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Host-derived protein profiles of human neonatal meconium across gestational ages.
Shitara, Yoshihiko; Konno, Ryo; Yoshihara, Masahito; Kashima, Kohei; Ito, Atsushi; Mukai, Takeo; Kimoto, Goh; Kakiuchi, Satsuki; Ishikawa, Masaki; Kakihara, Tomo; Nagamatsu, Takeshi; Takahashi, Naoto; Fujishiro, Jun; Kawakami, Eiryo; Ohara, Osamu; Kawashima, Yusuke; Watanabe, Eiichiro.
Afiliação
  • Shitara Y; Department of Pediatrics, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Konno R; Department of Applied Genomics, Kazusa DNA Research Institute, Chiba, Japan.
  • Yoshihara M; Institute for Advanced Academic Research (IAAR), Chiba University, Chiba, Japan.
  • Kashima K; Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
  • Ito A; Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Osaka, Japan.
  • Mukai T; Department of Pediatrics, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Kimoto G; Department of Pediatrics, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Kakiuchi S; Department of Pediatrics, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Ishikawa M; Department of Pediatrics, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Kakihara T; Department of Pediatrics, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Nagamatsu T; Department of Applied Genomics, Kazusa DNA Research Institute, Chiba, Japan.
  • Takahashi N; Department of Pediatric Surgery, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Fujishiro J; Department of Obstetrics and Gynecology, Faculty of Medicine, International University of Health and Welfare, Chiba, Japan.
  • Kawakami E; Department of Pediatrics, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Ohara O; Department of Pediatric Surgery, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
  • Kawashima Y; Institute for Advanced Academic Research (IAAR), Chiba University, Chiba, Japan.
  • Watanabe E; Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
Nat Commun ; 15(1): 5543, 2024 Jul 17.
Article em En | MEDLINE | ID: mdl-39019879
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Idade Gestacional / Proteômica / Aprendizado de Máquina / Mecônio Limite: Female / Humans / Male / Newborn / Pregnancy Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Idade Gestacional / Proteômica / Aprendizado de Máquina / Mecônio Limite: Female / Humans / Male / Newborn / Pregnancy Idioma: En Ano de publicação: 2024 Tipo de documento: Article