The structural diversity of heterocycle-fused potassium cyclopentadienides

Cyclopentadienides of d- and f-elements are highly important complexes with undoubted potential for practical applications. Annelation of a heterocyclic fragment with an η⁵-ring results in substantial improvement of the catalytic properties of these compounds, called “heterocenes”; the investigation of metal coordination with these specific ligands is a highly important problem. We prepared potassium derivatives 5–8 of heterocycle-annelated cyclopentadienes with different structures – derivatives of cyclopenta[1,2-b:4,3-b′]dithiophene (1), indeno[2,1-b]indole (2), indeno[1,2-b]indole (3), and indeno[1,2-b]indolizine (4) and studied the crystal and molecular structures of these salts by X-ray diffraction. We found that heterocycle-fused cyclopentadienides demonstrate remarkable diversity in metal–ligand coordination modes and crystal packing, with formation of two-dimensional polymeric (5), linear polymeric (6), tetrameric (7) and monomeric (8) structures. The NMR spectral data and results of DFT modeling indicate an increase in electron density in the cyclopentadienyl fragment, and this effect was found to be larger in the derivative of the new indolizine ligand precursor 4. The results of our study will be used in the design of next-generation catalysts of α-olefin polymerization.

Heterocycle-fused cyclopentadienides of potassium demonstrate remarkable diversity in metal–ligand coordination and crystal packing.     

Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer

Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.Mass spectrometry imaging (MSI) of biological tissue sections can provide topographically localized biochemical information to supplement conventional histopathological classification systems (1–3). Together with emerging metabolomics-based profiling approaches, MSI represents a highly promising approach in molecular systems oncology (4, 5) and is increasingly being used for the discovery of next-generation cancer biomarker panels (6, 7). Among the MSI techniques currently available, the three most commonly used are matrix-assisted laser desorption ionization (MALDI) (2, 6), secondary ion mass spectrometry (SIMS) (8, 9), and desorption electrospray ionization (DESI) (10, 11). With each of these described approaches, operating characteristics and experimental parameters can be modulated to suit specific analytical objectives and can be customized for the identification of particular biomolecular species. Here, we have opted to use the DESI technique as there are several practical advantages with this method for metabolome-wide imaging studies, primarily attributable to lack of requirement for matrix deposition and ambient ionization, which requires minimal sample preparation (11, 12).Currently MSI is likely to exert greatest influence at the prognostic and therapeutic stages of the disease continuum (Fig. 1), with three fundamental areas of application in cancer phenotyping. First, it offers a means of chemically mapping morphological regions of interest to develop next-generation prognostic and therapeutic biomarkers. Second, it permits compartmentalized assessment of the distribution and biochemical influence of chemotherapeutic agents and/or their downstream metabolites within different tissue regions, offering fresh insights into anti-cancer drug efficacy (13, 14). Third, MSI provides the opportunity to develop automated approaches for tissue classification based entirely on molecular ion patterns. Such automated, “machine-learned” strategies will lessen the logistical and financial burden being placed on pathology services in the modern cancer-screening era, while simultaneously ensuring quality control by minimizing interobserver variability (15).Open in a separate windowFig. 1.MS-based imaging technology in clinical settings.Until now the routine clinical application of MSI approaches has been restricted by inherent time/cost demands and associated heavy analytical workload. However, recent advances in MS technology combined with the richness of generated molecular information should ensure the widespread adoption of MSI technologies in the near- to midterm. The major impediment to this progress currently centers on the choice of chemo-informatics workflow. The standard approach applied to MSI datasets involves a series of steps designed to reduce bioanalytical complexities for improved information recovery, followed by pattern recognition analysis and molecular pattern interpretation. Conventional workflows, integrated into software packages such as BioMap (Novartis), SpectViewer (CEA), DataCubeExplorer (AMOLF), and Mirion (JLU) or within commercial packages from instrument manufacturers such as Xcalibur (Thermo Fisher Scientific) and FlexImaging (Bruker Daltonics) have capabilities limited to basic preprocessing and browsing through selected ion images. There is currently strong demand for more sophisticated chemo-informatics strategies that can streamline data processing and simultaneously maximize disease-relevant molecular information capture. In broad terms, these strategies involve (i) raw analytical signal preprocessing for improved information recovery; (ii) imaging informatics for correlation of MSI and histological information; and (iii) pattern recognition analysis for topographically localized biochemical feature extraction. These steps will influence one another and thus need to be considered within an integrated bioinformatics solution (16).Typically, data preprocessing methods involve peak detection or “binning” and filtering of solvent/matrix or noise-related peaks (17–19), followed by a normalization step. At present, the most widely applied approach involves integrating MSI spectra within a predefined “bin” size (typically ∼0.01 Da). This reduces mass detection accuracy and introduces biologically irrelevant spectral features, making unambiguous assignment of chemical species more difficult. In the case of normalization, the total ion current (TIC) scaling factor is frequently cited in the literature as an acceptable means of accounting for global intensity changes in a MSI dataset (20–22). However, we have recently demonstrated that the performance of this method can be compromised by single large molecular ion peak intensities (21, 23). An additional problem inherent to MS-based analysis of complex biological mixtures is the fact that molecules present in greater intensities within a given sample will tend to exhibit larger variations when subjected to repeated measurement (23). This disruption to variance constancy across the measurement range, known in statistical terms as heteroscedasticity, represents a significant barrier to the effective application of commonly used multivariate techniques for the downstream statistical interrogation of MSI datasets (23). To date a number of different strategies have been proposed in the literature to stabilize variance across the measurement range (24), and we have recently validated several variance-stabilizing normalization techniques in the context of MS-based profiling (23).Beyond these preprocessing steps, MSI data need to be effectively “fused” with conventional histopathological information to allow the construction of large-scale molecular databases composed of region-specific molecular biomarkers and facilitate future automated histology initiatives. Precise methods for coregistration of histological and MSI data are an essential prerequisite for these applications and represent a further challenge at present. Of the software packages currently available to the MSI analyst, only the proprietary Bruker package offers image coregistration and region-of-interest molecular ion pattern extraction, with the option to further process extracted spectra in the associated statistical toolbox ClinProTools (25). However, this approach (limited to data collected on Bruker instrumentation) requires the user to manually select features on the pre- and poststaining images to conduct coregistration and can be subject to considerable error. Other less refined platforms have sought to achieve this objective by visual selection of particular regions of interest on hematoxylin and eosin (H&E)-stained optical images, followed by selection of pixels occupying similar (but not precisely aligned) geographical coordinates on corresponding MSI heat maps (22, 26). This permits only very crude colocalization of features from the two imaging modalities and may be deemed sufficient perhaps only in instances where limited variation in cell typology is seen across the tissue section (e.g., cancerous cellular regions and healthy cellular regions only). A number of image informatics methods have been recently developed to segment and to align the objects between images (27, 28). These approaches can involve rigid or nonrigid transformation, depending on object deformation characteristics. The most commonly used methods are based on extensions of the Lucas–Canade algorithm and their relative advantages and limitations have been recently described within a unifying framework (27). However, there is no standardized image coregistration protocol in the context of histology-driven MSI, and the currently used marker-based/fiducial methods may lack the precision required for detailed definition of morphology-to-chemistry interrelationships.Histology-driven, automated tissue identification further requires efficient and robust extraction of tissue-specific molecular ion patterns (19). The multidimensional nature of MSI datasets calls for effective dimensionality reduction techniques that are capable of extracting tissue-specific multivariate molecular ion patterns. Currently, the most widely used supervised dimensionality reduction technique is partial least-squares discriminant analysis (PLS-DA) (29, 30). It has been shown that PLS-based discriminant components are derived by maximizing between-class variance (Table S1) (30). A more mathematically eloquent mode of discriminant analysis is to maximize the difference between class means while simultaneously minimizing within-class variability. This is the objective of linear discriminant analysis (LDA), which maximizes the ratio of between- vs. within-class variance (31). Unfortunately, LDA cannot be directly applied in circumstances where the number of variables exceeds the number of samples, as is the case with the dataset presented here. Principal component analysis (PCA) has been commonly applied as a preprocessing step before LDA (PCA-LDA) to mitigate this problem (32). However, a problem arises here with respect to the selection of an optimal number of components. Introducing too many components into a model will increase the likelihood of LDA model overfit, whereas retaining too few can result in the loss of discriminatory information (33). In the current study, we have proposed the use of a modified maximum margin criterion (Table S1) to improve supervised feature extraction, while simultaneously avoiding arbitrary selection of the number of principal components before discriminant analysis (34).Here, we have devised a comprehensive data analysis framework with the aim of addressing the current challenges outlined above in MSI data treatment and exploration. Specifically, innovative bioinformatics solutions proposed in this study are i) variance-stabilizing normalization for improved information recovery; ii) an automated image coregistration algorithm for intuitive, precise histology-to-chemistry feature correlation; and iii) a unique method for efficient extraction of tissue-specific multivariate ion patterns. As a validation step, the outlined workflow has been applied to the investigation of tumor-surrounding lipid signatures in colorectal cancer (Movies S1 and S2). We demonstrate that this platform provides in-depth insights into tumor biochemistry by simultaneously analyzing the spatial distribution of hundreds to thousands of lipid species across different cell types. This offers potential for the development of next-generation cancer biomarkers and also may have a translational impact beyond the field of clinical histopathology in personalized pharmacotherapy and drug discovery.     

Reduction of myocardial ischemia-reperfusion injury with pre- and postconditioning: molecular mechanisms and therapeutic targets

Reduction of infarct size as well as alleviation of other ischemia- and reperfusion-associated injuries are the goals of primary importance in cardiology. One of the remedies is considered to be myocardial preconditioning (PreCon) referred usually to as an increased myocardial tolerance to prolonged ischemia following brief ischemic or non-ischemic challenge. In this review, PreCon stimuli tested to date are considered including a number of mildly noxious factors applied either locally to the myocardium or systemically. Recently, one more mode of heart protection against reperfusion injury termed postconditioning (PostCon) has been developed. On the basis of ample evidence published, along with our findings, a detailed comparative analysis of PreCon and PostCon is presented, with special emphasis on the cellular, molecular, and pharmacological aspects of the topic as well as clinical applications, both implemented and awaiting practical approval.     

A role of RGS proteins in drug addiction

The diverse family of Regulators of G protein signaling (RGS) proteins are widely distributed proteins with multiple functions, including GAP activity for heterotrimeric G protein alpha subunits. Three members of the RGS family, RGS9-2, RGS4 and RGSz, have been shown to play an essential modulatory role in psychostimulant and opiate drug actions. Interestingly, these proteins show distinct structure, distribution pattern and cellular localization. In addition, each of these proteins is differentially regulated by drugs of abuse in particular brain networks and appears to modulate distinct signal transduction events. The striatal enriched RGS9 plays a prominent role in opiate and psychostimulant drug reward; RGS4 appears to modulate opiate dependence via actions in the locus coeruleus, whereas RGSz modulates analgesia via activation of the PKC pathway.     

Pressure-induced and store-operated cation influx in vascular smooth muscle cells is independent of TRPC1

Among the classical transient receptor potential (TRPC) subfamily, TRPC1 is described as a mechanosensitive and store-operated channel proposed to be activated by hypoosmotic cell swelling and positive pipette pressure as well as regulated by the filling status of intracellular Ca(2+) stores. However, evidence for a physiological role of TRPC1 may most compellingly be obtained by the analysis of a TRPC1-deficient mouse model. Therefore, we have developed and analyzed TRPC1(-/-) mice. Pressure-induced constriction of cerebral arteries was not impaired in TRPC1(-/-) mice. Smooth muscle cells from cerebral arteries activated by hypoosmotic swelling and positive pipette pressure showed no significant differences in cation currents compared to wild-type cells. Moreover, smooth muscle cells of TRPC1(-/-) mice isolated from thoracic aortas and cerebral arteries showed no change in store-operated cation influx induced by thapsigargin, inositol-1,4,5 trisphosphate, and cyclopiazonic acid compared to cells from wild-type mice. In contrast to these results, small interference RNAs decreasing the expression of stromal interaction molecule 1 (STIM1) inhibited thapsigargin-induced store-operated cation influx, demonstrating that STIM1 and TRPC1 are mutually independent. These findings also imply that, as opposed to current concepts, TRPC1 is not an obligatory component of store-operated and stretch-activated ion channel complexes in vascular smooth muscle cells.     

The HAL3-PPZ1 dependent regulation of nonsense suppression efficiency in yeast and its influence on manifestation of the yeast prion-like determinant [ISP(+)

The efficiency of stop codons read-through in yeast is controlled by multiple interactions of genetic and epigenetic factors. In this study, we demonstrate the participation of the Hal3-Ppz1 protein complex in regulation of read-through efficiency and manifestation of non-Mendelian anti-suppressor determinant [ISP(+)]. Over-expression of HAL3 in [ISP(+)] strain causes nonsense suppression, whereas its inactivation displays as anti-suppression of sup35 mutation in [isp(-)] strain. [ISP(+)] strains carrying hal3Delta deletion cannot be cured from [ISP(+)] in the presence of GuHCl. Since Hal3p is a negative regulatory subunit of Ppz1 protein phosphatase, consequences of PPZ1 over-expression and deletion are opposite to those of HAL3. The observed effects are mediated by the catalytic function of Ppz1 and are probably related to the participation of Ppz1 in regulation of eEF1Balpha elongation factor activity. Importantly, [ISP(+)] status of yeast strains is determined by fluctuation in Hal3p level, since [ISP(+)] strains have less Hal3p than their [isp(-)] derivatives obtained by GuHCl treatment. A model considering epigenetic (possibly prion) regulation of Hal3p amount as a mechanism underlying [ISP(+)] status of yeast cell is suggested.     

Mechanical and microstructural properties of hybrid poly(ethylene glycol)-soy protein hydrogels for wound dressing applications

Biomimetic hydrogel made of poly(ethylene glycol) and soy protein with a water content of 96% has been developed for moist wound dressing applications. In this study, such hybrid hydrogels were investigated by both tensile and unconfined compression measurements in order to understand the relationships between structural parameters of the network, its mechanical properties and protein absorption in vitro. Elastic moduli were found to vary from 1 to 17 kPa depending on the composition, while the Poisson's ratio (approximately 0.18) and deformation at break (approximately 300%) showed no dependence on this parameter. Further calculations yielded the crosslinking concentration, the average molecular weight between crosslinks (M(C)) and the mesh size. The results show that reactions between PEG and protein create polymeric chains comprising molecules of PEG and protein fragments between crosslinks. M(C) is three times higher than that expected for a "theoretical network." On the basis of this data, we propose a model for the 3D network of the hydrogel, which is found to be useful for understanding drug release properties and biomedical potential of the studied material.     

New data on Microphallus breviatus Deblock & Maillard, 1975 (Microphallidae: Digenea) with emphasis on the evolution of dixenous life cycles of microphallids

Infection by intramolluscan stages of Microphallus breviatus Deblock & Maillard, 1975 are common in Hydrobia ventrosa mudsnails in Iceland. Cercariae encyst inside the daughter sporocysts and develop there into metacercariae that become infective for the definitive hosts which are probably charadriiform waders. The adult stage was obtained in 1-day-old chicks that were experimentally infected with metacercariae from naturally infected hydrobians. New data are presented on the morphology and biology of the cercariae, and the adult is described for the first time. Comparisons are made between M. breviatus and closely related species. Differential diagnosis of M. breviatus is given. The morphological specializations in larvae of the hermaphroditic generation of the microphallids accompanying transition from trixenous life cycles to dixenous ones are considered, and the applicability of the term “life-cycle truncation” to microphallids with dixenous life cycles is discussed. Also, reasons for the broad distribution of dixenous life cycles within the family Microphallidae are reviewed.