Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases
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 a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/11/10/1531 |
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| author | Georgios K. Georgakilas Achilleas P. Galanopoulos Zafeiris Tsinaris Maria Kyritsi Varvara A. Mouchtouri Matthaios Speletas Christos Hadjichristodoulou |
| author_facet | Georgios K. Georgakilas Achilleas P. Galanopoulos Zafeiris Tsinaris Maria Kyritsi Varvara A. Mouchtouri Matthaios Speletas Christos Hadjichristodoulou |
| author_sort | Georgios K. Georgakilas |
| collection | DOAJ |
| description | 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 recognise 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 unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some 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 tested 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 putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with <i>M. tuberculosis</i> 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. |
| first_indexed | 2024-03-09T20:40:05Z |
| format | Article |
| id | doaj.art-a7a528df042f47ad846b50696730f7dc |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-09T20:40:05Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-a7a528df042f47ad846b50696730f7dc2023-11-23T23:01:06ZengMDPI AGBiology2079-77372022-10-011110153110.3390/biology11101531Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other DiseasesGeorgios K. Georgakilas0Achilleas P. Galanopoulos1Zafeiris Tsinaris2Maria Kyritsi3Varvara A. Mouchtouri4Matthaios Speletas5Christos Hadjichristodoulou6Laboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 41222 Larisa, GreeceLaboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 41222 Larisa, GreeceLaboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 41222 Larisa, GreeceLaboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 41222 Larisa, GreeceLaboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 41222 Larisa, GreeceDepartment of Immunology & Histocompatibility, Faculty of Medicine, University of Thessaly, 41500 Larisa, GreeceLaboratory of Hygiene and Epidemiology, Faculty of Medicine, University of Thessaly, 41222 Larisa, GreeceDuring 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 recognise 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 unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some 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 tested 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 putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with <i>M. tuberculosis</i> 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.https://www.mdpi.com/2079-7737/11/10/1531COVID-19T cell receptorpathogensdiseasescross-reactivity phenomenonMachine Learning |
| spellingShingle | Georgios K. Georgakilas Achilleas P. Galanopoulos Zafeiris Tsinaris Maria Kyritsi Varvara A. Mouchtouri Matthaios Speletas Christos Hadjichristodoulou Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases Biology COVID-19 T cell receptor pathogens diseases cross-reactivity phenomenon Machine Learning |
| title | Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases |
| title_full | Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases |
| title_fullStr | Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases |
| title_full_unstemmed | Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases |
| title_short | Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases |
| title_sort | machine learning assisted analysis of tcr profiling data unveils cross reactivity between sars cov 2 and a wide spectrum of pathogens and other diseases |
| topic | COVID-19 T cell receptor pathogens diseases cross-reactivity phenomenon Machine Learning |
| url | https://www.mdpi.com/2079-7737/11/10/1531 |
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