Dysregulation in Akt/mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS-CoV-2 infected cells
Bibliographic record
Abstract
How severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections engage cellular host pathways and innate immunity in infected cells remains largely elusive. We performed an integrative proteo-transcriptomics analysis in SARS-CoV-2 infected Huh7 cells to map the cellular response to the invading virus over time. We identified four pathways, ErbB, HIF-1, mTOR and TNF signaling, among others that were markedly modulated during the course of the SARS-CoV-2 infection in vitro. Western blot validation of the downstream effector molecules of these pathways revealed a dose-dependent activation of Akt, mTOR, S6K1 and 4E-BP1 at 24 hours post infection (hpi). However, we found a significant inhibition of HIF-1α through 24hpi and 48hpi of the infection, suggesting a crosstalk between the SARS-CoV-2 and the Akt/mTOR/HIF-1 signaling pathways. Inhibition of the mTOR signaling pathway using Akt inhibitor MK-2206 showed a significant reduction in virus production. Further investigations are required to better understand the molecular sequelae in order to guide potential therapy in the management of severe coronavirus disease 2019 (COVID-19) patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".