New SNPs for population genetic analysis reveal possible cryptic speciation of eastern Australian sea mullet (Mugil cephalus)

Sustainable management of sea mullet (Mugil cephalus) fisheries needs to account for recent observations of regional-scale differentiation. Population genetic analysis is sought to assess the situation of this ecologically and economically important fish species in eastern Australian waters. Here, we report (i) new population genetic markers [single nucleotide polymorphisms (SNPs) and potential microsatellites], (ii) first estimates of spatial genetic differentiation and (iii) prospective power tests for designing more comprehensive studies. Six DNA samples from three sampling regions (North Queensland, South Queensland and central New South Wales) on the eastern coast of Australia were used to prepare restriction site associated DNA (RAD) tag libraries from genomic DNA digested with EcoRI and MseI. A pooled sample of regional RAD tag libraries was sequenced using the Roche GS-FLX Titanium platform. A total of 172837 raw reads (17.4Mbp) were retrieved, 95500 of which were used to discover 1267 SNPs and 1417 microsatellites. A subset of 161 SNPs was validated based on 63 additional DNA samples genotyped using the Sequenom MassArray (iPLEX Gold chemistry). Altogether 92 SNPs (57%) were confirmed, with 40% of these marking fixed variants between northern and southern sampling regions. Our preliminary findings indicate a multispecies fishery stock of M. cephalus in eastern Australian waters, but suggest that strong genetic differentiation occurs north of major fishing grounds. Low potential differentiation within major fishing grounds (e.g. FST=0.0025) can be resolved with a likely power 67% by using standard sample sizes of 50 and validated subsets of available markers.

Phylodynamic evidence of the migration of turnip mosaic potyvirus from Europe to Australia and New Zealand

Turnip mosaic virus (TuMV) is a potyvirus that is transmitted by aphids and infects a wide range of plant species. We investigated the evolution of this pathogen by collecting 32 isolates of TuMV, mostly from Brassicaceae plants, in Australia and New Zealand. We performed a variety of sequence-based phylogenetic and population genetic analyses of the complete genomic sequences and of three non-recombinogenic regions of those sequences. The substitution rates, divergence times and phylogeographical patterns of the virus populations were estimated. Six inter- and seven intralineage recombination-type patterns were found in the genomes of the Australian and New Zealand isolates, and all were novel. Only one recombination-type pattern has been found in both countries. The Australian and New Zealand populations were genetically different, and were different from the European and Asian populations. Our Bayesian coalescent analyses, based on a combination of novel and published sequence data from three nonrecombinogenic protein-encoding regions, showed that TuMV probably started to migrate from Europe to Australia and New Zealand more than 80 years ago, and that distinct populations arose as a result of evolutionary drivers such as recombination. The basal-B2 subpopulation in Australia and New Zealand seems to be older than those of the world-B2 and -B3 populations. To our knowledge, our study presents the first population genetic analysis of TuMV in Australia and New Zealand. We have shown that the time of migration of TuMV correlates well with the establishment of agriculture and migration of Europeans to these countries.

N-syndecan and HB-GAM in neural migration and differentiation : Modulation of growth factor activity in brain

The juvenile sea squirt wanders through the sea searching for a suitable rock or hunk of coral to cling to and make its home for life. For this task it has a rudimentary nervous system. When it finds its spot and takes root, it doesn't need its brain any more so it eats it. It's rather like getting tenure. Daniel C. Dennett (from Consciousness Explained, 1991) The little sea squirt needs its brain for a task that is very simple and short. When the task is completed, the sea squirt starts a new life in a vegetative state, after having a nourishing meal. The little brain is more tightly structured than our massive primate brains. The number of neurons is exact, no leeway in neural proliferation is tolerated. Each neuroblast migrates exactly to the correct position, and only a certain number of connections with the right companions is allowed. In comparison, growth of a mammalian brain is a merry mess. The reason is obvious: Squirt brain needs to perform only a few, predictable functions, before becoming waste. The more mobile and complex mammals engage their brains in tasks requiring quick adaptation and plasticity in a constantly changing environment. Although the regulation of nervous system development varies between species, many regulatory elements remain the same. For example, all multicellular animals possess a collection of proteoglycans (PG); proteins with attached, complex sugar chains called glycosaminoglycans (GAG). In development, PGs participate in the organization of the animal body, like in the construction of parts of the nervous system. The PGs capture water with their GAG chains, forming a biochemically active gel at the surface of the cell, and in the extracellular matrix (ECM). In the nervous system, this gel traps inside it different molecules: growth factors and ECM-associated proteins. They regulate the proliferation of neural stem cells (NSC), guide the migration of neurons, and coordinate the formation of neuronal connections. In this work I have followed the role of two molecules contributing to the complexity of mammalian brain development. N-syndecan is a transmembrane heparan sulfate proteoglycan (HSPG) with cell signaling functions. Heparin-binding growth-associated molecule (HB-GAM) is an ECM-associated protein with high expression in the perinatal nervous system, and high affinity to HS and heparin. N-syndecan is a receptor for several growth factors and for HB-GAM. HB-GAM induces specific signaling via N-syndecan, activating c-Src, calcium/calmodulin-dependent serine protein kinase (CASK) and cortactin. By studying the gene knockouts of HB-GAM and N-syndecan in mice, I have found that HB-GAM and N-syndecan are involved as a receptor-ligand-pair in neural migration and differentiation. HB-GAM competes with the growth factors fibriblast growth factor (FGF)-2 and heparin-binding epidermal growth factor (HB-EGF) in HS-binding, causing NSCs to stop proliferation and to differentiate, and affects HB-EGF-induced EGF receptor (EGFR) signaling in neural cells during migration. N-syndecan signaling affects the motility of young neurons, by boosting EGFR-mediated cell migration. In addition, these two receptors form a complex at the surface of the neurons, probably creating a motility-regulating structure.

The oxygen migration in the apatite-type lanthanum silicate with the cation substitution

A theoretical model is proposed to determine the effects of Si substitution with Al on the oxygen diffusion in apatite-type lanthanum silicates based on density-functional theory (DFT) calculations for La10(SiO 4)4(AlO4)2O2. Substitution changes the stable configuration for excess oxygen from the split interstitial to a new cluster form with the original cluster. Al doping completely changes the migration mechanism from the interstitialcy one, which was proposed for the La9.33(SiO4)6O2 starting material, to a mechanism which contains an interstitial process. Nevertheless, the migration barrier is calculated to be 0.81 eV, which indicates small changes in oxygen conduction and is consistent with the observations. The present study indicates that the cation substitution on silicon site alone does not promise the improvement of the oxide ion conduction in the lanthanum silicate.

Elevated CDCP1 predicts poor patient outcome and mediates ovarian clear cell carcinoma by promoting tumor spheroid formation, cell migration and chemoresistance

Hematogenous metastases are rarely present at diagnosis of ovarian clear cell carcinoma (OCC). Instead dissemination of these tumors is characteristically via direct extension of the primary tumor into nearby organs and the spread of exfoliated tumor cells throughout the peritoneum, initially via the peritoneal fluid, and later via ascites that accumulates as a result of disruption of the lymphatic system. The molecular mechanisms orchestrating these processes are uncertain. In particular, the signaling pathways used by malignant cells to survive the stresses of anchorage-free growth in peritoneal fluid and ascites, and to colonize remote sites, are poorly defined. We demonstrate that the transmembrane glycoprotein CUB-domain-containing protein 1 (CDCP1) has important and inhibitable roles in these processes. In vitro assays indicate that CDCP1 mediates formation and survival of OCC spheroids, as well as cell migration and chemoresistance. Disruption of CDCP1 via silencing and antibody-mediated inhibition markedly reduce the ability of TOV21G OCC cells to form intraperitoneal tumors and induce accumulation of ascites in mice. Mechanistically our data suggest that CDCP1 effects are mediated via a novel mechanism of protein kinase B (Akt) activation. Immunohistochemical analysis also suggested that CDCP1 is functionally important in OCC, with its expression elevated in 90% of 198 OCC tumors and increased CDCP1 expression correlating with poor patient disease-free and overall survival. This analysis also showed that CDCP1 is largely restricted to the surface of malignant cells where it is accessible to therapeutic antibodies. Importantly, antibody-mediated blockade of CDCP1 in vivo significantly increased the anti-tumor efficacy of carboplatin, the chemotherapy most commonly used to treat OCC. In summary, our data indicate that CDCP1 is important in the progression of OCC and that targeting pathways mediated by this protein may be useful for the management of OCC, potentially in combination with chemotherapies and agents targeting the Akt pathway.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).