A metabolite-derived protein modification integrates glycolysis with KEAP1-NRF2 signalling.

Mechanisms that integrate the metabolic state of a cell with regulatory pathways are necessary to maintain cellular homeostasis. Endogenous, intrinsically reactive metabolites can form functional, covalent modifications on proteins without the aid of enzymes1,2, and regulate cellular functions such as metabolism3-5 and transcription6. An important 'sensor' protein that captures specific metabolic information and transforms it into an appropriate response is KEAP1, which contains reactive cysteine residues that collectively act as an electrophile sensor tuned to respond to reactive species resulting from endogenous and xenobiotic molecules. Covalent modification of KEAP1 results in reduced ubiquitination and the accumulation of NRF27,8, which then initiates the transcription of cytoprotective genes at antioxidant-response element loci. Here we identify a small-molecule inhibitor of the glycolytic enzyme PGK1, and reveal a direct link between glycolysis and NRF2 signalling. Inhibition of PGK1 results in accumulation of the reactive metabolite methylglyoxal, which selectively modifies KEAP1 to form a methylimidazole crosslink between proximal cysteine and arginine residues (MICA). This posttranslational modification results in the dimerization of KEAP1, the accumulation of NRF2 and activation of the NRF2 transcriptional program. These results demonstrate the existence of direct inter-pathway communication between glycolysis and the KEAP1-NRF2 transcriptional axis, provide insight into the metabolic regulation of the cellular stress response, and suggest a therapeutic strategy for controlling the cytoprotective antioxidant response in several human diseases

Long-term oxidization and phase transition of InN nanotextures

The long-term (6 months) oxidization of hcp-InN (wurtzite, InN-w) nanostructures (crystalline/amorphous) synthesized on Si [100] substrates is analyzed. The densely packed layers of InN-w nanostructures (5-40 nm) are shown to be oxidized by atmospheric oxygen via the formation of an intermediate amorphous In-Ox-Ny (indium oxynitride) phase to a final bi-phase hcp-InN/bcc-In2O3 nanotexture. High-resolution transmission electron microscopy, energy-dispersive X-ray spectroscopy, electron energy loss spectroscopy and selected area electron diffraction are used to identify amorphous In-Ox-Ny oxynitride phase. When the oxidized area exceeds the critical size of 5 nm, the amorphous In-Ox-Ny phase eventually undergoes phase transition via a slow chemical reaction of atomic oxygen with the indium atoms, forming a single bcc In2O3 phase

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

Genetic and phenotypic spectrum associated with IFIH1 gain-of-function

IFIH1 gain‐of‐function has been reported as a cause of a type I interferonopathy encompassing a spectrum of autoinflammatory phenotypes including Aicardi–Goutières syndrome and Singleton Merten syndrome. Ascertaining patients through a European and North American collaboration, we set out to describe the molecular, clinical and interferon status of a cohort of individuals with pathogenic heterozygous mutations in IFIH1. We identified 74 individuals from 51 families segregating a total of 27 likely pathogenic mutations in IFIH1. Ten adult individuals, 13.5% of all mutation carriers, were clinically asymptomatic (with seven of these aged over 50 years). All mutations were associated with enhanced type I interferon signaling, including six variants (22%) which were predicted as benign according to multiple in silico pathogenicity programs. The identified mutations cluster close to the ATP binding region of the protein. These data confirm variable expression and nonpenetrance as important characteristics of the IFIH1 genotype, a consistent association with enhanced type I interferon signaling, and a common mutational mechanism involving increased RNA binding affinity or decreased efficiency of ATP hydrolysis and filament disassembly rate

Soil water-holding capacity and monodominance in Southern Amazon tropical forests

Background and aims: We explored the hypothesis that low soil water-holding capacity is the main factor driving the monodominance of Brosimum rubescens in a monodominant forest in Southern Amazonia. Tropical monodominant forests are rare ecosystems with low diversity and high dominance of a single tree species. The causes of this atypical condition are still poorly understood. Some studies have shown a relationship between monodominance and waterlogging or soil attributes, while others have concluded that edaphic factors have little or no explanatory value, but none has accounted for soil-moisture variation other than waterlogging. This study is the first to explicitly explore how low soil water-holding capacity influences the monodominance of tropical forests. Methods: We conducted in situ measurements of vertical soil moisture using electrical resistance collected over 1 year at 0–5; 35–40 and 75–80 cm depths in a B. rubescens monodominant forest and in an adjacent mixed-species forest in the Amazon-Cerrado transition zone, Brazil. Minimum leaf water potential (Ψmin) of the seven most common species, including B. rubescens, and soil water-holding capacity for both forests were determined. Results: The vertical soil moisture decay pattern was similar in both forests for all depths. However, the slightly higher water availability in the monodominant forest and Ψmin similarity between B. rubescens and nearby mixed forest species indicate that low water-availability does not cause the monodominance. Conclusions: We reject the hypothesis that monodominance of B. rubescens is primarily determined by low soil water-holding capacity, reinforcing the idea that monodominance in tropical forests is not determined by a single factor

Metformin and the gastrointestinal tract

Metformin is an effective agent with a good safety profile that is widely used as a first-line treatment for type 2 diabetes, yet its mechanisms of action and variability in terms of efficacy and side effects remain poorly understood. Although the liver is recognised as a major site of metformin pharmacodynamics, recent evidence also implicates the gut as an important site of action. Metformin has a number of actions within the gut. It increases intestinal glucose uptake and lactate production, increases GLP-1 concentrations and the bile acid pool within the intestine, and alters the microbiome. A novel delayed-release preparation of metformin has recently been shown to improve glycaemic control to a similar extent to immediate-release metformin, but with less systemic exposure. We believe that metformin response and tolerance is intrinsically linked with the gut. This review examines the passage of metformin through the gut, and how this can affect the efficacy of metformin treatment in the individual, and contribute to the side effects associated with metformin intolerance

Calcium mobilization via intracellular ion channels, store organization and mitochondria in smooth muscle

In smooth muscle, Ca2+ release from the internal store into the cytoplasm occurs via inositol trisphosphate (IP3R) and ryanodine receptors (RyR). The internal Ca2+ stores containing IP3R and RyR may be arranged as multiple separate compartments with various IP3R and RyR arrangements, or there may be a single structure containing both receptors. The existence of multiple stores is proposed to explain several physiological responses which include the progression of Ca2+ waves, graded Ca2+ release from the store and various local responses and sensitivities. We suggest that, rather than multiple stores, a single luminally-continuous store exists in which Ca2+ is in free diffusional equilibrium throughout. Regulation of Ca2+ release via IP3R and RyR by the local Ca2+ concentration within the stores explains the apparent existence of multiple stores and physiological processes such as graded Ca2+ release and Ca2+ waves. Close positioning of IP3R on the store with mitochondria or with receptors on the plasma membrane creates ‘IP3 junctions’ to generate local responses on the luminally-continuous store

Potential cellular and biochemical mechanisms of exercise and physical activity on the ageing process

Exercise in young adults has been consistently shown to improve various aspects of physiological and psychological health but we are now realising the potential benefits of exercise with advancing age. Specifically, exercise improves cardiovascular, musculoskeletal, and metabolic health through reductions in oxidative stress, chronic low-grade inflammation and modulating cellular processes within a variety of tissues. In this this chapter we will discuss the effects of acute and chronic exercise on these processes and conditions in an ageing population, and how physical activity affects our vasculature, skeletal muscle function, our immune system, and cardiometabolic risk in older adults

Uniform Image Partitioning for Fractal Compression on Virtual Hexagonal Structure

Hexagonal structure is different from the traditional square structure for image representation. The geometrical arrangement of pixels on hexag-onal structure can be described in terms of a hexagonal grid. Uniformly sepa-rating image into seven similar copies with a smaller scale has commonly been used for parallel and accurate image processing including image compression on hexagonal structure. However, all the existing hardware for capturing image and for displaying image are produced based on square architecture. It has become a serious problem affecting the advanced research based on hexagonal structure. Furthermore, the current techniques used for uniform separation of images on hexagonal structure do not coincide with the rectangular shape of images. This has been an obstacle in the use of hexagonal structure for image processing. In this paper, we briefly review a newly developed virtual hexagonal structure that is scalable. Based on this virtual structure, algorithms for uni-form image separation are presented. The virtual hexagonal structure retains image resolution during the process of image separation, and does not intro-duce distortion. Furthermore, images can be smoothly and easily transferred between the traditional square structure and the hexagonal structure while the image shape is kept in rectangle. As an application of image partitioning, we present a Fractal Image Compression (FIC) method on the virtual image struc- ture by adopting Fisher's basic FIC method on the traditional square image structure. The modifcation on the definition of range block and domain block is implemented in order to utilize the enhanced image structure. The results of the FIC approach applied to testing images are analyzed and show higher fidelity