Stanniocalcin-1 Reduces Tumor Size in Human Hepatocellular Carcinoma

Growing evidence has revealed high expression levels of stanniocalcin-1 (STC1) in different types of human cancers. Numerous experimental studies using cancer cell lines demonstrated the involvement of STC1 in inflammatory and apoptotic processes; however the role of STC1 in carcinogenesis remains elusive. Hepatocellular carcinoma (HCC) an exemplified model of inflammation-related cancer, represents a paradigm of studying the association between STC1 and tumor development. Therefore, we conducted a statistical analysis on the expression levels of STC1 using clinicopathological data from 216 HCC patients. We found that STC1 was upregulated in the tumor tissues and its expression levels was positively correlated with the levels of interleukin (IL)-6 and IL-8. Intriguingly tumors with greater expression levels of STC1 (tumor/normal ≥ 2) were significantly smaller than the lower level (tumor/normal<2) samples (p = 0.008). A pharmacological approach was implemented to reveal the functional correlation between STC1 and the ILs in the HCC cell-lines. IL-6 and IL-8 treatment of Hep3B cells induced STC1 expression. Lentiviral-based STC1 overexpression in Hep3B and MHCC-97L cells however showed inhibitory action on the pro-migratory effects of IL-6 and IL-8 and reduced size of tumor spheroids. The inhibitory effect of STC1 on tumor growth was confirmed in vivo using the stable STC1-overexpressing 97L cells on a mouse xenograft model. Genetic analysis of the xenografts derived from the STC1-overexpressing 97L cells, showed upregulation of the pro-apoptotic genes interleukin-12 and NOD-like receptor family, pyrin domain-containing 3. Collectively, the anti-inflammatory and pro-apoptotic functions of STC1 were suggested to relate its inhibitory effect on the growth of HCC cells. This study supports the notion that STC1 may be a potential therapeutic target for inflammatory tumors in HCC patients.     

Formation of α- and β-conidia by Phaeocytostroma ambiguum

The formation of conidia in Phaeocytostroma ambiguum on different media and conditions was investigated in this study. Carnation leaf agar (CLA) and a 12 h photoperiod (24/18 °C) provided excellent conditions for the promotion of rapid formation of both alpha (α) and beta (β) conidia in a number of P. ambiguum isolates. The dimensions of α- and β-conidia amounted to 6.0–19.6 × 3.8–7.5 μm and 6.0–24.9 × 1.1–2.6 μm, respectively. They were produced on short or elongate, simple and branched conidiophores. β-conidia have not been described before in P. ambiguum. Intermediate conidia were rarely found. This revised version was published online in August 2006 with corrections to the Cover Date.     

Single point mutation on the gene encoding dysbindin results in recognition deficits

The dystrobrevin‐binding protein 1 (DTNBP1) gene is a candidate risk factor for schizophrenia and has been associated with cognitive ability in both patient populations and healthy controls. DTNBP1 encodes dysbindin protein, which is localized to synaptic sites and is reduced in the prefrontal cortex and hippocampus of patients with schizophrenia, indicating a potential role in schizophrenia etiology. Most studies of dysbindin function have focused on the sandy (sdy) mice that lack dysbindin protein and have a wide range of abnormalities. In this study, we examined dysbindin salt and pepper (spp) mice that possess a single point mutation on the Dtnbp1 gene predicted to reduce, but not eliminate, dysbindin expression. By western blot analysis, we found that spp homozygous (spp ?/?) mutants had reduced dysbindin and synaptosomal‐associated protein 25 (SNAP‐25) in the prefrontal cortex, but unaltered levels in hippocampus. Behaviorally, spp mutants performed comparably to controls on a wide range of tasks assessing locomotion, anxiety, spatial recognition and working memory. However, spp ?/? mice had selective deficits in tasks measuring novel object recognition and social novelty recognition. Our results indicate that reduced dysbindin and SNAP‐25 protein in the prefrontal cortex of spp ?/? is associated with selective impairments in recognition processing. These spp mice may prove useful as a novel mouse model to study cognitive deficits linked to dysbindin alterations. Our findings also suggest that aspects of recognition memory may be specifically influenced by DTNBP1 single nucleotide polymorphisms or risk haplotypes in humans and this connection should be further investigated.     

Construction of a genome-anchored,high-density genetic map for melon (<Emphasis Type="Italic">Cucumis melo</Emphasis> L.) and identification of <Emphasis Type="Italic">Fusarium oxysporum</Emphasis> f. sp. <Emphasis Type="Italic">melonis</Emphasis> race 1 resistance QTL

Key message

Four QTLs and an epistatic interaction were associated with disease severity in response to inoculation with Fusarium oxysporum f. sp. melonis race 1 in a recombinant inbred line population of melon.

Abstract

The USDA Cucumis melo inbred line, MR-1, harbors a wealth of alleles associated with resistance to several major diseases of melon, including powdery mildew, downy mildew, Alternaria leaf blight, and Fusarium wilt. MR-1 was crossed to an Israeli cultivar, Ananas Yok’neam, which is susceptible to all of these diseases, to generate a recombinant inbred line (RIL) population of 172 lines. In this study, the RIL population was genotyped to construct an ultra-dense genetic linkage map with 5663 binned SNPs anchored to the C. melo genome and exhibits the overall high quality of the assembly. The utility of the densely genotyped population was demonstrated through QTL mapping of a well-studied trait, resistance to Fusarium wilt caused by Fusarium oxysporum f. sp. melonis (Fom) race 1. A major QTL co-located with the previously validated resistance gene Fom-2. In addition, three minor QTLs and an epistatic interaction contributing to Fom race 1 resistance were identified. The MR-1 × AY RIL population provides a valuable resource for future QTL mapping studies and marker-assisted selection of disease resistance in melon.

     

CCN3 (NOV) is a novel angiogenic regulator of the CCN protein family

CCN3 (NOV) is a matricellular protein of the CCN family, which also includes CCN1 (CYR61), CCN2 (CTGF), CCN4 (WISP-1), CCN5 (WISP-2), and CCN6 (WISP-3). During development, CCN3 is expressed widely in derivatives of all three germ layers, and high levels of expression are observed in smooth muscle cells of the arterial vessel wall. Altered expression of CCN3 has been observed in a variety of tumors, including hepatocellular carcinomas, Wilm's tumors, Ewing's sarcomas, gliomas, rhabdomyosarcomas, and adrenocortical carcinomas. To understand its biological functions, we have investigated the activities of purified recombinant CCN3. We show that in endothelial cells, CCN3 supports cell adhesion, induces directed cell migration (chemotaxis), and promotes cell survival. Mechanistically, CCN3 supports human umbilical vein endothelial cell adhesion through multiple cell surface receptors, including integrins alphavbeta3, alpha5beta1, alpha6beta1, and heparan sulfate proteoglycans. In contrast, CCN3-induced cell migration is dependent on integrins alphavbeta3 and alpha5beta1, whereas alpha6beta1 does not play a role in this process. Although CCN3 does not contain a RGD sequence, it binds directly to immobilized integrins alphavbeta3 and alpha5beta1, with half-maximal binding occurring at 10 nm and 50 nm CCN3, respectively. Furthermore, CCN3 induces neovascularization when implanted in rat cornea, demonstrating that it is a novel angiogenic inducer. Together, these findings show that CCN3 is a ligand of integrins alphavbeta3 and alpha5beta1, acts directly upon endothelial cells to stimulate pro-angiogenic activities, and induces angiogenesis in vivo.     

The role of metal ions in chemical evolution: Polymerization of alanine and glycine in a cation-exchanged clay environment

Summary The effect of the exchangeable cation on the condensation of glycine and alanine was investigated using a series of homoionic bentonites. A cycling procedure of drying, warming and wetting was employed. Peptide bond formation was observed, and the effectiveness of metal ions to catalyze the condensation was Cu²⁺ > Ni²⁺ Zn²⁺ > Na⁺. Glycine showed 6% of the monomer incorporated into oligomers with the largest detected being the pentamer. Alanine showed less peptide bond formation (a maximum of 2%) and only the dimer was observed.     

A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.     

Projecting the future of an alpine ungulate under climate change scenarios

Climate change represents a primary threat to species persistence and biodiversity at a global scale. Cold adapted alpine species are especially sensitive to climate change and can offer key “early warning signs” about deleterious effects of predicted change. Among mountain ungulates, survival, a key determinant of demographic performance, may be influenced by future climate in complex, and possibly opposing ways. Demographic data collected from 447 mountain goats in 10 coastal Alaska, USA, populations over a 37‐year time span indicated that survival is highest during low snowfall winters and cool summers. However, general circulation models (GCMs) predict future increase in summer temperature and decline in winter snowfall. To disentangle how these opposing climate‐driven effects influence mountain goat populations, we developed an age‐structured population model to project mountain goat population trajectories for 10 different GCM/emissions scenarios relevant for coastal Alaska. Projected increases in summer temperature had stronger negative effects on population trajectories than the positive demographic effects of reduced winter snowfall. In 5 of the 10 GCM/representative concentration pathway (RCP) scenarios, the net effect of projected climate change was extinction over a 70‐year time window (2015–2085); smaller initial populations were more likely to go extinct faster than larger populations. Using a resource selection modeling approach, we determined that distributional shifts to higher elevation (i.e., “thermoneutral”) summer range was unlikely to be a viable behavioral adaptation strategy; due to the conical shape of mountains, summer range was expected to decline by 17%–86% for 7 of the 10 GCM/RCP scenarios. Projected declines of mountain goat populations are driven by climate‐linked bottom‐up mechanisms and may have wide ranging implications for alpine ecosystems. These analyses elucidate how projected climate change can negatively alter population dynamics of a sentinel alpine species and provide insight into how demographic modeling can be used to assess risk to species persistence.