SUMMARY The adipocyte-derived hormone adiponectin promotes metabolic and cardiovascular health. Circulating adiponectin increases in lean states such as caloric restriction (CR), but the reasons for this paradox remain unclear. Unlike white adipose tissue (WAT), bone marrow adipose tissue (MAT) increases during CR, and both MAT and serum adiponectin increase in many other clinical conditions. Thus, we investigated if MAT contributes to circulating adiponectin. We find that adiponectin secretion is greater from MAT than from WAT. Notably, specific inhibition of MAT formation in mice results in decreased circulating adiponectin during CR, despite unaltered adiponectin expression in WAT. Inhibiting MAT formation also alters skeletal muscle adaptation to CR, suggesting that MAT exerts systemic effects. Finally, we reveal that both MAT and serum adiponectin increase during cancer therapy in humans. These observations identify MAT as an endocrine organ that contributes significantly to increased serum adiponectin during CR, and perhaps in other adverse states.
The mechanism of protein translocation across the endoplasmic reticulum membrane of eukaryotic cells and the plasma membrane of prokaryotic cells are thought to be evolutionarily related. Protein targeting to the eukaryotic translocation apparatus is mediated by the signal recognition particle (SRP), a cytosolic ribonucleoprotein, and the SRP receptor, an endoplasmic reticulum membrane protein. During targeting, the 54K SRP subunit (M(r) 54,000; SRP54), a GTP-binding protein, binds to signal sequences and then interacts with the alpha-subunit of the SRP receptor (SR alpha), another GTP-binding protein. Two proteins from Escherichia coli, Ffh and FTsY, structurally resemble SRP54 and SR alpha. Like SRP54, Ffh is a subunit of a cytosolic ribonucleoprotein that also contains the E. coli 4.5S RNA. Although there is genetic and biochemical evidence that the E. coli Ffh/4.5S ribonucleoprotein has an SRP-like function, there is no evidence for an SR alpha-like role for FtsY. Here we show that the Ffh/4.5S ribonucleoprotein binds tightly to FtsY in a GTP-dependent manner. This interaction results in the stimulation of GTP hydrolysis which can be inhibited by synthetic signal peptides. These properties mimic those of mammalian SRP and its receptor, suggesting that the E. coli Ffh/4.5S ribonucleoprotein and FtsY have functions in protein targeting that are similar to those of their mammalian counterparts.
The signal recognition particle (SRP) consists of one RNA and six protein subunits. The N-terminal domain of the 54K subunit contains a putative GTP-binding site, whereas the C-terminal domain binds signal sequences and SRP RNA. Binding of SRP to the signal sequence as it emerges from the ribosome creates a cytosolic targeting complex containing the nascent polypeptide chain, the translating ribosome, and SRP. This complex is directed to the endoplasmic reticulum membrane as a result of its interaction with the SRP receptor, a membrane protein composed of two subunits, SR alpha and SR beta, each of which also contains a GTP-binding domain. In the presence of GTP, SRP receptor binding to SRP causes the latter to dissociate from both the signal sequence and the ribosome. GTP is then hydrolysed so that SRP can be released from the SRP receptor and returned to the cytosol. Here we show that the 54K subunit (M(r) 54,000) of SRP (SRP54) is a GTP-binding protein stabilized in a nucleotide-free state by signal sequences, and that the SRP receptor both increases the affinity of SRP54 for GTP and activates its GTPase. We propose that nucleotide-mediated conformational changes in SRP54 regulate the release of signal sequences and the docking of ribosomes at the endoplasmic reticulum.
Background: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
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