The SysteMHC Atlas project.

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts.

enviPath--The environmental contaminant biotransformation pathway resource.

The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.

Soil metaproteomics - Comparative evaluation of protein extraction protocols.

Metaproteomics and its potential applications are very promising to study microbial activity in environmental samples and to obtain a deeper understanding of microbial interactions. However, due to the complexity of soil samples the exhaustive extraction of proteins is a major challenge. We compared soil protein extraction protocols in terms of their protein extraction efficiency for two different soil types. Four different protein extraction procedures were applied based on (a) SDS extraction without phenol, (b) NaOH and subsequent phenol extraction, (c) SDS-phenol extraction and (d) SDS-phenol extraction with prior washing steps. To assess the suitability of these methods for the functional analysis of the soil metaproteome, they were applied to a potting soil high in organic matter and a forest soil. Proteins were analyzed by two-dimensional liquid chromatography/tandem mass spectrometry (2D-LC-MS/MS) and the number of unique spectra as well as the number of assigned proteins for each of the respective protocols was compared. In both soil types, extraction with SDS-phenol (c) resulted in "high" numbers of proteins. Moreover, a spiking experiment was conducted to evaluate protein recovery. To this end sterilized forest soil was amended with proteins from pure cultures of Pectobacterium carotovorum and Aspergillus nidulans. The protein recovery in the spiking experiment was almost 50%. Our study demonstrates that a critical evaluation of the extraction protocol is crucial for the quality of the metaproteomics data, especially in highly complex samples like natural soils.

Structure and function of the symbiosis partners of the lung lichen (Lobaria pulmonaria L. Hoffm.) analyzed by metaproteomics.

Environmental proteomics, also referred to as metaproteomics, is an emerging technology to study the structure and function of microbial communities. Here, we applied semi-quantitative label-free proteomics using one-dimensional gel electrophoresis combined with LC-MS/MS and normalized spectral counting together with fluorescence in situ hybridization and confocal laser scanning microscopy to characterize the metaproteome of the lung lichen symbiosis Lobaria pulmonaria. In addition to the myco- and photobiont, L. pulmonaria harbors proteins from a highly diverse prokaryotic community, which is dominated by Proteobacteria and including also Archaea. While fungal proteins are most dominant (75.4% of all assigned spectra), about the same amount of spectra were assigned to prokaryotic proteins (10%) and to the green algal photobiont (9%). While the latter proteins were found to be mainly associated with energy and carbohydrate metabolism, a major proportion of fungal and bacterial proteins appeared to be involved in PTMs and protein turnover and other diverse functions.

Proteome analysis of fungal and bacterial involvement in leaf litter decomposition.

Fungi and bacteria are key players in the decomposition of leaf litter, but their individual contributions to the process and their interactions are still poorly known. We combined semi-quantitative proteome analyses (1-D PAGE-LC-MS/MS) with qualitative and quantitative analyses of extracellular degradative enzyme activities to unravel the respective roles of a fungus and a bacterium during litter decomposition. Two model organisms, a mesophilic Gram-negative bacterium (Pectobacterium carotovorum) and an ascomycete (Aspergillus nidulans), were grown in both, pure culture and co-culture on minimal medium containing either glucose or beech leaf litter as sole carbon source. P. carotovorum grew best in co-culture with the fungus, whereas growth of A. nidulans was significantly reduced when the bacterium was present. This observation suggests that P. carotovorum has only limited capabilities to degrade leaf litter and profits from the degradation products of A. nidulans at the expense of fungal growth. In accordance with this interpretation, our proteome analysis revealed that most of the extracellular biodegradative enzymes (i.e. proteases, pectinases, and cellulases) in the cultures with beech litter were expressed by the fungus, the bacterium producing only low levels of pectinases.

A complete and flexible workflow for metaproteomics data analysis based on MetaProteomeAnalyzer and Prophane.

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.

A Single Metabolite which Modulates Lipid Metabolism Alters Hematopoietic Stem/Progenitor Cell Behavior and Promotes Lymphoid Reconstitution.

Fatty acid ß-oxidation (FAO), the breakdown of lipids, is a metabolic pathway used by various stem cells. FAO levels are generally high during quiescence and downregulated with proliferation. The endogenous metabolite malonyl-CoA modulates lipid metabolism as a reversible FAO inhibitor and as a substrate for de novo lipogenesis. Here we assessed whether malonyl-CoA can be exploited to steer the behavior of hematopoietic stem/progenitor cells (HSPCs), quiescent stem cells of clinical relevance. Treatment of mouse HSPCs in vitro with malonyl-CoA increases HSPC numbers compared with nontreated controls and ameliorates blood reconstitution capacity when transplanted in vivo, mainly through enhanced lymphoid reconstitution. Similarly, human HSPC numbers also increase upon malonyl-CoA treatment in vitro. These data corroborate that lipid metabolism can be targeted to direct cell fate and stem cell proliferation. Physiological modulation of metabolic pathways, rather than genetic or pharmacological inhibition, provides unique perspectives for stem cell manipulations in health and disease.

A computer-based prediction platform for the reaction of ozone with organic compounds in aqueous solution: kinetics and mechanisms.

Ozonation of secondary wastewater effluents can reduce the discharge of micropollutants by transforming their chemical structures. Therefore, a better understanding of the formation of transformation products during ozonation is important. In this study, a computer-based prediction platform for the kinetics and mechanisms of the reactions of ozone with organic compounds was developed to enable in silico predictions of transformation products. With the developed prediction platform, reaction kinetics expressed as second-order rate constants for the reactions of ozone with selected organic compounds (kO3, M-1 s-1) can be predicted based on an adapted kO3 prediction model from a previous study (Lee et al., Environ. Sci. Technol., 2015, 49, 9925-9935) (average model error of about a factor of 6 for 14 compound classes with 284 model compounds). Ozone reaction mechanisms reported in the literature have been reviewed and, using chemoinformatics tools, encoded into about 340 individual reaction rules that can be generally applied to predict the transformation products of micropollutants. Predictions for kO3 and/or transformation products were overall consistent with the experimental data for three micropollutants used as validation compounds (e.g., carbamazepine, tramadol, and triclosan). However, limitations of the current kO3 prediction platform were also identified: ambiguous assignment of the n-th highest occupied molecular orbital energy (EHOMO-n) to the reactive sites, potential errors associated with the use of a gas-phase geometry, and a poor kO3 prediction for certain compounds (cetirizine). Therefore, the current prediction tool should not be considered as a substitute for experimental studies and experimental data are still required in the future to obtain a more robust prediction model. Nonetheless, the developed prediction platform, made available as a stand-alone graphical user interface (GUI) application, will provide useful information about aqueous ozone chemistry to various groups of end-users such as environmental chemists, engineers, or toxicologists.

Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data.

Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules, e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity offered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.

Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions.

Leaf-litter decomposition is a central process in carbon cycling; however, our knowledge about the microbial regulation of this process is still scarce. Metaproteomics allows us to link the abundance and activity of enzymes during nutrient cycling to their phylogenetic origin based on proteins, the 'active building blocks' in the system. Moreover, we employed metaproteomics to investigate the influence of environmental factors and nutrients on the decomposer structure and function during beech litter decomposition. Litter was collected at forest sites in Austria with different litter nutrient content. Proteins were analyzed by 1-D-SDS-PAGE followed by liquid-chromatography and tandem mass-spectrometry. Mass spectra were assigned to phylogenetic and functional groups by a newly developed bioinformatics workflow, assignments being validated by complementary approaches. We provide evidence that the litter nutrient content and the stoichiometry of C:N:P affect the decomposer community structure and activity. Fungi were found to be the main producers of extracellular hydrolytic enzymes, with no bacterial hydrolases being detected by our metaproteomics approach. Detailed investigation of microbial succession suggests that it is influenced by litter nutrient content. Microbial activity was stimulated at higher litter nutrient contents via a higher abundance and activity of extracellular enzymes.

Synchronized quartz crystal microbalance and nanoplasmonic sensing of biomolecular recognition reactions.

We present a method providing synchronized measurements using the two techniques: quartz crystal microbalance with dissipation (QCM-D) monitoring and localized surface plasmon resonance (LSPR). This was achieved by letting a thin gold film perforated with short-ranged ordered plasmon-active nanoholes act as one of the electrodes of a QCM-D crystal. This enabled transmission-mode optical spectroscopy to be used to temporally resolve colorimetric changes of the LSPR active substrate induced upon biomolecular binding events. The LSPR response could thus be compared with simultaneously obtained changes in resonance frequency, Deltaf, and energy dissipation, DeltaD, of the QCM-D device. Since the LSPR technique is preferentially sensitive to changes within the voids of the nanoholes, while the QCM-D technique is preferentially sensitive to reactions on the planar region between the holes, a surface chemistry providing the same binding kinetics on both gold and silica was used. This was achieved by coating the substrate with poly(L-lysine)-graft-poly(ethylene glycol) (PLL-g-PEG), which was shown to bind in the same manner on silica and gold modified with a carboxyl-terminated thiol. In this way, the combined setup provided new information about structural changes upon PLL-g-PEG adsorption. We also demonstrate subsequent binding of NeutrAvidin and an immunoreaction utilizing biotin-modified IgG. The combined information from the synchronized measurements was also used in a new way to estimate the sensing volume of the LSPR sensor.