The Unipept web application (http://unipept.ugent.be) supports biodiversity analysis of large and complex metaproteome samples using tryptic peptide information obtained from shotgun MS/MS experiments. Its underlying index structure is designed to quickly retrieve all occurrences of a tryptic peptide in UniProtKB records. Taxon-specificity of the tryptic peptide is successively derived from these occurrences using a novel lowest common ancestor approach that is robust against taxonomic misarrangements, misidentifications, and inaccuracies. Not taking into account this identification noise would otherwise result in drastic loss of information. Dynamic treemaps visualize the biodiversity of metaproteome samples, which eases the exploration of samples with highly complex compositions. The potential of Unipept to gain novel insights into the biodiversity of a sample is evaluated by reanalyzing publicly available metaproteome data sets taken from the bacterial phyllosphere and the human gut.
Unipept (http://unipept.ugent.be) is a web application that offers a user-friendly way to explore the biodiversity of complex metaproteome samples by providing interactive visualizations. In this article, the updates and changes to Unipept since its initial release are presented. This includes the addition of interactive sunburst and treeview visualizations to the multipeptide analysis, the foundations of an application programming interface (API) and a command line interface, updated data sources, and the open-sourcing of the entire application under the MIT license.
Unipept (https://unipept.ugent.be) is a web application for metaproteome data analysis, with an initial focus on tryptic-peptide-based biodiversity analysis of MS/MS samples. Because the true potential of metaproteomics lies in gaining insight into the expressed functions of complex environmental samples, the 4.0 release of Unipept introduces complementary functional analysis based on GO terms and EC numbers. Integration of this new functional analysis with the existing biodiversity analysis is an important asset of the extended pipeline. As a proof of concept, a human faecal metaproteome data set from 15 healthy subjects was reanalyzed with Unipept 4.0, yielding fast, detailed, and straightforward characterization of taxon-specific catalytic functions that is shown to be consistent with previous results from a BLAST-based functional analysis of the same data.
Faecal metaproteomics provides insights in intestinal dysbiosis, inflammation in patients with CF and can be used to monitor different disease markers in parallel.
Metaproteomics has matured into a powerful tool to assess functional interactions in microbial communities. While many metaproteomic workflows are available, the impact of method choice on results remains unclear. Here, we carry out a community-driven, multi-laboratory comparison in metaproteomics: the critical assessment of metaproteome investigation study (CAMPI). Based on well-established workflows, we evaluate the effect of sample preparation, mass spectrometry, and bioinformatic analysis using two samples: a simplified, laboratory-assembled human intestinal model and a human fecal sample. We observe that variability at the peptide level is predominantly due to sample processing workflows, with a smaller contribution of bioinformatic pipelines. These peptide-level differences largely disappear at the protein group level. While differences are observed for predicted community composition, similar functional profiles are obtained across workflows. CAMPI demonstrates the robustness of present-day metaproteomics research, serves as a template for multi-laboratory studies in metaproteomics, and provides publicly available data sets for benchmarking future developments.
Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this field. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics.
Chitin is a valuable peat substrate amendment by increasing lettuce growth and reducing the survival of the zoonotic pathogen
Salmonella enterica
on lettuce leaves. The production of chitin-catabolic enzymes (chitinases) play a crucial role and are mediated through the microbial community. A higher abundance of plant-growth promoting microorganisms and genera involved in N and chitin metabolism are present in a chitin-enriched substrate. In this study, we hypothesize that chitin addition to peat substrate stimulates the microbial chitinase production. The degradation of chitin leads to nutrient release and the production of small chitin oligomers that are related to plant growth promotion and activation of the plant’s defense response. First a shotgun metagenomics approach was used to decipher the potential rhizosphere microbial functions then the nutritional content of the peat substrate was measured. Our results show that chitin addition increases chitin-catabolic enzymes, bacterial ammonium oxidizing and siderophore genes. Lettuce growth promotion can be explained by a cascade degradation of chitin to N-acetylglucosamine and eventually ammonium. The occurrence of increased ammonium oxidizing bacteria,
Nitrosospira
, and
amoA
genes results in an elevated concentration of plant-available nitrate. In addition, the increase in chitinase and siderophore genes may have stimulated the plant’s systemic resistance.
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