Altered gene expression in human brain microvascular endothelial cells in response to the infection of influenza H1N1 virus
Bibliographic record
Abstract
Influenza viruses not only cause respiratory illness, but also have been reported to elicit neurological manifestations following acute viral infection. The central nervous system (CNS) has a specific defense mechanism against pathogens structured by cerebral microvasculature lined with brain endothelial cells to form the blood-brain barrier (BBB). To investigate the response of human brain microvascular endothelial cells (hBMECs) to the Influenza A virus (IAV), we inoculated the cells with the A/WSN/33 (H1N1) virus. We then conducted an RNAseq experiment to determine the changes in gene expression levels and the activated disease pathways following infection. The analysis revealed an effective activation of the innate immune defense by inducing the pattern recognition receptors (PRRs). Along with the production of proinflammatory cytokines, we detected an upregulation of interferons and interferon-stimulated genes, such as IFN-β/λ, ISG15, CXCL11, CXCL3 and IL-6, etc. Moreover, infected hBMECs exhibited a disruption in the cytoskeletal structure both on the transcriptomic and cytological levels. The RNAseq analysis showed different pathways and candidate genes associated with the neuroactive ligand-receptor interaction, neuroinflammation, and neurodegenerative diseases, together with a predicted activation of the neuroglia. Likewise, some genes linked with the mitochondrial structure and function displayed a significantly altered expression. En masse, this data supports that hBMECs could be infected by the IAV, which induces the innate and inflammatory immune response. The results suggest that the influenza virus infection could potentially induce a subsequent aggravation of neurological disorders. Supplementary Information: The online version contains supplementary material available at 10.1186/s44149-022-00053-9.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".