Khoulali Celia, Pastor Juan Manuel, Galeano Javier, Vissenberg Kris, Miedes Eva
Department of Biotechnology-Plant Biology, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y Biosistemas, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
Biodiversity and Conservation of Plant Genetic Resources-UPM Research Group, Universidad Politécnica de Madrid, 28040 Madrid, Spain.
Int J Mol Sci. 2025 Mar 24;26(7):2946. doi: 10.3390/ijms26072946.
The plant cell wall (CW) is a physical barrier that plays a dual role in plant physiology, providing structural support for growth and development. Understanding the dynamics of CW growth is crucial for optimizing crop yields. In this study, we employed onion ( L.) epidermis as a model system, leveraging its layered organization to investigate growth stages. Microscopic analysis revealed proportional variations in cell size in different epidermal layers, offering insights into growth dynamics and CW structural adaptations. Fourier transform infrared spectroscopy (FTIR) identified 11 distinct spectral intervals associated with CW components, highlighting structural modifications that influence wall elasticity and rigidity. Biochemical assays across developmental layers demonstrated variations in cellulose, soluble sugars, and antioxidant content, reflecting biochemical shifts during growth. The differential expression of ten cell wall enzyme (CWE) genes, analyzed via RT-qPCR, revealed significant correlations between gene expression patterns and CW composition changes across developmental layers. Notably, the gene expression levels of the pectin methylesterase and fucosidase enzymes were associated with the contents in cellulose, soluble sugar, and antioxidants. To complement these findings, machine learning models, including Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Neural Networks, were employed to integrate FTIR data, biochemical parameters, and CWE gene expression profiles. Our models achieved high accuracy in predicting growth stages. This underscores the intricate interplay among CW composition, CW enzymatic activity, and growth dynamics, providing a predictive framework with applications in enhancing crop productivity and sustainability.
植物细胞壁（CW）是一种物理屏障，在植物生理学中发挥着双重作用，为生长和发育提供结构支持。了解细胞壁生长的动态对于优化作物产量至关重要。在本研究中，我们采用洋葱（L.）表皮作为模型系统，利用其分层结构来研究生长阶段。显微镜分析揭示了不同表皮层细胞大小的比例变化，为生长动态和细胞壁结构适应性提供了见解。傅里叶变换红外光谱（FTIR）确定了与细胞壁成分相关的11个不同光谱区间，突出了影响细胞壁弹性和刚性的结构修饰。对不同发育层进行的生化分析表明，纤维素、可溶性糖和抗氧化剂含量存在差异，反映了生长过程中的生化变化。通过RT-qPCR分析的十种细胞壁酶（CWE）基因的差异表达，揭示了基因表达模式与不同发育层细胞壁组成变化之间的显著相关性。值得注意的是，果胶甲酯酶和岩藻糖苷酶的基因表达水平与纤维素、可溶性糖和抗氧化剂的含量相关。为了补充这些发现，我们采用了包括支持向量机（SVM）、k近邻（kNN）和神经网络在内的机器学习模型，来整合FTIR数据、生化参数和CWE基因表达谱。我们的模型在预测生长阶段方面取得了很高的准确率。这强调了细胞壁组成、细胞壁酶活性和生长动态之间的复杂相互作用，提供了一个可用于提高作物生产力和可持续性的预测框架。
