Biology subjects

Moris, P.

Publications and source records attributed to Moris, P..

5 recordsLinked to original sources

bioRxiv2025

Elevated Plasmodium falciparum sexual conversion in HbAC and HbAS red blood cells without altered IgG/IgM levels to trophozoite and stage I gametocyte-specific antigens

Malaria transmission relies on the differentiation of asexual parasites into gametocytes, a process initiated by sexual conversion (SC). Mutations in the host hemoglobin beta (HBB) gene are known to influence parasite growth and disease outcome, yet their impact on SC remains unclear. We investigated the effect of HBB mutant genotypes on Plasmodium falciparum SC and humoral immunity in individuals from Nanoro, Burkina Faso. To measure SC rates in natural human malaria infections, we developed a new ex vivo SC assay (evSCA). We found that in human natural P. falciparum infections, SC rates were higher in individuals with HbAS or HbAC than in individuals with wild-type HBB (HbAA). Consistently, using an in vitro SC assay (ivSCA) based on the NF54-gexp02-Tom reporter line we found that cultures grown in HbAS red blood cells (RBCs) had higher SC rates than those grown in HbAA RBCs. Furthermore, IgG and IgM responses against trophozoite- and stage I gametocyte-infected RBC antigens, quantified by flow cytometry, did not differ between plasma from individuals with different HBB genotypes. These results demonstrate that exposure to RBCs with HBB mutations enhances SC, highlighting a host-genetic factor that may influence malaria transmission potential. AUTHOR SUMMARYTransmission of Plasmodium falciparum -a malaria causing parasite- depends on the parasites ability to produce gametocytes, the stage that infects mosquitoes. In many African regions where malaria is common, hemoglobin (Hb) variants such as hemoglobin S (the sickle-cell trait) and C are highly prevalent reflecting their role in protecting against severe disease. However, it remains unclear whether these variants also influence the parasites early shift toward gametocyte development (i.e., sexual conversion [SC]). In this study, we combined ex vivo and in vitro SC assays to measure SC in red blood cells with different hemoglobin genotypes. We found that SC was higher in parasites growing in red blood cells carrying hemoglobin AC and AS genotypes compared to normal AA hemoglobin. These findings provide experimental support for earlier epidemiological observations reporting that individuals with these hemoglobin variants often carry more gametocytes and are more infectious to mosquitoes. We also measured antibody levels in individuals with mutant RBCs and found no differences in immunity that could explain the variation in SC. Our results show that host genetic background can influence gametocyte development and may shape the infectious reservoir in high-burden settings. This knowledge is important for designing effective strategies for malaria elimination.

microbiology↗

bioRxiv2019

Treating biomolecular interaction as an image classification problem - a case study on T-cell receptor-epitope recognition prediction

The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both known and novel epitopes. In addition, we present a novel feature representation approach which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are particular to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets, and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to novel epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.

bioinformatics↗

bioRxiv2018

TCRex: a webtool for the prediction of T-cell receptor sequence epitope specificity

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individuals TCR repertoire and directly query their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

bioinformatics↗

bioRxiv2018

The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology

BackgroundTracking recent transmission is a vital part of controlling widespread pathogens such as Mycobacterium tuberculosis. Multiple methods with specific performance characteristics exist for detecting recent transmission chains, usually by clustering strains based on genotype similarities. With such a large variety of methods available, informed selection of an appropriate approach for determining transmissions within a given setting/time period is difficult.\n\nMethodsThis study combines whole genome sequence (WGS) data derived from 324 isolates collected 2005-2010 in Kinshasa, Democratic Republic of Congo (DRC), a high endemic setting, with phylodynamics to unveil the timing of transmission events posited by a variety of standard genotyping methods. Clustering data based on Spoligotyping, 24-loci MIRU-VNTR typing, WGS based SNP (Single Nucleotide Polymorphism) and core genome multi locus sequence typing (cgMLST) typing were evaluated.\n\nFindingsOur results suggest that clusters based on Spoligotyping could encompass transmission events that occurred over 70 years prior to sampling while 24-loci-MIRU-VNTR often represented two or more decades of transmission. Instead, WGS based genotyping applying low SNP or cgMLST allele thresholds allows for determination of recent transmission events in timespans of up to 10 years e.g. for a 5 SNP/allele cut-off.\n\nInterpretationWith the rapid uptake of WGS methods in surveillance and outbreak tracking, the findings obtained in this study can guide the selection of appropriate clustering methods for uncovering relevant transmission chains within a given time-period. For high resolution cluster analyses, WGS-SNP and cgMLST based analyses have similar clustering/timing characteristics even for data obtained from a high incidence setting.

epidemiology↗

bioRxiv2017

On the feasibility of mining CD8+ T-cell receptor patterns underlying immunogenic peptide recognition.

AbstractCurrent T-cell epitope prediction tools are a valuable resource in designing targeted immunogenicity experiments. They typically focus on, and are able to, accurately predict peptide binding and presentation by major histocompatibility complex (MHC) molecules on the surface of antigen-presenting cells. However, recognition of the peptide-MHC complex by a T-cell receptor is often not included in these tools. We developed a classification approach based on random forest classifiers to predict recognition of a peptide by a T-cell and discover patterns that contribute to recognition. We considered two approaches to solve this problem: (1) distinguishing between two sets of T-cell receptors that each bind to a known peptide and (2) retrieving T-cell receptors that bind to a given peptide from a large pool of T-cell receptors. Evaluation of the models on two HIV-1, B*08-restricted epitopes reveals good performance and hints towards structural CDR3 features that can determine peptide immunogenicity. These results are of particularly importance as they show that prediction of T-cell epitope and T-cell epitope recognition based on sequence data is a feasible approach. In addition, the validity of our models not only serves as a proof of concept for the prediction of immunogenic T-cell epitopes but also paves the way for more general and high performing models.

immunology↗