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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
TL;DR: This study underscores the complex cellular roles of Bmh1 and BmH2 coupled with response to rapamycin treatment and emphasizes the utility of multiplexed proteomic techniques to elucidate comprehensive proteomes and phosphoproteomes.
Abstract: We applied a multiplexed, mass spectrometry-based strategy to interrogate the proteome and phosphoproteome of three yeast strains under two growth conditions. The yeast proteins Bmh1 and Bmh2, analogs to the 14-3-3 protein family, have a wide-array of cellular functions including the regulation of phosphorylation events. Similarly, rapamycin is a drug that can regulate phosphorylation events. By performing a series of TMT10-plex experiments, we investigated the alterations in the proteome and phosphoproteome of wildtype and two deletion strains (bmh1Δ and bmh2Δ) of S. cerevisiae treated with rapamycin and DMSO as a control. Our 3×3+1 strategy allowed for triplicate analysis of each of the three strains, plus an additional sample consisting of an equal mix of all samples. We quantified over 4000 proteins and 20,000 phosphorylation events. Of these, we quantified over 3700 proteins across all 20 samples and over 14,300 phosphorylation events within each drug treatment. In total, data collected from four TMT10-plex experiments required approximately one week of data collection on the mass spectrometer. This study underscores the complex cellular roles of Bmh1 and Bmh2 coupled with response to rapamycin treatment and emphasizes the utility of multiplexed proteomic techniques to elucidate comprehensive proteomes and phosphoproteomes.

44 citations

Journal ArticleDOI
TL;DR: This study shows that the processes of export and polyadenylation are coupled via the Drosophila melanogaster CCCH-type zinc finger protein CG6694/dZC3H3 through both physical and functional interactions, and suggests a model wherein ZC 3H3 interfaces between the polyadenyation machinery, newly poly(A) mRNAs, and factors for transcript export.
Abstract: Coupling of messenger RNA (mRNA) nuclear export with prior processing steps aids in the fidelity and efficiency of mRNA transport to the cytoplasm. In this study, we show that the processes of export and polyadenylation are coupled via the Drosophila melanogaster CCCH-type zinc finger protein CG6694/dZC3H3 through both physical and functional interactions. We show that depletion of dZC3H3 from S2R+ cells results in transcript hyperadenylation. Using targeted coimmunoprecipitation and liquid chromatography mass spectrometry (MS)/MS techniques, we characterize interactions of known components of the mRNA nuclear export and polyadenylation machineries with dZC3H3. Furthermore, we demonstrate the functional conservation of this factor, as depletion of its human homologue ZC3H3 by small interfering RNA results in an mRNA export defect in human cells as well. Nuclear polyadenylated (poly(A)) RNA in ZC3H3-depleted cells is sequestered in foci removed from SC35-containing speckles, indicating a shift from the normal subnuclear distribution of poly(A) RNA. Our data suggest a model wherein ZC3H3 interfaces between the polyadenylation machinery, newly poly(A) mRNAs, and factors for transcript export.

44 citations

Journal ArticleDOI
TL;DR: Differences in the complementarity of two methods for phosphopeptide enrichment based on either titanium dioxide (TiO2) enrichment or phosphorylation motif-specific immunoaffinity precipitation (IAP) with four different antibodies suggest their collective importance in obtaining a comprehensive view of the phosphoproteome.
Abstract: A comprehensive view of protein phosphorylation remains an unmet challenge in the field of cell biology. Mass spectrometry-based proteomics is one of the most promising approaches for identifying thousands of phosphorylation events in a single experiment, yet the full breadth of the phosphoproteome has yet to be elucidated. In this article, we examined the complementarity of two methods for phosphopeptide enrichment based on either titanium dioxide (TiO2) enrichment or phosphorylation motif-specific immunoaffinity precipitation (IAP) with four different antibodies. Each method identified nearly 2000 phosphoproteins. However, distinct populations of phosphopeptides were observed. Despite quantifying over 10 000 unique phosphorylation events using TiO2 and over 3900 with IAP, less than 5% of the sites were in common. Agreeing with published literature, the ratio of pS:pT:pY phosphorylation for the TiO2-enriched data set approximated 90:10:<1. In contrast, that ratio for the combined IAP data sets was 51:29:...

44 citations

Journal ArticleDOI
TL;DR: It is concluded that false transfers by MBR are plentiful, but few are retained in the final dataset after applying the MaxQuant LFQ algorithm.
Abstract: Stochasticity between independent LC-MS/MS runs is a challenging problem in the field of proteomics, resulting in significant missing values (i.e., abundance measurements) among observed peptides. To address this issue, several approaches have been developed including computational methods such as MaxQuant's match-between-runs (MBR) algorithm. Often dozens of runs are all considered at once by MBR, transferring identifications from any one run to any of the others. To evaluate the error associated with these transfer events, we created a two-sample/two-proteome approach. In this way, samples containing no yeast lysate (n = 20) were assessed for false identification transfers from samples containing yeast (n = 20). While MBR increased the total number of spectral identifications by ∼40%, we also found that 44% of all identified yeast proteins had identifications transferred to at least one sample without yeast. However, of these only 2.7% remained in the final data set after applying the MaxQuant LFQ algorithm. We conclude that false transfers by MBR are plentiful, but few are retained in the final data set.

44 citations

Journal ArticleDOI
TL;DR: An image-based computational method is developed to quantify the size, number, and density of PBs in cells, and it is found that while 4E-T is required for steady-state PB assembly, its phosphorylation facilitates the formation of larger PBs upon oxidative stress.
Abstract: Processing bodies (PBs, or P bodies) are cytoplasmic granules involved in mRNA storage and degradation that participate in the regulation of gene expression. PBs concentrate nontranslated mRNAs and several factors involved in mRNA decay and translational repression, including the eukaryotic translation initiation factor 4E-transporter (4E-T). 4E-T is required for PB assembly, but little is known about the molecular mechanisms that regulate its function. Here, we demonstrate that oxidative stress promotes multisite 4E-T phosphorylation. We show that the c-Jun N-terminal kinase (JNK) is targeted to PBs in response to oxidative stress and promotes the phosphorylation of 4E-T. Quantitative mass spectrometry analysis reveals that JNK phosphorylates 4E-T on six proline-directed sites that are required for the formation of the 4E-T complex upon stress. We have developed an image-based computational method to quantify the size, number, and density of PBs in cells, and we find that while 4E-T is required for steady-state PB assembly, its phosphorylation facilitates the formation of larger PBs upon oxidative stress. Using polysomal mRNA profiling, we assessed global and specific mRNA translation but did not find that 4E-T phosphorylation impacts translational control. Collectively, these data support a model whereby PB assembly is regulated by a two-step mechanism involving a 4E-T-dependent assembly stage in unstressed cells and a 4E-T phosphorylation-dependent aggregation stage in response to stress stimuli.

43 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