Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China.
School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China.
Microbiome. 2023 Aug 7;11(1):176. doi: 10.1186/s40168-023-01602-1.
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.
微生物群落的高度多样性和复杂性使得识别和量化群落中大量表达的蛋白质成为一项艰巨的挑战。传统的宏蛋白质组学方法主要依赖于将 MS/MS 谱准确识别为消化样品中相应的短肽，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和功能分析。这些方法依赖于从特定于样本的宏基因组数据或公共存储库中获得的蛋白质序列数据库。由于这些蛋白质序列数据库的不完整性和不完善性，以及群落中不同细菌物种表达的同源蛋白质的优势，这种肽鉴定和蛋白质推断的计算过程具有挑战性且容易出错，这阻碍了跨多个样本的宏蛋白质组比较。
我们开发了 metaSpectraST，这是一种无监督且不依赖数据库的宏蛋白质组学工作流程，它通过根据实验观察到的 MS/MS 谱的光谱相似性对其进行聚类，来定量分析和比较宏蛋白质组学样本。我们将 metaSpectraST 应用于两只不同母鼠的断奶后幼鼠的粪便样本。获得了微生物群落的定量蛋白质组谱，而无需进行任何肽谱鉴定，并用于评估样本之间的总体相似性和突出任何区分标志物。与传统的依赖数据库的宏蛋白质组学分析相比，metaSpectraST 更成功地对样本进行分类并检测断奶后小鼠肠道微生物组的细微变化。metaSpectraST 还可以用作工具，从个体间差异较大的样本中选择合适的生物学重复。
metaSpectraST 能够快速定量分析宏蛋白质组学样本，而无需构建蛋白质序列数据库或识别 MS/MS 谱。它通过首先对所有 MS/MS 谱进行聚类，最大限度地保留了实验 MS/MS 谱中包含的信息，因此能够更好地分析复杂的微生物群落并突出其功能变化，与传统方法相比。在本节中标记 videobyte 为 ESM4 视频摘要。
