Differential expression of CDC25 phosphatases splice variants in human breast cancer cells

Background: CDC25 phosphatases control cell cycle progression by activating cyclin dependent kinases. The three CDC25 isoforms encoding genes are submitted to alternative splicing events which generate at least two variants for CDC25A and five for both CDC25B and CDC25C. An over-expression of CDC25 was reported in several types of cancer, including breast cancer, and is often associated with a poor prognosis. Nevertheless, most of the previous studies did not address the expression of CDC25 splice variants. Here, we evaluated CDC25 spliced transcripts expression in anti-cancerous drug-sensitive and resistant breast cancer cell lines in order to identify potential breast cancer biomarkers. Methods: CDC25 splice variants mRNA levels were evaluated by semi-quantitative RT-PCR and by an original real-time RT-PCR assay. Results: CDC25 spliced transcripts are differentially expres-sed in the breast cancer cell lines studied. An up-regulation of CDC25A2 variant and an increase of the CDC25C5/C1 ratio are associated to the multidrug-resistance in VCREMS and DOXOR breast cancer cells, compared to their sensitive counterpart cell line MCF-7. Additionally, CDC25B2 tran-script is exclusively over-expressed in VCREMS resistant cells and could therefore be involved in the development of certain type of drug resistance. Conclusions: CDC25 splice variants could represent interesting potential breast cancer prognostic biomarkers.

Synthesis, characterization, electrochemical behavior and in vitro protein tyrosine kinase inhibitory activity of the cymene-halogenobenzohydroxamato [Ru(eta(6)-cymene)(bha)Cl] complexes

The ruthenium(II)-cymene complexes [Ru(eta(6)-cymene)(bha)Cl] with substituted halogenobenzohydroxamato (bha) ligands (substituents = 4-F, 4-Cl, 4-Br, 2,4-F-2, 3,4-F-2, 2,5-F-2, 2,6-F-2) have been synthesized and characterized by elemental analysis, IR, H-1 NMR, C-13 NMR, cyclic voltammetry and controlled-potential electrolysis, and density functional theory (DFT) studies. The compositions of their frontier molecular orbitals (MOs) were established by DFT calculations, and the oxidation and reduction potentials are shown to follow the orders of the estimated vertical ionization potential and electron affinity, respectively. The electrochemical E-L Lever parameter is estimated for the first time for the various bha ligands, which can thus be ordered according to their electron-donor character. All complexes exhibit very strong protein tyrosine kinase (PTK) inhibitory activity, even much higher than that of genistein, the clinically used PTK inhibitory drug. The complex containing the 2,4-difluorobenzohydroxamato ligand is the most active one, and the dependences of the PTK activity of the complexes and of their redox potentials on the ring substituents are discussed. (C) 2012 Elsevier B.V. All rights reserved.

OsRMC, a negative regulator of salt stress response in rice, is regulated by two AP2/ERF transcription factors

High salinity causes remarkable losses in rice productivity worldwide mainly because it inhibits growth and reduces grain yield. To cope with environmental changes, plants evolved several adaptive mechanisms, which involve the regulation of many stress-responsive genes. Among these, we have chosen OsRMC to study its transcriptional regulation in rice seedlings subjected to high salinity. Its transcription was highly induced by salt treatment and showed a stress-dose-dependent pattern. OsRMC encodes a receptor-like kinase described as a negative regulator of salt stress responses in rice. To investigate how OsRMC is regulated in response to high salinity, a salt-induced rice cDNA expression library was constructed and subsequently screened using the yeast one-hybrid system and the OsRMC promoter as bait. Thereby, two transcription factors (TFs), OsEREBP1 and OsEREBP2, belonging to the AP2/ERF family were identified. Both TFs were shown to bind to the same GCC-like DNA motif in OsRMC promoter and to negatively regulate its gene expression. The identified TFs were characterized regarding their gene expression under different abiotic stress conditions. This study revealed that OsEREBP1 transcript level is not significantly affected by salt, ABA or severe cold (5 °C) and is only slightly regulated by drought and moderate cold. On the other hand, the OsEREBP2 transcript level increased after cold, ABA, drought and high salinity treatments, indicating that OsEREBP2 may play a central role mediating the response to different abiotic stresses. Gene expression analysis in rice varieties with contrasting salt tolerance further suggests that OsEREBP2 is involved in salt stress response in rice.

Cellular polarity in aging: role of redox regulation and nutrition

Cellular polarity concerns the spatial asymmetric organization of cellular components and structures. Such organization is important not only for biological behavior at the individual cell level, but also for the 3D organization of tissues and organs in living organisms. Processes like cell migration and motility, asymmetric inheritance, and spatial organization of daughter cells in tissues are all dependent of cell polarity. Many of these processes are compromised during aging and cellular senescence. For example, permeability epithelium barriers are leakier during aging; elderly people have impaired vascular function and increased frequency of cancer, and asymmetrical inheritance is compromised in senescent cells, including stem cells. Here, we review the cellular regulation of polarity, as well as the signaling mechanisms and respective redox regulation of the pathways involved in defining cellular polarity. Emphasis will be put on the role of cytoskeleton and the AMP-activated protein kinase pathway. We also discuss how nutrients can affect polarity-dependent processes, both by direct exposure of the gastrointestinal epithelium to nutrients and by indirect effects elicited by the metabolism of nutrients, such as activation of antioxidant response and phase-II detoxification enzymes through the transcription factor nuclear factor (erythroid-derived 2)-like 2 (Nrf2). In summary, cellular polarity emerges as a key process whose redox deregulation is hypothesized to have a central role in aging and cellular senescence.

A fluorescente bicyclic Calix[4] Arene-Oxacyclophane with planar chirality: Resolution, chiroptical, properties, and absolute configuration.

A new inherently chiral calix[4]arene ICC 1 has been disclosed. The dissymmetry of 1 is generated from a chirality plane in the quinol moiety of a 1,3-bridged bicyclic calix[4]arene. ICC 1 has been resolved by enantioselective HPLC, and the chiroptical properties of both isolated antipodes (pS)-1 and (pR)-1 confirm their enantiomeric nature. The absolute configuration of the (pS)-1/(pR)-1 enantiomeric pair was established through time-dependent density functional theory (TDDFT) calculations of electronic circular dichroism (CD) spectra. (C) 2014 Elsevier Ltd. All rights reserved.

Inherently chiral calix[4]arenes with planar chirality: two new entries to the family

The synthesis of two new inherently chiral calix[4]arenes (ICCs, 1 and 2), endowed with electron-rich concave surfaces, has been achieved through the desymmetrization of a lower rim distal-bridged oxacyclophane (OCP) macrocycle. The new highly emissive ICCs were resolved by chiral HPLC, and the enantiomeric nature of the isolated antipodes proved by electronic circular dichroism (CD). Using time-dependent density functional calculations of CD spectra, their absolute configurations were established. NMR studies with (S)-Pirkle's alcohol unequivocally showed that the host-guest interactions occur in the chiral pocket comprehending the calix-OCP exo cavities and the carbazole moieties.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.