Galectin-8 Modulates Innate and Adaptive Immune Response Genes in Bovine Neutrophils
Bibliographic record
Abstract
Galectins (Gals) are a family of animal lectins that bind β-galactosides through a carbohydrate recognition domain. Galectin-8 is a tandem-repeat galectin, secreted intracellularly and extracellularly. It is associated with neutrophil migration and has been studied as a possible therapeutic to combat inflammation. The objective of this study was to evaluate the translational and the transcriptional effects of recombinant Galectin-8 (rGal-8) on cow neutrophils. Blood was collected aseptically from Holstein-Friesian cows (n=10) from the North Carolina A&T State University Dairy Unit. Neutrophils isolated were treated with rGal-8 (2μg), or PBS (control) and were incubated at 37°C, 5% CO2 for 1 hour. Supernatant from treated neutrophils was evaluated for total protein concentration, and galectin-8 secretion using bovine Galectin-8 Enzyme Linked-Immuno-Sorbent Assay (ELISA) kit. Total RNA was extracted, reverse transcribed, and RT-qPCR was performed using the RT² Profiler Cow Innate & Adaptive Immune Responses Array with 84 genes. The Livak method was used to calculate transcript abundance and fold change (FC>2 considered significant). Total protein concentration increased (P=0.0361) after rGal-8 treatment compared to the untreated control. Galectin-8 secretion was not significantly different in control compared to treated group (P=0.5819). Out of the 84 genes, 81 genes were differentially expressed in response to rGal-8; 14 up-regulated, 5 down-regulated, 61 genes remained unchanged. Treatment with rGal8 induced the expression of IRF7. The top five up-regulated genes include FAS, CD40, CD86, IFNGR1, STAT1; down-regulated genes were TLR9, CD14, CCR6, TICAM1, and TLR1. Selected genes were probed to validate fold change; the levels of gene expression were comparable to data from RT2 array. Exposure of bovine neutrophils to rGal-8 modified expression of immune response genes. The functional significance of the change needs further studies.
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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 | 0.001 |
| 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".