Subpopulations of regulatory T cells are associated with subclinical atherosclerotic plaques, levels of LDL, and cardiorespiratory fitness in the elderly.

BACKGROUND: Atherosclerosis forms the pathological basis for the development of cardiovascular disease. Since pathological processes initially develop without clinically relevant symptoms, the identification of early markers in the subclinical stage plays an important role for initiating early interventions. There is evidence that regulatory T cells (Tregs) are involved in the development of atherosclerosis. Therefore, the present study aimed to identify and investigate associations with Tregs and their subsets in a cohort of healthy elderly individuals with and without subclinical atherosclerotic plaques (SAP). In addition, various lifestyle and risk factors, such as cardiorespiratory fitness, were investigated as associated signatures. METHODS: A cross-sectional study was performed in 79 participants (male: n = 50; age = 63.6 ± 3.7 years; body mass index = 24.9 ± 3.1 kg/m²; mean ± SD) who had no previous diagnosis of chronic disease and were not taking medication. Ultrasound of the carotids to identify SAP, cardiovascular function measurement for vascular assessment and a cardiorespiratory fitness test to determine peak oxygen uptake were performed. Additionally, tests were conducted to assess blood lipids and determine glucose levels. Immunophenotyping of Tregs and their subtypes (resting (rTregs) and effector/memory (mTregs)) was performed by 8-chanel flow cytometry. Participants were categorized according to atherosclerotic plaque status. Linear and logistic regression models were used to analyze associations between parameters. RESULTS: SAP was detected in a total of 29 participants. The participants with plaque were older (64.5 ± 3.6 years vs. 62.9 ± 3.5 years) and had higher peripheral systolic blood pressure (133.8 ± 14.7 mmHg vs. 125.8 ± 10.9 mmHg). The participants with SAP were characterized by a lower percentage of rTregs (28.8% ± 10.7% vs. 34.6% ± 10.7%) and a higher percentage of mTregs (40.3% ± 14.7% vs. 30.0% ± 11.9%). Multiple logistic regression identified age (odds ratio (OR) = 1.20 (95% confidence interval (95%CI): 1.01-1.42)) and mTregs (OR = 1.05 (95%CI: 1.02-1.10)) as independent risk factors for SAP. Stepwise linear regression could reveal an association of peak oxygen uptake (ß = 0.441), low-density lipoprotein (LDL) (ß = -0.096), and SAP (ß = 6.733) with mTregs and LDL (ß = 0.104) with rTregs. CONCLUSION: While at an early stage of SAP, the total proportion of Tregs gives no indication of vascular changes, this is indicated by a shift in the Treg subgroups. Factors such as serum LDL or cardiopulmonary fitness may be associated with this shift and may also be additional diagnostic indicators. This could be used to initiate lifestyle-based preventive measures at an early stage, which may have a protective effect against disease progression.

Cross-reactive MHC class I T cell epitopes may dictate heterologous immune responses between respiratory viruses and food allergens.

Respiratory virus infections play a major role in asthma, while there is a close correlation between asthma and food allergy. We hypothesized that T cell-mediated heterologous immunity may induce asthma symptoms among sensitized individuals and used two independent in silico pipelines for the identification of cross-reactive virus- and food allergen- derived T cell epitopes, considering individual peptide sequence similarity, MHC binding affinity and immunogenicity. We assessed the proteomes of human rhinovirus (RV1b), respiratory syncytial virus (RSVA2) and influenza-strains contained in the seasonal quadrivalent influenza vaccine 2019/2020 (QIV 2019/2020), as well as SARS-CoV-2 for human HLA alleles, in addition to more than 200 most common food allergen protein sequences. All resulting allergen-derived peptide candidates were subjected to an elaborate scoring system considering multiple criteria, including clinical relevance. In both bioinformatics approaches, we found that shortlisted peptide pairs that are potentially binding to MHC class II molecules scored up to 10 × lower compared to MHC class I candidate epitopes. For MHC class I food allergen epitopes, several potentially cross-reactive peptides from shrimp, kiwi, apple, soybean and chicken were identified. The shortlisted set of peptide pairs may be implicated in heterologous immune responses and translated to peptide immunization strategies with immunomodulatory properties.

Biological skin regeneration using epigenetic targets.

Epigenetics targets are the newest branches for building a novel platform of drugs for preventive and regenerative skin health care. Epigenetic regions [vascular endothelial growth factor (VEGF), epidermal growth factor receptor (EGFR), transforming growth factor beta (TGFß), DNA methyltransferases (DNMTs), histone deacetylase 1/2 (HDAC1/2), and miRNA) are innovative druggable targets. As we discuss here, a series of epigenetic-based small molecules are undergoing both clinical and preclinical trials for skin regeneration. Epigenetic writers, eraser targets, and epigenetic readers will become the key therapeutic windows for skin regenerative in the near future.

Homologies between SARS-CoV-2 and allergen proteins may direct T cell-mediated heterologous immune responses.

The outbreak of the new severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a public health emergency. Asthma does not represent a risk factor for COVID-19 in several published cohorts. We hypothesized that the SARS-CoV-2 proteome contains T cell epitopes, which are potentially cross-reactive to allergen epitopes. We aimed at identifying homologous peptide sequences by means of two distinct complementary bioinformatics approaches. Pipeline 1 included prediction of MHC Class I and Class II epitopes contained in the SARS-CoV-2 proteome and allergens along with alignment and elaborate ranking approaches. Pipeline 2 involved alignment of SARS-CoV-2 overlapping peptides with known allergen-derived T cell epitopes. Our results indicate a large number of MHC Class I epitope pairs including known as well as de novo predicted allergen T cell epitopes with high probability for cross-reactivity. Allergen sources, such as Aspergillus fumigatus, Phleum pratense and Dermatophagoides species are of particular interest due to their association with multiple cross-reactive candidate peptides, independently of the applied bioinformatic approach. In contrast, peptides derived from food allergens, as well as MHC class II epitopes did not achieve high in silico ranking and were therefore not further investigated. Our findings warrant further experimental confirmation along with examination of the functional importance of such cross-reactive responses.

Homologies between SARS-CoV-2 and allergen proteins may direct T cell-mediated heterologous immune responses.

The outbreak of the new Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a public health emergency. Asthma does not represent a risk factor for COVID-19 in several published cohorts. We hypothesized that the SARS-CoV-2 proteome contains T cell epitopes, which are potentially cross-reactive to allergen epitopes. We aimed at identifying homologous peptide sequences by means of two distinct complementary bioinformatics approaches. Pipeline 1 included prediction of MHC Class I and Class II epitopes contained in the SARS-CoV-2 proteome and allergens along with alignment and elaborate ranking approaches. Pipeline 2 involved alignment of SARS-CoV-2 overlapping peptides with known allergen-derived T cell epitopes. Our results indicate a large number of MHC Class I epitope pairs including known as well as de novo predicted allergen T cell epitopes with high probability for cross-reactivity. Allergen sources, such as Aspergillus fumigatus , Phleum pratense and Dermatophagoides species are of particular interest due to their association with multiple cross-reactive candidate peptides, independently of the applied bioinformatic approach. In contrast, peptides derived from food allergens, as well as MHC class II epitopes did not achieve high in silico ranking and were therefore not further investigated. Our findings warrant further experimental confirmation along with examination of the functional importance of such cross-reactive responses.

Characterization of the Proteins Involved in the DNA Repair Mechanism in M. smegmatis.

Several alkylating agents that either occur in the environment or are self-produced can cause DNA-damaging injuries in bacterial cells. Therefore, all microorganisms have developed repair systems that are able to counteract DNA alkylation damage. The adaptive response to alkylation stress in Escherichia coli consists of the Ada operon, which has been widely described; however, the homologous system in Mycobacterium tuberculosis (MTB) has been shown to have a different genetic organization but it is still largely unknown. In order to describe the defense system of MTB, we first investigated the proteins involved in the repair mechanism in the homologous non-pathogenic mycobacterium M. smegmatis. Ogt, Ada-AlkA and FadE8 proteins were recombinantly produced, purified and characterized. The biological role of Ogt was examined using proteomic experiments to identify its protein partners in vivo under stress conditions. Our results suggested the formation of a functional complex between Ogt and Ada-AlkA, which was confirmed both in silico by docking calculations and by gel filtration chromatography. We propose that this stable association allows the complex to fulfill the biological roles exerted by Ada in the homologous E. coli system. Finally, FadE8 was demonstrated to be structurally and functionally related to its E. coli homologous, AidB.

Proteomics of Galápagos Marine Iguanas Links Function of Femoral Gland Proteins to the Immune System.

Communication between individuals via molecules, termed chemosignaling, is widespread among animal and plant species. However, we lack knowledge on the specific functions of the substances involved for most systems. The femoral gland is an organ that secretes a waxy substance involved in chemical communication in lizards. Although the lipids and volatile substances secreted by the femoral glands have been investigated in several biochemical studies, the protein composition and functions of secretions remain completely unknown. Applying a proteomic approach, we provide the first attempt to comprehensively characterize the protein composition of femoral gland secretions from the Galápagos marine iguana. Using samples from several organs, the marine iguana proteome was assembled by next-generation sequencing and MS, resulting in 7513 proteins. Of these, 4305 proteins were present in the femoral gland, including keratins, small serum proteins, and fatty acid-binding proteins. Surprisingly, no proteins with discernible roles in partner recognition or inter-species communication could be identified. However, we did find several proteins with direct associations to the innate immune system, including lysozyme C, antileukoproteinase (ALP), pulmonary surfactant protein (SFTPD), and galectin (LGALS1) suggesting that the femoral glands function as an important barrier to infection. Furthermore, we report several novel anti-microbial peptides from the femoral glands that show similar action against Escherichia coli and Bacillus subtilis such as oncocin, a peptide known for its effectiveness against Gram-negative pathogens. This proteomics data set is a valuable resource for future functional protein analysis and demonstrates that femoral gland secretions also perform functions of the innate immune system.

Complete Genome Sequence of an Ebola Virus Isolate Imported from Sierra Leone to Germany Determined by Circle Sequencing.

We report here a complete genome sequence of Ebola virus Makona from a nonfatal patient sample that originated in Sierra Leone during the last Ebola virus outbreak in West Africa (species Zaire ebolavirus) using a highly accurate circle sequencing (Cir-seq) method.

First Genome Sequence of Potential Mycotoxin-Degrading Bacterium Devosia nanyangense DDB001.

Devosia sp. nov. DDB001, isolated from mycotoxin-contaminated soil, is a potential mycotoxin-degrading alphaproteobacterium. To our knowledge, this is the first draft genome announcement of a Devosia species.

ResA3: a web tool for resampling analysis of arbitrary annotations.

Resampling algorithms provide an empirical, non-parametric approach to determine the statistical significance of annotations in different experimental settings. ResA(3) (Resampling Analysis of Arbitrary Annotations, short: ResA) is a novel tool to facilitate the analysis of enrichment and regulation of annotations deposited in various online resources such as KEGG, Gene Ontology and Pfam or any kind of classification. Results are presented in readily accessible navigable table views together with relevant information for statistical inference. The tool is able to analyze multiple types of annotations in a single run and includes a Gene Ontology annotation feature. We successfully tested ResA using a dataset obtained by measuring incorporation rates of stable isotopes into proteins in intact animals. ResA complements existing tools and will help to evaluate the increasing number of large-scale transcriptomics and proteomics datasets (resa.mpi-bn.mpg.de).

sRNAdb: a small non-coding RNA database for gram-positive bacteria.

BACKGROUND: The class of small non-coding RNA molecules (sRNA) regulates gene expression by different mechanisms and enables bacteria to mount a physiological response due to adaptation to the environment or infection. Over the last decades the number of sRNAs has been increasing rapidly. Several databases like Rfam or fRNAdb were extended to include sRNAs as a class of its own. Furthermore new specialized databases like sRNAMap (gram-negative bacteria only) and sRNATarBase (target prediction) were established. To the best of the authors' knowledge no database focusing on sRNAs from gram-positive bacteria is publicly available so far. DESCRIPTION: In order to understand sRNA's functional and phylogenetic relationships we have developed sRNAdb and provide tools for data analysis and visualization. The data compiled in our database is assembled from experiments as well as from bioinformatics analyses. The software enables comparison and visualization of gene loci surrounding the sRNAs of interest. To accomplish this, we use a client-server based approach. Offline versions of the database including analyses and visualization tools can easily be installed locally on the user's computer. This feature facilitates customized local addition of unpublished sRNA candidates and related information such as promoters or terminators using tab-delimited files. CONCLUSION: sRNAdb allows a user-friendly and comprehensive comparative analysis of sRNAs from available sequenced gram-positive prokaryotic replicons. Offline versions including analysis and visualization tools facilitate complex user specific bioinformatics analyses.

Anti-Listeria activities of Galleria mellonella hemolymph proteins.

We report the use of antimicrobial hemolymph proteins from the model host Galleria mellonella as an inhibitor for various Listeria strains, providing a novel source for antilisterial therapeutics. We also have shown that specific virulence-associated genes known to mediate antimicrobial resistance of Listeria in mammalian models indicated a similar function in Galleria.

PCA2GO: a new multivariate statistics based method to identify highly expressed GO-Terms.

BACKGROUND: Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied. RESULTS: Here we present a new method, which we name PCA2GO, capable of GO analysis using complex multidimensional experimental settings. We employed principal component analysis (PCA) and developed a new score, which takes into account the relative frequency of certain GO terms and their specificity (hierarchical position) within the GO graph. We evaluated the correlation between our representation score R and a standard measure of enrichment, namely p-values to convey the versatility of our approach to other methods and point out differences between our method and commonly used enrichment analyses. Although p values and the R score formally measure different quantities they should be correlated, because relative frequencies of GO terms occurrences within a dataset are an indirect measure of protein numbers related to this term. Therefore they are also related to enrichment. We showed that our score enables us to identify more specific GO-terms i.e. those positioned further down the GO-graph than other common tools used for this purpose. PCA2GO allows visualization and detection of multidimensional dependencies both within the acyclic graph (GO tree) and the experimental settings. Our method is intended for the analysis of several experimental sets, not for one set, like standard enrichment tools. To demonstrate the usefulness of our approach we performed a PCA2GO analysis of a fractionated cardiomyocyte protein dataset, which was identified by enhanced liquid chromatography-mass spectrometry (GeLC-MS). The analysis enabled us to detect distinct groups of proteins, which accurately reflect properties of biochemical cell fractions. CONCLUSIONS: We conclude that PCA2GO is an alternative efficient GO analysis tool with unique features for detection and visualization of multidimensional dependencies within the dataset under study. PCA2GO reveals strongly correlated GO terms within the experimental setting (in this case different fractions) by PCA group formation and improves detection of more specific GO terms within experiment dependent GO term groups than standard p value calculations.

Comparative genome-wide analysis of small RNAs of major Gram-positive pathogens: from identification to application.

In the recent years, the number of drug- and multi-drug-resistant microbial strains has increased rapidly. Therefore, the need to identify innovative approaches for development of novel anti-infectives and new therapeutic targets is of high priority in global health care. The detection of small RNAs (sRNAs) in bacteria has attracted considerable attention as an emerging class of new gene expression regulators. Several experimental technologies to predict sRNA have been established for the Gram-negative model organism Escherichia coli. In many respects, sRNA screens in this model system have set a blueprint for the global and functional identification of sRNAs for Gram-positive microbes, but the functional role of sRNAs in colonization and pathogenicity for Listeria monocytogenes, Staphylococcus aureus, Streptococcus pyogenes, Enterococcus faecalis and Clostridium difficile is almost completely unknown. Here, we report the current knowledge about the sRNAs of these socioeconomically relevant Gram-positive pathogens, overview the state-of-the-art high-throughput sRNA screening methods and summarize bioinformatics approaches for genome-wide sRNA identification and target prediction. Finally, we discuss the use of modified peptide nucleic acids (PNAs) as a novel tool to inactivate potential sRNA and their applications in rapid and specific detection of pathogenic bacteria.