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.

Incorporation of codeine and metabolites into hair. Role of pigmentation.

Abstract: Xenobiotics circulating in the blood may become incorporated into growing hair. Melanin has affinity for many pharmacologically unrelated drugs and is responsible for the pigmentation in hair. To assess the role of pigmentation in the incorporation of drugs into hair, the distribution of codeine and its metabolites was studied in Sprague-Dawley (SD; white nonpigmented hair), Dark Agouti (DA; brown pigmented hair), and hooded Long-Evans (LE; both black pigmented and white nonpigmented hair) rats. Codeine was administered at a dose of 40 mg/kg/day i.p. for 5 days. Fourteen days after beginning the dosing protocol, hair was collected and analyzed for codeine, and its metabolite, morphine, by positive-ion chemical ionization GC/ion-trap MS. The plasma pharmacokinetics for codeine and morphine were also determined after a single 40 mg/kg injection (equivalent to first dose in 5-day dosing protocol) in all three strains of rats. Hair and plasma codeine and morphine concentrations were also determined after acid hydrolysis to evaluate the presence of glucuronide metabolites. Codeine concentrations in the hair of SD, DA, and pigmented LE hair were 0.98 +/- 0.10, 5.99 +/- 1.24, and 111.93 +/- 18.69 ng/mg hair, respectively; morphine concentrations were 0.34 +/- 0.04, 0.51 +/- 0.11, and 14.46 +/- 1.81 ng/mg hair, respectively; morphine glucuronide concentrations were 0.67 +/- 0.08, 1.04 +/- 0.37, and 13.80 +/- 3.60 ng/mg hair, respectively. Studies examining the in vitro binding of [3H] codeine and [3H]morphine to hair demonstrated both specific and nonspecific binding sites for codeine and morphine. Pigmented hair from LE rats possessed the greatest number of binding sites, white hair from SD rats contained the least, and brown hair from DA rats was intermediate. A time course study of codeine and its metabolites showed pigment-mediated differences in incorporation of codeine and metabolites within a few hours of drug administration. These data indicate that pigmented hair possesses a greater capacity to bind and incorporate codeine and its metabolites than does nonpigmented hair. Interpretation of hair concentrations of drugs should involve consideration of hair pigmentation.

An asymmetric interface between the regulatory and core particles of the proteasome.

Abstract: The eukaryotic proteasome is composed of a regulatory particle and a core particle. Now protein cross-linking is used to map the contacts between regulatory particle and core particle subunits, revealing that some Rpt subunits engage with the core particle in a dynamic fashion that is regulated by nucleotide binding and hydrolysis.

A phosphatase threshold sets the level of Cdk1 activity in early mitosis in budding yeast

Abstract: Entry into mitosis is initiated by synthesis of cyclins, which bind and activate cy- clin-dependent kinase 1 (Cdk1). Cyclin synthesis is gradual, yet activation of Cdk1 occurs in a stepwise manner: a low level of Cdk1 activity is initially generated that triggers early mitotic events, which is followed by full activation of Cdk1. Little is known about how stepwise acti- vation of Cdk1 is achieved. A key regulator of Cdk1 is the Wee1 kinase, which phosphorylates and inhibits Cdk1. Wee1 and Cdk1 show mutual regulation: Cdk1 phosphorylates Wee1, which activates Wee1 to inhibit Cdk1. Further phosphorylation events inactivate Wee1. We discovered that a specific form of protein phosphatase 2A (PP2A Cdc55 ) opposes the initial phosphorylation of Wee1 by Cdk1. In vivo analysis, in vitro reconstitution, and mathematical modeling suggest that PP2A Cdc55 sets a threshold that limits activation of Wee1, thereby al- lowing a low constant level of Cdk1 activity to escape Wee1 inhibition in early mitosis. These results define a new role for PP2A Cdc55 and reveal a systems-level mechanism by which dy-

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.