SARS-CoV-2 may hijack GPCR signaling pathways to dysregulate lung ion and fluid transport
Bibliographic record
Abstract
The tropism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a virus responsible for the ongoing coronavirus disease 2019 (COVID-19) pandemic, toward the host cells is determined, at least in part, by the expression and distribution of its cell surface receptor, angiotensin-converting enzyme 2 (ACE2). The virus further exploits the host cellular machinery to gain access into the cells; its spike protein is cleaved by a host cell surface transmembrane serine protease 2 (TMPRSS2) shortly after binding ACE2, followed by its proteolytic activation at a furin cleavage site. The virus primarily targets the epithelium of the respiratory tract, which is covered by a tightly regulated airway surface liquid (ASL) layer that serves as a primary defense mechanism against respiratory pathogens. The volume and viscosity of this fluid layer is regulated and maintained by a coordinated function of different transport pathways in the respiratory epithelium. We argue that SARS-CoV-2 may potentially alter evolutionary conserved second-messenger signaling cascades via activation of G protein-coupled receptors (GPCRs) or by directly modulating G protein signaling. Such signaling may in turn adversely modulate transepithelial transport processes, especially those involving cystic fibrosis transmembrane conductance regulator (CFTR) and epithelial Na + channel (ENaC), thereby shifting the delicate balance between anion secretion and sodium absorption, which controls homeostasis of this fluid layer. As a result, activation of the secretory pathways including CFTR-mediated Cl − transport may overwhelm the absorptive pathways, such as ENaC-dependent Na + uptake, and initiate a pathophysiological cascade leading to lung edema, one of the most serious and potentially deadly clinical manifestations of COVID-19.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".