and has equity and is a scientific advisory board member of Micronoma and has equity. R.K. is a board member of Microbiota Vault, Inc. R.K. is a board member of N=1 IBS advisory board and receives income. R.K. is a senior visiting fellow of the HKUST Jockey Club Institute for Advanced Study. The terms of these arrangements have been reviewed and approved by the University of California, San Diego, in accordance with its conflict of interest policies. D.M. is a consultant for, and has equity in, BiomeSense, Inc. The terms of these arrangements have been reviewed and approved by the University of California, San Diego. L.B. is a co-inventor on patent applications related to the use of HMOs in preventing necrotizing enterocolitis (NEC) and other inflammatory diseases.

Figures

**Figure 1:. Rapid shifts in key signalling metabolites during infant fecal metabolic gut development.**
a, Experimental overview. Open circles indicate random collection time within the first 60 days. n reflects the initial number of samples per biospecimen, prior to quality control. Images from flaticon.com, DBCLS (bioicons.com), or NIAID NIH BIOART (https://bioart.niaid.nih.gov/bioart/). b, UpSet plot of metabolic features across infant fecal (n=377) and plasma (n=34) samples, and maternal milk (n=119) samples. The left panel shows features unique to or shared between sample types, while the right panel shows chemical classes and pathways predictions based on NPC classification from CANOPUS (probability > 0.5). c, PCA of maternal milk showing development over time (PERMANOVA: p < 0.001). d, Boxplot showing the natural log peak areas of key discriminant features across lactation stages. Statistical significance assessed by Wilcoxon rank-sum test with Benjamini-Hochberg (BH) correction. Repeated measures were avoided by retaining only the highest value per subject within each lactation stage. e, PCA of fecal metabolic profiles across early infancy. Differences in the metabolic profiles were associated with infant age, delivery mode, maternal secretor status, and drinking water treatment (PERMANOVA: all p < 0.05). f, Longitudinal abundances of free di- and tri-hydroxylated bile acids in infant feces from birth to 6 months of life. g, Abundance trajectories of taurine (tau), glycine (gly), and amino acid (AA) conjugated bile acids, as well as oxo bile acids. h, Scatter plot showing the association between the natural log of bile salt hydrolase/transferase (*bsh)* gene read count matches and the ratio of amino acid to taurine conjugated bile acids using Pearson correlation for statistical significance (LME: β = 0.25, p < 0.001). A linear regression line with 95% CI is shown, with points colored by infant age. i, Longitudinal abundance trajectories of N-acyl lipids with a C2:0 fatty acid chain (left panel) and a C3:0 fatty acid chain (right panel) amidated to different head groups. j, Longitudinal abundances of short-chain acylcarnitines (left panel) and long-chain acylcarnitines (right panel) in infant feces. For panels **f, g, i, j**, β and p values were derived from LME with subject as a random effect, and scatter plot smoothing (‘LOESS’) was used for visualization purposes. All boxplots show the first (lower), median, and third (upper) quartiles, with whiskers 1.5 times the interquartile range.

**Figure 2:. Sources driving the developing gut microbiome.**
a, phylo-RPCA EMPeror plot of fecal samples with more than 100,000 reads (n=219) colored by infant age bin. b, Top and bottom 12 differentially abundant infant fecal genera identified by ANCOM-BC2 (p_adj < 0.05, LFC > 1). Full plot shown in Figure S3a. Enriched (yellow) or depleted (purple) after 30 days (later timepoints) are shown, black error bars indicate standard error. c, Scatterplot with lineplot of the natural log-ratio of the summed counts of enriched (numerator) and depleted (denominator) fecal genera found in b over time (LME: β = 0.026, p < 0.001); lineplot used binned timepoints. FEAST: Infant fecal samples as sinks. d, Stacked barplot of the mean percentage contribution of each maternal body site on infant fecal samples for matched (left) or all available (right) maternal samples. e, Stripplot/pointplot of the FEAST estimated percentage contribution of maternal fecal samples to matched (left) or all available (right) infant fecal samples. f, Stripplot/pointplot of the FEAST estimated percentage contribution of unknown elements to matched (left) or all available (right) infant fecal samples. Notation: ** = p < 0.01.

**Figure 3:. Delivery mode and water treatment are associated with infant fecal metabolic trajectories in early-life.**
a, PCA of infant fecal samples showing separation by delivery mode (PERMANOVA: p < 0.001). b, Longitudinal natural log ratios of the summed peak areas of the top 100 discriminating features for delivery mode in infant feces. Three outliers were removed for visualization. c, Barplot of metabolic features significantly differing between C-section and vaginal birth (p_adj < 0.05) in infant feces collected during days 10–30 after birth. For each infant, the sample with the highest log ratio within this interval was selected. * indicates p_adj < 0.10. d, Boxplot of the natural log ratios of amino acid to taurine conjugated bile acids by delivery mode (Wilcoxon rank-sum test, day 14–28: p = 0.042, p_adj = 0.13). e, Boxplot of *bsh* gene reads in infant feces by delivery mode (Wilcoxon rank-sum test, day 14–28: p = 0.07, p_adj = 0.08). f, Trajectories for valine-C2:0 and 2-aminovaleric acid-C2:0 by delivery mode in infant feces. g, PCA of infant fecal samples showing separation by household drinking water treatment (PERMANOVA: p = 0.020). h, Longitudinal natural log ratios of the summed peak areas of the top 100 discriminating features for treated vs. untreated drinking water. i, Molecular network of acylcarnitines associated with water treatment (untreated=brown, treated=dark green). Node size represents mean VIP score from three different PLS-DA models accounting for different purification methods, with larger nodes indicating higher VIP score. Only acylcarnitines with VIP > 1 and same directionality in all three models are shown. j, Boxplot showing differences in the natural log peak areas of various acylcarnitines enriched in the untreated water group at day 0–7. Statistical significance assessed by Wilcoxon rank-sum test with BH correction. k, Differentially abundant infant fecal genera by water treatment identified by ANCOMB-BC2 (p_adj < 0.05, log fold change (LFC) > 1). Enriched (grey) or depleted (brown) in treated water are shown, black errors indicate standard error. l, Scatterplot/lineplot of the natural log-ratio of the summed counts of enriched (numerator) and depleted (denominator) fecal genera found in k (LME: −0.058, p = 0.13); lineplot used binned timepoints. For **b, f,** and h, β and p values were derived from LME models with subject as random effect and smoothing for visualization. All boxplots show the first (lower), median, and third (upper) quartiles, with whiskers 1.5 times the interquartile range. For boxplots, repeated measures were avoided by retaining only the highest value per individual within each time window. For d and e, no statistically significant group differences were observed at Days 180–205, and therefore this timepoint was omitted from the visualization.

**Figure 4:. Maternal secretor status influences HMO profiles in human milk and infant feces.**
a, Longitudinal natural log ratios of the summed peak areas of the top 100 discriminating features for maternal secretor status in human milk. b, Boxplot of untargeted (predicted) and targeted 2’FL and DFLac, respectively, grouped by secretor status (Wilcoxon rank-sum test with BH correction). For subjects with multiple samples within a stage, only the sample with the highest 2′FL concentration was retained. c, PCA of infant fecal samples showing separation by maternal secretor status (PERMANOVA: p = 0.006). d, Longitudinal natural log ratios of the summed peak areas of the top 100 discriminating features for maternal secretor status in infant feces. e, Piechart showing the distribution of NPC predicted chemical classes or pathways (probability > 0.7) for the discriminant infant fecal features by maternal secretor status. Only features with VIP > 2.5 were included in this visualization to highlight which classes the most relevant features belonged to. f, Cross-biofluid molecular network of amino sugars, aminoglycosides, and saccharides, as predicted by CANOPUS, colored by biofluid and secretor status. Node size represents the mean VIP score of significant features across the two PLS-DA models for secretor status in human milk and infant feces, with larger nodes indicating higher VIP score. Selected features enriched in both maternal milk and infant feces are shown in boxplots. Boxplot of the natural log-transformed peak areas of g, a candidate trihydroxy bile acid and h, putative 3α,7α-(OH)₂-12-oxo grouped by secretor status (Wilcoxon rank-sum test with BH correction). All boxplots show the first (lower), median, and third (upper) quartiles, with whiskers 1.5 times the interquartile range. For boxplots in **f, g,** and h, repeated measures were avoided by retaining only the highest value per individual within each time window. For a and d, β and p values were derived from LME models with subject as random effect and smoothing added for visualization. For f and h, Days 180–205 were omitted from the visualization when no statistically significant group differences were observed.

**Figure 5:. Multi-omics integration identifies microbial mediators of metabolic trajectories.**
a, Heatmap of correlating metabolites and microbial species (OGU) from infant fecal samples collected at day ~30 identified by joint-RPCA. Metabolic features shown were selected based on previous analyses. Asterisk indicate MS/MS spectral match against microbeMASST, with (*) indicating a general match and (**) indicating a match to a monoculture of the same genus as taxa shown in the heatmap. Microbiome features shown were selected based on the overlap between 3 previous analyses, including needing to belong to the differentially abundant genera found in the ANCOM-BC2 analysis from Figure 2b, the top and bottom 20 most differentially ranked features for joint-RPCA Axis 1, and the top and bottom 10% differentially ranked features from TEMPTED Axis 1 (overlapping features=23).

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Update of

Environmental and Maternal Imprints on Infant Gut Metabolic Programming.
Kvitne KE, Allaband C, Onuora JC, Perry D, Zuffa S, Patel L, Charron-Lamoureux V, Mohanty I, Sejane K, Patan A, Al Mahmud A, Ahmed T, Bassani DG, González A, Hamer DH, Haque R, Ho B, Hossain MI, Islam MS, McDonald D, Pell LG, Qamar H, Roth DE, Saha S, Shah PS, Siddiqui MM, Sarker SA, Sultana S, Thomas S, Burnett LA, Tsunoda SM, Bode L, Dorrestein PC, Knight R. Kvitne KE, et al. bioRxiv [Preprint]. 2025 Jul 24:2025.07.24.666662. doi: 10.1101/2025.07.24.666662. bioRxiv. 2025. Update in: Cell Host Microbe. 2025 Dec 10;33(12):2130-2147.e7. doi: 10.1016/j.chom.2025.11.002. PMID: 40777448 Free PMC article. Updated. Preprint.

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Environmental and maternal imprints on infant gut metabolic development

Affiliations

Environmental and maternal imprints on infant gut metabolic development

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Update of

References

MeSH terms

Grants and funding

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Full Text Sources