PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping.

Toxicological evaluation of substances in regulation still often relies on animal experiments. Understanding the substances' mode-of-action is crucial to develop alternative test strategies. Omics methods are promising tools to achieve this goal. Until now, most attention was focused on transcriptomics, while proteomics is not yet routinely applied in toxicology despite the large number of datasets available in public repositories. Exploiting the full potential of these datasets is hampered by differences in measurement procedures and follow-up data processing. Here we present the tool PROTEOMAS, which allows meta-analysis of proteomic data from public origin. The workflow was designed for analyzing proteomic studies in a harmonized way and to ensure transparency in the analysis of proteomic data for regulatory purposes. It agrees with the Omics Reporting Framework guidelines of the OECD with the intention to integrate proteomics to other omic methods in regulatory toxicology. The overarching aim is to contribute to the development of AOPs and to understand the mode of action of substances. To demonstrate the robustness and reliability of our workflow we compared our results to those of the original studies. As a case study, we performed a meta-analysis of 25 proteomic datasets to investigate the toxicological effects of nanomaterials at the lung level. PROTEOMAS is an important contribution to the development of alternative test strategies enabling robust meta-analysis of proteomic data. This workflow commits to the FAIR principles (Findable, Accessible, Interoperable and Reusable) of computational protocols.

Metaproteome plasticity sheds light on the ecology of the rumen microbiome and its connection to host traits.

The arsenal of genes that microbes express reflect the way in which they sense their environment. We have previously reported that the rumen microbiome composition and its coding capacity are different in animals having distinct feed efficiency states, even when fed an identical diet. Here, we reveal that many microbial populations belonging to the bacteria and archaea domains show divergent proteome production in function of the feed efficiency state. Thus, proteomic data serve as a strong indicator of host feed efficiency state phenotype, overpowering predictions based on genomic and taxonomic information. We highlight protein production of specific phylogenies associated with each of the feed efficiency states. We also find remarkable plasticity of the proteome both in the individual population and at the community level, driven by niche partitioning and competition. These mechanisms result in protein production patterns that exhibit functional redundancy and checkerboard distribution that are tightly linked to the host feed efficiency phenotype. By linking microbial protein production and the ecological mechanisms that act within the microbiome feed efficiency states, our present work reveals a layer of complexity that bears immense importance to the current global challenges of food security and sustainability.

Investigating Lactococcus lactis MG1363 Response to Phage p2 Infection at the Proteome Level.

Phages are viruses that specifically infect and eventually kill their bacterial hosts. Bacterial fermentation and biotechnology industries see them as enemies, however, they are also investigated as antibacterial agents for the treatment or prevention of bacterial infections in various sectors. They also play key ecological roles in all ecosystems. Despite decades of research some aspects of phage biology are still poorly understood. In this study, we used label-free quantitative proteomics to reveal the proteotypes of Lactococcus lactis MG1363 during infection by the virulent phage p2, a model for studying the biology of phages infecting Gram-positive bacteria. Our approach resulted in the high-confidence detection and quantification of 59% of the theoretical bacterial proteome, including 226 bacterial proteins detected only during phage infection and 6 proteins unique to uninfected bacteria. We also identified many bacterial proteins of differing abundance during the infection. Using this high-throughput proteomic datasets, we selected specific bacterial genes for inactivation using CRISPR-Cas9 to investigate their involvement in phage replication. One knockout mutant lacking gene llmg_0219 showed resistance to phage p2 because of a deficiency in phage adsorption. Furthermore, we detected and quantified 78% of the theoretical phage proteome and identified many proteins of phage p2 that had not been previously detected. Among others, we uncovered a conserved small phage protein (pORFN1) coded by an unannotated gene. We also applied a targeted approach to achieve greater sensitivity and identify undetected phage proteins that were expected to be present. This allowed us to follow the fate of pORF46, a small phage protein of low abundance. In summary, this work offers a unique view of the virulent phages' takeover of bacterial cells and provides novel information on phage-host interactions.

A Metabolic Labeling Strategy for Relative Protein Quantification in Clostridioides difficile.

Clostridioides difficile (formerly Clostridium difficile) is a Gram-positive, anaerobe, spore-forming pathogen, which causes drug-induced diseases in hospitals worldwide. A detailed analysis of the proteome may provide new targets for drug development or therapeutic strategies to combat this pathogen. The application of metabolic labeling (ML) would allow for accurate quantification of significant differences in protein abundance, even in the case of very small changes. Additionally, it would be possible to perform more accurate studies of the membrane or surface proteomes, which usually require elaborated sample preparation. Such studies are therefore prone to higher standard deviations during the quantification. The implementation of ML strategies for C. difficile is complicated due to the lack in arginine and lysine auxotrophy as well as the Stickland dominated metabolism of this anaerobic pathogen. Hence, quantitative proteome analyses could only be carried out by label free or chemical labeling methods so far. In this paper, a ML approach for C. difficile is described. A cultivation procedure with 15N-labeled media for strain 630Δerm was established achieving an incorporation rate higher than 97%. In a proof-of-principle experiment, the performance of the ML approach in C. difficile was tested. The proteome data of the cytosolic subproteome of C. difficile cells grown in complex medium as well as two minimal media in the late exponential and early stationary growth phase obtained via ML were compared with two label free relative quantification approaches (NSAF and LFQ). The numbers of identified proteins were comparable within the three approaches, whereas the number of quantified proteins were between 1,110 (ML) and 1,861 (LFQ) proteins. A hierarchical clustering showed clearly separated clusters for the different conditions and a small tree height with ML approach. Furthermore, it was shown that the quantification based on ML revealed significant altered proteins with small fold changes compared to the label free approaches. The quantification based on ML was accurate, reproducible, and even more sensitive compared to label free quantification strategies.

Time-Resolved Analysis of Cytosolic and Surface-Associated Proteins of Staphylococcus aureus HG001 under Planktonic and Biofilm Conditions.

Staphylococcal biofilms are associated with persistent infections due to their capacity to protect bacteria against the host's immune system and antibiotics. Cell-surface-associated proteins are of great importance during biofilm formation. In the present study, an optimized biotinylation approach for quantitative GeLC-MS-based analysis of the staphylococcal cell-surface proteome was applied and the cytoplasmic protein fraction was analyzed to elucidate proteomic differences between colony biofilms and planktonic cells. The experimental setup enabled a time-resolved monitoring of the proteome under both culture conditions and the comparison of biofilm cells to planktonic cells at several time points. This allowed discrimination of differences attributed to delayed growth phases from responses provoked by biofilm conditions. Biofilm cells expressed CcpA-dependent catabolic proteins earlier than planktonic cells and strongly accumulated proteins that belong to the SigB stress regulon. The amount of the cell-surface protein and virulence gene regulator Rot decreased within biofilms and MgrA-dependent regulations appeared more pronounced. Biofilm cells simultaneously up-regulated activators (e.g., SarZ) as well as repressors (e.g., SarX) of RNAIII. A decreased amount of high-affinity iron uptake systems and an increased amount of the iron-storage protein FtnA possibly indicated a lower demand of iron in biofilms.