Profiling of blood miRNAomes revealed the potential regulatory role of miRNAs in various lameness phenotypes in feedlot cattle
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».