Sticholysin I–membrane interaction: An interplay between the presence of sphingomyelin and membrane fluidity

Sticholysin I (St I) is a pore-forming toxin (PFT) produced by the Caribbean Sea anemone Stichodactyla helianthus belonging to the actinoporin protein family, a unique class of eukaryotic PFT exclusively found in sea anemones. As for actinoporins, it has been proposed that the presence of sphingomyelin (SM) and the coexistence of lipid phases increase binding to the target membrane. However, little is known about the role of membrane structure and dynamics (phase state, fluidity, presence of lipid domains) on actinoporins' activity or which regions of the membrane are the most favorable platforms for protein insertion. To gain insight into the role of SM on the interaction of St I to lipid membranes we studied their binding to monolayers of phosphatidylcholine (PC) and SM in different proportions. Additionally, the effect of acyl chain length and unsaturation, two features related to membrane fluidity, was evaluated on St I binding to monolayers. This study revealed that St I binds and penetrates preferentially and with a faster kinetic to liquid-expanded films with high lateral mobility and moderately enriched in SM. A high content of SM induces a lower lateral diffusion and/or liquid-condensed phases, which hinder St I binding and penetration to the lipid monolayer. Furthermore, the presence of lipid domain borders does not appear as an important factor for St I binding to the lipid monolayer.     

Antibiotic drug tigecycline inhibited cell proliferation and induced autophagy in gastric cancer cells

Tigecycline acts as a glycylcycline class bacteriostatic agent, and actively resists a series of bacteria, specifically drug fast bacteria. However, accumulating evidence showed that tetracycline and their derivatives such as doxycycline and minocycline have anti-cancer properties, which are out of their broader antimicrobial activity. We found that tigecycline dramatically inhibited gastric cancer cell proliferation and provided an evidence that tigecycline induced autophagy but not apoptosis in human gastric cancer cells. Further experiments demonstrated that AMPK pathway was activated accompanied with the suppression of its downstream targets including mTOR and p70S6K, and ultimately induced cell autophagy and inhibited cell growth. So our data suggested that tigecycline might act as a candidate agent for pre-clinical evaluation in treatment of patients suffering from gastric cancer.     

A model for the coordinated stepping of cytoplasmic dynein

Cytoplasmic dynein play an important role in transporting various intracellular cargos by coupling their ATP hydrolysis cycle with their conformational changes. Recent experimental results showed that the cytoplasmic dynein had a highly variable stepping pattern including “hand-over-hand”, “inchworm” and “nonalternating-inchworm”. Here, we developed a model to describe the coordinated stepping patterns of cytoplasmic dynein, based on its working cycle, construction and the interaction between its leading head and tailing head. The kinetic model showed how change in the distance between the two heads influences the rate of cytoplasmic dynein under different stepping patterns. Numerical simulations of the distribution of step size and striding rate are in good quantitative agreement with experimental observations. Hence, our coordinated stepping model for cytoplasmic dynein successfully explained its diverse stepping patterns as a molecular motor. The cooperative mechanism carried out by the two heads of cytoplasmic dynein shed light on the strategies adopted by the cytoplasmic dynein in executing various functions.     

Integration,visualization and analysis of human interactome

Data integration and visualization are crucial to obtain meaningful hypotheses from the diversity of ‘omics’ fields and the large volume of heterogeneous and distributed data sets. In this review we focus on network analysis as a key technique to integrate, visualize and extrapolate relevant information from diverse data. We first describe challenges in integrating different types of data and then focus on systematically exploring network properties to gain insight into network function. We also describe the relationship between network structures and function of elements that form it. Next, we highlight the role of the interactome in connecting data derived from different experiments, and we stress the importance of network analysis to recognize interaction context-specific features. Finally, we present an example integration to demonstrate the value of the network approach in cancer research, and highlight the importance of dynamic data in the specific context of signaling pathways.     

miR-22 suppresses the proliferation and invasion of gastric cancer cells by inhibiting CD151

Gastric cancer (GC) is the second common cause of cancer-related death worldwide. microRNAs (miRNAs) play important roles in the carcinogenesis of GC. Here, we found that miR-22 was significantly decreased in GC tissue samples and cell lines. Ectopic overexpression of miR-22 remarkably suppressed cell proliferation and colony formation of GC cells. Moreover, overexpression of miR-22 significantly suppressed migration and invasion of GC cells. CD151 was found to be a target of miR-22. Furthermore, overexpression of CD151 significantly attenuated the tumor suppressive effect of miR-22. Taken together, miR-22 might suppress GC cells growth and motility partially by inhibiting CD151.     

RATES OF FITNESS DECLINE AND REBOUND SUGGEST PERVASIVE EPISTASIS

Unraveling the factors that determine the rate of adaptation is a major question in evolutionary biology. One key parameter is the effect of a new mutation on fitness, which invariably depends on the environment and genetic background. The fate of a mutation also depends on population size, which determines the amount of drift it will experience. Here, we manipulate both population size and genotype composition and follow adaptation of 23 distinct Escherichia coli genotypes. These have previously accumulated mutations under intense genetic drift and encompass a substantial fitness variation. A simple rule is uncovered: the net fitness change is negatively correlated with the fitness of the genotype in which new mutations appear—a signature of epistasis. We find that Fisher's geometrical model can account for the observed patterns of fitness change and infer the parameters of this model that best fit the data, using Approximate Bayesian Computation. We estimate a genomic mutation rate of 0.01 per generation for fitness altering mutations, albeit with a large confidence interval, a mean fitness effect of mutations of ?0.01, and an effective number of traits nine in mutS^? E. coli. This framework can be extended to confront a broader range of models with data and test different classes of fitness landscape models.