Glioblastoma stem cells:Molecular characteristics and therapeutic implications

Glioblastoma Multiforme(GBM)is a grade IV astrocytoma,with a median survival of 14.6 mo.Within GBM,stem-like cells,namely glioblastoma stem cells(GSCs),have the ability to self-renew,differentiate into distinct lineages within the tumor and initiate tumor xenografts in immunocompromised animal models.More importantly,GSCs utilize cell-autonomous and tumor microenvironment-mediated mechanisms to overcome current therapeutic approaches.They are,therefore,very important therapeutic targets.Although the functional criteria defining GSCs are well defined,their molecular characteristics,the mechanisms whereby they establish the cellular hierarchy within tumors,and their contribution to tumor heterogeneity are not well understood.This review is aimed at summarizing current findings about GSCs and their therapeutic importance from a molecular and cellular point of view.A better characterization of GSCs is crucial for designing effective GSCtargeted therapies.     

Differential effect of activin on mouse embryonic stem cell differentiation in insulin-secreting cells under nestin-positive selection and spontaneous differentiation protocols

Parallel to the importance of the development of cell therapies to treat diabetes is the production of sufficient numbers of pancreatic endocrine cells that function like primary islets. To increase the efficiency of endocrine pancreatic-like cell differentiation from mouse embryonic stem cells (ESCs), we applied activin-B to nestin-positive selection (protocol 1) and spontaneous differentiation (protocol 2) in different groups including: [A] activin-B, or [B] basic fibroblast growth factor (bFGF), and/or [C] activin-B+bFGF. The differentiated cells expressed most pancreatic-related genes. The number of insulin- and C peptide-positive cells, as well as dithizone-positive clusters in group A of protocol 1 was higher than in the other groups. Significant insulin concentrations in protocol 1 were produced when glucose was added to the medium, in comparison with protocol 2. Moreover, insulin release was increased significantly in group A of protocol 1 even with lower glucose. In conclusion, Addition of activin-B in a nestin-positive selection protocol increased the insulin-secreting cells in comparison with the same protocol with bFGF and/or spontaneous differentiation in presence of bFGF and/or activin-B alone. However, improvements of the current method are required to generate a sufficient source of true beta-cells for the treatment of diabetes mellitus.     

Incorporating metadata in HIV transmission network reconstruction: A machine learning feasibility assessment

HIV molecular epidemiology estimates the transmission patterns from clustering genetically similar viruses. The process involves connecting genetically similar genotyped viral sequences in the network implying epidemiological transmissions. This technique relies on genotype data which is collected only from HIV diagnosed and in-care populations and leaves many persons with HIV (PWH) who have no access to consistent care out of the tracking process. We use machine learning algorithms to learn the non-linear correlation patterns between patient metadata and transmissions between HIV-positive cases. This enables us to expand the transmission network reconstruction beyond the molecular network. We employed multiple commonly used supervised classification algorithms to analyze the San Diego Primary Infection Resource Consortium (PIRC) cohort dataset, consisting of genotypes and nearly 80 additional non-genetic features. First, we trained classification models to determine genetically unrelated individuals from related ones. Our results show that random forest and decision tree achieved over 80% in accuracy, precision, recall, and F1-score by only using a subset of meta-features including age, birth sex, sexual orientation, race, transmission category, estimated date of infection, and first viral load date besides genetic data. Additionally, both algorithms achieved approximately 80% sensitivity and specificity. The Area Under Curve (AUC) is reported 97% and 94% for random forest and decision tree classifiers respectively. Next, we extended the models to identify clusters of similar viral sequences. Support vector machine demonstrated one order of magnitude improvement in accuracy of assigning the sequences to the correct cluster compared to dummy uniform random classifier. These results confirm that metadata carries important information about the dynamics of HIV transmission as embedded in transmission clusters. Hence, novel computational approaches are needed to apply the non-trivial knowledge collected from inter-individual genetic information to metadata from PWH in order to expand the estimated transmissions. We note that feature extraction alone will not be effective in identifying patterns of transmission and will result in random clustering of the data, but its utilization in conjunction with genetic data and the right algorithm can contribute to the expansion of the reconstructed network beyond individuals with genetic data.     

Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms

Cluster Computing - Due to the limitations associated with the processing capability of mobile devices in cloud environments, various tasks are offloaded to the cloud server. This has led to an...     

Ribozymes in the age of molecular therapeutics

Ribozymes are RNA molecules capable of sequence-specific cleavage of other RNA molecules. Since the discovery of the first group I intron ribozyme in 1982, new classes of ribozymes, each with their own unique reaction, target site specifications, and potential applications, have been identified. These include hammerhead, hairpin, hepatitis delta, varkud satellite, groups I and II intron, and RNase P ribozymes, as well as the ribosome and spliceosome. Meanwhile, ribozyme engineering has enabled the in vitro selection of synthetic ribozymes with unique properties. This, along with advances in ribozyme delivery methods and expression systems, has led to an explosion in the potential therapeutic applications of ribozymes, whether for anti-cancer or anti-viral therapy, or for gene repair.     

Interaction of drugs amlodipine and paroxetine with the metabolizing enzyme CYP2B4: a molecular dynamics simulation study

The interactions of the drugs amlodipine and paroxetine, which are prescribed respectively for treatment of hypertension and depression, with the metabolizing enzyme cytochrome CYP2B4 as the drug target, have been studied by molecular dynamics (MD) simulation. Poly ethylene glycol was used to control the drugs’ interactions with each other and with the target CYP2B4. Thirteen simulation systems were carefully designed, and the results obtained from MD simulations indicated that amlodipine in the PEGylated form prescribed with paroxetine in the nonPEGylated form promotes higher cytochrome stability and causes fewer fluctuations as the drugs approach the target CYP2B4 and interact with it. The simulation results led us to hypothesize that the combination of the drugs with a specific drug ratio, as proposed in this work, manifests more effective diffusivity and less instability while metabolizing with enzyme CYP2B4. Also, the active residues in the CYP2B4 enzyme that interact with the drugs were determined by MD simulation, which were consistent with the reported experimental results.

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Graphical Abstract Efficient drug-enzyme interactions, as a result of PEGylation.

     

Artificial signal peptide prediction by a hidden markov model to improve protein secretion via Lactococcus lactis bacteria

A hidden Markov model (HMM) has been utilized to predict and generate artificial secretory signal peptide sequences. The strength of signal peptides of proteins from different subcellular locations via Lactococcus lactis bacteria correlated with their HMM bit scores in the model. The results show that the HMM bit score +12 are determined as the threshold for discriminating secreteory signal sequences from the others. The model is used to generate artificial signal peptides with different bit scores for secretory proteins. The signal peptide with the maximum bit score strongly directs proteins secretion.     

Direct competition results from strong competition for limited resource

We study a model of competition for resource through a chemostat-type model where species consume the common resource that is constantly supplied. We assume that the species and resources are characterized by a continuous trait. As already proved, this model, although more complicated than the usual Lotka–Volterra direct competition model, describes competitive interactions leading to concentrated distributions of species in continuous trait space. Here we assume a very fast dynamics for the supply of the resource and a fast dynamics for death and uptake rates. In this regime we show that factors that are independent of the resource competition become as important as the competition efficiency and that the direct competition model is a good approximation of the chemostat. Assuming these two timescales allows us to establish a mathematically rigorous proof showing that our resource-competition model with continuous traits converges to a direct competition model. We also show that the two timescales assumption is required to mathematically justify the corresponding classic result on a model consisting of only finite number of species and resources (MacArthur in, Theor Popul Biol 1:1–11, 1970). This is performed through asymptotic analysis, introducing different scales for the resource renewal rate and the uptake rate. The mathematical difficulty relies in a possible initial layer for the resource dynamics. The chemostat model comes with a global convex Lyapunov functional. We show that the particular form of the competition kernel derived from the uptake kernel, satisfies a positivity property which is known to be necessary for the direct competition model to enjoy the related Lyapunov functional.