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.

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.

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.

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.

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).

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.

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.