Evaluating Immune Dysregulation in Patients With COVID-19 Requires a More Accurate Definition of the CD45RA+ T-cell Phenotype
Notice bibliographique
Résumé
To the Editor—The coronavirus disease 2019 (COVID-19) pandemic has disproportionally affected the elderly. The recently published study conducted in Wuhan, China, by Qin et al indicated dysregulation of the immune response specifically related to T lymphocytes, suggesting that they are highly involved in the pathophysiology of COVID-19 [1]. T-cell dysregulation is a major contributor to age-related changes of the immune system in the elderly, where T-cell responses become defective. The causes of immunodeficiency are multifactorial, including T-cell phenotypic changes, signal transduction failure, and thymic involution [2, 3]. Dysregulated T-cell responses have been linked to a variety of different diseases typically seen in the elderly, notably cardiovascular disease and Alzheimer’s [4, 5]. Furthermore, an immune phenotype known as the immune risk phenotype (IRP) has been used as a marker to track these changes, and is defined by a low CD4:CD8 T-cell ratio and an expansion of CD8+CD28− T cells in those cytomegalovirus seropositive [6]. It has been shown that IRP-positive individuals have an expansion of CD8+ effector memory T cells (TEM cells) that are low functioning and late-differentiated, causing memory inflation [7]. The recent COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2, has disproportionately impacted the elderly, with severe cases being linked to increases in proinflammatory cytokines in serum [1]. Qin et al sought to characterize the T-lymphocyte responses in COVID-19, with aims to differentiate between nonsevere and severe cases. The severe cases had a significantly higher average age compared to the nonsevere cases, indicating a worse outcome in the elderly. Additionally, the severe group had an increased incidence of cardiovascular disease as compared to the nonsevere population. As the IRP is seen at increased levels in the elderly and has been associated with increased incidence of cardiovascular disease, it would be interesting to see the significance of the IRP in terms of COVID-19 response. The Wuhan study also identified several differences in T-cell populations between the severe and nonsevere COVID-19 cases. Most notably, there were significantly higher levels of CD3+CD4+CD45RA+ T cells in the severe cases, which was attributed to increases in naive cells. Although naive T cells are characterized by the presence of a combination of surface markers including CD45RA, this marker alone cannot be used to define naive subsets. Furthermore, CD45RA is re-expressed during late differentiation and is part of a proinflammatory phenotype identified in the elderly [8]. This terminally differentiated T-cell population has been associated with immune dysregulation in the elderly and is further characterized by low CD28 and increased CD57 expression [9]. The Wuhan study failed to further characterize the CD45RA+ T-cell subset, making it impossible to attribute the increase specifically to the naive T-cell subset. Furthermore, the study did not report on CD8+CD45RA+ T-cell subsets, which are thought to play an important role in the inflammatory aging process. Improved characterization of terminally differentiated CD45RA+ T cells, along with screening for IRP positivity, may be beneficial in identifying those with potential for severe COVID-19. Financial support. This work was supported by the Canadian Institutes of Health Research (CIHR grant number 0996); the Anna-Maria Solinas Laroche Allergy and Clinical Immunology Research fund; and the Montreal General Hospital Foundation to C. P. Potential conflicts of interest. The authors: No reported conflicts of interest. Both authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,011 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».