Tofacitinib Mitigates the Increased SARS-CoV-2 Infection Susceptibility Caused by an IBD Risk Variant in the PTPN2 Gene
Bibliographic record
Abstract
BACKGROUND & AIMS: Coronavirus disease (COVID-19), caused by severe acquired respiratory syndrome-Coronavirus-2 (SARS-CoV-2), triggered a global pandemic with severe medical and socioeconomic consequences. Although fatality rates are higher among the elderly and those with underlying comorbidities, host factors that promote susceptibility to SARS-CoV-2 infection and severe disease are poorly understood. Although individuals with certain autoimmune/inflammatory disorders show increased susceptibility to viral infections, there is incomplete knowledge of SARS-CoV-2 susceptibility in these diseases. The aim of our study was to investigate whether the autoimmunity risk gene, PTPN2, which also confers elevated risk to develop inflammatory bowel disease, affects susceptibility to SARS-CoV-2 viral uptake. METHODS: Using samples from PTPN2 genotyped patients with inflammatory bowel disease, PTPN2-deficient mice, and human intestinal and lung epithelial cell lines, we investigated how PTPN2 affects expression of the SARS-CoV-2 receptor angiotensin converting enzyme 2 (ACE2), and uptake of virus-like particles expressing the SARS-CoV2 spike protein and live SARS-CoV-2 virus. RESULTS: We report that the autoimmune PTPN2 loss-of-function risk variant rs1893217 promotes expression of the SARS-CoV-2 receptor, ACE2, and increases cellular entry of SARS-CoV-2 spike protein and live virus. Elevated ACE2 expression and viral entry were mediated by increased Janus kinase-signal transducers and activators of transcription signaling and were reversed by the Janus kinase inhibitor, tofacitinib. CONCLUSION: Collectively, our findings uncover a novel risk biomarker for increased expression of the SARS-CoV-2 receptor and viral entry, and identify a clinically approved therapeutic agent to mitigate this risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".