Toward understanding calmodulin plasticity by molecular dynamics

Aim: Calmodulin interacts in many different ways with its ligands. We aim to shed light on its plasticity analysing the changes followed by the linker region and the relative position of the lobes using conventional Molecular Dynamics (cMD), accelerated MD (aMD) and scaled MD (sMD). Materials & Methods: Three different structures of calmodulin are compared, obtaining a total of 2.5 μs of molecular dynamics, which have been analysed using the principal component analysis and clustering methodologies Results: sMD simulations reach conformations that cMD is not able to, without compromising the stability of the protein. On the other hand, aMD requires optimization of the setup parameters to be useful. Conclusion: SMD is useful to study flexible proteins, highlighting those factors that justify its promiscuit

The linker domain of the Ha-Ras hypervariable region regulates interactions with exchange factors, Raf-1 and phosphoinositide 3-kinase

Ha-Ras and Ki-Ras have different distributions across plasma membrane microdomains. The Ras C-terminal anchors are primarily responsible for membrane microlocalization, but recent work has shown that the interaction of Ha-Ras with lipid rafts is modulated by GTP loading via a mechanism that requires the hypervariable region (HVR). We have now identified two regions in the HVR linker domain that regulate Ha-Ras raft association. Release of activated Ha-Ras from lipid rafts is blocked by deleting amino acids 173-179 or 166-172. Alanine replacement of amino acids 173-179 but not 166-172 restores wild type micro-localization, indicating that specific N-terminal sequences of the linker domain operate in concert with a more C-terminal spacer domain to regulate Ha-Ras raft association. Mutations in the linker domain that confine activated Ha-RasG12V to lipid rafts abrogate Raf-1, phosphoinositide 3-kinase, and Akt activation and inhibit PC 12 cell differentiation. N-Myristoylation also prevents the release of activated Ha-Ras from lipid rafts and inhibits Raf-1 activation. These results demonstrate that the correct modulation of Ha-Ras lateral segregation is critical for downstream signaling. Mutations in the linker domain also suppress the dominant negative phenotype of Ha-RasS17N, indicating that HVR sequences are essential for efficient interaction of Ha-Ras with exchange factors in intact cells

Ribonucleoprotein HNRNPA2B1 interacts with and regulates oncogenic KRAS in Pancreatic Ductal Adenocarcinoma Cells.

BACKGROUND & AIMS: Development of pancreatic ductal adenocarcinoma (PDAC) involves activation of c-Ki-ras2 Kirsten rat sarcoma oncogene homolog (KRAS) signaling, but little is known about the roles of proteins that regulate the activity of oncogenic KRAS. We investigated the activities of proteins that interact with KRAS in PDAC cells. METHODS: We used mass spectrometry to demonstrate that heterogeneous nuclear ribonucleoproteins (HNRNP) A2 and B1 (encoded by the gene HNRNPA2B1) interact with KRAS G12V. We used co-immunoprecipitation analyses to study interactions between HNRNPA2B1 and KRAS in KRAS-dependent and KRAS-independent PDAC cell lines. We knocked down HNRNPA2B1 using small hairpin RNAs and measured viability, anchorage-independent proliferation, and growth of xenograft tumors in mice. We studied KRAS phosphorylation using the Phos-tag system. RESULTS: We found that interactions between HRNPA2B1 and KRAS correlated with KRAS-dependency of some human PDAC cell lines. Knock down of HNRNPA2B1 significantly reduced viability, anchorage-independent proliferation, and formation of xenograft tumors by KRAS-dependent PDAC cells. HNRNPA2B1 knock down also increased apoptosis of KRAS-dependent PDAC cells, inactivated c-akt murine thymoma oncogene homolog 1 signaling via mammalian target of rapamycin, and reduced interaction between KRAS and phosphatidylinositide 3-kinase. Interaction between HNRNPA2B1 and KRAS required KRAS phosphorylation at serine 181. CONCLUSIONS: In KRAS-dependent PDAC cell lines, HNRNPA2B1 interacts with and regulates the activity of KRAS G12V and G12D. HNRNPA2B1 is required for KRAS activation of c-akt murine thymoma oncogene homolog 1-mammalian target of rapamycin signaling, interaction with phosphatidylinositide 3-kinase, and PDAC cell survival and tumor formation in mice. HNRNPA2B1 might be a target for treatment of pancreatic cancer

Colloquium on Solid State Devices

[no abstract

Ikaros-1 couples cell cycle arrest of late striatal precursors with neurogenesis of enkephalinergic neurons.

During central nervous system development, several transcription factors regulate the differentiation of progenitor cells to postmitotic neurons. Here we describe a novel role for Ikaros-1 in the generation of late-born striatal neurons. Our results show that Ikaros-1 is expressed in the boundary of the striatal germinal zone (GZ)/mantle zone (MZ), where it induces cell cycle arrest of neural progenitors by up-regulation of the cyclin-dependent kinase inhibitor (CDKi) p21(Cip1/Waf1). This effect is coupled with the neuronal differentiation of late precursors, which in turn is critical for the second wave of striatal neurogenesis that gives rise to matrix neurons. Consistently, Ikaros(-/-) mice had fewer striatal projecting neurons and, in particular, enkephalin (ENK)-positive neurons. In addition, overexpression of Ikaros-1 in primary striatal cultures increases the number of calbindin- and ENK-positive neurons. Our results also show that Ikaros-1 acts downstream of the Dlx family of transcription factors, insofar as its expression is lost in Dlx1/2 double knockout mice. However, we demonstrate that Ikaros-1 and Ebf-1 independently regulate the final determination of the two populations of striatal projection neurons of the matrix compartment, ENK- and substance P-positive neurons. In conclusion, our findings identify Ikaros-1 as a modulator of cell cycle exit of neural progenitors that gives rise to the neurogenesis of ENK-positive striatal neurons.We thank M.T. Mun ̃oz, A. Lo ́pez, T. Gil, and M. Bonete for technical support and Dr. Maria Calvo and Anna Bosch from the confocal microscopy unit at the Serveis Cientı ́fico-Te`cnics (Universitat de Barcelona) for their sup-port and advice on confocal techniques. We also thank Dr.K. Campbell for providing Dlx5/6Cre-IRES-EGFP trans-genic mice, Dr. Rudolf Grosschedl for Ebf1–/– mice, and Dr.Susan Winandy for Ikaros constructs. We are also very grateful to Robin Rycroft for the English language revisionS

MCR-ALS on metabolic networks: Obtaining more meaningful pathways

[EN] With the aim of understanding the flux distributions across a metabolic network, i.e. within living cells, Principal Component Analysis (PCA) has been proposed to obtain a set of orthogonal components (pathways) capturing most of the variance in the flux data. The problems with this method are (i) that no additional information can be included in the model, and (ii) that orthogonality imposes a hard constraint, not always reasonably. To overcome these drawbacks, here we propose to use a more flexible approach such as Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to obtain this set of biological pathways through the network. By using this method, different constraints can be included in the model, and the same source of variability can be present in different pathways, which is reasonable from a biological standpoint. This work follows a methodology developed for Pichia pastoris cultures grown on different carbon sources, lately presented in González-Martínez et al. (2014). In this paper a different grey modelling approach, which aims to incorporate a priori knowledge through constraints on the modelling algorithms, is applied to the same case of study. The results of both models are compared to show their strengths and weaknesses.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grants DPI2011-28112-C04-01 and DPI2011-28112-C04-02. The authors are also grateful to Biopolis SL for supporting this research.Folch-Fortuny, A.; Tortajada Serra, M.; Prats-Montalbán, JM.; Llaneras Estrada, F.; Picó Marco, JA.; Ferrer Riquelme, AJ. (2015). MCR-ALS on metabolic networks: Obtaining more meaningful pathways. Chemometrics and Intelligent Laboratory Systems. 142:293-303. https://doi.org/10.1016/j.chemolab.2014.10.004S29330314

MIA and NIR Chemical Imaging for pharmaceutical product characterization

[EN] This paper presents a three step methodology based on the use of chemical oriented models (MCR and CLS) for extracting out the chemical distribution maps (CDMs) from hyperspectral images, afterwards performing multivariate image analysis (MIA) on the CDMs, and !nally extracting 'channel' and textural features from the score images related to quality characteristics These features show complementary properties to those directly obtained from the CDMs, since they take advantage of their internal correlation structure. The approach has been successfully applied to the evaluation of homogeneity and cluster presence of API in a novel formulation developed to improve the dissolution of poorly soluble drugs. © 2012 Elsevier B.V. All rights reserved.Research in this study was partially supported by the Spanish Ministry of Science and Innovation and FEDER funds from the European Union through grant DPI2011-28112-C04-02, and also by NSF-Engineering Research Center for Structured Organic Particulate Systems (ERC-SOPS, EEC-0540855) and the program NSF-Major Research Instrumentation grant 0821113.Prats-Montalbán, JM.; Jerez-Rozo, J.; Romanach, R.; Ferrer Riquelme, AJ. (2012). MIA and NIR Chemical Imaging for pharmaceutical product characterization. Chemometrics and Intelligent Laboratory Systems. 117(117):240-249. https://doi.org/10.1016/j.chemolab.2012.04.002S24024911711

KRAS phosphorylation regulates cell polarization and tumorigenic properties in colorectal cancer.

Oncogenic mutations of KRAS are found in the most aggressive human tumors, including colorectal cancer. It has been suggested that oncogenic KRAS phosphorylation at Ser181 modulates its activity and favors cell transformation. Using nonphosphorylatable (S181A), phosphomimetic (S181D), and phospho-/dephosphorylatable (S181) oncogenic KRAS mutants, we analyzed the role of this phosphorylation to the maintenance of tumorigenic properties of colorectal cancer cells. Our data show that the presence of phospho-/dephosphorylatable oncogenic KRAS is required for preserving the epithelial organization of colorectal cancer cells in 3D cultures, and for supporting subcutaneous tumor growth in mice. Interestingly, gene expression differed according to the phosphorylation status of KRAS. In DLD-1 cells, CTNNA1 was only expressed in phospho-/dephosphorylatable oncogenic KRAS-expressing cells, correlating with cell polarization. Moreover, lack of oncogenic KRAS phosphorylation leads to changes in expression of genes related to cell invasion, such as SERPINE1, PRSS1,2,3, and NEO1, and expression of phosphomimetic oncogenic KRAS resulted in diminished expression of genes involved in enterocyte differentiation, such as HNF4G. Finally, the analysis, in a public data set of human colorectal cancer, of the gene expression signatures associated with phosphomimetic and nonphosphorylatable oncogenic KRAS suggests that this post-translational modification regulates tumor progression in patients

Near infrared hyperspectral imaging for forensic analysis of document forgery

[EN] Hyperspectral images in the near infrared range (HSI-NIR) were evaluated as a nondestructive method to detect fraud in documents. Three different types of typical forgeries were simulated by (a) obliterating text, (b) adding text and (c) approaching the crossing lines problem. The simulated samples were imaged in the range of 928 2524 nm with spectral and spatial resolutions of 6.3 nm and 10 mm, respectively. After data pre-processing, different chemometric techniques were evaluated for each type of forgery. Principal component analysis (PCA) was performed to elucidate the first two types of adulteration, (a) and (b). Moreover, Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) was used in an attempt to improve the results of the type (a) obliteration and type (b) adding text problems. Finally, MCR-ALS and Partial Least Squares Discriminant Analysis (PLS-DA), employed as a variable selection tool, were used to study the type (c) forgeries, i.e. crossing lines problem. Type (a) forgeries (obliterating text) were successfully identified in 43% of the samples using both the chemometric methods (PCA and MCR-ALS). Type (b) forgeries (adding text) were successfully identified in 82% of the samples using both the methods (PCA and MCR-ALS). Finally, type (c) forgeries (crossing lines) were successfully identified in 85% of the samples. The results demonstrate the potential of HSI-NIR associated with chemometric tools to support document forgery identificationINCTAA (Processes no. : CNPq 573894/2008-6; FAPESP 2008/57808-1), NUQAAPE, FACEPE, CNPq, CAPES, Spanish Ministry of Science and Innovation MICINN (grant DPI2011-28112-C04-02).Silva, CS.; Pimentel, MF.; Honorato, RS.; Pasquini, C.; Prats Montalbán, JM.; Ferrer Riquelme, AJ. (2014). Near infrared hyperspectral imaging for forensic analysis of document forgery. Analyst. 139(20):5176-5184. https://doi.org/10.1039/C4AN00961DS517651841392

A three-dimensional discriminant analysis approach for hyperspectral images

Raman hyperspectral imaging is a powerful technique that provides both chemical and spatial information of a sample matrix being studied. The generated data are composed of three-dimensional (3D) arrays containing the spatial information across the x- and y-axis, and the spectral information in the z-axis. Unfolding procedures are commonly employed to analyze this type of data in a multivariate fashion, where the spatial dimension is reshaped and the spectral data fits into a two-dimensional (2D) structure and, thereafter, common first-order chemometric algorithms are applied to process the data. There are only a few algorithms capable of working with the full 3D array. Herein, we propose new algorithms for 3D discriminant analysis of hyperspectral images based on a three-dimensional principal component analysis linear discriminant analysis (3D-PCA-LDA) and a three-dimensional discriminant analysis quadratic discriminant analysis (3D-PCA-QDA) approach. The analysis was performed in order to discriminate simulated and real-world data, comprising benign controls and ovarian cancer samples based on Raman hyperspectral imaging, in which 3D-PCA-LDA and 3D-PCA-QDA achieved far superior performance than classical algorithms using unfolding procedures (PCA-LDA, PCA-QDA, partial lest squares discriminant analysis [PLS-DA], and support vector machines [SVM]), where the classification accuracies improved from 66% to 83% (simulated data) and from 50% to 100% (real-world dataset) after employing the 3D techniques. 3D-PCA-LDA and 3D-PCA-QDA are new approaches for discriminant analysis of hyperspectral images multisets to provide faster and superior classification performance than traditional techniques