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Steven P. Gygi

Bio: Steven P. Gygi is an academic researcher from Harvard University. The author has contributed to research in topics: Phosphorylation & Proteome. The author has an hindex of 172, co-authored 704 publications receiving 129173 citations. Previous affiliations of Steven P. Gygi include University of Rochester Medical Center & Cell Signaling Technology.


Papers
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Journal ArticleDOI
Abstract: Isobaric tag-based sample multiplexing strategies are extensively used for global protein abundance profiling. However, such analyses are often confounded by ratio compression resulting from the co-isolation, co-fragmentation, and co-quantification of co-eluting peptides, termed "interference." Recent analytical strategies incorporating ion mobility and real-time database searching have helped to alleviate interference, yet further assessment is needed. Here, we present the strain-specific peptide (SSP) interference reference sample, a tandem mass tag (TMT)pro-labeled quality control that leverages the genetic variation in the proteomes of eight phylogenetically divergent mouse strains. Typically, a peptide with a missense mutation has a different mass and retention time than the reference or native peptide. TMT reporter ion signal for the native peptide in strains that encode the mutant peptide suggests interference which can be quantified and assessed using the interference-free index (IFI). We introduce the SSP by investigating interference in three common data acquisition methods and by showcasing improvements in the IFI when using ion mobility-based gas-phase fractionation. In addition, we provide a user-friendly, online viewer to visualize the data and streamline the calculation of the IFI. The SSP will aid in developing and optimizing isobaric tag-based experiments.

7 citations

Journal ArticleDOI
TL;DR: This multifunctional proteolytic system has emerged as a regulator of mammalian gene expression, in part through conditional targeting of TFs that include ATF3, GR, and PREP1, strongly suggesting that GR is a physiological substrate of the Arg/N-degron pathway.
Abstract: The Arg/N-degron pathway targets proteins for degradation by recognizing their N-terminal or internal degrons. Our previous work produced double-knockout (2-KO) HEK293T human cell lines that lacked the functionally overlapping UBR1 and UBR2 E3 ubiquitin ligases of the Arg/N-degron pathway. Here, we studied these cells in conjunction with RNA-sequencing, mass spectrometry (MS), and split-ubiquitin binding assays. 1) Some mRNAs, such as those encoding lactate transporter MCT2 and β-adrenergic receptor ADRB2, are strongly (∼20-fold) up-regulated in 2-KO cells, whereas other mRNAs, including those encoding MAGEA6 (a regulator of ubiquitin ligases) and LCP1 (an actin-binding protein), are completely repressed in 2-KO cells, in contrast to wild-type cells. 2) Glucocorticoid receptor (GR), an immunity-modulating transcription factor (TF), is up-regulated in 2-KO cells and also physically binds to UBR1, strongly suggesting that GR is a physiological substrate of the Arg/N-degron pathway. 3) PREP1, another TF, was also found to bind to UBR1. 4) MS-based analyses identified ∼160 proteins whose levels were increased or decreased by more than 2-fold in 2-KO cells. For example, the homeodomain TF DACH1 and the neurofilament subunits NF-L (NFEL) and NF-M (NFEM) were expressed in wild-type cells but were virtually absent in 2-KO cells. 5) The disappearance of some proteins in 2-KO cells took place despite up-regulation of their mRNAs, strongly suggesting that the Arg/N-degron pathway can also modulate translation of specific mRNAs. In sum, this multifunctional proteolytic system has emerged as a regulator of mammalian gene expression, in part through conditional targeting of TFs that include ATF3, GR, and PREP1.

6 citations

Posted ContentDOI
04 Jun 2017-bioRxiv
TL;DR: A quantitative view of the protein and posttranslational economy of the fertilization response in the frog egg is presented and new, broadly applicable analytical methods for phosphorylation are developed that provide absolute quantification with confidence intervals for improved interpretability of post- translational modification analysis.
Abstract: Fertilization triggers release from meiotic arrest and initiates events that prepare for the ensuing developmental program Protein degradation and phosphorylation are known to regulate protein activity during this process However, the full extent of protein loss and phospho-regulation is still unknown We examined absolute protein and phospho-site dynamics after fertilization by mass spectrometry-based proteomics To do this, we developed a new approach for calculating the stoichiometry of phospho-sites from multiplexed proteomics that is compatible with dynamic, stable and multi-site phosphorylation Overall, the data suggest that degradation is limited to a few low abundance proteins However, this degradation promotes extensive dephosphorylation that occurs over a wide range of abundances during meiotic exit We also show that eggs release a large amount of protein into the medium just after fertilization, most likely related to the blocks to polyspermy Concomitantly, there is a substantial increase in phosphorylation likely tied to calcium activated kinases We identify putative degradation targets as well as new components of the block to polyspermy The analytical approaches demonstrated here are broadly applicable to studies of dynamic biological systems

6 citations

Posted ContentDOI
29 Jul 2020-bioRxiv
TL;DR: This approach correctly identifies the three factors known to undergo Hedgehog-regulated ciliary redistribution and reveals two such additional proteins, which promise a rapid characterization of ciliopathies and their underlying signaling malfunctions.
Abstract: Author(s): May, Elena; Kalocsay, Marian; D’Auriac, Ines Galtier; Gygi, Steven; Nachury, Maxence; Mick, David | Abstract: ABSTRACT The primary cilium is a signaling compartment that interprets Hedgehog signals through changes of its protein, lipid and second messenger compositions. Here, we combine proximity labeling of cilia with quantitative mass spectrometry to unbiasedly profile the time-dependent alterations of the ciliary proteome in response to Hedgehog. This approach correctly identifies the three factors known to undergo Hedgehog-regulated ciliary redistribution and reveals two such additional proteins. First, we find that a regulatory subunit of the cAMP-dependent protein kinase (PKA) rapidly exits cilia together with the G protein-coupled receptor GPR161 in response to Hedgehog; and we propose that the GPR161/PKA module senses and amplifies cAMP signals to modulate ciliary PKA activity. Second, we identify the putative phosphatase Paladin as a cell type-specific regulator of Hedgehog signaling that enters primary cilia upon pathway activation. The broad applicability of quantitative ciliary proteome profiling promises a rapid characterization of ciliopathies and their underlying signaling malfunctions.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

32,980 citations

28 Jul 2005
TL;DR: PfPMP1)与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作�ly.
Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1(PfPMP1)与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用,在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员,通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

18,940 citations

Journal ArticleDOI
23 Jan 2009-Cell
TL;DR: The current understanding of miRNA target recognition in animals is outlined and the widespread impact of miRNAs on both the expression and evolution of protein-coding genes is discussed.

18,036 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency.
Abstract: Background: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments. Content: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement. Summary: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of qPCR results.

12,469 citations