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.

Proteomic Profiling of γ-Secretase Substrates and Mapping of Substrate Requirements

Abstract: The presenilin/γ-secretase complex, an unusual intramembrane aspartyl protease, plays an essential role in cellular signaling and membrane protein turnover. Its ability to liberate numerous intracellular signaling proteins from the membrane and also mediate the secretion of amyloid-β protein (Aβ) has made modulation of γ-secretase activity a therapeutic goal for cancer and Alzheimer disease. Although the proteolysis of the prototypical substrates Notch and β-amyloid precursor protein (APP) has been intensely studied, the full spectrum of substrates and the determinants that make a transmembrane protein a substrate remain unclear. Using an unbiased approach to substrate identification, we surveyed the proteome of a human cell line for targets of γ-secretase and found a relatively small population of new substrates, all of which are type I transmembrane proteins but have diverse biological roles. By comparing these substrates to type I proteins not regulated by γ-secretase, we determined that besides a short ectodomain, γ-secretase requires permissive transmembrane and cytoplasmic domains to bind and cleave its substrates. In addition, we provide evidence for at least two mechanisms that can target a substrate for γ cleavage: one in which a substrate with a short ectodomain is directly cleaved independent of sheddase association, and a second where a substrate requires ectodomain shedding to instruct subsequent γ-secretase processing. These findings expand our understanding of the mechanisms of substrate selection as well as the diverse cellular processes to which γ-secretase contributes.

Msp1/ATAD1 maintains mitochondrial function by facilitating the degradation of mislocalized tail-anchored proteins.

Abstract: The majority of ER-targeted tail-anchored (TA) proteins are inserted into membranes by the Guided Entry of Tail-anchored protein (GET) system. Disruption of this system causes a subset of TA proteins to mislocalize to mitochondria. We show that the AAA+ ATPase Msp1 limits the accumulation of mislocalized TA proteins on mitochondria. Deletion of MSP1 causes the Pex15 and Gos1 TA proteins to accumulate on mitochondria when the GET system is impaired. Likely as a result of failing to extract mislocalized TA proteins, yeast with combined mutation of the MSP1 gene and the GET system exhibit strong synergistic growth defects and severe mitochondrial damage, including loss of mitochondrial DNA and protein and aberrant mitochondrial morphology. Like yeast Msp1, human ATAD1 limits the mitochondrial mislocalization of PEX26 and GOS28, orthologs of Pex15 and Gos1, respectively. GOS28 protein level is also increased in ATAD1−/− mouse tissues. Therefore, we propose that yeast Msp1 and mammalian ATAD1 are conserved members of the mitochondrial protein quality control system that might promote the extraction and degradation of mislocalized TA proteins to maintain mitochondrial integrity.

Cdk1 coordinates cell-surface growth with the cell cycle

Abstract: The mechanisms that control cell growth during the cell cycle are poorly understood. In budding yeast, cyclin dependent kinase 1 (Cdk1) triggers polarization of the actin cytoskeleton and bud emergence in late G1 through activation of the Cdc42 GTPase. However, Cdk1 is not thought to be required for subsequent growth of the bud. Here, we show that Cdk1 has an unexpected role in controlling bud growth after bud emergence. Moreover, we show that G1 cyclin-Cdk1 complexes specifically phosphorylate multiple proteins associated with Cdc24, the guanine nucleotide-exchange factor (GEF) that activates the Cdc42 GTPase. A mutant form of a Cdc24-associated protein that fails to undergo Cdk1-dependent phosphorylation causes defects in bud growth. These results provide a direct link between Cdk1 activity and the control of polarized cell growth.

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.