Whole Genome Methylation Analysis Reveals Role of DNA Methylation in Cow’s Ileal and Ileal Lymph Node Responses to Mycobacterium avium subsp. paratuberculosis Infection
Bibliographic record
Abstract
Johne’s Disease (JD), caused by Mycobacterium avium subsp paratuberculosis (MAP), is an incurable disease of ruminants and other animal species and is characterized by an imbalance of gut immunity. The role of MAP infection on the epigenetic modeling of gut immunity during the progression of JD is still unknown. This study investigated the DNA methylation patterns in ileal (IL) and ileal lymph node (ILLN) tissues from cows diagnosed with persistent subclinical MAP infection over a one to 4 years period. DNA samples from IL and ILLN tissues from cows negative (MAPneg) ( n = 3) or positive for MAP infection (MAPinf) ( n = 4) were subjected to whole genome bisulfite sequencing. A total of 11,263 and 62,459 differentially methylated cytosines (DMCs), and 1259 and 8086 differentially methylated regions (DMRs) (FDR<0.1) were found between MAPinf and MAPneg IL and ILLN tissues, respectively. The DMRs were found on 394 genes (denoted DMR genes) in the IL and on 1305 genes in the ILLN. DMR genes with hypermethylated promoters/5′UTR [3 (IL) and 88 (ILLN)] or hypomethylated promoters/5′UTR [10 (IL) and 25 (ILLN)] and having multiple functions including response to stimulus/immune response ( BLK, BTC, CCL21, AVPR1A, CHRNG, GABRA4, TDGF1 ), cellular processes ( H2AC20, TEX101, GLA, NCKAP5L, RBM27, SLC18A1, H2AC20BARHL2, NLGN3, SUV39H1, GABRA4, PPA1, UBE2D2 ) and metabolic processes ( GSTO2, H2AC20, SUV39H1, PPA1, UBE2D2 ) are potential DNA methylation candidate genes of MAP infection. The ILLN DMR genes were enriched for more biological process (BP) gene ontology (GO) terms ( n = 374), most of which were related to cellular processes (27.6%), biological regulation (16.6%), metabolic processes (15.4%) and response to stimulus/immune response (8.2%) compared to 75 BP GO terms (related to cellular processes, metabolic processes and transport, and system development) enriched for IL DMR genes. ILLN DMR genes were enriched for more pathways ( n = 47) including 13 disease pathways compared with 36 enriched pathways, including 7 disease/immune pathways for IL DMR genes. In conclusion, the results show tissue specific responses to MAP infection with more epigenetic changes (DMCs and DMRs) in the ILLN than in the IL tissue, suggesting that the ILLN and immune processes were more responsive to regulation by methylation of DNA relative to IL tissue. Our data is the first to demonstrate a potential role for DNA methylation in the pathogenesis of MAP infection in dairy cattle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".