Research Pieter Meysman

Abstract

The adaptive immune system works by recognizing and responding to infected or malignant cells by recognizing peptides bound to major histocompatibility complex (MHC) molecules. This induces an immune response by producing antibodies or directly attacking infected or abnormal cells to eliminate the threat. Mass spectrometry-based immunopeptidomics is a key approach to understand the adaptive immune system by identifying and characterizing peptides presented on MHC molecules. However, there is a lack of optimized bioinformatics tools for immunopeptidomics data analysis, resulting in very low spectrum annotation rates and missing out on important insights into the immune system. To overcome this challenge, we will develop a powerful de novo immunopeptide sequencing solution using deep learning to uncover increased biological knowledge from immunopeptidomics data. We will apply this tool to study the presence of aberrant peptides, e.g. due to errors in translation or transcriptional splicing, and non-human peptides, originating from pathogens and other organisms, in the human immunopeptidome. These innovations have the potential to unlock new biological and biomedical insights into the adaptive immune system that will catalyze the development of novel immunotherapies and vaccines.

Researcher(s)

• Promoter: Bittremieux Wout
• Co-promoter: Meysman Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Next Generation Sequencing (NGS) has emerged as a suitable tool to evaluate and characterize the T Cell Receptor (TCR) immune repertoire. This approach paves the way for the use of the TCR repertoire study as a novel complex biomarker to track the exposure of individuals to non-self antigens and to develop new therapeutic strategies by improving our comprehension of the adaptive immune response against infectious agents and cancer antigens. The collaboration between Italy's Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori" (IRST) and Belgium's University of Antwerp involves both these fields and aims to identify and characterize specific human TCRs via NGS to develop therapeutic TCR-T cells, exploiting experimental protocols, in silico analysis, and in vitro assays for T cell stimulation with antigens' pools. The first objective is to study the T cell response to mRNA-based COVID vaccines in healthy subjects and lymphoma patients from Emilia Romagna region through RNA samples sequencing and subsequent analysis of TCR specificity against SARS-CoV-2 proteins. The correlation between TCR diversity and strength with the amount of neutralizing antibodies will also be examined in consideration of the predicted HLA context, leading to an actionable SARS-Cov2 bulk TCR-seq database. Genomic instability, which is a common feature in cancer cells, often leads to the generation of chromosomal rearrangements and aneuploidy. Many are the examples today of gene fusions that promote cell transformation in different oncological settings. Those events lead to the unique opportunity to generate neoantigens that could be presented to the immune system in an HLA-restricted manner. Therefore, the second objective is the identification and validation of neoantigens originated from genetic fusion events in cancer patients already available in IRST. Subsequently, experiments will be focused on the identification of antigen-specific T cells and TCRs against neoantigens originating from selected genetic fusion events. To these ends, computational protein reconstruction and prioritization from the already available fusion genes database will be performed to retrieve fusion transcripts in hematological tumors. These peptides will be later synthesized and pooled to stimulate T cells, with subsequent expansion and characterization of the expressed receptors. Before that, a proof-of-concept T cell expansion and following TCR-identification experiments with model protein(s) for a known neoantigen will be set up.

Researcher(s)

• Promoter: Lion Eva
• Co-promoter: Campillo Davó Diana
• Co-promoter: Meysman Pieter

Research team(s)

• Laboratory for Experimental Hematology (LEH)

Project type(s)

• Research Project

Abstract

Disentangling the complex reticular components of the immune system represents an obstacle to a deep understanding of the interactions underpinning immune responses. The solution to this challenge likely lies in a systemic design that can illuminate emerging unique and shared biomarkers. However, thus far, the potential of fully integrated immunological data has remained largely untapped. By leveraging an unprecedentedly large cohort for the field, we aim to bridge the gap and build a framework for multi-view biological data fusion with a focus on the often overlooked T cell layer. The views of each cohort will be combined into a latent space. We will group individuals based on emerging new patterns, validate previously published biomarkers, deconvolute group parameters and perform response phenotyping. We will then overlay the T-cell receptor level on this space in an innovative integration to focus on the cellular mediated response. Informed by the discovered features, the T cell analysis will then be driven by epitope and disease specificity and compounded by a longitudinal aspect, to guide the development of the framework's modules. We foresee that this novel framework has great potential for transversal applicability within bioinformatics, biomedical and pharmaceutical companies. Specifically, we anticipate this framework could spark a paradigm shift towards more informed holistic therapeutics designs.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Co-promoter: Ogunjimi Benson
• Fellow: Affaticati Fabio

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Current serology-based testing methods to support Lyme disease (LD) diagnosis are critically flawed: they lack sensitivity (25–50%) and specificity for early LD diagnosis, and are unable to differentiate active from past infections in the burdensome late LD stages such as Lyme arthritis (LA). However, in contrast to Borrelia-specific antibodies, T cells are consistently recruited in early LD. Furthermore, different T cell subsets are thought to play a key role in developing autoimmune-driven LA in some patients, although underlying mechanisms remain enigmatic. With this FWO-SB project, we will investigate the potential of T cells as a new avenue for diagnosis. Building upon the recent advances in single-cell immune profiling, this project will deliver a novel, integrative approach for characterizing disease-associated T-cell signatures in unprecedented detail, combining T cell phenotype with its receptor specificity information on a single-cell level. We will construct this detailed immune map in patients with acute and autoimmune-driven Lyme, and contextualize it through comparative analysis with various forms of chronic autoimmune arthritis. This novel methodology will allow us to convert complex immune sequencing data into clinically actionable insights and extremely specific biomarkers. This empowers us to address the imperative diagnostic needs in Lyme, and might shed light on the elusive pathogenesis of acute and chronic LA disease states.

Researcher(s)

• Promoter: Ogunjimi Benson
• Co-promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Fellow: Van Deuren Vincent

Research team(s)

• Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID)

Project type(s)

• Research Project

Abstract

Life-science is a data science; it relies on the generation, sharing and integrated analysis of vast quantities of digital data. ELIXIR is a European Research Infrastructure that brings together international resources in life-sciences to form a single infrastructure enabling scientists to find and share data, exchange expertise and access advanced tools and large scale computational facilities, across borders and disciplines. The Belgian ELIXIR Node offers a portfolio of services in data management and analysis to help researchers adopt best practices of Open Science and perform their research efficiently. We bring together expertise in Flanders in human health and plant sciences, focusing on federated learning, and enabling data integration and interpretation. A new priority area is the establishment of a sensitive data infrastructure in Belgium. We also provide training for researchers and developers. Our mission is to ensure that researchers in Flanders and Belgium can focus on their research question, rather than on technical details of data, interoperability, compute resources, etc. by providing tailored solutions based on an interoperable infrastructure across Europe.

Researcher(s)

• Promoter: Meysman Pieter
• Co-promoter: Vandeweyer Geert

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Our kidneys play a crucial role in body homeostasis, i.e. they provide in a balanced ionic composition, volume, pH and osmolarity of our body fluid, thereby ensuring proper functioning of all our organs. Hence, acute kidney injury (AKI), commonly caused by hampered blood flow, toxins and drugs that typically affect the renal tubular epithelial cells, has far-reaching health consequences with >13.3 million patients each year. When AKI is not lethal, functional recovery of the tubular epithelium usually occurs spontaneously. However, AKI has also been identified as an important risk factor for development of chronic kidney disease (CKD) due to inefficiencies in spontaneous epithelial recovery. To date, there are no treatments that directly heal the injured kidney. Yet, the intriguing biological phenomenon of "nephrectomy-induced renal recovery" might provide a new perspective. This phenomenon comes down to the observation that an acutely injured kidney shows a remarkable degree of recovery and is able to avert progression to CKD when the healthy contralateral kidney is removed shortly after the initial insult. Recently we demonstrated, for the first time, that nephrectomy 1) stimulates proliferation of renal progenitor cells, 2) suppresses detrimental cells, 3) might cause a pro-repair wave of cell death and 4) induces a maximal repair response when performed at the right time after injury. In this project, we aim for profound mechanistic insight in these unexplored processes as they may foster design of new therapeutic strategies. Hereto, we make use of state-of-the-art single cell transcriptomics as well as unique transgenic mouse models to investigate the repair response in relation to its long term outcome.

Researcher(s)

• Promoter: Vervaet Benjamin
• Co-promoter: Meysman Pieter
• Fellow: Van Campen Elien

Research team(s)

• Cell death signaling (CDS)

Project type(s)

• Research Project

Abstract

Precision medicine is an approach to tailor healthcare individually, on the basis of the genes, lifestyle and environment of an individual. It is based on technologies that allow clinicians to predict more accurately which treatment and prevention strategies for a given disease will work in which group of affected individuals. Key drivers for precision medicine are advances in technology, such as the next generation sequencing technology in genomics, the increasing availability of health data and the growth of data sciences and artificial intelligence. In these domains, 6 strong research teams of the UAntwerpen are now joining forces to translate their research and offer a technology platform for precision medicine (PreMeT) towards industry, hospitals, research institutes and society. The mission of PreMeT is to enable precision medicine through an integrated approach of genomics and big data analysis.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Bittremieux Wout
• Co-promoter: Kooy Frank
• Co-promoter: Loeys Bart
• Co-promoter: Meester Josephina
• Co-promoter: Meysman Pieter
• Co-promoter: Mortier Geert
• Co-promoter: Op de Beeck Ken
• Co-promoter: Van Camp Guy
• Co-promoter: Van Hul Wim
• Co-promoter: Verstraeten Aline
• Fellow: Bosschaerts Tom
• Fellow: Gauglitz Julia

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Leishmaniasis is a severe global disease with no licensed vaccine to date. The disease outcome is driven by the complex interplay between the Leishmania parasite and the host immune response. For example, the diverse clinical presentations of in cutaneous leishmaniasis (CL) have been characterized by either a protective, anergic or pathogenic T cell response. However, due to the size and complexity of the parasite, it is still unknown how these different T cell responses arise and drive the disease course. T cells are activated by the recognition of its T cell receptor (TCR) to the Leishmania antigen presented by MHC (aMHC) molecules on antigen-presenting cells (APCs). We hypothesize that diverse Leishmania-specific aMHC-TCR interactions primarily underlie the different T cell and disease phenotypes. This is in line with our recent immunopeptidomics data that demonstrated the presence of diverse Leishmania antigens across different CL presentations. In this jPPP, we aim to trial and showcase our pipeline to identify these Leishmania antigen- specific T cells in lesion biopsies, and use a novel approach to characterize their protective/detrimental function in a spatially resolved manner. The acquired data will significantly increase the feasibility score in the upcoming grant applications wherein we aim to elevate and expand the project' scope by generating the first Leishmania T-cell epitope map and how it drives the complete disease spectrum of leishmaniasis, eventually to provide candidate antigens for vaccine development and diagnostic assays

Researcher(s)

• Promoter: Meysman Pieter
• Co-promoter: Laukens Kris

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

The emergence of immunotherapy has improved cancer treatment in many different ways. One of the specific approaches is T-cell receptor (TCR)-T-cell therapy, in which potent TCRs are introduced into patient T cells in the laboratory, after which they can specifically destroy unwanted cells in the body. Although this therapy shows promising results, identification of potent TCRs remains a major hurdle. Due to the immense diversity of TCR repertoires, it is challenging to efficiently detect tumor-reactive T cells in the blood. In addition, TCRs are antigen-specific, meaning that different TCRs are required for different (sub)cancer types. The aim of project PrioriTCR is to develop an immunoinformatics platform that simplifies and accelerates the identification of potent TCRs. This proof-of-concept project is designed for the Wilms' Tumor 1 (WT1) antigen, which is overexpressed in a variety of solid tumors and blood cancers. WT1 is considered a virtually universal cancer marker, promising to target with specific immunotherapy. By combining limited laboratory experiments with blood samples from cancer patients with artificial intelligence, this project will result in the identification of new candidate potent WT1-specific TCRs for development of next-generation T-cell therapies. The developed computer models can be further extended for TCRs against other cancer markers.

Researcher(s)

• Promoter: Lion Eva
• Co-promoter: Laukens Kris
• Co-promoter: Meysman Pieter

Research team(s)

• Laboratory for Experimental Hematology (LEH)

Project type(s)

• Research Project

Abstract

Chikungunya virus (CHIKV) is a reemerging human pathogen that has seen a rapid global spread in the past decade. It is the most wide-spread member of a group of mosquito transmitted, arthritogenic viruses that can leave up to half of the patients with chronic joint pains long after the initial infection. The chronic joint pain is caused by sustained inflammation and bears hallmarks of auto-immune rheumatoid arthritis. However, the antigenic driver of the prolonged joint inflammation, and the relative contribution of auto-reactive immune cells have not been elucidated. Using next-gen T-cell Receptor (TCR) sequencing on peripheral blood CD4 T cells from CHIKV patients that do or do not develop sustained joint pains we will identify the TCR signatures specifically associated with development of chronic disease. These preliminary findings will extend the role of T cells in the etiology of chronic CHIKV to humans, provide a basis for the search for the antigenic drivers of the disease and potentially identify biomarkers for progression to chronic CHIKV.

Researcher(s)

• Promoter: Ogunjimi Benson
• Co-promoter: Laukens Kris
• Co-promoter: Meysman Pieter

Research team(s)

• Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID)

Project type(s)

• Research Project

Abstract

Current serology-based testing methods to support Lyme disease (LD) diagnosis are critically flawed: they lack sensitivity (25–50%) and specificity for diagnosis in early LD, and are unable to differentiate active from past infections in the burdensome late LD stages such as Lyme arthritis (LA). In contrast to Borrelia-specific antibodies, T cells have been shown to be consistently recruited in early LD. Furthermore, different T cell subsets are thought to play a key role in the development of later postinfectious (autoimmune-driven) LA. With this FWO-SB project, we will investigate the potential of T cells as a new avenue for diagnosis. Building upon the recent advances in single-cell immune profiling, this project will deliver a novel framework for characterizing disease-associated T-cell signatures in unprecedented detail, integrating the T cell phenotype with its receptor specificity on a single-cell level. We will address the critical need for post-analysis tools for such complex datasets by developing novel immunoinformatic workflows, allowing efficient extraction of clinically actionable insights and extremely specific biomarkers. Employing the developed methodology and tools across various types of LD, LA, and other relevant autoimmune-driven arthritides will then empower us in addressing the imperative diagnostic need and in shedding light on the elusive pathogenic mechanisms behind postinfectious LA.

Researcher(s)

• Promoter: Ogunjimi Benson
• Co-promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Fellow: Van Deuren Vincent

Research team(s)

• Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID)

Project type(s)

• Research Project

Abstract

The aim of this project is to develop a robust workflow and to identify promising T-cell receptors (TCRs) for the development of T cell-based immunotherapies, focusing on the leukemia-associated antigen Wilms' tumor-1 (WT1). For this, a unique collection of blood samples is available from acute myeloid leukemia (AML) patients in the context of our academic clinical trials investigating WT1-loaded dendritic cell (DC) vaccination, a cellular immunotherapy designed to activate WT1-specific T cells. By combining specialized cell sorting techniques with in-house developed bioinformatic tools, single-cell TCR and RNA sequencing will be integrated with cutting-edge computational models to link the specificity and transcriptomic profile of these T cells with patients' clinical responses.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Lion Eva
• Co-promoter: Meysman Pieter
• Fellow: Gielis Sofie

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

High throughput sequencing allows characterization of the human immune system, but the resulting data cannot simply be translated into clinical insights. We have therefore developed artificial intelligence models that can translate T-cell receptor and gene expression data into useful insights about an individual's immune status. For example, we have developed the first online platform to predict the epitopes of T cells. We have demonstrated the power of this for predicting and modeling vaccination-induced immune responses.

Researcher(s)

• Promoter: Meysman Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

The human immune system, and in turn its response to pathogens, cancer, and other diseases, is governed by a complex interaction between different immune cell types. T cells play a major role in the immune defense as they initiate specific elimination pathways for cancer and infected cells. The rise of single-cell sequencing has opened new doors to study the functional T-cell repertoire by mapping the T-cell subtypes in immune repertoires. However, interpretation of single-cell data is sorely lacking and no methods are available to link T-cell functionalities with their actual pathogenic targets.  This project aims to leverage single-cell data richness to visualize key interplays between T-cell subtypes, as well as other immune cells. To this end, the team will develop a user-friendly tool to functionally annotate groups of disease relevant T cells within single-cell data that are both clinically relevant and directly actionable. Following identification of the key active T cells, integration with single cell transcriptomics of other immune cell types will support the explanation of their activity. This will aid understanding of the functional immune compartment in different pathologies including cancer, infections and autoimmune disorders.

Researcher(s)

• Promoter: Meysman Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Infection with the Leishmania parasite can lead to a wide spectrum of clinical presentations, ranging from diverse cutaneous presentations (localized, mucocutaneous, disseminated) to a deadly systemic disease (visceral leishmaniasis). Yet, the underlying factors driving this disease spectrum remain mostly unknown. Although Leishmania is an obligatory intracellular parasite surviving in the phagolysosome of key antigen presenting cells, it remains mostly unexplored whether the complex host-parasite interplay translates in an altered net effect on the MHC-presented peptidome to T cells (giving rise to a differential antigen-specific T cell repertoire), and whether and how this is associated with certain disease presentations that have distinct immunopathology patterns. Although attempts were made in murine models, discordant data has often been found between experimental in vivo models, in vitro settings and patients regarding the host immune response after Leishmania infection. By applying a new high-throughput MS-based method on an unique set of patient tissue samples, we aim to perform the first comprehensive screening of the antigenic repertoire and study whether and how this differs between in vitro and in vivo conditions, blood and tissue compartments, and across the clinical presentations.

Researcher(s)

• Promoter: Baggerman Geert
• Co-promoter: Cuypers Bart
• Co-promoter: Meysman Pieter
• Co-promoter: Pepermans Elise

Research team(s)

• Ecosphere

Project type(s)

• Research Project

Abstract

T-cell immunity is a key player in the development and progression of multiple sclerosis (MS). The T-cell receptor (TCR) expressed on the cell surface of T-cells is one of the primary determinants of self versus non-self. The composition of the TCR sequence space targeting MS-involved antigens, such as myelin, is currently poorly characterized. We hypothesize that the TCR space holds invaluable knowledge which may unlock the molecular mechanisms of self-antigen reactivity. To this end, we aim to map the MS antigen-specific TCR space with state-of-the-art immunoinformatics data mining models.

Researcher(s)

• Promoter: Meysman Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Current standards of viral diagnostics rely on in-vitro methods detecting either genome or proteins of a pathogen or host antibodies against pathogenic antigens. As a result, multiple assays are required when a sample is screened for several viruses, making the process time-demanding and cost-ineffective. Moreover, some of the methods fail in the case of acute and latent infections. With this FWO-SB project, I will investigate the potential of T cell receptor (TCR) repertoires to overcome this shortcoming and introduce a new approach for the simultaneous diagnosis of multiple viral infections. To discover the TCR signatures that differ between infected and uninfected individuals, I will search for pathogen-associated patterns in TCR repertoires by applying state-of-the-art immunoinformatics and machine learning methods. The obtained results will be used to build a classification model that utilizes the TCR repertoire to predict whether an individual is virus-positive or virus-negative. The insights from this project will broaden our understanding of pathogen-induced TCR repertoire changes and serve as a foundation for the development of a computational diagnostic framework. This will have a high impact on the broad field of diagnostics as the TCR repertoire is playing an important role in various non-infectious diseases, such as cancer and autoimmune diseases.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Fellow: Postovskaya Anna

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Recent developments in the field of sequencing technology allow for the characterization of adaptive immune receptor repertoires with unprecedented detail. T-cell receptor (TCR) sequencing holds tremendous promise for understanding the involvement and dynamics of adaptive immune components in autoimmune disorders. As the field is rapidly evolving from pre-processing of TCR-seq data to functional analysis of adaptive immune repertoires, new opportunities emerge for the development of comprehensive approaches for the post-analysis of immune receptor profiles. These approaches can offer comprehensive solutions to address clinical questions in the research on autoimmune disorders. An important example is multiple sclerosis (MS), a neuroinflammatory disease of the central nervous system, for which very little is known about the specific T-cell clones involved in its pathogenesis. By analysing the adaptive immune repertoire of MS patients, we postulate it is possible to uncover key drivers of the MS disease process. The identified T-cell clones will present themselves as highly specific biomarkers and therapeutic targets. This translational research project will lead to novel approaches for the identification of condition-associated T-cell clones, to new monitoring tools to evaluate the efficacy of MS-therapies and to a model to predict the disease course of MS.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Cools Nathalie
• Co-promoter: Meysman Pieter
• Fellow: Valkiers Sebastiaan

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Mathematical simulation models have become indispensable tools for forecasting and studying the effectiveness of intervention strategies such as lockdowns and screening during the SARS-CoV-2 pandemic. Estimation of key modeling quantities uses the serological footprint of an infection on the host. However, although depending on the type of assay, SARS-CoV-2 antibody titers were frequently not found in young and/or asymptomatic individuals and were shown to wane after a relatively short period, especially in asymptomatic individuals. In contrast, T-cells have been found in different situations – also without antibodies being present - ranging from convalescent asymptomatic to mild SARS-CoV-2 patients and their household members, thereby indicating that T-cells offer more sensitivity to detect past exposure to SARS-CoV-2 than the detection of antibodies can. In this project, we will gather on a population level T-cell and antibody SARS-CoV-2 specific data from different well-described cohorts including 300 individuals (and 200 household members) who have had proven covid-19 infection > 3 months earlier, 100 general practitioners, 100 hospital workers, 500 randomly selected individuals and 75 pre-covid-era PBMC/sera. This data will be used in comparative simulation models and will lead to a reassessment of several key epidemiological estimates such as herd immunity and the reproduction number R that will significantly inform covid-19 related public health interventions.

Researcher(s)

• Promoter: Ogunjimi Benson
• Co-promoter: Beutels Philippe
• Co-promoter: Coenen Samuel
• Co-promoter: Laukens Kris
• Co-promoter: Lion Eva
• Co-promoter: Meysman Pieter
• Co-promoter: Van Damme Pierre

Research team(s)

• Centre for Health Economics Research and Modelling of Infectious Diseases (CHERMID)

Project type(s)

• Research Project

Abstract

Infectious disease laboratory diagnostic testing is still based on targeted test methods (Ag detection, PCR, ELISA, agglutination, ELISPOT, etc.). However, rapid evolutions in sequencing applications might soon dramatically change our diagnostic algorithms. For instance, metagenomic sequencing is an untargeted diagnostic tool for direct (in theory any) infectious pathogen detection without preassumptions on the causative agent. However, acute infectious pathogens rapidly disappear from the infected individual (causing diagnostic methods based on direct pathogen detection to fail) leaving behind its immune imprint (primed B and T cells). We here wish to demonstrate that immune repertoire sequencing (a cutting-edge sequencing tool that allows high-throughput mapping of B and T cell receptor variable domains) focused on recently activated immune cells is an indirect untargeted diagnostic tool for acute infectious pathogen detection. This method could therefore be an alternative to current indirect targeted assays (serology and T cell assays). To prove this concept, we will exploit recently collected acute COVID-19 patient samples.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Co-promoter: Ogunjimi Benson

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Machine learning can be used to elucidate the presence or absence of interactions. In particular for life science research, the prediction of molecular interactions that underlie the mechanics of cells, pathogens and the immune system is a problem of great relevance. Here we aim to establish a fundamentally new technology that can predict unknown interaction graphs with models trained on the vast amount of molecular interaction data that is nowadays available thanks to high-throughput experimental techniques. This will be accomplished using a machine learning workflow that can learn the patterns in molecular sequences that underlie interactions. We will tackle this problem in a generalizable way using the latest generation of neural networks approaches by establishing a generic encoding for molecular sequences that can be readily translated to various biological problems. This encoding will be fed into an advanced deep neural network to model general molecular interactions, which can then be fine-tuned to highly specific use cases. The features that underlie the successful network will then be translated into novel visualisations to allow interpretation by biologists. We will assess the performance of this framework using both computationally simulated and real-life experimental sequence and interaction data from a diverse range of relevant use cases.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Bittremieux Wout
• Co-promoter: Meysman Pieter
• Fellow: Postovskaya Anna

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

The T-cell repertoire is a key player in the adaptive immune system and is thus important in infectious disease defense, vaccine development, auto-immune disorders and oncology immunotherapies. T-cell receptor sequencing allows characterization of a full repertoire with a single experiment, however the data this generates cannot be readily translated into medical action. With artificial intelligence models we can translate T-cell receptor sequencing data to actionable insight into the immune status of an individual.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Co-promoter: Ogunjimi Benson

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Cancer is one of the leading causes of death worldwide. Over the past decades, new therapies have been developed that target the patients' immune system to mount an antitumor response. The efficacy of these immunotherapies has already been demonstrated in various clinical trials. Nevertheless, these therapies show a large variation in their individual responses as some patients respond well to the therapy, while others do not. In this project, we will investigate the differences between the T cell receptor (TCR) repertoires of responders and non-responders as a possible marker for immunotherapy responsiveness. We will apply state-of-the-art data mining methods and newly developed immunoinformatics tools to uncover those features that make a patient a clinical responder or non-responder. This will reveal the underlying mechanism of DC-based vaccine responsiveness. This can potentially accelerate general health care in terms of personalized medicine and will save costs.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Co-promoter: Smits Evelien
• Co-promoter: Van Tendeloo Vigor
• Fellow: Gielis Sofie

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

The relationship between pathogens and their host is often complex and their evolutionary arms race intricate. Subclinical infections are a common occurrence; host organisms are infected by a normally disease-inducing pathogen, but no symptoms are displayed. This allows pathogens to establish natural reservoirs of asymptomatic carriers that can aid in their transmission to those hosts that are susceptible to the disease. The goal of this fundamental research project is to gain understanding of the general molecular mechanisms that underlie why some animal species - or even some individuals - remain mostly asymptomatic following infection with specific pathogens, while others progress into symptomatic disease. To this end, a large collection of pathogen-host interaction networks will be established for both symptomatic and asymptomatic hosts. State-of-the-art data mining methods will then be applied to discover rules and patterns in the interaction network that are associated with disease susceptibility. Finally, these patterns will be filtered and validated using integrated multi-level 'omics information derived from both the pathogen and the host species. The results of this project will lead to both novel methodology to tackle previously uncharacterised host-pathogen interactions and deliver fundamental new insights in the biological drivers of disease susceptibility.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Fellow: Moris Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

This project aims to construct a computational framework to predict which T-cells can react to a tumour-associated epitope. Key problems that will be investigated are the optimal feature representation as well as the most performant classification strategy. As a proof-of-concept, we will apply the framework on a unique dataset generated by combining a tetramer assay with single cell sequencing.

Researcher(s)

• Promoter: Meysman Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

The foodborne pathogen Salmonella poses a significant threat to human health worldwide. This is further complicated by the emergent spread of antibiotic resistant strains. Salmonella serotypes and subtypes can have different niches, from a broad range to a very specific niche, e.g. humans. Such bacteria can become very efficient in infecting humans and will contribute even more to the spread of antibiotic resistance. To combat the emergent spread of multiresistant bacteria, molecular monitoring of bacterial strains that show increased adaptation towards the human host, combined with high resistance and virulence, it is vital. While researchers can relatively accurately predict alarming resistant and virulent phenotypes based on whole genome sequencing data, niche adaptation prediction techniques are lagging behind. I will solve these problems by (i) analysing niche adaptation from a broad perspective and (ii) implementing cutting edge computational technologies to predict niche adaptation in Salmonella. This methodology will be built and tested on a model Salmonella serotype, Salmonella Concord. Salmonella Concord is intrinsically a highly virulent and resistant serotype, and shows geographical restriction (the Horn of Africa). It has been reported in Belgium through adopted children, mainly from Ethiopia. Insights from my research will empower health care innovations, and the predictive model will significantly improve risk assessment of pathogenic bacteria.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Fellow: Cuypers Wim

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Dr. Pieter Meysman has been active at the Biodata Mining research lab under the guidance of Prof. Kris Laukens since the 1st of January 2013 as post-doctoral researcher. His research focus is on the application of state-of-the-art computer science techniques in the area of 'data mining' on biomedical data, under the header of bioinformatics. In his first four years at the University of Antwerp, he has published 31 scientific papers, included eight as first author and three as last author.

Researcher(s)

• Promoter: Meysman Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

The relationship between pathogens and their host is often complex and their evolutionary arms race intricate. Subclinical infections are a common occurrence; host organisms are infected by a normally disease-inducing pathogen, but no symptoms are displayed. This allows pathogens to establish natural reservoirs of asymptomatic carriers that can aid in their transmission to those hosts that are susceptible to the disease. The goal of this fundamental research project is to gain understanding of the general molecular mechanisms that underlie why some animal species - or even some individuals - remain mostly asymptomatic following infection with specific pathogens, while others progress into symptomatic disease. To this end, a large collection of pathogen-host interaction networks will be established for both symptomatic and asymptomatic hosts. State-of-the-art data mining methods will then be applied to discover rules and patterns in the interaction network that are associated with disease susceptibility. Finally, these patterns will be filtered and validated using integrated multi-level 'omics information derived from both the pathogen and the host species. The results of this project will lead to both novel methodology to tackle previously uncharacterised host-pathogen interactions and deliver fundamental new insights in the biological drivers of disease susceptibility.

Researcher(s)

• Promoter: Laukens Kris
• Co-promoter: Meysman Pieter
• Fellow: Moris Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project

Abstract

Herpes zoster (shingles) is caused by the varicella zoster virus and is responsible for a substantial decrease in the quality of life, especially among the elderly. In this project we intend to identify the HLA alleles that increase or decrease chance of herpes zoster in the Belgian population. These results will be combined with computational models to uncover the link between viral peptide affinity and disease susceptibility.

Researcher(s)

• Promoter: Meysman Pieter

Research team(s)

• ADReM Data Lab (ADReM)

Project type(s)

• Research Project