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.

Endocytosis of Ubiquitylation-Deficient EGFR Mutants via Clathrin-Coated Pits is Mediated by Ubiquitylation.

Abstract: Signaling by epidermal growth factor receptor (EGFR) is controlled by endocytosis. However, mechanisms of EGFR endocytosis remain poorly understood. Here, we found that the EGFR mutant lacking known ubiquitylation, acetylation and clathrin adaptor AP-2-binding sites (21KRΔAP2) was internalized at relatively high rates via the clathrin-dependent pathway in human duodenal adenocarcinoma HuTu-80 cells. RNA interference analysis revealed that this residual internalization is strongly inhibited by depletion of Grb2 and the E2 ubiquitin-conjugating enzyme UbcH5b/c, and partially affected by depletion of the E3 ubiquitin ligase Cbl and ubiquitin-binding adaptors, indicating that an ubiquitylation process is involved. Several new ubiquitin conjugation sites were identified by mass spectrometry in the 21KRΔAP2 mutant, suggesting that cryptic ubiquitylation may mediate endocytosis of this mutant. Total internal reflection fluorescence microscopy imaging of HuTu-80 cells transfected with labeled ubiquitin adaptor epsin1 demonstrated that the ubiquitylation-deficient EGFR mutant was endocytosed through a limited population of epsin-enriched clathrin-coated pits (CCPs), although with a prolonged CCP lifetime. Native EGFR was recruited with the same efficiency into CCPs containing either AP-2 or epsin1 that were tagged with fluorescent proteins by genome editing of MDA-MD-231 cells. We propose that two redundant mechanisms, ubiquitylation and interaction with AP-2, contribute to EGFR endocytosis via CCPs in a stochastic fashion.

Role of Insulin-dependent Cortical Fodrin/Spectrin Remodeling in Glucose Transporter 4 Translocation in Rat Adipocytes

Abstract: Fodrin or nonerythroid spectrin is an abundant component of the cortical cytoskeletal network in rat adipocytes. Fodrin has a highly punctate distribution in resting cells, and insulin causes a dramatic remodeling of fodrin to a more diffuse pattern. Insulin-mediated remodeling of actin occurs to a lesser extent than does that of fodrin. We show that fodrin interacts with the t-soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) syntaxin 4, and this interaction is increased by insulin stimulation and decreased by prior latrunculin A treatment. Latrunculin A disrupts all actin filaments, inhibits glucose transporter 4 (GLUT4) translocation, and causes fodrin to partially redistribute from the plasma membrane to the cytosol. In contrast, cytochalasin D disrupts only the short actin filament signal, and cytochalasin D neither inhibits GLUT4 translocation nor fodrin redistribution in adipocytes. Together, our data suggest that insulin induces remodeling of the fodrin-actin network, which is required for the fusion of GLUT4 storage vesicles with the plasma membrane by permitting their access to the t-SNARE syntaxin 4.

Kinase-independent function of E-type cyclins in liver cancer.

Abstract: E-type cyclins (cyclins E1 and E2) are components of the core cell cycle machinery and are overexpressed in many human tumor types. E cyclins are thought to drive tumor cell proliferation by activating the cyclin-dependent kinase 2 (CDK2). The cyclin E1 gene represents the site of recurrent integration of the hepatitis B virus in the pathogenesis of hepatocellular carcinoma, and this event is associated with strong up-regulation of cyclin E1 expression. Regardless of the underlying mechanism of tumorigenesis, the majority of liver cancers overexpress E-type cyclins. Here we used conditional cyclin E knockout mice and a liver cancer model to test the requirement for the function of E cyclins in liver tumorigenesis. We show that a ubiquitous, global shutdown of E cyclins did not visibly affect postnatal development or physiology of adult mice. However, an acute ablation of E cyclins halted liver cancer progression. We demonstrated that also human liver cancer cells critically depend on E cyclins for proliferation. In contrast, we found that the function of the cyclin E catalytic partner, CDK2, is dispensable in liver cancer cells. We observed that E cyclins drive proliferation of tumor cells in a CDK2- and kinase-independent mechanism. Our study suggests that compounds which degrade or inhibit cyclin E might represent a highly selective therapeutic strategy for patients with liver cancer, as these compounds would selectively cripple proliferation of tumor cells, while sparing normal tissues.

Follistatin-like 3 (FSTL3) Mediated Silencing of Transforming Growth Factor β (TGFβ) Signaling Is Essential for Testicular Aging and Regulating Testis Size

Abstract: Follistatin-like 3 (FSTL3) is a glycoprotein that binds and inhibits the action of TGFβ ligands such as activin. The roles played by FSTL3 and activin signaling in organ development and homeostasis are not fully understood. The authors show mice deficient in FSTL3 develop markedly enlarged testes that are also delayed in their age-related regression. These FSTL3 knockout mice exhibit increased Sertoli cell numbers, allowing for increased spermatogenesis but otherwise showing normal testicular function. The data show that FSTL3 deletion leads to increased AKT signaling and SIRT1 expression in the testis. This demonstrates a cross-talk between TGFβ ligand and AKT signaling and leads to a potential mechanism for increased cellular survival and antiaging. The findings identify crucial roles for FSTL3 in limiting testis organ size and promoting age-related testicular regression.

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.