Enzymatic machinery of wood-inhabiting fungi that degrade temperate tree species.

Deadwood provides habitat for fungi and serves diverse ecological functions in forests. We already have profound knowledge of fungal assembly processes, physiological and enzymatic activities, and resulting physico-chemical changes during deadwood decay. However, in situ detection and identification methods, fungal origins, and a mechanistic understanding of the main lignocellulolytic enzymes are lacking. This study used metaproteomics to detect the main extracellular lignocellulolytic enzymes in 12 tree species in a temperate forest that have decomposed for 8 ½ years. Mainly white-rot (and few brown-rot) Basidiomycota were identified as the main wood decomposers, with Armillaria as the dominant genus; additionally, several soft-rot xylariaceous Ascomycota were identified. The key enzymes involved in lignocellulolysis included manganese peroxidase, peroxide-producing alcohol oxidases, laccase, diverse glycoside hydrolases (cellulase, glucosidase, xylanase), esterases, and lytic polysaccharide monooxygenases. The fungal community and enzyme composition differed among the 12 tree species. Ascomycota species were more prevalent in angiosperm logs than in gymnosperm logs. Regarding lignocellulolysis as a function, the extracellular enzyme toolbox acted simultaneously and was interrelated (e.g. peroxidases and peroxide-producing enzymes were strongly correlated), highly functionally redundant, and present in all logs. In summary, our in situ study provides comprehensive and detailed insight into the enzymatic machinery of wood-inhabiting fungi in temperate tree species. These findings will allow us to relate changes in environmental factors to lignocellulolysis as an ecosystem function in the future.

The nutritional composition and cell size of microbial biomass for food applications are defined by the growth conditions.

BACKGROUND: It is increasingly recognized that conventional food production systems are not able to meet the globally increasing protein needs, resulting in overexploitation and depletion of resources, and environmental degradation. In this context, microbial biomass has emerged as a promising sustainable protein alternative. Nevertheless, often no consideration is given on the fact that the cultivation conditions affect the composition of microbial cells, and hence their quality and nutritional value. Apart from the properties and nutritional quality of the produced microbial food (ingredient), this can also impact its sustainability. To qualitatively assess these aspects, here, we investigated the link between substrate availability, growth rate, cell composition and size of Cupriavidus necator and Komagataella phaffii. RESULTS: Biomass with decreased nucleic acid and increased protein content was produced at low growth rates. Conversely, high rates resulted in larger cells, which could enable more efficient biomass harvesting. The proteome allocation varied across the different growth rates, with more ribosomal proteins at higher rates, which could potentially affect the techno-functional properties of the biomass. Considering the distinct amino acid profiles established for the different cellular components, variations in their abundance impacts the product quality leading to higher cysteine and phenylalanine content at low growth rates. Therefore, we hint that costly external amino acid supplementations that are often required to meet the nutritional needs could be avoided by carefully applying conditions that enable targeted growth rates. CONCLUSION: In summary, we demonstrate tradeoffs between nutritional quality and production rate, and we discuss the microbial biomass properties that vary according to the growth conditions.

lesSDRF is more: maximizing the value of proteomics data through streamlined metadata annotation.

Public proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication.

Unipept Desktop 2.0: Construction of Targeted Reference Protein Databases for Metaproteogenomics Analyses.

Unipept Desktop 2.0 is the most recent iteration of the Unipept Desktop tool that adds support for the analysis of metaproteogenomics datasets. Unipept Desktop now supports the automatic construction of targeted protein reference databases that only contain proteins (originating from the UniProtKB resource) associated with a predetermined list of taxa. This improves both the taxonomic and functional resolution of a metaproteomic analysis and yields several technical advantages. By limiting the proteins present in a reference database, it is also possible to perform (meta)proteogenomics analyses. Since the protein reference database resides on the user's local machine, they have complete control over the database used during an analysis. Data no longer need to be transmitted over the Internet, decreasing the time required for an analysis and better safeguarding privacy-sensitive data. As a proof of concept, we present a case study in which a human gut metaproteome dataset is analyzed with Unipept Desktop 2.0 using different targeted databases based on matched 16S rRNA gene sequencing data.

Blood-borne extracellular vesicles of bacteria and intestinal cells in patients with psychotic disorders.

BACKGROUND: Human cells and bacteria secrete extracellular vesicles (EV) which play a role in intercellular communication. EV from the host intestinal epithelium are involved in the regulation of bacterial gene expression and growth. Bacterial EV (bactEV) produced in the intestine can pass to various tissues where they deliver biomolecules to many kinds of cells, including neurons. Emerging data indicate that gut microbiota is altered in patients with psychotic disorders. We hypothesized that the amount and content of blood-borne EV from intestinal cells and bactEV in psychotic patients would differ from healthy controls. METHODS: We analyzed for human intestinal proteins by proteomics, for bactEV by metaproteomic analysis, and by measuring the level of lipopolysaccharide (LPS) in blood-borne EV from patients with psychotic disorders (n = 25), tested twice, in the acute phase of psychosis and after improvement, with age- and sex-matched healthy controls (n = 25). RESULTS: Patients with psychotic disorders had lower LPS levels in their EV compared to healthy controls (p = .027). Metaproteome analyses confirmed LPS finding and identified Firmicutes and Bacteroidetes as dominating phyla. Total amounts of human intestine proteins in EV isolated from blood was lower in patients compared to controls (p = .02). CONCLUSIONS: Our results suggest that bactEV and host intestinal EV are decreased in patients with psychosis and that this topic is worthy of further investigation given potential pathophysiological implications. Possible mechanisms involve dysregulation of the gut microbiota by host EV, altered translocation of bactEV to systemic circulation where bactEV can interact with both the brain and the immune system.

Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work.

The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.

Sensitive and Specific Spectral Library Searching with CompOmics Spectral Library Searching Tool and Percolator.

Maintaining high sensitivity while limiting false positives is a key challenge in peptide identification from mass spectrometry data. Here, we investigate the effects of integrating the machine learning-based postprocessor Percolator into our spectral library searching tool COSS (CompOmics Spectral library Searching tool). To evaluate the effects of this postprocessing, we have used 40 data sets from 2 different projects and have searched these against the NIST and MassIVE spectral libraries. The searching is carried out using 2 spectral library search tools, COSS and MSPepSearch with and without Percolator postprocessing, and using sequence database search engine MS-GF+ as a baseline comparator. The addition of the Percolator rescoring step to COSS is effective and results in a substantial improvement in sensitivity and specificity of the identifications. COSS is freely available as open source under the permissive Apache2 license, and binaries and source code are found at https://github.com/compomics/COSS.

Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms.

It is important for the proteomics community to have a standardized manner to represent all possible variations of a protein or peptide primary sequence, including natural, chemically induced, and artifactual modifications. The Human Proteome Organization Proteomics Standards Initiative in collaboration with several members of the Consortium for Top-Down Proteomics (CTDP) has developed a standard notation called ProForma 2.0, which is a substantial extension of the original ProForma notation developed by the CTDP. ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. ProForma 2.0 supports use cases needed for bottom-up and middle-/top-down proteomics approaches and allows the encoding of highly modified proteins and peptides using a human- and machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated by mass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical) and atomic isotopes. Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification document and information about software implementations are available at http://psidev.info/proforma.

Pout2Prot: An Efficient Tool to Create Protein (Sub)groups from Percolator Output Files.

In metaproteomics, the study of the collective proteome of microbial communities, the protein inference problem is more challenging than in single-species proteomics. Indeed, a peptide sequence can be present not only in multiple proteins or protein isoforms of the same species, but also in homologous proteins from closely related species. To assign the taxonomy and functions of the microbial species, specialized tools have been developed, such as Prophane. This tool, however, is not directly compatible with post-processing tools such as Percolator. In this manuscript we therefore present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein group and protein subgroup output files (.tsv) that can be used directly with Prophane. We investigated different grouping strategies and compared existing protein grouping tools to develop an advanced protein grouping algorithm that offers a variety of different approaches, allows grouping for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes.

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. Video Abstract.

Critical Assessment of MetaProteome Investigation (CAMPI): a multi-laboratory comparison of established workflows.

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.

A proteomics sample metadata representation for multiomics integration and big data analysis.

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

Research culture: science from bench to society.

Research is a long process in which the collaboration between stakeholders involved in academia, industry and governments is crucial. Ideally, these stakeholders should work together to better align the innovation process with the values, needs and expectations of the research community. Reflecting on how we perform research and how our discoveries can benefit society is therefore of the utmost importance. The complete system of shared values concerning the research process is embedded in the concept of research culture, which has been gaining more attention in recent years. With the hope of increasing awareness of research culture among established scientists and early-career professionals, in this manuscript we discuss what research culture is, what it consists of and how it can positively influence scientific developments.

Cov-MS: A Community-Based Template Assay for Mass-Spectrometry-Based Protein Detection in SARS-CoV-2 Patients.

Rising population density and global mobility are among the reasons why pathogens such as SARS-CoV-2, the virus that causes COVID-19, spread so rapidly across the globe. The policy response to such pandemics will always have to include accurate monitoring of the spread, as this provides one of the few alternatives to total lockdown. However, COVID-19 diagnosis is currently performed almost exclusively by reverse transcription polymerase chain reaction (RT-PCR). Although this is efficient, automatable, and acceptably cheap, reliance on one type of technology comes with serious caveats, as illustrated by recurring reagent and test shortages. We therefore developed an alternative diagnostic test that detects proteolytically digested SARS-CoV-2 proteins using mass spectrometry (MS). We established the Cov-MS consortium, consisting of 15 academic laboratories and several industrial partners to increase applicability, accessibility, sensitivity, and robustness of this kind of SARS-CoV-2 detection. This, in turn, gave rise to the Cov-MS Digital Incubator that allows other laboratories to join the effort, navigate, and share their optimizations and translate the assay into their clinic. As this test relies on viral proteins instead of RNA, it provides an orthogonal and complementary approach to RT-PCR using other reagents that are relatively inexpensive and widely available, as well as orthogonally skilled personnel and different instruments. Data are available via ProteomeXchange with identifier PXD022550.

Universal Spectrum Identifier for mass spectra.

Mass spectra provide the ultimate evidence to support the findings of mass spectrometry proteomics studies in publications, and it is therefore crucial to be able to trace the conclusions back to the spectra. The Universal Spectrum Identifier (USI) provides a standardized mechanism for encoding a virtual path to any mass spectrum contained in datasets deposited to public proteomics repositories. USI enables greater transparency of spectral evidence, with more than 1 billion USI identifications from over 3 billion spectra already available through ProteomeXchange repositories.

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS): an open community for bioinformatics training and research.

The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS; eubic-ms.org) was founded in 2014 to unite European computational mass spectrometry researchers and proteomics bioinformaticians working in academia and industry. EuBIC-MS maintains educational resources (proteomics-academy.org) and organises workshops at national and international conferences on proteomics and mass spectrometry. Furthermore, EuBIC-MS is actively involved in several community initiatives such as the Human Proteome Organization's Proteomics Standards Initiative (HUPO-PSI). Apart from these collaborations, EuBIC-MS has organised two Winter Schools and two Developers' Meetings that have contributed to the strengthening of the European mass spectrometry network and fostered international collaboration in this field, even beyond Europe. Moreover, EuBIC-MS is currently actively developing a community-driven standard dedicated to mass spectrometry data annotation (SDRF-Proteomics) that will facilitate data reuse and collaboration. This manuscript highlights what EuBIC-MS is, what it does, and what it already has achieved. A warm invitation is extended to new researchers at all career stages to join the EuBIC-MS community on its Slack channel (eubic.slack.com).

MegaGO: A Fast Yet Powerful Approach to Assess Functional Gene Ontology Similarity across Meta-Omics Data Sets.

The study of microbiomes has gained in importance over the past few years and has led to the emergence of the fields of metagenomics, metatranscriptomics, and metaproteomics. While initially focused on the study of biodiversity within these communities, the emphasis has increasingly shifted to the study of (changes in) the complete set of functions available in these communities. A key tool to study this functional complement of a microbiome is Gene Ontology (GO) term analysis. However, comparing large sets of GO terms is not an easy task due to the deeply branched nature of GO, which limits the utility of exact term matching. To solve this problem, we here present MegaGO, a user-friendly tool that relies on semantic similarity between GO terms to compute the functional similarity between multiple data sets. MegaGO is high performing: Each set can contain thousands of GO terms, and results are calculated in a matter of seconds. MegaGO is available as a web application at https://megago.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the MIT license and is available at https://github.com/MEGA-GO/.

Unipept Desktop: A Faster, More Powerful Metaproteomics Results Analysis Tool.

Metaproteomics has become an important research tool to study microbial systems, which has resulted in increased metaproteomics data generation. However, efficient tools for processing the acquired data have lagged behind. One widely used tool for metaproteomics data interpretation is Unipept, a web-based tool that provides, among others, interactive and insightful visualizations. Due to its web-based implementation, however, the Unipept web application is limited in the amount of data that can be analyzed. In this manuscript we therefore present Unipept Desktop, a desktop application version of Unipept that is designed to drastically increase the throughput and capacity of metaproteomics data analysis. Moreover, it provides a novel comparative analysis pipeline and improves the organization of experimental data into projects, thus addressing the growing need for more efficient and versatile analysis tools for metaproteomics data.

Extracellular vesicles in patients in the acute phase of psychosis and after clinical improvement: an explorative study.

Extracellular vesicles (EVs) are cell-derived structures that transport proteins, lipids and nucleic acids between cells, thereby affecting the phenotype of the recipient cell. As the content of EVs reflects the status of the originating cell, EVs can have potential as biomarkers. Identifying EVs, including their cells of origin and their cargo, may provide insights in the pathophysiology of psychosis. Here, we present an in-depth analysis and proteomics of EVs from peripheral blood in patients (n = 25) during and after the acute phase of psychosis. Concentration and protein content of EVs in psychotic patients were twofold higher than in 25 age- and sex-matched healthy controls (p < 0.001 for both concentration and protein content), and the diameter of EVs was larger in patients (p = 0.02). Properties of EVs did not differ significantly in blood sampled during and after the acute psychotic episode. Proteomic analyses on isolated EVs from individual patients revealed 1,853 proteins, whereof 45 were brain-elevated proteins. Of these, five proteins involved in regulation of plasticity of glutamatergic synapses were significantly different in psychotic patients compared to controls; neurogranin (NRGN), neuron-specific calcium-binding protein hippocalcin (HPCA), kalirin (KALRN), beta-adducin (ADD2) and ankyrin-2 (ANK2). To summarize, our results show that peripheral EVs in psychotic patients are different from those in healthy controls and point at alterations on the glutamatergic system. We suggest that EVs allow investigation of blood-borne brain-originating biological material and that their role as biomarkers in patients with psychotic disorders is worthy of further exploration.

Unipept CLI 2.0: adding support for visualizations and functional annotations.

SUMMARY: Unipept is an ecosystem of tools developed for fast metaproteomics data-analysis consisting of a web application, a set of web services (application programming interface, API) and a command-line interface (CLI). After the successful introduction of version 4 of the Unipept web application, we here introduce version 2.0 of the API and CLI. Next to the existing taxonomic analysis, version 2.0 of the API and CLI provides access to Unipept's powerful functional analysis for metaproteomics samples. The functional analysis pipeline supports retrieval of Enzyme Commission numbers, Gene Ontology terms and InterPro entries for the individual peptides in a metaproteomics sample. This paves the way for other applications and developers to integrate these new information sources into their data processing pipelines, which greatly increases insight into the functions performed by the organisms in a specific environment. Both the API and CLI have also been expanded with the ability to render interactive visualizations from a list of taxon ids. These visualizations are automatically made available on a dedicated website and can easily be shared by users. AVAILABILITY AND IMPLEMENTATION: The API is available at http://api.unipept.ugent.be. Information regarding the CLI can be found at https://unipept.ugent.be/clidocs. Both interfaces are freely available and open-source under the MIT license. CONTACT: pieter.verschaffelt@ugent.be. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.