The mechanism by which gap junction proteins, connexins, act as potent tumor suppressors remains poorly understood. In this study human breast tumor cells were found to exhibit diverse gap junction phenotypes including (a) undetectable Cx43 and no intercellular communication (

The present study was designed to determine the specific roles played by lysosomes and proteasomes in the degradation of Cx43 in both gap junctional intercellular communication-deficient MDA-MB-231 and -competent BICR-M1R k cells. In MDA-MB-231 cells, immunolocalization and brefeldin A protein transport blocking studies revealed that there was a propensity for newly synthesized Cx43 to be transported to lysosomes. On the other hand, light and electron microscopic analysis of BICR-M1R k cells showed that Cx43 gap junctions were prevalent with a subpopulation of intracellular Cx43 localized to lysosomes. In both cell types, Western blots revealed a notable increase in total cellular Cx43 in response to lysosome inhibitors. Interestingly, lactacystin inhibition of proteosomal degradation in MDA-MB-231 cells resulted in a marked increase in phosphorylated Cx43 at the expense of non-phosphorylated Cx43, and this change corresponded with an increase in "oversized" gap junction plaques. In BICR-M1R k cells, lactacystin treatment partially prevented the BFA-induced loss of gap junctions. Together, our data suggests that lysosomes play a key role in not only degrading internalized gap junction in BICR-M1R k cells but also in degrading Cx43 delivered from early secretory compartments to lysosomes in MDA-MB-231 cells. Overall proteasomal degradation regulates the stability of phosphorylated Cx43 and appears to promote the internalization of Cx43 from the cell surface.

To study the roles of translational accuracy, translational efficiency, and the Hill-Robertson effect in codon usage bias, we studied the intragenic spatial distribution of synonymous codon usage bias in four prokaryotic (Escherichia coli, Bacillus subtilis, Sulfolobus tokodaii, and Thermotoga maritima) and two eukaryotic (Saccharomyces cerevisiae and Drosophila melanogaster) genomes. We generated supersequences at each codon position across genes in a genome and computed the overall bias at each codon position. By quantitatively evaluating the trend of spatial patterns using isotonic regression, we show that in yeast and prokaryotic genomes, codon usage bias increases along translational direction, which is consistent with purifying selection against nonsense errors. Fruit fly genes show a nearly symmetric M-shaped spatial pattern of codon usage bias, with less bias in the middle and both ends. The low codon usage bias in the middle region is best explained by interference (the Hill-Robertson effect) between selections at different codon positions. In both yeast and fruit fly, spatial patterns of codon usage bias are characteristically different from patterns of GC-content variations. Effect of expression level on the strength of codon usage bias is more conspicuous than its effect on the shape of the spatial distribution.

Light‐driven swimming particles hold great potential in wide applications ranging from next‐generation drug delivery to versatile microrobotic devices. It is desired that the self‐propelled microparticles should swim not only autonomously but also directionally to achieve their goals in their potential applications. This paper presents the first example of fully organic colloidal particles of a spiropyran‐terminated hyperbranched polymer that can be driven and meanwhile steered by a UV light source, swimming straight towards the UV source. The mean‐square velocities of the photochromic suspension particles are about 20 μm s−1, and increase to about 54 μm s−1 with the addition of NaCl of 0.5%. The phototactic propulsion is supposed to be originated from the UV irradiation‐induced interfacial tension gradient on the surface of the colloidal particles. This finding allows for the design of new microengines for next‐generation drug delivery systems, microrobotic devices, and self‐adaptive photocatalysts, etc.

To study the evolution of the yeast protein interaction network, we first classified yeast proteins by their evolutionary histories into isotemporal categories, then analyzed the interaction tendencies within and between the categories, and finally reconstructed the main growth path. We found that two proteins tend to interact with each other if they are in the same or similar categories, but tended to avoid each other otherwise, and that network evolution mirrors the universal tree of life. These observations suggest synergistic selection during network evolution and provide insights into the hierarchical modularity of cellular networks.B iological networks are the basis of cellular functions (1, 2).Understanding network evolution may shed light on the hierarchical modularity, scale-free property, and various uses of the building blocks of biological networks (3-12). The yeast protein interaction network is one of the best annotated complex networks to date (13)(14)(15)(16)(17). Previous studies on the evolution of this network focused either on gene duplication and molecular evolution at the protein level (9, 10) or on the global statistical properties (12). Neither approach can delineate the network evolutionary path, and there is no other comparable protein interaction data for the system-level comparison approach (5). Therefore, uncovering the growth patterns and the evolutionary path of the protein interaction network is a serious challenge (3,4,6,7,9,12).Parts of the present yeast protein interaction network would have been inherited from the last common ancestor of the three domains of life: Eubacteria, Archaea, and Eukaryotes. Thus, an analysis of the evolution of the yeast protein interaction network may provide new insights into the origin of eukaryotic cells (18-21), which has been a controversial issue.A key question in the evolution of biological complexity (6, 7, 9, 12, 21, 22) is, how have integrated biological systems evolved? Darwinists (21, 23) proposed natural selection as the driving force of evolution. However, the striking similarities between biological and nonbiological complexities have led to the argument that a set of universal (or ahistorical) rules account for the formation of all complexities (22,24,25). The yeast protein interaction network is an example of a complex biological system and contributes to the complexity at the cellular level (26). By analyzing the growth pattern and reconstructing the evolutionary path of the yeast protein interaction network, we can address whether or not network growth is contingent on evolutionary history, which is the key disagreement between the Darwinian view and the universality view (22,23,27).In this article, we studied how the yeast protein interaction network has evolved. We used graph theory to model the yeast protein interaction network. Each yeast protein is a node in the graph. Each pairwise interaction is a link between two nodes. Evolution of the yeast protein interaction network can then be inferred by analyzing the growth pattern ...