The Molecular Chaperone HSP90 Promotes Notch Signaling in the Germline of Caenorhabditis elegans.

In a genetic screen to identify genes that promote GLP-1/Notch signaling in Caenorhabditis elegans germline stem cells, we found a single mutation, om40, defining a gene called ego-3. ego-3(om40) causes several defects in the soma and the germline, including paralysis during larval development, sterility, delayed proliferation of germline stem cells, and ectopic germline stem cell proliferation. Whole genome sequencing identified om40 as an allele of hsp-90, previously known as daf-21, which encodes the C. elegans ortholog of the cytosolic form of HSP90. This protein is a molecular chaperone with a central position in the protein homeostasis network, which is responsible for proper folding, structural maintenance, and degradation of proteins. In addition to its essential role in cellular function, HSP90 plays an important role in stem cell maintenance and renewal. Complementation analysis using a deletion allele of hsp-90 confirmed that ego-3 is the same gene. hsp-90(om40) is an I→N conservative missense mutation of a highly conserved residue in the middle domain of HSP-90 RNA interference-mediated knockdown of hsp-90 expression partially phenocopied hsp-90(om40), confirming the loss-of-function nature of hsp-90(om40) Furthermore, reduced HSP-90 activity enhanced the effect of reduced function of both the GLP-1 receptor and the downstream LAG-1 transcription factor. Taken together, our results provide the first experimental evidence of an essential role for HSP90 in Notch signaling in development.

Using GIS and secondary data to target diabetes-related public health efforts.

OBJECTIVES: To efficiently help communities prevent and manage diabetes, health departments need to be able to target populations with high risk but low resources. To aid in this process, we mapped county-level diabetes-related rates and resources/use using publicly available secondary data to identify Michigan counties with high diabetes prevalence and low or no medical and/or community resources. METHODS: We collected county-level diabetes-related rates and resources from Web-based sources and mapped them using geographic information systems (GIS) software. Data included age-adjusted county diabetes rates, diabetes-related medical resource and resource use (i.e., the number of endocrinologists and percentage of Medicare patients with diabetes who received hemoglobin A1c testing in the past year), community resources (i.e., the number of certified diabetes self-management education and diabetes support groups), as well as population estimates and demographics (e.g., rural residence, education, poverty, and race/ethnicity). We created GIS maps highlighting areas that had higher-than-median rates of disease and lower-than-median resources. We also conducted linear, logistic, and Poisson regression analyses to confirm GIS findings. RESULTS: There were clear regional trends in resource distribution across Michigan. The 15 counties in the Upper Peninsula were lacking in medical resources but higher in community resources compared with the 68 counties in the Lower Peninsula. There was little apparent association between need (diabetes prevalence) and diabetes-related resources/use. Specific counties with high diabetes prevalence and low resources were easily identified using GIS mapping. CONCLUSION: Using public data and mapping tools identified diabetes health-service shortage areas for targeted public health programming.