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

Abstract

Background

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.

Methods

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).

Results

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.

Conclusions

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.

Impact

  • 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.

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Funding

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 (2022). https://doi.org/10.1038/s41390-022-02002-1

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