Single-nucleus transcriptomics identifies separate classes of UCP1 and futile cycle adipocytes.

Adipose tissue can recruit catabolic adipocytes that utilize chemical energy to dissipate heat. This process occurs either by uncoupled respiration through uncoupling protein 1 (UCP1) or by utilizing ATP-dependent futile cycles (FCs). However, it remains unclear how these pathways coexist since both processes rely on the mitochondrial membrane potential. Utilizing single-nucleus RNA sequencing to deconvolute the heterogeneity of subcutaneous adipose tissue in mice and humans, we identify at least 2 distinct subpopulations of beige adipocytes: FC-adipocytes and UCP1-beige adipocytes. Importantly, we demonstrate that the FC-adipocyte subpopulation is highly metabolically active and utilizes FCs to dissipate energy, thus contributing to thermogenesis independent of Ucp1. Furthermore, FC-adipocytes are important drivers of systemic energy homeostasis and linked to glucose metabolism and obesity resistance in humans. Taken together, our findings identify a noncanonical thermogenic adipocyte subpopulation, which could be an important regulator of energy homeostasis in mammals.

The novel adrenergic agonist ATR-127 targets skeletal muscle and brown adipose tissue to tackle diabesity and steatohepatitis.

OBJECTIVE: Simultaneous activation of ß2- and ß3-adrenoceptors (ARs) improves whole-body metabolism via beneficial effects in skeletal muscle and brown adipose tissue (BAT). Nevertheless, high-efficacy agonists simultaneously targeting these receptors whilst limiting activation of ß1-ARs - and thus inducing cardiovascular complications - are currently non-existent. Therefore, we here developed and evaluated the therapeutic potential of a novel ß2-and ß3-AR, named ATR-127, for the treatment of obesity and its associated metabolic perturbations in preclinical models. METHODS: In the developmental phase, we assessed the impact of ATR-127's on cAMP accumulation in relation to the non-selective ß-AR agonist isoprenaline across various rodent ß-AR subtypes, including neonatal rat cardiomyocytes. Following these experiments, L6 muscle cells were stimulated with ATR-127 to assess the impact on GLUT4-mediated glucose uptake and intramyocellular cAMP accumulation. Additionally, in vitro, and in vivo assessments are conducted to measure ATR-127's effects on BAT glucose uptake and thermogenesis. Finally, diet-induced obese mice were treated with 5 mg/kg ATR-127 for 21 days to investigate the effects on glucose homeostasis, body weight, fat mass, skeletal muscle glucose uptake, BAT thermogenesis and hepatic steatosis. RESULTS: Exposure of L6 muscle cells to ATR-127 robustly enhanced GLUT4-mediated glucose uptake despite low intramyocellular cAMP accumulation. Similarly, ATR-127 markedly increased BAT glucose uptake and thermogenesis both in vitro and in vivo. Prolonged treatment of diet-induced obese mice with ATR-127 dramatically improved glucose homeostasis, an effect accompanied by decreases in body weight and fat mass. These effects were paralleled by an enhanced skeletal muscle glucose uptake, BAT thermogenesis, and improvements in hepatic steatosis. CONCLUSIONS: Our results demonstrate that ATR-127 is a highly effective, novel ß2- and ß3-ARs agonist holding great therapeutic promise for the treatment of obesity and its comorbidities, whilst potentially limiting cardiovascular complications. As such, the therapeutic effects of ATR-127 should be investigated in more detail in clinical studies.

Integrating a Multi-label Deep Learning Approach with Protein Information to Compare Bioactive Peptides in Brain and Plasma.

Peptide therapeutics is gaining momentum. Advances in the field of peptidomics have enabled researchers to harvest vital information from various organisms and tissue types concerning peptide existence, expression and function. The development of mass spectrometry techniques for high-throughput peptide quantitation has paved the way for the identification and discovery of numerous known and novel peptides. Though much has been achieved, scientists are still facing difficulties when it comes to reducing the search space of the large mass spectrometry-generated peptidomics datasets and focusing on the subset of functionally relevant peptides. Moreover, there is currently no straightforward way to analytically compare the distributions of bioactive peptides in distinct biological samples, which may reveal much useful information when seeking to characterize tissue- or fluid-specific peptidomes. In this chapter, we demonstrate how to identify, rank, and compare predicted bioactive peptides and bioactivity distributions from extensive peptidomics datasets. To aid this task, we utilize MultiPep, a multi-label deep learning approach designed for classifying peptide bioactivities, to identify bioactive peptides. The predicted bioactivities are synergistically combined with protein information from the UniProt database, which assist in navigating through the jungle of putative therapeutic peptides and relevant peptide leads.