New findings from the laboratory of Pere Puigserver [Cell Metab. 2012 Apr 4;15(4):505-17] explain how Yin Yang 1 deficiency in skeletal muscle protects against rapamycin-induced diabetic-like symptoms through activation of insulin/igf signaling

Chronic inhibition of mTOR by rapamycin led to insulin resistance and lipid dysregulation associated with defective insulin/IGF signaling and suppression of key genes of this pathway, such as Igf1-2, Irs1-2, and Akt1-3. Skeletal muscle-specific YY1 knockout (YY1mKO) mice were protected against the rapamycin-induced diabetic-like effects including insulin resistance, dyslipidemia, and suppression of insulin signaling. YY1mKO mice exhibited hyper-activation of insulin/IGF signaling and increased expression of the genes of this pathway. In contrast to wild-type mice, rapamycin did not suppress insulin/IGF signaling genes in YY1mKO mice. Upon rapamycin treatment YY1 functioned as a transcriptional repressor of insulin signaling genes through interaction with the polycomb protein Pc2 and recruitment at the promoters of these genes, which was associated with H3K27 trimethylation. Moreover, mTOR regulated the interaction between YY1 and Pc2 through YY1 phosphorylation at T30 and S365. In summary, we show that YY1 mediates suppression of insulin signaling genes through polycomb proteins and epigenetic changes in response to mTOR inhibition, and this accounts, at least in part, for the prodiabetic effects of rapamycin in vivo.

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From multiplexing to hyperplexing – increasing sample throughput in quantitative proteomics.

Mass spectrometry has emerged as a powerful tool for monitoring changes in protein abundance and posttranslational modifications, but widespread access to quantitative mass spectrometry is limited by a bottleneck in sample analysis. Recent work in the Gygi lab to develop and refine multiplexed quantitative methods promises to relieve this block and allow more complex experimentation. In work recently published in Science Signaling, postdoctoral fellow Noah Dephoure demonstrated a method called “Hyperplexing” that uses a combination of metabolic and chemical labels to increase the multiplexing capacity of large-scale quantitative proteomics. Using this method he monitored changes in protein abundance for thousands of proteins in the budding yeast after treatment with the immunosuppressant drug rapamycin. Hyperplexing allowed this experiment to be performed with biological triplicates, monitoring all 18 samples, six time-points from each of three biological replicates, simultaneously. The replicate analysis enabled by hyperplexing provided the statistical power to confidently assign significant changes. This technique should facilitate the analysis of dynamic cellular events via quantitative mass spectrometry.
 
reference: Hyperplexing: a method for higher-order multiplexed quantitative proteomics provides a map of the dynamic response to rapamycin in yeast. Dephoure N, Gygi SP. Sci Signal. 2012 Mar 27;5(217):rs2. PMID: 22457332

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A novel method identifies prominent off-targeted transcripts in RNAi screens

The RNA interference (RNAi) pathway is routinely exploited as a tool to discover novel gene/phenotype associations in high-throughput screens. A potential limitation of the approach, however, is that siRNAs can non-specifically down regulate the expression of other genes through microRNA-like effects. The extent to which such off-target effects can affect RNAi screen datasets is not clear at the moment. In a study by Frederic Sigoillot and colleagues in the King lab [A bioinformatics method identifies prominent off-targeted transcripts in RNAi screens. [Sigoillot FD, Lyman S, Huckins JF, Adamson B, Chung E, Quattrochi B, King RW. Nat Methods. 2012 Feb 19.] it was found that the problem can be pervasive. The reported bioinformatics method, "Genome-wide enrichment for seed sequence matches" (GESS), identifies transcripts that are sensitive to off-target effects in multiple RNAi screen datasets. The information provided by the method can be directly exploited to enrich candidate gene lists for true positive gene/phenotype association while excluding some false positive candidates as it was found in analyzing results from the co-published study by Adamson et al. [A genome-wide homologous recombination screen identifies the RNA-binding protein RBMX as a component of the DNA-damage response. Adamson B, Smogorzewska A, Sigoillot FD, King RW, Elledge SJ. Nat Cell Biol. 2012 Feb 19.]. The authors note that the off-target effects detected by GESS may only be the tip of the iceberg.

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