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Host-derived protein profiles of human neonatal meconium across gestational ages

Author

Listed:
  • Yoshihiko Shitara

    (The University of Tokyo)

  • Ryo Konno

    (Kazusa DNA Research Institute)

  • Masahito Yoshihara

    (Chiba University
    Chiba University
    Osaka University)

  • Kohei Kashima

    (The University of Tokyo)

  • Atsushi Ito

    (The University of Tokyo)

  • Takeo Mukai

    (The University of Tokyo)

  • Goh Kimoto

    (The University of Tokyo)

  • Satsuki Kakiuchi

    (The University of Tokyo)

  • Masaki Ishikawa

    (Kazusa DNA Research Institute)

  • Tomo Kakihara

    (The University of Tokyo)

  • Takeshi Nagamatsu

    (International University of Health and Welfare)

  • Naoto Takahashi

    (The University of Tokyo)

  • Jun Fujishiro

    (The University of Tokyo)

  • Eiryo Kawakami

    (Chiba University
    Chiba University
    RIKEN Information R&D and Strategy Headquarters, RIKEN)

  • Osamu Ohara

    (Kazusa DNA Research Institute)

  • Yusuke Kawashima

    (Kazusa DNA Research Institute)

  • Eiichiro Watanabe

    (The University of Tokyo
    Gunma Children’s Medical Center)

Abstract

Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted an extensive proteomic analysis of first meconium from 259 newborns across varied gestational ages to delineate protein composition and elucidate its relevance to neonatal diseases. The first meconium samples were collected, with the majority obtained before feeding, and the mean time for the first meconium passage from the anus was 11.9 ± 9.47 h. Our analysis revealed 5370 host-derived meconium proteins, which varied depending on sex and gestational age. Specifically, meconium from preterm infants exhibited elevated concentrations of proteins associated with the extracellular matrix. Additionally, the protein profiles of meconium also exhibited unique variations depending on both specific diseases, including gastrointestinal diseases, congenital heart diseases, and maternal conditions. Furthermore, we developed a machine learning model to predict gestational ages using meconium proteins. Our model suggests that newborns with gastrointestinal diseases and congenital heart diseases may have immature gastrointestinal systems. These findings highlight the intricate relationship between clinical parameters and meconium protein composition, offering potential for a novel approach to assess neonatal gastrointestinal health.

Suggested Citation

  • Yoshihiko Shitara & Ryo Konno & Masahito Yoshihara & Kohei Kashima & Atsushi Ito & Takeo Mukai & Goh Kimoto & Satsuki Kakiuchi & Masaki Ishikawa & Tomo Kakihara & Takeshi Nagamatsu & Naoto Takahashi &, 2024. "Host-derived protein profiles of human neonatal meconium across gestational ages," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49805-w
    DOI: 10.1038/s41467-024-49805-w
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
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