mProphet: automated data processing and statistical validation for large-scale SRM experiments

Reiter, Lukas; Rinner, Oliver; Picotti, Paola; Hüttenhain, Ruth; Beck, Martin; Brusniak, Mi-Youn; Hengartner, Michael O.; Aebersold, Ruedi

doi:10.1038/nmeth.1584

Cited by 491 publications

(544 citation statements)

References 37 publications

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“…SRM assay development and implementation of H-PINS technology was performed essentially as described previously (12,28) with exceptions described in SI Text. Decoy peptides were generated using the mProphet algorithm (42).…”

Section: Methodsmentioning

confidence: 99%

Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins

Mirzaei

Knijnenburg

Kim

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Regulation of gene expression involves the orchestrated interaction of a large number of proteins with transcriptional regulatory elements in the context of chromatin. Our understanding of gene regulation is limited by the lack of a protein measurement technology that can systematically detect and quantify the ensemble of proteins associated with the transcriptional regulatory elements of specific genes. Here, we introduce a set of selected reaction monitoring (SRM) assays for the systematic measurement of 464 proteins with known or suspected roles in transcriptional regulation at RNA polymerase II transcribed promoters in Saccharomyces cerevisiae. Measurement of these proteins in nuclear extracts by SRM permitted the reproducible quantification of 42% of the proteins over a wide range of abundances. By deploying the assay to systematically identify DNA binding transcriptional regulators that interact with the environmentally regulated FLO11 promoter in cell extracts, we identified 15 regulators that bound specifically to distinct regions along ∼600 bp of the regulatory sequence. Importantly, the dataset includes a number of regulators that have been shown to either control FLO11 expression or localize to these regulatory regions in vivo. We further validated the utility of the approach by demonstrating that two of the SRM-identified factors, Mot3 and Azf1, are required for proper FLO11 expression. These results demonstrate the utility of SRM-based targeted proteomics to guide the identification of gene-specific transcriptional regulators. C ritical to understanding gene regulation is the ability to determine the composition of the regulatory complexes that assemble at specific genes and to determine how the composition of these complexes change in response to cellular, genetic, and environmental signals. Despite considerable efforts to address these key questions, the lack of methods for routine analysis of the ensemble of transcription factors (TFs) associated with specific transcriptional regulatory elements (TREs) remains a significant limitation.Current approaches for studying TF-TRE interactions include the EMSA (1, 2), protein binding microarrays (PBMs) (3), the yeast one-hybrid method (4), and chromatin immunoprecipitation (ChIP)-based methods (5, 6). Although each of these methods can provide information about TF-DNA interactions, their utility for routine analysis of TF-DNA interactions and complexes assembled at TREs is limited by the need for genetic engineering, protein detection reagents, and/or purified proteins. Notably, ChIP-chip has been used to systematically study the localization of most transcriptional regulators (TRs) across the yeast genome (7), but this tour de force required the creation and independent assay of 203 TR-specific, epitope-tagged strains.Another approach for studying TF-TRE interactions that does not require genetic engineering or protein detection reagents, and can readily provide information about the ensemble of TFs associated with a TRE, is DNA affinity purification fo...

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Section: Methodsmentioning

confidence: 99%

Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins

Mirzaei

Knijnenburg

Kim

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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“…In a typical S/MRM experiment, the signal of three to six transitions per targeted MHC peptide ligand is recorded over the chromatographic elution profile of the targeted peptide. This type of targeted data acquisition results in a peak group (coeluting fragment ion traces) that is subsequently analyzed using software tools such as Skyline (89), mProphet, and mQuest (90,91). Importantly, selection of optimal transitions has to be carried out before an S/MRM experiment, typically involving the use of synthetic peptides, that optionally are heavy isotope labeled (92)(93)(94).…”

Section: Identification and Quantification Of Mhc-associated Peptidesmentioning

confidence: 99%

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry*

Caron

Kowalewski

Koh

et al. 2015

Molecular & Cellular Proteomics

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118

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The myriad of peptides presented at the cell surface by class I and class II major histocompatibility complex (MHC) molecules are referred to as the immunopeptidome and are of great importance for basic and translational science. For basic science, the immunopeptidome is a critical component for understanding the immune system; for translational science, exact knowledge of the immunopeptidome can directly fuel and guide the development of next-generation vaccines and immunotherapies against autoimmunity, infectious diseases, and cancers. In this mini-review, we summarize established isolation techniques as well as emerging mass spectrometry-based platforms (i.e. SWATH-MS) to identify and quantify MHC-associated peptides. We also highlight selected biological applications and discuss important current technical limitations that need to be solved to accelerate the development of this field. Molecular & Cellular

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“…To control correct peak group selection, we added one synthetic decoy transition for each target transition (calculated by the random mass shift method). Peak group selection and signal integration were done in Skyline after fitting an mProphet model (17) to the acquired data. False-negative peak groups were inferred using the data acquired for decoy transitions.…”

Section: Protein Extraction and Quantification By Label-free Selectedmentioning

confidence: 99%

Autocrine and Paracrine Regulation of Keratinocyte Proliferation through a Novel Nrf2–IL-36γ Pathway

Kurinna

Muzumdar

Köhler

et al. 2016

The Journal of Immunology

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The Nrf2 transcription factor is well known for its cytoprotective functions through regulation of genes involved in the detoxification of reactive oxygen species or toxic compounds. Therefore, activation of Nrf2 is a promising strategy for the protection of tissues from various types of insults and for cancer prevention. However, recent studies revealed a proinflammatory activity of activated Nrf2 and a stimulating effect on epithelial cell proliferation, but the underlying mechanisms of action and the responsible target genes are largely unknown. Using a combination of gene expression profiling, chromatin immunoprecipitation, and targeted proteomics via selected reaction monitoring, we show that the gene encoding the proinflammatory cytokine IL-36γ is a novel direct target of Nrf2 in keratinocytes and hepatocytes in vitro and in vivo. As a consequence, upregulation of IL-36γ expression occurred upon genetic or pharmacological activation of Nrf2 in the epidermis and in the normal and regenerating liver. Functional in vitro studies demonstrate that IL-36γ directly stimulates proliferation of keratinocytes. In particular, it induces expression of keratinocyte mitogens in fibroblasts, suggesting that the Nrf2–IL-36γ axis promotes keratinocyte proliferation through a double paracrine loop. These results provide mechanistic insight into Nrf2 action in the control of inflammation and cell proliferation through regulation of a proinflammatory cytokine with a key function in various inflammatory diseases.

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mProphet: automated data processing and statistical validation for large-scale SRM experiments

Cited by 491 publications

References 37 publications

Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins

Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry*

Autocrine and Paracrine Regulation of Keratinocyte Proliferation through a Novel Nrf2–IL-36γ Pathway

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