Hyperhomocysteinemia increases intimal hyperplasia in a rat carotid endarterectomy model

AbstractPurpose: This preliminary study investigated the ability to elevate the serum homocysteine (H[e]) levels and investigated the increases in postoperative neointimal hyperplasia (IH) in an environment with hyperhomocysteinemia and the resultant restenosis in a rat carotid endarterectomy (CEA) model. Method: The 9 rats for the control group were fed rat chow, and the 8 rats for the H(e) group were fed H(e)-supplemented rat chow for 2 weeks before and after CEA. The animals underwent anesthesia, and a left common CEA was performed. After 14 days, the serum H(e) levels were measured and the left carotid artery was harvested and elastin stained. Morphometric measurements were used to calculate the area of stenosis of the lumen. The mean and the standard deviation of the mean were determined. The 2 groups were compared with the Mann-Whitney test and a linear regression model. Three additional rats per group were studied, with carotid artery sectioning with double immunohistochemical staining for 5-bromodeoxyuridine (BrdU) and α–smooth muscle (α-SM) actin. Results: The serum H(e) level in the H(e) group was 36.32 μmol/L ± 15.28, and in the control group the level was 5.53 μmol/L ± 2.06 (P = .0007). IH presented as percent lumen stenosis was 21.89% ± 4.82% in the H(e) group and 4.82% ± 1.64% in the control group (P = .0007). The linear regression model of the serum H(e) levels and the percent stenosis showed a linear relationship (r2 = .72). The α-SM actin staining revealed that nearly all of the cells in the IH area were of smooth muscle or myofibroblast origin and that 10.1% ± 2.6% of the cells were stained for BrdU in the control group versus 23% ± 7.1% in the H(e) group. Also, 9.3% ± 2.6% of the cells in the IH area were stained for BrdU and for α-SM actin versus 19.1% ± 5.6% stained for both BrdU and α-SM actin in the H(e) group. Conclusion: This is the first study to examine IH after CEA and hyperhomocysteinemia in rats. The study shows that the elevation of serum H(e) levels can be obtained by feeding rats modified diets with added H(e). The consistent elevation of serum H(e) levels was associated with more than 4 times the amount of IH after a CEA in a rat model. (J Vasc Surg 1998;28:909-18.

A Method to Quantify Mouse Coat-Color Proportions

Coat-color proportions and patterns in mice are used as assays for many processes such as transgene expression, chimerism, and epigenetics. In many studies, coat-color readouts are estimated from subjective scoring of individual mice. Here we show a method by which mouse coat color is quantified as the proportion of coat shown in one or more digital images. We use the yellow-agouti mouse model of epigenetic variegation to demonstrate this method. We apply this method to live mice using a conventional digital camera for data collection. We use a raster graphics editing program to convert agouti regions of the coat to a standard, uniform, brown color and the yellow regions of the coat to a standard, uniform, yellow color. We use a second program to quantify the proportions of these standard colors. This method provides quantification that relates directly to the visual appearance of the live animal. It also provides an objective analysis with a traceable record, and it should allow for precise comparisons of mouse coats and mouse cohorts within and between studies

Post hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies

Rare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work

Financing U.S. Graduate Medical Education: A Policy Position Paper of the Alliance for Academic Internal Medicine and the American College of Physicians

In this position paper, the Alliance for Academic Internal Medicine and the American College of Physicians examine the state of graduate medical education (GME) financing in the United States and recent proposals to reform GME funding. They make a series of recommendations to reform the current funding system to better align GME with the needs of the nation's health care workforce. These recommendations include using Medicare GME funds to meet policy goals and to ensure an adequate supply of physicians, a proper specialty mix, and appropriate training sites; spreading the costs of financing GME across the health care system; evaluating the true cost of training a resident and establishing a single per-resident amount; increasing transparency and innovation; and ensuring that primary care residents receive training in well-functioning ambulatory settings that are financially supported for their training roles

Genome-wide association of familial prostate cancer cases identifies evidence for a rare segregating haplotype at 8q24.21

Previous genome-wide association studies (GWAS) of prostate cancer risk focused on cases unselected for family history and have reported over 100 significant associations. The International Consortium for Prostate Cancer Genetics (ICPCG) has now performed a GWAS of 2511 (unrelated) familial prostate cancer cases and 1382 unaffected controls from 12 member sites. All samples were genotyped on the Illumina 5M+exome single nucleotide polymorphism (SNP) platform. The GWAS identified a significant evidence for association for SNPs in six regions previously associated with prostate cancer in population-based cohorts, including 3q26.2, 6q25.3, 8q24.21, 10q11.23, 11q13.3, and 17q12. Of note, SNP rs138042437 (p = 1.7e−8) at 8q24.21 achieved a large estimated effect size in this cohort (odds ratio = 13.3). 116 previously sampled affected relatives of 62 risk-allele carriers from the GWAS cohort were genotyped for this SNP, identifying 78 additional affected carriers in 62 pedigrees. A test for an excess number of affected carriers among relatives exhibited strong evidence for co-segregation of the variant with disease (p = 8.5e−11). The majority (92 %) of risk-allele carriers at rs138042437 had a consistent estimated haplotype spanning approximately 100 kb of 8q24.21 that contained the minor alleles of three rare SNPs (dosage minor allele frequencies <1.7 %), rs183373024 (PRNCR1), previously associated SNP rs188140481, and rs138042437 (CASC19). Strong evidence for co-segregation of a SNP on the haplotype further characterizes the haplotype as a prostate cancer pre-disposition locus

REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants

Supplemental Data Supplemental Data include one figure and five tables and can be found with this article online at http://dx.doi.org/10.1016/j.ajhg.2016.08.016. Supplemental Data Document S1. Figure S1 and Tables S1–S5 Download Document S2. Article plus Supplemental Data Download Web Resources ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/ dbNSFP, https://sites.google.com/site/jpopgen/dbNSFP Human Gene Mutation Database, http://www.hgmd.cf.ac.uk/ REVEL, https://sites.google.com/site/revelgenomics/ SwissVar, http://swissvar.expasy.org/ The vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10−12) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046–0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027–0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale

Evidence of maternal QTL affecting growth and obesity in adult mice

Most quantitative trait loci (QTL) studies fail to account for the effect that the maternal genotype may have on an individual’s phenotypes, even though maternal effect QTL have been shown to account for considerable variation in growth and obesity traits in mouse models. Moreover, the fetal programming theory suggests that maternal effects influence an offspring’s adult fitness, although the genetic nature of fetal programming remains unclear. Within this context, our study focused on mapping genomic regions associated with maternal effect QTL by analyzing the phenotypes of chromosomes 2 and 7 subcongenic mice from genetically distinct dams. We analyzed 12 chromosome 2 subcongenic strains that spanned from 70 to 180 Mb with CAST/EiJ donor regions on the background of C57BL/6 J, and 14 chromosome 7 subcongenic strains that spanned from 81 to 111 Mb with BALB/cByJ donor regions on C57BL/6ByJ background. Maternal QTL analyses were performed on the basis of overlapping donor regions between subcongenic strains. We identified several highly significant (P < 5 × 10−4) maternal QTL influencing total body weight, organ weight, and fat pad weights in both sets of subcongenics. These QTL accounted for 1.9-11.7% of the phenotypic variance for growth and obesity and greatly narrowed the genomic regions associated with the maternal genetic effects. These maternal effect QTL controlled phenotypic traits in adult mice, suggesting that maternal influences at early stages of development may permanently affect offspring performance. Identification of maternal effects in our survey of two sets of subcongenic strains, representing approximately 5% of the mouse genome, supports the hypothesis that maternal effects represent significant sources of genetic variation that are largely ignored in genetic studies

The case for strategic international alliances to harness nutritional genomics for public and personal health

Nutrigenomics is the study of how constituents of the diet interact with genes, and their products, to alter phenotype and, conversely, how genes and their products metabolise these constituents into nutrients, antinutrients, and bioactive compounds. Results from molecular and genetic epidemiological studies indicate that dietary unbalance can alter gene-nutrient interactions in ways that increase the risk of developing chronic disease. The interplay of human genetic variation and environmental factors will make identifying causative genes and nutrients a formidable, but not intractable, challenge. We provide specific recommendations for how to best meet this challenge and discuss the need for new methodologies and the use of comprehensive analyses of nutrient-genotype interactions involving large and diverse populations. The objective of the present paper is to stimulate discourse and collaboration among nutrigenomic researchers and stakeholders, a process that will lead to an increase in global health and wellness by reducing health disparities in developed and developing countrie

A written self-help intervention for depressed adults comparing behavioural activation combined with physical activity promotion with a self-help intervention based upon behavioural activation alone: study protocol for a parallel group pilot randomised controlled trial (BAcPAc)

notes: PMCID: PMC4061537types: Journal Article© 2014 Farrand et al.; licensee BioMed Central Ltd.Challenges remain to find ways to support patients with depression who have low levels of physical activity (PA) to overcome perceived barriers and enhance the perceived value of PA for preventing future relapse. There is an evidence-base for behavioural activation (BA) for depression, which focuses on supporting patients to restore activities that have been avoided, but practitioners have no specific training in promoting PA. We aimed to design and evaluate an integrated BA and PA (BAcPAc) practitioner-led, written, self-help intervention to enhance both physical and mental health.NPRI-

Germline Mutations in HOXB13 and Prostate-Cancer Risk

Family history is a significant risk factor for prostate cancer, although the molecular basis for this association is poorly understood. Linkage studies have implicated chromosome 17q21-22 as a possible location of a prostate-cancer susceptibility gene