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.

mTOR inhibition activates overall protein degradation by the ubiquitin proteasome system as well as by autophagy

Abstract: Growth factors and nutrients enhance protein synthesis and suppress overall protein degradation by activating the protein kinase mammalian target of rapamycin (mTOR). Conversely, nutrient or serum deprivation inhibits mTOR and stimulates protein breakdown by inducing autophagy, which provides the starved cells with amino acids for protein synthesis and energy production. However, it is unclear whether proteolysis by the ubiquitin proteasome system (UPS), which catalyzes most protein degradation in mammalian cells, also increases when mTOR activity decreases. Here we show that inhibiting mTOR with rapamycin or Torin1 rapidly increases the degradation of long-lived cell proteins, but not short-lived ones, by stimulating proteolysis by proteasomes, in addition to autophagy. This enhanced proteasomal degradation required protein ubiquitination, and within 30 min after mTOR inhibition, the cellular content of K48-linked ubiquitinated proteins increased without any change in proteasome content or activity. This rapid increase in UPS-mediated proteolysis continued for many hours and resulted primarily from inhibition of mTORC1 (not mTORC2), but did not require new protein synthesis or key mTOR targets: S6Ks, 4E-BPs, or Ulks. These findings do not support the recent report that mTORC1 inhibition reduces proteolysis by suppressing proteasome expression [Zhang Y, et al. (2014) Nature 513(7518):440–443]. Several growth-related proteins were identified that were ubiquitinated and degraded more rapidly after mTOR inhibition, including HMG-CoA synthase, whose enhanced degradation probably limits cholesterol biosynthesis upon insulin deficiency. Thus, mTOR inhibition coordinately activates the UPS and autophagy, which provide essential amino acids and, together with the enhanced ubiquitination of anabolic proteins, help slow growth.

Succinate dehydrogenase is a direct target of sirtuin 3 deacetylase activity.

Abstract: Background Sirtuins (SIRT1-7) are a family of NAD-dependent deacetylases and/or ADP-ribosyltransferases that are involved in metabolism, stress responses and longevity. SIRT3 is localized to mitochondria, where it deacetylates and activates a number of enzymes involved in fuel oxidation and energy production. Methodology/Principal Findings In this study, we performed a proteomic screen to identify SIRT3 interacting proteins and identified several subunits of complex II and V of the electron transport chain. Two subunits of complex II (also known as succinate dehydrogenase, or SDH), SDHA and SDHB, interacted specifically with SIRT3. Using mass spectrometry, we identified 13 acetylation sites on SDHA, including six novel acetylated residues. SDHA is hyperacetylated in SIRT3 KO mice and SIRT3 directly deacetylates SDHA in a NAD-dependent manner. Finally, we found that SIRT3 regulates SDH activity both in cells and in murine brown adipose tissue. Conclusions/Significance Our study identifies SDHA as a binding partner and substrate for SIRT3 deacetylase activity. SIRT3 loss results in decreased SDH enzyme activity, suggesting that SIRT3 may be an important physiological regulator of SDH activity.

Accumulation of succinate controls activation of adipose tissue thermogenesis.

Abstract: Thermogenesis by brown and beige adipose tissue, which requires activation by external stimuli, can counter metabolic disease1. Thermogenic respiration is initiated by adipocyte lipolysis through cyclic AMP–protein kinase A signalling; this pathway has been subject to longstanding clinical investigation2–4. Here we apply a comparative metabolomics approach and identify an independent metabolic pathway that controls acute activation of adipose tissue thermogenesis in vivo. We show that substantial and selective accumulation of the tricarboxylic acid cycle intermediate succinate is a metabolic signature of adipose tissue thermogenesis upon activation by exposure to cold. Succinate accumulation occurs independently of adrenergic signalling, and is sufficient to elevate thermogenic respiration in brown adipocytes. Selective accumulation of succinate may be driven by a capacity of brown adipocytes to sequester elevated circulating succinate. Furthermore, brown adipose tissue thermogenesis can be initiated by systemic administration of succinate in mice. Succinate from the extracellular milieu is rapidly metabolized by brown adipocytes, and its oxidation by succinate dehydrogenase is required for activation of thermogenesis. We identify a mechanism whereby succinate dehydrogenase-mediated oxidation of succinate initiates production of reactive oxygen species, and drives thermogenic respiration, whereas inhibition of succinate dehydrogenase supresses thermogenesis. Finally, we show that pharmacological elevation of circulating succinate drives UCP1-dependent thermogenesis by brown adipose tissue in vivo, which stimulates robust protection against diet-induced obesity and improves glucose tolerance. These findings reveal an unexpected mechanism for control of thermogenesis, using succinate as a systemically-derived thermogenic molecule.

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.