Meta-analysis of fecal metaproteomic datasets reveals robust human and microbial signatures for inflammatory bowel disease

Introduction: Metaproteomics provides valuable insights into the processes within the human gastrointestinal tract, helping us understand the interplay between humans, their gut microbiome, and various diseases, including colon cancer, diabetes, and inflammatory bowel diseases (IBD). However, comparing different studies is challenging due to significant variations between individuals and differences in experimental and bioinformatic workflows. This complicates the integration of studies and the identification of general biomarkers for different diseases.

Materials and Methods: To draw general conclusions from metaproteomics toward the development of general biomarkers, we conducted a fecal metaproteomics metastudy using the following inclusion criteria: (i) based on Data-Dependent Acquisition (DDA), (ii) published before November 2023, (iii) no chemical labeling, and (iv) spectral data available on PRIDE. We performed a complete bioinformatic re-analysis of the data from all studies using the MetaProteomeAnalyzer software and developed a novel biostatistical workflow for data analysis.

Results: Nine studies met the inclusion criteria, covering a total of 429 samples, While clustering revealed a separation based on different labs, experimental workflows, and mass spectrometers rather than diseases, variance analysis enabled separation based on diseases. For example, focusing on IBD, we identified 28 human biomarker candidates, mostly neutrophil- and immunoglobulin-derived, and 55 microbial biomarker candidates. These biomarkers allowed for a distinction between healthy and diseased states with an accuracy of 95%. Moreover, integrating all the different datasets resulted in smaller p-values than individual studies, providing more robust results.

Conclusion: Our study demonstrates a promising strategy for conducting metaproteomics, confirming known biomarker candidates, and identifying novel candidates for fecal diagnosis of diseases such as IBD. This approach enhances the validity of results and supports the development of general biomarkers for disease diagnosis.