Profiling of blood miRNAomes revealed the potential regulatory role of miRNAs in various lameness phenotypes in feedlot cattle
Bibliographic record
Abstract
Lameness is a collective term for multiple foot diseases in cattle including, but not limited to, foot rot (FR), digital dermatitis (DD), and toe tip necrosis (TTN), which is a critical welfare concern. The diagnosis of specific phenotypes of lameness in feedlot cattle is challenging and primarily relies on visual assessments. However, different lameness phenotypes share similar clinical symptoms and there is a limited understanding of potential biomarkers relating to such disease for further molecular diagnosis. This study aimed to identify blood miRNA profiles of feedlot cattle with various lameness phenotypes and whether they could be potential diagnostic markers to differentiate lameness phenotypes and predictive lameness recovery. MicroRNAome profiles were generated for the whole blood samples collected from feedlot cattle at Week 0 (W0) before treatment ( n = 106) and longitudinal miRNA expression profiles relating to lameness recovery from W0 to W2 ( n = 140) using RNA-seq. Ten miRNAs were selected to verify miRNA sequencing accuracy using stem-loop RT-qPCR. A total of 321 miRNAs were identified to be expressed in bovine blood samples with three (all downregulated, average log 2 fold change = -1.32), seven (two downregulated with average log 2 fold change = -1.15, five upregulated with average log 2 fold change = 1.68), six (three downregulated with average log 2 fold change = -1.23, three upregulated with average log 2 fold change = 3.31), and fourteen (eight downregulated with average log 2 fold change = -1.24, six upregulated with average log 2 fold change = 1.26) miRNAs differentially expressed (DE) miRNAs in DD, FR, TTN, and FR combined with DD (FRDD) compared to healthy control at W0 (defined as pre-treatment DE miRNAs), respectively. The predicted functions of identified DE miRNAs among different lameness phenotypes were mainly related to Zinc-finger, muscle cell development, and host inflammatory responses. Furthermore, the longitudinal miRNA expression profiles revealed that a total of eight miRNA changed patterns from W0 to W2, with the BTB/POZ-like domain being the most enriched function by longitudinal miRNA expression profiles in both unrecovered and recovered cattle. A total of nine miRNAs (five downregulated with average log 2 fold change = -2.4, four upregulated with average log 2 fold change = 3.7) from W0 to W2 were differentially expressed in unrecovered cattle compared to the recovered cattle, with functions associated with transcription regulation and Zinc-finger. Moreover, the area under the receiver operating characteristics (ROC) curve (AUC) revealed that pre-treatment DE miRNAs could serve as good diagnostic markers to differentiate any two of four phenotypes of lameness, with bta-miR-339b being able to differentiate all lameness phenotypes. Moreover, pre-treatment DE miRNAs could also predict the recovery of three lameness phenotypes (DD, FRDD, TTN) with good to excellent predictiveness. Our results comprehensively assessed the blood miRNAomes in response to various lameness phenotypes, promoting the understanding of miRNA-regulated mechanisms of lameness in feedlot cattle. The diagnostic miRNA markers were also identified to differentiate within lameness phenotypes and predictive lameness recovery, shedding light on accurate on-farm lameness detection.
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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".