负反馈回路是沃伯格效应的基础。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Alok Jaiswal, Raghvendra Singh
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引用次数: 0

摘要

癌细胞利用有氧糖酵解或沃伯格效应进行增殖,同时产生乳酸。虽然乳酸盐的产生对癌症的进展有广泛影响,但人们并不知道这种效应如何增加细胞增殖以及与氧化磷酸化的关系。在这里,我们阐明了负反馈回路(NFL)是沃伯格效应的原因。此外,我们还发现有氧糖酵解是氧化磷酸化的放大器。另一方面,静止是癌症干细胞的一个重要特性。基于NFL,我们发现有氧糖酵解和氧化磷酸化发挥协同作用,是实现细胞静止的必要条件。此外,我们的研究结果表明,处于缺氧龛位的细胞具有高度增殖性,但通过严重缺氧提高其 NADH/NAD+ 比率,接近达到静止状态。这项研究的结果有助于更好地理解新陈代谢、细胞周期、癌变和干性之间的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A negative feedback loop underlies the Warburg effect.

A negative feedback loop underlies the Warburg effect.

Aerobic glycolysis, or the Warburg effect, is used by cancer cells for proliferation while producing lactate. Although lactate production has wide implications for cancer progression, it is not known how this effect increases cell proliferation and relates to oxidative phosphorylation. Here, we elucidate that a negative feedback loop (NFL) is responsible for the Warburg effect. Further, we show that aerobic glycolysis works as an amplifier of oxidative phosphorylation. On the other hand, quiescence is an important property of cancer stem cells. Based on the NFL, we show that both aerobic glycolysis and oxidative phosphorylation, playing a synergistic role, are required to achieve cell quiescence. Further, our results suggest that the cells in their hypoxic niche are highly proliferative yet close to attaining quiescence by increasing their NADH/NAD+ ratio through the severity of hypoxia. The findings of this study can help in a better understanding of the link among metabolism, cell cycle, carcinogenesis, and stemness.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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