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.

The relaxin receptor RXFP1 signals through a mechanism of autoinhibition

Abstract: The relaxin family peptide receptor 1 (RXFP1) is the receptor for relaxin-2, an important regulator of reproductive and cardiovascular physiology. RXFP1 is a multi-domain G protein-coupled receptor (GPCR) with an ectodomain consisting of a low-density lipoprotein receptor class A (LDLa) module and leucine-rich repeats. The mechanism of RXFP1 signal transduction is clearly distinct from that of other GPCRs, but remains very poorly understood. In the present study, we determine the cryo-electron microscopy structure of active-state human RXFP1, bound to a single-chain version of the endogenous agonist relaxin-2 and the heterotrimeric G_s protein. Evolutionary coupling analysis and structure-guided functional experiments reveal that RXFP1 signals through a mechanism of autoinhibition. Our results explain how an unusual GPCR family functions, providing a path to rational drug development targeting the relaxin receptors. The RXFP1 relaxin receptor is a critical mediator of physiological adaptation to pregnancy and an emerging drug target. RXFP1 activation was found to entail an unexpected mechanism of ectodomain disinhibition resulting in downstream signaling.

Ms1 gas-phase enrichment using notched isolation waveforms

Abstract: A method of performing mass spectrometry survey scans The method includes using at least one isolation waveform of an ion trap, the isolation waveform with at least a first and second notch Methods may include determining a first mass-to-charge ratio (m/z) spectrum of a first plurality of ions from a first plurality of discrete m/z ranges; determining a second m/z spectrum of a second plurality of ions from a second plurality of discrete m/z ranges different from the first plurality of discrete m/z ranges; and creating a combined m/z spectrum by combining the first and second m/z spectra

Changes in Cell Morphology Are Coordinated with Cell Growth through the TORC1 Pathway

Abstract: Summary Background Growth rate is determined not only by extracellular cues such as nutrient availability but also by intracellular processes. Changes in cell morphology in budding yeast, mediated by polarization of the actin cytoskeleton, have been shown to reduce cell growth. Results Here we demonstrate that polarization of the actin cytoskeleton inhibits the highly conserved Target of Rapamycin Complex 1 (TORC1) pathway. This downregulation is suppressed by inactivation of the TORC1 pathway regulatory Iml1 complex, which also regulates TORC1 during nitrogen starvation. We further demonstrate that attenuation of growth is important for cell recovery after conditions of prolonged polarized growth. Conclusions Our results indicate that extended periods of polarized growth inhibit protein synthesis, mass accumulation, and the increase in cell size at least in part through inhibiting the TORC1 pathway. We speculate that this mechanism serves to coordinate the ability of cells to increase in size with their biosynthetic capacity.

Enrichment of neurodegenerative microglia signature in brain-derived extracellular vesicles isolated from Alzheimer’s disease mouse model

Abstract: Extracellular vesicles (EVs) are secreted by any neuronal cells in the central nervous system (CNS) for molecular clearance, cellular communications and disease spread in multiple neurodegenerative diseases, including Alzheimers disease (AD), although their exact molecular mechanism is poorly understood. We hypothesize that high-resolution proteomic profiling of EVs separated from animal models of AD would determine the composition of EV contents and their cellular origin. Here, we examined recently developed transgenic mice (CAST.APP/PS1), which express familial AD-linked mutations of amyloid precursor protein (APP) and presenilin-1 (PS1) in the CAST/EiJ mouse strain and develop hippocampal neurodegeneration. Quantitative proteomics analysis of EVs separated from CAST.APP/PS1 and age-matched control mice by tandem mass tag-mass spectrometry identified a total of 3,444 unique proteins, which are enriched in neuron, astrocyte, oligodendrocyte and microglia-specific molecules. CAST.APP/PS1-derived EVs show significant enrichment of Psen1, APP, Itgax, and reduction of Wdr61, Pmpca, Aldh1a2, Calu, Anp32b, Actn4 and Ndufv2 compared to WT-derived EVs, suggesting the involvement of Ab-processing complex and disease-associated / neurodegenerative microglia (DAM/MGnD) in EV secretion. In addition, Itgax and Apoe, the DAM/MGnD markers, in EV show a positive correlation with Itgax and Apoe mRNA expression from brain tissue in CAST.APP/PS1 mice. These datasets indicate the significant contribution of Ab plaque and neurodegeneration-induced DAM/MGnD microglia for EV secretion in CAST.APP/PS1 mice and shed light on understanding the AD pathogenesis.

Genome-wide transcript and protein analysis reveals distinct features of aging in the mouse heart

Abstract: Understanding the molecular mechanisms underlying age-related changes in the heart is challenging due to the contributions from numerous genetic and environmental factors. Genetically diverse outbred mice provide a model to study the genetic regulation of aging processes in healthy tissues from individuals undergoing natural aging in a controlled environment. We analyzed transcriptome and proteome data from outbred mice at 6, 12 and 18 months of age to reveal a scenario of cardiac hypertrophy, fibrosis, extracellular matrix remodeling, and reemergence of fetal gene expression patterns. We observed widespread changes in protein trafficking and sorting, and post-translational disruption of the stoichiometry of the protein quality control system itself. We identified genome hotspots of age-by-genetic effects that regulate proteins from the proteasome and endoplasmic reticulum stress response, suggesting that genetic variation in these modules may contribute to individual variation in the aging heart.

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.