Johannes Söllner

Identification of in vivo expressed vaccine candidate antigens from Staphylococcus aureus

For the design of potent subunit vaccines, it is of paramount importance to identify all antigens immunologically recognized by a patient population infected with a pathogen. We have developed a rapid and efficient procedure to identify such commonly recognized antigens, and here we provide a comprehensive in vivo antigenic profile of Staphylococcus aureus, an important human pathogen. S. aureus peptides were displayed on the surface of Escherichia coli via fusion to one of two outer membrane proteins (LamB and FhuA) and probed with sera selected for high Ab titer and opsonic activity. A total of 60 antigenic proteins were identified, most of which are located or predicted to be located on the surface of the bacterium or secreted. The identification of these antigens and their reactivity with individual sera from patients and healthy individuals greatly facilitate the selection of promising vaccine candidates for further evaluation. This approach, which makes use of whole genome sequence information, has the potential to greatly accelerate and facilitate the formulation of novel vaccines and is applicable to any pathogen that induces Abs in humans and/or experimental animals.

Functional Selection of Vaccine Candidate Peptides from Staphylococcus aureus Whole-Genome Expression Libraries In Vitro

ABSTRACT An in vitro protein selection method, ribosome display, has been applied to comprehensively identify and map the immunologically relevant proteins of the human pathogen Staphylococcus aureus. A library built up from genomic fragments of the virulent S. aureus COL strain (methicillin-resistant S. aureus) allowed us to screen all possible encoded peptides for immunoreactivity. As selective agents, human sera exhibiting a high antibody titer and opsonic activity against S. aureus were used, since these antibodies indicate the in vivo expression and immunoreactivity of the corresponding proteins. Identified clones cluster in distinct regions of 75 genes, most of them classifiable as secreted or surface-localized proteins, including previously identified virulence factors. In addition, 14 putative novel short open reading frames were identified and their immunoreactivity and in vivo mRNA expression were confirmed, underscoring the annotation-independent, true genomic nature of our approach. Evidence is provided that a large fraction of the identified peptides cannot be expressed in an in vivo-based surface display system. Thus, in vitro protein selection, not biased by the context of living entities, allows screening of genomic expression libraries with a large number of different ligands simultaneously. It is a powerful approach for fingerprinting the repertoire of immune reactive proteins serving as target candidates for active and passive vaccination against pathogens.

Analysis and prediction of protective continuous B-cell epitopes on pathogen proteins

BackgroundThe application of peptide based diagnostics and therapeutics mimicking part of protein antigen is experiencing renewed interest. So far selection and design rationale for such peptides is usually driven by T-cell epitope prediction, available experimental and modelled 3D structure, B-cell epitope predictions such as hydrophilicity plots or experience. If no structure is available the rational selection of peptides for the production of functionally altering or neutralizing antibodies is practically impossible. Specifically if many alternative antigens are available the reduction of required synthesized peptides until one successful candidate is found is of central technical interest. We have investigated the integration of B-cell epitope prediction with the variability of antigen and the conservation of patterns for post-translational modification (PTM) prediction to improve over state of the art in the field. In particular the application of machine-learning methods shows promising results.ResultsWe find that protein regions leading to the production of functionally altering antibodies are often characterized by a distinct increase in the cumulative sum of three presented parameters. Furthermore the concept to maximize antigenicity, minimize variability and minimize the likelihood of post-translational modification for the identification of relevant sites leads to biologically interesting observations. Primarily, for about 50% of antigen the approach works well with individual area under the ROC curve (AROC) values of at least 0.65. On the other hand a significant portion reveals equivalently low AROC values of < = 0.35 indicating an overall non-Gaussian distribution. While about a third of 57 antigens are seemingly intangible by our approach our results suggest the existence of at least two distinct classes of bioinformatically detectable epitopes which should be predicted separately. As a side effect of our study we present a hand curated dataset for the validation of protectivity classification. Based on this dataset machine-learning methods further improve predictive power to a class separation in an equilibrated dataset of up to 83%.ConclusionWe present a computational method to automatically select and rank peptides for the stimulation of potentially protective or otherwise functionally altering antibodies. It can be shown that integration of variability, post-translational modification pattern conservation and B-cell antigenicity improve rational selection over random guessing. Probably more important, we find that for about 50% of antigen the approach works substantially better than for the overall dataset of 57 proteins. Essentially as a side effect our method optimizes for presumably best applicable peptides as they tend to be likely unmodified and as invariable as possible which is answering needs in diagnosis and treatment of pathogen infection. In addition we show the potential for further improvement by the application of machine-learning methods, in particular Random Forests.

Concept and application of a computational vaccinology workflow.

BackgroundThe last years have seen a renaissance of the vaccine area, driven by clinical needs in infectious diseases but also chronic diseases such as cancer and autoimmune disorders. Equally important are technological improvements involving nano-scale delivery platforms as well as third generation adjuvants. In parallel immunoinformatics routines have reached essential maturity for supporting central aspects in vaccinology going beyond prediction of antigenic determinants. On this basis computational vaccinology has emerged as a discipline aimed at ab-initio rational vaccine design.Here we present a computational workflow for implementing computational vaccinology covering aspects from vaccine target identification to functional characterization and epitope selection supported by a Systems Biology assessment of central aspects in host-pathogen interaction. We exemplify the procedures for Epstein Barr Virus (EBV), a clinically relevant pathogen causing chronic infection and suspected of triggering malignancies and autoimmune disorders.ResultsWe introduce pBone/pView as a computational workflow supporting design and execution of immunoinformatics workflow modules, additionally involving aspects of results visualization, knowledge sharing and re-use. Specific elements of the workflow involve identification of vaccine targets in the realm of a Systems Biology assessment of host-pathogen interaction for identifying functionally relevant targets, as well as various methodologies for delineating B- and T-cell epitopes with particular emphasis on broad coverage of viral isolates as well as MHC alleles.Applying the workflow on EBV specifically proposes sequences from the viral proteins LMP2, EBNA2 and BALF4 as vaccine targets holding specific B- and T-cell epitopes promising broad strain and allele coverage.ConclusionBased on advancements in the experimental assessment of genomes, transcriptomes and proteomes for both, pathogen and (human) host, the fundaments for rational design of vaccines have been laid out. In parallel, immunoinformatics modules have been designed and successfully applied for supporting specific aspects in vaccine design. Joining these advancements, further complemented by novel vaccine formulation and delivery aspects, have paved the way for implementing computational vaccinology for rational vaccine design tackling presently unmet vaccine challenges.

Mapping of molecular pathways, biomarkers and drug targets for diabetic nephropathy

Purpose: For diseases with complex phenotype such as diabetic nephropathy (DN), integration of multiple Omics sources promises an improved description of the disease pathophysiology, being the basis for novel diagnostics and therapy, but equally important personalization aspects.

Application of systems biology for understanding of disease and selection of molecular targets for therapy

The Herpesviridae are a large family of DNA viruses. Several members of this family are known to cause disease in animals and human. In particular there are several lines of evidence for involvement in human cancer and autoimmune conditions. Although this group of viruses has been investigated intensively for several decades molecular mechanisms and roles in the development of associated diseases are not yet fully understood. Bioinformatics and Systems Biology allow to incorporate current knowledge with high throughput Omics data to analyze them in the context of molecular networks. Among the applications of this development, effects of pathogen proteins and non-coding RNAs can be more readily understood by viewing their human targets in their functional-dependency network. Combination of generated networks with disease specific transcriptomics data allows to analyze the effect of pathogen proteins in the context of medically relevant phenomena. Additional enrichment with drug-target information allows generating new hypotheses for drug re-use or drug repositioning of currently available drugs to treat herpes virus associated diseases. Here we show publicly available Epstein-Barr virus data in the context of a meta-network which combines a proprietary human interaction network (omicsNET), drug-target networks, host-pathogen networks and orthology networks. EBV, a clinically relevant human Lymphocryptovirus, is of high immunological interest also due to its potential to bias natural immune responses by establishing (life-long) latent infection in memory B-cells. We thereby exemplify implications of a Systems view for better understanding of pathogen mode of action. Our work towards a methodological basis for selection of drugs which could be of interest in the treatment of this class of pathogens is presented. While investigating options for development of novel therapies we place particular focus on the application potentials of Systems Biology for selection of candidate vaccine targets in light of chronic infections not amenable to classical vaccination strategies.