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.

Quick quantitative analysis of protein or protein function in composition mixture

Abstract: PROBLEM TO BE SOLVED: To provide a method used in a proteome analysis for conquering the limitation peculiar to the conventional technique, and to provide a reagent. SOLUTION: An automation LC/MS/MS system comprises an auto sampler that is subjected to fluid connection to a capillary HPLC (a), an electro spray ionization triple quadrupole electrode MS/MS apparatus that is subjected to fluid connection to the capillary HPLC (b), and device control and a data analysis system that are electrically connected to the autosampler, the capillary HPLC, and an MS/MS apparatus.

DAGBagM: Learning directed acyclic graphs of mixed variables with an application to identify prognostic protein biomarkers in ovarian cancer

Abstract: Motivation Directed gene/protein regulatory networks inferred by applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of clinical outcomes. However, there remain unsolved challenges in DAG learning to jointly model clinical outcome variables, which often take binary values, and biomarker measurements, which usually are continuous variables. Therefore, in this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models for continuous and binary variables, DAGBagM allows for either type of nodes to be parents or children nodes in the learned graph. DAGBagM also employs a bootstrap aggregating strategy to reduce false positives and achieve better estimation accuracy. Moreover, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges for DAG reconstruction. Results As shown by simulation studies, DAGBagM performs better in identifying edges between continuous and binary nodes, as compared to commonly used strategies of either treating binary variables as continuous or discretizing continuous variables. Moreover, DAGBagM outperforms several popular DAG structure learning algorithms including the score-based hill climbing (HC) algorithm, constraint-based PC-algorithm (PC-alg), and the hybrid method max-min hill climbing (MMHC) even for constructing DAG with only continuous nodes. The HC implementation in the R package DAGBagM is much faster than that in a widely used DAG learning R package bnlearn. When applying DAGBagM to proteomics datasets from ovarian cancer studies, we identify potential prognostic protein biomarkers in ovarian cancer. Availability and implementation DAGBagM is made available as a github repository https://github.com/jie108/dagbagM.

Adjusting precursor ion populations in mass spectrometry using dynamic isolation waveforms

Abstract: A mass spectrometry technique for isolating a plurality of isolated ions from a plurality of injected ions using a dynamic isolation waveform to create at least one isolation notch. Isolating the plurality of isolated ions may include collecting at least a first target ion, but not a second target ion, using the at least one isolation notch for a first period of time; changing at least one property of the at least one isolation notch; and collecting at least the first target ion and the second target ion using the at least one isolation notch for a second period of time.

Proteomic analysis identifies the E3 ubiquitin ligase Pdzrn3 as a regulatory target of Wnt5a-Ror signaling.

Abstract: Wnt5a-Ror signaling is a conserved pathway that regulates morphogenetic processes during vertebrate development [R. T. Moon et al, Development 119, 97-111 (1993); I. Oishi et al, Genes Cells 8, 645-654 (2003)], but its downstream signaling events remain poorly understood. Through a large-scale proteomic screen in mouse embryonic fibroblasts, we identified the E3 ubiquitin ligase Pdzrn3 as a regulatory target of the Wnt5a-Ror pathway. Upon pathway activation, Pdzrn3 is degraded in a β-catenin-independent, ubiquitin-proteasome system-dependent manner. We developed a flow cytometry-based reporter to monitor Pdzrn3 abundance and delineated a signaling cascade involving Frizzled, Dishevelled, Casein kinase 1, and Glycogen synthase kinase 3 that regulates Pdzrn3 stability. Epistatically, Pdzrn3 is regulated independently of Kif26b, another Wnt5a-Ror effector. Wnt5a-dependent degradation of Pdzrn3 requires phosphorylation of three conserved amino acids within its C-terminal LNX3H domain [M. Flynn, O. Saha, P. Young, BMC Evol. Biol. 11, 235 (2011)], which acts as a bona fide Wnt5a-responsive element. Importantly, this phospho-dependent degradation is essential for Wnt5a-Ror modulation of cell migration. Collectively, this work establishes a Wnt5a-Ror cell morphogenetic cascade involving Pdzrn3 phosphorylation and degradation.

ADAM17 cytoplasmic domain modulates Thioredoxin-1 conformation and activity.

Abstract: The activity of Thioredoxin-1 (Trx-1) is adjusted by the balance of its monomeric, active and its dimeric, inactive state. The regulation of this balance is not completely understood. We have previously shown that the cytoplasmic domain of the transmembrane protein A Disintegrin And Metalloprotease 17 (ADAM17cyto) binds to Thioredoxin-1 (Trx-1) and the destabilization of this interaction favors the dimeric state of Trx-1. Here, we investigate whether ADAM17 plays a role in the conformation and activation of Trx-1. We found that disrupting the interacting interface with Trx-1 by a site-directed mutagenesis in ADAM17 (ADAM17cytoF730A) caused a decrease of Trx-1 reductive capacity and activity. Moreover, we observed that ADAM17 overexpressing cells favor the monomeric state of Trx-1 while knockdown cells do not. As a result, there is a decrease of cell oxidant levels and ADAM17 sheddase activity and an increase in the reduced cysteine-containing peptides in intracellular proteins in ADAM17cyto overexpressing cells. A mechanistic explanation that ADAM17cyto favors the monomeric, active state of Trx-1 is the formation of a disulfide bond between Cys824 at the C-terminal of ADAM17cyto with the Cys73 of Trx-1, which is involved in the dimerization site of Trx-1. In summary, we propose that ADAM17 is able to modulate Trx-1 conformation affecting its activity and intracellular redox state, bringing up a novel possibility for positive regulation of thiol isomerase activity in the cell by mammalian metalloproteinases.

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.