Comprehensive Summary
This study focuses on the role of microRNAs (miRNAs), a type of short non-coding RNAs, in regulating cell signaling transcription within white adipose tissue (WAT) during mouse torpor. The researchers obtained male adult mice and induced torpor via adjusting temperature in the experiment group, sampling mice with the lowest CO2 production as ones that had reached sufficient torpor. Of these mice, RNA was extracted from the WAT, used to construct libraries, and processed via comparing against several reference sequence databases for miRDeep2 analysis. A random forest feature was used to identify candidate biomarkers for torpor in WAT. It was found that eight miRNAs were found to be downregulated during hibernation, including miR-12193-5p, miR-194-1-3p, miR-215-3p, miR-26a-2-3p, miR-29b-1-5p, miR-363-5p, miR-449a-3p and miR-490-3p, while four miRNAs were upregulated in WAT of hibernators, including miR-124-3p, miR-188-5p, miR-3088-3p and miR-3103-3p. This suggests links to steroid metabolism and cell growth and division, as the increased miRNA inhibition is involved in the steroid hormone biosynthesis pathway. The study demonstrates that miRNA can be used to identify biomarkers for torpor, furthermore indicating that WAT plays more than just a role as a lipid storage, but also an active participant in the metabolic pathway of torpor.
Outcomes and Implications
This research is important as it demonstrates the abilities of using AI to be able to find biomarkers from experimental data. In this study, biomarkers were used to identify torpor. However, using this same technology, it could also identify biomarkers that are a sign for other symptoms, such as serious diseases and illnesses. As such, this technology could be extrapolated to find other kinds of biomarkers and advance treatment towards such related illnesses.