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Adaptations of animal cells to growth in suspension culture concern in particular viral vaccine production, where very specific aspects of virus-host cell interaction need to be taken into account to achieve high cell specific yields and overall process productivity. So far, the complexity of alterations on the metabolism, enzyme, and proteome level required for adaptation is only poorly understood. In this study, for the first time, we combined several complex analytical approaches with the aim to track cellular changes on different levels and to unravel interconnections and correlations. Therefore, a Madin-Darby canine kidney (MDCK) suspension cell line, adapted earlier to growth in suspension, was cultivated in a 1-L bioreactor. Cell concentrations and cell volumes, extracellular metabolite concentrations, and intracellular enzyme activities were determined. The experimental data set was used as the input for a segregated growth model that was already applied to describe the growth dynamics of the parental adherent cell line. In addition, the cellular proteome was analyzed by liquid chromatography coupled to tandem mass spectrometry using a label-free protein quantification method to unravel altered cellular processes for the suspension and the adherent cell line. Four regulatory mechanisms were identified as a response of the adaptation of adherent MDCK cells to growth in suspension. These regulatory mechanisms were linked to the proteins caveolin, cadherin-1, and pirin. Combining cell, metabolite, enzyme, and protein measurements with mathematical modeling generated a more holistic view on cellular processes involved in the adaptation of an adherent cell line to suspension growth.
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 feld. 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.
The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE.
The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms.
The BAM Data Store
(2023)
As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities.
The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis.
Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.
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.
Motivation: Inferring taxonomy in mass spectrometry-based shotgun proteomics is a complex task. In multi-species or viral samples of unknown taxonomic origin, the presence of proteins and corresponding taxa must be inferred from a list of identified peptides, which is often complicated by protein homology: many proteins do not only share peptides within a taxon but also between taxa. However, the correct taxonomic inference is crucial when identifying different viral strains with high-sequence homology—considering, e.g., the different epidemiological characteristics of the various strains of severe acute respiratory syndrome-related coronavirus-2. Additionally, many viruses mutate frequently, further complicating the correct identification of viral proteomic samples.
Results: We present PepGM, a probabilistic graphical model for the taxonomic assignment of virus proteomic samples with strain-level resolution and associated confidence scores. PepGM combines the results of a standard proteomic database search algorithm with belief propagation to calculate the marginal distributions, and thus confidence scores, for potential taxonomic assignments. We demonstrate the performance of PepGM using several publicly available virus proteomic datasets, showing its strain-level resolution performance. In two out of eight cases, the taxonomic assignments were only correct on the species level, which PepGM clearly indicates by lower confidence scores.
Availability and implementation: PepGM is written in Python and embedded into a Snakemake workflow. It is available at https://github.com/BAMeScience/PepGM.
Mistle: bringing spectral library predictions to metaproteomics with an efficient search index
(2023)
Motivation: Deep learning has moved to the forefront of tandem mass spectrometry-driven proteomics and authentic prediction for peptide fragmentation is more feasible than ever. Still, at this point spectral prediction is mainly used to validate database search results or for confined search spaces. Fully predicted spectral libraries have not yet been efficiently adapted to large search space problems that often occur in metaproteomics or proteogenomics.
Results: In this study, we showcase a workflow that uses Prosit for spectral library predictions on two common metaproteomes and implement an indexing and search algorithm, Mistle, to efficiently identify experimental mass spectra within the library. Hence, the workflow emulates a classic protein sequence database search with protein digestion but builds a searchable index from spectral predictions as an in-between step.
We compare Mistle to popular search engines, both on a spectral and database search level, and provide evidence that this approach is more accurate than a database search using MSFragger. Mistle outperforms other spectral library search engines in terms of run time and proves to be extremely memory efficient with a 4- to 22-fold decrease in RAM usage. This makes Mistle universally applicable to large search spaces, e.g. covering comprehensive sequence databases of diverse microbiomes.
Availability and implementation: Mistle is freely available on GitHub at https://github.com/BAMeScience/Mistle.
Metaproteomics, the study of the collective proteome within a microbial ecosystem, has substantially grown over the past few years. This growth comes from the increased awareness that it can powerfully supplement metagenomics and metatranscriptomics analyses. Although metaproteomics is more challenging than single-species proteomics, its added value has already been demonstrated in various biosystems, such as gut microbiomes or biogas plants. Because of the many challenges, a variety of metaproteomics workflows have been developed, yet it remains unclear what the impact of the choice of workflow is on the obtained results. Therefore, we set out to compare several well-established workflows in the first community-driven, multi-lab comparison in metaproteomics: the critical assessment of metaproteome investigation (CAMPI) study. In this benchmarking study, we evaluated the influence of different workflows on sample preparation, mass spectrometry acquisition, and bioinformatic analysis on two samples: a simplified, lab-assembled human intestinal sample and a complex human fecal sample. We find that the same overall biological meaning can be inferred from the metaproteome data, regardless of the chosen workflow. Indeed, taxonomic and functional annotations were very similar across all sample-specific data sets. Moreover, this outcome was consistent regardless of whether protein groups or peptides, or differences at the spectrum or peptide level were used to infer these annotations. Where differences were observed, those originated primarily from different wet-lab methods rather than from different bioinformatic pipelines. The CAMPI study thus provides a solid foundation for benchmarking metaproteomics workflows, and will therefore be a key reference for future method improvement. [doi:10.25345/C5SX64D9M] [dataset license: CC0 1.0 Universal (CC0 1.0)]
MALDI-TOF-MS-Based Identification of Monoclonal Murine Anti-SARS-CoV-2 Antibodies within One Hour
(2022)
During the SARS-CoV-2 pandemic, many virus-binding monoclonal antibodies have been developed for clinical and diagnostic purposes. This underlines the importance of antibodies as universal bioanalytical reagents. However, little attention is given to the reproducibility crisis that scientific studies are still facing to date. In a recent study, not even half of all research antibodies mentioned in publications could be identified at all. This should spark more efforts in the search for practical solutions for the traceability of antibodies. For this purpose, we used 35 monoclonal antibodies against SARS-CoV-2 to demonstrate how sequence-independent antibody identification can be achieved by simple means applied to the protein. First, we examined the intact and light chain masses of the antibodies relative to the reference material NIST-mAb 8671. Already half of the antibodies could be identified based solely on these two parameters. In addition, we developed two complementary peptide mass fingerprinting methods with MALDI-TOF-MS that can be performed in 60 min and had a combined sequence coverage of over 80%. One method is based on the partial acidic hydrolysis of the protein by 5 mM of sulfuric acid at 99 degrees C. Furthermore, we established a fast way for a tryptic digest without an alkylation step. We were able to show that the distinction of clones is possible simply by a brief visual comparison of the mass spectra. In this work, two clones originating from the same immunization gave the same fingerprints. Later, a hybridoma sequencing confirmed the sequence identity of these sister clones. In order to automate the spectral comparison for larger libraries of antibodies, we developed the online software ABID 2.0. This open-source software determines the number of matching peptides in the fingerprint spectra. We propose that publications and other documents critically relying on monoclonal antibodies with unknown amino acid sequences should include at least one antibody fingerprint. By fingerprinting an antibody in question, its identity can be confirmed by comparison with a library spectrum at any time and context.