Scholarlink

<p>The gut microbiome plays a fundamental role in human health and disease. Individual variations in the microbiome and the corresponding functional implications are key considerations to enhance precision health and medicine. Metaproteomics has recently revealed protein expression that might be associated with human health and disease. Existing studies focused on either human proteins or bacterial proteins that can be identified from (meta)proteomics data sets, but not both. In this study, we examined the feasibility of identifying both human and bacterial proteins that are differentially expressed between healthy and diseased individuals from metaproteomics data sets. We further evaluated different strategies of using identified peptides and proteins for building predictive models. By leveraging existing metaproteomics data sets and a tool that we have developed for metaproteomics data analysis (MetaProD), we were able to derive both human and bacterial differentially expressed proteins that could serve as potential biomarkers for all diseases we studied. We also built predictive models using identified peptides and proteins as features for prediction of human diseases. Our results showed peptide-based identifications over protein-based ones often produce the most accurate models and that feature selection can offer improvements. Prediction accuracy could be further improved, in some cases, by including bacterial identifications, but missing data in bacterial identifications remains problematic.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Identification and applications of disease-associated differential human and bacterial proteins with metaproteomic evidence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月29日

查看全文
  • Jamie Canderan,
  • Moses Stamboulian,
  • Yuzhen Ye

摘要

The gut microbiome plays a fundamental role in human health and disease. Individual variations in the microbiome and the corresponding functional implications are key considerations to enhance precision health and medicine. Metaproteomics has recently revealed protein expression that might be associated with human health and disease. Existing studies focused on either human proteins or bacterial proteins that can be identified from (meta)proteomics data sets, but not both. In this study, we examined the feasibility of identifying both human and bacterial proteins that are differentially expressed between healthy and diseased individuals from metaproteomics data sets. We further evaluated different strategies of using identified peptides and proteins for building predictive models. By leveraging existing metaproteomics data sets and a tool that we have developed for metaproteomics data analysis (MetaProD), we were able to derive both human and bacterial differentially expressed proteins that could serve as potential biomarkers for all diseases we studied. We also built predictive models using identified peptides and proteins as features for prediction of human diseases. Our results showed peptide-based identifications over protein-based ones often produce the most accurate models and that feature selection can offer improvements. Prediction accuracy could be further improved, in some cases, by including bacterial identifications, but missing data in bacterial identifications remains problematic.