The interplay of secondary Hg⋯S, Hg⋯N and Hg⋯π bonding interactions in supramolecular structures of phenylmercury(ii) dithiocarbamates

Three new phenylmercury(II) and one mercury(II) dithiocarbamate complexes viz. PhHg S2CN(PyCH2) Bz (1), PhHg S2CN(PyCH2)CH3 (2), PhHg S2CN(Bz)CH3 (3), and [Hg (NCS2(PyCH2)Bz)(2)] (4) (Py = pyridine; Bz = benzyl) have been synthesized and characterized by elemental analyses, IR, electronic absorption, H-1 and C-13 NMR spectroscopy. The crystal structures of 1, 2 and 3 showed a linear S-Hg-C core at the centre of the molecule, in which the metal atom is bound to the sulfur atom of the dithiocarbamate ligand and a carbon atom of the aromatic ring. In contrast the crystal structure of 4 showed a linear S-Hg-S core at the Hg(II) centre of the molecule. Weak intermolecular Hg center dot center dot center dot N (Py) interactions link molecules into a linear chain in the case of 1, whereas chains of dimers are formed in 2 through intermolecular Hg center dot center dot center dot N (Py) and Hg center dot center dot center dot S interactions. 3 forms a conventional face-to-edge dimeric structure through intermolecular Hg center dot center dot center dot S secondary bonding and 4 forms a linear chain of dimers through face-to-face Hg center dot center dot center dot S secondary bonding. In order to elucidate the nature of these secondary bonding interactions and the electronic absorption spectra of the complexes, ab initio quantum chemical calculations at the MP2 level and density functional theory calculations were carried out for 1-3. Complexes 1 and 2 exhibited photoluminescent properties in the solid state as well as in the solution phase. Studies indicate that Hg center dot center dot center dot S interactions decrease and Hg center dot center dot center dot N interactions increase the chances of photoluminescence in the solid phase

Gene-nutrient interactions on the phosphoenolpyruvate carboxykinase influence insulin sensitivity in metabolic syndrome subjects

Genetic background may interact with habitual dietary fat composition, and affect development of the metabolic syndrome (MetS). The phosphoenolpyruvate carboxykinase gene (PCK1) plays a significant role regulating glucose metabolism, and fatty acids are key metabolic regulators, which interact with transcription factors and influence glucose metabolism. We explored genetic variability at the PCK1 gene locus in relation to degree of insulin resistance and plasma fatty acid levels in MetS subjects. Moreover, we analyzed the PCK1 gene expression in the adipose tissue of a subgroup of MetS subjects according to the PCK1 genetic variants.

Plant–pathogen interactions: disease resistance in modern agriculture

The growing human population will require a significant increase in agricultural production. This challenge is made more difficult by the fact that changes in the climatic and environmental conditions under which crops are grown have resulted in the appearance of new diseases, whereas genetic changes within the pathogen have resulted in the loss of previously effective sources of resistance. To help meet this challenge, advanced genetic and statistical methods of analysis have been used to identify new resistance genes through global screens, and studies of plant-pathogen interactions have been undertaken to uncover the mechanisms by which disease resistance is achieved. The informed deployment of major, race-specific and partial, race-nonspecific resistance, either by conventional breeding or transgenic approaches, will enable the production of crop varieties with effective resistance without impacting on other agronomically important crop traits. Here, we review these recent advances and progress towards the ultimate goal of developing disease-resistant crops.

Nutrimetabonomics: applications for nutritional sciences, with specific reference to gut microbial interactions

Understanding the role of the diet in determining human health and disease is one major objective of modern nutrition. Mammalian biocomplexity necessitates the incorporation of systems biology technologies into contemporary nutritional research. Metabonomics is a powerful approach that simultaneously measures the low-molecular-weight compounds in a biological sample, enabling the metabolic status of a biological system to be characterized. Such biochemical profiles contain latent information relating to inherent parameters, such as the genotype, and environmental factors, including the diet and gut microbiota. Nutritional metabonomics, or nutrimetabonomics, is being increasingly applied to study molecular interactions between the diet and the global metabolic system. This review discusses three primary areas in which nutrimetabonomics has enjoyed successful application in nutritional research: the illumination of molecular relationships between nutrition and biochemical processes; elucidation of biomarker signatures of food components for use in dietary surveillance; and the study of complex trans-genomic interactions between the mammalian host and its resident gut microbiome. Finally, this review illustrates the potential for nutrimetabonomics in nutritional science as an indispensable tool to achieve personalized nutrition.

Ketamine, propofol, and the EEG: A neural field analysis of HCN1-mediated interactions

Ketamine and propofol are two well-known, powerful anesthetic agents, yet at first sight this appears to be their only commonality. Ketamine is a dissociative anesthetic agent, whose main mechanism of action is considered to be N-methyl-D-aspartate (NMDA) antagonism; whereas propofol is a general anesthetic agent, which is assumed to primarily potentiate currents gated by γ-aminobutyric acid type A (GABAA) receptors. However, several experimental observations suggest a closer relationship. First, the effect of ketamine on the electroencephalogram (EEG) is markedly changed in the presence of propofol: on its own ketamine increases θ (4–8 Hz) and decreases α (8–13 Hz) oscillations, whereas ketamine induces a significant shift to beta band frequencies (13–30 Hz) in the presence of propofol. Second, both ketamine and propofol cause inhibition of the inward pacemaker current Ih, by binding to the corresponding hyperpolarization-activated cyclic nucleotide-gated potassium channel 1 (HCN1) subunit. The resulting effect is a hyperpolarization of the neuron’s resting membrane potential. Third, the ability of both ketamine and propofol to induce hypnosis is reduced in HCN1-knockout mice. Here we show that one can theoretically understand the observed spectral changes of the EEG based on HCN1-mediated hyperpolarizations alone, without involving the supposed main mechanisms of action of these drugs through NMDA and GABAA, respectively. On the basis of our successful EEG model we conclude that ketamine and propofol should be antagonistic to each other in their interaction at HCN1 subunits. Such a prediction is in accord with the results of clinical experiment in which it is found that ketamine and propofol interact in an infra-additive manner with respect to the endpoints of hypnosis and immobility.

The FunFOLD2 server for the prediction of protein-ligand interactions

The FunFOLD2 server is a new independent server that integrates our novel protein–ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures. The protein–ligand interactions can also be interactively visualized in 3D using the Jmol plug-in. The raw machine readable data are provided for developers, which comply with the Critical Assessment of Techniques for Protein Structure Prediction data standards for FN predictions. The FunFOLD2 webserver is freely available to all at the following web site: http://www.reading.ac.uk/bioinf/FunFOLD/FunFOLD_form_2_0.html.

The energy-cycle analysis of the interactions between shallow and deep atmospheric convection

Interactions between different convection modes can be investigated using an energy–cycle description under a framework of mass–flux parameterization. The present paper systematically investigates this system by taking a limit of two modes: shallow and deep convection. Shallow convection destabilizes itself as well as the other convective modes by moistening and cooling the environment, whereas deep convection stabilizes itself as well as the other modes by drying and warming the environment. As a result, shallow convection leads to a runaway growth process in its stand–alone mode, whereas deep convection simply damps out. Interaction between these two convective modes becomes a rich problem, even when it is limited to the case with no large–scale forcing, because of these opposing tendencies. Only if the two modes are coupled at a proper level can a self–sustaining system arise, exhibiting a periodic cycle. The present study establishes the conditions for self–sustaining periodic solutions. It carefully documents the behaviour of the two mode system in order to facilitate the interpretation of global model behaviours when this energy–cycle is implemented as a closure into a convection parameterization in future.