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1.
Microorganisms ; 8(10)2020 Oct 04.
Artigo em Inglês | MEDLINE | ID: mdl-33020444

RESUMO

The microbial diversity encompassed by the environmental biosphere is largely unexplored, although it represents an extensive source of new knowledge and potentially of novel enzymatic catalysts for biotechnological applications. To determine the taxonomy of microorganisms, proteotyping by tandem mass spectrometry has proved its efficiency. Its latest extension, phylopeptidomics, adds a biomass quantitation perspective for mixtures of microorganisms. Here, we present an application of phylopeptidomics to rapidly and sensitively screen microorganisms sampled from an industrial environment, i.e., a pool where radioactive material is stored. The power of this methodology is demonstrated through the identification of both prokaryotes and eukaryotes, whether as pure isolates or present as mixtures or consortia. In this study, we established accurate taxonomical identification of environmental prokaryotes belonging to the Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria phyla, as well as eukaryotes from the Ascomycota phylum. The results presented illustrate the potential of tandem mass spectrometry proteotyping, in particular phylopeptidomics, to screen for and rapidly identify microorganisms.

2.
Microbiome ; 8(1): 30, 2020 03 06.
Artigo em Inglês | MEDLINE | ID: mdl-32143687

RESUMO

BACKGROUND: There is an important need for the development of fast and robust methods to quantify the diversity and temporal dynamics of microbial communities in complex environmental samples. Because tandem mass spectrometry allows rapid inspection of protein content, metaproteomics is increasingly used for the phenotypic analysis of microbiota across many fields, including biotechnology, environmental ecology, and medicine. RESULTS: Here, we present a new method for identifying the biomass contribution of any given organism based on a signature describing the number of peptide sequences shared with all other organisms, calculated by mathematical modeling and phylogenetic relationships. This so-called "phylopeptidomics" principle allows for the calculation of the relative ratios of peptide-specified taxa by the linear combination of such signatures applied to an experimental metaproteomic dataset. We illustrate its efficiency using artificial mixtures of two closely related pathogens of clinical interest, and with more complex microbiota models. CONCLUSIONS: This approach paves the way to a new vision of taxonomic changes and accurate label-free quantitative metaproteomics for fine-tuned functional characterization. Video abstract.


Assuntos
Proteínas de Bactérias/análise , Microbiota , Modelos Teóricos , Peptídeos/genética , Filogenia , Proteômica/métodos , Bactérias/classificação , Bactérias/metabolismo , Proteínas de Bactérias/genética , Biomassa , Bases de Dados de Proteínas , Proteoma , Espectrometria de Massas em Tandem
3.
Methods Mol Biol ; 1197: 275-85, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25172287

RESUMO

Currently, proteomic tools are able to establish a complete list of the most abundant proteins present in a sample, providing the opportunity to study at high resolution the physiology of any bacteria for which the genome sequence is available. For a comprehensive list, proteins should be first resolved into fractions that are then proteolyzed by trypsin. The resulting peptide mixtures are analyzed by a high-throughput tandem mass spectrometer that records thousands of MS/MS spectra for each fraction. These spectra are then assigned to peptides, which are used as evidence of the existence of proteins. In addition to generating a list of protein identifications, this shortcut to proteomics uses the number of spectra recorded for each protein to quantify the observations. Here, we describe one of the most simple sample preparation methods for high-throughput proteomics of bacteria, as well as the subsequent data processing to extract quantitative information based on the spectral count approach.


Assuntos
Bactérias/metabolismo , Proteômica , Proteínas de Bactérias/análise , Bases de Dados de Proteínas , Espectrometria de Massas em Tandem
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