Biology subjects

Laukens, K.

Publications and source records attributed to Laukens, K..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in TimsTOF

Immunopeptidomics plays a crucial role in identifying targets for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. Low abundant peptides often occur in the field of immunopeptidomics, which is why the highly sensitive timsTOF instruments are increasingly gaining popularity. To improve PSM rescoring for immunopeptides measured using timsTOF instruments, we trained a deep learning-based fragment ion intensity prediction model. 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project were analyzed on a timsTOF-Pro to generate a ground-truth dataset, containing 93,227 MS/MS spectra of 74,847 unique peptides, that was used to fine-tune an existing Prosit model. By applying our fragment ion intensity prediction model, we demonstrate up to 3-fold improvement in the identification of immunopeptides. Furthermore, our approach increased detection of immunopeptides even from low input samples.

bioinformatics↗

bioRxiv2023

Modeling Antigen-Specific T Cell Dynamics Following Hepatitis B Vaccination indicates differences between conventional and regulatory T cell dynamics

Our study aims to investigate the dynamics of conventional memory T cells (Tconv) and regulatory T cells (Treg) following activation, and to explore potential differences between these two cell types. To achieve this, we developed advanced statistical mixed models based on mathematical models of ordinary differential equations (ODE), which allowed us to transform post-vaccination immunological processes into mathematical formulas. These models were applied on in-house data from a de novo Hepatitis B vaccination trial. By accounting for inter- and intra-individual variability, our models provided good fits for both antigen-specific Tconv and Treg cells, overcoming the challenge of studying these complex processes. Our modeling approach provided a deeper understanding of the immunological processes underlying T cell development after vaccination. Specifically, our analysis revealed several important findings regarding the dynamics of Tconv and Treg cells, as well as their relationship to seropositivity for HSV-1 and EBV, and the dynamics of antibody response to vaccination. Firstly, our modeling indicated that Tconv dynamics suggest the existence of two T cell types, in contrast to Treg dynamics where only one T cell type is predicted. Secondly, we found that individuals who converted to a positive antibody response to the vaccine earlier had lower decay rates for both Tregs and Tconv cells, which may have important implications for the development of more effective vaccination strategies. Additionally, our modeling showed that HSV-1 seropositivity negatively influenced Tconv cell expansion after the second vaccination, while EBV seropositivity was associated with higher Treg expansion rates after vaccination. Overall, this study provides a critical foundation for understanding the dynamic processes underlying T cell development after vaccination.

immunology↗

bioRxiv2023

The pitfalls of negative data bias for the T-cell epitope specificity challenge

Summary / AbstractEven high-performing machine learning models can have problems when deployed in a real-world setting if the data used to train and test the model contains biases. TCR-epitope binding prediction for novel epitopes is a very important but yet unsolved problem in immunology. In this article, we describe how the technique used to create negative data for the TCR-epitope interaction prediction task can lead to a strong bias and makes that the performance drops to random when tested in a more realistic scenario.

bioinformatics↗

bioRxiv2023

A new Plasmodium vivax reference genome for South American isolates

BackgroundPlasmodium vivax is the second most important cause of human malaria worldwide, and accounts for the majority of malaria cases in South America. A high-quality reference genome exists for Papua Indonesia (PvP01) and Thailand (PvW1), but is lacking for South America. A reference genome specifically for South America would be beneficial though, as P. vivax is a genetically diverse parasite with geographical clustering. ResultsThis study presents a new high-quality assembly of a South American P. vivax isolate, referred to as PvPAM. The genome was obtained from a low input patient sample from the Peruvian Amazon and sequenced using PacBio technology, resulting in a highly complete assembly with 6497 functional genes. Telomeric ends were present in 17 out of 28 chromosomal ends, and additional (sub)telomeric regions are present in 12 unassigned contigs. A comparison of multigene families between PvPAM and the PvP01 genome revealed remarkable variation in vir genes, and the presence of merozoite surface proteins (MSP) 3.6 and 3.7. Three dhfr and dhps drug resistance associated mutations are present in PvPAM, similar to those found in other Peruvian isolates. Mapping of publicly available South American whole genome sequencing (WGS) data to PvPAM resulted in significantly fewer variants and truncated reads compared to the use of PvP01 or PvW1 as reference genomes. To minimize the number of core genome variants in non-South American samples, PvW1 is most suited for Southeast Asian isolates, both PvPAM and PvW1 are suited for South Asian isolates, and PvPAM is recommended for African isolates. Interestingly, non-South American samples still contained the least subtelomeric variants when mapped to PvPAM, indicating high quality of the PvPAM subtelomeric regions. ConclusionsOur findings show that the PvPAM reference genome more accurately represents South American P. vivax isolates in comparison to PvP01 and PvW1. In addition, PvPAM has a high level of completeness, and contains a similar number of annotated genes as PvP01 or PvW1. The PvPAM genome therefore will be a valuable resource to improve future genomic analyses on P. vivax isolates from the South American continent.

genetics↗

bioRxiv2023

Multi-view learning to unravel the different levels underlying hepatitis B vaccine response

The immune system acts as the intricate apparatus dedicated to mounting a defence that ensures host survival from microbial threats. To engage this faceted immune response and provide protection against infectious diseases, vaccinations are the critical tool developed. However, vaccine responses are governed by levels that when interrogated separately only explain a fraction of the immune reaction. To address this knowledge gap, we conducted a feasibility study to determine if multi-view modelling can aid in gaining actionable insights on response markers shared across populations, capture the immune system diversity, and disentangle confounders. We thus sought to assess this multi-view modelling capacity on the responsiveness to Hepatitis B virus (HBV) vaccination. Seroconversion to vaccine induced antibodies against HBV surface antigen (anti-HBs) in early-converters (n=21; <2 month) and late-converters (n=9; <6 months), was defined based on the anti-HBs titres (>10IU/L). The multi-view data encompassed bulk RNA-seq, CD4+ T cell parameters (including T-cell receptor data), flow cytometry data, and clinical metadata (including age and gender). The modelling included testing single-view and multi-view joint dimensionality reductions. Multi-view joint dimensionality reduction out-performed single-view methods in terms of area under curve and balanced accuracy, confirming an increase in predictive power to be gained. The interpretation of the findings showed that age, gender, inflammation-related gene sets and pre-existing vaccine specific T-cells were associated with vaccination responsiveness. This multi-view dimensionality reduction approach complements the clinical seroconversion and all single modalities. Importantly, this modelling could identify what features predict HBV vaccine response. This methodology could be extended to other vaccination trials to identify key features regulating responsiveness.

systems biology↗

bioRxiv2022

Predicting MmpR-based bedaquiline resistance using sequence- and structure-based features

Accurate molecular detection of resistance to bedaquiline, a core drug for the treatment of drug resistant tuberculosis, remains challenging. In this study, we investigated whether three sequence- and nine structure-based features describing the impact of Rv0678 variants on the MmpR transcriptional repressor could predict the bedaquiline phenotype of isolates containing Rv0678 variants. Paired genotypic and phenotypic data was used to train a binary random forest classifier. The mean value of the individual features was similar for resistant and susceptible variants (p[≥]0.05). Leave one out cross validation showed predictability of bedaquiline resistance from Rv0678 variants when using a binary classifier with a combination of sequence and structural features (ROC AUC = 0.766; f1 score = 0.746), but the performance was too low for clinically use. Evolutionary conservation of the affected residue was the most important individual feature (mean decrease in impurity = 0.226 {+/-} 0.003) to discriminate bedaquiline resistance from susceptibility. Prediction of bedaquiline resistance was only possible when restricting the data to missense variants, as the selected features could not be applied to Rv0678 insertions and deletions. Additionally, prediction of bedaquiline resistance was only possible when restricting the data to isolates for which the phenotype was determined using Mycobacterial Growth Indicator Tubes, suggesting possible misclassification of the bedaquiline phenotype by other methods. The results of this study suggest that structural features describing Rv0678 missense variants could be used to predict bedaquiline resistance, albeit not yet at the performance level required for clinical practice. Author summaryGuidelines by the World Health Organization indicate that antibiotic resistant tuberculosis should be treated with bedaquiline, a newly discovered antibiotic. However, resistance to bedaquiline has already emerged and is spreading rapidly. More than 500 different variants have been discovered in the bedaquiline resistance genes in Mycobacterium tuberculosis. It is not known which DNA variants specifically drive bedaquiline resistance due to insufficiency of data. Here, we used a machine learning technique to predict whether a variant in the Rv0678 gene confers bedaquiline resistance. To this end, for each variant we defined features that describe the impact of the variant on the MmpR (encoded by the Rv0678 gene) protein structure. These features were then fed into a classification algorithm to predict bedaquiline resistance. We successfully developed a bedaquiline resistance prediction model, although performance was limited. We also found that accurate predictions were only possible when restricting the data to samples that were tested on the MGIT platform, a World Health Organization endorsed method to test for bedaquiline resistance. Our study shows that predicting bedaquiline resistance from protein structural features is feasible. Furthermore, our study provides new insights into phenotypic testing platforms to assess bedaquiline resistance.

microbiology↗

bioRxiv2022

Selective whole-genome sequencing of Plasmodium parasites directly from blood samples by Nanopore adaptive sampling

BackgroundWhole-genome sequencing (WGS) is becoming an increasingly popular tool to study the population genetics and drug resistance of Plasmodium spp. However, the predominance of human DNA in a malaria patient blood sample requires time-consuming lab procedures to filter out human DNA or enrich Plasmodium DNA. Here, we investigated the potential of adaptive sampling to enrich for Plasmodium DNA while sequencing unenriched patient blood samples on a minION device. ResultsTo compare adaptive sampling versus regular sequencing, a dilution series consisting of 0% up to 100% P. falciparum DNA in human DNA was sequenced. Half of the flowcell channels were run in adaptive sampling mode, enriching for the P. falciparum reference genome, resulting in a 3.2 fold enrichment of P. falciparum bases on average. Samples with a lower concentration of parasite DNA had a higher enrichment potential. We confirmed these findings by sequencing two P. falciparum patient blood samples with common levels of parasitaemia (0.1% and 0.2%). The estimated enrichment was 3.9 and 5.8, which was sufficient to cover at least 97% of the P. falciparum reference genome at a median depth of 20 (highest parasitaemia) or 5 (lowest parasitaemia). A comparison of 38 drug resistance variants (WHO) obtained via adaptive sequencing or Sanger sequencing showed a high concordance between the two methods, suggesting that the obtained sequencing data is of sufficient quality to address common clinical research questions for patients with parasitaemias of 0.1% and higher. ConclusionsOur results demonstrate that adaptive Nanopore sequencing has the potential to replace more time-consuming Plasmodium-enrichment protocols and sequence directly from patient blood, given further improvements in cost-efficiency.

microbiology↗

bioRxiv2022

Semi-supervised machine learning for sensitive open modification spectral library searching

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular post-processing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semi-supervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semi-supervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

bioinformatics↗

bioRxiv2022

Interpretable deep learning to uncover the molecular binding patterns determining TCR-epitope interactions

The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR-epitope pair is still a challenging task, especially for novel epitopes, because the underlying patterns are largely unknown to domain experts and machine learning models. To achieve a deeper understanding of TCR-epitope interactions, we have used interpretable deep learning techniques to gain insights into the performance of TCR-epitope binding machine learning models. We demonstrate how interpretable AI techniques can be linked to the three-dimensional structure of molecules to offer novel insights into the factors that determine TCR affinity on a molecular level. Additionally, our results show the importance of using interpretability techniques to verify the predictions of machine learning models for challenging molecular biology problems where small hard-to-detect problems can accumulate to inaccurate results.

bioinformatics↗

bioRxiv2022

Open modification searching of SARS-CoV-2-human protein interaction data reveals novel viral modification sites

The outbreak of the SARS-CoV-2 coronavirus, the causative agent of the COVID-19 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions (PPIs) through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications (PTMs). We have reanalyzed public affinity purification mass spectrometry data using open modification searching to investigate the presence of PTMs in the context of the SARS-CoV-2 virus-host PPI network. Based on an over two-fold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. Additionally, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

bioinformatics↗

bioRxiv2021

Investigating the Role of Chromatin Remodeler FOXA1 in Ferroptotic Cell Death

Ferroptosis is a lipid peroxidation-dependent mechanism of regulated cell death known to suppress tumor proliferation and progression. Although several genetic and protein hallmarks have been identified in ferroptotic cell death, it remains challenging to fully characterize ferroptosis signaling pathways and to find suitable biomarkers. Moreover, changes taking place in the epigenome of ferroptotic cells remain poorly studied. In this context, we aimed to investigate the role of chromatin remodeler forkhead box protein A1 (FOXA1) in RSL3-treated multiple myeloma cells because, similar to ferroptosis, this transcription factor has been associated with changes in the lipid metabolism, DNA damage, and epithelial-to-mesenchymal transition (EMT). RNA sequencing and Western blot analysis revealed that FOXA1 expression is consistently upregulated upon ferroptosis induction in different in vitro and in vivo disease models. In silico motif analysis and transcription factor enrichment analysis further suggested that ferroptosis-mediated FOXA1 expression is orchestrated by specificity protein 1 (Sp1), a transcription factor known to be influenced by lipid peroxidation. Remarkably, FOXA1 upregulation in ferroptotic myeloma cells did not alter hormone signaling or EMT, two key downstream signaling pathways of FOXA1. CUT&RUN genome-wide transcriptional binding site profiling showed that GPX4-inhibition by RSL3 triggered loss of binding of FOXA1 to pericentromeric regions in multiple myeloma cells, suggesting that this transcription factor is possibly involved in genomic instability, DNA damage, or cellular senescence under ferroptotic conditions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/461056v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1fe44acorg.highwire.dtl.DTLVardef@4ea970org.highwire.dtl.DTLVardef@560fd0org.highwire.dtl.DTLVardef@283874_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Predicting compound-protein interaction using hierarchical graph convolutional networks

MotivationConvolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction. ResultsExperiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction. Availability and implementationhttps://github.com/banhdzui/cpi_hgcn.git

bioinformatics↗

bioRxiv2021

Four layer multi-omics reveals molecular responses to aneuploidy in Leishmania

Aneuploidy causes system-wide disruptions in the stochiometric balances of transcripts, proteins, and metabolites, often resulting in detrimental effects for the organism. The protozoan parasite Leishmania has an unusually high tolerance for aneuploidy, but the molecular and functional consequences for the pathogen remain poorly understood. Here, we addressed this question in vitro and present the first integrated analysis of the genome, transcriptome, proteome, and metabolome of highly aneuploid Leishmania donovani strains. Our analyses unambiguously establish that aneuploidy in Leishmania proportionally impacts the average transcript- and protein abundance levels of affected chromosomes, ultimately correlating with the degree of metabolic differences between strains. This proportionality was present in both proliferative and non-proliferative in vitro promastigotes. However, protein complex subunits and non-cytoplasmic proteins, showed dosage compensation, responding less or even not at all to aneuploidy-induced dosage changes. In contrast to other Eukaryotes, we did not observe the widespread regulation at the transcript level that typically modulates some of the negative effects of aneuploidy. Further, the majority of differentially expressed proteins between aneuploid strains were encoded by non-aneuploid chromosomes and were not driven by a significant underlying transcript change, suggesting that aneuploidy is accompanied by extensive post-transcriptional protein-level modulation. This makes Leishmania a unique Eukaryotic model for elucidating post-transcriptional protein-abundance modulation in the context of aneuploidy.

systems biology↗

bioRxiv2021

Constrained standardization of count data from massive parallel sequencing

In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets with about 2 million count records in less than a second whilst removing unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANds simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.

bioinformatics↗

bioRxiv2021

clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences

The T-cell receptor (TCR) determines the specificity of a T-cell towards an epitope. As of yet, the rules for antigen recognition remain largely undetermined. Current methods for grouping TCRs according to their epitope specificity remain limited in performance and scalability. Multiple methodologies have been developed, but all of them fail to efficiently cluster large data sets exceeding 1 million sequences. To account for this limitation, we developed clusTCR, a rapid TCR clustering alternative that efficiently scales up to millions of CDR3 amino acid sequences. Benchmarking comparisons revealed similar accuracy of clusTCR with other TCR clustering methods. clusTCR offers a drastic improvement in clustering speed, which allows clustering of millions of TCR sequences in just a few minutes through efficient similarity searching and sequence hashing. clusTCR was written in Python 3. It is available as an anaconda package (https://anaconda.org/svalkiers/clustcr) and on github (https://github.com/svalkiers/clusTCR).

bioinformatics↗

bioRxiv2021

Predicted epitope binding core motifs and common amino acid polymorphisms distinguish protective- from susceptibility-associated HLA alleles in leishmaniasis

Susceptibility for leishmaniasis is largely dependent on genetic- and immune factors of the host. Despite the previously described association of human leukocyte antigen (HLA) gene cluster variants as genetic susceptibility factors, little is known on the mechanisms that mediate these associations. To characterize the functionality underpinning these associations between HLA and disease, we predicted the epitope binding repertoires for all known leishmaniasis-associated HLA variants collected in a thorough literature review. We identified several amino acid polymorphisms in the HLA sequences that distinguished protective-from risk-associated HLA-DRB1 alleles. Proteome-wide and multi-species T cell epitope binding predictions were carried out across these alleles, enabling us to map the effects on the epitope binding repertoires. The protective-associated HLA-DRB1 alleles were characterized by common binding core motifs, which map to the identified amino acid polymorphisms. These results strongly suggest that polymorphism in the HLA region, resulting in differential antigen presentation, affects the association between HLA and leishmaniasis disease development. Finally, we established a valuable open-access resource of putative epitopes, of which a set of 14 HLA-unrestricted strong-binding epitopes, conserved across species, were prioritized for further epitope discovery in the search for novel subunit-based vaccines.

immunology↗

bioRxiv2021

Large-scale tandem mass spectrum clustering using fast nearest neighbor searching

RationaleAdvanced algorithmic solutions are necessary to process the ever increasing amounts of mass spectrometry data that is being generated. Here we describe the falcon spectrum clustering tool for efficient clustering of millions of MS/MS spectra. Methodsfalcon succeeds in efficiently clustering large amounts of mass spectral data using advanced techniques for fast spectrum similarity searching. First, high-resolution spectra are binned and converted to low-dimensional vectors using feature hashing. Next, the spectrum vectors are used to construct nearest neighbor indexes for fast similarity searching. The nearest neighbor indexes are used to efficiently compute a sparse pairwise distance matrix without having to exhaustively perform all pairwise spectrum comparisons within the relevant precursor mass tolerance. Finally, density-based clustering is performed to group similar spectra into clusters. ResultsSeveral state-of-the-art spectrum clustering tools were evaluated using a large draft human proteome dataset consisting of 25 million spectra, indicating that alternative tools produce clustering results with different characteristics. Notably, falcon generates larger highly pure clusters than alternative tools, leading to a larger reduction in data volume without the loss of relevant information for more efficient downstream processing. Conclusionsfalcon is a highly efficient spectrum clustering tool. It is publicly available as open source under the permissive BSD license at https://github.com/bittremieux/falcon.

bioinformatics↗

bioRxiv2020

Tracking SARS-CoV-2 T cells with epitope T-cell receptor recognition models

Despite the general agreement on the importance of T cells during SARS-CoV-2 infection, the clinical impact of specific and cross-reactive T-cell responses remains uncertain, while this knowledge may indicate how to adjust vaccines and maintain robust long-term protection against continuously emerging variants. To characterize CD8+ T-cell response to epitopes unique to SARS-CoV-2 (SC-unique) or shared with other coronaviruses (CoV-common), we trained a large number of TCR-epitope recognition models for MHC-I-presented SARS-CoV-2 epitopes from publicly available data. Applying those models to longitudinal COVID-19 TCR repertoires of critical and non-critical COVID-19 patients, we discovered that notwithstanding comparable CD8+ T-cell depletion and the sizes of putative CoV-common CD8+ TCR repertoires in all symptomatic patients at the initial stage of the disease, the temporal dynamics of putative SC2-unique TCRs differed depending on the disease severity. Only non-critical patients had developed large and diverse SC2-unique CD8+ T-cell response by the second week of the disease. Additionally, only this patient group demonstrated redundancy in CD8+ TCRs putatively recognizing unique and common SARS-CoV-2 epitopes. Our findings thus emphasize the role of the de novo CD8+ T-cell response and support the argument against the clinical benefit of pre-existing cross-reactive CD8+ T cells. Now, the analytical framework of this study can not only be employed to track specific and cross-reactive SARS-CoV-2 CD8+ T cells in any TCR repertoire but also be generalized to more epitopes and be employed for adaptive immune response assessment and monitoring to inform public health decisions.

bioinformatics↗