The repertoire of Adhesion G protein-coupled receptors in adipocytes and their functional relevance.

BACKGROUND: G protein-coupled receptors (GPCR) are well-characterized regulators of a plethora of physiological functions among them the modulation of adipogenesis and adipocyte function. The class of Adhesion GPCR (aGPCR) and their role in adipose tissue, however, is poorly studied. With respect to the demand for novel targets in obesity treatment, we present a comprehensive study on the expression and function of this enigmatic GPCR class during adipogenesis and in mature adipocytes. METHODS: The expression of all aGPCR representatives was determined by reanalyzing RNA-Seq data and by performing qPCR in different mouse and human adipose tissues under low- and high-fat conditions. The impact of aGPCR expression on adipocyte differentiation and lipid accumulation was studied by siRNA-mediated knockdown of all expressed members of this receptor class. The biological characteristics and function of mature adipocytes lacking selected aGPCR were analyzed by mass spectrometry and biochemical methods (lipolysis, glucose uptake, adiponectin secretion). RESULTS: More than ten aGPCR are significantly expressed in visceral and subcutaneous adipose tissues and several aGPCR are differentially regulated under high-caloric conditions in human and mouse. Receptor knockdown of six receptors resulted in an impaired adipogenesis indicating their expression is essential for proper adipogenesis. The altered lipid composition was studied in more detail for two representatives, ADGRG2/GPR64 and ADGRG6/GPR126. While GPR126 is mainly involved in adipocyte differentiation, GPR64 has an additional role in mature adipocytes by regulating metabolic processes. CONCLUSIONS: Adhesion GPCR are significantly involved in qualitative and quantitative adipocyte lipid accumulation and can control lipolysis. Factors driving adipocyte formation and function are governed by signaling pathways induced by aGPCR yielding these receptors potential targets for treating obesity.

Genetic basis of functional variability in adhesion G protein-coupled receptors.

The enormous sizes of adhesion G protein-coupled receptors (aGPCRs) go along with complex genomic exon-intron architectures giving rise to multiple mRNA variants. There is a need for a comprehensive catalog of aGPCR variants for proper evaluation of the complex functions of aGPCRs found in structural, in vitro and animal model studies. We used an established bioinformatics pipeline to extract, quantify and visualize mRNA variants of aGPCRs from deeply sequenced transcriptomes. Data analysis showed that aGPCRs have multiple transcription start sites even within introns and that tissue-specific splicing is frequent. On average, 19 significantly expressed transcript variants are derived from a given aGPCR gene. The domain architecture of the N terminus encoded by transcript variants often differs and N termini without or with an incomplete seven-helix transmembrane anchor as well as separate seven-helix transmembrane domains are frequently derived from aGPCR genes. Experimental analyses of selected aGPCR transcript variants revealed marked functional differences. Our analysis has an impact on a rational design of aGPCR constructs for structural analyses and gene-deficient mouse lines and provides new support for independent functions of both, the large N terminus and the transmembrane domain of aGPCRs.

Correction: Metabolites of lactic acid bacteria present in fermented foods are highly potent agonists of human hydroxycarboxylic acid receptor 3.

[This corrects the article DOI: 10.1371/journal.pgen.1008145.].

Metabolites of lactic acid bacteria present in fermented foods are highly potent agonists of human hydroxycarboxylic acid receptor 3.

The interplay of microbiota and the human host is physiologically crucial in health and diseases. The beneficial effects of lactic acid bacteria (LAB), permanently colonizing the human intestine or transiently obtained from food, have been extensively reported. However, the molecular understanding of how LAB modulate human physiology is still limited. G protein-coupled receptors for hydroxycarboxylic acids (HCAR) are regulators of immune functions and energy homeostasis under changing metabolic and dietary conditions. Most mammals have two HCAR (HCA1, HCA2) but humans and other hominids contain a third member (HCA3) in their genomes. A plausible hypothesis why HCA3 function was advantageous in hominid evolution was lacking. Here, we used a combination of evolutionary, analytical and functional methods to unravel the role of HCA3 in vitro and in vivo. The functional studies included different pharmacological assays, analyses of human monocytes and pharmacokinetic measurements in human. We report the discovery of the interaction of D-phenyllactic acid (D-PLA) and the human host through highly potent activation of HCA3. D-PLA is an anti-bacterial metabolite found in high concentrations in LAB-fermented food such as Sauerkraut. We demonstrate that D-PLA from such alimentary sources is well absorbed from the human gut leading to high plasma and urine levels and triggers pertussis toxin-sensitive migration of primary human monocytes in an HCA3-dependent manner. We provide evolutionary, analytical and functional evidence supporting the hypothesis that HCA3 was consolidated in hominids as a new signaling system for LAB-derived metabolites.

Profiling of genetic switches using boolean implications in expression data.

Correlation analysis assuming coexpression of the genes is a widely used method for gene expression analysis in molecular biology. Yet growing extent, quality and dimensionality of the molecular biological data permits emerging, more sophisticated approaches like Boolean implications. We present an approach which is a combination of the SOM (self organizing maps) machine learning method and Boolean implication analysis to identify relations between genes, metagenes and similarly behaving metagene groups (spots). Our method provides a way to assign Boolean states to genes/metagenes/spots and offers a functional view over significantly variant elements of gene expression data on these three different levels. While being able to cover relations between weakly correlated entities Boolean implication method also decomposes these relations into six implication classes. Our method allows one to validate or identify potential relationships between genes and functional modules of interest and to assess their switching behaviour. Furthermore the output of the method renders it possible to construct and study the network of genes. By providing logical implications as updating rules for the network it can also serve to aid modelling approaches.

Analysis of microRNA expression using machine learning.

The systematic analysis of miRNA expression and its potential mRNA targets constitutes a basal objective in miRNA research in addition to miRNA gene detection and miRNA target prediction. In this chapter we address methodical issues of miRNA expression analysis using self-organizing maps (SOM), a neural network machine learning algorithm with strong visualization and second-level analysis capabilities widely used to categorize large-scale, high-dimensional data. We shortly review selected experimental and theoretical aspects of miRNA expression analysis. Then, the protocol of our SOM method is outlined with special emphasis on miRNA/mRNA coexpression. The method allows extracting differentially expressed RNA transcripts, their functional context, and also characterization of global properties of expression states and profiles. In addition to the separate study of miRNA and mRNA expression landscapes, we propose the combined analysis of both entities using a covariance SOM.

MicroRNA expression landscapes in stem cells, tissues, and cancer.

MicroRNAs play critical roles in the regulation of gene expression with two major functions: marking mRNA for degradation in a sequence-specific manner or repressing translation. Publicly available data sets on miRNA and mRNA expression in embryonal and induced stem cells, human tissues, and solid tumors are analyzed in this case study using self-organizing maps (SOMs) to characterize miRNA expression landscapes in the context of cell fate commitment, tissue-specific differentiation, and its dysfunction in cancer. The SOM portraits of the individual samples clearly reveal groups of miRNA specifically overexpressed without the need of additional pairwise comparisons between the different systems. Sets of miRNA differentially over- and underexpressed in different systems have been detected in this study. The individual portraits of the expression landscapes enable a very intuitive, image-based perception which clearly promotes the discovery of qualitative relationships between the systems studied. We see perspectives for broad applications of this method in standard analysis to many kinds of high-throughput data of single miRNA and especially combined miRNA/mRNA data sets.