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Wei, S.-Y.

Publications and source records attributed to Wei, S.-Y..

2 recordsLinked to original sources
bioRxiv2023

m6A mRNA Methylation in Brown Adipose Tissue Regulates Systemic Insulin Sensitivity via an Inter-Organ Prostaglandin Signaling Axis

Brown adipose tissue (BAT) has the capacity to regulate systemic metabolism through the secretion of signaling lipids. N6-methyladenosine (m6A) is the most prevalent and abundant post-transcriptional mRNA modification and has been reported to regulate BAT adipogenesis and energy expenditure. In this study, we demonstrate that the absence of m6A methyltransferase-like 14 (METTL14), modifies the BAT secretome to initiate inter-organ communication to improve systemic insulin sensitivity. Importantly, these phenotypes are independent of UCP1-mediated energy expenditure and thermogenesis. Using lipidomics, we identified prostaglandin E2 (PGE2) and prostaglandin F2a (PGF2a) as M14KO-BAT-secreted insulin sensitizers. Notably, circulatory PGE2 and PGF2a levels are inversely correlated with insulin sensitivity in humans. Furthermore, in vivo administration of PGE2 and PGF2a in high-fat diet-induced insulin-resistant obese mice recapitulates the phenotypes of METTL14 deficient animals. PGE2 or PGF2a improves insulin signaling by suppressing the expression of specific AKT phosphatases. Mechanistically, METTL14-mediated m6A installation promotes decay of transcripts encoding prostaglandin synthases and their regulators in human and mouse brown adipocytes in a YTHDF2/3-dependent manner. Taken together, these findings reveal a novel biological mechanism through which m6A-dependent regulation of BAT secretome regulates systemic insulin sensitivity in mice and humans. HighlightsO_LIMettl14KO-BAT improves systemic insulin sensitivity via inter-organ communication; C_LIO_LIPGE2 and PGF2a are BAT-secreted insulin sensitizers and browning inducers; C_LIO_LIPGE2 and PGF2a sensitize insulin responses through PGE2-EP-pAKT and PGF2a-FP-AKT axis; C_LIO_LIMETTL14-mediated m6A installation selectively destabilizes prostaglandin synthases and their regulator transcripts; C_LIO_LITargeting METTL14 in BAT has therapeutic potential to enhance systemic insulin sensitivity C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/542169v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@16b3393org.highwire.dtl.DTLVardef@1072ae3org.highwire.dtl.DTLVardef@190a6d7org.highwire.dtl.DTLVardef@1b98bce_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗
bioRxiv2019

State Change Probability: A Measure of the Complexity of Cardiac RR Interval Time Series Using Physiological State Change with Statistical Hypothesis Testing

The complexity of biological signals has been proposed to reflect the adaptability of a given biological system to different environments. Two measures of complexity--multiscale entropy (MSE) and entropy of entropy (EoE)--have been proposed, to evaluate the complexity of heart rate signals from different perspectives. The MSE evaluates the information content of a long time series across multiple temporal scales, while the EoE characterizes variation in amount of information, which is interpreted as the \"state changing,\" of segments in a time series. However, both are problematic when analyzing white noise and are sensitive to data size. Therefore, based on the concept of \"state changing,\" we propose state change probability (SCP) as a measure of complexity. SCP utilizes a statistical hypothesis test to determine the physiological state changes between two consecutive segments in heart rate signals. The SCP value is defined as the ratio of the number of state changes to total number of consecutive segment pairs. Two common statistical tests, the t-test and Wilcoxon rank-sum test, were separately used in the SCP algorithm for comparison, yielding similar results. The SCP method is capable of reasonably evaluating the complexity of white noise and other signals, including 1/f noise, periodic signals, and heart rate signals, from healthy subjects, as well as subjects with congestive heart failure or atrial fibrillation. The SCP method is also insensitive to data size. A universal SCP threshold value can be applied, to differentiate between healthy and pathological subjects for data sizes ranging from 100 to 10,000 points. The SCP algorithm is slightly better than the EoE method when differentiating between subjects, and is superior to the MSE method.

physiology↗