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.

Tra1 has specific regulatory roles, rather than global functions, within the SAGA co-activator complex.

Abstract: The SAGA complex is a conserved, multifunctional co-activator that has broad roles in eukaryotic transcription. Previous studies suggested that Tra1, the largest SAGA component, is required either for SAGA assembly or for SAGA recruitment by DNA-bound transcriptional activators. In contrast to Saccharomyces cerevisiae and mouse, a tra1Δ mutant is viable in Schizosaccharomyces pombe, allowing us to test these issues in vivo. We find that, in a tra1Δ mutant, SAGA assembles and is recruited to some, but not all, promoters. Consistent with these findings, Tra1 regulates the expression of only a subset of SAGA-dependent genes. We previously reported that the SAGA subunits Gcn5 and Spt8 have opposing regulatory roles during S. pombe sexual differentiation. We show here that, like Gcn5, Tra1 represses this pathway, although by a distinct mechanism. Thus, our study reveals that Tra1 has specific regulatory roles, rather than global functions, within SAGA.

Defective NADPH production in mitochondrial disease complex I causes inflammation and cell death.

Abstract: Electron transport chain (ETC) defects occurring from mitochondrial disease mutations compromise ATP synthesis and render cells vulnerable to nutrient and oxidative stress conditions. This bioenergetic failure is thought to underlie pathologies associated with mitochondrial diseases. However, the precise metabolic processes resulting from a defective mitochondrial ETC that compromise cell viability under stress conditions are not entirely understood. We design a whole genome gain-of-function CRISPR activation screen using human mitochondrial disease complex I (CI) mutant cells to identify genes whose increased function rescue glucose restriction-induced cell death. The top hit of the screen is the cytosolic Malic Enzyme (ME1), that is sufficient to enable survival and proliferation of CI mutant cells under nutrient stress conditions. Unexpectedly, this metabolic rescue is independent of increased ATP synthesis through glycolysis or oxidative phosphorylation, but dependent on ME1-produced NADPH and glutathione (GSH). Survival upon nutrient stress or pentose phosphate pathway (PPP) inhibition depends on compensatory NADPH production through the mitochondrial one-carbon metabolism that is severely compromised in CI mutant cells. Importantly, this defective CI-dependent decrease in mitochondrial NADPH production pathway or genetic ablation of SHMT2 causes strong increases in inflammatory cytokine signatures associated with redox dependent induction of ASK1 and activation of stress kinases p38 and JNK. These studies find that a major defect of CI deficiencies is decreased mitochondrial one-carbon NADPH production that is associated with increased inflammation and cell death. Mitochondrial oxidative phosphorylation produces ATP and is an important source for cellular energy equivalents. Here the authors perform a cell-based screen to identify genes that alleviate perturbed mitochondrial complex I function and identify malic enzyme (ME-1), which promotes survival by production of NADPH and glutathione.

Sir3-Nucleosome Interactions in Spreading of Silent Chromatin in Saccharomyces cerevisiae

Abstract: Silent chromatin in Saccharomyces cerevisiae is established in a stepwise process involving the SIR complex, comprised of the histone deacetylase Sir2 and the structural components Sir3 and Sir4. The Sir3 protein, which is the primary histone-binding component of the SIR complex, forms oligomers in vitro and has been proposed to mediate the spreading of the SIR complex along the chromatin fiber. In order to analyze the role of Sir3 in the spreading of the SIR complex, we performed a targeted genetic screen for alleles of SIR3 that dominantly disrupt silencing. Most mutations mapped to a single surface in the conserved N-terminal BAH domain, while one, L738P, localized to the AAA ATPase-like domain within the C-terminal half of Sir3. The BAH point mutants, but not the L738P mutant, disrupted the interaction between Sir3 and nucleosomes. In contrast, Sir3-L738P bound the N-terminal tail of histone H4 more strongly than wild-type Sir3, indicating that misregulation of the Sir3 C-terminal histone-binding activity also disrupted spreading. Our results underscore the importance of proper interactions between Sir3 and the nucleosome in silent chromatin assembly. We propose a model for the spreading of the SIR complex along the chromatin fiber through the two distinct histone-binding domains in Sir3.

The effects of mass accuracy, data acquisition speed, and search algorithm choice on peptide identification rates in phosphoproteomics

Abstract: Proteomic analyses via tandem mass spectrometry have been greatly enhanced by the recent development of fast, highly accurate instrumentation. However, successful application of these developments to high-throughput experiments requires careful optimization of many variables which adversely affect each other, such as mass accuracy and data collection speed. We examined the performance of three shotgun-style acquisition methods ranging in their data collection speed and use of mass accuracy in identifying proteins from yeast-derived complex peptide and phosphopeptide-enriched mixtures. We find that the combination of highly accurate precursor masses generated from one survey scan in the FT-ICR cell, coupled with ten data-dependent tandem MS scans in a lower-resolution linear ion trap, provides more identifications in both mixtures than the other examined methods. For phosphopeptide identifications in particular, this method identified over twice as many unique phosphopeptides as the second-ranked, lower-resolution method from triplicate 90-min analyses (744 ± 50 vs. 308 ± 50, respectively). We also examined the performance of four popular peptide assignment algorithms (Mascot, Sequest, OMSSA, and Tandem) in analyzing the results from both high-and low-resolution data. When compared in the context of a false positive rate of approximately 1%, the performance differences between algorithms were much larger for phosphopeptide analyses than for an unenriched, complex mixture. Based upon these findings, acquisition speed, mass accuracy, and the choice of assignment algorithm all largely affect the number of peptides and proteins identified in high-throughput studies.

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.