Global transcriptome profiling of mild relapsing‐remitting versus primary progressive multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Genetic research in multiple sclerosis (MS) mostly compares patients with MS with healthy controls, but does not differentiate between MS disease courses. We compared peripheral blood gene expression patterns between extremes of MS phenotypes, i.e. patients with mild relapsing-remitting MS (mRRMS) and primary progressive MS (PPMS). METHODS: We analyzed global gene expression profiles of peripheral blood samples of age- and gender-matched patients with mRRMS and PPMS. Detailed bioinformatic and gene set enrichment analysis, pathway and principle component analyses were used to identify differentially expressed genes and pathways. RESULTS: A total of 84 genes were significantly deregulated between the groups. Of those, 19 had been previously reported to be deregulated in patients with MS as compared with healthy controls, including major histocompatibility complex, interferon receptor 2 and interleukin 6 receptor. Detailed molecular pathway analysis revealed significant up-regulation of antigen processing and presentation, leukocyte transendothelial migration, nucleotide-binding oligomerization domain-like receptor signaling, chemokine signaling and down-regulation of RNA transport, spliceosome and aminoacyl-tRNA biosynthesis pathways in PPMS compared with mRRMS. CONCLUSION: Our analyses show significant differences between mRRMS and PPMS gene expression. Surprisingly, the differentially expressed genes were mostly involved in immunological and inflammatory pathways, suggesting that the difference in MS phenotypes is caused primarily by a difference in immune responses. It should be kept in mind that our analyses were in peripheral blood only, and that the observed differences in inflammatory pathways may be a substrate of the analysed tissue. Further research into gene expression differences between disease courses including analyses in central nervous system tissue is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".