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Increasing the power of interpretation for soil metaproteomics data

Jouffret, Virginie, Miotello, Guylaine, Culotta, Karen, Ayrault, Sophie, Pible, Olivier, Armengaud, Jean·2021·Microbiome
DOI 10.1186/s40168-021-01139-1PMC PMC8482631
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Abstract

Background: Soil and sediment microorganisms are highly phylogenetically diverse but are currently largely under-represented in public molecular databases. Their functional characterization by means of metaproteomics is usually performed using metagenomic sequences acquired for the same sample. However, such hugely diverse metagenomic datasets are difficult to assemble; in parallel, theoretical proteomes from isolates available in generic databases are of high quality. Both these factors advocate for the use of theoretical proteomes in metaproteomics interpretation pipelines. Here, we examined a number of database construction strategies with a view to increasing the outputs of metaproteomics studies performed on soil samples. Results: The number of peptide-spectrum matches was found to be of comparable magnitude when using public or sample-specific metagenomics-derived databases. However, numbers were significantly increased when a combination of both types of information was used in a two-step cascaded search. Our data also indicate that the functional annotation of the metaproteomics dataset can be maximized by using a combination of both types of databases. Conclusions: A two-step strategy combining sample-specific metagenome database and public databases such as the non-redundant NCBI database and a massive soil gene catalog allows maximizing the metaproteomic interpretation both in terms of ratio of assigned spectra and retrieval of function-derived information.

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Fig. 1 Workflows assessed for metaproteomics analysis of environmental samples. The left (green) and top (blue) arrows show the experimental steps tested to obtain MS/MS spectra and metagenomics reads. The dashed-line panel presents the bioinformatics analysis, with, at the top, database construction either from metagenomics data with or without assembly step, or from generic databases. The bottom part shows the process used to refine and enhance the attribution rate through multiple search rounds. A first analysis on full databases was used to select protein-based subsets at various p-values. New searches on these subsets then identified the databases performing best
Fig. 1 Workflows assessed for metaproteomics analysis of environmental samples. The left (green) and top (blue) arrows show the experimental steps tested to obtain MS/MS spectra and metagenomics reads. The dashed-line panel presents the bioinformatics analysis, with, at the top, database construction either from metagenomics data with or without assembly step, or from generic databases. The bottom part shows the process used to refine and enhance the attribution rate through multiple search rounds. A first analysis on full databases was used to select protein-based subsets at various p-values. New searches on these subsets then identified the databases performing bestCC BY · PubMed Central
Fig. 2 Attribution rates for MS/MS spectra, obtained with sample-specific metagenomics databases. Databases were generated using the following workflows: FragGeneScan only (FGS), MEGAHIT and FragGeneScan (MGH-FGS), MEGAHIT and six-frame translation (MGH-6RF), sixgill, and PLASS. The results obtained when using the complete databases are indicated on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are shown on the left. Searches were performed at decoy-free FDR 1% and results are ordered based on the database size, using a logarithmic X-axis. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.
Fig. 2 Attribution rates for MS/MS spectra, obtained with sample-specific metagenomics databases. Databases were generated using the following workflows: FragGeneScan only (FGS), MEGAHIT and FragGeneScan (MGH-FGS), MEGAHIT and six-frame translation (MGH-6RF), sixgill, and PLASS. The results obtained when using the complete databases are indicated on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are shown on the left. Searches were performed at decoy-free FDR 1% and results are ordered based on the database size, using a logarithmic X-axis. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.CC BY · PubMed Central
Fig. 3 Attribution rates for MS/MS spectra when using generic databases. The results obtained with SGC and NCBI are shown as a function of the database size using a logarithmic X-axis. The results obtained with the whole databases are presented on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are indicated on the left (two-step search). The SGC+NCBI databases were obtained by merging the reduced NCBI and SGC protein databases at each p-value cutoff. Searches were performed at decoy-free FDR 1%. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.
Fig. 3 Attribution rates for MS/MS spectra when using generic databases. The results obtained with SGC and NCBI are shown as a function of the database size using a logarithmic X-axis. The results obtained with the whole databases are presented on the right (one-step search), whereas the results obtained on refined databases containing proteins selected at distinct p-values during the first-round search are indicated on the left (two-step search). The SGC+NCBI databases were obtained by merging the reduced NCBI and SGC protein databases at each p-value cutoff. Searches were performed at decoy-free FDR 1%. The small numbers indicate the p-value threshold used for each search. Three biological replicates were used to calculate the mean and standard deviation values.CC BY · PubMed Central
Fig. 4 Functional annotation of peptides and proteins identified at FDR 1%. The databases used to identify proteins are indicated on the left. For each database, the percentage of PSMs annotated are indicated in terms of GO_BP, GO_MF terms, and EC numbers obtained either by Unipept or using Diamond BLASTp queries on Uniref50. In the latter case, the GO OWL tools were used to retrieve GO slim annotations; EC numbers and KO entries were retrieved using the GhostKOALA web-service. The grey squared areas on the EC lines represent the KEGG KO annotation level, from which EC numbers were extracted
Fig. 4 Functional annotation of peptides and proteins identified at FDR 1%. The databases used to identify proteins are indicated on the left. For each database, the percentage of PSMs annotated are indicated in terms of GO_BP, GO_MF terms, and EC numbers obtained either by Unipept or using Diamond BLASTp queries on Uniref50. In the latter case, the GO OWL tools were used to retrieve GO slim annotations; EC numbers and KO entries were retrieved using the GhostKOALA web-service. The grey squared areas on the EC lines represent the KEGG KO annotation level, from which EC numbers were extractedCC BY · PubMed Central
Fig. 5 KO and taxonomical annotation of soil metagenomics data and metaproteomics data. Metagenomics data were annotated using the MG-RAST pipeline, with KO and taxonomic analysis. Peptides and sequences attributed at FDR 1% of the combined reduced sample-specific and generic database FGS_0.30+SGC_0.30+NCBI_0.30 were analyzed using Unipept and GhostKOALA to retrieve taxonomic and functional annotations, respectively. The PSMs for the three biological replicates were cumulated. A Functional annotation of three KEGG categories: metabolism (left), genetic information processing (center), and environmental information processing (right). A total of 16% of reads and 72% of PSMs were annotated in terms of function. B Taxonomic annotation of metagenomics data (MetaG) and metaproteomics data (MetaP) at Kingdom (left), Bacterial phyla (center), and Eukaryotic phyla (right) levels. Phyla represented less than 1% of the total were merged under “Other phyla”. A total of 17% of reads and 21% of PSMs were unequivocally annotated in terms of taxonomy
Fig. 5 KO and taxonomical annotation of soil metagenomics data and metaproteomics data. Metagenomics data were annotated using the MG-RAST pipeline, with KO and taxonomic analysis. Peptides and sequences attributed at FDR 1% of the combined reduced sample-specific and generic database FGS_0.30+SGC_0.30+NCBI_0.30 were analyzed using Unipept and GhostKOALA to retrieve taxonomic and functional annotations, respectively. The PSMs for the three biological replicates were cumulated. A Functional annotation of three KEGG categories: metabolism (left), genetic information processing (center), and environmental information processing (right). A total of 16% of reads and 72% of PSMs were annotated in terms of function. B Taxonomic annotation of metagenomics data (MetaG) and metaproteomics data (MetaP) at Kingdom (left), Bacterial phyla (center), and Eukaryotic phyla (right) levels. Phyla represented less than 1% of the total were merged under “Other phyla”. A total of 17% of reads and 21% of PSMs were unequivocally annotated in terms of taxonomyCC BY · PubMed Central

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Metadata

Authors
Jouffret, V., Miotello, G., Culotta, K., Ayrault, S., Pible, O., & Armengaud, J.
ORCID
Armengaud, Jean
Venue
Microbiome
Publisher
Springer Science and Business Media LLC
Published
2021-09-29
Volume
9
Issue
1
ISSN
2049-2618
DOI
10.1186/s40168-021-01139-1
PMC
PMC8482631
Cited by
52
References
2
Language
en
Licence

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