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.

TMTpro reagents: a set of isobaric labeling mass tags enables simultaneous proteome-wide measurements across 16 samples.

Abstract: Isobaric labeling empowers proteome-wide expression measurements simultaneously across multiple samples. Here an expanded set of 16 isobaric reagents based on an isobutyl-proline immonium ion reporter structure (TMTpro) is presented. These reagents have similar characteristics to existing tandem mass tag reagents but with increased fragmentation efficiency and signal. In a proteome-scale example dataset, we compared eight common cell lines with and without Torin1 treatment with three replicates, quantifying more than 8,800 proteins (mean of 7.5 peptides per protein) per replicate with an analysis time of only 1.1 h per proteome. Finally, we modified the thermal stability assay to examine proteome-wide melting shifts after treatment with DMSO, 1 or 20 µM staurosporine with five replicates. This assay identified and dose-stratified staurosporine binding to 228 cellular kinases in just one, 18-h experiment. TMTpro reagents allow complex experimental designs—all with essentially no missing values across the 16 samples and no loss in quantitative integrity. A set of isobaric labeling reagents called TMTpro enables deep quantitative comparisons of proteome measurements across 16 samples.

An ERK/Cdk5 axis controls the diabetogenic actions of PPARγ

Abstract: Blocking ERK/MAP kinases improves insulin sensitivity thorough a mechanism similar to the actions of the anti-diabetic thiazolidinediones drugs on PPARγ. The nuclear receptor PPARγ is required for glucose metabolism and is a drug target in diabetes. Earlier work has suggested that PPARγ phosphorylation has an adverse effect on insulin sensitivity and that the kinase Cdk5 is the main kinase responsible for phosphorylation at this site. Now Bruce Spiegelman and colleagues report the paradoxical observation that insulin resistance is worsened in mice lacking Cdk5 in adipose tissue. The authors further show that Cdk5 suppresses the MEK/ERK signalling pathway that is central to the regulation of cell growth and proliferation, and that ERK directly phosphorylates PPARγ. MEK/ERK inhibitors can improve insulin resistance in obese wild-type and ob/ob mice. This work suggests that MEK/ERK inhibitors — already approved for cancer treatment — might also be effective against diabetes. Obesity-linked insulin resistance is a major precursor to the development of type 2 diabetes. Previous work has shown that phosphorylation of PPARγ (peroxisome proliferator-activated receptor γ) at serine 273 by cyclin-dependent kinase 5 (Cdk5) stimulates diabetogenic gene expression in adipose tissues1. Inhibition of this modification is a key therapeutic mechanism for anti-diabetic drugs that bind PPARγ, such as the thiazolidinediones and PPARγ partial agonists or non-agonists2. For a better understanding of the importance of this obesity-linked PPARγ phosphorylation, we created mice that ablated Cdk5 specifically in adipose tissues. These mice have both a paradoxical increase in PPARγ phosphorylation at serine 273 and worsened insulin resistance. Unbiased proteomic studies show that extracellular signal-regulated kinase (ERK) kinases are activated in these knockout animals. Here we show that ERK directly phosphorylates serine 273 of PPARγ in a robust manner and that Cdk5 suppresses ERKs through direct action on a novel site in MAP kinase/ERK kinase (MEK). Importantly, pharmacological inhibition of MEK and ERK markedly improves insulin resistance in both obese wild-type and ob/ob mice, and also completely reverses the deleterious effects of the Cdk5 ablation. These data show that an ERK/Cdk5 axis controls PPARγ function and suggest that MEK/ERK inhibitors may hold promise for the treatment of type 2 diabetes.

The metabolic function of cyclin D3–CDK6 kinase in cancer cell survival

Abstract: D-type cyclins (D1, D2 and D3) and their associated cyclin-dependent kinases (CDK4 and CDK6) are components of the core cell cycle machinery that drives cell proliferation. Inhibitors of CDK4 and CDK6 are currently being tested in clinical trials for patients with several cancer types, with promising results. Here, using human cancer cells and patient-derived xenografts in mice, we show that the cyclin D3-CDK6 kinase phosphorylates and inhibits the catalytic activity of two key enzymes in the glycolytic pathway, 6-phosphofructokinase and pyruvate kinase M2. This re-directs the glycolytic intermediates into the pentose phosphate (PPP) and serine pathways. Inhibition of cyclin D3-CDK6 in tumour cells reduces flow through the PPP and serine pathways, thereby depleting the antioxidants NADPH and glutathione. This, in turn, increases the levels of reactive oxygen species and causes apoptosis of tumour cells. The pro-survival function of cyclin D-associated kinase operates in tumours expressing high levels of cyclin D3-CDK6 complexes. We propose that measuring the levels of cyclin D3-CDK6 in human cancers might help to identify tumour subsets that undergo cell death and tumour regression upon inhibition of CDK4 and CDK6. Cyclin D3-CDK6, through its ability to link cell cycle and cell metabolism, represents a particularly powerful oncoprotein that affects cancer cells at several levels, and this property can be exploited for anti-cancer therapy.

Role for perinuclear chromosome tethering in maintenance of genome stability

Abstract: Repetitive DNA sequences, which constitute half the genome in some organisms, often undergo homologous recombination. This can instigate genomic instability resulting from a gain or loss of DNA. Assembly of DNA into silent chromatin is generally thought to serve as a mechanism ensuring repeat stability by limiting access to the recombination machinery. Consistent with this notion is the observation, in the budding yeast Saccharomyces cerevisiae, that stability of the highly repetitive ribosomal DNA (rDNA) sequences requires a Sir2-containing chromatin silencing complex that also inhibits transcription from foreign promoters and transposons inserted within the repeats by a process called rDNA silencing. Here we describe a protein network that stabilizes rDNA repeats of budding yeast by means of interactions between rDNA-associated silencing proteins and two proteins of the inner nuclear membrane (INM). Deletion of either the INM or silencing proteins reduces perinuclear rDNA positioning, disrupts the nucleolus-nucleoplasm boundary, induces the formation of recombination foci, and destabilizes the repeats. In addition, artificial targeting of rDNA repeats to the INM suppresses the instability observed in cells lacking an rDNA-associated silencing protein that is typically required for peripheral tethering of the repeats. Moreover, in contrast to Sir2 and its associated nucleolar factors, the INM proteins are not required for rDNA silencing, indicating that Sir2-dependent silencing is not sufficient to inhibit recombination within the rDNA locus. These findings demonstrate a role for INM proteins in the perinuclear localization of chromosomes and show that tethering to the nuclear periphery is required for the stability of rDNA repeats. The INM proteins studied here are conserved and have been implicated in chromosome organization in metazoans. Our results therefore reveal an ancient mechanism in which interactions between INM proteins and chromosomal proteins ensure genome stability.

Mapping and analysis of phosphorylation sites: a quick guide for cell biologists

Abstract: A mechanistic understanding of signaling networks requires identification and analysis of phosphorylation sites. Mass spectrometry offers a rapid and highly sensitive approach to mapping phosphorylation sites. However, mass spectrometry has significant limitations that must be considered when planning to carry out phosphorylation-site mapping. Here we provide an overview of key information that should be taken into consideration before beginning phosphorylation-site analysis, as well as a step-by-step guide for carrying out successful experiments.

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

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.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

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。

Machine learning

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.).

The MIQE Guidelines: Minimum Information for Publication of Quantitative Real-Time PCR Experiments

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.