Is miR-144 an effective inhibitor of PTEN mRNA: a controversy in breast cancer

Breast cancer is the first common cancer among women worldwide. One of the major signaling pathways playing a role in the onset and progression of this disease is PI3K/Akt/mTOR, which can be inhibited by PTEN. miRNAs are small non-coding molecules that regulate the expression of their targets by inhibition or suppression, and thus, their dysregulated expression results in the development of cancer. Using various software applications predicting miRNAs and evaluating GEO microarray data, miR-144 was selected as an inhibitor of PTEN. The expression of miR-144 and PTEN was evaluated in 18 triple negative breast cancer (TNBC) clinical samples and cell lines including 4T1, MDA-MB-231, MDA-MB-468, SK-BR-3, and MCF-7 in comparison with normal cells. PTEN and miR-144 expression analysis revealed their elevated expression in MCF-7 cells. MDA-MB-468, SK-BR-3, and MDA-MB-231 cells showed decreased levels of PTEN and increased levels of miR-144. In contrast, 4T1 cells had an increased expression of PTEN and decreased expression of miR-144. In clinical samples, miR-144 was up-regulated in 22% of the cases and PTEN was down-regulated in 78% of the cases. The results showed that the expression of PTEN and miR-144 was inversely correlated in metastatic breast cancer cell lines. However, in TNBC clinical samples, there was no correlation between the expression of miR-144 and PTEN. Literature shows that there are other influencing factors affecting the expression of miRNAs. Therefore, care should be taken in interpreting the results of gene expression studies and its relation with cancer diagnosis/prognosis.     

Processing of intermediates in recombination and DNA repair: identification of a new endonuclease that specifically cleaves Holliday junctions.

The formation and subsequent resolution of Holliday junctions are critical stages in recombination. We describe a new Escherichia coli endonuclease that resolves Holliday intermediates by junction cleavage. The 14 kDa Rus protein binds DNA containing a synthetic four-way junction (X-DNA) and introduces symmetrical cuts in two strands to give nicked duplex products. Rus also processes Holliday intermediates made by RecA into products that are characteristic of junction resolution. The cleavage activity on X-DNA is remarkably similar to that of RuvC. Both proteins preferentially cut the same two strands at the same location. Increased expression of Rus suppresses the DNA repair and recombination defects of ruvA, ruvB and ruvC mutants. We conclude that all ruv strains are defective in junction cleavage, and discuss pathways for Holliday junction resolution by RuvAB, RuvC, RecG and Rus.     

Modeling the Afferent Dynamics of the Baroreflex Control System

In this study we develop a modeling framework for predicting baroreceptor firing rate as a function of blood pressure. We test models within this framework both quantitatively and qualitatively using data from rats. The models describe three components: arterial wall deformation, stimulation of mechanoreceptors located in the BR nerve-endings, and modulation of the action potential frequency. The three sub-systems are modeled individually following well-established biological principles. The first submodel, predicting arterial wall deformation, uses blood pressure as an input and outputs circumferential strain. The mechanoreceptor stimulation model, uses circumferential strain as an input, predicting receptor deformation as an output. Finally, the neural model takes receptor deformation as an input predicting the BR firing rate as an output. Our results show that nonlinear dependence of firing rate on pressure can be accounted for by taking into account the nonlinear elastic properties of the artery wall. This was observed when testing the models using multiple experiments with a single set of parameters. We find that to model the response to a square pressure stimulus, giving rise to post-excitatory depression, it is necessary to include an integrate-and-fire model, which allows the firing rate to cease when the stimulus falls below a given threshold. We show that our modeling framework in combination with sensitivity analysis and parameter estimation can be used to test and compare models. Finally, we demonstrate that our preferred model can exhibit all known dynamics and that it is advantageous to combine qualitative and quantitative analysis methods.     

BiomeNet: A Bayesian Model for Inference of Metabolic Divergence among Microbial Communities

Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic subnetworks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from other unsupervised methods by allowing researchers to discriminate groups of samples through the metabolic patterns it discovers in the data, and by providing a framework for interpreting them. We describe a collapsed Gibbs sampler for inference of the mixture weights under BiomeNet, and we use simulation to validate the inference algorithm. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among inflammatory bowel disease (IBD) patients. Based on the discriminatory subnetworks for this metabosystem, we inferred that the community is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD. Because this metabosystem has a greater capacity to exploit host-associated glycans, we speculate that IBD-associated communities might arise from opportunist growth of bacteria that can circumvent the host''s nutrient-based mechanism for bacterial partner selection.     

Surface Engineering of TiO2 ETL for Highly Efficient and Hysteresis‐Less Planar Perovskite Solar Cell (21.4%) with Enhanced Open‐Circuit Voltage and Stability

Interfacial studies and band alignment engineering on the electron transport layer (ETL) play a key role for fabrication of high‐performance perovskite solar cells (PSCs). Here, an amorphous layer of SnO₂ (a‐SnO₂) between the TiO₂ ETL and the perovskite absorber is inserted and the charge transport properties of the device are studied. The double‐layer structure of TiO₂ compact layer (c‐TiO₂) and a‐SnO₂ ETL leads to modification of interface energetics, resulting in improved charge collection and decreased carrier recombination in PSCs. The optimized device based on a‐SnO₂/c‐TiO₂ ETL shows a maximum power conversion efficiency (PCE) of 21.4% as compared to 19.33% for c‐TiO₂ based device. Moreover, the modified device demonstrates a maximum open‐circuit voltage (V_oc) of 1.223 V with 387 mV loss in potential, which is among the highest reported value for PSCs with negligible hysteresis. The stability results show that the device on c‐TiO₂/a‐SnO₂ retains about 91% of its initial PCE value after 500 h light illumination, which is higher than pure c‐TiO₂ (67%) based devices. Interestingly, using a‐SnO₂/c‐TiO₂ ETL the PCE loss was only 10% of initial value under continuous UV light illumination after 30 h, which is higher than that of c‐TiO₂ based device (28% PCE loss).