Next-Generation Sequencing Identifies MicroRNAs that Associate with Pathogenic Autoimmune Neuroinflammation in Rats
Bibliographic record
Abstract
MicroRNAs (miRNAs) are known to regulate most biological processes and have been found dysregulated in a variety of diseases, including multiple sclerosis (MS). In this study, we characterized miRNAs that associate with susceptibility to develop experimental autoimmune encephalomyelitis (EAE) in rats, a well-established animal model of MS. Using Illumina next-generation sequencing, we detected 544 miRNAs in the lymph nodes of EAE-susceptible Dark Agouti and EAE-resistant Piebald Virol Glaxo rats during immune activation. Forty-three miRNAs were found differentially expressed between the two strains, with 81% (35 out of 43) showing higher expression in the susceptible strain. Only 33% of tested miRNAs displayed differential expression in naive lymph nodes, suggesting that a majority of regulated miRNAs are EAE dependent. Further investigation of a selected six miRNAs indicates differences in cellular source and kinetics of expression. Several of the miRNAs, including miR-146a, miR-21, miR-181a, miR-223, and let-7, have previously been implicated in immune system regulation. Moreover, 77% (33 out of 43) of the miRNAs were associated with MS and other autoimmune diseases. Target genes likely regulated by the miRNAs were identified using computational predictions combined with whole-genome expression data. Differentially expressed miRNAs and their targets involve functions important for MS and EAE, such as immune cell migration through targeting genes like Cxcr3 and cellular maintenance and signaling by regulation of Prkcd and Stat1. In addition, we demonstrated that these three genes are direct targets of miR-181a. Our study highlights the impact of multiple miRNAs, displaying diverse kinetics and cellular sources, on development of pathogenic autoimmune inflammation.
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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 | 0.000 |
| 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) | 0.000 | 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".