Comparative miRNAome analysis revealed different miRNA expression profiles in bovine sera and exosomes
Bibliographic record
Abstract
BACKGROUND: Extra-cellular components, such as serum and exosome, have drawn great attention as a readily accessible source of biomarkers for mammalian health. However, the contribution of different blood components to the signature of respective microRNAs (miRNAs) remains unknown, especially in cattle. In this study we aimed to investigate the miRNAs from bovine sera and exosomes, and to provide insights into their future applications. RESULTS: Blood collected from four healthy dairy cows were used for this study. The serum and exosomal RNAs were extracted using two commonly used commercial kits (Norgen and Invitrogen total RNA isolation kit), respectively. The miRNA profiles were then generated using RNA-seq. Sera had higher complexity of miRNAome consisting of 328 ± 17 miRNAs, while less number of miRNAs (260 ± 15, P = 0.001) was detected in exosomes. The profile of total detected miRNAs in sera and exosomes was different, while exosomes represented about 78 % of total miRNAs expressed in sera, suggesting that exosomes are the major miRNAs carriers in bovine sera. A total of 24 and 3 miRNAs (RPM > 5) were exclusively expressed in sera and exosomes, respectively. In addition, 12 miRNAs were differentially expressed between sera and exosomes (FDR < 0.05), with the expression of four of them being further validated by stem-loop RT-qPCR. Moreover, functional analysis showed that uniquely and highly expressed miRNAs in sera were mainly related to diseases and disorders, while the predicted functions of those in exosomes were enriched in tissue development and lipid metabolism. CONCLUSION: Our results provide evidence that bovine sera and exosomes miRNAomes are different with regarding to the miRNA numbers, types and expressions. Based on their distinct profiles, miRNAs from sera and exosomes may reflect different aspects of physiological and pathological conditions in cattle. The functional analysis suggest that sera may be preferable for the purpose of detecting inflammation in cattle, while exosomes may be a better choice for monitoring the status of muscle development and lipid metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".