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Sepsis still remains a major cause for morbidity and mortality in patients. The molecular mechanisms underlying the disease are still enigmatic. A great number of therapeutic approaches have failed and treatment strategies are limited to date. Among those few admitted for clinical intervention, intensive insulin treatment has proven to be effective in the reduction of disease related complications in critically ill patients. Insulin effectively reduces glucose levels and thereby contributes to protection. On the other hand insulin is a potent signaling pathway activator. One of those is the PI3K signaling axis. Activation of PI3K is known to limit pro-inflammatory gene expression. Here we can show that in a mouse model of insulin hypersensitivity induced by the deletion of the PI3K antagonist PTEN, specifically in hepatic tissue, significant protection is conferred in murine models of lethal endotoxemia and sepsis. Acute inflammatory responses are diminished, glucose metabolism normalized and vascular activation is reduced. Furthermore we investigated the hepatic gene expression profile of relevant anti-inflammatory genes in PTEN deficient mice and found marked upregulation of PPARγ and HO-1. We conclude from our data that insulin hypersensitivity via sustained activation of the PI3K signaling pathway exerts protective effects in acute inflammatory processes.

A CpG island (CGI) lies at the 5' end of the Airn macro non-protein-coding (nc) RNA that represses the flanking Igf2r promoter in cis on paternally inherited chromosomes. In addition to being modified on maternally inherited chromosomes by a DNA methylation imprint, the Airn CGI shows two unusual organization features: its position immediately downstream of the Airn promoter and transcription start site and a series of tandem direct repeats (TDRs) occupying its second half. The physical separation of the Airn promoter from the CGI provides a model to investigate if the CGI plays distinct transcriptional and epigenetic roles. We used homologous recombination to generate embryonic stem cells carrying deletions at the endogenous locus of the entire CGI or just the TDRs. The deleted Airn alleles were analyzed by using an ES cell imprinting model that recapitulates the onset of Igf2r imprinted expression in embryonic development or by using knock-out mice. The results show that the CGI is required for efficient Airn initiation and to maintain the unmethylated state of the Airn promoter, which are both necessary for Igf2r repression on the paternal chromosome. The TDRs occupying the second half of the CGI play a minor role in Airn transcriptional elongation or processivity, but are essential for methylation on the maternal Airn promoter that is necessary for Igf2r to be expressed from this chromosome. Together the data indicate the existence of a class of regulatory CGIs in the mammalian genome that act downstream of the promoter and transcription start.

Imprinted macro non-protein-coding (nc) RNAs are cis-repressor transcripts that silence multiple genes in at least three imprinted gene clusters in the mouse genome. Similar macro or long ncRNAs are abundant in the mammalian genome. Here we present the full coding and non-coding transcriptome of two mouse tissues: differentiated ES cells and fetal head using an optimized RNA-Seq strategy. The data produced is highly reproducible in different sequencing locations and is able to detect the full length of imprinted macro ncRNAs such as Airn and Kcnq1ot1, whose length ranges between 80-118 kb. Transcripts show a more uniform read coverage when RNA is fragmented with RNA hydrolysis compared with cDNA fragmentation by shearing. Irrespective of the fragmentation method, all coding and non-coding transcripts longer than 8 kb show a gradual loss of sequencing tags towards the 3' end. Comparisons to published RNA-Seq datasets show that the strategy presented here is more efficient in detecting known functional imprinted macro ncRNAs and also indicate that standardization of RNA preparation protocols would increase the comparability of the transcriptome between different RNA-Seq datasets.