Lipolysis drives expression of the constitutively active receptor GPR3 to induce adipose thermogenesis

Thermogenic adipocytes possess a therapeutically appealing, energy-expending capacity, which is canonically cold-induced by ligand-dependent activation of β-adrenergic G protein-coupled receptors (GPCRs). Here, we uncover an alternate paradigm of GPCR-mediated adipose thermogenesis through the constitutively active receptor, GPR3. We show that the N terminus of GPR3 confers intrinsic signaling activity, resulting in continuous Gs-coupling and cAMP production without an exogenous ligand. Thus, transcriptional induction of Gpr3 represents the regulatory parallel to ligand-binding of conventional GPCRs. Consequently, increasing Gpr3 expression in thermogenic adipocytes is alone sufficient to drive energy expenditure and counteract metabolic disease in mice. Gpr3 transcription is cold-stimulated by a lipolytic signal, and dietary fat potentiates GPR3-dependent thermogenesis to amplify the response to caloric excess. Moreover, we find GPR3 to be an essential, adrenergic-independent regulator of human brown adipocytes. Taken together, our findings reveal a noncanonical mechanism of GPCR control and thermogenic activation through the lipolysis-induced expression of constitutively active GPR3.ISSN:0092-8674ISSN:1097-417

Targeted and selective knockout of the TLQP-21 neuropeptide unmasks its unique role in energy homeostasis

Objective: Pro-peptide precursors are processed into biologically active peptide hormones or neurotransmitters, each playing an essential role in physiology and disease. Genetic loss of function of a pro-peptide precursor results in the simultaneous ablation of all biologically-active peptides within that precursor, often leading to a composite phenotype that can be difficult to align with the loss of specific peptide components. Due to this biological constraint and technical limitations, mice carrying the selective ablation of individual peptides encoded by pro-peptide precursor genes, while leaving the other peptides unaffected, have remained largely unaddressed. Methods: We developed and characterized a mouse model carrying the selective knockout of the TLQP-21 neuropeptide (ΔTLQP-21) encoded by the Vgf gene. To achieve this goal, we used a knowledge-based approach by mutating a codon in the Vgf sequence leading to the substitution of the C-terminal Arginine of TLQP-21, which is the pharmacophore as well as an essential cleavage site from its precursor, into Alanine (R21→A). Results: We provide several independent validations of this mouse, including a novel in-gel digestion targeted mass spectrometry identification of the unnatural mutant sequence, exclusive to the mutant mouse. ΔTLQP-21 mice do not manifest gross behavioral and metabolic abnormalities and reproduce well, yet they have a unique metabolic phenotype characterized by an environmental temperature-dependent resistance to diet-induced obesity and activation of the brown adipose tissue. Conclusions: The ΔTLQP-21 mouse line can be a valuable resource to conduct mechanistic studies on the necessary role of TLQP-21 in physiology and disease, while also serving as a platform to test the specificity of novel antibodies or immunoassays directed at TLQP-21. Our approach also has far-reaching implications by informing the development of knowledge-based genetic engineering approaches to generate selective loss of function of other peptides encoded by pro-hormones genes, leaving all other peptides within the pro-protein precursor intact and unmodified

A Hitchhiker's guide to functional magnetic resonance imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.This article has been developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). We are also thankful to FCT-ANR/NEU-OSD/0258/2012 founded by FCT/MEC. RM and PSM are supported by FCT fellowship grants, from the Ph.D.-iHES program, with the references PDE/BDE/113604/2015 and PDE/BDE/113601/2015, respectively. PM is supported by a grant from the project “Better mental health during ageing based on temporal prediction of individual brain ageing trajectories (TEMPO)” (Contract grant number: P-139977) funded by Fundação Calouste Gulbenkian.info:eu-repo/semantics/publishedVersio