Gene expression alterations in Rocky Mountain elk infected with chronic wasting disease
Bibliographic record
Abstract
Chronic wasting disease (CWD) is an invariably fatal neurologic disease that naturally infects mule deer, white tailed deer and elk. The understanding of CWD neurodegeneration at a molecular level is very limited. In this study, microarray analysis was performed to determine changes in the gene expression profiles in six different tissues including brain, midbrain, thalamus, spleen, RPLN and tonsil of CWD-infected elk in comparison to non-infected healthy elk, using 24,000 bovine specific oligo probes. In total, 329 genes were found to be differentially expressed (> 2.0-fold) between CWD negative and positive brain tissues, with 132 genes upregulated and 197 genes downregulated. There were 249 DE genes in the spleen (168 up- and 81 downregulated), 30 DE genes in the retropharyngeal lymph node (RPLN) (18 up- and 12 downregulated), and 55 DE genes in the tonsil (21 up- and 34 downregulated). Using Gene Ontology (GO), the DE genes were assigned to functional groups associated with cellular process, biological regulation, metabolic process, and regulation of biological process. For all brain tissues, the highest ranking networks for DE genes identified by Ingenuity Pathway Analysis (IPA) were associated with neurological disease, cell morphology, cellular assembly and organization. Quantitative real-time PCR (qRT-PCR) validated the expression of DE genes primarily involved in different regulatory pathways, including neuronal signaling and synapse function, calcium signaling, apoptosis and cell death and immune cell trafficking and inflammatory response. This is the first study to evaluate altered gene expression in multiple organs including brain from orally infected elk and the results will improve our understanding of CWD neurodegeneration at the molecular level.
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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".