Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden.
Division of Dermatology and Venereology, Department of Clinical Sciences, Lund University, Lund, Sweden.
Nat Commun. 2024 Aug 20;15(1):7128. doi: 10.1038/s41467-024-51589-y.
Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.
基于质谱的肽组学的最新进展推动了数千种内源性肽在不同生物系统中的鉴定和定量。然而，蛋白水解处理产生的广泛肽组学图谱给下游分析带来了一些挑战，并限制了临床样本的可比性。在这里，我们提出了一种将肽聚集到肽簇中的算法，降低了肽组学数据的维度，改善了蛋白酶切割位点的定义，增强了样本间的可比性，并能够实施类似于其他组学领域中使用的大规模数据分析方法。我们通过对高度定义的猪伤口感染和人类临床非愈合伤口的伤口液肽组进行大规模定量分析来展示该算法。这揭示了在细菌定植的最早阶段特征性表型特异性肽区域和蛋白水解活性。我们在 1 型糖尿病患者的尿肽组上验证了该方法，发现了潜在的亚群并提高了分类准确性。
