Algorithmic Modeling Quantifies the Complementary Contribution of Metabolic Inhibitions to Gemcitabine Efficacy

Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process blocks the cell cycle in the early S phase, eventually resulting in apoptosis. The incorporation of gemcitabine into DNA takes place in competition with the natural nucleoside dCTP. The mechanisms of indirect competition between these cascades for common resources are given with the race for DNA incorporation; in clinical studies dedicated to singling out mechanisms of resistance, ribonucleotide reductase (RR) and deoxycytidine kinase (dCK) and human equilibrative nucleoside transporter1 (hENT1) have been associated to efficacy of gemcitabine with respect to their roles in the synthesis cascades of dFdC-TP and dCTP. However, the direct competition, which manifests itself in terms of inhibitions between these cascades, remains to be quantified. We propose an algorithmic model of gemcitabine mechanism of action, verified with respect to independent experimental data. We performed in silico experiments in different virtual conditions, otherwise difficult in vivo, to evaluate the contribution of the inhibitory mechanisms to gemcitabine efficacy. In agreement with the experimental data, our model indicates that the inhibitions due to the association of dCTP with dCK and the association of gemcitabine diphosphate metabolite (dFdC-DP) with RR play a key role in adjusting the efficacy. While the former tunes the catalysis of the rate-limiting first phosphorylation of dFdC, the latter is responsible for depletion of dCTP pools, thereby contributing to gemcitabine efficacy with a dependency on nucleoside transport efficiency. Our simulations predict the existence of a continuum of non-efficacy to high-efficacy regimes, where the levels of dFdC-TP and dCTP are coupled in a complementary manner, which can explain the resistance to this drug in some patients.     

Expression of ERCC1 and its clinicopathological correlations in non-small cell lung cancer

Excision Repair Cross-Complementing Group 1 (ERCC1) is an important DNA repair gene, playing critical role in nucleotide excision repair pathway and having a significant influence on genomic instability. Some studies support that ERCC1 might be a potential predictive and prognostic marker in non-small cell lung cancer (NSCLC). ERCC1 has also been shown to be a promising biomarker in NSCLC treated with a cisplatin-based regimen. Therefore, the determination of ERCC1 expression at DNA, mRNA and protein level in different stages of NSCLC is still an important topic in the cancer. Ninety-one formalin-fixed paraffin-embedded tumor samples histopathologically diagnosed as NSCLC were examined in this study. ERCC1 expression at protein level were scored by immunohistochemistry. The gene amplification and mRNA expression levels for ERCC1 were determined by real-time quantitative PCR. There was complete concordance among the three methods in 39 tumor samples (42.9%). A strong correlation was found between DNA amplification and mRNA expression (r = 0.662) while there was no correlation between mRNA and protein assessment for ERCC1 expression (r = −0.013). ERCC1 expression at mRNA and DNA level (63.1 and 84.2%, respectively) in tumors at stage III was higher than at the other stages. In contrast, the protein expression at stage II and III (56.6 and 52.6%, respectively) of NSCLC was lower than that of tumors with stage I NSCLC. These results show that the mechanism by which ERCC1 expression might play a role in tumor behavior. This study was also confirmed that the appropriate validation and qualification in methods used for ERCC1 status were needed before its clinical application and implementation.     

Myoblasts and macrophages share molecular components that contribute to cell-cell fusion

Cell-cell fusion is critical to the normal development of certain tissues, yet the nature and degree of conservation of the underlying molecular components remains largely unknown. Here we show that the two guanine-nucleotide exchange factors Brag2 and Dock180 have evolutionarily conserved functions in the fusion of mammalian myoblasts. Their effects on muscle cell formation are distinct and are a result of the activation of the GTPases ARF6 and Rac, respectively. Inhibition of ARF6 activity results in a lack of physical association between paxillin and beta(1)-integrin, and disruption of paxillin transport to sites of focal adhesion. We show that fusion machinery is conserved among distinct cell types because Dock180 deficiency prevented fusion of macrophages and the formation of multinucleated giant cells. Our results are the first to demonstrate a role for a single protein in the fusion of two different cell types, and provide novel mechanistic insight into the function of GEFs in the morphological maturation of multinucleated cells.     

Probing structurally altered and aggregated states of therapeutically relevant proteins using GroEL coupled to bio-layer interferometry

The ability of a GroEL-based bio-layer interferometry (BLI) assay to detect structurally altered and/or aggregated species of pharmaceutically relevant proteins is demonstrated. Assay development included optimizing biotinylated-GroEL immobilization to streptavidin biosensors, combined with biophysical and activity measurements showing native and biotinylated GroEL are both stable and active. First, acidic fibroblast growth factor (FGF-1) was incubated under conditions known to promote (40°C) and inhibit (heparin addition) molten globule formation. Heat exposed (40°C) FGF-1 exhibited binding to GroEL-biosensors, which was significantly diminished in the presence of heparin. Second, a polyclonal human IgG solution containing 6–8% non-native dimer showed an increase in higher molecular weight aggregates upon heating by size exclusion chromatography (SEC). The poly IgG solution displayed binding to GroEL-biosensors initially with progressively increased binding upon heating. Enriched preparations of the IgG dimers or monomers showed significant binding to GroEL-biosensors. Finally, a thermally treated IgG1 monoclonal antibody (mAb) solution also demonstrated increased GroEL-biosensor binding, but with different kinetics. The bound complexes could be partially to fully dissociated after ATP addition (i.e., specific GroEL binding) depending on the protein, environmental stress, and the assay’s experimental conditions. Transmission electron microscopy (TEM) images of GroEL-mAb complexes, released from the biosensor, also confirmed interaction of bound complexes at the GroEL binding site with heat-stressed mAb. Results indicate that the GroEL-biosensor-BLI method can detect conformationally altered and/or early aggregation states of proteins, and may potentially be useful as a rapid, stability-indicating biosensor assay for monitoring the structural integrity and physical stability of therapeutic protein candidates.     

A Generic Shallow Lake Ecosystem Model Based on Collective Expert Knowledge

We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake ecosystem model by augmenting the individual cognitive maps drawn by 8 scientists working in the area of shallow lake ecology. We calculated graph theoretical indices of the individual cognitive maps and the collective cognitive map produced by augmentation. There were a total of 32 variables with 113 connections in the collective cognitive map. The graph theoretical indices revealed internal cycles showing non-linear dynamics in the shallow lake ecosystem. The ecological processes were organized democratically without a top-down hierarchical structure. The most central variable in the collective map was submerged plants. The strongest connections were suspended solids concentration decreasing water clarity, phosphorus concentration increasing the phytoplankton biomass, higher water clarity increasing submerged plants, benthivorous fish biomass reducing submerged plants and increasing suspended solids concentration, and submerged plants decreasing suspended solids. The steady state condition of the generic model was a characteristic turbid shallow lake ecosystem. The generic shallow lake ecosystem model had the tendency to go into a turbid state since there were no dynamic environmental changes that could cause shifts between a turbid and a clearwater state, and the generic model indicated that only a dynamic disturbance regime could maintain the clearwater state. The model developed herein captured the empirical behavior of shallow lakes, and contained the basic model of the Alternative Stable States Theory. In addition, our model expanded the basic model by quantifying the relative effects of connections and by extending it with 22 more variables and 99 more weighted causal connections. Using our expanded model we ran 4 simulations: harvesting submerged plants, nutrient reduction, fish removal without nutrient reduction, and biomanipulation. Only biomanipulation, which included fish removal and nutrient reduction, had the potential to shift the turbid state into clearwater state. The structure and relationships in the generic model as well as the outcomes of the management simulations were supported by actual field studies in shallow lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to understand the complex structure of shallow lake ecosystems as a whole and obtain a valid generic model based on tacit knowledge of experts in the field.     

Predictive models in ecology: Comparison of performances and assessment of applicability

Ecological systems are governed by complex interactions which are mainly nonlinear. In order to capture the inherent complexity and nonlinearity of ecological, and in general biological systems, empirical models recently gained popularity. However, although these models, particularly connectionist approaches such as multilayered backpropagation networks, are commonly applied as predictive models in ecology to a wide variety of ecosystems and questions, there are no studies to date aiming to assess the performance, both in terms of data fitting and generalizability, and applicability of empirical models in ecology. Our aim is hence to provide an overview for nature of the wide range of the data sets and predictive variables, from both aquatic and terrestrial ecosystems with different scales of time-dependent dynamics, and the applicability and robustness of predictive modeling methods on such data sets by comparing different empirical modeling approaches. The models used in this study range from predicting the occurrence of submerged plants in shallow lakes to predicting nest occurrence of bird species from environmental variables and satellite images. The methods considered include k-nearest neighbor (k-NN), linear and quadratic discriminant analysis (LDA and QDA), generalized linear models (GLM) feedforward multilayer backpropagation networks and pseudo-supervised network ARTMAP.Our results show that the predictive performances of the models on training data could be misleading, and one should consider the predictive performance of a given model on an independent test set for assessing its predictive power. Moreover, our results suggest that for ecosystems involving time-dependent dynamics and periodicities whose frequency are possibly less than the time scale of the data considered, GLM and connectionist neural network models appear to be most suitable and robust, provided that a predictive variable reflecting these time-dependent dynamics included in the model either implicitly or explicitly. For spatial data, which does not include any time-dependence comparable to the time scale covered by the data, on the other hand, neighborhood based methods such as k-NN and ARTMAP proved to be more robust than other methods considered in this study. In addition, for predictive modeling purposes, first a suitable, computationally inexpensive method should be applied to the problem at hand a good predictive performance of which would render the computational cost and efforts associated with complex variants unnecessary.     

Characterization of two genes encoding metal tolerance proteins from Beta vulgaris subspecies maritima that confers manganese tolerance in yeast

Manganese (Mn²⁺) is an essential micronutrient in plants. However increased Mn²⁺ levels are toxic to plant cells. Metal tolerance proteins (MTPs), member of cation diffusion facilitator protein (CDF) family, have important roles in metal homeostatis in different plant species and catalyse efflux of excess metal ions. In this study, we identified and characterized two MTP genes from Beta vulgaris spp. maritima (B. v. ssp. maritima). Overexpression of these two genes provided Mn tolerance in yeast cells. Sequence analyses displayed BmMTP10 and BmMTP11as members of the Mn-CDF family. Functional analyses of these proteins indicated that they are specific to Mn²⁺ with a role in reducing excess cellular Mn²⁺ levels when expressed in yeast. GFP-fusion constructs of both proteins localized to the Golgi apparatus as a punctuated pattern. Finally, Q-RT-PCR results showed that BmMTP10 expression was induced threefold in response to the excess Mn²⁺ treatment. On the other hand BmMTP11 expression was not affected in response to excess Mn²⁺ levels. Thus, our results suggest that the BmMTP10 and BmMTP11 proteins from B. v. ssp. maritima have non-redundant functions in terms of Mn²⁺ detoxification with a similar in planta localization and function as the Arabidopsis Mn-CDF homolog AtMTP11 and this conservation shows the evolutionary importance of these vesicular proteins in heavy metal homeostatis among plant species.