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.

High-density chemical cross-linking for modeling protein interactions.

Abstract: Detailed mechanistic understanding of protein complex function is greatly enhanced by insights from its 3-dimensional structure. Traditional methods of protein structure elucidation remain expensive and labor-intensive and require highly purified starting material. Chemical cross-linking coupled with mass spectrometry offers an alternative that has seen increased use, especially in combination with other experimental approaches like cryo-electron microscopy. Here we report advances in method development, combining several orthogonal cross-linking chemistries as well as improvements in search algorithms, statistical analysis, and computational cost to achieve coverage of 1 unique cross-linked position pair for every 7 amino acids at a 1% false discovery rate. This is accomplished without any peptide-level fractionation or enrichment. We apply our methods to model the complex between a carbonic anhydrase (CA) and its protein inhibitor, showing that the cross-links are self-consistent and define the interaction interface at high resolution. The resulting model suggests a scaffold for development of a class of protein-based inhibitors of the CA family of enzymes. We next cross-link the yeast proteasome, identifying 3,893 unique cross-linked peptides in 3 mass spectrometry runs. The dataset includes 1,704 unique cross-linked position pairs for the proteasome subunits, more than half of them intersubunit. Using multiple recently solved cryo-EM structures, we show that observed cross-links reflect the conformational dynamics and disorder of some proteasome subunits. We further demonstrate that this level of cross-linking density is sufficient to model the architecture of the 19-subunit regulatory particle de novo.

Multiplexed, Proteome-Wide Protein Expression Profiling: Yeast Deubiquitylating Enzyme Knockout Strains.

Abstract: Characterizing a protein's function often requires a description of the cellular state in its absence. Multiplexing in mass spectrometry-based proteomics has now achieved the ability to globally measure protein expression levels in yeast from 10 cell states simultaneously. We applied this approach to quantify expression differences in wild type and nine deubiquitylating enzyme (DUB) knockout strains with the goal of creating "information networks" that might provide deeper, mechanistic insights into a protein's biological role. In total, more than 3700 proteins were quantified with high reproducibility across three biological replicates (30 samples in all). DUB mutants demonstrated different proteomics profiles, consistent with distinct roles for each family member. These included differences in total ubiquitin levels and specific chain linkages. Moreover, specific expression changes suggested novel functions for several DUB family members. For instance, the ubp3Δ mutant showed large expression changes for members of the cytochrome C oxidase complex, consistent with a role for Ubp3 in mitochondrial regulation. Several DUBs also showed broad expression changes for phosphate transporters as well as other components of the inorganic phosphate signaling pathway, suggesting a role for these DUBs in regulating phosphate metabolism. These data highlight the potential of multiplexed proteome-wide analyses for biological investigation and provide a framework for further study of the DUB family. Our methods are readily applicable to the entire collection of yeast deletion mutants and may help facilitate systematic analysis of yeast and other organisms.

Profiling DNA damage-induced phosphorylation in budding yeast reveals diverse signaling networks.

Abstract: The DNA damage response (DDR) is regulated by a protein kinase signaling cascade that orchestrates DNA repair and other processes. Identifying the substrate effectors of these kinases is critical for understanding the underlying physiology and mechanism of the response. We have used quantitative mass spectrometry to profile DDR-dependent phosphorylation in budding yeast and genetically explored the dependency of these phosphorylation events on the DDR kinases MEC1, RAD53, CHK1, and DUN1. Based on these screens, a database containing many novel DDR-regulated phosphorylation events has been established. Phosphorylation of many of these proteins has been validated by quantitative peptide phospho-immunoprecipitation and examined for functional relevance to the DDR through large-scale analysis of sensitivity to DNA damage in yeast deletion strains. We reveal a link between DDR signaling and the metabolic pathways of inositol phosphate and phosphatidyl inositol synthesis, which are required for resistance to DNA damage. We also uncover links between the DDR and TOR signaling as well as translation regulation. Taken together, these data shed new light on the organization of DDR signaling in budding yeast.

Global analysis of protein expression and phosphorylation levels in nicotine-treated pancreatic stellate cells

Abstract: Smoking is a risk factor in pancreatic disease; however, the biochemical mechanisms correlating smoking with pancreatic dysfunction remain poorly understood. Strategies using multiplexed isobaric tag-based mass spectrometry facilitate the study of drug-induced perturbations on biological systems. Here, we present the first large-scale analysis of the proteomic and phosphoproteomic alterations in pancreatic stellate cells following treatment with two nicotinic acetylcholine receptor (nAChR) ligands: nicotine and α-bungarotoxin. We treated cells with nicotine or α-bungarotoxin for 12 h in triplicate and compared alterations in protein expression and phosphorylation levels to mock-treated cells using a tandem mass tag (TMT9plex)-based approach. Over 8100 proteins were quantified across all nine samples, of which 46 were altered in abundance upon treatment with nicotine. Proteins with increased abundance included those associated with neurons, defense mechanisms, indicators of pancreatic disease, and lysosoma...

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.