Transcriptome Analysis of the Medulla Tissue from Cattle in Response to Bovine Spongiform Encephalopathy using Digital Gene Expression Tag Profiling
Bibliographic record
Abstract
Bovine spongiform encephalopathy (BSE) is a transmissible, fatal neurodegenerative disorder of cattle produced by prions. The use of excessive parallel sequencing for comparison of gene expression in bovine control and infected tissues may help to elucidate the molecular mechanisms associated with this disease. In this study, tag profiling Solexa sequencing was used for transcriptome analysis of bovine brain tissues. Replicate libraries were prepared from mRNA isolated from control and infected (challenged with 100 g of BSE-infected brain) medulla tissues 45 mo after infection. For each library, 5-6 million sequence reads were generated and approximately 67-70% of the reads were mapped against the Bovine Genome database to approximately 13,700-14,120 transcripts (each having at least one read). About 42-47% of the total reads mapped uniquely. Using the GeneSifter software package, 190 differentially expressed (DE) genes were identified (>2.0-fold change, p < .01): 73 upregulated and 117 downregulated. Seventy-nine DE genes had functions described in the Gene Ontology (GO) database and 16 DE genes were involved in 38 different pathways described in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Digital analysis expression by tag profiling may be a powerful approach to comprehensive transcriptome analysis to identify changes associated with disease progression, leading to a better understanding of the underlying mechanism of pathogenesis of BSE.
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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) | 0.000 | 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 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".