Towards precision medicine in kidney transplantation: Epitope based HLA-matching and improved DSA diagnostics

Refinement of immunological pre-transplant risk assessment, specifically by improving the molecular compatibility between donor and recipient and by enhancing the characterization of the recipient’s pre-established immunological memory, is key to transit to an improved organ allocation and a personalized immunosuppression in solid organ transplantation (SOT). For both of these diagnostic clarifications, novel conceptual and technological achievements have been attained, but still need to be perfected. This doctoral thesis is dedicated to both topics. Publication 1 presents a study on the immunogenicity of human leukocyte antigen (HLA) epitopes, the results and insights of which contribute to better prediction of newly formed (de novo) immune responses in allograft recipients. The study presented in Publication 2 addresses the question of which pre-transplant donor specific HLA antibodies (DSA) are clinically relevant and why. The results demonstrate how different antibody-IgG compositions affect complement activation, the most detrimental antibody effector function in relation to antibody-mediated rejection (AMR). Finally, Publication 3 reviews relevant issues in the context of DSA characterization by the Single Antigen Bead (SAB) assay, which has become the central assay for HLA-antibody characterization in transplant diagnostics

In Silico Studies on DARC.

International audienceThe Duffy Antigen/Receptor for Chemokine (DARC) is a seven segment transmembrane protein. It was firstly discovered as a blood group antigen and was the first specific gene locus assigned to a specific autosome in man. It became more famous as an erythrocyte receptor for malaria parasites (Plasmodium vivax and Plasmodium knowlesi), and finally for chemokines. DARC is an unorthodox chemokine receptor as (i) it binds chemokines of both CC and CXC classes and (ii) it lacks the Asp-Arg-Tyr consensus motif in its second cytoplasmic loop hence cannot couple to G proteins and activate their signaling pathways. DARC had also been associated to cancer progression, numerous inflammatory diseases, and possibly to AIDS. In this review, we will summarize important biological data on DARC. Then we shall focus on recent development of the elaboration and analyzes of structural models of DARC. We underline the difficulty to propose pertinent structural models of transmembrane protein using comparative modeling process, and other dedicated approaches as the Protein Blocks. The chosen structural models encompass most of the biochemical data known to date. Finally, we present recent development of protein - protein docking between DARC structural models and CXCL-8 structures. We propose a hierarchal search based on separated rigid and flexible docking

Modelling the genomic structure, and antiviral susceptibility of Human Cytomegalovirus

Human Cytomegalovirus (HCMV) is found ubiquitously in humans worldwide, and once acquired, the infection persists within the host throughout their life. Although Immunocompetent people rarely are affected by HCMV infections, their related diseases pose a major health problem worldwide for those with compromised or suppressed immune systems such as transplant recipients. Additionally, congenital transmission of HCMV is the most common infectious cause of birth defects globally and is associated with a substantial economic burden. This thesis explores the application of statistical modelling and genomics to unpick three key areas of interest in HCMV research. First, a comparative genomics analysis of global HCMV strains was undertaken to delineate the molecular population structure of this highly variable virus. By including in-house sequenced viruses of African origin and by developing a statistical framework to deconvolute highly variable regions of the genome, novel and important insights into the co-evolution of HCMV with its host were uncovered. Second, a rich database relating mutations to drug sensitivity was curated for all the antiviral treated herpesviruses. This structured information along with the development of a mutation annotation pipeline, allowed the further development of statistical models that predict the phenotype of a virus from its sequence. The predictive power of these models was validated for HSV1 by using external unseen mutation data provided in collaboration with the UK Health Security Agency. Finally, a nonlinear mixed effects model, expanded to account for Ganciclovir pharmacokinetics and pharmacodynamics, was developed by making use of rich temporal HCMV viral load data. This model allowed the estimation of the impact of immune-clearance versus antiviral inhibition in controlling HCMV lytic replication in already established infections post-haematopoietic stem cell transplant

Deep sequencing of blood and gut T-cell receptor beta-chains reveals gluten-induced immune signatures in celiac disease

Celiac disease (CD) patients mount an abnormal immune response to gluten. T-cell receptor (TCR) repertoires directed to some immunodominant gluten peptides have previously been described, but the global immune response to in vivo gluten exposure in CD has not been systematically investigated yet. Here, we characterized signatures associated with gluten directed immune activity and identified gluten-induced T-cell clonotypes from total blood and gut TCR repertoires in an unbiased manner using immunosequencing. CD patient total TCR repertoires showed increased overlap and substantially altered TRBV-gene usage in both blood and gut samples, and increased diversity in the gut during gluten exposure. Using differential abundance analysis, we identified gluten-induced clonotypes in each patient that were composed of a large private and an important public component. Hierarchical clustering of public clonotypes associated with dietary gluten exposure identified subsets of highly similar clonotypes, the most proliferative of which showing significant enrichment for the motif ASS[LF] R[SW][TD][DT][TE][QA][YF] in PBMC repertoires. These results show that CD-associated clonotypes can be identified and that common gluten associated immune response features can be characterized in vivo from total repertoires, with potential use in disease stratification and monitoring.Peer reviewe

T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification

Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their specific recognition of antigens plays a crucial role in the immune response against diseases, including cancer. The diversity and specificity of TCRs make them ideal for targeting cancer cells, and recent advancements in sequencing technologies have enabled the comprehensive profiling of TCR repertoires. This has led to the discovery of TCRs with potent anti-cancer activity and the development of TCR-based immunotherapies. In this study, we investigate the use of sparse coding for the multi-class classification of TCR protein sequences with cancer categories as target labels. Sparse coding is a popular technique in machine learning that enables the representation of data with a set of informative features and can capture complex relationships between amino acids and identify subtle patterns in the sequence that might be missed by low-dimensional methods. We first compute the k-mers from the TCR sequences and then apply sparse coding to capture the essential features of the data. To improve the predictive performance of the final embeddings, we integrate domain knowledge regarding different types of cancer properties. We then train different machine learning (linear and non-linear) classifiers on the embeddings of TCR sequences for the purpose of supervised analysis. Our proposed embedding method on a benchmark dataset of TCR sequences significantly outperforms the baselines in terms of predictive performance, achieving an accuracy of 99.8\%. Our study highlights the potential of sparse coding for the analysis of TCR protein sequences in cancer research and other related fields

A large peptidome dataset improves HLA class I epitope prediction across most of the human population

Published in final edited form as: Nat Biotechnol. 2020 February ; 38(2): 199–209. doi:10.1038/s41587-019-0322-9.Prediction of HLA epitopes is important for the development of cancer immunotherapies and vaccines. However, current prediction algorithms have limited predictive power, in part because they were not trained on high-quality epitope datasets covering a broad range of HLA alleles. To enable prediction of endogenous HLA class I-associated peptides across a large fraction of the human population, we used mass spectrometry to profile >185,000 peptides eluted from 95 HLA-A, -B, -C and -G mono-allelic cell lines. We identified canonical peptide motifs per HLA allele, unique and shared binding submotifs across alleles and distinct motifs associated with different peptide lengths. By integrating these data with transcript abundance and peptide processing, we developed HLAthena, providing allele-and-length-specific and pan-allele-pan-length prediction models for endogenous peptide presentation. These models predicted endogenous HLA class I-associated ligands with 1.5-fold improvement in positive predictive value compared with existing tools and correctly identified >75% of HLA-bound peptides that were observed experimentally in 11 patient-derived tumor cell lines.P01 CA229092 - NCI NIH HHS; P50 CA101942 - NCI NIH HHS; T32 HG002295 - NHGRI NIH HHS; T32 CA009172 - NCI NIH HHS; U24 CA224331 - NCI NIH HHS; R21 CA216772 - NCI NIH HHS; R01 CA155010 - NCI NIH HHS; U01 CA214125 - NCI NIH HHS; T32 CA207021 - NCI NIH HHS; R01 HL103532 - NHLBI NIH HHS; U24 CA210986 - NCI NIH HHSAccepted manuscrip

Epitope-Based Immunoinformatics and Molecular Docking Studies of Nucleocapsid Protein and Ovarian Tumor Domain of Crimean–Congo Hemorrhagic Fever Virus

Crimean–Congo hemorrhagic fever virus (CCHFV), the fatal human pathogen is transmitted to humans by tick bite, or exposure to infected blood or tissues of infected livestock. The CCHFV genome consists of three RNA segments namely, S, M, and L. The unusual large viral L protein has an ovarian tumor (OTU) protease domain located in the N terminus. It is likely that the protein may be autoproteolytically cleaved to generate the active virus L polymerase with additional functions. Identification of the epitope regions of the virus is important for the diagnosis, phylogeny studies, and drug discovery. Early diagnosis and treatment of CCHF infection is critical to the survival of patients and the control of the disease. In this study, we undertook different in silico approaches using molecular docking and immunoinformatics tools to predict epitopes which can be helpful for vaccine designing. Small molecule ligands against OTU domain and protein–protein interaction between a viral and a host protein have been studied using docking tools