High-throughput metaproteomics data analysis with Unipept: A tutorial

Mesuere, Bart; Jeugt, Felix Van der; Willems, Toon; Naessens, Tom; Devreese, Bart; Martens, Lennart; Dawyndt, Peter

doi:10.1016/j.jprot.2017.05.022

Cited by 48 publications

(53 citation statements)

References 36 publications

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“…Taxonomic classification of peptides was done by the lowest common ancestor method using UniPept (Mesuere et al, 2018). Identification of 13 C-labelled peptides and quantification of 13 C incorporation were done by comparing measured and expected isotopologue patterns, chromatographic retention times and fragmentation patterns as previously described (Seifert et al, 2012;Taubert et al, 2012).…”

Section: Sip-metaproteomicsmentioning

confidence: 99%

“…Peptide identification is based on the NCBI nr database as reference. Peptides were classified according to their lowest common ancestor using UniPept (Mesuere et al, 2018). Figure S2 13 C incorporation patterns in peptides of major taxonomic groups.…”

Section: Supporting Informationmentioning

confidence: 99%

“…Peptide identification is based on the NCBI nr database as reference. Peptides were classified according to their lowest common ancestor using UniPept (Mesuere et al ., ).…”

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confidence: 97%

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Communal metabolism by Methylococcaceae and Methylophilaceae is driving rapid aerobic methane oxidation in sediments of a shallow seep near Elba, Italy

Taubert

Grob

Crombie

et al. 2019

Environmental Microbiology

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Summary The release of abiotic methane from marine seeps into the atmosphere is a major source of this potent greenhouse gas. Methanotrophic microorganisms in methane seeps use methane as carbon and energy source, thus significantly mitigating global methane emissions. Here, we investigated microbial methane oxidation at the sediment–water interface of a shallow marine methane seep. Metagenomics and metaproteomics, combined with 13C‐methane stable isotope probing, demonstrated that various members of the gammaproteobacterial family Methylococcaceae were the key players for methane oxidation, catalysing the first reaction step to methanol. We observed a transfer of carbon to methanol‐oxidizing methylotrophs of the betaproteobacterial family Methylophilaceae, suggesting an interaction between methanotrophic and methylotrophic microorganisms that allowed for rapid methane oxidation. From our microcosms, we estimated methane oxidation rates of up to 871 nmol of methane per gram sediment per day. This implies that more than 50% of methane at the seep is removed by microbial oxidation at the sediment–water interface, based on previously reported in situ methane fluxes. The organic carbon produced was further assimilated by different heterotrophic microbes, demonstrating that the methane‐oxidizing community supported a complex trophic network. Our results provide valuable eco‐physiological insights into this specialized microbial community performing an ecosystem function of global relevance.

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Section: Sip-metaproteomicsmentioning

confidence: 99%

Section: Supporting Informationmentioning

confidence: 99%

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Communal metabolism by Methylococcaceae and Methylophilaceae is driving rapid aerobic methane oxidation in sediments of a shallow seep near Elba, Italy

Taubert

Grob

Crombie

et al. 2019

Environmental Microbiology

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“…Only peptides containing a maximum of 1 missed cleavage, having at least 8 amino acids in their sequence without any methionine and cysteine and identified in at least 6 Peptide Sequence Matches (PSM) were considered. The list of peptides from the 15 bacterial species was then searched with the Unipept software(34,35) to delete peptide sequences also found in the human proteome, and to associate each peptide to the bacterial proteome it belongs to. Finally, for each bacterium separately, a list of potentially observable peptides was built and imported into Skyline 4.1.0.11796(36,37).…”

mentioning

confidence: 99%

“…An additional step of refinement was done to establish, for each species, the list of bacterial peptides to be searched for in the DIA runs. To this purpose, we used the Unipept software(34,35) that enables to match peptide sequences with all matching taxa in UniProtKB databases. Starting from the non-redundant list of all peptides identified the bacteria, Unipept was used to confirm in which of the 15 bacterial species these could theoretically be found.…”

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confidence: 99%

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

Roux‐Dalvai

Gotti

Leclercq

et al. 2019

Preprint

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The identification of microbial species in biological samples is essential to many applications in health, food safety and environment. MALDI-TOF MS technology has become a tool of choice for microbial identification but it has several drawbacks including: it requires a long step of bacterial culture prior to analysis (24h), it has a low specificity and is not quantitative. We have developed a new strategy for identifying bacterial species in biological samples using specific LC-MS/MS peptidic signatures. In the first training step, deep proteome coverage of bacteria of interest is obtained in Data Independent Acquisition (DIA) mode, followed by the use of machine learning to define the peptides the most susceptible to distinguish each bacterial species from the others. Then, in the second step, this peptidic signature is monitored in biological samples using targeted proteomics. This method, which allows the bacterial identification from clinical specimens in less than 4h, has been applied to fifteen species representing 84% of all Urinary Tract Infections (UTI). More than 31000 peptides in 200 samples have been quantified by DIA and analyzed by machine learning to determine an 82 peptides signature and build prediction models able to classify the fifteen bacterial species. This peptidic signature was validated for its use in routine conditions using Parallel Reaction Monitoring on a capillary flow chromatography coupled to a Thermo Scientific™ Q Exactive HF-X instrument. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the 1×105 CFU/mL threshold commonly used by clinical laboratories. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.

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Advances in Omics and Bioinformatics Tools for Phyllosphere Studies

Bansal

2021

Phytomicrobiome Interactions and Sustainable Agriculture

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High-throughput metaproteomics data analysis with Unipept: A tutorial

Cited by 48 publications

References 36 publications

Communal metabolism by Methylococcaceae and Methylophilaceae is driving rapid aerobic methane oxidation in sediments of a shallow seep near Elba, Italy

Communal metabolism by Methylococcaceae and Methylophilaceae is driving rapid aerobic methane oxidation in sediments of a shallow seep near Elba, Italy

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning

Advances in Omics and Bioinformatics Tools for Phyllosphere Studies

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