背景:微生物群落的高度多样性和复杂性使鉴定和量化群落中表达的大量蛋白质成为一项艰巨的挑战。传统的元蛋白质组学方法在很大程度上依赖于MS/MS谱对消化样品中相应短肽的准确鉴定。然后进行蛋白质推断以及随后对检测到的蛋白质进行分类学和功能分析。这些方法取决于源自样品特异性宏基因组数据或来自公共存储库的蛋白质序列数据库的可用性。由于这些蛋白质序列数据库的不完整和不完善,以及群落中不同细菌物种表达的同源蛋白的优势,这种肽鉴定和蛋白质推断的计算过程是具有挑战性和容易出错的,这阻碍了跨多个样本的元蛋白质组的比较。
结果:我们开发了metaSpectraST,无监督和数据库独立的元蛋白质组学工作流程,它通过根据光谱相似性对实验观察到的MS/MS光谱进行聚类来定量分析和比较元蛋白质组学样品。我们在断奶后立即将metaSpectraST应用于从两只不同母鼠的同窝中收集的粪便样本。在没有任何肽谱鉴定的情况下获得不同小鼠的微生物群落的定量蛋白质组谱,并用于评估样品之间的总体相似性并突出任何区别标记。与传统的依赖数据库的元蛋白质组学分析相比,metaSpectraST在对样本进行分类和检测断奶后小鼠肠道微生物组的细微微生物组变化方面更为成功。metaSpectraST也可以用作从具有广泛个体间差异的样品中选择合适的生物学重复的工具。
结论:metaSpectraST可以定量地快速分析蛋白质组学样品,无需构建蛋白质序列数据库或鉴定MS/MS谱。它最大限度地保留了包含在实验MS/MS光谱的信息,通过聚类所有的第一,从而能够更好地描绘复杂的微生物群落,并突出其功能的变化,与传统方法相比。将本节中的videobyte标记为ESM4视频摘要。
The high diversity and complexity of the microbial community make it a formidable challenge to identify and quantify the large number of proteins expressed in the community. Conventional metaproteomics approaches largely rely on accurate identification of the MS/MS spectra to their corresponding short peptides in the digested samples, followed by protein inference and subsequent taxonomic and functional analysis of the detected proteins. These approaches are dependent on the availability of protein sequence databases derived either from sample-specific metagenomic data or from public repositories. Due to the incompleteness and imperfections of these protein sequence databases, and the preponderance of homologous proteins expressed by different bacterial species in the community, this computational process of peptide identification and protein inference is challenging and error-prone, which hinders the comparison of metaproteomes across multiple samples.
We developed metaSpectraST, an unsupervised and database-independent metaproteomics workflow, which quantitatively profiles and compares metaproteomics samples by clustering experimentally observed MS/MS spectra based on their spectral similarity. We applied metaSpectraST to fecal samples collected from littermates of two different mother mice right after weaning. Quantitative proteome profiles of the microbial communities of different mice were obtained without any peptide-spectrum identification and used to evaluate the overall similarity between samples and highlight any differentiating markers. Compared to the conventional database-dependent metaproteomics analysis, metaSpectraST is more successful in classifying the samples and detecting the subtle microbiome changes of mouse gut microbiomes post-weaning. metaSpectraST could also be used as a tool to select the suitable biological replicates from samples with wide inter-individual variation.
metaSpectraST enables rapid profiling of metaproteomic samples quantitatively, without the need for constructing the protein sequence database or identification of the MS/MS spectra. It maximally preserves information contained in the experimental MS/MS spectra by clustering all of them first and thus is able to better profile the complex microbial communities and highlight their functional changes, as compared with conventional approaches. tag the videobyte in this section as ESM4 Video Abstract.