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. 2023 Feb 3;22(2):442-453.
doi: 10.1021/acs.jproteome.2c00614. Epub 2023 Jan 23.

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

Jamie Canderan  1 , Moses Stamboulian  1 , Yuzhen Ye  2
Affiliations

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

Jamie Canderan et al. J Proteome Res. .

Abstract

The microbiome has been shown to be important for human health because of its influence on disease and the immune response. Mass spectrometry is an important tool for evaluating protein expression and species composition in the microbiome but is technically challenging and time-consuming. Multiplexing has emerged as a way to make spectrometry workflows faster while improving results. Here, we present MetaProD (MetaProteomics in Django) as a highly configurable metaproteomic data analysis pipeline supporting label-free and multiplexed mass spectrometry. The pipeline is open-source, uses fully open-source tools, and is integrated with Django to offer a web-based interface for configuration and data access. Benchmarking of MetaProD using multiple metaproteomics data sets showed that MetaProD achieved fast and efficient identification of peptides and proteins. Application of MetaProD to a multiplexed cancer data set resulted in identification of more differentially expressed human proteins in cancer tissues versus healthy tissues as compared to previous studies; in addition, MetaProD identified bacterial proteins in those samples, some of which are differentially abundant.

Keywords: bacterial proteins; differentially expressed proteins; isobaric labeling; mass spectrometry; metaproteomics; multiplexing; proteomics.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Overview of the MetaProD workflow showing the profiling step (green), full-proteome step (red), and postprocessing (blue).
Figure 2
Figure 2
Profile and full-proteome performance of the different engine combinations for the SIHUMIx S07 data set at 90% of NSAF. (a) Profile performance of the search engine combinations showing the number of correct and inaccurate proteomes selected during the profile step along with the runtime of the SearchGUI step for the combination. (b) Full-proteome performance of the search engine combinations showing the total number of PSMs identified along with the number of PSMs believed to come from species not present in the sample. The runtime for both SearchGUI steps (profile + proteome) for each combination is also shown.
Figure 3
Figure 3
Breakdown of the family-level identifications for the C,4 10 ppm configuration on the CAMPI F07 data sets using NSAF and peptide counts.
Figure 4
Figure 4
Total number of peptides selected when filtering results by the percent of missing channels prior to imputation for the colon cancer data set.
Figure 5
Figure 5
Volcano plot of the differentially expressed proteins (shown as genes) in the colon cancer data set showing significantly more downregulated genes.
Figure 6
Figure 6
Comparison of the overlap of the differentially expressed upregulated genes with a fold change (FC) ≥ 2 and a p-value <0.05 found by the CPTAC pipeline and MetaProD.
Figure 7
Figure 7
Genus-level breakdown of the colon cancer data showing the top 10 genuses by peptide count and the total percentage of any remaining genus. The full set of identified peptides (unfiltered) was used for this analysis.
See this image and copyright information in PMC

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