A leucine-rich repeat motif of Leishmania parasite surface antigen 2 binds to macrophages through the complement receptor 3

Membrane glycoconjugates on the Leishmania parasites, notably leishmanolysin and lipophosphoglycan, have been implicated in attachment and invasion of host macrophages. However, the function of parasite surface Ag 2 (PSA-2) and membrane proteophosphoglycan (PPG) has not been elucidated. In this study we demonstrate that native and recombinant Leishmania infantum PSA-2, which consists predominantly of 15 leucine-rich repeats (LRR) and a recombinant LRR domain derived from L. major PPG, bind to macrophages. The interaction is restricted to macrophages and appears to be calcium independent. We have investigated the PSA-2-macrophage interaction to identify the host receptor involved in binding and we show that binding of PSA-2 to macrophages can be blocked by Abs to the complement receptor 3 (CR3, Mac-1). Data derived from mouse macrophage studies were further confirmed using cell lines expressing human CR3, and showed that PSA-2 also binds to the human receptor. This is the first demonstration of a functional role for PSA-2. Our data indicate that in addition to leishmanolysin and lipophosphoglycan, parasite attachment and invasion of macrophages involve a third ligand comprising the LRRs shared by PSA-2 and PPG and that these interactions occur via the CR3.     

Symmetric K+ and Mg2+ ion-binding sites in the 5S rRNA loop E inferred from molecular dynamics simulations

Potassium binding to the 5 S rRNA loop E motif has been studied by molecular dynamics at high (1.0 M) and low (0.2 M) concentration of added KCl in the presence and absence of Mg²⁺. A clear pattern of seven deep groove K⁺ binding sites or regions, in all cases connected with guanine N7/O6 atoms belonging to GpG, GpA, and GpU steps, was identified, indicating that the LE deep groove is significantly more ionophilic than the equivalent groove of regular RNA duplexes. Among all, two symmetry-related sites (with respect to the central G·A pair) were found to accommodate K⁺ ions with particularly long residence times. In a preceding molecular dynamics study by Auffinger et al. in the year 2003, these two sites were described as constituting important Mg²⁺ binding locations. Altogether, the data suggest that these symmetric sites correspond to the loop E main ion binding regions. Indeed, they are located in the deep groove of an important ribosomal protein binding motif associated with a fragile pattern of non-Watson-Crick pairs that has certainly to be stabilized by specific Mg²⁺ ions in order to be efficiently recognized by the protein. Besides, the other sites accommodate monovalent ions in a more diffuse way pointing out their lesser significance for the structure and function of this motif. Ion binding to the shallow groove and backbone atoms was generally found to be of minor importance since, at the low concentration, no well defined binding site could be characterized while high K⁺ concentration promoted mostly unspecific potassium binding to the RNA backbone. In addition, several K⁺ binding sites were located in positions equivalent to water molecules from the first hydration shell of divalent ions in simulations performed with magnesium, indicating that ion binding regions are able to accommodate both mono- and divalent ionic species. Overall, the simulations provide a more precise but, at the same time, a more intricate view of the relations of this motif with its ionic surrounding.     

Sequence Representation and Prediction of Protein Secondary Structure for Structural Motifs in Twilight Zone Proteins

Characterizing and classifying regularities in protein structure is an important element in uncovering the mechanisms that regulate protein structure, function and evolution. Recent research concentrates on analysis of structural motifs that can be used to describe larger, fold-sized structures based on homologous primary sequences. At the same time, accuracy of secondary protein structure prediction based on multiple sequence alignment drops significantly when low homology (twilight zone) sequences are considered. To this end, this paper addresses a problem of providing an alternative sequences representation that would improve ability to distinguish secondary structures for the twilight zone sequences without using alignment. We consider a novel classification problem, in which, structural motifs, referred to as structural fragments (SFs) are defined as uniform strand, helix and coil fragments. Classification of SFs allows to design novel sequence representations, and to investigate which other factors and prediction algorithms may result in the improved discrimination. Comprehensive experimental results show that statistically significant improvement in classification accuracy can be achieved by: (1) improving sequence representations, and (2) removing possible noise on the terminal residues in the SFs. Combining these two approaches reduces the error rate on average by 15% when compared to classification using standard representation and noisy information on the terminal residues, bringing the classification accuracy to over 70%. Finally, we show that certain prediction algorithms, such as neural networks and boosted decision trees, are superior to other algorithms.This research was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC).     

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−¹³C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.We acknowledge the support of NIH Grant DK58533 and the DuPont-MIT Alliance.     

Metabolic memory - the implications for diabetic complications

Many important biochemical mechanisms are activated in the presence of high levels of glucose, which occur in diabetes. Large randomised studies have established that early intensive glycaemic control reduces the risk of diabetic complications. This phenomenon has recently been dubbed 'metabolic memory'. It has been suggested that early glycaemia normalisation can halt the hyperglycaemia-induced pathological processes associated with enhanced oxidative stress and glycation of cellular proteins and lipids. The phenomenon of metabolic memory suggests that early aggressive treatment and strict glycaemic control could prevent chronic diabetic complications.