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.

Initiation of myoblast to brown fat switch by a PRDM16-C/EBP-beta transcriptional complex.

Abstract: Brown adipose cells are specialized to dissipate chemical energy in the form of heat, as a physiological defence against cold and obesity. PRDM16 (PR domain containing 16) is a 140 kDa zinc finger protein that robustly induces brown fat determination and differentiation. Recent data suggests that brown fat cells arise in vivo from a Myf5-positive, myoblastic lineage by the action of PRDM16 (ref. 3); however, the molecular mechanisms responsible for this developmental switch is unclear. Here we show that PRDM16 forms a transcriptional complex with the active form of C/EBP-beta (also known as LAP), acting as a critical molecular unit that controls the cell fate switch from myoblastic precursors to brown fat cells. Forced expression of PRDM16 and C/EBP-beta is sufficient to induce a fully functional brown fat program in naive fibroblastic cells, including skin fibroblasts from mouse and man. Transplantation of fibroblasts expressing these two factors into mice gives rise to an ectopic fat pad with the morphological and biochemical characteristics of brown fat. Like endogenous brown fat, this synthetic brown fat tissue acts as a sink for glucose uptake, as determined by positron emission tomography with fluorodeoxyglucose. These data indicate that the PRDM16-C/EBP-beta complex initiates brown fat formation from myoblastic precursors, and may provide opportunities for the development of new therapeutics for obesity and type-2 diabetes.

Human ESCRT and ALIX proteins interact with proteins of the midbody and function in cytokinesis

Abstract: TSG101 and ALIX both function in HIV budding and in vesicle formation at the multivesicular body (MVB), where they interact with other Endosomal Sorting Complex Required for Transport (ESCRT) pathway factors required for release of viruses and vesicles. Proteomic analyses revealed that ALIX and TSG101/ESCRT‐I also bind a series of proteins involved in cytokinesis, including CEP55, CD2AP, ROCK1, and IQGAP1. ALIX and TSG101 concentrate at centrosomes and are then recruited to the midbodies of dividing cells through direct interactions between the central CEP55 ‘hinge’ region and GPP‐based motifs within TSG101 and ALIX. ESCRT‐III and VPS4 proteins are also recruited, indicating that much of the ESCRT pathway localizes to the midbody. Depletion of ALIX and TSG101/ESCRT‐I inhibits the abscission step of HeLa cell cytokinesis, as does VPS4 overexpression, confirming a requirement for these proteins in cell division. Furthermore, ALIX point mutants that block CEP55 and CHMP4/ESCRT‐III binding also inhibit abscission, indicating that both interactions are essential. These experiments suggest that the ESCRT pathway may be recruited to facilitate analogous membrane fission events during HIV budding, MVB vesicle formation, and the abscission stage of cytokinesis.

Proteomics: the move to mixtures

Abstract: Proteomics can be defined as the systematic analysis of proteins for their identity, quantity and function. In contrast to a cell's static genome, the proteome is both complex and dynamic. Proteome analysis is most commonly accomplished by the combination of two-dimensional gel electrophoresis (2DE) and mass spectrometry (MS). However, this technique is under scrutiny because of a failure to detect low-abundance proteins from the analysis of whole cell lysates. Alternative approaches integrate a diversity of separation technologies and make use of the tremendous peptide separation and sequencing power provided by MS/MS. When liquid chromatography is combined with tandem mass spectrometry (LC/MS/MS) and applied to the direct analysis of mixtures, many of the limitations of 2DE for proteome analysis can be overcome. This tutorial addresses current approaches to identify and characterize large numbers of proteins and measure dynamic changes in protein expression directly from complex protein mixtures (total cell lysates).

The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry

Abstract: The success in profiling the phosphoproteome by mass spectrometry-based proteomics has been intimately related to the availability of methods that selectively enrich for phosphopeptides. To this end, we describe a protocol that combines two sequential enrichment steps. First, strong cation exchange (SCX) chromatography separates peptides by solution charge. Phosphate groups contribute to solution charge by adding a negative charge at pH 2.7. Therefore, at that pH, phosphopeptides are expected to elute earlier than their nonphosphorylated homologs. Second, immobilized metal affinity chromatography (IMAC) takes advantage of phosphate's affinity for metal ions such as Fe(3+) to uniformly enrich for phosphopeptides from the previously collected SCX fractions. We have successfully employed the SCX/IMAC enrichment strategy in the exploration of phosphoproteomes from several systems including mouse liver and Drosophila embryos characterizing over 5,500 and 13,000 phosphorylation events, respectively. The SCX/IMAC enrichment protocol requires 2 days, and the entire procedure from cells to a phosphorylation data set can be completed in less than 10 days.

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.