Aging affects isoproterenol-induced water drinking, astrocyte density, and central neuronal activation in female Brown Norway rats.

Age-dependent impairments in the central control of compensatory responses to body fluid challenges have received scant experimental attention, especially in females. In the present study, we found that water drinking in response to ß-adrenergic activation with isoproterenol (30 µg/kg, s.c.) was reduced by more than half in aged (25 mo) vs. young (5 mo) ovariectomized female Brown Norway rats. To determine whether this age-related decrease in water intake was accompanied by changes in central nervous system areas associated with fluid balance, we assessed astrocyte density and neuronal activation in the SFO, OVLT, SON, AP and NTS of these rats using immunohistochemical labeling for GFAP and c-fos, respectively. GFAP labeling intensity was increased in the SFO, AP, and NTS of aged females independent of treatment, and was increased in the OVLT of isoproterenol-treated rats independent of age. Fos immunolabeling in response to isoproterenol was reduced in both the SFO and the OVLT of aged females compared to young females, but was increased in the SON of female rats of both ages. Finally, fos labeling in the AP and caudal NTS of aged rats was elevated after vehicle control treatment and did not increase in response to isoproterenol as it did in young females. Thus, age-related declines in water drinking are accompanied by site-specific, age-related changes in astrocyte density and neuronal activation. We suggest that astrocyte density may alter the detection and/or processing of signals related to isoproterenol treatment, and thereby alter neuronal activation in areas associated with fluid balance.

Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts.

BACKGROUND: The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs. RESULTS: For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms. CONCLUSIONS: LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin .