Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires

Sidhom, John-William; Baras, Alexander S.

doi:10.1038/s41598-021-93608-8

Cited by 10 publications

(23 citation statements)

References 42 publications

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“…However, the sample cohorts were exceptionally small, meaning that overfitting may have occurred, producing a falsely high accuracy [89]. The machine learning algorithm, DeepTCR, is a multipleinstance deep learning repertoire classifier assessing a combination of CDR3 sequence and V/D/J gene-segment usage [91], which has been used successfully to predict patients with severe versus milder SARS-CoV-2 infection from their repertoire sequencing [92]. However, it did not generalise between two separate cohorts, likely due to geographical and demographic differences, although overfitting could not be excluded.…”

Section: Machine Learning To Predict Diagnosis Exposure To Infection ...mentioning

confidence: 99%

“…Multiplex Identification of T-cell Receptor Antigen Specificity (MIRA) was applied to these sequences and SARS-CoV-2 antigen specificity was predicted and shown to differ (a) between CD4 and CD8 T cells and (b) between individuals with mild and severe disease. As a consequence of this approach, it was possible to construct an epitope-specific classifier to predict whether patients had mild or severe disease [92].…”

Section: Machine Learning To Identify New Antigen-specific Sequencesmentioning

confidence: 99%

“…Furthermore, the frequencies of specific clonotypes, even those that are SARS-CoV-2-specific, are not particularly high, in contrast to findings in severe COVID-19, in which there are smaller numbers of more frequent SARS-CoV-2-specific sequences. Notwithstanding, a broad range of SARS-CoV-2-specific sequences are seen in mild disease, with many CDR3 sequences shared between multiple individuals, i.e., public CDR3 sequences [92,[94][95][96][97]. One caveat is the fact that younger age may confound this observation.…”

Section: Association Between Higher Repertoire Diversity and Improved...mentioning

confidence: 99%

“…The second approach is prediction by analogy to TCR sequences, the specificity of which is already known. The Multiplex Identification of the T-cell Receptor Antigen Specificity (MIRA) platform [80] was used for this purpose in several studies [92,98,99]. Between these two approaches, numerous potentially SARS-CoV-2-specific TCR sequences were identified.…”

Section: Importance Of Sars-cov-2 Specific Tcr Sequences and Motifsmentioning

confidence: 99%

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Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

et al. 2022

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Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

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Section: Machine Learning To Predict Diagnosis Exposure To Infection ...mentioning

confidence: 99%

Section: Machine Learning To Identify New Antigen-specific Sequencesmentioning

confidence: 99%

Section: Association Between Higher Repertoire Diversity and Improved...mentioning

confidence: 99%

Section: Importance Of Sars-cov-2 Specific Tcr Sequences and Motifsmentioning

confidence: 99%

See 2 more Smart Citations

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

et al. 2022

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“…The majority of work focuses on developing computational methods to unveil differences and similarities between healthy and infected subjects related to the TCR repertoire diversity, CDR3 length distribution and the V and J gene segment preference [11][12][13][14] . Other studies have attempted to combine the aforementioned TCRrelated statistics with Machine Learning (ML), aiming to provide predictive tools to distinguish healthy and infected subjects 44,45 . To our knowledge however, no studies exist in the literature which attempt to approach this field from the viewpoint of the extent each SARS-CoV-2 antigen is recognized by the TCR repertoire of COVID-19 infected and non-infected individuals.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted analysis on TCR profiling data from COVID-19-convalescent and healthy individuals unveils cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and other diseases

Γεωργακίλας

Galanopoulos

Tsinaris

et al. 2022

Preprint

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During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community’s focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS© study, provides thousands of TCRs that can specifically recognize SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML) assisted approach for analysing TCR-Seq data from the antigens’ point of view, with the ability to accurately distinguish between COVID-19-convalescent and healthy individuals in the case of MIRA dataset. Most SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was validated by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding the cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

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Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities

Xu¹,

Chour²,

DeLucia³

et al. 2023

Cell Systems

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Deep learning identifies antigenic determinants of severe SARS-CoV-2 infection within T-cell repertoires

Cited by 10 publications

References 42 publications

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Machine learning assisted analysis on TCR profiling data from COVID-19-convalescent and healthy individuals unveils cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and other diseases

Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities

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