Di-μ-benzoato-κ3 O,O′:O;κ3 O:O,O′-bis­[(acetato-κO)(1,10-phenanthroline-κ2 N,N′)lead(II)] dihydrate

The title compound, [Pb2(CH3COO)2(C7H5O2)2(C12H8N2)2]·2H2O, consists of dimeric units built up around a crystallographic centre of symmetry and two non-coordinating water mol­ecules. Each PbII unit is six-coordinated by a bidentate 1,10-phenanthroline (phen) ligand, a monodentate acetate anion and a bidentate benzoate anion, which also acts as a bridge linking the two PbII atoms. The crystal packing is stabilized by O—H⋯O hydrogen bonds and by π–π inter­actions between the phen rings of neighboring mol­ecules, with a centroid–centroid distance of 3.577 (3) Å

Bis(μ-3-hydroxy­benzoato-κ3 O,O′:O)bis­[aqua­(3-hydroxy­benzoato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)lead(II)] monohydrate

In the centrosymmetric binuclear title complex, [Pb2(C7H5O3)4(C12H8N2)2(H2O)2]·H2O, each Pb atom is eight-coordinated in a PbO6N2 environment by two N atoms from the 1,10-phenanthroline (phen) ligand, five carboxylate O atoms from four 3-hydroxy­benzoate anions and one O atom from the coordinated water mol­ecule in a distorted bicapped trigonal-prismatic geometry. The benzoate groups coordinate each PbII atom in two different ways. Two benzoate ions behave as bidentate ligands to the Pb atom, and another benzoate ion bridges the Pb atoms, forming a binuclear structure. The dimeric units are packed via O—H⋯O hydrogen bonds and π–π inter­actions between the aromatic rings of neighboring mol­ecules, with centroid–centroid distances of 3.552 (2) and 3.641 (2) Å

Quantitative definition and monitoring of the host cell protein proteome using iTRAQ: a study of an industrial mAb producing CHO-S cell line

There are few studies defining CHO host cell proteins (HCPs) and the flux of these throughout a downstream purification process. Here we have applied quantitative iTRAQ proteomics to follow the HCP profile of an antibody (mAb) producing CHO-S cell line throughout a standard downstream purification procedure consisting of a Protein A, cation and anion exchange process. We used both 6 sample iTRAQ experiment to analyze technical replicates of three samples, which were culture harvest (HCCF), Protein A flow through and Protein A eluate and an 8 sample format to analyze technical replicates of four sample types; HCCF compared to Protein A eluate and subsequent cation and anion exchange purification. In the 6 sample iTRAQ experiment, 8781 spectra were confidently matched to peptides from 819 proteins (including the mAb chains). Across both the 6 and 8 sample experiments 936 proteins were identified. In the 8 sample comparison, 4187 spectra were confidently matched to peptides from 219 proteins. We then used the iTRAQ data to enable estimation of the relative change of individual proteins across the purification steps. These data provide the basis for application of iTRAQ for process development based upon knowledge of critical HCPs

Plato's Cave Algorithm: Inferring Functional Signaling Networks from Early Gene Expression Shadows

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships

Combination of contrast with stress echocardiography: A practical guide to methods and interpretation

Contrast echocardiography has an established role for enhancement of the right heart Doppler signals, the detection of intra-cardiac shunts, and most recently for left ventricular cavity opacification (LVO). The use of intravenously administered micro-bubbles to traverse the myocardial microcirculation in order to outline myocardial viability and perfusion has been the source of research studies for a number of years. Despite the enthusiasm of investigators, myocardial contrast echocardiography (MCE) has not attained routine clinical use and LV opacification during stress has been less widely adopted than the data would support. The purpose of this review is to facilitate an understanding of the involved imaging technologies that have made this technique more feasible for clinical practice, and to guide its introduction into the practice of the non-expert user

The translational landscape of the splicing factor SRSF1 and its role in mitosis

The shuttling serine/arginine rich (SR) protein SRSF1 (previously known as SF2/ASF) is a splicing regulator that also activates translation in the cytoplasm. In order to dissect the gene network that is translationally regulated by SRSF1, we performed a high-throughput deep sequencing analysis of polysomal fractions in cells overexpressing SRSF1. We identified approximately 1500 mRNAs that are translational targets of SRSF1. These include mRNAs encoding proteins involved in cell cycle regulation, such as spindle, kinetochore, and M phase proteins, which are essential for accurate chromosome segregation. Indeed, we show that translational activity of SRSF1 is required for normal mitotic progression. Furthermore, we found that mRNAs that display alternative splicing changes upon SRSF1 overexpression are also its translational targets, strongly suggesting that SRSF1 couples pre-mRNA splicing and translation. These data provide insights on the complex role of SRSF1 in the control of gene expression at multiple levels and its implications in cancer.Fil: Maslon, Magdalena M. . Institute of Genetics and Molecular Medicine, University of Edinburgh; Reino UnidoFil: Heras, Sara R.. Institute of Genetics and Molecular Medicine, University of Edinburgh; Reino Unido. Universidad de Granada; EspañaFil: Bellora, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Patagonia Norte. Instituto de Investigación En Biodiversidad y Medioambiente; Argentina. Universitat Pompeu Fabra; EspañaFil: Eyras, Eduardo. Universitat Pompeu Fabra; España. Catalan Institution for Research and Advanced Studies (ICREA), Barcelona; EspañaFil: Cáceres, Javier F.. University Of Edinburgh; Reino Unid