Evidence map›Paper›PMID 41436733›Full record

ArticleNature communications2025

Decoding necrosome assembly: harmonizing signal amplification and attenuation through optimal RIP3 stoichiometry.

Xiang Li, Yating Cao, Fei Xu, Yiting Zhang, Yue Kong, Chengjie Lan, Rongfeng Zhu, Cheng Lin, Chuan-Qi Zhong, Zhilong Liu and 6 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Xiang Li *Department of Physics, Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen, China. xianglibp@xmu.edu.cn.ORCID http://orcid.org/0000-0003-2511-0348
Yating Cao *State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Fei Xu *Department of Physics, Anhui Normal University, Wuhu, China.
Yiting ZhangState Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Yue KongState Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Chengjie LanState Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.
Rongfeng ZhuInstitute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen, China.
Cheng LinDepartment of Physics, Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen, China.
Chuan-Qi ZhongState Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China.ORCID http://orcid.org/0000-0002-8354-7727
Zhilong LiuDepartment of Physics, Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen, China.
Hong QiComplex Systems Research Center, Shanxi University, Taiyuan, China.ORCID http://orcid.org/0000-0003-2230-1763
Yichuan HuangDepartment of Physics, Fujian Provincial Key Lab for Soft Functional Materials Research, Xiamen University, Xiamen, China.
Yunshan XiaoDepartment of Obstetrics, School of Medicine, Xiamen University, Xiamen, China.
Gui-Quan SunComplex Systems Research Center, Shanxi University, Taiyuan, China.
Jianwei ShuaiOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China. shuaijw@wiucas.ac.cn.
Xin ChenState Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China. xchen@xmu.edu.cn.ORCID http://orcid.org/0000-0002-2477-6087

Funding

National Natural Science Foundation of China (National Science Foundation of China) 31871386, 32070736, and 32370803Natural Science Foundation of Fujian Province (Fujian Provincial Natural Science Foundation) 2023J06003
6 · The paper itself

Abstract

Necrosome assembly is essential for necroptosis, a process implicated in neurodegeneration, ischemic injury, and inflammatory diseases. Yet the spatiotemporal rules governing this assembly remain elusive. Leveraging quantitative STORM and mathematical modeling, we define an approximately 3:1 ratio of RIP3 to RIP1 in necrosomes as the optimal stoichiometry for necroptosis, enabling signal amplification and a threshold response. Surprisingly, excessive RIP3 oligomerization attenuates signaling, acting as an intrinsic size control mechanism. RIP3 assembly is dynamically regulated: it is constrained by stimulation and RIP1, promoted by RIP3 itself, and unexpectedly limited by downstream MLKL. A complementary balance between necrosome quantity and RIP3 assembly degree ensures efficient MLKL phosphorylation. In contrast, Caspase-8 assembly is limited by c-FLIP and recruited linearly by RIP1, while its distinct behavior from RIP3 underlies the biphasic necroptotic response to RIP1. These findings uncover the flexible, multi-strategic nature of signalosomes and offer valuable insights for therapeutic and synthetic biology.

Indexed as

NecroptosisReceptor-Interacting Protein Serine-Threonine KinasesAnimalsCASP8 and FADD-Like Apoptosis Regulating ProteinCaspase 8HEK293 CellsHumansMiceNuclear Pore Complex ProteinsPhosphorylationProtein KinasesSignal TransductionCASP8 and FADD-Like Apoptosis Regulating ProteinCaspase 8Nuclear Pore Complex ProteinsProtein KinasesReceptor-Interacting Protein Serine-Threonine KinasesRIPK3 protein, humanRipk3 protein, mouse

Identifiers

PMID41436733
PMCPMC12796345

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.