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.

Transcriptional role of cyclin D1 in development revealed by a genetic–proteomic screen

Abstract: Cyclin D1 belongs to the core cell cycle machinery, and it is frequently overexpressed in human cancers. The full repertoire of cyclin D1 functions in normal development and oncogenesis is unclear at present. Here we developed Flag- and haemagglutinin-tagged cyclin D1 knock-in mouse strains that allowed a high-throughput mass spectrometry approach to search for cyclin D1-binding proteins in different mouse organs. In addition to cell cycle partners, we observed several proteins involved in transcription. Genome-wide location analyses (chromatin immunoprecipitation coupled to DNA microarray; ChIP-chip) showed that during mouse development cyclin D1 occupies promoters of abundantly expressed genes. In particular, we found that in developing mouse retinas-an organ that critically requires cyclin D1 function-cyclin D1 binds the upstream regulatory region of the Notch1 gene, where it serves to recruit CREB binding protein (CBP) histone acetyltransferase. Genetic ablation of cyclin D1 resulted in decreased CBP recruitment, decreased histone acetylation of the Notch1 promoter region, and led to decreased levels of the Notch1 transcript and protein in cyclin D1-null (Ccnd1(-/-)) retinas. Transduction of an activated allele of Notch1 into Ccnd1(-/-) retinas increased proliferation of retinal progenitor cells, indicating that upregulation of Notch1 signalling alleviates the phenotype of cyclin D1-deficiency. These studies show that in addition to its well-established cell cycle roles, cyclin D1 has an in vivo transcriptional function in mouse development. Our approach, which we term 'genetic-proteomic', can be used to study the in vivo function of essentially any protein.

MURC/Cavin-4 and cavin family members form tissue-specific caveolar complexes

Abstract: Polymerase I and transcript release factor (PTRF)/Cavin is a cytoplasmic protein whose expression is obligatory for caveola formation. Using biochemistry and fluorescence resonance energy transfer–based approaches, we now show that a family of related proteins, PTRF/Cavin-1, serum deprivation response (SDR)/Cavin-2, SDR-related gene product that binds to C kinase (SRBC)/Cavin-3, and muscle-restricted coiled-coil protein (MURC)/Cavin-4, forms a multiprotein complex that associates with caveolae. This complex can constitutively assemble in the cytosol and associate with caveolin at plasma membrane caveolae. Cavin-1, but not other cavins, can induce caveola formation in a heterologous system and is required for the recruitment of the cavin complex to caveolae. The tissue-restricted expression of cavins suggests that caveolae may perform tissue-specific functions regulated by the composition of the cavin complex. Cavin-4 is expressed predominantly in muscle, and its distribution is perturbed in human muscle disease associated with Caveolin-3 dysfunction, identifying Cavin-4 as a novel muscle disease candidate caveolar protein.

The Parkinson's disease-associated DJ-1 protein is a transcriptional co-activator that protects against neuronal apoptosis

Abstract: Mutations in the DJ-1 gene cause early-onset autosomal recessive Parkinson's disease (PD), although the role of DJ-1 in the degeneration of dopaminergic neurons is unresolved Here we show that the major interacting-proteins with DJ-1 in dopaminergic neuronal cells are the nuclear proteins p54nrb and pyrimidine tract-binding protein-associated splicing factor (PSF), two multifunctional regulators of transcription and RNA metabolism PD-associated DJ-1 mutants exhibit decreased nuclear distribution and increased mitochondrial localization, resulting in diminished co-localization with co-activator p54nrb and repressor PSF Unlike pathogenic DJ-1 mutants, wild-type DJ-1 acts to inhibit the transcriptional silencing activity of the PSF In addition, the transcriptional silencer PSF induces neuronal apoptosis, which can be reversed by wild-type DJ-1 but to a lesser extent by PD-associated DJ-1 mutants DJ-1-specific small interfering RNA sensitizes cells to PSF-induced apoptosis Both DJ-1 and p54nrb block oxidative stress and mutant alpha-synuclein-induced cell death Thus, DJ-1 is a neuroprotective transcriptional co-activator that may act in concert with p54nrb and PSF to regulate the expression of a neuroprotective genetic program Mutations that impair the transcriptional co-activator function of DJ-1 render dopaminergic neurons vulnerable to apoptosis and may contribute to the pathogenesis of PD

ms3 eliminates ratio distortion in isobaric multiplexed quantitative

Abstract: of a two-proteome model sample to accurately measure the extent of the interference effect on quantitative data from a large-scale proteomics experiment. We used this model to evaluate several analytical strategies as potential solutions to the outlined diffi-culties, and we show that performing an additional isolation and fragmentation event (MS3 scan) overcomes these problems by eliminating the interference effect.We used sixplexed TMT reagents to build a model two- proteome peptide mixture sample from Lys-C protein digests of yeast (

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.