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.

Proteogenomic Network Analysis of Context-Specific KRAS Signaling in Mouse-to-Human Cross-Species Translation

Abstract: The highest frequencies of KRAS mutations occur in colorectal carcinoma (CRC) and pancreatic ductal adenocarcinoma (PDAC). The ability to target downstream pathways mediating KRAS oncogenicity is limited by an incomplete understanding of the contextual cues modulating the signaling output of activated K-RAS. We performed mass spectrometry on mouse tissues expressing wild-type or mutant Kras to determine how tissue context and genetic background modulate oncogenic signaling. Mutant Kras dramatically altered the proteomes and phosphoproteomes of preneoplastic and neoplastic colons and pancreases in a context-specific manner. We developed an approach to statistically humanize the mouse networks with data from human cancer and identified genes within the humanized CRC and PDAC networks synthetically lethal with mutant KRAS. Our studies demonstrate the context-dependent plasticity of oncogenic signaling, identify non-canonical mediators of KRAS oncogenicity within the KRAS-regulated signaling network, and demonstrate how statistical integration of mouse and human datasets can reveal cross-species therapeutic insights.

Combining amine metabolomics and quantitative proteomics of cancer cells using derivatization with isobaric tags.

Abstract: Quantitative metabolomics and proteomics technologies are powerful approaches to explore cellular metabolic regulation. Unfortunately, combining the two technologies typically requires different LC-MS setups for sensitive measurement of metabolites and peptides. One approach to enhance the analysis of certain classes of metabolites is by derivatization with various types of tags to increase ionization and chromatographic efficiency. We demonstrate here that derivatization of amine metabolites with tandem mass tags (TMT), typically used in multiplexed peptide quantitation, facilitates amino acid analysis by standard nanoflow reversed-phase LC-MS setups used for proteomics. We demonstrate that this approach offers the potential to perform experiments at the MS1-level using duplex tags or at the MS2-level using novel 10-plex reporter ion-containing isobaric tags for multiplexed amine metabolite analysis. We also demonstrate absolute quantitative measurements of amino acids conducted in parallel with multiple...

Dose-related distribution of codeine and its metabolites into rat hair.

Abstract: Drugs and endogenous compounds may be incorporated into the matrix of a growing hair shaft. However, the relationship between incorporation and dose or time course of plasma concentrations is poorly defined. The purpose of this study was to compare plasma and hair concentrations of codeine and its metabolites after various doses of codeine. Male Sprague-Dawley rats had a 1" x 1" square shaved from their backs. Codeine was administered by intraperitoneal injection (10, 20, 40, or 60 mg/kg/day) daily for 5 days. Fourteen days after beginning drug administration, the original patch was reshaved and newly grown hair was analyzed for codeine and morphine using GC/MS. The mean concentrations of codeine in hair for the 10, 20, 40, and 60 mg/kg/day groups were 0.29, 0.57, 0.96, and 1.93 ng/mg hair, respectively, and the concentrations of morphine were 0.15, 0.28, 0.49, and 0.79 ng/mg hair, respectively. The plasma concentration time courses for codeine and morphine were determined after single doses of either 20 or 40 mg/kg. Peak plasma codeine concentrations for the 20 and 40 mg/kg groups were 1,441 and 2,452 ng/ml plasma, respectively, and the areas under the plasma concentration vs. time curve were 699 and 1,581 ng-hr/ml, respectively. Morphine glucuronide, but not codeine glucuronide, was measured in the hair of rats administered codeine. Codeine was also administered to rats by constant intravenous infusion (40 mg/kg/day for 5 days). The concentration of codeine in rat hair after this route of administration was 2.92 +/- 0.72 ng/mg hair. Codeine and morphine are incorporated into rat hair in a dose-proportional fashion. Morphine glucuronide can be found in rat hair after codeine administration. The codeine concentration in hair is the same whether the drug is administered by constant intravenous infusion or daily intraperitoneal injections if the areas under the plasma concentration vs. time curve values are considered.

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.