MicroRNAs are endogenous ∼23-nucleotide RNAs that can pair to sites in the messenger RNAs of protein-coding genes to downregulate the expression from these messages. MicroRNAs are known to influence the evolution and stability of many mRNAs, but their global impact on protein output had not been examined. Here we use quantitative mass spectrometry to measure the response of thousands of proteins after introducing microRNAs into cultured cells and after deleting mir-223 in mouse neutrophils. The identities of the responsive proteins indicate that targeting is primarily through seed-matched sites located within favourable predicted contexts in 3′ untranslated regions. Hundreds of genes were directly repressed, albeit each to a modest degree, by individual microRNAs. Although some targets were repressed without detectable changes in mRNA levels, those translationally repressed by more than a third also displayed detectable mRNA destabilization, and, for the more highly repressed targets, mRNA destabilization usually comprised the major component of repression. The impact of microRNAs on the proteome indicated that for most interactions microRNAs act as rheostats to make fine-scale adjustments to protein output.Large-scale approaches for studying the regulatory effects of microRNAs (miRNAs) have revealed important insights into target recognition and function. These approaches include computational analysis of the selective maintenance or avoidance of miRNA complementary sites during evolution [1][2][3][4][5][6][7][8] and experimental identification of messages destabilized or those preferentially associated with argonaute proteins in the presence of a miRNA [7][8][9][10][11][12][13][14][15] . Despite their utility, none of these approaches directly measures the influence of a miRNA on protein output, which is the most relevant readout of its regulatory effects. The influence of miRNAs on protein output has instead been limited to single-protein analyses, primarily immunoblotting and reporter assays, and a medium-size proteomics analysis with detection of 504 proteins 16 . ©2008 Macmillan Publishers Limited. All rights reservedCorrespondence and requests for materials should be addressed to S.P.G. (steven_gygi@hms.harvard.edu) or D.P.B (dbartel@wi.mit.edu).. * These authors contributed equally to this work. Author Contributions The order of listing of the first three authors is arbitrary. C.S. and F.C. performed the experimental work with cells and animals. J.V. performed the mass spectrometry and associated computational analysis. D.B. performed the computational analysis of targeting. All authors contributed to the design of the study and preparation of the manuscript. Author Information Array data and small RNA sequencing data were deposited in the Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/) under accession number GSE12075. NIH Public Access Proteomic consequences of added miRNAsTo acquire data sufficient to investigate the effects of miRNA regulation on the proteome, we applied a quantitative-ma...

SUMMARY Although most tissues in an organism are genetically identical, the biochemistry of each is optimized to fulfill its unique physiological roles, with important consequences for human health and disease. Each tissue’s unique physiology requires tightly regulated gene and protein expression coordinated by specialized, phosphorylation-dependent intracellular signaling. To better understand the role of phosphorylation in maintenance of physiological differences among tissues, we performed proteomic and phosphoproteomic characterizations of nine mouse tissues. We identified 12,039 proteins, including 6296 phosphoproteins harboring nearly 36,000 phosphorylation sites. Comparing protein abundances and phosphorylation levels revealed specialized, interconnected phosphorylation networks within each tissue while suggesting that many proteins are regulated by phosphorylation independently of their expression. Our data suggest that the ‘typical’ phosphoprotein is widely expressed, yet displays variable, often tissue-specific phosphorylation that tunes protein activity to the specific needs of each tissue. We offer this dataset as an online resource for the biological research community.

Despite the success of tyrosine kinase-based cancer therapeutics, for most solid tumors the tyrosine kinases that drive disease remain unknown, limiting our ability to identify drug targets and predict response. Here we present the first large-scale survey of tyrosine kinase activity in lung cancer. Using a phosphoproteomic approach, we characterize tyrosine kinase signaling across 41 non-small cell lung cancer (NSCLC) cell lines and over 150 NSCLC tumors. Profiles of phosphotyrosine signaling are generated and analyzed to identify known oncogenic kinases such as EGFR and c-Met as well as novel ALK and ROS fusion proteins. Other activated tyrosine kinases such as PDGFRalpha and DDR1 not previously implicated in the genesis of NSCLC are also identified. By focusing on activated cell circuitry, the approach outlined here provides insight into cancer biology not available at the chromosomal and transcriptional levels and can be applied broadly across all human cancers.

Data analysis and interpretation remain major logistical challenges when attempting to identify large numbers of protein phosphorylation sites by nanoscale reverse-phase liquid chromatography/tandem mass spectrometry (LC-MS/MS) (Supplementary Figure 1 online). In this report we address challenges that are often only addressable by laborious manual validation, including data set error, data set sensitivity and phosphorylation site localization. We provide a large-scale phosphorylation data set with a measured error rate as determined by the target-decoy approach, we demonstrate an approach to maximize data set sensitivity by efficiently distracting incorrect peptide spectral matches (PSMs), and we present a probability-based score, the Ascore, that measures the probability of correct phosphorylation site localization based on the presence and intensity of site-determining ions in MS/MS spectra. We applied our methods in a fully automated fashion to nocodazole-arrested HeLa cell lysate where we identified 1,761 nonredundant phosphorylation sites from 491 proteins with a peptide false-positive rate of 1.3%.