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Metabolomics efficiently discriminates monozygotic twins in peripheral blood.

Kuo ZengJiang DuYun-Zhou ChenDan-Yang WangMao-Ling SunYu-Zhang LiDong-Yi WangShu-Han LiuXiu-Mei ZhuPeng LvZhe DuKun LiuJun Yao
Published in: International journal of legal medicine (2024)
Monozygotic (MZ) twins cannot be distinguished using conventional forensic STR typing because they present identical STR genotypings. However, MZ twins do not always live in the same environment and often have different dietary and other lifestyle habits. Metabolic profiles are deyermined by individual characteristics and are also influenced by the environment in which they live. Therefore, they are potential markers capable of identifying MZ twins. Moreover, the production of proteins varies from organism to organism and is influenced by both the physiological state of the body and the external environment. Hence, we used metabolomics and proteomics to identify metabolites and proteins in peripheral blood to discriminate MZ twins. We identified 1749 known metabolites and 622 proteins in proteomic analysis. The metabolic profiles of four pairs of MZ twins revealed minor differences in intra-MZ twins and major differences in inter-MZ twins. Each pair of MZ twins exhibited distinct characteristics, and four metabolites-methyl picolinate, acesulfame, paraxanthine, and phenylbenzimidazole sulfonic acid-were observed in all four MZ twin pairs. These four differential exogenous metabolites conincidently show that the different external environments and life styles can be well distinguished by metabolites, considering that twins do not all have the same eating habits and living environments. Moreover, MZ twins showed different protein profiles in serum but not in whole blood. Thus, our results indicate that differential metabolites provide potential biomarkers for the personal identification of MZ twins in forensic medicine.
Keyphrases
  • gestational age
  • ms ms
  • peripheral blood
  • mass spectrometry
  • risk assessment
  • type diabetes
  • weight loss
  • preterm birth
  • small molecule
  • single cell
  • human health
  • bioinformatics analysis