Systems Vaccinology: Applications, Trends, and Perspectives.

The strategies employed in vaccinology have improved since the seminal work of Edward Jenner in the eighteenth century. Stimulated by failure to develop vaccines for cancers and chronic infectious diseases as well as an emergence of a multitude of new technologies not available earlier, vaccinology has moved from a largely experimental art to a new phase of innovation. Currently, immune reactions can be predicted and modeled before they occur and formulations can be optimized in advance for genetic background, age, sex, lifestyle, environmental factors, and microbiome. A multitude of scientific insights and technological advancements have led us to this current status, yet possibly none of the recent developments is individually more promising to achieve these goals than the interdisciplinary science of systems vaccinology. This review summarizes current trends and applications of systems vaccinology, including technically tangible areas of vaccine and immunology research which allow the transformative process into a truly broad understanding of vaccines, thereby effectively modeling interaction of vaccines with health and disease. It is becoming clear that a multitude of factors have to be considered to understand inter-patient variability of vaccine responses including those characterized from the interfaces between the immune system, microbiome, metabolome, and the nervous system.

Computational peptide vaccinology.

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Synthetic lethality for linking the mycophenolate mofetil mode of action with molecular disease and drug profiles.

Systematic study of the effect of mycophenolate mofetil (MMF) on the molecular level in the context of other drugs and molecular disease profiles became possible due to the availability of large scale molecular profiles on both disease characterization and drug mode of action. Such analysis is of particular value in elucidating alternative drug use for addressing clinically unmet needs, and the concept of synthetic lethality provides an alternative tool for such repositioning strategies. Resting on consolidation of transcriptomics data and literature mining, a MMF molecular footprint became available including a set of 170 genes specifically affected by the drug. Analysis of this profile on a molecular pathway level reveals a set of 14 pathways as affected. Next to assignment of molecular pathways and associated diseases synergistic drug combinations are proposed by utilizing the synthetic lethal interaction network. Of particular interest is the combination of MMF with adenosine deaminase inhibitors, sulfasalazine, and other selected drugs interfering with calcium-based regulatory pathways and metabolism. Indeed analysis of drugs in clinical trials positively identifies combinations with MMF in the context of synthetic lethality and affected pathways, particularly in diseases such as multiple sclerosis, vasculitis, GVHD and lupus nephritis. Importantly, the synthetic lethal interaction of the drug mode of action is an interesting basis for rational repositioning strategies by suggesting combinations which exhibit a synergistic rather than a mere additive effect, as for example is evident for the combination of tacrolimus and MMF. Inherent is also the assessment of possible adverse effects of drug combinations.

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. EXPERIMENTAL DESIGN: Molecular features on DN were retrieved from public domain Omics studies and by mining scientific literature, patent text and clinical trial specifications. Molecular feature sets were consolidated on a human protein interaction network and interpreted on the level of molecular pathways in the light of the pathophysiology of the disease and its clinical context defined as associated biomarkers and drug targets. RESULTS: About 1000 gene symbols each could be assigned to the pathophysiological description of DN and to the clinical context. Direct feature comparison showed minor overlap, whereas on the level of molecular pathways, the complement and coagulation cascade, PPAR signaling, and the renin-angiotensin system linked the disease descriptor space with biomarkers and targets. CONCLUSION AND CLINICAL RELEVANCE: Only the combined molecular feature landscapes closely reflect the clinical implications of DN in the context of hypertension and diabetes. Omics data integration on the level of interaction networks furthermore provides a platform for identification of pathway-specific biomarkers and therapy options.

Beyond epitopes: future and application of computational vaccinology.

Vaccine research has significantly changed face within the last decade. Newly developed vaccines usually comprise defined subunits and often next generation adjuvants. On the downside, as in many areas ultimately of interest to clinical development, basic research does only slowly translate to bedside therapy. Part of the reason can be found in regulatory processes. On the other hand new technologies such as NGS (Next Generation Sequencing), Systems Biology, recently unimagined computing and storage power and suitable information technologies allow new perspectives and approaches to the field, enlarging the gap between possible and approved even more. Computational vaccinology is an aid to vaccine developers to help bridge this gap, but naturally only if it is accepted as tool and made use of. The aim of this commentary is to point out recent developments and trends and show how this can invigorate vaccine development. It is felt necessary to make a case for rational vaccine design augmented by computational vaccinology for the community to harness the full potential of emerging and already burgeoning technologies and concepts such as Next Generation Sequencing and Systems Biology.

Concept and application of a computational vaccinology workflow.

BACKGROUND: The 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. RESULTS: We 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. CONCLUSION: Based 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.

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

BACKGROUND: The 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. RESULTS: We 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 < or = 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%. CONCLUSION: We 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.

Mouse mammary tumor virus integration site selection in human and mouse genomes.

Based on integration site preferences, retroviruses can be placed into three groups. Viruses that comprise the first group, murine leukemia virus and foamy virus, integrate preferentially near transcription start sites. The second group, notably human immunodeficiency virus and simian immunodeficiency virus, preferentially targets transcription units. Avian sarcoma-leukosis virus (ASLV) and human T-cell leukemia virus (HTLV), forming the third group, show little preference for any genomic feature. We have previously shown that some human cells sustain mouse mammary tumor virus (MMTV) infection; therefore, we infected a susceptible human breast cell line, Hs578T, and, without introducing a species-specific bias, compared the MMTV integration profile to those of other retroviruses. Additionally, we infected a mouse cell line, NMuMG, and thus we could compare MMTV integration site selection in human and mouse cells. In total, we examined 468 unique MMTV integration sites. Irrespective of whether human or mouse cells were infected, no integration bias favoring transcription start sites was detected, a profile that is reminiscent of that of ASLV and HTLV. However, in contrast to ASLV and HTLV, not even a modest tendency in favor of integration within genes was observed. Similarly, repetitive sequences and genes that are frequently tagged by MMTV in mammary tumors were not preferentially targeted in cell culture either in mouse or in human cells; hence, we conclude that MMTV displays the most random dispersion of integration sites among retroviruses determined so far.

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools.

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.

Selection and combination of machine learning classifiers for prediction of linear B-cell epitopes on proteins.

Recently, new machine learning classifiers for the prediction of linear B-cell epitopes were presented. Here we show the application of Receiver Operator Characteristics (ROC) convex hulls to select optimal classifiers as well as possibilities to improve the post test probability (PTP) to meet real world requirements such as high throughput epitope screening of whole proteomes. The major finding is that ROC convex hulls present an easy to use way to rank classifiers based on their prediction conservativity as well as to select candidates for ensemble classifiers when validating against the antigenicity profile of 10 HIV-1 proteins. We also show that linear models are at least equally efficient to model the available data when compared to multi-layer feed-forward neural networks.

Machine learning approaches for prediction of linear B-cell epitopes on proteins.

Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set.

Functional selection of vaccine candidate peptides from Staphylococcus aureus whole-genome expression libraries in vitro.

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.

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.