In-vitro transcriptomic profiling of indigenous Gaddi vis-à-vis exotic Labrador dogs: insights from systems biology
Bibliographic record
Abstract
Introduction: The domestication of dogs is regarded as an evolutionary adaptation influenced by artificial selective pressures, leading to the fruition of diverse canine breeds across regions. Indigenous breeds, developed in tandem with local environments, display unique conformations and disease resistance, yet many remain understudied at the molecular level. The Gaddi dog, originating in the northern parts of India and used by local tribes for livestock guarding, exemplifies such a breed with potential for transcriptomic research. Despite its vital role, it remains unrecognized by the National Bureau of Animal Genetic Resources (NBAGR). This study addresses the gaps in understanding the genetics and immune responses of Indigenous breeds, emphasizing their importance as holders of unique genetic heritage. This study explores the molecular profiles of Indigenous Gaddi dogs and exotic Labrador retrievers, focusing on their immune responses to TLR ligand-induced infections. Methods: < 0.05) was conducted to identify enriched pathways in each breed. Results: The analysis revealed that Labrador dogs had more DEGs across all treatment groups than Gaddi dogs. The enriched pathways in Labradors included Th1, Th2, Th17 cell differentiation, and T-cell receptor signaling. In contrast, Gaddi dogs significantly enriched 'Wnt' signaling, T cell activation, and immune regulation pathways. The differential expression (DE) analysis of miRNA-Seq results indicated that Labradors had more DE miRNAs (with expression levels of the original level >1.5 and <-1.5), such as miR-204, miR-206, miR-106a, miR-132, miR-335, and miR-676, which help regulate inflammation, autophagy, and immune responses. Gaddi dogs had unique miRNAs (miR-551 and miR-1249) associated with tumor suppression and inflammation. Discussion: The study highlights distinct immunological profiles between Labrador and Gaddi dogs, with no shared genes responding to TLR-ligand stimulation. The functional enrichment of miRNA targets demonstrated consistent regulatory patterns at both the mRNA and miRNA levels. These findings emphasize the importance of preserving the genetic diversity of indigenous Gaddi dogs and utilizing advanced sequencing techniques to explore immunological diversity for disease resistance and the selection of breeding individuals.
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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 | 0.001 | 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".