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Metabolome and microbiome multi-omics integration from a murine lung inflammation model of bronchopulmonary dysplasia



Respiratory tract microbial dysbiosis can exacerbate inflammation and conversely inflammation may cause dysbiosis. Dysbiotic microbiome metabolites may lead to bronchopulmonary dysplasia (BPD). Hyperoxia and lipopolysaccharide (LPS) interaction alters lung microbiome and metabolome, mediating BPD lung injury sequence.


C57BL6/J mice were exposed to 21% (normoxia) or 70% (hyperoxia) oxygen during postnatal days (PND) 1–14. Pups were injected with LPS (6 mg/kg) or equal PBS volume, intraperitoneally on PND 3, 5, and 7. At PND14, the lungs were collected for microbiome and metabolomic analyses (n = 5/group).


Microbiome alpha and beta diversity were similar between groups. Metabolic changes included hyperoxia 31 up/18 down, LPS 7 up/4 down, exposure interaction 8. Hyperoxia increased Intestinimonas abundance, whereas LPS decreased Clostridiales, Dorea, and Intestinimonas; exposure interaction affected Blautia. Differential co-expression analysis on multi-omics data identified exposure-altered modules. Hyperoxia metabolomics response was integrated with a published matching transcriptome, identifying four induced genes (ALDOA, GAA, NEU1, RENBP), which positively correlated with BPD severity in a published human newborn cohort.


We report hyperoxia and LPS lung microbiome and metabolome signatures in a clinically relevant BPD model. We identified four genes correlating with BPD status in preterm infants that are promising targets for therapy and prevention.


  • Using multi-omics, we identified and correlated key biomarkers of hyperoxia and LPS on murine lung micro-landscape and examined their potential clinical implication, which shows strong clinical relevance for future research.

  • Using a double-hit model of clinical relevance to bronchopulmonary dysplasia, we are the first to report integrated metabolomic/microbiome landscape changes and identify novel disease biomarker candidates.

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Fig. 1: Analytical approach.
Fig. 2: Hyperoxia and LPS exposures alter the microbial and metabolomic landscapes.
Fig. 3: The impact of individual hyperoxia and LPS exposures on microbiome diversity in the murine lung.
Fig. 4: Microbiome–metabolite correlation for individual exposure-associated microbiome genera and metabolites.
Fig. 5: Differential correlation for single-omic and multi-omic microbiome and metabolomic modules induced by either hyperoxia or LPS exposure.
Fig. 6: A murine integrated transcriptomics/metabolomic signature of hyperoxia associates with blood transcriptome in human newborns at high risk for BPD.


  1. Integrative HMP (iHMP) Research Network Consortium. The Integrative Human Microbiome Project: dynamic analysis of microbiome-host omics profiles during periods of human health and disease. Cell Host Microbe 16, 276–289 (2014).

  2. Dickson, R. P. et al. The lung microbiota of healthy mice are highly variable, cluster by environment, and reflect variation in baseline lung innate immunity. Am. J. Respir. Crit. Care Med. 198, 497–508 (2018).

    Article  CAS  Google Scholar 

  3. Dickson, R. P., Erb-Downward, J. R., Martinez, F. J. & Huffnagle, G. B. The microbiome and the respiratory tract. Annu. Rev. Physiol. 78, 481–504 (2016).

    Article  CAS  Google Scholar 

  4. Marsland, B. J. & Gollwitzer, E. S. Host-microorganism interactions in lung diseases. Nat. Rev. Immunol. 14, 827–835 (2014).

    Article  CAS  Google Scholar 

  5. Wypych, T. P., Wickramasinghe, L. C. & Marsland, B. J. The influence of the microbiome on respiratory health. Nat. Immunol. 20, 1279–1290 (2019).

    Article  CAS  Google Scholar 

  6. Lal, C. V. et al. The airway microbiome at birth. Sci. Rep. 6, 31023 (2016).

    Article  CAS  Google Scholar 

  7. Pammi, M. et al. Airway microbiome and development of bronchopulmonary dysplasia in preterm infants: a systematic review. J. Pediatr. 204, 126.e2–133.e2 (2019).

    Article  Google Scholar 

  8. Segal, L. N. et al. Randomised, double-blind, placebo-controlled trial with azithromycin selects for anti-inflammatory microbial metabolites in the emphysematous lung. Thorax 72, 13–22 (2017).

    Article  Google Scholar 

  9. Ashley, S. L. et al. Lung and gut microbiota are altered by hyperoxia and contribute to oxygen-induced lung injury in mice. Sci. Transl. Med. 12, eaau9959 (2020).

  10. Jobe, A. J. The new BPD: an arrest of lung development. Pediatr. Res. 46, 641–643 (1999).

    Article  CAS  Google Scholar 

  11. Walsh, M. C. et al. Summary Proceedings from the Bronchopulmonary Dysplasia Group. Pediatrics 117, S52–S56 (2006).

    Article  Google Scholar 

  12. Madurga, A., Mizikova, I., Ruiz-Camp, J. & Morty, R. E. Recent advances in late lung development and the pathogenesis of bronchopulmonary dysplasia. Am. J. Physiol. Lung Cell. Mol. Physiol. 305, L893–L905 (2013).

    Article  CAS  Google Scholar 

  13. Menon, R. T., Shrestha, A. K., Reynolds, C. L., Barrios, R. & Shivanna, B. Long-term pulmonary and cardiovascular morbidities of neonatal hyperoxia exposure in mice. Int. J. Biochem. Cell Biol. 94, 119–124 (2018).

    Article  CAS  Google Scholar 

  14. Park, J. R., Lee, H., Kim, S. I. & Yang, S. R. The Tri-peptide Ghk-Cu complex ameliorates lipopolysaccharide-induced acute lung injury in mice. Oncotarget 7, 58405–58417 (2016).

    Article  Google Scholar 

  15. Bos, L. D. et al. Alterations in exhaled breath metabolite-mixtures in two rat models of lipopolysaccharide-induced lung injury. J. Appl. Physiol. 115, 1487–1495 (2013).

    Article  CAS  Google Scholar 

  16. Naz, S., Garcia, A., Rusak, M. & Barbas, C. Method development and validation for rat serum fingerprinting with Ce-Ms: application to ventilator-induced-lung-injury study. Anal. Bioanal. Chem. 405, 4849–4858 (2013).

    Article  CAS  Google Scholar 

  17. Shrestha, A. K. et al. Consequences of early postnatal lipopolysaccharide exposure on developing lungs in mice. Am. J. Physiol. Lung Cell. Mol. Physiol. 316, L229–l244 (2019).

    Article  CAS  Google Scholar 

  18. Shrestha, A. K. et al. Interactive and independent effects of early lipopolysaccharide and hyperoxia exposure on developing murine lungs. Am. J. Physiol. Lung Cell. Mol. Physiol. 319, L981–l996 (2020).

    Article  CAS  Google Scholar 

  19. Contrepois, K., Liang, L. & Snyder, M. Can metabolic profiles be used as a phenotypic readout of the genome to enhance precision medicine? Clin. Chem. 62, 676–678 (2016).

    Article  CAS  Google Scholar 

  20. Tesson, B. M., Breitling, R. & Jansen, R. C. DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics 11, 497 (2010).

    Article  Google Scholar 

  21. Shrestha, A. K. et al. Lung omics signatures in a bronchopulmonary dysplasia and pulmonary hypertension-like murine model. Am. J. Physiol. Lung Cell. Mol. Physiol. 315, L734–l741 (2018).

    Article  CAS  Google Scholar 

  22. Chong, J., Wishart, D. S. & Xia, J. Using metaboanalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinformatics 68, e86 (2019).

    Article  Google Scholar 

  23. Pietrzyk, J. J. et al. Gene expression profiling in preterm infants: new aspects of bronchopulmonary dysplasia development. PLoS ONE 8, e78585 (2013).

    Article  CAS  Google Scholar 

  24. Edgar, R. C. Search and clustering orders of magnitude faster than blast. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  Google Scholar 

  25. Edgar, R. C. Uparse: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    Article  CAS  Google Scholar 

  26. Quast, C. et al. The Silva Ribosomal RNA Gene Database Project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  Google Scholar 

  27. McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  CAS  Google Scholar 

  28. Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).

    Google Scholar 

  29. Amara, C. S. et al. Serum metabolic profiling identified a distinct metabolic signature in bladder cancer smokers: a key metabolic enzyme associated with patient survival. Cancer Epidemiol. Biomark. Prev. 28, 770–781 (2019).

    Article  CAS  Google Scholar 

  30. Vantaku, V. et al. Large-scale profiling of serum metabolites in African American and European American patients with bladder cancer reveals metabolic pathways associated with patient survival. Cancer 125, 921–932 (2019).

    Article  CAS  Google Scholar 

  31. Gohlke, J. H. et al. Methionine-homocysteine pathway in African-American prostate cancer. JNCI Cancer Spectr. 3, pkz019 (2019).

    Article  Google Scholar 

  32. Jin, F. et al. Tobacco-specific carcinogens induce hypermethylation, DNA adducts, and DNA damage in bladder cancer. Cancer Prev. Res. 10, 588–597 (2017).

    Article  CAS  Google Scholar 

  33. Whitlock, M. C. & Schluter, D. The Analysis of Biological Data 2nd edn (Roberts and Company Publishers, 2015).

  34. Package ‘Pheatmap’1.0.12. https://Cran.R-Project.Org/Web/Packages/Pheatmap/Pheatmap.Pdf (2018).

  35. Grimm, S. L. et al. Effect of sex chromosomes versus hormones in neonatal lung injury. JCI Insight 6, e146863 (2021).

  36. Jobe, A. H. & Bancalari, E. Bronchopulmonary dysplasia. Am. J. Respir. Crit. Care Med. 163, 1723–1729 (2001).

    Article  CAS  Google Scholar 

  37. Zoetis, T. & Hurtt, M. E. Species comparison of lung development. Birth Defects Res. B Dev. Reprod. Toxicol. 68, 121–124 (2003).

    Article  CAS  Google Scholar 

  38. Wagner, B. D. et al. Airway microbial community turnover differs by BPD severity in ventilated preterm infants. PLoS ONE 12, e0170120 (2017).

    Article  Google Scholar 

  39. Lal, C. V. et al. Early airway microbial metagenomic and metabolomic signatures are associated with development of severe bronchopulmonary dysplasia. Am. J. Physiol. Lung Cell. Mol. Physiol. 315, L810–L815 (2018).

    Article  CAS  Google Scholar 

  40. Gentle, S. J. et al. Bronchopulmonary dysplasia is associated with reduced oral nitrate reductase activity in extremely preterm infants. Redox Biol. 38, 101782 (2021).

    Article  CAS  Google Scholar 

  41. Fanos, V. et al. Urinary metabolomics of bronchopulmonary dysplasia (BPD): preliminary data at birth suggest it is a congenital disease. J. Matern. Fetal Neonatal Med. 27(Suppl 2), 39–45 (2014).

    Article  CAS  Google Scholar 

  42. Pintus, M. C. et al. Urinary (1)H-NMR metabolomics in the first week of life can anticipate BPD diagnosis. Oxid. Med. Cell. Longev. 2018, 7620671 (2018).

    Article  Google Scholar 

  43. Piersigilli, F. & Bhandari, V. Metabolomics of bronchopulmonary dysplasia. Clin. Chim. Acta 500, 109–114 (2020).

    Article  CAS  Google Scholar 

  44. Huffnagle, G. B., Dickson, R. P. & Lukacs, N. W. The respiratory tract microbiome and lung inflammation: a two-way street. Mucosal Immunol. 10, 299–306 (2017).

    Article  CAS  Google Scholar 

  45. Schmidt, D. R. et al. Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J. Clin. 71, 333–358 (2021).

  46. Kang, M. K. et al. Prognostic significance of genetic variants in glut1 in stage III non-small cell lung cancer treated with radiotherapy. Thorac. Cancer 12, 874–879 (2021).

    Article  CAS  Google Scholar 

  47. Keeler, A. M. et al. Airway smooth muscle dysfunction in Pompe (Gaa(-/-)) mice. Am. J. Physiol. Lung Cell. Mol. Physiol. 312, L873–l881 (2017).

    Article  Google Scholar 

  48. Cross, A. S. et al. Neu1 and Neu3 sialidase activity expressed in human lung microvascular endothelia: Neu1 restrains endothelial cell migration, whereas Neu3 does not. J. Biol. Chem. 287, 15966–15980 (2012).

    Article  CAS  Google Scholar 

  49. Luzina, I. G. et al. Elevated expression of Neu1 sialidase in idiopathic pulmonary fibrosis provokes pulmonary collagen deposition, lymphocytosis, and fibrosis. Am. J. Physiol. Lung Cell. Mol. Physiol. 310, L940–L954 (2016).

    Article  Google Scholar 

  50. Tada, M., Takahashi, S., Miyano, M. & Miyake, Y. Tissue-specific regulation of renin-binding protein gene expression in rats. J. Biochem. 112, 175–182 (1992).

    Article  CAS  Google Scholar 

  51. Nagpal, R. et al. Comparative microbiome signatures and short-chain fatty acids in mouse, rat, non-human primate, and human feces. Front. Microbiol. 9, 2897 (2018).

    Article  Google Scholar 

  52. Khaliullin, T. O. et al. Comparative analysis of lung and blood transcriptomes in mice exposed to multi-walled carbon nanotubes. Toxicol. Appl. Pharmacol. 390, 114898 (2020).

    Article  CAS  Google Scholar 

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M.P. is funded by NIH grants R03HD098482 and R21HD091718. The metabolomics core was supported by the CPRIT Core Facility Support Awards RP170005, RP210227 (N.P.) “Proteomic and Metabolomic Core Facility,” NCI Cancer Center Support Grant P30CA125123, NIH/NCI R01CA220297, and NIH/NCI R01CA216426 intramural funds from the Dan L. Duncan Cancer Center (DLDCC). F.C. and C.C. were partially supported by The Cancer Prevention Institute of Texas (CPRIT) RP170005, NIH P30 shared resource grant CA125123, NIEHS center grants P30 ES030285 and P42 ES027725, and NIMHD P50MD015496.

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All authors included in this paper fulfill the criteria of authorship. M.P. conceived the ideas. A.E.S., B.S., and M.P. collected the samples. K.H., V.P., C.S.R.A., and N.P. processed the samples and performed the omics profiling. A.E.S., C.F., S.L.G., M.J.R., C.C., and M.P. analyzed the data. A.E.S., C.F., C.C., S.L.G., and M.P. prepared the first draft. All authors have read and approved the current version.

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Correspondence to Cristian Coarfa.

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El Saie, A., Fu, C., Grimm, S.L. et al. Metabolome and microbiome multi-omics integration from a murine lung inflammation model of bronchopulmonary dysplasia. Pediatr Res 92, 1580–1589 (2022).

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