Biology subjects

Kesmir, C.

Publications and source records attributed to Kesmir, C..

6 recordsLinked to original sources

bioRxiv2023

A novel bioinformatics pipeline for the identification of immune inhibitory receptors as potential therapeutic targets

Blocking inhibitory receptors like PD-1 and CTLA-4 has revolutionized cancer treatment in recent years. However, despite major successes in melanoma and lung cancer, the majority of cancer types are not responsive to these immunotherapies. As such, there is an ongoing need for the identification of novel inhibitory receptors as drug targets. Most inhibitory receptors signal via immunoreceptor tyrosine-based inhibitory motifs (ITIMs) and previous studies have estimated that our genome contains over 1600 ITIM- bearing transmembrane proteins. However, further testing and development of this large number of candidates requires increased understanding of their expression patterns and likelihood to function as inhibitory receptor. To assist in the selection of novel inhibitory receptor as therapeutic targets, we designed a novel bioinformatics pipeline integrating machine learning-guided structural predictions and sequence-based likelihood models to identify 51 known and 390 putative inhibitory receptors. Using publicly available transcriptomics data of immune cells, we determined the expression of these novel inhibitory receptors, and classified them into previously proposed functional categories. Known and putative inhibitory receptors were expressed across a wide variety of immune cells, and we found cell type-specific patterns in expression of these receptors. We used our pipeline to study inhibitory receptor expression patterns in single cell transcriptomics data of tumour infiltrating T cells. We determined that putative immune inhibitory receptors were expressed differentially in CD4+ and CD8+ T cell subsets, including exhausted CD8+ T cells and CD4+ memory T cells, which could allow for subset-specific targeting. In conclusion, we present an inhibitory receptor pipeline that identifies 51 known and 390 novel inhibitory receptors. This pipeline will support future drug target selection across diseases where therapeutic targeting of immune inhibitory receptors is warranted.

immunology↗

bioRxiv2023

ProRafts A machine-learning predictor for raftophilicity, the protein affinity for biomembrane rafts.

BackgroundProtein raftophilicity refers to the affinity of proteins for cell biomembrane lipid domains, called rafts. Rafts are fluctuating nanoscale platforms that are enriched in cholesterol and sphingolipids, and that are considered relevant for cell signalling, viral function, and biomembrane trafficking. The dynamic partitioning of proteins into rafts depends on the physical and physico-chemical properties of the biomembranes where such proteins are embedded or attached; however it also depends on specific protein "features", such as acylation, glypidation, specific amino acid sequence motifs, transmembrane hydrophobic length, and surface accessible area to solvent. In this paper we present a method, and the resulting "ProRafts" predictor, that can be used to predict if a given mammal protein may be "raftophilic" or "non-raftophilic", without having an a priori knowledge of the physical and physico-chemical properties of the biomembranes where such protein is embedded or attached. ProRafts is based on a machine-learning algorithm, XGBoost, where data regarding the features of known raftophilic human-proteins fed the algorithm. ResultsProRafts enabled to predict correctly more than 80% of human proteins that are a priori known to be raftophilic; this is a promising result considering the limited size of the training dataset that we could build with data retrieved from protein databases. In addition, although we used protein features of known human raftophilic proteins, it was possible to identify accurately raft-proteins from other mammals than humans, such as mouse and rats. This finding suggests that certain protein features are sufficient to predict raftophilicity of proteins from different species. Moreover, our results indicated that phosphorylation may play a more relevant role for protein raftophilicity than indicated by previous studies. ConclusionRaftophilic proteins can be used as biomarkers in medical research, or can serve as targeting sites for therapeutics. In this respect, the machine learning method presented in this paper is a useful tool to guide experimental validations of raftophilicity of proteins in biomembranes, and facilitate the choice of proteins that can be used for experiments on biomimetic membranes.

biophysics↗

bioRxiv2020

Identification of a novel conserved signaling motif in CD200 Receptor required for its inhibitory function

The inhibitory signaling of CD200 receptor 1 (CD200R) has been attributed to its NPxY signaling motif. However, NPxY-motifs are present in multiple protein families and are mostly known to mediate protein trafficking between subcellular locations rather than signaling. Therefore, we investigated whether additional motifs specify the inhibitory function of CD200R. We performed phylogenetic analysis of the intracellular domain of CD200R in mammals, birds, bony fish, amphibians and reptiles. Indeed, the tyrosine of the NPxY-motif is fully conserved across species, in line with its central role in CD200R signaling. In contrast, P295 of the NPxY-motif is not conserved. Instead, a conserved stretch of negatively charged amino acids, EEDE279, and two conserved residues P285 and K292 in the flanking region prior to the NPxY-motif are required for CD200R mediated inhibition of p-Erk, p-Akt308, p-Akt473, p-rpS6 and LPS-induced IL-8 secretion. Altogether, we show that instead of the more common NPxY-motif, CD200R signaling can be assigned to a unique signaling motif in mammals defined by: EEDExxPYxxYxxKxNxxY.

immunology↗

bioRxiv2020

Quantifying the impact of Human Leukocyte Antigen on the human gut microbiome

ObjectiveThe gut microbiome is affected by a number of factors, including the innate and adaptive immune system. The major histocompatibility complex (MHC), or the human leukocyte antigen (HLA) in humans, performs an essential role in vertebrate immunity, and is very polymorphic in different populations. HLA determines the specificity of T lymphocyte and natural killer (NK) cell responses, including against the commensal bacteria present in the human gut. Thus, it is likely that our HLA molecules and thereby the adaptive immune response, can shape the composition of our microbiome. Here, we investigated the effect of HLA haplotype on the microbiome composition. ResultsWe performed HLA typing and microbiota composition analyses on 3,002 public human gut microbiome datasets. We found that (i) individuals with functionally similar HLA molecules (i.e. presenting similar peptides) are also similar in their microbiota, and (ii) HLA homozygosity correlated with microbiome diversity, suggesting that diverse immune responses limit microbiome diversity. ConclusionOur results show a statistical association between host HLA haplotype and gut microbiome composition. Because the HLA haplotype is a readily measurable parameter of the human immune system, these results open the door to incorporating the immune system into predictive microbiome models. IMPORTANCEThe microorganisms that live in the digestive tracts of humans, known as the gut microbiome, are essential for hosts survival as they support crucial functions. For example, they support the host in facilitating the uptake of nutrients and give colonization resistance against pathogens. The composition of the gut microbiome varies among humans. Studies have proposed multiple factors driving the observed variation, including; diet, lifestyle, and health condition. Another major influence on the microbiome is the hosts genetic background. We hypothesized the immune system to be one of the most important genetic factors driving the differences observed between gut microbiomes. Therefore, we are interested in linking the polymorphic molecules that play a role in human immune responses to the composition of the microbiome. HLA molecules are the most polymorphic molecules in our genome and therefore makes an excellent candidate to test such an association/link. To our knowledge for the first time, our results indicate a significant impact of the HLA on the human gut microbiome composition.

bioinformatics↗

bioRxiv2018

Estimating HLA disease associations using similarity trees

The human leukocyte antigen (HLA) is associated with many (infectious) disease outcomes. These associations are perhaps best documented for HIV-1. For example, the HLA-B*58:01 allele is associated with control of the virus, while HLA-B*18:01 is considered detrimental. In HLA disease association studies, it is often ignored that certain HLA molecules are functionally very similar to others. For instance, HLA-B*18:03 differs \"only\" at 3 positions in its peptide binding site from HLA-B*18:01, and not surprisingly, HLA-B*18:03 is also associated with fast progression to AIDS. Here, we present a Bayesian method that takes functional HLA similarities into account to find HLA associations with quantitative traits such as HIV-1 viral load. The method is based on the so-called phylogenetic mixed model (a model for the evolution of a quantitative trait on the branches of a phylogeny), and can easily be modified to study a wide range of research questions, like the role of the heterozygote advantage, or KIR ligands on disease outcomes. We show that in the case of HIV-1, our model is significantly better at predicting set-point virus load than a model that ignores HLA similarities altogether. Furthermore, our method provides a comprehensible visualization of HLA associations. The software is available online at www.github.com/chvandorp/MHCshrubs

immunology↗

bioRxiv2018

Robust self-nonself discrimination requires negative T cell selection on non-random peptides

Self-foreign discrimination by the immune system was long thought to arise because negative selection in the thymus silences self-reactive T cells. Yet recent data show that this silencing is remarkably incomplete. Here we ask how a repertoire containing many self-reactive cells can nevertheless discriminate self from foreign. We address this question using realistic-scale computational models of the T cell repertoire. Our models show that when foreign peptides dif-fer systematically from self, moderate T cell cross-reactivity skews the post-selection repertoire towards foreign recognition. When no such systematic differences exist, self-foreign discrim-ination is only possible if peptide presentation in the thymus minimizes the co-occurrence of similar, redundant self peptides. These results imply that negative selection needs to be based on non-random self peptides to allow robust self-foreign discrimination for both self-similar and -dissimilar pathogens.

immunology↗