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.

Phosphoproteome Analysis of Fission Yeast

Abstract: Phosphorylation is a key regulator of many events in eukaryotic cells The acquisition of large-scale phosphorylation data sets from model organisms can pinpoint conserved regulatory inputs and reveal kinase-substrate relationships Here, we provide the first large-scale phosphorylation analysis of the fission yeast, Schizosaccharomyces pombe Protein from thiabendazole-treated cells was separated by preparative SDS-PAGE and digested with trypsin The resulting peptides were subjected to either IMAC or TiO2 phosphopeptide enrichment methods and then analyzed by LC-MS/MS using an LTQ-Orbitrap mass spectrometer In total, 2887 distinct phosphorylation sites were identified from 1194 proteins with an estimated false-discovery rate of <05% at the peptide level A comparison of the two different enrichment methods is presented, supporting the finding that they are complementary Finally, phosphorylation sites were examined for phosphorylation-specific motifs and evolutionary conservation These analyses revealed both motifs and specific phosphorylation events identified in S pombe were conserved and predicted novel phosphorylation in mammals

Chemical Genetic Screen for AMPKα2 Substrates Uncovers a Network of Proteins Involved in Mitosis

Abstract: The energy-sensing AMP-activated protein kinase (AMPK) is activated by low nutrient levels. Functions of AMPK, other than its role in cellular metabolism, are just beginning to emerge. Here we use a chemical genetics screen to identify direct substrates of AMPK in human cells. We find that AMPK phosphorylates 28 previously unidentified substrates, several of which are involved in mitosis and cytokinesis. We identify the residues phosphorylated by AMPK in vivo in several substrates, including protein phosphatase 1 regulatory subunit 12C (PPP1R12C) and p21-activated protein kinase (PAK2). AMPK-induced phosphorylation is necessary for PPP1R12C interaction with 14-3-3 and phosphorylation of myosin regulatory light chain. Both AMPK activity and PPP1R12C phosphorylation are increased in mitotic cells and are important for mitosis completion. These findings suggest that AMPK coordinates nutrient status with mitosis completion, which may be critical for the organism's response to low nutrients during development, or in adult stem and cancer cells.

Identification of β-Secretase (BACE1) Substrates Using Quantitative Proteomics

Abstract: β-site APP cleaving enzyme 1 (BACE1) is a transmembrane aspartyl protease with a lumenal active site that sheds the ectodomains of membrane proteins through juxtamembrane proteolysis. BACE1 has been studied principally for its role in Alzheimer's disease as the β-secretase responsible for generating the amyloid-β protein. Emerging evidence from mouse models has identified the importance of BACE1 in myelination and cognitive performance. However, the substrates that BACE1 processes to regulate these functions are unknown, and to date only a few β-secretase substrates have been identified through candidate-based studies. Using an unbiased approach to substrate identification, we performed quantitative proteomic analysis of two human epithelial cell lines stably expressing BACE1 and identified 68 putative β-secretase substrates, a number of which we validated in a cell culture system. The vast majority were of type I transmembrane topology, although one was type II and three were GPI-linked proteins. Intriguingly, a preponderance of these proteins are involved in contact-dependent intercellular communication or serve as receptors and have recognized roles in the nervous system and other organs. No consistent sequence motif predicting BACE1 cleavage was identified in substrates versus non-substrates. These findings expand our understanding of the proteins and cellular processes that BACE1 may regulate, and suggest possible mechanisms of toxicity arising from chronic BACE1 inhibition.

S-Nitrosylation links obesity-associated inflammation to endoplasmic reticulum dysfunction

Abstract: The association between inflammation and endoplasmic reticulum (ER) stress has been observed in many diseases. However, if and how chronic inflammation regulates the unfolded protein response (UPR) and alters ER homeostasis in general, or in the context of chronic disease, remains unknown. Here, we show that, in the setting of obesity, inflammatory input through increased inducible nitric oxide synthase (iNOS) activity causes S-nitrosylation of a key UPR regulator, IRE1α, which leads to a progressive decline in hepatic IRE1α-mediated XBP1 splicing activity in both genetic (ob/ob) and dietary (high-fat diet–induced) models of obesity. Finally, in obese mice with liver-specific IRE1α deficiency, reconstitution of IRE1α expression with a nitrosylation-resistant variant restored IRE1α-mediated XBP1 splicing and improved glucose homeostasis in vivo. Taken together, these data describe a mechanism by which inflammatory pathways compromise UPR function through iNOS-mediated S-nitrosylation of IRE1α, which contributes to defective IRE1α activity, impaired ER function, and prolonged ER stress in obesity.

An integrated microfluidics-tandem mass spectrometry system for automated protein analysis.

Abstract: We describe an integrated analytical system consisting of a microfluidics device micromachined using photolithography/etching technology, a panel of computer-controlled high-voltage relays, and an electrospray ionization tandem mass spectrometer. Movement of solvents and samples on the device and off the device to the mass spectrometer was achieved by directed electroosmotic pumping induced by the activation of a suitable constellation of high-voltage relays. The system was used for the sequential automated analysis of protein digests. We demonstrate low femtomole per microliter sensitivity of detection and compatibility of the system with the automated analysis of proteins separated by two-dimensional gel electrophoresis.

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.