MetaProD: A Highly-Configurable Mass Spectrometry Analyzer for Multiplexed Proteomic and Metaproteomic Data

Area of Science:

• Microbiology
• Bioinformatics
• Proteomics

Background:

• The human microbiome plays a crucial role in health, influencing disease and immune responses.
• Mass spectrometry is vital for microbiome analysis but is often time-consuming and complex.
• Multiplexing techniques can enhance the speed and accuracy of mass spectrometry workflows.

Purpose of the Study:

• To introduce MetaProD (MetaProteomics in Django), a configurable, open-source pipeline for metaproteomic data analysis.
• To support both label-free and multiplexed mass spectrometry data.
• To provide a web-based interface for user-friendly configuration and data access.

Main Methods:

• Development of the MetaProD pipeline using open-source tools and Django framework.
• Benchmarking MetaProD with multiple metaproteomic datasets for performance evaluation.
• Application of MetaProD to a multiplexed cancer dataset for differential protein expression analysis.

Main Results:

• MetaProD demonstrated fast and efficient identification of peptides and proteins.
• The pipeline identified a greater number of differentially expressed human proteins in cancer versus healthy tissues compared to prior studies.
• Bacterial proteins, including differentially abundant species, were identified in the cancer samples.

Conclusions:

• MetaProD offers a significant advancement in metaproteomic data analysis, enhancing speed and efficiency.
• The pipeline facilitates deeper insights into host-microbe interactions, particularly in disease contexts like cancer.
• MetaProD's open-source nature and web interface promote accessibility and broader adoption in microbiome research.