GALNT2 as a novel modulator of adipogenesis and adipocyte insulin signaling

Background/objectives: A better understanding of adipose tissue biology is crucial to tackle insulin resistance and eventually coronary heart disease and diabetes, leading causes of morbidity and mortality worldwide. GALNT2, a GalNAc-transferase, positively modulates insulin signaling in human liver cells by down-regulating ENPP1, an insulin signaling inhibitor. GALNT2 expression is increased in adipose tissue of obese as compared to that of non-obese individuals. Whether this association is secondary to a GALNT2-insulin sensitizing effect exerted also in adipocytes is unknown. We then investigated in mouse 3T3-L1 adipocytes the GALNT2 effect on adipogenesis, insulin signaling and expression levels of both Enpp1 and 72 adipogenesis-related genes. Methods: Stable over-expressing GALNT2 and GFP preadipocytes (T 0 ) were generated. Adipogenesis was induced with (R+) or without (R−) rosiglitazone and investigated after 15 days (T 15 ). Lipid accumulation (by Oil Red-O staining) and intracellular triglycerides (by fluorimetric assay) were measured. Lipid droplets (LD) measures were analyzed at confocal microscope. Gene expression was assessed by RT-PCR and insulin-induced insulin receptor (IR), IRS1, JNK and AKT phosphorylation by Western blot. Results: Lipid accumulation, triglycerides and LD measures progressively increased from T 0 to T 15 R- and furthermore to T 15 R+. Such increases were significantly higher in GALNT2 than in GFP cells so that, as compared to T 15 R+GFP, T 15 R- GALNT2 cells showed similar (intracellular lipid and triglycerides accumulation) or even higher (LD measures, p < 0.01) values. In GALNT2 preadipocytes, insulin-induced IR, IRS1 and AKT activation was higher than that in GFP cells. GALNT2 effect was totally abolished during adipocyte maturation and completely reversed at late stage maturation. Such GALNT2 effect trajectory was paralleled by coordinated changes in the expression of Enpp1 and adipocyte-maturation key genes. Conclusions: GALNT2 is a novel modulator of adipogenesis and related cellular phenotypes, thus becoming a potential target for tackling the obesity epidemics and its devastating sequelae

Does degradation from selective logging and illegal activities differently impact forest resources? A case study in Ghana

Degradation, a reduction of the ecosystem’s capacity to supply goods and services, is widespread in tropical forests and mainly caused by human disturbance. To maintain the full range of forest ecosystem services and support the development of effective conservation policies, we must understand the overall impact of degradation on different forest resources. This research investigates the response to disturbance of forest structure using several indicators: soil carbon content, arboreal richness and biodiversity, functional composition (guild and wood density), and productivity. We drew upon large field and remote sensing datasets from different forest types in Ghana, characterized by varied protection status, to investigate impacts of selective logging, and of illegal land use and resources extraction, which are the main disturbance causes in West Africa. Results indicate that functional composition and the overall number of species are less affected by degradation, while forest structure, soil carbon content and species abundance are seriously impacted, with resources distribution reflecting the protection level of the areas. Remote sensing analysis showed an increase in productivity in the last three decades, with higher resiliency to change in drier forest types, and stronger productivity correlation with solar radiation in the short dry season. The study region is affected by growing anthropogenic pressure on natural resources and by an increased climate variability: possible interactions of disturbance with climate are also discussed, together with the urgency to reduce degradation in order to preserve the full range of ecosystem functions

The spatial association between environmental pollution and long-term cancer mortality in Italy

Tumours are nowadays the second world-leading cause of death after cardiovascular diseases. During the last decades of cancer research, lifestyle and random/genetic factors have been blamed for cancer mortality, with obesity, seden-tary habits, alcoholism, and smoking contributing as supposed major causes. However, there is an emerging consensus that environmental pollution should be considered one of the main triggers. Unfortunately, all this preliminary scien-tific evidence has not always been followed by governments and institutions, which still fail to pursue research on can-cer's environmental connections. In this unprecedented national-scale detailed study, we analyzed the links between cancer mortality, socio-economic factors, and sources of environmental pollution in Italy, both at wider regional and finer provincial scales, with an artificial intelligence approach. Overall, we found that cancer mortality does not have a random or spatial distribution and exceeds the national average mainly when environmental pollution is also higher, despite healthier lifestyle habits. Our machine learning analysis of 35 environmental sources of pollution showed that air quality ranks first for importance concerning the average cancer mortality rate, followed by sites to be reclaimed, urban areas, and motor vehicle density. Moreover, other environmental sources of pollution proved to be relevant for the mortality of some specific cancer types. Given these alarming results, we call for a rearrangement of the priority of cancer research and care that sees the reduction and prevention of environmental contamination as a priority action to put in place in the tough struggle against cancer

Constant Chemical Potential-Quantum Mechanical-Molecular Dynamics simulations of the Graphene-electrolyte double layer

We present the coupling of two frameworks -- the pseudo-open boundary simulation method known as constant potential Molecular Dynamics simulations (C$\mu$MD), combined with QMMD calculations -- to describe the properties of graphene electrodes in contact with electrolytes. The resulting C$\mu$QMMD model was then applied to three ionic solutions (LiCl, NaCl and KCl in water) at bulk solution concentrations ranging from 0.5 M up to 6 M in contact with a charged graphene electrode. The new approach we are describing here provides a simulation protocol to control the concentration of the electrolyte solutions while including the effects of a fully polarizable electrode surface. Thanks to this coupling, we are able to accurately model both the electrode and solution side of the double layer and provide a thorough analysis of the properties of electrolytes at charged interfaces, such as the screening ability of the electrolyte and the electrostatic potential profile. We also report the calculation of the integral electrochemical double layer capacitance in the whole range of concentrations analysed for each ionic species, while the QM simulations provide access to the differential and integral quantum capacitance. We highlight how subtle features, such as the adsorption of potassium at the interface or the tendency of the ions to form clusters, emerge from our simulations, contribute to explaining the ability of graphene to store charge and suggest implications for desalination.Comment: 28 pages, 10 figure