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Although metaproteomics, the study of the collective proteome of microbial communities, has become increasingly powerful and popular over the past few years, the field has lagged behind on the availability of user-friendly, end-to-end pipelines for data analysis. We therefore describe the Connection from two commonly used metaproteomics data processing tools in the field, MetaProteomeAnalyzer and PeptideShaker, to Unipept for downstream analysis.
Through these connections, direct end-to-end pipelines are built from database searching to taxonomic and functional annotation.
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.
In biological fluids, proteins associate with nanoparticles, leading to a protein 'corona' defining the biological identity of the particle. However, a comprehensive knowledge of particle-guided protein fingerprints and their dependence on nanomaterial properties is incomplete. We studied the long-lived ('hard') blood plasma derived corona on monodispersed amorphous silica nanoparticles differing in size (20, 30, and 100 nm). Employing label-free liquid chromatography mass spectrometry, one- and two-dimensional gel electrophoresis, and immunoblotting the composition of the protein corona was analyzed not only qualitatively but also quantitatively. Detected proteins were bioinformatically classified according to their physicochemical and biological properties. Binding of the 125 identified proteins did not simply reflect their relative abundance in the plasma but revealed an enrichment of specific lipoproteins as well as proteins involved in coagulation and the complement pathway. In contrast, immunoglobulins and acute phase response proteins displayed a lower affinity for the particles. Protein decoration of the negatively charged particles did not correlate with protein size or charge, demonstrating that electrostatic effects alone are not the major driving force regulating the nanoparticle–protein interaction. Remarkably, even differences in particle size of only 10 nm significantly determined the nanoparticle corona, although no clear correlation with particle surface volume, protein size, or charge was evident. Particle size quantitatively influenced the particles decoration with 37% of all identified proteins, including (patho)biologically relevant candidates. We demonstrate the complexity of the plasma corona and its still unresolved physicochemical regulation, which need to be considered in nanobioscience in the future.
Metaproteomics, the study of the collective protein composition of multi-organism systems, provides deep insights into the biodiversity of microbial communities and the complex functional interplay between microbes and their hosts or environment. Thus, metaproteomics has become an indispensable tool in various fields such as microbiology and related medical applications. The computational challenges in the analysis of corresponding datasets differ from those of pure-culture proteomics, e.g., due to the higher complexity of the samples and the larger reference databases demanding specific computing pipelines. Corresponding data analyses usually consist of numerous manual steps that must be closely synchronized. With MetaProteomeAnalyzer and Prophane, we have established two open-source software solutions specifically developed and optimized for metaproteomics. Among other features, peptide-spectrum matching is improved by combining different search engines and, compared to similar tools, metaproteome annotation benefits from the most comprehensive set of available databases (such as NCBI, UniProt, EggNOG, PFAM, and CAZy). The workflow described in this protocol combines both tools and leads the user through the entire data analysis process, including protein database creation, database search, protein grouping and annotation, and results visualization. To the best of our knowledge, this protocol presents the most comprehensive, detailed and flexible guide to metaproteomics data analysis to date. While beginners are provided with robust, easy-to-use, state-of-the-art data analysis in a reasonable time (a few hours, depending on, among other factors, the protein database size and the number of identified peptides and inferred proteins), advanced users benefit from the flexibility and adaptability of the workflow.
To gain a thorough appreciation of microbiome dynamics, researchers characterize the functional relevance of expressed microbial genes or proteins. This can be accomplished through metaproteomics, which characterizes the protein expression of microbiomes. Several software tools exist for analyzing microbiomes at the functional level by measuring their combined proteome-level response to environmental perturbations. In this survey, we explore the performance of six available tools, to enable researchers to make informed decisions regarding software choice based on their research goals. Tandem mass spectrometry-based proteomic data obtained from dental caries plaque samples grown with and without sucrose in paired biofilm reactors were used as representative data for this evaluation. Microbial peptides from one sample pair were identified by the X! tandem search algorithm via SearchGUI and subjected to functional analysis using software tools including eggNOG-mapper, MEGAN5, MetaGOmics, MetaProteomeAnalyzer (MPA), ProPHAnE, and Unipept to generate functional annotation through Gene Ontology (GO) terms. Among these software tools, notable differences in functional annotation were detected after comparing differentially expressed protein functional groups. Based on the generated GO terms of these tools we performed a peptide-level comparison to evaluate the quality of their functional annotations. A BLAST analysis against the NCBI non-redundant database revealed that the sensitivity and specificity of functional annotation varied between tools. For example, eggNOG-mapper mapped to the most number of GO terms, while Unipept generated more accurate GO terms. Based on our evaluation, metaproteomics researchers can choose the software according to their analytical needs and developers can use the resulting feedback to further optimize their algorithms. To make more of these tools accessible via scalable metaproteomics workflows, eggNOG-mapper and Unipept 4.0 were incorporated into the Galaxy platform.
Results suggest a reduction in immune gene repertoires in termites and possible complementary expression between termite castes. With comparative genomics we will investigate the evolution of gene families related to immunity, try to understand where reductions and expansions take place and relate these changes to shifts in sociality and ecology. The role of TEs in expansions and contractions of immune gene families will be investigated. For these analyses, we propose to generate high quality, highly contiguous genomes of species from different levels of sociality, covering all major termite families. With comparative transcriptomics we will investigate the expression of immune genes in different castes. Via network analyses we will identify pathways indicated in differential immunity between castes and between species of different sociality levels. We will investigate how these pathways have been rewired along the transitions to higher levels of sociality and how, intra-specifically, they change between castes.
Untargeted accurate strain-level classification of a priori unidentified organisms using tandem mass spectrometry is a challenging task. Reference databases often lack taxonomic depth, limiting peptide assignments to the species level. However, the extension with detailed strain information increases runtime and decreases statistical power. In addition, larger databases contain a higher number of similar proteomes. We present TaxIt, an iterative workflow to address the increasing search space required for MS/MS-based strain-level classification of samples with unknown taxonomic origin. TaxIt first applies reference sequence data for initial identification of species candidates, followed by automated acquisition of relevant strain sequences for low level classification. Furthermore, proteome similarities resulting in ambiguous taxonomic assignments are addressed with an abundance weighting strategy to increase the confidence in candidate taxa. For benchmarking the performance of our method, we apply our iterative workflow on several samples of bacterial and viral origin. In comparison to noniterative approaches using unique peptides or advanced abundance correction, TaxIt identifies microbial strains correctly in all examples presented (with one tie), thereby demonstrating the potential for untargeted and deeper taxonomic classification. TaxIt makes extensive use of public, unrestricted, and continuously growing sequence resources such as the NCBI databases and is available under open-source BSD license at https://gitlab.com/rki_bioinformatics/TaxIt.
Bioinformatics meets virology: The European virus bioinformatics center's second annual meeting
(2018)
The Second Annual Meeting of the European Virus Bioinformatics Center (EVBC), held in Utrecht, Netherlands, focused on computational approaches in virology, with topics including (but not limited to) virus discovery, diagnostics, (meta-)genomics, modeling, epidemiology, molecular structure, evolution, and viral ecology. The goals of the Second Annual Meeting were threefold: (i) to bring together virologists and bioinformaticians from across the academic, industrial, professional, and training sectors to share best practice; (ii) to provide a meaningful and interactive scientific environment to promote discussion and collaboration between students, postdoctoral fellows, and both new and established investigators; (iii) to inspire and suggest new research directions and questions. Approximately 120 researchers from around the world attended the Second Annual Meeting of the EVBC this year, including 15 renowned international speakers. This report presents an overview of new developments and novel research findings that emerged during the meeting.
Virologists. You might know a couple of them, but unless you are a virologist yourself, the probability that you have collaborated with one in the past is low. The community is relatively small, but they pack a heavy punch and are expected to play a leading role in the research into pathogens that lies ahead. You may ask why we think virologists are our future. Suffice it to say that it is not just because they have invented technologies that belong to the space age, including use of viruses as vehicles to shuttle genes into cells[1], organic nanoparticles with specific tools attached to their surfaces to get inside target cells[2], and using genetically modified viruses as therapies to fight against cancer[3]. Did you know that virologists currently only know of about 3,200 viral species but that more than 320,000 mammal-associated viruses[4] are thought to await discovery? Just think about the viruses hidden in the Arctic ice[5] or in the insects and other animals from once cut-off regions in the world, which now face ever-increasing human exposure[6]. But a heroic (as well as an apocalyptic) role for virologists may also be on the horizon, as the adoption of phage therapy may, in the future, be used to control harmful bacteria when antibiotics fail
Probability based taxonomic profiling of viral and microbiome samples using PepGM and Unipept
(2022)
In mass spectrometry based proteomics, protein homology leads to many shared peptides within and between species. This complicates
taxonomic inference in samples of unknown taxonomic origin. PepGM uses a graphical model for taxonomic profiling of viral proteomes and
metaproteomic datasets providing taxonomic confidence scores. To build the graphical model, a list of potentially present taxa needs to be
inferred. To this end, we integrate Unipept, which enables the fast querying of potentially present taxa. Together, they allow for taxonomic
inference with statistically sound confidence scores.