Different culture media and purification methods unveil the core proteome of Propionibacterium freudenreichii-derived extracellular vesicles.

Bacterial extracellular vesicles (EVs) are natural lipidic nanoparticles implicated in intercellular communication. Although EV research focused mainly on pathogens, the interest in probiotic-derived EVs is now rising. One example is Propionibacterium freudenreichii, which produces EVs with anti-inflammatory effects on human epithelial cells. Our previous study with P. freudenreichii showed that EVs purified by size exclusion chromatography (SEC) displayed variations in protein content according to bacterial growth conditions. Considering these content variations, we hypothesized that a comparative proteomic analysis of EVs recovered in different conditions would elucidate whether a representative vesicular proteome existed, possibly providing a robust proteome dataset for further analysis. Therefore, P. freudenreichii was grown in two culture media, and EVs were purified by sucrose density gradient ultracentrifugation (UC). Microscopic and size characterization confirmed EV purification, while shotgun proteomics unveiled that they carried a diverse set of proteins. A comparative analysis of the protein content of UC- and SEC-derived EVs, isolated from cultures either in UF (cow milk ultrafiltrate medium) or YEL (laboratory yeast extract lactate medium), showed that EVs from all these conditions shared 308 proteins. This EV core proteome was notably enriched in proteins related to immunomodulation. Moreover, it showed distinctive features, including highly interacting proteins, compositional biases for some specific amino acids, and other biochemical parameters. Overall, this work broadens the toolset for the purification of P. freudenreichii-derived EVs, identifies a representative vesicular proteome, and enumerates conserved features in vesicular proteins. These results hold the potential for providing candidate biomarkers of purification quality, and insights into the mechanisms of EV biogenesis and cargo sorting.

Extracellular Vesicles Produced by the Probiotic Propionibacterium freudenreichii CIRM-BIA 129 Mitigate Inflammation by Modulating the NF-κB Pathway.

Extracellular vesicles (EVs) are nanometric spherical structures involved in intercellular communication, whose production is considered to be a widespread phenomenon in living organisms. Bacterial EVs are associated with several processes that include survival, competition, pathogenesis, and immunomodulation. Among probiotic Gram-positive bacteria, some Propionibacterium freudenreichii strains exhibit anti-inflammatory activity, notably via surface proteins such as the surface-layer protein B (SlpB). We have hypothesized that, in addition to surface exposure and secretion of proteins, P. freudenreichii may produce EVs and thus export immunomodulatory proteins to interact with the host. In order to demonstrate their production in this species, EVs were purified from cell-free culture supernatants of the probiotic strain P. freudenreichii CIRM-BIA 129, and their physicochemical characterization, using transmission electron microscopy and nanoparticle tracking analysis (NTA), revealed shapes and sizes typical of EVs. Proteomic characterization showed that EVs contain a broad range of proteins, including immunomodulatory proteins such as SlpB. In silico protein-protein interaction predictions indicated that EV proteins could interact with host proteins, including the immunomodulatory transcription factor NF-κB. This potential interaction has a functional significance because EVs modulate inflammatory responses, as shown by IL-8 release and NF-κB activity, in HT-29 human intestinal epithelial cells. Indeed, EVs displayed an anti-inflammatory effect by modulating the NF-κB pathway; this was dependent on their concentration and on the proinflammatory inducer (LPS-specific). Moreover, while this anti-inflammatory effect partly depended on SlpB, it was not abolished by EV surface proteolysis, suggesting possible intracellular sites of action for EVs. This is the first report on identification of P. freudenreichii-derived EVs, alongside their physicochemical, biochemical and functional characterization. This study has enhanced our understanding of the mechanisms associated with the probiotic activity of P. freudenreichii and identified opportunities to employ bacterial-derived EVs for the development of bioactive products with therapeutic effects.

Transcriptome analysis of Corynebacterium pseudotuberculosis biovar Equi in two conditions of the environmental stress.

Corynebacterium pseudotuberculosis has been widely studied in an effort to understand its biological evolution. Transcriptomics has revealed possible candidates for virulence and pathogenicity factors of strain 1002 (biovar Ovis). Because C. pseudotuberculosis is classified into two biovars, Ovis and Equi, it was interesting to assess the transcriptional profile of biovar Equi strain 258, the causative agent of ulcerative lymphangitis. The genome of this strain was re-sequenced; the reassembly was completed using optical mapping technology, and the sequence was subsequently re-annotated. Two growth conditions that occur during the host infection process were simulated for the transcriptome: the osmotic and acid medium. Genes that may be associated with the microorganism's resilience under unfavorable conditions were identified through RNAseq, including genes present in pathogenicity islands. The RT-qPCR was performed to confirm the results in biological triplicate for each condition for some genes. The results extend our knowledge of the factors associated with the spread and persistence of C. pseudotuberculosis during the infection process and suggest possible avenues for studies related to the development of vaccines, diagnosis, and therapies that might help minimize damage to agribusinesses.

Functional annotation of hypothetical proteins from the Exiguobacterium antarcticum strain B7 reveals proteins involved in adaptation to extreme environments, including high arsenic resistance.

Exiguobacterium antarcticum strain B7 is a psychrophilic Gram-positive bacterium that possesses enzymes that can be used for several biotechnological applications. However, many proteins from its genome are considered hypothetical proteins (HPs). These functionally unknown proteins may indicate important functions regarding the biological role of this bacterium, and the use of bioinformatics tools can assist in the biological understanding of this organism through functional annotation analysis. Thus, our study aimed to assign functions to proteins previously described as HPs, present in the genome of E. antarcticum B7. We used an extensive in silico workflow combining several bioinformatics tools for function annotation, sub-cellular localization and physicochemical characterization, three-dimensional structure determination, and protein-protein interactions. This genome contains 2772 genes, of which 765 CDS were annotated as HPs. The amino acid sequences of all HPs were submitted to our workflow and we successfully attributed function to 132 HPs. We identified 11 proteins that play important roles in the mechanisms of adaptation to adverse environments, such as flagellar biosynthesis, biofilm formation, carotenoids biosynthesis, and others. In addition, three predicted HPs are possibly related to arsenic tolerance. Through an in vitro assay, we verified that E. antarcticum B7 can grow at high concentrations of this metal. The approach used was important to precisely assign function to proteins from diverse classes and to infer relationships with proteins with functions already described in the literature. This approach aims to produce a better understanding of the mechanism by which this bacterium adapts to extreme environments and to the finding of targets with biotechnological interest.

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

BACKGROUND: Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. RESULTS: Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. CONCLUSIONS: The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

In Silico Protein-Protein Interactions: Avoiding Data and Method Biases Over Sensitivity and Specificity.

The study of protein-protein interactions (PPIs) can help researchers raise new hypotheses about an organism or disease and guide new experiments. Various methods for the identification and analysis of PPIs have been discussed in the literature. These methods are generally categorized as experimental or computational - each having its own advantages and disadvantages. Experimental methods provide insights into the real state of biological interactions but tend to be time-consuming and costly. Computational methods, on the other hand, can study thousands of PPIs at a very low cost and in much less time; however, the accuracy of such in silico prediction results heavily depends on the specific computational approach used. Furthermore, there is no gold standard for these computational methods; a method that works well for predicting one PPI may perform poorly (by generating false positives and false negatives) for a different PPI. Therefore, all such predictions must be carefully validated, preferably with experimental data. In this paper, we review the existing computational approaches and emphasize the use of biological data as inputs for accurate predictions of PPIs. We also discuss how such input datasets and approaches may influence the sensitivity and specificity of the predicted PPI networks.

Genome Sequence of Corynebacterium ulcerans Strain FRC11.

Here, we present the genome sequence of Corynebacterium ulcerans strain FRC11. The genome includes one circular chromosome of 2,442,826 bp (53.35% G+C content), and 2,210 genes were predicted, 2,146 of which are putative protein-coding genes, with 12 rRNAs and 51 tRNAs; 1 pseudogene was also identified.

Genome Sequence of Corynebacterium ulcerans Strain 210932.

In this work, we present the complete genome sequence of Corynebacterium ulcerans strain 210932, isolated from a human. The species is an emergent pathogen that infects a variety of wild and domesticated animals and humans. It is associated with a growing number of cases of a diphtheria-like disease around the world.

An improved interolog mapping-based computational prediction of protein-protein interactions with increased network coverage.

Automated and efficient methods that map ortholog interactions from several organisms and public databases (pDB) are needed to identify new interactions in an organism of interest (interolog mapping). When computational methods are applied to predict interactions, it is important that these methods be validated and their efficiency proven. In this study, we compare six Blast+ metrics over three datasets to identify the best metric for protein-protein interaction predictions. Using Blast+ to align the protein pairs, the ortholog interactions from DIP were mapped to String, Intact and Psibase pDBs. For each interaction mapped to each pDBs, we retrieved the alignment score, e-value, bitscore, similarity, identity and coverage. We evaluated these Blast+ values, and combinations thereof, with the Receiver Operating Characteristic (ROC) curves and computed the Area Under Curve (AUC). To validate these predictions, we used a subset of the Database of Interacting Proteins (DIP) composed of experimental interactions curated by the International Molecular Exchange (IMEx). The cut-off point for each metric/pDB was computed aiming to identify the best one that separates the true and false predicted interactions. In contrast to other methods that only compute the first Blast hit, we considered the first 20 hits, thus increasing the number of predicted interaction pairs. In addition, we identified the contribution of each individual pDB, as well as their combined contribution to the prediction. The best metric had an AUC of 0.96 for a single pDB and AUC of 0.93 for combined pDBs. Compared to other studies, with a cut-off point of 0.70 representing a specificity of 0.95 and a sensitivity of 0.90 for individual pDB, our method efficiently predicts protein-protein interactions.

C. pseudotuberculosis Phop confers virulence and may be targeted by natural compounds.

The bacterial two-component system (TCS) regulates genes that are crucial for virulence in several pathogens. One of such TCS, the PhoPR system, consisting of a transmembrane sensory histidine kinase protein (PhoR) and an intracellular response regulator protein (PhoP), has been reported to have a major role in mycobacterial pathogenesis. We knocked out the phoP in C. pseudotuberculosis, the causal organism of caseous lymphadenitis (CLA), and using a combination of in vitro and in vivo mouse system, we showed for the first time, that the PhoP of C. pseudotuberculosis plays an important role in the virulence and pathogenicity of this bacterium. Furthermore, we modeled the PhoP of C. pseudotuberculosis and our docking results showed that several natural compounds including Rhein, an anthraquinone from Rheum undulatum, and some drug-like molecules may target PhoP to inhibit the TCS of C. pseudotuberculosis, and therefore may facilitate a remarkable attenuation of bacterial pathogenicity being the CLA. Experiments are currently underway to validate these in silico docking results.

SpliceProt: a protein sequence repository of predicted human splice variants.

The mechanism of alternative splicing in the transcriptome may increase the proteome diversity in eukaryotes. In proteomics, several studies aim to use protein sequence repositories to annotate MS experiments or to detect differentially expressed proteins. However, the available protein sequence repositories are not designed to fully detect protein isoforms derived from mRNA splice variants. To foster knowledge for the field, here we introduce SpliceProt, a new protein sequence repository of transcriptome experimental data used to investigate for putative splice variants in human proteomes. Current version of SpliceProt contains 159 719 non-redundant putative polypeptide sequences. The assessment of the potential of SpliceProt in detecting new protein isoforms resulting from alternative splicing was performed by using publicly available proteomics data. We detected 173 peptides hypothetically derived from splice variants, which 54 of them are not present in UniprotKB/TrEMBL sequence repository. In comparison to other protein sequence repositories, SpliceProt contains a greater number of unique peptides and is able to detect more splice variants. Therefore, SpliceProt provides a solution for the annotation of proteomics experiments regarding splice isofoms. The repository files containing the translated sequences of the predicted splice variants and a visualization tool are freely available at http://lbbc.inca.gov.br/spliceprot.

Segtor: rapid annotation of genomic coordinates and single nucleotide variations using segment trees.

Various research projects often involve determining the relative position of genomic coordinates, intervals, single nucleotide variations (SNVs), insertions, deletions and translocations with respect to genes and their potential impact on protein translation. Due to the tremendous increase in throughput brought by the use of next-generation sequencing, investigators are routinely faced with the need to annotate very large datasets. We present Segtor, a tool to annotate large sets of genomic coordinates, intervals, SNVs, indels and translocations. Our tool uses segment trees built using the start and end coordinates of the genomic features the user wishes to use instead of storing them in a database management system. The software also produces annotation statistics to allow users to visualize how many coordinates were found within various portions of genes. Our system currently can be made to work with any species available on the UCSC Genome Browser. Segtor is a suitable tool for groups, especially those with limited access to programmers or with interest to analyze large amounts of individual genomes, who wish to determine the relative position of very large sets of mapped reads and subsequently annotate observed mutations between the reads and the reference. Segtor (http://lbbc.inca.gov.br/segtor/) is an open-source tool that can be freely downloaded for non-profit use. We also provide a web interface for testing purposes.