Morphogenesis of post-Golgi transport carriers

The trans-Golgi network (TGN) is one of the main, if not the main, sorting stations in the process of intracellular protein trafficking. It is therefore of central importance to understand how the key players in the TGN-based sorting and delivery process, the post-Golgi carriers (PGCs), form and function. Over the last few years, modern morphological approaches have generated new insights into the questions of PGC biogenesis, structure and dynamics. Here, we present a view by which the “lifecycle” of a PGC consists of several distinct stages: the formation of TGN tubular export domains (where different cargoes are segregated from each other and from the Golgi enzymes); the docking of these tubular domains onto molecular motors and their extrusion towards the cell periphery along microtubules; the fission of the forming PGC from the donor membrane; and the delivery of the newly formed PGC to its specific acceptor organelle. It is now important to add the many molecular machineries that have been described as operating at the TGN to this “morphofunctional map” of the TGN export process.     

Genetic population structure as indirect measure of dispersal ability in a Lake Tanganyika cichlid

Diversification and speciation processes are influenced by intrinsic (ecological specialization, dispersal) and extrinsic (habitat structure and instability) factors, but the effect of ecological characteristics on dispersal is difficult to assess. This study uses mitochondrial control region sequences to investigate the population structure and demographic history of the endemic Lake Tanganyika cichlid Neolamprologus caudopunctatus with a preference for the rock-sand interface along two stretches of continuous, rocky shoreline, and across a sandy bay representing a potential dispersal barrier. Populations along uninterrupted habitat were not differentiated; whereas, the sandy bay separated two reciprocally monophyletic clades. The split between the two clades between 170,000 and 260,000 years BP coincides with a period of rising water level following a major lowstand, and indicates that clades remained isolated throughout subsequent lake level fluctuations. Low long-term effective population sizes were inferred from modest genetic diversity estimates, and may be due to recent population expansions starting from small population sizes 45,000–60,000 years BP. Comparisons with available data from specialized rock-dwelling species of the␣same area suggest that habitat structure and lake level fluctuations determine phylogeographic patterns on large scales, while fine-scale population structure and demography are modulated by species-specific ecologies.     

CCN proteins: A centralized communication network

The CCN family of proteins includes six members presently known as CCN1, CCN2, CCN3, CCN4, CCN5 and CCN6. These proteins were originally designated CYR61, CTGF, NOV, and WISP-1, WISP-2, WISP-3. Although these proteins share a significant amount of structural features and a partial identity with other large families of regulatory proteins, they exhibit different biological functions. A critical examination of the progress made over the past two decades, since the first CCN proteins were discovered brings me to the conclusion that most of our present knowledge regarding the functions of these proteins was predicted very early after their discovery. In an effort to point out some of the gaps that prevent us to reach a comprehensive view of the functional interactions between CCN proteins, it is necessary to reconsider carefully data that was already published and put aside, either because the scientific community was not ready to accept them, or because they were not fitting with the « consensus » when they were published. This review article points to avenues that were not attracting the attention that they deserved. However, it is quite obvious that the six members of this unique family of tetra-modular proteins must act in concert, either simultaneously or sequentially, on the same sites or at different times in the life of living organisms. A better understanding of the spatio-temporal regulation of CCN proteins expression requires considering the family as such, not as a set of single proteins related only by their name. As proposed in this review, there is enough convincing pieces of evidence, at the present time, in favor of these proteins playing a role in the coordination of multiple signaling pathways, and constituting a Centralized Communication Network. Deciphering the hierarchy of regulatory circuits involved in this complex system is an important challenge for the near future. In this article, I would like to briefly review the concept of a CCN family of proteins and critically examine the progress made over the past 10 years in the understanding of their biological functions and involvement in both normal and pathological processes.     

External sensilla of the locust abdomen provide the central nervous system with an interganglionic network

External mechanoreceptors and contact chemoreceptors on the cuticle of the sixth abdominal segment of locusts have divergent primary projections of their sensory neurons that form arbours in the segmental and anterior abdominal ganglia. Homologous interganglionic projections from adjacent segments converge in the neuropile of each abdominal ganglion. Of the contributing types of sensilla, three were previously unknown for locust pregenital segments: tactile mechanosensory hairs with dual innervation, external proprioceptors of the hairplate type covered by intersegmental membranes and single campaniform sensilla that monitor cuticular strain in sternites and tergites. In general, interdependence of motor coordination in the abdominal segments is based on a neural network that relies heavily on intersegmental primary afferents that cooperate to identify the location, parameters and strength of external stimuli.     

Modeling epidemic spread with awareness and heterogeneous transmission rates in networks

During an epidemic outbreak in a human population, susceptibility to infection can be reduced by raising awareness of the disease. In this paper, we investigate the effects of three forms of awareness (i.e., contact, local, and global) on the spread of a disease in a random network. Connectivity-correlated transmission rates are assumed. By using the mean-field theory and numerical simulation, we show that both local and contact awareness can raise the epidemic thresholds while the global awareness cannot, which mirrors the recent results of Wu et al. The obtained results point out that individual behaviors in the presence of an infectious disease has a great influence on the epidemic dynamics. Our method enriches mean-field analysis in epidemic models.     

Evolution on distributive lattices

We consider the directed evolution of a population after an intervention that has significantly altered the underlying fitness landscape. We model the space of genotypes as a distributive lattice; the fitness landscape is a real-valued function on that lattice. The risk of escape from intervention, i.e., the probability that the population develops an escape mutant before extinction, is encoded in the risk polynomial. Tools from algebraic combinatorics are applied to compute the risk polynomial in terms of the fitness landscape. In an application to the development of drug resistance in HIV, we study the risk of viral escape from treatment with the protease inhibitors ritonavir and indinavir.     

Learnability-based further prediction of gene functions in Gene Ontology

Currently the functional annotations of many genes are not specific enough, limiting their further application in biology and medicine. It is necessary to push the gene functional annotations deeper in Gene Ontology (GO), or to predict further annotated genes with more specific GO terms. A framework of learnability-based further prediction of gene functions in GO is proposed in this paper. Local classifiers are constructed in local classification spaces rooted at qualified parent nodes in GO, and their classification performances are evaluated with the averaged Tanimoto index (ATI). Classification spaces with higher ATIs are selected out, and genes annotated only to the parent classes are predicted to child classes. Through learnability-based further predicting, the functional annotations of annotated genes are made more specific. Experiments on the fibroblast serum response dataset reported further functional predictions for several human genes and also gave interesting clues to the varied learnability between classes of different GO ontologies, different levels, and different numbers of child classes.