The Role of CyaY in Iron Sulfur Cluster Assembly on the E. coli IscU Scaffold Protein

Progress in understanding the mechanism underlying the enzymatic formation of iron-sulfur clusters is difficult since it involves a complex reaction and a multi-component system. By exploiting different spectroscopies, we characterize the effect on the enzymatic kinetics of cluster formation of CyaY, the bacterial ortholog of frataxin, on cluster formation on the scaffold protein IscU. Frataxin/CyaY is a highly conserved protein implicated in an incurable ataxia in humans. Previous studies had suggested a role of CyaY as an inhibitor of iron sulfur cluster formation. Similar studies on the eukaryotic proteins have however suggested for frataxin a role as an activator. Our studies independently confirm that CyaY slows down the reaction and shed new light onto the mechanism by which CyaY works. We observe that the presence of CyaY does not alter the relative ratio between [2Fe2S]2+ and [4Fe4S]2+ but directly affects enzymatic activity

1H-NMR-Based Metabolic Profiling of Maternal and Umbilical Cord Blood Indicates Altered Materno-Foetal Nutrient Exchange in Preterm Infants

Background: Adequate foetal growth is primarily determined by nutrient availability, which is dependent on placental nutrient transport and foetal metabolism. We have used 1H nuclear magnetic resonance (NMR) spectroscopy to probe the metabolic adaptations associated with premature birth. Methodology: The metabolic profile in 1H NMR spectra of plasma taken immediately after birth from umbilical vein, umbilical artery and maternal blood were recorded for mothers delivering very-low-birth-weight (VLBW) or normo-ponderal full-term (FT) neonates. Principal Findings: Clear distinctions between maternal and cord plasma of all samples were observed by principal component analysis (PCA). Levels of amino acids, glucose, and albumin-lysyl in cord plasma exceeded those in maternal plasma, whereas lipoproteins (notably low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) and lipid levels were lower in cord plasma from both VLBW and FT neonates. The metabolic signature of mothers delivering VLBW infants included decreased levels of acetate and increased levels of lipids, pyruvate, glutamine, valine and threonine. Decreased levels of lipoproteins glucose, pyruvate and albumin-lysyl and increased levels of glutamine were characteristic of cord blood (both arterial and venous) from VLBW infants, along with a decrease in levels of several amino acids in arterial cord blood. Conclusion: These results show that, because of its characteristics and simple non-invasive mode of collection, cord plasma is particularly suited for metabolomic analysis even in VLBW infants and provides new insights into the materno-foetal nutrient exchange in preterm infants

Dynein and Dynactin Leverage Their Bivalent Character to Form a High-Affinity Interaction

Amanda E. Siglin is with Thomas Jefferson University, Shangjin Sun is with University of Delaware, Jeffrey K. Moore is with Washington University in Saint Louis, Sarah Tan is with UT Austin, Martin Poenie is with UT Austin, James D. Lear is with University of Pennsylvania, Tatyana Polenova is with University of Delaware, John A. Cooper is with Washington University in Saint Louis, and John C. Williams is with Thomas Jefferson University and Beckman Research Institute at City of Hope.Cytoplasmic dynein and dynactin participate in retrograde transport of organelles, checkpoint signaling and cell division. The principal subunits that mediate this interaction are the dynein intermediate chain (IC) and the dynactin p150Glued; however, the interface and mechanism that regulates this interaction remains poorly defined. Herein, we use multiple methods to show the N-terminus of mammalian dynein IC, residues 10–44, is sufficient for binding p150Glued. Consistent with this mapping, monoclonal antibodies that antagonize the dynein-dynactin interaction also bind to this region of the IC. Furthermore, double and triple alanine point mutations spanning residues 6 to 19 in the yeast IC homolog, Pac11, produce significant defects in spindle positioning. Using the same methods we show residues 381 to 530 of p150Glued form a minimal fragment that binds to the dynein IC. Sedimentation equilibrium experiments indicate that these individual fragments are predominantly monomeric, but admixtures of the IC and p150Glued fragments produce a 2:2 complex. This tetrameric complex is sensitive to salt, temperature and pH, suggesting that the binding is dominated by electrostatic interactions. Finally, circular dichroism (CD) experiments indicate that the N-terminus of the IC is disordered and becomes ordered upon binding p150Glued. Taken together, the data indicate that the dynein-dynactin interaction proceeds through a disorder-to-order transition, leveraging its bivalent-bivalent character to form a high affinity, but readily reversible interaction.This work was supported in part by National Institutes of Health R21NS071166 (J.C.W.), R01GM085306 (J.C.W. & T.P.), NCRR SRR022316A (J.C.W.), GM 47337 (J.A.C.), NCRR 5P20RR017716-07 (T.P.), 5-T32-DK07705 (A.E.S) and The American Heart Association 0715196U (A.E.S). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Cellular and Molecular Biolog

A comparison of different chemometrics approaches for the robust classification of electronic nose data

Accurate detection of certain chemical vapours is important, as these may be diagnostic for the presence of weapons, drugs of misuse or disease. In order to achieve this, chemical sensors could be deployed remotely. However, the readout from such sensors is a multivariate pattern, and this needs to be interpreted robustly using powerful supervised learning methods. Therefore, in this study, we compared the classification accuracy of four pattern recognition algorithms which include linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), random forests (RF) and support vector machines (SVM) which employed four different kernels. For this purpose, we have used electronic nose (e-nose) sensor data (Wedge et al., Sensors Actuators B Chem 143:365-372, 2009). In order to allow direct comparison between our four different algorithms, we employed two model validation procedures based on either 10-fold cross-validation or bootstrapping. The results show that LDA (91.56% accuracy) and SVM with a polynomial kernel (91.66% accuracy) were very effective at analysing these e-nose data. These two models gave superior prediction accuracy, sensitivity and specificity in comparison to the other techniques employed. With respect to the e-nose sensor data studied here, our findings recommend that SVM with a polynomial kernel should be favoured as a classification method over the other statistical models that we assessed. SVM with non-linear kernels have the advantage that they can be used for classifying non-linear as well as linear mapping from analytical data space to multi-group classifications and would thus be a suitable algorithm for the analysis of most e-nose sensor data