Chronic Endocannabinoid System Stimulation Induces Muscle Macrophage and Lipid Accumulation in Type 2 Diabetic Mice Independently of Metabolic Endotoxaemia

Aims

Obesity and type 2 diabetes are characterised by low-grade inflammation, metabolic endotoxaemia (i.e., increased plasma lipopolysaccharides [LPS] levels) and altered endocannabinoid (eCB)-system tone. The aim of this study was to decipher the specific role of eCB-system stimulation or metabolic endotoxaemia in the onset of glucose intolerance, metabolic inflammation and altered lipid metabolism.

Methods

Mice were treated with either a cannabinoid (CB) receptor agonist (HU210) or low-dose LPS using subcutaneous mini-pumps for 6 weeks. After 3 weeks of the treatment under control (CT) diet, one-half of each group of mice were challenged with a high fat (HF) diet for the following 3-week period.

Results

Under basal conditions (control diet), chronic CB receptor agonist treatment (i.e., 6 weeks) induced glucose intolerance, stimulated metabolic endotoxaemia, and increased macrophage infiltration (CD11c and F4/80 expression) in the muscles; this phenomenon was associated with an altered lipid metabolism (increased PGC-1α expression and decreased CPT-1b expression) in this tissue. Chronic LPS treatment tended to increase the body weight and fat mass, with minor effects on the other metabolic parameters. Challenging mice with an HF diet following pre-treatment with the CB agonist exacerbated the HF diet-induced glucose intolerance, the muscle macrophage infiltration and the muscle''s lipid content without affecting the body weight or the fat mass.

Conclusion

Chronic CB receptor stimulation under basal conditions induces glucose intolerance, stimulates metabolic inflammation and alters lipid metabolism in the muscles. These effects worsen following the concomitant ingestion of an HF diet. Here, we highlight the central roles played by the eCB system and LPS in the pathophysiology of several hallmarks of obesity and type 2 diabetes.     

Sulphide oxidation to elemental sulphur in a membrane bioreactor: performance and characterization of the selected microbial sulphur-oxidizing community

In leather tanning industrial areas sulphide management represents a major problem. However, biological sulphide oxidation to sulphur represents a convenient solution to this problem. Elemental sulphur is easy to separate and the process is highly efficient in terms of energy consumption and effluent quality. As the oxidation process is performed by specialized bacteria, selection of an appropriate microbial community is fundamental for obtaining a good yield. Sulphur oxidizing bacteria (SOB) represent a wide-ranging and highly diversified group of microorganisms with the capability of oxidizing reduced sulphur compounds. Therefore, it is useful to select new microbes that are able to perform this process efficiently. For this purpose, an experimental membrane bioreactor for sulphide oxidation was set up, and the selected microbial community was characterized by constructing 16S rRNA gene libraries and subsequent screening of clones. Fluorescence in situ hybridization (FISH) was then used to assess the relative abundance of different bacterial groups. Sulphide oxidation to elemental sulphur proceeded in an efficient (up to 79% conversion) and stable way in the bioreactor. Both analysis of clone libraries and FISH experiments revealed that the dominant operational taxonomic unit (OTU) in the bioreactor was constituted by Gammaproteobacteria belonging to the Halothiobacillaceae family. FISH performed with the specifically designed probe tios_434 demonstrated that this OTU constituted 90.6+/-1.3% of the bacterial community. Smaller fractions were represented by bacteria belonging to the classes Betaproteobacteria, Alphaproteobacteria, Deltaproteobacteria, Clostridia, Mollicutes, Sphingobacteria, Bacteroidetes and Chlorobia. Phylogenetic analysis revealed that clone sequences from the dominant OTU formed a stable clade (here called the TIOS44 cluster), within the Halothiobacillaceae family, with sequences from many organisms that have not yet been validly described. The data indicated that bacteria belonging to the TIOS44 cluster were responsible for the oxidation process.     

Effects of tunable excitation in carotenoids explained by the vibrational energy relaxation approach

Carotenoids are fundamental building blocks of natural light harvesters with convoluted and ultrafast energy deactivation networks. In order to disentangle such complex relaxation dynamics, several studies focused on transient absorption measurements and their dependence on the pump wavelength. However, such findings are inconclusive and sometimes contradictory. In this study, we compare internal conversion dynamics in \(\beta\)-carotene, pumped at the first, second, and third vibronic progression peak. Instead of employing data fitting algorithms based on global analysis of the transient absorption spectra, we apply a fully quantum mechanical model to treat the high-frequency symmetric carbon–carbon (C=C and C–C) stretching modes explicitly. This model successfully describes observed population dynamics as well as spectral line shapes in their time-dependence and allows us to reach two conclusions: Firstly, the broadening of the induced absorption upon excess excitation is an effect of vibrational cooling in the first excited state (\(S_{1}\)). Secondly, the internal conversion rate between the second excited state (\(S_{2}\)) and \(S_{1}\) crucially depends on the relative curve displacement. The latter point serves as a new perspective on solvent- and excitation wavelength-dependent experiments and lifts contradictions between several studies found in literature.     

The Minimal Complexity of Adapting Agents Increases with Fitness

What is the relationship between the complexity and the fitness of evolved organisms, whether natural or artificial? It has been asserted, primarily based on empirical data, that the complexity of plants and animals increases as their fitness within a particular environment increases via evolution by natural selection. We simulate the evolution of the brains of simple organisms living in a planar maze that they have to traverse as rapidly as possible. Their connectome evolves over 10,000s of generations. We evaluate their circuit complexity, using four information-theoretical measures, including one that emphasizes the extent to which any network is an irreducible entity. We find that their minimal complexity increases with their fitness.     

The Arabidopsis oligopeptidases TOP1 and TOP2 are salicylic acid targets that modulate SA‐mediated signaling and the immune response

Salicylic acid (SA) is a small phenolic molecule with hormonal properties, and is an essential component of the immune response. SA exerts its functions by interacting with protein targets; however, the specific cellular components modulated by SA and critical for immune signal transduction are largely unknown. To uncover cellular activities targeted by SA, we probed Arabidopsis protein microarrays with a functional analog of SA. We demonstrate that thimet oligopeptidases (TOPs) constitute a class of SA‐binding enzymes. Biochemical evidence demonstrated that SA interacts with TOPs and inhibits their peptidase activities to various degrees both in vitro and in plant extracts. Functional characterization of mutants with altered TOP expression indicated that TOP1 and TOP2 mediate SA‐dependent signaling and are necessary for the immune response to avirulent pathogens. Our results support a model whereby TOP1 and TOP2 act in separate pathways to modulate SA‐mediated cellular processes.     

Mass spectrometry-based functional proteomics: from molecular machines to protein networks

The study of protein-protein interactions by mass spectrometry is an increasingly important part of post-genomics strategies to understand protein function. A variety of mass spectrometry-based approaches allow characterization of cellular protein assemblies under near-physiological conditions and subsequent assignment of individual proteins to specific molecular machines, pathways and networks, according to an increasing level of organizational complexity. An appropriate analytical strategy can be individually tailored--from an in-depth analysis of single complexes to a large-scale characterization of entire molecular pathways or even an analysis of the molecular organization of entire expressed proteomes. Here we review different options regarding protein-complex purification strategies, mass spectrometry analysis and bioinformatic methods according to the specific question that is being addressed.