Preventing dysbiosis of the neonatal mouse intestinal microbiome protects against late-onset sepsis

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Abstract

Late-onset sepsis (LOS) is thought to result from systemic spread of commensal microbes from the intestines of premature infants. Clinical use of probiotics for LOS prophylaxis has varied owing to limited efficacy, reflecting an incomplete understanding of relationships between development of the intestinal microbiome, neonatal dysbiosis and LOS. Using a model of LOS, we found that components of the developing microbiome were both necessary and sufficient to prevent LOS. Maternal antibiotic exposure that eradicated or enriched transmission of Lactobacillus murinus exacerbated and prevented disease, respectively. Prophylactic administration of some, but not all Lactobacillus spp. was protective, as was administration of Escherichia coli. Intestinal oxygen level was a major driver of colonization dynamics, albeit via mechanisms distinct from those in adults. These results establish a link between neonatal dysbiosis and LOS, and provide a basis for rational selection of probiotics that modulate primary succession of the microbiome to prevent disease.

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Fig. 1: Neonatal dysbiosis becomes LOS when K. pneumoniae are not cleared following translocation.
Fig. 2: The microbiome alters susceptibility to LOS and neonatal dysbiosis.
Fig. 3: Perinatal antibiotics alter communities of endogenous lactobacilli.
Fig. 4: A microbiome dominated by obligate anaerobes provides resistance to LOS.
Fig. 5: Obligate anaerobes cannot engraft into susceptible pups to protect against LOS and neonatal dysbiosis.

Data availability

The whole-genome sequencing data of L. murinus isolates and metagenomic sequencing data for this study are linked to NCBI BioProject number PRJNA542320. The 16S rRNA sequencing data for this study are linked to NCBI BioProject number PRJNA587139. All other data are available upon reasonable request without restrictions.

References

  1. 1.

    Liu, L. et al. Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet 385, 430–440 (2015).

  2. 2.

    Stoll, B. J. et al. Trends in care practices, morbidity, and mortality of extremely preterm neonates, 1993–2012. JAMA 314, 1039–1051 (2015).

  3. 3.

    Dong, Y. & Speer, C. P. Late-onset neonatal sepsis: recent developments. Arch. Dis. Child. Fetal Neonatal Ed. 100, F257–F263 (2015).

  4. 4.

    Kuppala, V., Meinzen-Derr, J. & Morrow, A. Prolonged initial empirical antibiotic treatment is associated with adverse outcomes in premature infants. J. Pediatr. 159, 720–725 (2011).

  5. 5.

    Fajardo, C., Alshaikh, B. & Harabor, A. Prolonged use of antibiotics after birth is associated with increased morbidity in preterm infants with negative cultures. J. Matern. Fetal Neonatal Med. 64, 1–7 (2018).

  6. 6.

    Gibson, M. K. et al. Developmental dynamics of the preterm infant gut microbiota and antibiotic resistome. Nat. Microbiol. 1, 16024 (2016).

  7. 7.

    Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016).

  8. 8.

    Parm, Ü., Metsvaht, T., Ilmoja, M.-L. & Lutsar, I. Gut colonization by aerobic microorganisms is associated with route and type of nutrition in premature neonates. Nutr. Res. 35, 496–503 (2015).

  9. 9.

    Bokulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl. Med. 8, 343ra82 (2016).

  10. 10.

    Korpela, K. et al. Intestinal microbiota development and gestational age in preterm neonates. Sci. Rep. 8, 2453 (2018).

  11. 11.

    Tarr, P. I. & Warner, B. B. Gut bacteria and late-onset neonatal bloodstream infections in preterm infants. Semin. Fetal Neonatal Med. 21, 388–393 (2016).

  12. 12.

    Graham, P. L., Della-Latta, P., Wu, F., Zhou, J. & Saiman, L. The gastrointestinal tract serves as the reservoir for Gram-negative pathogens in very low birth weight infants. Pediatr. Infect. Dis. J. 26, 1153–1156 (2007).

  13. 13.

    Knoop, K. A. et al. Microbial antigen encounter during a preweaning interval is critical for tolerance to gut bacteria. Sci. Immunol. 2, eaao1314 (2017).

  14. 14.

    Kim, Y.-G. et al. Neonatal acquisition of Clostridia species protects against colonization by bacterial pathogens. Science 356, 315–319 (2017).

  15. 15.

    Tourneur, E. & Chassin, C. Neonatal immune adaptation of the gut and its role during infections. Clin. Dev. Immunol. 2013, 270301–270317 (2013).

  16. 16.

    Dermyshi, E. et al. The ‘golden age’ of probiotics: a systematic review and meta-analysis of randomized and observational studies in preterm infants. Neonatology 112, 9–23 (2017).

  17. 17.

    van den Akker, C. H. P. et al. Probiotics for preterm infants: a strain-specific systematic review and network meta-analysis. J. Pediatr. Gastroenterol. Nutr. 67, 103–122 (2018).

  18. 18.

    Deshmukh, H. S. et al. The microbiota regulates neutrophil homeostasis and host resistance to Escherichia coli K1 sepsis in neonatal mice. Nat. Med. 20, 524–530 (2014).

  19. 19.

    Ulas, T. et al. S100-alarmin-induced innate immune programming protects newborn infants from sepsis. Nat. Immunol. 18, 622–632 (2017).

  20. 20.

    Howe, K. et al. Development of stable reporter system cloning luxCDABE genes into chromosome of Salmonella enterica serotypes using Tn7 transposon. BMC Microbiol. 10, 197 (2010).

  21. 21.

    Langstraat, J., Bohse, M. & Clegg, S. Type 3 fimbrial shaft (MrkA) of Klebsiella pneumoniae, but not the fimbrial adhesin (MrkD), facilitates biofilm formation. Infect. Immun. 69, 5805–5812 (2001).

  22. 22.

    Ko, K. S. The contribution of capsule polysaccharide genes to virulence of Klebsiella pneumoniae. Virulence 8, 485–486 (2017).

  23. 23.

    Pamer, E. G. Resurrecting the intestinal microbiota to combat antibiotic-resistant pathogens. Science 352, 535–538 (2016).

  24. 24.

    Patel, S. & Bernice, F. Vancomycin. in StatPearls https://www.ncbi.nlm.nih.gov/pubmed/29083794 (StatPearls Publishing, 2018).

  25. 25.

    Miranda, J. C. et al. Gentamicin absorption during prophylactic use for necrotizing enterocolitis. Dev. Pharmacol. Ther. 7, 303–306 (1984).

  26. 26.

    Milani, C. et al. Phylotype-level profiling of lactobacilli in highly complex environments by means of an internal transcribed spacer-based metagenomic approach. Appl. Environ. Microbiol. 84, e00706–e00718 (2018).

  27. 27.

    Eren, A. M. et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013).

  28. 28.

    Ammor, M. S., Belén Flórez, A. & Mayo, B. Antibiotic resistance in non-enterococcal lactic acid bacteria and bifidobacteria. Food Microbiol. 24, 559–570 (2007).

  29. 29.

    Elkins, C. A. & Mullis, L. B. Bile-mediated aminoglycoside sensitivity in Lactobacillus species likely results from increased membrane permeability attributable to cholic acid. Appl. Environ. Microbiol. 70, 7200–7209 (2004).

  30. 30.

    Yassour, M. et al. Strain-level analysis of mother-to-child bacterial transmission during the first few months of life. Cell Host Microbe 24, 146–154 (2018).

  31. 31.

    Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145 (2018).

  32. 32.

    Taft, D. H. et al. Center variation in intestinal microbiota prior to late-onset sepsis in preterm infants. PloS One 10, e0130604 (2015).

  33. 33.

    Madan, J. C. et al. Gut microbial colonisation in premature neonates predicts neonatal sepsis. Arch. Dis. Child. Fetal Neonatal Ed. 97, F456–F462 (2012).

  34. 34.

    Mai, V. et al. Distortions in development of intestinal microbiota associated with late onset sepsis in preterm infants. PloS One 8, e52876 (2013).

  35. 35.

    Litvak, Y., Byndloss, M. X. & Baumler, A. J. Colonocyte metabolism shapes the gut microbiota. Science 362, eaat9076 (2018).

  36. 36.

    Rivera-Chávez, F. et al. Depletion of butyrate-producing Clostridia from the gut microbiota drives an aerobic luminal expansion of Salmonella. Cell Host Microbe 19, 443–454 (2016).

  37. 37.

    Cramton, S. E., Ulrich, M., Götz, F. & Döring, G. Anaerobic conditions induce expression of polysaccharide intercellular adhesin in Staphylococcus aureus and Staphylococcus epidermidis. Infect. Immun. 69, 4079–4085 (2001).

  38. 38.

    Byndloss, M. X. & Baumler, A. J. The germ-organ theory of non-communicable diseases. Nat. Rev. Microbiol. 16, 103–110 (2018).

  39. 39.

    Lodinová-Zádniková, R. & Sonnenborn, U. Effect of preventive administration of a nonpathogenic Escherichia coli strain on the colonization of the intestine with microbial pathogens in newborn infants. Biol. Neonate 71, 224–232 (1997).

  40. 40.

    Birchenough, G. M. H. et al. Altered innate defenses in the neonatal gastrointestinal tract in response to colonization by neuropathogenic Escherichia coli. Infect. Immun. 81, 3264–3275 (2013).

  41. 41.

    Grozdanov, L. et al. Analysis of the genome structure of the nonpathogenic probiotic Escherichia coli strain Nissle 1917. J. Bacteriol. 186, 5432–5441 (2004).

  42. 42.

    Reed, B. D., Schibler, K. R., Deshmukh, H., Ambalavanan, N. & Morrow, A. L. The impact of maternal antibiotics on neonatal disease. J. Pediatr. 197, 97–103 (2018).

  43. 43.

    Schulman, J. et al. Neonatal intensive care unit antibiotic use. Pediatrics 135, 826–833 (2015).

  44. 44.

    Rao, S. C., Athalye-Jape, G. K., Deshpande, G. C., Simmer, K. N. & Patole, S. K. Probiotic supplementation and late-onset sepsis in preterm infants: a meta-analysis. Pediatrics 137, e20153684 (2016).

  45. 45.

    Viswanathan, S., Lau, C., Akbari, H., Hoyen, C. & Walsh, M. C. Survey and evidence-based review of probiotics used in very low birth weight preterm infants within the United States. J. Perinatol. 36, 1106–1111 (2016).

  46. 46.

    Suez, J., Zmora, N., Segal, E. & Elinav, E. The pros, cons, and many unknowns of probiotics. Nat. Med. 25, 716–729 (2019).

  47. 47.

    Segers, M. E. & Lebeer, S. Towards a better understanding of Lactobacillus rhamnosus GG–host interactions. Microb. Cell Fact. 13, S7 (2014).

  48. 48.

    Panigrahi, P. et al. A randomized synbiotic trial to prevent sepsis among infants in rural India. Nature 548, 407–412 (2017).

  49. 49.

    Kelly, C. J. et al. Crosstalk between microbiota-derived short-chain fatty acids and intestinal epithelial HIF augments tissue barrier function. Cell Host Microbe 17, 662–671 (2015).

  50. 50.

    Byndloss, M. X. et al. Microbiota-activated PPAR-γ signaling inhibits dysbiotic Enterobacteriaceae expansion. Science 357, 570–575 (2017).

  51. 51.

    Lee, A. & Gemmell, E. Changes in the mouse intestinal microflora during weaning: role of volatile fatty acids. Infect. Immun. 5, 1–7 (1972).

  52. 52.

    Hosny, M., Cassir, N. & La Scola, B. Updating on gut microbiota and its relationship with the occurrence of necrotizing enterocolitis. Hum. Microbiome J. 4, 14–19 (2017).

  53. 53.

    Corsini, I. et al. Peroxisome proliferator-activated receptor-γ agonist pioglitazone reduces the development of necrotizing enterocolitis in a neonatal preterm rat model. Pediatr. Res. 81, 364–368 (2017).

  54. 54.

    Friedman, E. S. et al. Microbes versus chemistry in the origin of the anaerobic gut lumen. Proc. Natl Acad. Sci. USA 115, 4170–4175 (2018).

  55. 55.

    de Goffau, M. C. et al. Human placenta has no microbiome but can contain potential pathogens. Nature 572, 329–334 (2019).

  56. 56.

    Pedersen, M. B., Gaudu, P., Lechardeur, D., Petit, M.-A. & Gruss, A. Aerobic respiration metabolism in lactic acid bacteria and uses in biotechnology. Annu. Rev. Food Sci. Technol. 3, 37–58 (2012).

  57. 57.

    Wilck, N. et al. Salt-responsive gut commensal modulates TH17 axis and disease. Nature 551, 585–589 (2017).

  58. 58.

    Singh, A. K., Hertzberger, R. Y. & Knaus, U. G. Hydrogen peroxide production by lactobacilli promotes epithelial restitution during colitis. Redox Biol. 16, 11–20 (2018).

  59. 59.

    Lee, Y.-S. et al. Microbiota-derived lactate accelerates intestinal stem-cell-mediated epithelial development. Cell Host Microbe 24, 833–846 (2018).

  60. 60.

    Litvak, Y. et al. Commensal Enterobacteriaceae protect against Salmonella colonization through oxygen competition. Cell Host Microbe 25, 128–139 (2019).

  61. 61.

    Hinsa, S. M., Espinosa-Urgel, M., Ramos, J. L. & O'Toole, G. A. Transition from reversible to irreversible attachment during biofilm formation by Pseudomonas fluorescens WCS365 requires an ABC transporter and a large secreted protein. Mol. Microbiol. 49, 905–918 (2003).

  62. 62.

    McKenzie, G. J. & Craig, N. L. Fast, easy and efficient: site-specific insertion of transgenes into enterobacterial chromosomes using Tn7 without need for selection of the insertion event. BMC Microbiol. 6, 39 (2006).

  63. 63.

    Kumar, R. et al. Getting started with microbiome analysis: sample acquisition to bioinformatics. Curr. Protoc. Hum. Genet. 82, 18.8.1–18.8.29 (2014).

  64. 64.

    Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

  65. 65.

    Meadow, J. q2oligo—convert QIIME files into oligotyping subsets. GitHub https://github.com/jfmeadow/q2oligo (2014).

  66. 66.

    Bergey, D. H. Bergey's Manual of Systematics of Archaea and Bacteria. Bergey's Manual Trust https://doi.org/10.1002/9781118960608 (2015).

  67. 67.

    Ormerod, K. L. et al. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals. Microbiome 4, 36 (2016).

  68. 68.

    Tegtmeier, D., Riese, C., Geissinger, O., Radek, R. & Brune, A. Breznakia blatticola gen. nov. sp. nov. and Breznakia pachnodae sp. nov., two fermenting bacteria isolated from insect guts, and emended description of the family Erysipelotrichaceae. Syst. Appl. Microbiol. 39, 319–329 (2016).

  69. 69.

    Weingarten, R. A. et al. Genomic analysis of hospital plumbing reveals diverse reservoir of bacterial plasmids conferring carbapenem resistance. mBio 9, e02011–e02017 (2018).

  70. 70.

    Nurk, S. et al. Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. J. Comput. Biol. 20, 714–737 (2013).

  71. 71.

    Koren, S. et al. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 27, 722–736 (2017).

  72. 72.

    Treangen, T. J., Ondov, B. D., Koren, S. & Phillippy, A. M. The harvest suite for rapid core-genome alignment and visualization of thousands of intraspecific microbial genomes. Genome Biol. 15, 524 (2014).

  73. 73.

    Wyres, K. L. et al. Identification of Klebsiella capsule synthesis loci from whole genome data. Microb. Genomics 2, 1–15 (2016).

  74. 74.

    Johnson, R. C. et al. Investigation of a cluster of Sphingomonas koreensis infections. N. Engl. J. Med. 379, 2529–2539 (2018).

  75. 75.

    Wood, D. E. & Salzberg, S. L. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15, R46 (2014).

  76. 76.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Meth. 9, 357–359 (2012).

  77. 77.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Software 1, 1–48 (2015).

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Acknowledgements

We thank members of the Weaver laboratory for their helpful comments, G. Gaskins and C. Rutledge for their administrative support and S.E. Winter (UT Southwestern), N. Ambalavanan, C.O. Elson III, R.G. Lorenz, R.D. Hatton and L.A. Harrington (UAB) for their advice. We thank M. Wu (University of North Dakota), G. O’Toole (Dartmouth University) and C. Orihuela (UAB) for providing critical reagents to this study (Supplementary Tables 3 and 4) . We also thank D. O’Quinn, J. Wright and H. Turner for their technical assistance. Additionally, we are grateful to T. Schaub and S. Sinclair at the UAB Gnotobiotic Animal Core, S. Samuel and K. Zinn at the UAB Small Animal Imaging Core, and J. Day and the La Jolla Institute of Allergy and Immunology Sequencing Facility. Finally, we thank W. Duck and P. Basu Thakur for their advice and technical assistance and P. Thomas and M. Park for bioinformatics analysis. We acknowledge the following for their support of the Microbiome Resource at the University of Alabama at Birmingham: the Comprehensive Cancer Center (P30AR050948), the Center for Clinical Translational Science (UL1TR001417) and the University-Wide Institutional Core and Heflin Center for Genomic Sciences. This study used the Nephele platform from the National Institute of Allergy and Infectious Diseases Office of Cyber Infrastructure and Computational Biology in Bethesda and utilized the high-performance computational capabilities of the National Institutes of Health (NIH) Biowulf Linux cluster. Whole-genome sequencing was performed at the NIH Intramural Sequencing Center. This work was supported by NIH F30DK105680 (J.R.S.), the UAB Medical Scientist Training Program, supported by NIH T32GM008361 (E.G.B., D.D., V.A.L. and J.R.S.), NHGRI Intramural Research funds (J.A.S.) and UAB institutional funds (C.T.W. and D.A.R.).

Author information

G.B. and D.A.R. conceived of the original model. E.G.B. generated Kp-43816lux. J.R.S. generated Kp-39gfp. J.R.S. and E.G.B. designed and performed all experiments with guidance from C.T.W., D.A.R. and M.J.G. J.A.S., C.D. and S.C. helped to perform and analyze metagenomics sequencing and strain-tracking experiments. Some immunofluorescence experiments were performed by C.L.Z. Some bioluminescence experiments were performed by D.J.S. D.D. helped prepare figures for the initial submission. V.A.L. fit the statistical models for the analysis of bioluminescence data. 16S rRNA microbiome sequencing and initial analysis were performed by C.D.M. and R.K. J.R.S. analyzed all the data, wrote the original manuscript and prepared the final figures. C.T.W., D.A.R. and J.A.S. secured funding for this project. J.R.S. and C.T.W. authored the final manuscript.

Correspondence to Jeffrey R. Singer or Casey T. Weaver.

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The authors declare no competing interests.

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Peer review information Alison Farrell was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 K. pneumoniae bioluminescence correlates with CFU.

(a) Schematic illustration for K. pneumoniae infections. Litters of pups (n = 6-12) were infected intragastrically (i.g.) with 107 CFU Kp-43816 or Kp-39 on P5. On day 1 or day 3 following infection, intestinal organs were removed, luminescence signal measured, homogenized, and CFU determined by selective plating on MacConkey Agar. All colonies grown on MacConkey Agar were bioluminescent (right). Representative images of pups infected with Kp-43816 (b) and (c) and correlation between luminescent signal and CFU for each organ isolated. Each point in (b) and (c) represents an individual organ (small intestines, colon, cecum, mesentery, or liver) from a mouse infected with Kp-43816 (b) collected on day 1 (n = 21) or day 3 (n = 15) post infection, or infected with Kp-39 an day 1 (n = 9) or day 3 (n = 6). Regression analysis performed using ordinary least squares model on log-transformed CFU and bioluminescence data. P values were calculated to test the null hypothesis that the slope of regression line = 0. Number of pups of either sex, n.

Extended Data Fig. 2 K. pneumoniae susceptibility to perinatal antibiotics that alter diversity of the neonatal intestinal microbiome.

(a) Growth inhibition disk diffusion assay showing antibiotic susceptibility of Kp-39 (left) and (right) to vancomycin (30ug) or gentamicin (10ug) grown under aerobic or anaerobic conditions. Bars display mean ± SD. No inhibition detected, nd. Data representative of 3 similar experiments; n = 3 plates per condition tested. (b) Beta-diversity (upper panel) and alpha-diversity (lower panel) measurements from microbiome communities of P5 pups reared with or without maternal antibiotics. Lines display mean ± SD. Beta-diversity and alpha-diversity metrics were calculated from the microbiome sequencing data described in Fig. 3a.

Extended Data Fig. 3 Perinatal antibiotics alter diversity of the intestinal microbiome.

(a) Initial Shannon entropy analysis of reads classified to Gammapreoteobacteria (left) or Lactobacillales (right). Oligotyping analysis was performed to resolve Lactobacillales oligotypes, but not carried out on Gammaproteobacteria reads due to low entropy. Shannon entropy by nucleotide position was calculated from the combined sequencing data described in Fig. 3e using the standard oligotyping pipeline. (b) Ratio (top) and absolute counts (bottom) of Lactobacillales reads in each sample aligning to any of the 8 oligotypes compared with reads failing to align. 465,160 reads from 36 samples were input and 453,693 were used in the final analysis after quality filtering. (c) 26 isolates from P5 intestinal samples were identified via BLAST of full length 16S gene sequence. Sequences from the V4 region of each oligotype resolved from previous analysis were aligned to each isolate. Table shows the number of isolates with > 98% match to a previously identified oligotype and the Shannon entropy measurements following oligotyping to highlight any unresolved diversity.

Extended Data Fig. 4 Lactobacilli species have divergent antibiotic resistance partially mediated by presence of bile acids.

Growth inhibition disk diffusion assay showing antibiotic susceptibility of endogenous isolates of L. murinus V10 (a,b) and L. johnsonii G2A (d,e) to vancomycin or gentamicin grown under aerobic or anaerobic conditions and with or without the addition of bile acids. Bars display mean ± SD. Growth curves of L. murinus (c) and L. johnsonii (f) under aerobic or anaerobic conditions and with or without the addition of bile acids (mean ± SD). No inhibition detected, nd. (a,d) Data are representative of 3 similar experiments; n = 3 plates per condition tested. (b,e) Data representative of 2 independent experiments; n = 3 plates per condition. (c,f) Data representative of 2 or 3 independent experiments; n = 3 wells per condition.

Extended Data Fig. 5 Vertical transmission of L. murinus in Vanc-reared litters.

(a) Experimental design. Littermate dams were separated 1 or 2 days prior to birth and drinking water was supplemented with vancomycin (Vanc) or left unchanged (SPF) until P5. SPF in black and Vanc in blue. Pup colon contents, maternal vaginal swabs, and fecal samples were homogenized in MRS and plated for single colony isolation of lactobacilli. DNA was simultaneously extracted from unenriched colon contents and ‘plate swipes’ after colony picking and subjected to shotgun metagenomics sequencing. (b) Taxonomic classification and (c) possible routes of vertical transmission based on colony isolates from dam and pup samples. Large circles represent the presence of maternal Lactobacillus isolate for given taxonomy. Small circles represent the presence of pup isolate. Black circles indicate that no isolates were identified from the sample. Data are from 2 pups per litter in 2 litters of either SPF or Vanc-reared pups and their dam. (d) A phylogenetic tree built from core SNPs from 26 L. murinus isolate genomes. The V10 isolate was selected as the reference. The branches of the tree associated with each litter/dam share the same color. The starred clade (*) is composed of isolates from a single pup that do not cluster with the associated dam. Phylogenetic relationships between all 26 L. murinus isolates identified in this study based on 187 core SNPs were used. (e) Observed allele read counts for 187 core SNPs. Each bar shows the number of metagenomic reads aligned to that position in the reference. Bars are colored to show the relative abundance of each base at that position. Allele frequencies of 187 core SNPs from plate-swabs of fecal and vaginal lactobacilli of a single vanc dam and colon contents of her pups.

Extended Data Fig. 6 Protective probiosis of lactobacilli is not generalizable across species and strains.

(a) A phylogenetic tree constructed from 259 single copy genes with shared homology across all genomes using ‘Codon Tree’ protocol of PATRIC’s Phylogentic Tree building service. Colors indicate whether strains protect (black) or do not protect (red) against neonatal dysbiosis. Reference strains not used in the study are in gray. Gent pups received PBS or probiotic Lactobacillus rhamnosus GG (LGG) i.g prior to infection with 107 CFU Kp-43816lux (b) or (c) and monitored daily for sepsis (curve) or abdominal bioluminescence, respectively. Pooled from 2 independent experiments using within-litter controls: Gent (n = 10); LGG (n = 13) (b), or pooled from 3 separate cages of within-litter controls in a single infection experiment: Gent (n = 12); LGG (n = 10) (c). (d) Gent pups received PBS, fructooligosaccharide (FOS), L. plantarum (), or FOS and L. plantarum i.g. prior to infection with 107 CFU Kp-39lux. Pooled from 4 independent experiments using within-litter controls: Gent (n = 12); Fructooligosaccharide (n = 13); L. plantarum (n = 10), L. plantarum and fructooligosaccharide (n = 13). (e) Gent pups received PBS or L. murinus (ATCC 35020) i.g. prior to infection with 107 CFU Kp-39lux. Pooled from 3 independent experiments from 4 litters with within-litter controls: Gent (n = 21); L. murinus (n = 18). (f) Gent pups received PBS or L. reuteri (S1P1) i.g. prior to infection with 107 CFU Kp-39lux. Representative abdominal bioluminescence is shown and quantitated. Box and whisker plots show median and IQR with lines extending as the 1st and 4th quartile. Data are representative of 2 independent experiments: Gent (n = 3); L. reuteri (n = 3). Number of pups of either sex, n.

Extended Data Fig. 7 Probiotic activity of lactobacilli does not correlate with in vitro growth inhibition or in vivo engraftment.

(a) Growth inhibition colony diffusion assay showing ability of lactobacilli to directly inhibit growth of Kp-39 (left) or Kp-43816 (right). Bars display mean ± SD. Data are representative of 2 independent experiments; n = 3 plates per condition tested. (b) Lactobacilli CFU from small intestine or colon contents of P7 littermates reared with gentamicin and given 50 µl PBS, L. murinus V10, LGG, or L. johnsonii G2A, i.g on P5 and P6. Box and whisker plots show median and IQR with lines extending as the 1st and 4th quartile. Pooled from 2 independent experiments: Gent (n = 7); L. murinus (n = 8); LGG (n = 8); L. johnsonii (n = 6). Number of pups of either sex, n.

Extended Data Fig. 8 Microbiome abundance during primary succession.

DNA was extracted from intestinal tissue at indicated days from birth to weaning and bacterial abundance was measured by qPCR of 16S rRNA gene (mean ± SE). Data are from a single experiment: P0 (n = 3); P3 (n = 5); P5 (n = 5); P7 (n = 5); P10 (n = 5); P14 (n = 5); P21 (n = 5). Number of pups of either sex, n.

Extended Data Fig. 9 The aerobic neonatal intestine supports rapid K. pneumoniae expansion.

(a) Representative images validating the specificity of hypoxyprobe staining across all ages assayed (P5, P12, and P21). Non-specific staining is not observed with secondary antibody alone or when PMDZ is withheld from administration. Images are representative of controls used in every experiment using PMDZ hypoxyprobe assay. (b) Representative images of hypoxyprobe assay measuring ratio of PMDZ adducts (red) to DAPI (blue) from randomly selected sections of mid-distal colon in pups at different ages prior to weaning (upper panel). Average intensity of all fields (left) and average intensity per mouse (right) are displayed (lower panel). Violin plots show median (solid line) and quartiles (dashed line) of all measurements. Data are representative of 2 independent experiments; n = 5 animals per age. (c) Growth curves of K. pneumoniae in minimal media under aerobic or anaerobic conditions with either glucose (left) or amino acids (right) as a sole carbon source (mean ± SD). Data are representative of 3 independent experiments; n = 3 wells per condition.

Extended Data Fig. 10 Neither butyrate nor PPARg activation protect against neonatal dysbiosis.

(a) (b) Gent pups received PBS or tributyrin i.g. for 3 days following infection with 107 CFU . Abdominal bioluminescence was measured daily. Pooled from 2 independent experiments using within-litter controls (a): Gent (n = 7); Tributyrin (n = 8). Representative of 2 independent experiments using within-litter controls (b); n = 4 mice per group. (c,d) Gene expression of Pparg and Angptl4 in colon of uninfected P7 gent pups receiving 3 days treatment with PBS or tributyrin. (c) Gent pups received PBS or PPARg agonist rosiglitazone i.g. for 3 days following infection with 107 CFU Kp-39lux. Abdominal bioluminescence was measured daily. Pooled from 2 independent experiments using within-litter controls: Gent (n = 6), Rosiglitazone (n = 7). (d) Gene expression of Pparg and Angptl4 in colon of uninfected P7 gent pups receiving 3 days treatment with PBS or rosiglitazone. Box and whisker plots show median and IQR with lines extending as the 1st and 4th quartile. Data are representative of 2 independent experiments using controls; n = 3 mice per group. (e) Representative images of hypoxyprobe assay measuring ratio of PMDZ adducts (red) to DAPI (blue) from randomly selected sections of mid-distal colon in P7 littermates reared with gentamicin and given 50 µl PBS (Gent), fecal microbiome transplant (FMT), L. rhamnosus GG (LGG), L. johnsonii G2A, or L. murinus V10 i.g on P5 and P6. Average intensity of all high-powered fields (top) and average intensity per mouse (bottom) are displayed. Violin plots show median (solid line) and quartiles (dashed line) of all measurements. Pooled from 2 independent experiments: Gent (n = 7); FMT (n = 8); LGG (n = 7); L. johnsonii (n = 8); L. murinus (n = 6). Number of pups of either sex, n.

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Singer, J.R., Blosser, E.G., Zindl, C.L. et al. Preventing dysbiosis of the neonatal mouse intestinal microbiome protects against late-onset sepsis. Nat Med 25, 1772–1782 (2019) doi:10.1038/s41591-019-0640-y

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