LTC Interface

A portion of a four-year study concentrating on the design and implementation of long-term care service arrays and peer-support mechanisms

The Effect of Peer Support on Recidivism Rates for Mental Health Hospital Admissions and Crisis stabilization episodes

Telephone survey with urban/rural Georgians over age 55 to provide public input into the planning for the future of aging services in Georgia

Molecule Microscopy

Contains research objectives, summary of research on five research projects and reports on four research projects.Joint Services Electronics Program (Contract DAAB07-74-C-0630)National Institutes of Health (Grant 1 PO1 HL14322-03)National Institutes of Health (Grant 5 SO5 RR07047-08)Environmental Measurements Project Laboratory grant from the Dean of Science, M.I.T.Boehringer Mannheim Gmb

Apple skin patterning is associated with differential expression of MYB10

Background: Some apple (Malus × domestica Borkh.) varieties have attractive striping patterns, a quality attribute that is important for determining apple fruit market acceptance. Most apple cultivars (e.g. ‘Royal Gala’) produce fruit with a defined fruit pigment pattern, but in the case of ‘Honeycrisp’ apple, trees can produce fruits of two different kinds: striped and blushed. The causes of this phenomenon are unknown. Results: Here we show that striped areas of ‘Honeycrisp’ and ‘Royal Gala’ are due to sectorial increases in anthocyanin concentration. Transcript levels of the major biosynthetic genes and MYB10, a transcription factor that upregulates apple anthocyanin production, correlated with increased anthocyanin concentration in stripes. However, nucleotide changes in the promoter and coding sequence of MYB10 do not correlate with skin pattern in ‘Honeycrisp’ and other cultivars differing in peel pigmentation patterns. A survey of methylation levels throughout the coding region of MYB10 and a 2.5 Kb region 5’ of the ATG translation start site indicated that an area 900 bp long, starting 1400 bp upstream of the translation start site, is highly methylated. Cytosine methylation was present in all three contexts, with higher methylation levels observed for CHH and CHG (where H is A, C or T) than for CG. Comparisons of methylation levels of the MYB10 promoter in ‘Honeycrisp’ red and green stripes indicated that they correlate with peel phenotypes, with an enrichment of methylation observed in green stripes. Conclusions: Differences in anthocyanin levels between red and green stripes can be explained by differential transcript accumulation of MYB10. Different levels of MYB10 transcript in red versus green stripes are inversely associated with methylation levels in the promoter region. Although observed methylation differences are modest, trends are consistent across years and differences are statistically significant. Methylation may be associated with the presence of a TRIM retrotransposon within the promoter region, but the presence of the TRIM element alone cannot explain the phenotypic variability observed in ‘Honeycrisp’. We suggest that methylation in the MYB10 promoter is more variable in ‘Honeycrisp’ than in ‘Royal Gala’, leading to more variable color patterns in the peel of this cultivar.https://doi.org/10.1186/1471-2229-11-9

Metabolomic analysis of insulin resistance across different mouse strains and diets

Insulin resistance is a major risk factor for many diseases. However, its underlying mechanism remains unclear in part because it is triggered by a complex relationship between multiple factors, including genes and the environment. Here, we used metabolomics combined with computational methods to identify factors that classified insulin resistance across individual mice derived from three different mouse strains fed two different diets. Three inbred ILSXISS strains were fed high-fat or chow diets and subjected to metabolic phenotyping and metabolomics analysis of skeletal muscle. There was significant metabolic heterogeneity between strains, diets, and individual animals. Distinct metabolites were changed with insulin resistance, diet, and between strains. Computational analysis revealed 113 metabolites that were correlated with metabolic phenotypes. Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sensitivity, we identified C22:1-CoA, C2-carnitine, and C16-ceramide as the best classifiers. Strikingly, when these three metabolites were combined into one signature, they classified mice based on insulin sensitivity more accurately than each metabolite on its own or other published metabolic signatures. Furthermore, C22:1-CoA was 2.3-fold higher in insulin-resistant mice and correlated significantly with insulin resistance. We have identified a metabolomic signature composed of three functionally unrelated metabolites that accurately predicts whole-body insulin sensitivity across three mouse strains. These data indicate the power of simultaneous analysis of individual, genetic, and environmental variance in mice for identifying novel factors that accurately predict metabolic phenotypes like whole-body insulin sensitivity