Emerging Vaccine Informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning

Evolutionarily Conserved Protein Sequences of Influenza A Viruses, Avian and Human, as Vaccine Targets

10.1371/journal.pone.0001190PLoS ONE21

HLA class I supertype and supermotif definition by chemometric approaches.

Activation of cytotoxic T cells in human requires specific binding of antigenic peptides to human leukocyte antigen (HLA) molecules. HLA is the most polymorphic protein in the human body, currently 1814 different alleles collected in the HLA sequence database at the European Bioinformatics Institute. Most of the HLA molecules recognise different peptides. Also, some peptides can be recognised by several of HLA molecules. In the present project, all available class I HLA alleles are classified into supertypes. Super - binding motifs for peptides binding to some supertypes are defined where binding data are available. A variety of chemometric techniques are used in the project, including 2D and 3D QSAR techniques and different variable selection methods like SIMCA, GOLPE and genetic algorithm. Principal component analysis combined with molecular interaction fields calculation by the program GRID is used in the class I HLA classification. This thesis defines an HLA-A3 supermotif using two QSAR methods: the 3D-QSAR method CoMSIA, and a recently developed 2D-QSAR method, which is named the additive method. Four alleles with high phenotype frequency were included in the study: HLA-A*0301, HLA-A*1101, HLA-A*3101 and HLA- A*6801. An A*020T binding motif is also defined using amino acid descriptors and variable selection methods. Novel peptides have been designed according to the motifs and the binding affinity is tested experimentally. The results of the additive method are used in the online server, MHCPred, to predict binding affinity of unknown peptides. In HLA classification, the HLA-A, B and C molecules are classified into supertypes separately. A total of eight supertypes are observed for class I HLA, including A2, A3, A24, B7, B27, B44, CI and C4 supertype. Using the HLA classification, any newly discovered class I HLA molecule can be grouped into a supertype easily, thus simplifying the experimental function characterisation process

Opportunities and obstacles for deep learning in biology and medicine

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network\u27s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates

Bioinformatische Analyse des Humanen Leukozyten Antigens in Chronisch Entzündlichen Darmerkrankungen

The Human Leucocyte Antigen (HLA) has been identified as a genetic risk factor for Inflammatory Bowel Diseases (IBD). The causative role of the HLA in IBD remains to be revealed. The aim of this thesis is to gain as much understanding as possible about the HLA alleles genetically associated with Ulcerative Colitis (UC), a subtype of IBD. This cumulative thesis includes two papers published in peer-reviewed journals and an additional manuscript which is in progress. In the first publication, presented in this thesis (Section 6.3), we built an imputation panel that enabled the analysis of IBD and the associated HLA alleles and their corresponding haplotypes across different ancestries. In the second publication (Section 7.3), I analyzed the interaction of peptides with a defined set of HLA alleles associated with UC. This study was the first to use ultra-high density microarray data for predicting the binding status of HLA alleles and peptides. In the final manuscript (Section 8.3), I studied the genetics of UC in Caucasian individuals. I analyzed, what the genetics, combined with prior knowledge about different genes and their protein function, can tell us about a hypothesized peptide that might be a key player in the pathogenesis of UC. Next to some improvements in the imputation of HLA genomes and the binding prediction, this thesis points out first concrete candidate peptides and suggests a path to continue to discover more about the contribution of the HLA in UC

Investigating the potential of the Metastasis Associated Antigen 1 (MTA1) for cancer immunotherapy in a murine model

Immunotherapeutic approaches to target antigens associated with metastasis could provide a valuable means of targeting metastatic cells specifically. Metastasis Associated Antigen (MTA1) is one such relatively novel antigen, which has been associated with aggressive tumours, and shown to be over expressed in breast, oesophageal, colorectal, gastric and pancreatic cancer, amongst others. Various studies have indicated that MTA1 is essential for the transformation of cells and hence targeting it is unlikely to generate antigen loss variants. This study proposed to investigate MTA1 as a potential target for immunotherapy in a murine tumour model. We have shown that murine MTA1 (mMTA1) mRNA is highly expressed in most of the tumour cell lines as compared to normal tissues, which express mMTA1 at very low levels. Furthermore, to rule out any post-transcriptional modifications, MTA1 protein levels were also confirmed by western blotting. It was observed that most of the cell lines expressed MTA1 at high levels, whereas no protein expression was detected in the normal tissues by western blotting. Next, we decided to identify MHC class I and II restricted immunogenic peptides from murine and human MTA1 gene for syngeneic and transgenic mice respectively

Shared Minor Histocompatibility Antigen Discovery and Targeting in the context of Allogeneic Hematopoietic Stem Cell Transplant for Hematologic Malignancies

In allogeneic hematopoietic stem cell transplantation (alloHCT), donor-derived T cells that recognize minor histocompatibility antigens (mHAs) are able to eliminate leukemia cells via the “graft versus leukemia effect” (GvL). However, donor T cells can also recognize non-leukemia host antigens and cause inflammatory tissue damage termed “graft versus host disease” (GvHD). Therapies that boost T cell responses can improve alloHCT efficacy, but are limited by concurrent increases in incidence and severity of GvHD. Therapies that prevent GvHD by impairing T cell responses also increase relapse rates. Thus, it is critical to understand the biological differences between GvL and GvHD to develop treatment strategies that separate GvL from GvHD. mHAs with expression restricted to hematopoietic tissue (GvL mHAs) are attractive targets for T cell responses that could drive GvL without causing GvHD. Prior work to identify mHAs has focused on a small set of mHAs or population-level SNP association studies. We sought to broaden the array of known mHAs capable of mediating an anti-leukemia response, including discovery of GvL mHAs that are highly shared across donor-recipient pairs (DRPs). To do this, we applied experimental and computational antigen discovery and validation methods to two alloHCT datasets. We found that the total number of predicted mHAs varied by HLA allele, and number of each class of mHA significantly differed by recipient genomic ancestry. From the pool of predicted mHAs, we identified the smallest sets of GvL mHAs needed to cover 100% of DRPs with a given HLA allele. We used two different mass spectrometry methods to search for high population frequency GvL mHAs presented by three common HLA alleles. We validated a total of 26 novel predicted GvL mHAs that have a high degree of sharing within DRPs expressing HLA-A*02:01, HLA-B*35:01, and HLA-C*07:02, increasing the number of known GvL mHAs by more than 200%. We confirmed immunogenicity of an example novel mHA via T cell co-culture with peptide-pulsed dendritic cells, and we continue to seek additional mHA-targeting T cell clones against our novel antigens. This work demonstrates that identification of shared mHAs is a feasible and promising technique for expanding GvL mHA-targeting immunotherapies.Doctor of Philosoph

The good and the bad of T cell cross-reactivity: challenges and opportunities for novel therapeutics in autoimmunity and cancer

T cells are main actors of the immune system with an essential role in protection against pathogens and cancer. The molecular key event involved in this absolutely central task is the interaction of membrane-bound specific T cell receptors with peptide-MHC complexes which initiates T cell priming, activation and recall, and thus controls a range of downstream functions. While textbooks teach us that the repertoire of mature T cells is highly diverse, it is clear that this diversity cannot possibly cover all potential foreign peptides that might be encountered during life. TCR cross-reactivity, i.e. the ability of a single TCR to recognise different peptides, offers the best solution to this biological challenge. Reports have shown that indeed, TCR cross-reactivity is surprisingly high. Hence, the T cell dilemma is the following: be as specific as possible to target foreign danger and spare self, while being able to react to a large spectrum of body-threatening situations. This has major consequences for both autoimmune diseases and cancer, and significant implications for the development of T cell-based therapies. In this review, we will present essential experimental evidence of T cell cross-reactivity, implications for two opposite immune conditions, i.e. autoimmunity vs cancer, and how this can be differently exploited for immunotherapy approaches. Finally, we will discuss the tools available for predicting cross-reactivity and how improvements in this field might boost translational approaches