4.8 Article

Immune cells use active tugging forces to distinguish affinity and accelerate evolution

出版社

NATL ACAD SCIENCES
DOI: 10.1073/pnas.2213067120

关键词

immune response; physical dynamics of cells; adaptive evolution; antigen recognition

向作者/读者索取更多资源

Cells can use mechanical work to drive their own evolution, similar to the adaptive immune system. Immune B cells extract antigens from other cells' surfaces using cytoskeletal forces. This force usage can accelerate adaptation but may also cause cell population extinction, resulting in an optimal range of pulling strength. The extraction of environmental signals through active force can make biological systems more evolvable at a moderate energy cost.
Cells are known to exert forces to sense their physical surroundings for guidance of motion and fate decisions. Here, we propose that cells might do mechanical work to drive their own evolution, taking inspiration from the adaptive immune system. Growing evidence indicates that immune B cells-capable of rapid Darwinian evolution-use cytoskeletal forces to actively extract antigens from other cells' surfaces. To elucidate the evolutionary significance of force usage, we develop a theory of tug-of-war antigen extraction that maps receptor binding characteristics to clonal reproductive fitness, revealing physical determinants of selection strength. This framework unifies mechanosensing and affinity-discrimination capabilities of evolving cells: Pulling against stiff antigen tethers enhances discrimination stringency at the expense of absolute extraction. As a consequence, active force usage can accelerate adaptation but may also cause extinction of cell populations, resulting in an optimal range of pulling strength that matches molecular rupture forces observed in cells. Our work suggests that nonequilibrium, physical extraction of environmental signals can make biological systems more evolvable at a moderate energy cost.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据