Long noncoding RNAs are dynamically regulated during ß-cell mass expansion in mouse pregnancy and control ß-cell proliferation in vitro.

Pregnancy is associated with increased ß-cell proliferation driven by prolactin. Long noncoding RNAs (lncRNA) are the most abundant RNA species in the mammalian genome, yet, their functional importance is mainly elusive. AIMS/HYPOTHESIS: This study tests the hypothesis that lncRNAs regulate ß-cell proliferation in response to prolactin in the context of ß-cell mass compensation in pregnancy. METHODS: The expression profile of lncRNAs in mouse islets at day 14.5 of pregnancy was explored by a bioinformatics approach, further confirmed by quantitative PCR at different days of pregnancy, and islet specificity was evaluated by comparing expression in islets versus other tissues. In order to establish the role of the candidate lncRNAs we studied cell proliferation in mouse islets and the MIN6 ß-cell line by EdU incorporation and cell count. RESULTS: We found that a group of lncRNAs is differentially regulated in mouse islets at 14.5 days of pregnancy. At different stages of pregnancy, these lncRNAs are dynamically expressed, and expression is prolactin dependent in mouse islets and MIN6 cells. One of those lncRNAs, Gm16308 (Lnc03), is dynamically regulated during pregnancy, prolactin-dependent and islet-enriched. Silencing Lnc03 in primary ß-cells and MIN6 cells inhibits, whereas over-expression stimulates, proliferation even in the absence of prolactin, demonstrating that Lnc03 regulates ß-cell growth. CONCLUSIONS/INTERPRETATION: During pregnancy mouse islet proliferation is correlated with dynamic changes of lncRNA expression. In particular, Lnc03 regulates mouse ß-cell proliferation and may be a crucial component of ß-cell proliferation in ß-cell mass adaptation in both health and disease.

BioMet Toolbox: genome-wide analysis of metabolism.

The rapid progress of molecular biology tools for directed genetic modifications, accurate quantitative experimental approaches, high-throughput measurements, together with development of genome sequencing has made the foundation for a new area of metabolic engineering that is driven by metabolic models. Systematic analysis of biological processes by means of modelling and simulations has made the identification of metabolic networks and prediction of metabolic capabilities under different conditions possible. For facilitating such systemic analysis, we have developed the BioMet Toolbox, a web-based resource for stoichiometric analysis and for integration of transcriptome and interactome data, thereby exploiting the capabilities of genome-scale metabolic models. The BioMet Toolbox provides an effective user-friendly way to perform linear programming simulations towards maximized or minimized growth rates, substrate uptake rates and metabolic production rates by detecting relevant fluxes, simulate single and double gene deletions or detect metabolites around which major transcriptional changes are concentrated. These tools can be used for high-throughput in silico screening and allows fully standardized simulations. Model files for various model organisms (fungi and bacteria) are included. Overall, the BioMet Toolbox serves as a valuable resource for exploring the capabilities of these metabolic networks. BioMet Toolbox is freely available at www.sysbio.se/BioMet/.