Identification of Appropriate Endogenous Controls for Circulating miRNA Quantification in Working Dogs under Physiological Stress Conditions.
Gabriella GuelfiCamilla CapacciaMichele Matteo SantoroSilvana DiverioPublished in: Animals : an open access journal from MDPI (2023)
Cell-free miRNAs, called circulating miRNAs (cmiRNAs), can act in a paracrine manner by facilitating a diversity of signaling mechanisms between cells. Real-time qPCR is the most accepted method for quantifying miRNA expression levels. The use of stable miRNA endogenous control (EC) for qPCR data normalization allows an accurate cross-sample gene expression comparison. The appropriate selection of EC is a crucial step because qPCR data can change drastically when normalization is performed using an unstable versus a stable EC. To find EC cmiRNA with stable expression in search and rescue (SAR) working dogs, we explored the serum miRNome by Next-Generation Sequencing (NGS) at T0 (resting state) and T1 immediately after SAR performance (state of physiologically recovered stress). The cmiRNAs selected in the NGS circulating miRNome as probable ECs were validated by qPCR, and miRNA stability was evaluated using the Delta Ct, BestKeeper, NormFinder, and GeNorm algorithms. Finally, RefFinder was used to rank the stability orders at both T0 and T1 by establishing miR-320 and miR-191 as the best-circulating ECs. We are confident that this study not only provides a helpful result in itself but also an experimental design for selecting the best endogenous controls to normalize gene expression for genes beyond circulating miRNAs.
Keyphrases
- gene expression
- resting state
- cell free
- poor prognosis
- long non coding rna
- functional connectivity
- cell proliferation
- dna methylation
- machine learning
- electronic health record
- induced apoptosis
- computed tomography
- genome wide
- big data
- magnetic resonance
- deep learning
- bioinformatics analysis
- cell cycle arrest
- binding protein
- circulating tumor
- contrast enhanced
- signaling pathway
- artificial intelligence
- mass spectrometry
- transcription factor
- positron emission tomography
- dual energy