精确分析胰腺癌诱发恶病质中的蛋白质序列和翻译后修饰异质性

Allyse M. Emmel, Tara S. Umberger, Emma H. Doud, T. Zimmers, Amber L. Mosley
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摘要

背景与假设:恶病质是一种常见于癌症患者的消耗性综合征,不能仅用热量摄入减少来解释。恶病质会导致生活质量下降和死亡率升高,目前尚无有效的治疗方法。为了更好地了解恶病质,我们旨在分析胰腺导管腺癌(PDAC)小鼠恶病质模型中蛋白质的异质性。由于肌肉是分解代谢过程中随时可用的蛋白质资源,我们推测在恶病质进展过程中,肌肉蛋白质的蛋白质序列和翻译后修饰(PTM)会发生显著变化。随着这些蛋白质被清除,对序列变异的精确分析可以确定蛋白质/肌肉分解的确切机制,从而更好地阐明PDAC诱导的恶病质的分子过程。实验设计:我们对PDAC诱导的恶病质小鼠模型的心肌和骨骼肌样本的两个蛋白质组学数据集进行了生物信息学分析。两种蛋白质组学软件算法SEQUEST(蛋白质组发现者(PD))和PEAKS(一种机器学习算法)被用来鉴定所有样本蛋白质及其PTMs。由于 PEAKS 能比 PD 鉴定出更多的 PTM,我们比较了 PD 和 PEAKS 鉴定出的最丰富的蛋白质,假设 PEAKS 会产生更大的蛋白质序列覆盖率。最后,我们汇编了两个数据集中 25 个最丰富蛋白质的未发表 PTM 和蛋白质加工事件。结果:值得注意的是,PEAKS报告的心肌序列覆盖率高于PD,而骨骼肌样本在两种算法中的覆盖率相似。这种差异可能表明,骨骼肌的缓存过程比心肌的降解速度更快,从而阻碍了 PEAKS 提高骨骼肌序列覆盖率。此外,还记录了几种未发表的修饰,包括肌动蛋白和乙酰-CoA乙酰转移酶的修饰。潜在影响:这些新发现的蛋白质修饰可能表明了 PDAC 诱导的恶病质过程中以前未知的分子过程。这些修饰可作为未来研究的基石,以确定治疗恶病质的新靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Precision analysis of protein sequence and post-translational modification heterogeneity in pancreatic cancer induced cachexia
Background and Hypothesis:Cachexia is a wasting syndrome commonly occurring in cancer patients that cannot be explained by decreased calorie intake alone. It results in decreased quality of life and increased mortality, and there are currently no effective treatments. To better understand cachexia, we aimed to profile protein heterogeneity seen in pancreatic ductal adenocarcinoma (PDAC) mouse models with cachexia. Since muscle is a readily available protein resource during catabolism, we hypothesize there are significant changes in protein sequence and post-translational modifications (PTMs) of muscle proteins during cachexia progression. As these proteins are scavenged, precise analysis of sequence variation can identify the exact mechanism of protein/muscle breakdown and better elucidate the molecular processes of PDAC-induced cachexia. Experimental Design:We performed bioinformatic analysis of two proteomics datasets of cardiac and skeletal muscle samples from PDAC-induced cachexia mouse models. Two proteomics software algorithms, SEQUEST (within Proteome Discoverer (PD)) and PEAKS (a machine-learning algorithm), were used to identify all sample proteins and their PTMs. Because PEAKS can identify more PTMs than PD, we compared the most abundant proteins identified in PD and PEAKS, hypothesizing PEAKS would yield greater protein sequence coverage. Finally, we compiled unpublished PTMs and protein processing events for 25 of the most abundant proteins in both datasets. Results:Notably, PEAKS reported greater sequence coverage for cardiac muscle than PD, while the skeletal muscle sample had similar coverage in both algorithms. This discrepancy may suggest cachectic processes degrade skeletal muscle at a greater rate than cardiac muscle, preventing PEAKS from increasing skeletal muscle sequence coverage relative to PD. In addition, several unpublished modifications, including those of actin and acetyl-CoA acetyltransferase, were recorded. Potential Impact:These newly discovered protein modifications may indicate previously unknown molecular processes in the course of PDAC-induced cachexia. These modifications may serve as cornerstones of future research to identify novel therapeutic targets in cachexia treatment.
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