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

Yoshihiko Shitara, Ryo Konno, Masahito Yoshihara, Kohei Kashima, Atsushi Ito, Takeo Mukai, Goh Kimoto, Satsuki Kakiuchi, Masaki Ishikawa, Tomo Kakihara, Takeshi Nagamatsu, Naoto Takahashi, Jun Fujishiro, Eiryo Kawakami, Osamu Ohara, Yusuke Kawashima () and Eiichiro Watanabe ()
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Yoshihiko Shitara: The University of Tokyo
Ryo Konno: Kazusa DNA Research Institute
Masahito Yoshihara: Chiba 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
Osamu Ohara: Kazusa DNA Research Institute
Yusuke Kawashima: Kazusa DNA Research Institute
Eiichiro Watanabe: The University of Tokyo

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49805-w

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