Associations of cord metabolome and biochemical parameters with the neonatal deaths of cloned pigs.
Zheng AoZhimin WuHuaxing ZhaoZhenfang WuZicong LiPublished in: Reproduction in domestic animals = Zuchthygiene (2021)
Neonatal cloned pigs generated via somatic cell nuclear transfer (SCNT) have high incidences of malformation and mortality. The mechanisms underlying the massive loss of cloned pig neonates remain unclear. We compared the cord serum metabolic profiles and biochemical indexes of SCNT-derived piglets that died within 4 days (SCNT-DW4), SCNT-derived piglets that survived over 4 days (SCNT-SO4) and artificial insemination (AI)-generated piglets that survived over 4 days (AI-SO4) to investigate the associations of serum metabolomics and biochemical indexes in umbilical cord (UC) sera at delivery with the neonatal loss of cloned pigs. Results showed that compared with SCNT-SO4 and AI-SO4 piglets, SCNT-DW4 piglets had lower birth weight, placental indexes, placental vascularization scores, UC scores, vitality scores, serum glucose and levels but higher creatinine, urea nitrogen and uric acid levels in cord sera. Metabolomics analysis revealed alterations in lipid, glucose and purine metabolism in the cord sera of SCNT-DW4 piglets. These results indicated that the disturbance of the cord serum metabolome might be associated with the low birth weight and malformations of cloned neonates. These effects were likely the consequences of the impaired placental morphology and function of SCNT-derived piglets. This study provides helpful information regarding the potential mechanisms responsible for the neonatal death of cloned pigs and also offers an important basis for the design of effective strategies to improve the survival rate of these animals.
Keyphrases
- low birth weight
- uric acid
- preterm infants
- artificial intelligence
- umbilical cord
- birth weight
- single cell
- human milk
- metabolic syndrome
- preterm birth
- blood glucose
- gene expression
- weight gain
- type diabetes
- insulin resistance
- risk factors
- adipose tissue
- genome wide
- deep learning
- skeletal muscle
- fatty acid
- social media
- free survival