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Updated: May 15, 2025

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整合RNAseq转录组学和N-甘氨基组学揭示了生物合成途径,并预测了结构特定的N-甘氨基表达

Michael Russelle S Alvarez¹, Xavier A Holmes¹, Armin Oloumi¹

¹Department of Chemistry, University of California, Davis Davis California USA cblebrilla@ucdavis.edu.

Chemical science

|April 7, 2025

概括

机器学习模型从糖基因表达中预测蛋白质N-甘氨酸的丰富性,有助于发现癌症和自身免疫性疾病等疾病的治疗点.

科学领域:

糖化学和系统生物学系统生物学
计算生物学计算生物学
生物化学生物化学

背景情况:

蛋白N-糖化在疾病发病过程中至关重要,但由于生物合成途径重叠,鉴定涉及的特定糖基因是复杂的.
异常的N-糖化与各种疾病有关,包括癌症,神经退行性和自身免疫性疾病,突出了对治疗点的需求.

研究的目的:

开发监督机器学习模型,以根据糖基因表达来预测N-甘氨酸的丰富性.
阐明不同细胞类型和条件的甘氨基基表达和N-甘氨酸组成之间的相互作用.

主要方法:

经过训练的回归模型使用配对的LC-MS/MS N-glycomic和3'-TagSeq转录组数据集.
利用来自多种组织来源 (B细胞,大脑,结肠,肺,肌肉,前列腺) 的数据,包括约400个N-甘氨酸化合物和>160个甘氨基基基因.
经验证的模型准确性 (R2 > 0.8) 和评估的葡萄糖基因重要性评分用于N-葡萄糖的预测.

主要成果:

准确的预测模型 (R2>0.8) 已开发用于N-甘氨酸在各种细胞类型的丰富度,包括肺癌和B细胞.
模型重要性得分确定了特定的甘氨基基因和N-甘氨酸类型之间的显著关联.
预测在不同的细胞输入量中保持一致,优于LC-MS/MS甘氨基化合物.

结论:

开发的机器学习模型可靠地预测N-糖化,即使细胞输入低,包括单细胞样本.
这些发现提供了关于细胞N-糖化机制的见解,并确定了与糖化相关疾病的潜在治疗策略.
这项研究为了解糖原-N-甘氨酸相互作用及其与疾病相关性提供了一个框架.

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