Genome-Wide Network Analysis Reveals the Global Properties of IFN-β Immediate Transcriptional Effects in Humans
Bibliographic record
Abstract
IFN-beta effectively controls clinical exacerbations and magnetic resonance imaging activity in most multiple sclerosis patients. However, its mechanism of action has not been yet fully elucidated. In this study we used DNA microarrays to analyze the longitudinal transcriptional profile of blood cells within a week of IFN-beta administration. Using differential expression and gene ontology analyses we found evidence of a general decrease in the cellular activity of T lymphocytes resembling the endogenous antiviral response of IFNs. In contrast, most of the differentially expressed genes (DEGs) from untreated individuals were involved in cellular physiological processes. We then used mutual information (MI) to build networks of coregulated genes in both treated and untreated individuals. Interestingly, the connectivity distribution (k) of networks generated with high MI values displayed scale-free properties. Conversely, the observed k for networks generated with suboptimal MI values approximated a Poisson distribution, suggesting that MI captures biologically relevant interactions. Gene networks from individuals treated with IFN-beta revealed a tight core of immune- and apoptosis-related genes associated with higher values of MI. In contrast, networks obtained from untreated individuals primarily reflected cellular housekeeping functions. Finally, we trained a neural network to reverse engineer the directionality of the main interactions observed at the biological process level. This is the first study that incorporates network analysis to investigate gene regulation in response to a therapeutic drug in humans. Implications of this method in the creation of personalized models of response to therapy are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".