The CD4+ T cell methylome contributes to a distinct CD4+ T cell transcriptional signature in Mycobacterium bovis-infected cattle
Bibliographic record
Abstract
We hypothesised that epigenetic regulation of CD4(+) T lymphocytes contributes to a shift toward a dysfunctional T cell phenotype which may impact on their ability to clear mycobacterial infection. Combined RNA-seq transcriptomic profiling and Reduced Representation Bisulfite Sequencing identified 193 significantly differentially expressed genes and 760 differentially methylated regions (DMRs), between CD4(+) T cells from M. bovis infected and healthy cattle. 196 DMRs were located within 10 kb of annotated genes, including GATA3 and RORC, both of which encode transcription factors that promote TH2 and TH17 T helper cell subsets respectively. Gene-specific DNA methylation and gene expression levels for the TNFRSF4 and Interferon-γ genes were significantly negatively correlated suggesting a regulatory relationship. Pathway analysis of DMRs identified enrichment of genes involved in the anti-proliferative TGF-β signaling pathway and TGFB1 expression was significantly increased in peripheral blood leukocytes from TB-infected cattle. This first analysis of the bovine CD4(+) T cell methylome suggests that DNA methylation directly contributes to a distinct gene expression signature in CD4(+) T cells from cattle infected with M. bovis. Specific methylation changes proximal to key inflammatory gene loci may be critical to the emergence of a non-protective CD4(+) T cell response during mycobacterial infection in cattle.
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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".