| 论文题目: | System-level Metabolic Modeling Facilitates Unveiling Metabolic Signature in Exceptional Longevity |
| 作者: | Li, GH; Han, FF ; Xiao, FH ; Gu, KSY; Shen, Q; Xu, WH; Li, WX; Wang, YL; Liang, B; Huang, JF; Xiao, WZ; Kong, QP |
| 联系作者: | kongqp@mail.kiz.ac.cn |
| 发表年度: | 2022 |
| DOI: | doi: 10.1111/acel.13595 |
| 摘要: | Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system-level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome-wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI-60 cancer cell lines; it dramatically improved the performance of the modeling with an R-2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long-lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age-related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans |
| 刊物名称: | Aging Cell |
| 论文出处: | https://onlinelibrary.wiley.com/doi/10.1111/acel.13595 |
| 影响因子: | 9.304(2020IF) |
