15 Differentially Expressed Long Non-coding RNA Associated with Gastrointestinal Nematode Infection in Divergent Immune Response Sheep
Bibliographic record
Abstract
Abstract Gastrointestinal nematode infection is one of the major production problems for sheep producers worldwide due its high incidence, morbidity, and mortality in affected flocks. The study of long non-coding RNA (lncRNA) in liver tissue of high (HIR) and low immune responder (LIR) sheep to GINs using RNA-Sequencing technology may provide a better understanding of the gene regulation mechanism associated with the host response to the infection. The aim of this study was to identify differentially expressed (DE) lncRNA between HIR and LIR natural infested sheep and control group. Liver tissue samples from the 13 divergent animals (out of a population of 211) based on their immunoglobulin G levels after vaccination using Hen Egg White (HEW) Lysozyme, and immature abomasum worm counts [HIR (> 4000) (n = 5), LIR (< 1500) (n=5) and control (no parasite challenge) (n=4) groups] were used to perform transcriptomic analysis using RNA-Sequencing. The “Large Gap read mapping “and “Transcript Discovery” tools from CLC Genomics Workbench 20.0.4 (CLC Bio, Aarhus, Denmark), were used to map reads to a reference genome (Oar_rambouillet_v1.0) and transcript discovery, respectively. The FEELnc software was used to identify, from predicted transcript model, potential lncRNAs and classify those transcripts into intro putative lncRNAs and protein coding RNAs. As preliminary results, 8 and 48 DE lncRNAs for HIR and LIR compared to control group were identified, respectively using an adjusted p-value False Discovery Rate (FDR) < 0.05 and Fold change (FC) abs > 2. Functional analyses using the list of DE lncRNAs identified metabolic pathways related to immune function. In depth analysis will help to better understand the physiological mechanisms of resilience of high immune sheep.
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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".