A Computational Framework for Protein Identification and Quantification in Metaproteomics Using Data-Independent Acquisition

总结 表征人类微生物组的广泛努力极大地增加了关于微生物组的知识。 微生物组的多样性及其在健康和疾病中的组成。人类微生物群中的生态失调 是许多疾病的基础，如肥胖症、糖尿病和炎症性肠病。 基于质谱（MS）的元蛋白质组学已广泛用于微生物组研究， 深入了解微生物群落的功能状态。具有数据依赖采集的质谱法 (DDA)是元蛋白质组学中鉴定和定量微生物蛋白质的最常用方法， 但是这种技术在再现性和全面性方面基本上受到限制。蛋白质组学 理论上，数据独立采办（DIA）可以解决与DDA相关的基本问题 法然而，缺乏生物信息学工具仍然是DIA背景下尚未解决的挑战， 只有少数DIA应用于微生物组或宿主-微生物相互作用的报道。 基于质谱的元蛋白质组学是一个具有挑战性的测量，由于与数千个物种的高度复杂性 丰度差异很大为了获得微生物功能状态的全面表征， 群落不仅需要考虑来自优势微生物的蛋白质， 微生物的该提案解决了通过免疫组织化学来鉴定和定量蛋白质的需求。 使用DIA数据来识别和量化肽及其变体的一组计算工具的可用性 在微生物菌株水平上。通过新提出的用于假肽鉴定的方法来控制假肽鉴定 多粒度的发现率评估。蛋白质推断和定量通过以下方法优化： 包含来自基因组/转录组测序数据的信息的线性编程模型， 元蛋白质组样品复制品。这一改进将增加鉴定的蛋白质变体的数量， 特别是来自低丰度微生物的那些，这可以帮助准确地表征功能 微生物群落的组成，并揭示功能冗余。

Summary Extensive efforts to characterize the human microbiome have tremendously increased the knowledge about the diversity of the microbiome and about its composition in health and in disease. Dysbiosis in human microbiota underlies the development of many diseases, such as obesity, diabetes, and inflammatory bowel disease. Metaproteomics based on mass spectrometry (MS) has become widely used in microbiome research for gaining insights into the functional states of microbial communities. Mass spectrometry with data-dependent acquisition (DDA) is the most common method of choice for identifying and quantifying microbial proteins in metaproteomics, but this technique is fundamentally limited in terms of reproducibility and comprehensiveness. Proteomics using data-independent acquisition (DIA) can, in theory, resolve the fundamental problems associated with the DDA method. However, the lack of bioinformatics tools still presents unresolved challenges in the context of DIA, and only few DIA applications on microbiome or host-microbe interactions have been reported. MS-based metaproteomics is a challenging measurement due to the high complexity with thousands of species at vastly different abundances. To obtain a comprehensive characterization of the functional state of microbial communities requires considering proteins not just from dominant microorganisms but also low-abundance microorganisms. This proposal addresses the need for identifying and quantifying proteins through the availability of a set of computational tools that use DIA data to identify and quantify peptides and their variants at the microbial strain level. The false peptide identifications are controlled by newly proposed methods for false discovery rate assessment at multiple granularities. The protein inference and quantification are optimized by linear programming models that contain information from genome/transcriptome sequencing data and metaproteome sample replicas. The improvement will increase the number of identified protein variants, especially those from the low-abundance microorganisms, which can help accurately characterize the functional composition in microbial communities and reveal the functional redundancy.