A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease
January 10th, 2025
Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.
Our research focus has been on detecting and quantifying microbial proteins and understanding their role in clinical disease. This field of research is called clinical metaproteomics. In this study, we have developed a bioinformatics workflow that will enable researchers to understand how bacterial activity can influence disease progression.
Metaproteomic analysis of clinical samples presents numerous challenges, including handling very large protein sequence databases for sensitive and accurate microbial peptide and protein identification from mass spectrometry data, in addition to performing taxonomic and functional annotations of quantified peptides and proteins to enable biological interpretation of results. The workflow offers multiple advantages, including database reduction using our database reduction workflow, the ability to search for microbial peptides using multiple search algorithms, the ability to verify microbial peptides detected in the mass spectrometry data, the ability to quantify the microbial proteins along with the host proteins, and the biological interpretation of the data using statistical and visual analysis. We have utilized the clinical metaproteomics workflow to identify microbial peptide panel for cystic fibrosis disease-progression studies to study the co-infection status during COVID-19 pandemic waves.
These studies have been published in peer-reviewed academic journals. We are currently using this workflow for an ongoing study to develop a predictive target-peptide panel for ovarian cancer. The Galaxy P team is involved in multiomics research, and we're developing several advanced workflows for proteogenomics and metaproteomics analysis.
We are currently also working on developing workflows for immunopeptidomics, which will enable researchers to detect and characterize peptides presented to the immune system, some during cancer progression which are called neoantigens, and also with other diseases where these might also be microbial peptides. To begin, obtain a list of species that are linked to the disease or the condition of interest. Use the species list file titled Species.
tabular"as the input for UniProt. Download the proteome in FASTA format to generate a protein sequence database. Run the protein database downloader to generate two additional protein sequence databases, a human Swiss-Prot database containing only reviewed entries and a contaminant protein database containing a common repository of adventitious proteins, or cRAP.
Use the three protein databases as inputs for FASTA merge files and filter unique sequences to exclude duplicates. Using the large database generated and mass spectrometry dataset as inputs, run MetaNovo to generate a reduced protein sequence database, then run FASTA merge files and filter unique sequences on the MetaNovo generated database, human Swiss-Prot and cRAP databases to create a reduced target database containing microbial, human and contaminant protein sequences for peptide detection. Execute Search GUI"to generate an archive file containing peptide spectrum matches, or PSMs.
Use the Search GUI"archive file as input for Peptide-Shaker"to generate the PSM, peptide and protein reports. Run MaxQuant"to produce protein groups and peptides files. Using text manipulation tools, organize the obtained outputs from Search GUI, Peptide-Shaker"and MaxQuant.
Concatenate the two peptide lists into a single dataset labeled SGPS-MQ-Peptides.tabular. Group the concatenated peptide list to eliminate duplicate peptide sequences and obtain the final list of unique microbial peptides. For PepQuery2 verification, input the list of distinct microbial peptides, MS spectral datasets, the human UniProt reference database with isoforms, and the contaminant protein sequence database.
Run Cut"on the peptide reports from Search GUI, Peptide-Shaker"and MaxQuant"to extract the peptide sequences and associated protein entries. Concatenate the peptide sequences and protein entries from both programs to create a new combined peptide protein dataset, then run the Query Tabular"on the combined peptide protein dataset and the verified peptides to assign each verified peptide to its associated protein entry. Group to retain unique verified peptides and their associated UniProt IDs.
Next, run Query Tabular"to extract the UniProt IDs, generating a list labeled Uniprot-ID from verified Peptides.tabular. Upload the UniProt IDs to UniProt to retrieve the associated protein sequences and save them as a new UniProt FASTA file. Run FASTA merge files and filter unique sequences on the newly-generated UniProt FASTA, the human UniProt database with isoforms and the cRAP contaminant database to create a verified database for peptide quantification.
Use the verified protein sequence database and MS dataset as inputs for MaxQuant. From the MaxQuant"peptides file, select only microbial peptides and run Cut"to extract only microbial peptide sequences from the selection file. Group the Cut"file to compile a list of quantified microbial peptides.
Use the list-of-quantified-microbial-peptides file as input for Unipept to perform taxonomic and functional annotations. Extract the Unipept outputs, specifically the microbial taxonomy tree and the microbial enzyme commission-proteins tree. To view the microbial taxonomy and EC protein trees, select the data-set and open the options.
Click on Visualize, followed by Unipept Taxonomy Viewer. For the taxonomic and functional annotations in a table format, click the eye icon of the tabular dataset named Unipept_peptinfo. Scroll to review each peptide on its own row and its corresponding columns of information.
Before conducting statistical analysis with MSstatsTMT, run Select"on the MaxQuant"protein groups file to create separate data-sets for microbial and human proteins. These proteins contain taxonomy tags that indicate their source. Exclude any contaminant proteins labeled with the tag con_.
Retain only microbial proteins with tags such as _9laco"and human proteins with the tag _human"in the Microbial_Proteins"tabular and Human_Proteins"tabular respectively. Finally, using MSstatsTMT, perform statistical analysis with the MaxQuant"evidence file and the selected microbial or human proteins. Click on the eye icon to view the resulting plots.
A total of 2, 595, 745 protein sequences were compiled into a comprehensive database, which was then reduced to a more targeted database containing 21, 289 protein sequences for effective peptide identification. Using Search GUI, Peptide-Shaker"and MaxQuant, 196 distinct microbial peptides were identified. PepQuery2 confirmed 134 microbial peptides linked to 73 protein sequences, forming a verified database for quantification.
MaxQuant"analysis provided a peptides file containing 3, 203 peptides, with 155 quantified microbial peptides. Unipept analysis revealed lactobacillus as the most abundant genus, and class 2 transferases as the most prevalent enzyme category among 155 quantified microbial peptides. MSstatsTMT"analysis produced volcano and comparison plots illustrating differentially-expressed proteins, showing that three lactobacillus proteins were down-regulated in ovarian cancer cases versus benign cases.
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Overview
This study focuses on clinical metaproteomics, which provides insights into the human microbiome and its role in disease. A bioinformatics workflow was developed to facilitate the analysis of microbial proteins in clinical samples.
Key Study Components
Area of Science
- Clinical metaproteomics
- Microbial protein analysis
- Bioinformatics
Background
- Understanding microbial proteins can aid in disease progression studies.
- Challenges include handling large protein databases for accurate identification.
- Taxonomic and functional annotations are crucial for biological interpretation.
- Research has implications for diseases like cystic fibrosis and COVID-19.
Purpose of Study
- To develop a workflow for analyzing microbial proteins in clinical samples.
- To understand the influence of bacterial activity on disease progression.
- To identify microbial peptide panels for specific disease studies.
Methods Used
- Mass spectrometry-based metaproteomic analysis.
- Database reduction workflow for efficient protein identification.
- Multiple search algorithms for microbial peptide detection.
- Statistical and visual analysis for data interpretation.
Main Results
- Successful identification of microbial peptides related to cystic fibrosis.
- Insights into co-infection status during COVID-19 pandemic waves.
- Demonstrated advantages of the developed bioinformatics workflow.
- Facilitated understanding of microbial contributions to disease.
Conclusions
- The workflow enhances the analysis of microbial proteins in clinical research.
- It provides a framework for future studies on microbial influences in diseases.
- Clinical metaproteomics can lead to better understanding and treatment of diseases.
Frequently Asked Questions
Executive Industry Relevance
Clinical metaproteomics workflows that integrate mass spectrometry data with advanced bioinformatics are critical for elucidating host-microbiome interactions in disease. This Galaxy-based pipeline enables sensitive detection and quantification of microbial proteins in complex clinical samples, directly supporting target validation and mechanistic de-risking in translational research. The approach enhances predictive confidence at the discovery-to-preclinical interface, informing risk-adjusted portfolio decisions for microbiome-related therapeutic programs.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables interrogation of microbial protein activity linked to disease progression.
- Supports functional target validation by quantifying both host and microbial proteins.
- Facilitates mechanistic de-risking through taxonomic and functional annotation of microbial peptides.
- Improves predictive confidence for prioritizing microbiome-related targets.
Screening & Assay Development
- Prepares validated peptide panels for downstream screening and biomarker studies.
- Standardizes peptide-spectrum match generation and verification for reproducible outputs.
- Enables scalable, quantitative workflows for high-throughput clinical sample analysis.
- Supports reliable evaluation of microbial contributions to disease phenotypes.
Translational & Preclinical Research
- Aligns microbial protein quantification with disease-relevant clinical endpoints.
- Provides continuity from discovery through preclinical validation of host-microbiome interactions.
- Enables risk-adjusted advancement of microbiome-targeted therapeutic hypotheses.
- Delivers mechanistic insights for translational biomarker development.
Pipeline & Workflow Integration
This workflow bridges early discovery, screening, and translational research by integrating mass spectrometry data processing, peptide verification, and statistical analysis within a unified Galaxy platform.
- Discovery Biology: Supports hypothesis testing and pathway clarification for host-microbiome interactions.
- Screening: Delivers reproducible, quantitative peptide and protein outputs for assay development.
- Analytics: Provides statistical and visual outputs for comparing protein expression across clinical conditions.
- Translational Research: Connects microbial protein signatures to disease states for biomarker alignment.
- Enterprise Reuse: Offers a modular, accessible workflow for broad application across disease areas and sample types.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in microbiome research.
- Operational Value: Standardizes and scales metaproteomic analysis for diverse clinical samples.
- Strategic Value: Enables informed go/no-go decisions and capital-efficient portfolio management.
- Portfolio Impact: Supports risk-adjusted prioritization of microbiome-targeted programs.
Implementation Considerations
- Requires expertise in mass spectrometry and bioinformatics data analysis.
- Depends on access to high-quality instrumentation and computational infrastructure.
- Demands cross-team standardization for database generation and peptide verification.
- Adaptable to various clinical sample types and disease contexts.
- Scalability may be limited by database size and computational resources.
