Evaluating Immune Dysregulation in Patients With COVID-19 Requires a More Accurate Definition of the CD45RA+ T-cell Phenotype
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".