Anti-cancer study and whey protein complexation of new lanthanum(III) complex with the aim of achieving bioactive anticancer metal-based drugs

In this study, a new lanthanum (III)-amino acid complex utilizing cysteine has been synthesized and characterized. The anticancer activities of the prepared La(III) complex against MCF-7 cell lines were studied. Results of MTT assay showed that at all three incubation times, the cytotoxic effect of prepared La(III) complex on MCF-7 breast cancer cell lines displays a time- and dose-dependent inhibitory effects. The interactions of the La(III) complex with two whey proteins (bovine serum albumin, BSA, and Bovine β-lactoglobulin, βLG) have been explored by using spectroscopic and molecular dicking methods. The obtained results indicated that La(III) complex strongly quenched the fluorescence of two carrier proteins in static quenching mode and also, BSA hah stronger binding affinity toward studied complex than βLG whit binding constant values of K_{BSA-La?Complex}?～?0.11?×?10⁴ M^?1 and K_{βLG-La?Complex}?～?0.63?×?10³ M^?1 at 300 K. The thermodynamic parameters revealed the contribution of hydrogen bond and Vander Waals interactions in both systems. The distances of the La(III) complex whit whey proteins were calculated using Förster energy transfer theory and proved existence of the energy transfer between two proteins and prepared La(III) complex with a high probability. FT-IR and UV–Vis absorption measurements indicated that the binding of the La(III) to BSA and βLG may induce conformational and micro-environmental changes of the proteins. The docking results indicate that the La(III) complex bind to residues located in the site II of BSA and second site of βLG.

Communicated by Ramaswamy H. Sarma     

NetCooperate: a network-based tool for inferring host-microbe and microbe-microbe cooperation

Background

Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm.

Results

NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms’ niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds.

Conclusions

The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html.     

SDF2XYZ2SDF: how to exploit TINKER power in cheminformatics projects

An open-source tool capable of converting SD files (and virtually any other format through OpenBabel) into MMFF-typed XYZ coordinate files to be used with TINKER is described. SDF2XYZ2SDF allows including the power of TINKER molecular mechanics computations in automated cheminformatics workflows, such as conformational searches and virtual screening protocols.     

A stable biologically motivated learning mechanism for visual feature extraction to handle facial categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task.     

Encapsulation of Pseudomonas sp. ADP cells in electrospun microtubes for atrazine bioremediation

Electrospun hollow polymeric microfibers (microtubes) were evaluated as an encapsulation method for the atrazine degrading bacterium Pseudomonas sp. ADP. Pseudomonas sp. ADP cells were successfully incorporated in a formulation containing a core solution of polyethylene oxide dissolved in water and spun with an outer shell solution made of polycaprolactone and polyethylene glycol dissolved in a chloroform and dimethylformamide. The resulting microtubes, collected as mats, were partially collapsed with a ribbon-like structure. Following encapsulation, the atrazine degradation rate was low (0.03?±?0.01?mg atrazine/h/g fiber) indicating that the electrospinning process negatively affected cell activity. Atrazine degradation was restored to 0.5?±?0.1?mg atrazine/h/g fiber by subjecting the microtubes to a period of growth. After 3 and 7?days growth periods, encapsulated cells were able to remove 20.6?±?3 and 47.6?±?5.9?mg atrazine/g mat, respectively, in successive batches under non-growth conditions (with no additional electron donor) until atrazine was detected in the medium. The loss of atrazine degrading capacity was regained following an additional cell-growth period.     

Analyzing Gene Expression from Whole Tissue vs. Different Cell Types Reveals the Central Role of Neurons in Predicting Severity of Alzheimer’s Disease

Alterations in gene expression resulting from Alzheimer’s disease have received considerable attention in recent years. Although expression has been investigated separately in whole brain tissue, in astrocytes and in neurons, a rigorous comparative study quantifying the relative utility of these sources in predicting the progression of Alzheimer’s disease has been lacking. Here we analyze gene expression from neurons, astrocytes and whole tissues across different brain regions, and compare their ability to predict Alzheimer’s disease progression by building pertaining classification models based on gene expression sets annotated to different biological processes. Remarkably, we find that predictions based on neuronal gene expression are significantly more accurate than those based on astrocyte or whole tissue expression. The findings explicate the central role of neurons, particularly as compared to glial cells, in the pathogenesis of Alzheimer’s disease, and emphasize the importance of measuring gene expression in the most relevant (pathogenically ‘proximal’) single cell types.     

RNA inhibition highlights cyclin d1 as a potential therapeutic target for mantle cell lymphoma

Mantle cell lymphoma is characterized by a genetic translocation results in aberrant overexpression of the CCND1 gene, which encodes cyclin D1. This protein functions as a regulator of the cell cycle progression, hence is considered to play an important role in the pathogenesis of the disease. In this study, we used RNA interference strategies to examine whether cyclin D1 might serve as a therapeutic target for mantle cell lymphoma. Knocking down cyclin D1 resulted in significant growth retardation, cell cycle arrest, and most importantly, induction of apoptosis. These results mark cyclin D1 as a target for mantle cell lymphoma and emphasize the therapeutic potential hidden in its silencing.