Nucleus pulposus cell network modelling in the intervertebral disc.

IF 3.5 2区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

NPJ Systems Biology and Applications Pub Date : 2025-01-31 DOI:10.1038/s41540-024-00479-6

Sofia Tseranidou, Maria Segarra-Queralt, Francis Kiptengwer Chemorion, Christine Lyn Le Maitre, Janet Piñero, Jérôme Noailly

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Abstract

Intervertebral disc degeneration (IDD) results from an imbalance between anabolic and catabolic processes in the extracellular matrix (ECM). Due to complex biochemical interactions, a comprehensive understanding is needed. This study presents a regulatory network model (RNM) for nucleus pulposus cells (NPC), representing normal intervertebral disc (IVD) conditions. The RNM includes 33 proteins, and 153 interactions based on literature, incorporating key NPC regulatory mechanisms. A semi-quantitative approach calculates the basal steady state, accurately reflecting normal NPC activity. Model validation through published studies replicated pro-catabolic and pro-anabolic shifts, emphasizing the roles of transforming growth factor beta (TGF-β) and interleukin-1 receptor antagonist (IL-1Ra) in ECM regulation. This IVD RNM is a valuable tool for predicting IDD progression, offering insights into ECM degradation mechanisms and guiding experimental research on IVD health and degeneration.

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椎间盘髓核细胞网络模型。

椎间盘退变（IDD）是由细胞外基质（ECM）中合成代谢和分解代谢过程的不平衡引起的。由于复杂的生物化学相互作用，需要全面的了解。本研究提出了一个代表正常椎间盘（IVD）状况的髓核细胞（NPC）的调节网络模型（RNM）。RNM包括33个蛋白，文献中有153个相互作用，包含了关键的NPC调控机制。半定量方法计算基本稳定状态，准确反映正常的NPC活动。通过发表的研究验证模型，复制了促分解代谢和促合成代谢的转变，强调了转化生长因子β (TGF-β)和白细胞介素-1受体拮抗剂（IL-1Ra）在ECM调节中的作用。这种IVD RNM是预测IDD进展的有价值的工具，为ECM降解机制提供了见解，并指导了IVD健康和退化的实验研究。

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来源期刊

NPJ Systems Biology and Applications Mathematics-Applied Mathematics

CiteScore

5.80

自引率

0.00%

发文量

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.