Matriptase regulates c-Met mediated proliferation and invasion in inflammatory breast cancer.

The poor prognosis for patients with inflammatory breast cancer (IBC) compared to patients with other types of breast cancers emphasizes the need to better understand the molecular underpinnings of this disease with the goal of developing effective targeted therapeutics. Dysregulation of matriptase expression, an epithelial-specific member of the type II transmembrane serine protease family, has been demonstrated in many different cancer types. To date, no studies have assessed the expression and potential pro-oncogenic role of matriptase in IBC. We examined the functional relationship between matriptase and the HGF/c-MET signaling pathway in the IBC cell lines SUM149 and SUM190, and in IBC patient samples. Matriptase and c-Met proteins are localized on the surface membrane of IBC cells and their expression is strongly correlated in infiltrating cancer cells and in the cancer cells of lymphatic emboli in patient samples. Abrogation of matriptase expression by silencing with RNAi or inhibition of matriptase proteolytic activity with a synthetic inhibitor impairs the conversion of inactive pro-HGF to active HGF and subsequent c-Met-mediated signaling, leading to efficient impairment of proliferation and invasion of IBC cells. These data show the potential of matriptase inhibitors as a novel targeted therapy for IBC, and lay the groundwork for the development and testing of such drugs

A three-stage approach for genome-wide association studies with family data for quantitative traits

Background: Genome-wide association (GWA) studies that use population-based association approaches may identify spurious associations in the presence of population admixture. In this paper, we propose a novel three-stage approach that is computationally efficient and robust to population admixture and more powerful than the family-based association test (FBAT) for GWA studies with family data. We propose a three-stage approach for GWA studies with family data. The first stage is to perform linear regression ignoring phenotypic correlations among family members. SNPs with a first stage p-value below a liberal cut-off (e.g. 0.1) are then analyzed in the second stage that employs a linear mixed effects (LME) model that accounts for within family correlations. Next, SNPs that reach genome-wide significance (e.g. $10^{-6}$ for 34,625 genotyped SNPs in this paper) are analyzed in the third stage using FBAT, with correction of multiple testing only for SNPs that enter the third stage. Simulations are performed to evaluate type I error and power of the proposed method compared to LME adjusting for 10 principal components (PC) of the genotype data. We also apply the three-stage approach to the GWA analyses of uric acid in Framingham Heart Study's SNP Health Association Resource (SHARe) project. Results: Our simulations show that whether or not population admixture is present, the three-stage approach has no inflated type I error. In terms of power, using LME adjusting PC is only slightly more powerful than the three-stage approach. When applied to the GWA analyses of uric acid in the SHARe project of FHS, the three-stage approach successfully identified and confirmed three SNPs previously reported as genome-wide significant signals. Conclusions: For GWA analyses of quantitative traits with family data, our three-stage approach provides another appealing solution to population admixture, in addition to LME adjusting for genetic PC

Shared Mycobacterium avium Genotypes Observed among Unlinked Clinical and Environmental Isolates

Our understanding of the sources of Mycobacterium avium infection is partially based on genotypic matching of pathogen isolates from cases and environmental sources. These approaches assume that genotypic identity is rare in isolates from unlinked cases or sources. To test this assumption, a high-resolution PCR-based genotyping approach, large-sequence polymorphism (LSP)-mycobacterial interspersed repetitive unit–variable-number tandem repeat (MIRU-VNTR), was selected and used to analyze clinical and environmental isolates of M. avium from geographically diverse sources. Among 127 clinical isolates from seven locations in North America, South America, and Europe, 42 genotypes were observed. Among 12 of these genotypes, matches were seen in isolates from apparently unlinked patients in two or more geographic locations. Six of the 12 were also observed in environmental isolates. A subset of these isolates was further analyzed by alternative strain genotyping methods, pulsed-field gel electrophoresis and MIRU-VNTR, which confirmed the existence of geographically dispersed strain genotypes. These results suggest that caution should be exercised in interpreting high-resolution genotypic matches as evidence for an acquisition event

Assessing the psychometric and ecometric properties of neighborhood scales using adolescent survey data from urban and rural Scotland

This work was supported by NHS Health Scotland and the University of St Andrews.Background:  Despite the well-established need for specific measurement instruments to examine the relationship between neighborhood conditions and adolescent well-being outcomes, few studies have developed scales to measure features of the neighborhoods in which adolescents reside. Moreover, measures of neighborhood features may be operationalised differently by adolescents living in different levels of urban/rurality. This has not been addressed in previous studies. The objectives of this study were to: 1) establish instruments to measure adolescent neighborhood features at both the individual and neighborhood level, 2) assess their psychometric and ecometric properties, 3) test for invariance by urban/rurality, and 4) generate neighborhood level scores for use in further analysis. Methods:  Data were from the Scottish 2010 Health Behaviour in School-aged Children Survey, which included an over-sample of rural adolescents. The survey responses of interest came from questions designed to capture different facets of the local area in which each respondent resided. Intermediate data zones were used as proxies for neighborhoods. Internal consistency was evaluated by Cronbach’s alpha. Invariance was examined using confirmatory factor analysis. Multilevel models were used to estimate ecometric properties and generate neighborhood scores. Results:  Two constructs labeled neighborhood social cohesion and neighborhood disorder were identified. Adjustment was made to the originally specified model to improve model fit and measures of invariance. At the individual level, reliability was .760 for social cohesion and .765 for disorder, and between .524 and .571 for both constructs at the neighborhood level. Individuals in rural areas experienced greater neighborhood social cohesion and lower levels of neighborhood disorder compared with those in urban areas. Conclusions:  The scales are appropriate for measuring neighborhood characteristics experienced by adolescents across urban and rural Scotland, and can be used in future studies of neighborhoods and health. However, trade-offs between neighborhood sample size and reliability must be considered.Publisher PDFPeer reviewe

International Child Development Accounts

This Working Paper has been submitted for inclusion in the new online edition of theEncyclopedia of Social Work, which is published by Oxford University Press. The paper discusses efforts to implement Child Development Accounts in the United States and numerous other countries. Child Development Accounts (CDAs) are subsidized savings or investment accounts to help people accumulate assets for developmental purposes and life course needs. They are envisioned as universal (everyone participates), progressive (greater subsidies for the poor), and potentially lifelong national policy. These features distinguish CDAs from most existing asset-building policies and programs around the world, which are typically regressive, giving greater benefits to the well-off. With policy innovation in recent years, several countries now have national CDA policies, and four states in the United States have statewide programs. Some of these are designed to be universal and progressive. Evidence indicates that true universality can be achieved, but only with automatic account opening and automatic deposits. In the absence of automatic features, advantaged families participate and benefit more. Today, momentum for universal and automatic features is gradually gaining traction and accelerating. At this stage in the emergence of inclusive asset-based policy, this is the most important development

Evaluation of Cage Designs and Feeding Regimes for Honey Bee (Hymenoptera: Apidae) Laboratory Experiments

The aim of this study was to improve cage systems for maintaining adult honey bee (Apis mellifera L.) workers under in vitro laboratory conditions. To achieve this goal, we experimentally evaluated the impact of different cages, developed by scientists of the international research network COLOSS (Prevention of honey bee COlony LOSSes), on the physiology and survival of honey bees. We identified three cages that promoted good survival of honey bees. The bees from cages that exhibited greater survival had relatively lower titers of deformed wing virus, suggesting that deformed wing virus is a significant marker reflecting stress level and health status of the host. We also determined that a leak- and drip-proof feeder was an integral part of a cage system and a feeder modified from a 20-ml plastic syringe displayed the best result in providing steady food supply to bees. Finally, we also demonstrated that the addition of protein to the bees' diet could significantly increase the level of vitellogenin gene expression and improve bees' survival. This international collaborative study represents a critical step toward improvement of cage designs and feeding regimes for honey bee laboratory experiment

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition