Research team

ADReM Data Lab (ADReM)

Expertise

Both biological sciences and clinical medicine are currently overwhelmed by vast amounts of complex data and are becoming increasingly dependent on information technology for data analysis, interpretation and organisation. Although powerful data mining techniques are being developed within and outside the University, they are still underutilized in the life sciences. We aim to bring state-of-the-art data mining techniques to life science applications by setting up interdisciplinary collaborations between computer scientists, life scientists and clinicians. Core activities are: 1) the introduction, adaptation and application of innovative pattern mining and machine learning techniques to heterogeneous 'omics data (genome, transcriptome, proteome and metabolome) and to clinical information; 2) using these techniques to generate computational (network) models for biological systems and diseases; and 3) the development of interactive and intuitive visualizations of complex life science data and pattern mining results.

The genomic basis of rapid change in a functionally significant trait: osteoderm evolution in a girdled lizard 01/11/2021 - 31/10/2023

Abstract

The expression of osteoderms (bony deposits embedded in the dermal layer of skin in vertebrates) is thought to provide many adaptive functions, including protection against predators or sexual rivals, and aiding in thermo- and hydro-regulation. Cordyline lizards are a subfamily of Southern African lizards that exhibit substantial variation in this adaptive trait. Within this group, the Cape cliff lizard (Hemicordylus capensis) shows extensive intrecific variation. However, little is known about the evolutionary basis for this variation. This project aims to unravel the genomic basis of variation in osteoderm expression in this species, using an integrative approach that combines genomic and transcriptomic methods with phenotypic data. To this end, genetic material will be collected in the field, allowing me to assemble a reference genome for this species and produce genomic data from populations that differ in their environments. I will combine these data with phenotypic data to test for associations between genomic differentiation and phenotypic variation. Furthermore, I will collect and analyse transcriptomic data to test for differential gene expression associated with osteoderm expression variation. Overall, this project will shed light on the evolutionary basis of an ecologically important functional trait. The high-quality genomic resources to be produced will provide useful tools for the research community.

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Research team(s)

Identifying compensatory mutations in XDR-Mtb strains: understanding the dynamics and mechanisms of transmission 01/11/2021 - 31/10/2023

Abstract

Tuberculosis (TB) continues to be a major global public health threat. The development of drug resistance, and extensively drug resistant (XDR) strains of Mycobacterium tuberculosis (Mtb) especially, pose a major problem to TB control. Transmission of XDR-Mtb strains is in conflict with the dogma that XDR-Mtb strains are less transmissible due to a cumulative fitness costs of resistance conferring mutations. For rifampicin resistant strains for example, it has been shown that mycobacteria can acquire compensatory mutations in the rpoC gene to overcome the fitness cost of resistance conferring mutations. Due to the limited access to large sample sets of XDR-Mtb strains, the transmissibility of XDR-Mtb strains and the effect of compensatory mutations to reverse the fitness cost remain poorly studied. The FWO-funded TORCH consortium houses a whole genome sequence database of around 1000 XDR-Mtb strains collected in the Western Cape Province in South Africa over a 14-year period (2006 to 2020). This exceptional dataset of XDR strains allows an in-depth analysis of XDR-TB transmission dynamics and its evolutionary mechanisms. By combining bioinformatics analyses (identifying XDR-Mtb associated genomic convergence) with spatio-temporal analyses of the XDR-Mtb strains by transmission status we expect to identify novel compensatory mutations and their relative importance to the transmission of XDR-Mtb strains.

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Research team(s)

LeapSEQ: Lean data processing solutions for adaptive and portable genome sequencing, applied to infectious disease monitoring. 01/10/2021 - 30/09/2023

Abstract

Infectious diseases are becoming an increasing challenge to public health worldwide with urbanization, increased travel, climate change, habitat destruction, and deforestation fuelling local outbreaks and global spread. Metagenomic sequencing provides an attractive solution to identify all genomic material present in a patient sample without prior knowledge of the target. While metagenomic sequencing thus far relied on large, expensive and operationally demanding DNA-sequencers reserved for expert labs, the recent introduction of USB-stick sized nanopore sequencing devices offers an attractive portable and affordable solution for metagenomic sequencing in low-cost settings around the world. However for the context of pathogen detection, this technology still suffers from major data roadblocks in terms of data interpretation. In this strategic basic research project, we aim to remove significant roadblocks that stand between nanopore sequencing and its implementation for portable pathogen detection, characterisation and monitoring. These roadblocks include: (1) the reliance on expert bioinformatics skills to convert the sequencer data into interpretable results; (2) the lack of realtime interaction with the ongoing sequencing process; and (3) the selectivity challenge of detection low abundant pathogens within highly abundant host DNA. We will tackle these problems by implementing a Lean and Adaptive bioinformatics solution for Portable Sequencing ("LeapSEQ") based on in house developed data processing techniques. We will optimise and validate this tool with highly relevant infectious disease use cases together with strategic partners of ITM and UA and explore its valorisation potential in the context of global pathogen identification.

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Precision Medicine Technologies (PreMeT) 01/01/2021 - 31/12/2026

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.

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Research team(s)

The genomic and ecological basis of rapid change in a functionally significant trait: osteoderm evolution in a girdled lizard. 01/01/2021 - 31/12/2024

Abstract

Osteoderms are bony elements that are expressed in the skin of a few disparate groups of tetrapods (i.e. in crocodiles, turtles, armadillos, and some lizard and frog species) – but not in other taxa. In humans, osteoderms are frequent complications of injury and in a few rare inherited disorders. Osteoderms spark interest because they are ecologically relevant (they are likely to function in body protection, thermoregulation and water budget maintenance, in mineral storage) but at the same time exhibit an unusually binary distribution (i.e., they are expressed completely, or not at all). The latter element facilitates research into the genomic substrate of the trait. One species of cordylid lizard, Hemicordylus capensis, uniquely displays intraspecific variation in osteoderms: the trait has evolved repeatedly and therefore is present in some populations, but not in others. The species thus offers exceptional opportunities for learning how, why and when this remarkable trait evolves. With this project, we aim to resolve those issues through a thoroughly integrated approach combining state-of-the-art genomic, functional morphological and ecological techniques. We will also explore if we can extrapolate the findings on this study system to other taxa that (occasionally) express osteoderms, including humans. The project will allow a rare complete view of the evolution of an ecologically relevant phenotypic characteristic with a remarkably discontinuous variation and an unusually disparate taxonomic distribution.

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MIMICRY - Modulating Immunity and the Microbiome for effective CRC. 01/01/2021 - 31/12/2024

Abstract

A fundamentally important biological question is how our bodies maintain a critical balance between inflammation and immune tolerance, and how this may be modified or evaded by cancers. The human colon, a tissue where many inflammatory diseases and cancers arise, performs this balancing act basally in the presence of dietary antigens and the normal microbiome. Within this homeostatic state, colorectal polyps and colorectal cancer (CRC) arise and can evade clearance by the immune system despite treatment by immune checkpoint inhibitors. We hypothesize that these pre-malignant lesions subvert the default tolerogenic state of the colon and induce additional immunosuppressive mechanisms. Deciphering the complex interaction between the epithelium, immune system and microbiome requires a talented group of researchers with complementary expertise. The unique composition of this 'MIMICRY' iBOF consortium aims to combine human samples, state-of-the-art immunology, novel tools, and in vivo mouse models to study the multi-factorial aspects of colorectal cancers. These will help develop novel immunotherapeutic strategies.

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Machine learning framework for T-cell receptor repertoire-based viral diagnostics. 01/11/2020 - 31/10/2022

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.

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Investigation of non-thermal plasma therapy with first-line treatments of recurrent and metastatic head and neck squamous cell carcinoma: a novel combination with platinum-based chemotherapy and immunotherapy. 01/11/2020 - 31/10/2022

Abstract

Head and neck squamous cell carcinoma (HNSCC) is the 6th most common cancer worldwide, and advanced HNSCC patients often experience relapse or metastasis (R/M HNSCC) resulting in dismal prognoses. These patients receive immunotherapy (ICI) alone or in combination with platinum-based chemotherapeutics (PLAT) as first-line treatment. While these combination treatments have some clinical benefit, they are limited by low response rates and severe side effects in already weakened patients. To address this, I will investigate a novel combination strategy with non-thermal plasma (NTP). NTP, an ionised gas, is a localised therapy that induces immunogenic cancer cell death (ICD), which can activate the patient's anti-cancer immunity. To date, no adverse side effects have been reported with the clinical use of NTP. Therefore, we hypothesize that combining NTP with PLAT/ICI will be well-tolerated and improve clinical efficacy in R/M HNSCC. In this project, I will perform 3D in vitro experiments on cell lines and primary patient material, and two mouse models will be used to validate the safety and clinical efficacy of this combination strategy. The successful completion of my project will help integrate NTP into current first-line therapies of R/M HNSCC as a new combination strategy to improve treatment efficacy and quality of life for those patients. This study will also be a stepping stone towards a broader implementation of NTP technology in other cancer types.

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Approaching multiple sclerosis from a computational perspective through bioinformatic analysis of the T-cell repertoire. 01/11/2020 - 31/10/2022

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.

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A framework to deduce the convoluted repertoire and epitope hierarchy of human T cell responses in visceral leishmaniasis: patient meets in silico. 01/11/2020 - 31/10/2022

Abstract

Visceral leishmaniasis (VL) is one of the most severe parasitic infectious diseases with 0.4 million cases annually. There are currently no vaccines for VL, although there is evidence of acquired T cell-mediated immunity and resistance to reinfection. Indeed, VL vaccine development is severely hampered by the absence of a good animal model and the multitude of possible Leishmania antigens that remain uncharacterized because of the low-throughput screening methodologies currently applied. As such, there is a complete lack of insight in epitope reactivity, epitope dominance hierarchy and antigenic variation. In this project, we aim to unlock this status quo by implementing a patient-centered framework integrated with in silico epitope prediction tools and in vitro immunopeptidomics that can comprehensively deduce and confirm the Leishmania epitope hierarchy in patients. Additionally, we will phenotype and monitor the human Leishmania-specific T-cell response and repertoire during the complete course of infection using single-cell RNAseq, single-cell TCRseq and CITE-seq. These recent, state-of-the art tools allow unprecedented resolution by providing an exhaustive, timely and high-throughput immune profiling. We believe that this framework can be directly wheeled for diagnostic tools and to expedite vaccine development against Leishmania and serve as a proof of concept for similar complex eukaryotic pathogens.

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From physical plasma to cellular pathway: a multi-disciplinary approach to unravel the response pathways induced by nonthermal plasma for cancer therapy. 01/10/2020 - 30/09/2023

Abstract

Cancer therapy has been rapidly transforming in part due to progress in seemingly unrelated fields. This has led to the development of profound tools for studying cancer pathways and innovative therapies. Non-thermal plasma (NTP) is a novel treatment that has been emerging for cancer immunotherapy. Bioinformatics is another field experiencing rapid growth, as the ability to collect and process large amounts of 'omics' data has become increasingly accessible. In the context of oncology, this has led to success in elucidating therapy-induced pathways and therapy target discovery. Therefore, in my project, I will use a combination of experimental and bioinformatics approaches to study fundamental effects of NTP on cancerous cells: 1) mechanisms driving cell sensitivity and 2) immunological changes to be exploited for combination therapy. In vitro experiments will be performed to categorize cells into sensitivity groups based on NTP-induced cell death; cellular redox and death modalities will also be studied. Transcriptome analysis and bioinformatics techniques will be used to uncover the activated pathways. Signature gene sets from transcriptome data will be studied to obtain a more comprehensive picture of the immunologic changes in NTP-treated cells. All in silico results will be validated experimentally. Success of this project will benefit multiple science fields and open new lines of research while providing insight into underlying mechanisms of NTP-induced cancer response.

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Project website

Microbial Systems Technology (MST). 01/01/2020 - 31/12/2025

Abstract

Microorganisms have been exploited from the earliest times for baking, brewing, and food preservation. More recently, the enormous versatility in biochemical and physiological properties of microbes has been exploited to create new chemicals and nanomaterials, and has led to bio-electrical systems employed for clean energy and waste management. Moreover, it has become clear that humans, animals and plants are greatly influenced by their microbiome, leading to new medical treatments and agricultural applications. Recent progress in molecular biology and genetic engineering provide a window of opportunity for developing new microbiology-based technology. Just as advances in physics and engineering transformed life in the 20th century, rapid progress in (micro)biology is poised to change the world in the decades to come. The Excellence Centre "Microbial Systems Technology" (MST) will assemble and consolidate the expertise in microbial ecology and technology at UAntwerpen, embracing state-of-the-art technologies and interdisciplinary systems biology approaches to better understand microbes and their environment and foster the development of transformational technologies and applications. MST connects recently established research lines across UAntwerpen in the fields of microbial ecology, medical microbial ecology, plant physiology, biomaterials and nanotechnology with essential expertise in Next Generation Sequencing and Bioinformatics. By joining forces, new and exciting developments can be more quickly integrated into research activities, thus catalyzing the development of novel microbial products and processes, including functional food, feed and fertilizers, probiotics, and novel biosensors and bio-electronics applications. This way, MST aims for an essential contribution to the sustainable improvement of human health and the environment.

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Transferable deep learning for sequence based prediction of molecular interactions. 01/10/2019 - 30/09/2023

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.

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Unlocking the TCR repertoire for personalized cancer immunotherapies. 01/01/2019 - 31/12/2022

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.

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Shifting rainfall regimes: a multi-scale analysis of ecosystem response (REGIME SHIFT). 01/01/2019 - 31/12/2022

Abstract

Recent climate change research reveals a novel and significant trend: weather patterns at mid-latitudes, such as in temperate western Europe, are getting more persistent. With respect to rainfall, this means longer droughts, but also longer periods with excessive rain. No comprehensive study has hitherto investigated the ecological consequences of such regime shifts. Can ecosystems adapt, or will the alternation between drought stress and soil water saturation exhaust them? Will this select for communities with novel trait combinations and more volatile species dynamics? And will these novel systems still be robust in the face of further changes in the environment? This study explores the potential impact of the ongoing shift in the frequency of dry/wet cycles at multiple, connected levels of biological organization. It does so in a new, large-scale set-up at UAntwerp built in the framework of the developing European infrastructure for ecosystem research 'AnaEE'. The design simulates changes in rainfall and associated temperature changes in the open air, using a gradient with eight precipitation regimes so that non-linearity and tipping points can be discerned with great precision. The project scope ranges from plants to soil biota such as bacteria and fungi, and from metabolism and genetic regulation assessed with bioinformatics to ecosystem processes. This multi-scale approach explicitly acknowledges the interwoven nature of ecosystems, with knowledge of molecular and cellular changes being instrumental to mechanistically explain the whole-system-scale effects on productivity, greenhouse gas fluxes and biodiversity dynamics. Different experiments are planned each year: (i) year 1 features a gradient in alternating dry/wet cycles, from 1 to 60 days, across a full growing season; (ii) year 2 focuses on legacy effects and the importance of changes of soil communities; (iii) year 3 matches precipitation regimes to corresponding temperature regimes to study the impact of drought-associated warming (an important natural feedback that can greatly increase plant stress). A series of connected, hypothesis-driven measurements is carried out, which will be integrated using structural equation modelling (path analysis) and ecosystem modelling. The project team has successfully collaborated in the past, and the complementary expertise brought together here should yield both significantly increased understanding of key processes as well as new avenues to climate change impact mitigation.

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Development of novel methods to predict the drug resistance phenotype of Mycobacterium tuberculosis variants. 01/01/2019 - 31/12/2022

Abstract

Every year, ten million people develop tuberculosis (TB) and 1,7 million people die from TB. About 600,000 people are diagnosed with TB resistant to rifampicin, a key first-line TB drug. Drug resistant TB thus poses a global public health problem and threatens global TB control. Whole Genome Sequencing (WGS) is an innovative method to detect drug resistance, but current drug resistance tools can only predict the resistance profile for a small proportion of the over 2000 genetic variants that may be associated with resistance. Furthermore, in late 2018, the WHO will prioritize fluoroquinolones (FQ) and bedaquiline (BDQ) as two of the three core drugs for treatment of rifampicin resistant TB. While FQs are a well-studied class of antibiotics, BDQ is a new drug. Consequently, genotype-phenotype data is limited, and no mutations have been statistically associated with BDQ resistance. New tools to predict resistance are needed to make optimal clinical use of WGS. In this project, I will develop two different methods for prediction of drug resistance in Mycobacterium tuberculosis. In the first method, I will apply pattern mining methods to assess alteration of the drug binding site as a resistance mechanism, by modelling the drug-drug target interaction. In the second method, I will assess regulatory resistance mechanisms, such as upregulation of efflux pumps, by modelling transcriptional changes caused by genetic mutations.

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A study of the plasmodium vivax reticulocyte invasion pathways and ligan candidates, with special attention to the promising PvTRAg and PvRBP multigenic families. 01/01/2019 - 31/12/2022

Abstract

Plasmodium vivax is one of the 5 species causing malaria in humans, and the leading cause of malaria outside Africa. A key step in P. vivax infection is the invasion of reticulocytes (young red blood cells) by the parasite. This invasion is made possible through several interactions between host receptors (reticulocyte membrane) and parasite ligands. While these interactions are well studied for P. falciparum, they remain elusive (and are not comparable) in P. vivax, due to the inability of long-term cultures. However, identifying parasite ligands and characterising the pathways used by the parasite to enter reticulocytes is essential for drug and vaccine development, and is the question that lies at the core of this project. In order to achieve P. vivax elimination, a better understanding of the ligands involved in invasion is necessary. We hypothesize that alternate pathways are used by P. vivax to invade reticulocytes, and that the PvTRAg and PvRBP multigenic families contain important invasion ligands. Therefore, we will carry out the first study integrating newly characterized P. vivax invasion phenotypes with transcriptomic and (epi-)genomic data in field isolates. As such, we expect to advance the knowledge on the role and regulation of PvTrag and PvRBP families in invasion and to discover new potential ligands. Candidate target ligands will be validated by ex vivo invasion assays, and will finally help us to identify the most suited drug and vaccine candidates.

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A personalized recommendation system for whole genome sequencing based and individualized tuberculosis treatment. 01/01/2019 - 31/12/2022

Abstract

Tuberculosis (TB) continues to be a global public health problem with 10.4 million new cases and 1.4 million TB deaths annually. About 600 000 of these new TB cases are resistant to rifampicin, the most important first line drug. In this PhD project I aim to bring Whole Genome Sequencing (WGS) as a diagnostic method, currently mostly used in research, to the patient. Interpretation of WGS data requires expertise which is unavailable in high burden countries, I will bridge this gap by developing a software suite which automatically interprets the WGS data and recommends the optimal individualized treatment regimen for each patient, also taking clinical patient information into account. Additionally, the software will also be able to, dependent on the region and based on the prevalence of drug resistance in that region, detect unexpected drug resistance patterns in patients. For these patients, additional drug susceptibility tests (DSTs) will be recommended. The software will have an easy to use interface where health care workers can enter the clinical patient data. The WGS results will be combined with the clinical information and the results will be made available to the health care worker. Depending on the expertise of the health care worker, additional details describing the decision process towards the optimal regimen and DST recommendation can be made available. This all-inclusive software suite will allow for easier diagnosis and treatment of drug resistant TB.

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Tracing ions in mass spectra to identify small molecules (TractION). 01/11/2017 - 31/03/2022

Abstract

Currently, data analysis and interpretation is the most time consuming step in structural elucidation of small molecules. This still requires a lot of manual intervention time by highly trained MS experts. Moreover, the manual nature of this step makes it vulnerable to human errors. The goal of this project is to reduce the current bottleneck of data interpretation by the evaluation and development of an automatic identification pipeline. This pipeline is based on advanced spectral libraries together with adapted search algorithms and state-of-the-art pattern mining technology.

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Intelligent quality control for mass spectrometry-based proteomics 01/10/2017 - 30/09/2022

Abstract

As mass spectrometry proteomics has matured over the past few years, a growing emphasis has been placed on quality control (QC), which is becoming a crucial factor to endorse the generated experimental results. Mass spectrometry is a highly complex technique, and because its results can be subject to significant variability, suitable QC is necessary to model the influence of this variability on experimental results. Nevertheless, extensive quality control procedures are currently lacking due to the absence of QC information alongside the experimental data and the high degree of difficulty in interpreting this complex information. For mass spectrometry proteomics to mature a systematic approach to quality control is essential. To this end we will first provide the technical infrastructure to generate QC metrics as an integral element of a mass spectrometry experiment. We will develop the qcML standard file format for mass spectrometry QC data and we will establish procedures to include detailed QC data alongside all data submissions to PRIDE, a leading public repository for proteomics data. Second, we will use this newly generated wealth of QC data to develop advanced machine learning techniques to uncover novel knowledge on the performance of a mass spectrometry experiment. This will make it possible to improve the experimental set-up, optimize the spectral acquisition, and increase the confidence in the generated results, massively empowering biological mass spectrometry.

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Identification of compensatory mutations in XDR-Mtb strains: understanding the dynamics and mechanisms of transmission. 01/11/2020 - 31/10/2021

Abstract

Tuberculosis (TB) continues to be a major global public health threat. The development of drug resistance, and extensively drug resistant (XDR) strains of Mycobacterium tuberculosis (Mtb) especially, pose a major problem to TB control. Transmission of XDR-Mtb strains is in conflict with the dogma that XDR-Mtb strains are less transmissible due to a cumulative fitness costs of resistance conferring mutations. For rifampicin resistant strains for example, it has been shown that mycobacteria can acquire compensatory mutations in the rpoC gene to overcome the fitness cost of resistance conferring mutations. Due to the limited access to large sample sets of XDR-Mtb strains, the transmissibility of XDR-Mtb strains and the effect of compensatory mutations to reverse the fitness cost remain poorly studied. The FWO-funded TORCH consortium houses a whole genome sequence database of around 1000 XDR-Mtb strains collected in the Western Cape Province in South Africa over a 14-year period (2006 to 2019). This exceptional dataset of XDR strains allows an in-depth analysis of XDR-TB transmission dynamics and its evolutionary mechanisms. By combining bioinformatics analyses (identifying XDR-Mtb associated genomic convergence) with spatio-temporal analyses of the XDR-Mtb strains by transmission status we expect to identify novel compensatory mutations and their relative importance to the transmission of XDR-Mtb strains.

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Research team(s)

Celluloepidemiology: generating and modeling SARS-COV-2 specific T-cell responses on a population level for more accurate interventions in public health. 01/11/2020 - 31/10/2021

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.

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Diagnosis through Sorted Immune Repertoires (DiagnoSIR). 20/10/2020 - 19/07/2021

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.

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Flanders AI. 01/07/2019 - 31/12/2021

Abstract

The Flemish AI research program aims to stimulate strategic basic research focusing on AI at the different Flemish universities and knowledge institutes. This research must be applicable and relevant for the Flemish industry. Concretely, 4 grand challenges 1. Help to make complex decisions: focusses on the complex decision-making despite the potential presence of wrongful or missing information in the datasets. 2. Extract and process information at the edge: focusses on the use of AI systems at the edge instead of in the cloud through the integration of software and hardware and the development of algorithms that require less power and other resources. 3. Interact autonomously with other decision-making entities: focusses on the collaboration between different autonomous AI systems. 4. Communicate and collaborate seamlessly with humans: focusses on the natural interaction between humans and AI systems and the development of AI systems that can understand complex environments and can apply human-like reasoning.

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Biomina infrastructure. 01/07/2019 - 31/12/2019

Abstract

To meet the enormous amount of data nowadays generated in the life sciences, and the associated need for bioinformatics support, the biomina consortium was established in Antwerpen (biomina = biomedical informatics network Antwerpen), an collaborative network that unites life scientists and data scientists in different faculties around biocomputing. From this initiative, technical and administrative support of bioinformatics initiatives at UA is consolidated.

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iNNOCENS: data driven clinical decision support for improved neonatal care. 01/05/2019 - 30/04/2020

Abstract

Analysis of patient related vital parameters generated in a continuous manner on a neonatal intensive care department offers the opportunity to develop computational models that can predict care-related complications. This project aims to develop a machine learning model that can predict acquired brain injury of prematurity. The model can than be implemented to generate bedside visualizations in the context of a self-learning digital early warning system.

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T Cell Receptor sequence mining platform MinTR. 01/04/2019 - 31/03/2020

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.

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Coordination Biomina support. 01/01/2019 - 31/12/2021

Abstract

To meet the enormous amount of data nowadays generated in the life sciences, and the associated need for bioinformatics support, the biomina consortium was established in Antwerpen (biomina = biomedical informatics network Antwerpen), an collaborative network that unites life scientists and data scientists in different faculties around biocomputing. From this initiative, technical and administrative support of bioinformatics initiatives at UA is consolidated.

Researcher(s)

Research team(s)

A multi-omic approach to characterize gene dosage compensation in Leishmania. 01/10/2018 - 30/09/2021

Abstract

Leishmania is a protozoan parasite with a remarkable tolerance for aneuploidy, while this phenomenon is often deleterious in other organisms. The result of aneuploidy is that all genes of an affected chromosome have an altered gene dosage (i.e. more or less copies) compared to the euploid situation. In Leishmania, we have previously shown that the majority of transcripts and proteins follow dosage changes in a same in vitro condition, while for the remaining products dosage compensation occurs by an unknown mechanism. This project investigates whether (i) dosage compensation occurs by alterations of transcript stability, translation efficiency and/or protein stability, driven by specific transcript and protein biomolecular features and (ii) whether dosage compensation regulation is modulated during the life cycle. As such, we will determine the relative contribution of each regulation layer to the overall compensation and establish a conceptual model of dosage compensation in Trypanosomatids. This is the first integrated multi-omic of dosage compensation in Leishmania, but also in Trypanosomatids in general. The study will lead to novel insights in how this compensation is regulated in aneuploid cells, and investigate if this has a life-stage specific component to it. These fundamental mechanisms are still incompletely understood in all eukaryotes and trough this study, we believe it is possible to gain insights in potentially hitherto unrevealed regulatory mechanisms in eukaryotes.

Researcher(s)

Research team(s)

Mining multi-omics interaction data to reveal the determinants and evolution of host-pathogen disease susceptibility. 01/10/2018 - 30/09/2020

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)

Research team(s)

Single molecule long-read sequencing technology: beyond state-of-the-art in biological and medical research. 01/05/2018 - 30/04/2021

Abstract

This project aims to advance the currently available sequencing technologies at the University of Antwerp (UA) by acquiring a third generation sequencing (3GS) platform. The flagship of the third generation, single-molecule longread sequencers, PacBio Sequel, harnesses the natural process of DNA replication and enables real-time observation of DNA synthesis. 3GS promises to open new avenues for sequencing-based research beyond the current state-of-the-art for this consortium, which consists of more than 14 UA research groups in various disciplines of medicine, biology and bioinformatics. Furthermore, several third parties have also committed to utilize this technology for their ongoing and future research studies. 3GS will be utilized by this consortium to (i) sequence prokaryotic and eukaryotic genomes, and difficult-to-sequence genome regions, (ii) identify new genes and mutations in various rare Mendelian disorders, (iii) identify epigenetic modifications to better understand biological processes like gene expression and host-pathogen interactions, (iv) precisely profile the human, murine, and environmental microbiome in disease and under various environmental stressors, and (v) develop novel preventive therapies for infection-prone disorders for better drug targeting. The analysis of the large amount of genomic and transcriptomic data generated by the various research groups will be coordinated by the UZA/UA bioinformatics group Biomina.

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Research team(s)

Comprehensive Liquid Chromatography - Ion Mobility - Quadrupole-Time-of-Flight Mass Spectrometry for innovative metabolomics. 01/05/2018 - 30/04/2021

Abstract

The requested infrastructure (comprehensive liquid chromatograph-ion mobility-quadrupole time of flight mass spectrometer LCxLC-IM-QTOFMS) combines several state-of-the-art technologies into one platform which aims at bringing metabolomics research to the next level. As such, the infrastructure will deliver a combined five-dimension separation and detection technology, the first of its kind in Belgium. This instrument will be dedicated to metabolomics research, the science of endogenous metabolites in cells, tissues or organisms. The infrastructure will be able to optimally separate, detect and identify the very broad and complex chemical space of metabolites ranging from very polar (e.g. amino acids) to non-polar (e.g. lipids and hormones) at low nanomolar concentration range. Within UA, there is a growing need to combine the currently scattered efforts in metabolomics, an Emerging Frontline Research Domain in the UA research scene. Research ranges from drug discovery (mode of action and pharmacokinetic profiling), biomarker and toxicity studies to advanced data-analysis and systems biology approaches, but a dedicated metabolomics infrastructure to strengthen these studies is currently missing. As such, the investment in a core facility together with the gathering of nine research groups from five departments and two faculties would centralize the metabolomics research. This will position UA as a key player in the academic metabolomics research in the BeNeLux and worldwide.

Researcher(s)

Research team(s)

Systems biological analysis of niche adaptation in resistant and virulent Salmonella pathogens. 01/01/2018 - 31/12/2021

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)

Research team(s)

An interdisciplinary study on the role of the HLA genes and T-cell diversity as risk factors for herpes zoster. 01/01/2018 - 31/12/2021

Abstract

Chickenpox is a consequence of primary infection of varicella-zoster virus (VZV). Afterwards, VZV remains latent in neural ganglia until symptomatic reactivation called herpes zoster (HZ, shingles). In this project, we will first develop a novel computational framework that will allow us to estimate the probability that a pathogen-derived antigen is adequately recognised by the major histocompatibility complexes (MHC) encoded by HLA genes. Antigen bounding by MHC molecules is a necessary step prior to recognition (and further management) of infected cells. Next, we will obtain HLA data from 150 HZ patients and 150 matched controls. This will allow us to estimate whether and which HLA A/B/C genes are enriched or depleted in HZ patients. Our computational framework will allow us to estimate which VZV proteins are most likely of importance in controlling VZV. We will assess whether the HLA data is readily translated into the diversity of the T-cell receptor (TCR) against VZV, and against which of the most important VZV proteins. Finally, we will differentiate blood-derived inducible pluripotent stem cells (iPSC) into neuronal cells, infect these neuronal cells with VZV and study whether depletion of VZV-specific T-cells affects VZV proliferation, thereby confirming our earlier obtained HLA-TCR predictions.

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Research team(s)

Rapid vaccine development through immunoinformatics ans immunisequencing. 01/01/2018 - 31/10/2020

Abstract

Vaccines are used to stimulate the immune system in its defense against pathogens and cancer. Vaccine development involves extensive clinical trials that study the changes in antibodies and immune cells in response to the vaccine to determine their efficacy and safety. This is often an extensive and costly process, with a high failure rate. This project aims to develop a computational framework for use within vaccine clinical trials to make the process more efficient, more rapid and more accurate. The basis of this framework is the new immunological and molecular insights that have been gained through the advent of immunosequencing and immune-informatics technologies, and it builds further upon a successful collaboration between immunologists and data scientists.

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Research team(s)

Unraveling the potential of circular RNAs as novel biomarkers of radiation exposure and- sensitivity and their functional characterization in the radiation response 15/10/2017 - 14/10/2021

Abstract

Biomarkers for radiation exposure are important for a number of reasons. With a growing nuclear threat, the identification of efficient biomarkers for radiation exposure that enable fast triage of exposed individuals is becoming increasingly important. Likewise, the identification of robust biomarkers of radiosensitivity should help tuning current tumor radiotherapies to more personalized schemes. Current golden standard methods for biodosimetry such as cytogenetics assays fall short in several aspects related to emergencies, in that their analysis is very laborious, time-consuming and expensive and therefore not amenable for fast screening of large cohorts. In the last decade, gene expression signatures have emerged as potential biomarkers that could be useful for the abovementioned purposes1–6. We have recently taken this research a step further with the identification of exon expression signatures as robust radiation biomarkers7 which are more sensitive than gene signatures, and therefore more suitable in the case of low-dose exposures. One of the main disadvantages of classical mRNA biomarkers is their inherent instability. Circular RNAs (circRNAs) are a recently described class of non-coding RNA molecules9,10, of which the expression varies according to the cell/tissue-type and developmental timing11–16. Due to their covalently closed circular structure, circRNAs are resistant to exonuclease degradation, and therefore remarkably stable17. This, together with observations that circRNAs are highly abundant in blood cells18 and furthermore enriched in exosomes from human serum19 gives them a very high potential as biomarkers in general, and radiation biomarkers in particular. Hence, in this PhD project, we will identify circRNA biomarkers for radiation exposure and radiosensitivity and characterize the functions of the most promising ones.

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Research team(s)

The development of a metabolomics-based in vitro model for human hepatotoxicity 01/10/2017 - 30/06/2019

Abstract

In order to comply with the REACH-regulations (Registration Evaluation and Authorization of Chemicals) and to improve animal welfare, a human based in vitro model to investigate the hepatotoxicity of new chemical entities (NCEs) will be developed. The designed model is based on an innovative approach combining in vitro methodology with LC-MS metabolomics, a recent – omics domain that examines alterations in the endogenous metabolic profile of cells and organisms. The basis of our model is the hepatic human HepaRG® cell line, which closely resembles primary hepatocytes in terms of metabolic capacity and toxicological response. In a first phase, the model will be developed by exposing HepaRG cell cultures to well known hepatotoxic compounds, such as acetaminophen, valproic acid, fluoxetine, bosentan and aflatoxine B1; the intracellular profile of the endogenous metabolites will be investigated in an untargeted approach using Liquid Chromatography coupled to High Resolution Mass Spectrometry (LC-QTOF-MS). Differences between the metabolic profiles of exposed and unexposed cells will be examined using bioinformatics tools in order to identify possible biomarkers characteristic for the multiple hepatotoxic modes of action (MoA). A database containing the reference compounds, their MoAs and the corresponding specific biomarkers will be used in a targeted approach to investigate the hepatotoxic MoA of NCEs, including pharmaceutical and/or industrial compounds.

Researcher(s)

Research team(s)

Establishment of Belgian Elixir Node. 01/01/2017 - 31/12/2019

Abstract

Consolidation of VariantDB as a collaborative variant interpretation platform within ELIXIR. Given the rapid implementation of next-generation sequencing in various domains, we believe that one of the major bottlenecks will become the interpretation of the resulting data. We are convinced that a structural solution should support distributed big data storage, coupled to centralized and intelligent querying. Today, due to dispersed data, investigators resort to multiple databases and ad- hoc communication with collaborators to assess variant pathogenicity. Considering today's challenges, we aim at providing an integrated platform offering researchers intelligent decision support and seamless collaboration options. First, phenotypic information is coupled to interpretation and ranking of individual variants in the context of a single sample. Second, we integrate the ELIXIR service NGS-Logistics, to enable platform wide analysis of variant prevalence. Third, we provide automatic selection of similar patients and matched control cohorts from the available samples, to perform valid enrichment analysis. Within ELIXIR, NGS-Logistics already adheres to the philosophy of distributed storage and centralized analysis, on the level of variant calling. By implementing the features proposed above, VariantDB could complement this service at the level of variant interpretation. As a service, it will be an asset in both routine and research applications. Finally, the proposed platform is made available to all institutions, bringing new collaboration opportunities to ELIXIR partners.

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Research team(s)

Efficient mining for unexpected patterns in complex biological data. 01/10/2016 - 30/09/2020

Abstract

The last decade, life sciences have become increasingly overwhelmed and driven by large amounts of complex data. Thanks to disruptive new technologies, the speed at which the biomolecules (such as DNA, metabolites or proteins) of a living system can be analyzed, is already for several years increasing faster than the capacity of computer processors and hard drives. This trend means that "traditional techniques" to analyze and interpret biomolecular data become less suitable in the current era. Indeed, extracting relevant knowledge from these data relies on a range of dedicated "big data" techniques, falling under the terms "data mining" and "machine learning". This project addresses "pattern mining", a specific class of techniques that is very relevant for life science. Pattern mining allows for the discovery of previously unseen, interesting patterns in complex data. Traditionally, frequent pattern mining deals with finding the most frequent sets or "combinations" of items in a dataset. There are however major problems with such pattern lists, which we will address in this project. First, these pattern lists are often huge, and no domain expert is typically able to investigate and try to interpret every pattern in a pattern mining result list. Second, many of the patterns in such a list are not interesting for the domain expert, for example because they are trivial. In this project, we develop a generic formal and statistically sound framework to re-define pattern interestingness given the specific life science context. After definition of novel pattern mining interestingness criteria, we will develop efficient algorithms to mine such patterns. The algorithms will be validated on toy datasets and golden standard data. Finally we will put these methods into force to extract novel knowledge from large scale microbial gene expression compendia, a huge set of human genome sequences and drug-compound interaction networks, with the goals to generate fundamentally new biological or biomedical insights.

Researcher(s)

Research team(s)

Deciphering hidden inheritance patterns using frequent itemset mining techniques on high throughput genomic data. 01/10/2016 - 15/04/2020

Abstract

Today, technologies exist that are able to screen complete human genomes for genetic defects, hereby producing massive amounts of data. These techniques include microarrays for the detection of duplicated or missing genomic material and next-generation sequencing for the detection of variation at the nucleotide level. In parallel, extensive public resources contain additional biological information on the observed variation to aid in interpretation of the data. While some variants show full penetrance, others can be present in both seemingly healthy and severely impaired family members, indicating that disease modifying variants play a role in the clinical presentation. This led to the formulation of a 'many genes, common pathways' paradigm. To study genetic variation under this paradigm, novel models placing interpretation of individual results in a context of multiple patients are mandatory. Searching for common patterns over large patient cohorts might identify recurrently affected pathways with a critical role in the studied disease. Simultaneously considering multiple variants affecting such a pathway will thus help to explain both the observed phenotype and combined with pedigree information, the intrafamilial variability. Here, we will investigate how we can apply state-of-the-art data mining methods to reveal hidden relationships between variants, with the goal of gaining new insights in the molecular pathology of heritable diseases, focusing on cognitive disorders.

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Research team(s)

Project website

Mining multi-omics interaction data to reveal the determinants and evolution of host-pathogen disease susceptibility. 01/10/2016 - 30/09/2018

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)

Research team(s)

MALDI Mass Spectrometry Imaging (MALDI-MSI): Bridging proteomics and imaging. 01/05/2016 - 30/04/2020

Abstract

The instrument acquired in this project is a matrix assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometer capable of mass spectrometry imaging (MSI). This technique is especially developed for the identification of biomolecules in a manner that retains cytological and histological patterning. This novel technical process, abbreviated to MALDI-MSI represents an interesting and extremely productive intersection between mass spectrometric and imaging platforms. Therefore, this grant is bridging 3 University of Antwerp CORE facilities (Center for Proteomics, Bio-Imaging lab and the Biomedical Microscopic Imaging Core). Using this MALDI-MSI platform, multiple research groups, brought together by a common interest in investigating molecular damage associated with aberrant aging mechanisms, will be able to identify a diverse range of small molecules (peptides and metabolites) as well as larger proteins directly on tissue slides, preserving the topological, histological and cytological data. This is not possible with routine proteomics and metabolomics technologies nor with advanced imaging techniques.

Researcher(s)

Research team(s)

    Development of immunoinformatics tools for the discovery of T-cell epitope recognition rules. 01/02/2016 - 31/01/2020

    Abstract

    Herpes viruses are ubiquitous in human society and cause several common diseases, such as cold sores (Herpes simplex) and chickenpox (Varicella). The eight species of herpes viruses known to primarily infect humans are all clinically relevant and of these, five are known to be extremely widespread amongst humans with seroprevalence rates as high as 90%. Not all individuals are equally susceptible to equivalent viral pathogens. After infection, some individuals do not become symptomatic, while others experience a high severity of the disease with serious complications. For example, a relatively benign disease such as chickenpox can become life-threatening in a small set of individuals. These differences in disease susceptibility are likely to be caused in part due to the variation in the human immune system, but remain largely unknown up to date. A key step in the activation of the adaptive immune system is the presentation of viral epitopes, usually peptides (p), by the major histocompatibility complex (MHC) present on antigen presenting cells (APC) and the recognition of this complex by a T-cell receptor (TCR). There exist many allelic variants of the genes coding for the MHC genes within the population and each variant has a different propensity to bind immunogenic (viral) peptides. This variability in the MHC alleles is one of the underlying factors that leads to differences in disease susceptibility. Previous research has demonstrated that high accuracy models can be established for the affinity of the MHC molecules for the presentation of peptides, based on machine learning methods. The resulting affinity prediction models have made it possible to assess the affinity for almost all human MHC alleles for any given peptide. However, the MHC recognition variability is only part of the story, as each individual has a unique repertoire of T-cells with a large diversity of TCR variants. The variability in TCR epitope recognition is also an important factor in differences between individual immune responses. Unfortunately, few TCR recognition models exist and they are all very limited in scope and accuracy. Therefore, the scope of this project is to develop, evaluate and apply state-of-the-art computational approaches to enable the interpretation of complex MHC-p-TCR interaction data and to elucidate the patterns that govern this system. Within this scope, a key point of interest will be the modelling of the molecular interaction between the MHC complex, encoded by its corresponding HLA allele, the antigen-specific TCR and the peptide antigen itself. Ultimately, this will result in the development of computational tools capable of predicting personalized immune responses to Herpes viruses and the efficacy of vaccine-induced viral protection.

    Researcher(s)

    Research team(s)

    The development of a metabolomics-based in vitro model for human hepatotoxicity. 01/10/2015 - 30/09/2017

    Abstract

    In order to comply with the REACH-regulations (Registration Evaluation and Authorization of Chemicals) and to improve animal welfare, a human based in vitro model to investigate the hepatotoxicity of new chemical entities (NCEs) will be developed. The designed model is based on an innovative approach combining in vitro methodology with LC-MS metabolomics, a recent – omics domain that examines alterations in the endogenous metabolic profile of cells and organisms. The basis of our model is the hepatic human HepaRG® cell line, which closely resembles primary hepatocytes in terms of metabolic capacity and toxicological response. In a first phase, the model will be developed by exposing HepaRG cell cultures to well known hepatotoxic compounds, such as acetaminophen, valproic acid, fluoxetine, bosentan and aflatoxine B1; the intracellular profile of the endogenous metabolites will be investigated in an untargeted approach using Liquid Chromatography coupled to High Resolution Mass Spectrometry (LC-QTOF-MS). Differences between the metabolic profiles of exposed and unexposed cells will be examined using bioinformatics tools in order to identify possible biomarkers characteristic for the multiple hepatotoxic modes of action (MoA). A database containing the reference compounds, their MoAs and the corresponding specific biomarkers will be used in a targeted approach to investigate the hepatotoxic MoA of NCEs, including pharmaceutical and/or industrial compounds.

    Researcher(s)

    Research team(s)

    A systems biology approach for a comprehensive understanding of development and adaptation in Leishmania donovani. 01/10/2015 - 30/09/2017

    Abstract

    This PhD project will undertake a systems biology approach to improve the understanding of Leishmania development and adaptation using a holistic view of cellular processes. As such, our goal is to stepwise unravel the complexity of the interactions between the different 'omic levels.

    Researcher(s)

    Research team(s)

      Gene expression analyses for the differentiation between viral and bacterial meningitis in children. 01/07/2015 - 30/06/2017

      Abstract

      Rapid detection of bacterial meningitis in children remains an important goal for emergency room doctors and paediatricians. The differentiation between viral and bacterial meningitis in children is mainly based on clinical scoring systems that are, however, neither 100% sensitive nor 100% specific. Additionally, adequate sampling of cerebrospinal fluid (CSF) is not always achievable. Recently, the value of gene expression analyses for infectious diseases has been illustrated in several clinical and experimental settings. Several studies were able to show a difference in gene expression between different types of influenza, between different types of bacterial infections, between tuberculosis and other inflammatory or infectious diseases in African children and between some viral infections and some bacterial infections. However, none of these studies specifically addressed the value of gene expression analyses in differentiating between viral and bacterial meningitis. In this multicentre prospective study, we will use whole blood gene expression analyses to differentiate between viral and bacterial infections in children with meningitis (N = 80). This study will add to the important clinical differentiation between viral and bacterial meningitis in children. Furthermore, we believe that the determination of the gene expression signalling in bacterial (but also viral) meningitis will elucidate the pathophysiology of this disease.

      Researcher(s)

      Research team(s)

        Development of an integrated strategy to characterize new lead compounds based on natural pro-drugs and their metabolites. 01/10/2014 - 30/09/2017

        Abstract

        Many natural products are pro-drugs that are metabolized and activated after oral administration. Nevertheless this aspect is usually overlooked when searching for new lead compounds for therapeutic agents. In this project Filipendula ulmaria (meadowsweet) and Herniaria hirsuta have been selected as case studies for the characterization of new leads for anti-inflammatory drugs, and drugs for nephrolithiasis. An LC-MS and 1H-NMR platform will be used for the fast metabolomic profiling of plant extracts, prepared using comprehensive extraction methods to cover the full range of constituents. Secondly, the platform will be extended with a dialysis model simulating human gastro-intestinal (GI) metabolization, which will be applied to activate potential pro-drugs. In addition, the dialysate containing the GI metabolites will be treated with microsomal S9 fractions to mimic liver metabolization. The resulting metabolized samples (before and after S9 treatment) will be profiled in the same LC-MS and 1H-NMR platform, and compared with the original profiles. At the same time all extracts and metabolized extracts will be pharmacologically evaluated in a range of in vitro assays related to anti-inflammatory and anti-nephrolithiasis properties. Pharmacological and chromatographic / phytochemical data will be analyzed in a metabolomics approach using multivariate data analysis in order to characterize the pharmacologically active constituents and their metabolites as new lead compounds.

        Researcher(s)

        Research team(s)

        Interactome of living cells in contact with nanoparticles. 01/05/2014 - 20/08/2014

        Abstract

        The interdisciplinary PhD project aims to investigate the behavior and fate of engineered nanomaterials in contact with biologica I fluids and living ce lis, and consequent biological signalling responses. Emphasis is put on signalling mechanisms in immune relevant cells, underlying possible immunomodulatory effects. The nature of what is adsorbed on the nanoparticle surface is connected to the observed interactions and outcomes on cells. Both the adsorbed biomolecule corona, as weil as biomarker expression signatures and pathways will be investigated and combined via a comprehensive -omics analysis and bio-informaties approach. By modulating the nanoparticles' surface properties, e.g. through functionalization, avoidance of particular mechanisms leading to undesired outcome will be realized.

        Researcher(s)

        Research team(s)

        Deciphering hidden inheritance patterns using advanced data mining techniques on high throughput genomic data. 01/10/2013 - 31/10/2016

        Abstract

        In this project, we will investigate how we can apply state-of-the-art data mining methods to reveal hidden relationships between variants, with the goal of gaining new insights in the molecular pathology of heritable diseases, focusing on cognitive and cardiac disorders.

        Researcher(s)

        Research team(s)

        A systems biology approach for a comprehensive understanding of development and adaptation in Leishmania donovani. 01/10/2013 - 30/09/2015

        Abstract

        This PhD project will undertake a systems biology approach to improve the understanding of Leishmania development and adaptation using a holistic view of cellular processes. As such, our goal is to stepwise unravel the complexity of the interactions between the different 'omic levels.

        Researcher(s)

        Research team(s)

          An integrated informatics platform for mass spectrometry-based protein assays (InSPECtor). 01/03/2013 - 28/02/2017

          Abstract

          This project represents a research agreement between the UA and on the onther hand IWT. UA provides IWT research results mentioned in the title of the project under the conditions as stipulated in this contract.

          Researcher(s)

          Research team(s)

          Evolving graph patterns. 01/01/2013 - 31/12/2016

          Abstract

          The goals of this project are first addressed from a theoretical perspective. Furthermore, techniques are studied using both synthetic and real experimental data. The concept of evolving graph patterns is relevant for a large series of application domains. However, we will particularly validate our approaches with bioinformatics applications, for which the extraction of this new pattern type is highly interesting.

          Researcher(s)

          Research team(s)

          Integrative bioinformatics analysis of combined epigenome, transcriptome and proteome data. 01/10/2012 - 30/09/2016

          Abstract

          Rapid evolution in analytical technologies including next generation sequencing and mass spectrometry recently boosted the systematic analysis of molecular layers such as the transcriptome, epigenome and proteome. Datasets of enormous size and diversity are now routinely generated in Systems Biology research projects. The advances in the acquisition of these diverse data are followed by the development of new bioinformatics approaches, typically each dedicated to the analysis and interpretation of a specific data type. The separate analysis of each data type does not suffice anymore to satisfy the need for a multi-perspective understanding of biological processes and diseases, which is imperative in modern Systems Biology. Different 'omics datasets should not only be analysed separately, but also be integrated and compared, in order to reveal patterns that encompass multiple 'omics layers. This is an underexplored research area in the bioinformatics field. At the PPES lab of Proteomics & Epigenetic Signaling, parallel transcriptomic (Illumina Array, miRNA QPCR array), epigenomic (MBD2seq, Illumina CpG array) and chemoproteomic (SILAC/iTrAQ) assays have been performed on different cancer cell types treated with the very potent tumor selective anticancer drug Withaferin A, to get a comprehensive view of cellular networks targeted during chemosensitisation. By an integrated analysis of our available datasets, we want to identify key proteins/nodes/pathways responsible for the potent chemosensitizing anti-cancer effects of Withaferin A. In this doctoral project, novel bioinformatic methodologies will be developed and studied which enable the integrative analysis of these three different quantitative omics data types (transcriptome, epigenome and proteome) with high relevance for research ongoing in and outside the University.

          Researcher(s)

          Research team(s)

            Next generation sequencing technology opening new frontiers in biological and medical research. 28/06/2012 - 31/12/2017

            Abstract

            The aim of this project is to develop a next generation sequencing (NGS) platform to advance in a collaborative way biological and medical research within the Antwerp research community. The consortium involves more than 16 research groups in various disciplines of medicine, biology and biomedical informatics. The goals are to identify new genes and mutations in various rare Mendelian disorders, to achieve more insights in the genetic causes of cancer and to unravel more precisely the genetic determinants of infectious diseases. This new knowledge will improve both the diagnosis and management of these human diseases. The project will also focus on the interaction between environment and genes. More specifically, the effect of environmental stressors on genetic variation in aquatic organisms, the effect of teratogenic factors on embryonic development in vertebrates and the effects of environmental conditions on growth in maize and Arabidopsis lines will be studied. The analysis of the large amount of genomic and transcriptomic data, generated by the various research groups, will be coordinated by the recently founded UZA/UA bioinformatics group Biomina

            Researcher(s)

            Research team(s)

            Intelligent analysis and data-mining of mass spectrometry-based proteome data. 01/07/2009 - 30/06/2013

            Abstract

            Mass spectrometry is a powerful analytical technique to elucidate the structure of molecules, like proteins. Until now a significant fraction of the data coming from MS analysis remains uninterpretable. This projects aims to apply state-of-the-art data mining techniques to a large set of mass spectra, aiming to find new relevant patterns that may point towards unknown structural modifications.

            Researcher(s)

            Research team(s)

            Anoxia resistance in vertebrates: metabolomics of brains en hearts that never stop. 01/10/2007 - 30/09/2011

            Abstract

            The crucian carp can survive for weeks in total anoxia, amongst others because of a unique anaerobic pathway producing ethanol. During this period brain and heart remain fully functional. This study compares the 'metabolomics' of the main organs of the anoxic crucian carp with those of the closely related, but not anoxia resistent, common carp. New methodologies for NMR and data analaysis will ne optimised.

            Researcher(s)

            Research team(s)

              Proteome research on the physiology underlying cryotolerance and desiccation in banana meristems. 01/01/2006 - 31/12/2009

              Abstract

              The project's main objective is "To gain a fundamental insight in the cryo-physiology through a proteome study of banana meristems". It aims to contribute to a more fundamental knowledge of some of the physiological processes that underlie adaptation and acclimatization, as weIl as the mechanisms of stress injury. Many of these stress parameters are correlated. For example, water stress is often associated with salt stress in the root and/or heath stress in the leaves. Resistance towards freezing is highly dependent upon resistance towards tissue dehydration. Plants also often exhibit a cross- resistance what implies that the underlying mechanisms for resistance against different stress factors have common characteristics. These mechanisms are complex and their unraveling can only take place through long term and multidisciplinary research.

              Researcher(s)

              Research team(s)

              Hormone homeostasis and signal transduction during oxidative stress in plants : identification of signal components by means of an integrated proteomic and immunological approach. 01/01/2005 - 31/12/2008

              Abstract

              This project envisages identifying components of the signal transduction cascades during oxidative stress in plants by means of an integrated immunological and proteomic approach. During its development a plant is continuously exposed to various kinds of stress. Its ability to react to these changes in a fast and appropriate manner is essential for its survival. Very often, changes in hormone homeostasis play an important role. Our knowledge regarding the role of plant growth regulators such abscisic acid (ABA), ethylene, jasmonic acid (JA) and salicylic acid (SA) as important signal molecules in stress responses is already substantial. Stress induced auxin and cytokinin action has been described in a restricted set of stress phenomena. Various studies point to oxidative stress as a central component in many cellular responses (Desikan et al., 2001; Neill et al., 2002). Oxidative stress results from an imbalance in the production and metabolism of reactive oxygen species (ROS). ROS are produced as a response to exposure to biotic and abiotic stress and act as a signal that triggers a variety of molecular, biochemical and physiological events. Nitrogen monoxide (NO) is very often an important component in the underlying signal transduction cascades. NO, a gaseous free radical, can interact with ROS in a number of ways and as such influences the response to biotic and abiotic stress factors (Delledonne et al., 1998; Neill et al., 2002; Neill et al., 2003). As is the case in animal models, cyclic GMP and cyclic ADPR are put forward as signal molecules in the plant NO signal transduction system. Cyclic GMP is produced in plants as a response to NO application (Pfeiffer et al., 1994) and in turn cADPR synthesis is thought to be induced by cGMP. Both have been shown to mimic certain functions of NO (Durner et al., 1998), and a specific inhibitor of guanylyl cyclase (ODQ) inhibits Arabidopsis thaliana NO-induced cell death. A membrane permeable cGMP-analog, 8-Br-cGMP, eliminates this inhibitory effect (Clarke et al., 2000). In animal systems, stress induced cADPR synthesis is activated via cGMP-dependent protein kinase. Until now, the existence of such a protein in higher plants has not been reported yet. The recent isolation of the gene for a putative cyclic nucleotide dependent protein kinase in our lab is a convincing candidate for this function. In its promoter region a considerable number of stress-regulated elements are found, such as the ABA-response element (ABRE), the "heat shock"-response element, the ABRE related "GC-repeat" (Litis et al., 1992) and the cold and water stress regulated dehydration (DRE)/"C-repeat"-response elements (Baker et al., 1994). The additional presence of a salicylic acid regulated promoter element (TCA), a wound induced response element, (WUN) (Pastuglia et al., 1997), and of the "TC-rich repeat", a stress and defence induced response element (Klotz en Lagrimini 1996), strengthens our proposition for a stress related function of this putative cyclic nucleotide dependent protein kinase.

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              Integration of proteome data from tobacco BY-2 cell suspension cultures in a centralised bioinformatics platform. 01/01/2005 - 31/12/2007

              Abstract

              This project fits within the context of the proteome-analytical study of the regulation of the plant cell-cycle, using the tobacco BY-2 cell cuspension culture. This research continuously yields large and complex data-sets, of which the processing, integration and analysis are a major challenge. The project aims at bringing together all databases and tools on a central bioinformatics platform in order to achieve efficient data-handling. The new platform consists of a computer-cluster with sufficient processing power and storage capacity, on which a range of bioinformatics tools will be installed. The platform enables a number of new applications: construction of relational proteome databases, clustering of proteins based on parameters such as observerd expression levels, in silico proteolytic cleavage of complete databases to aid the interpretation of experimental data, etcetera. Although the analysis of tobacco BY-2 proteome data is the primary focus of this project, the platform will be valuable to other projects as well.

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              Study of the effects of cyclic nucleotides and cytokinins on the proteome during the cell cycle in plants. 01/10/2003 - 30/09/2006

              Abstract

              This project aims to elucidate the molecular mechanisms, by which cyclic nucleotides and cytokinins regulate the higher plant cell cycle. Earlier research yielded some interesting binding proteins, among which cAMP-binding Nucleoside Diphosphate Kinases and a cytokinin-binding Adenosine Kinase. By using Nicotiana tabacum BY-2 (Bright Yellow-2) cell suspension cultures as well as Arabidopsis thaliana whole plants and cell cultures, the interactions of cyclic nucleotides and cytokinins with the plant proteome are further investigated. This study consists of three major parts. The first part focusses on searching and identifying proteins that are affected by cyclic nucleotides or cytokinins. Either affinity chromatography and photo-affinity labeling techniques are used to find proteins that interact directly with these signalling molecules. By means of two-dimensional electrophoresis and specific detection techniques, proteins undergoing post-translational modifications, affected by cAMP or cytokinins, are revealed. Differential proteome analysis can show which gene products are up- or downregulated by these regulators. Potentially interesting proteins are systematically identified with several mass spectrometrical techniques. The second part of this study consists of cloning the relevant genes, by means of a PCR strategy. Transcript levels of these individual genes are studied. Recombinant GFP-fusion constructs are expressed in suspension cells as well as in whole plants, to investigate the spatio-temporal distribution of the gene products. In the third part of the project, gene products that show potential cAMP or cytokinin regulated functions, will be thoroughly characterised. The proteins are therefore produced in an expression system. Interacting partners are searched by means of affinity chromatography and biosensor techniques, and subsequently identified. All identity data arising from this project will be continuously implemented in the tobacco BY-2 proteome database, which has already been published online. Besides identity data, information regarding relevant interactions with other components, post-translational modifications and expression information are added. The combination of these approaches will provide a comprehensive picture of the signal transduction chain of cAMP and cytokinins in relation to the plant cell cycle.

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              Functional involvement of cyclic nucleotide-binding proteins in the cell cycle of the Nicotiana tabacum BY-2 cell suspension culture. 01/10/2001 - 30/09/2003

              Abstract

              Synchronized tobacco BY-2 cell cultures are used as a model system to study the putative role played by cGMP on developmental processes of higher plants. The following aspects are being investigated: What is the role of cGMP during cell cycle progression? Characterization of a cGMP specific phosphodiesterase. Which are the target proteins of cGMP action?

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                Functional involvement of cyclic GMP in the developmental physiology of higher plants. 01/10/1999 - 30/09/2001

                Abstract

                Synchronized tobacco BY-2 cell cultures are used as a model system to study the putative role played by cGMP on developmental processes of higher plants. The following aspects are being investigated: What is the role of cGMP during cell cycle progression? Characterization of a cGMP specific phosphodiesterase. Which are the target proteins of cGMP action?

                Researcher(s)

                Research team(s)