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.

UCP1 governs liver extracellular succinate and inflammatory pathogenesis

Abstract: Non-alcoholic fatty liver disease (NAFLD), the most prevalent liver pathology worldwide, is intimately linked with obesity and type 2 diabetes. Liver inflammation is a hallmark of NAFLD and is thought to contribute to tissue fibrosis and disease pathogenesis. Uncoupling protein 1 (UCP1) is exclusively expressed in brown and beige adipocytes, and has been extensively studied for its capacity to elevate thermogenesis and reverse obesity. Here we identify an endocrine pathway regulated by UCP1 that antagonizes liver inflammation and pathology, independent of effects on obesity. We show that, without UCP1, brown and beige fat exhibit a diminished capacity to clear succinate from the circulation. Moreover, UCP1KO mice exhibit elevated extracellular succinate in liver tissue that drives inflammation through ligation of its cognate receptor succinate receptor 1 (SUCNR1) in liver-resident stellate cell and macrophage populations. Conversely, increasing brown and beige adipocyte content in mice antagonizes SUCNR1-dependent inflammatory signalling in the liver. We show that this UCP1-succinate–SUCNR1 axis is necessary to regulate liver immune cell infiltration and pathology, and systemic glucose intolerance in an obesogenic environment. As such, the therapeutic use of brown and beige adipocytes and UCP1 extends beyond thermogenesis and may be leveraged to antagonize NAFLD and SUCNR1-dependent liver inflammation. UCP1 is exclusively expressed in brown and beige adipocytes, where it drives thermogenesis through futile substrate cycling. Mills et al. identify a endocrine pathway mediated by the UCP1 catabolic circuit that antagonizes liver inflammation by lowering the concentration of succinate in the liver extracellular fluid.

LIN28 phosphorylation by MAPK/ERK couples signalling to the post-transcriptional control of pluripotency.

Abstract: Signalling and post-transcriptional gene control are both critical for the regulation of pluripotency, yet how they are integrated to influence cell identity remains poorly understood. LIN28 (also known as LIN28A), a highly conserved RNA-binding protein, has emerged as a central post-transcriptional regulator of cell fate through blockade of let-7 microRNA biogenesis and direct modulation of mRNA translation. Here we show that LIN28 is phosphorylated by MAPK/ERK in pluripotent stem cells, which increases its levels via post-translational stabilization. LIN28 phosphorylation had little impact on let-7 but enhanced the effect of LIN28 on its direct mRNA targets, revealing a mechanism that uncouples LIN28's let-7-dependent and -independent activities. We have linked this mechanism to the induction of pluripotency by somatic cell reprogramming and the transition from naive to primed pluripotency. Collectively, our findings indicate that MAPK/ERK directly impacts LIN28, defining an axis that connects signalling, post-transcriptional gene control, and cell fate regulation.

Cooperative interaction between phosphorylation sites on PERIOD maintains circadian period in Drosophila.

Abstract: Circadian rhythms in Drosophila rely on cyclic regulation of the period (per) and timeless (tim) clock genes. The molecular cycle requires rhythmic phosphorylation of PER and TIM proteins, which is mediated by several kinases and phosphatases such as Protein Phosphatase-2A (PP2A) and Protein Phosphatase-1 (PP1). Here, we used mass spectrometry to identify 35 “phospho-occupied” serine/threonine residues within PER, 24 of which are specifically regulated by PP1/PP2A. We found that cell culture assays were not good predictors of protein function in flies and so we generated per transgenes carrying phosphorylation site mutations and tested for rescue of the per01 arrhythmic phenotype. Surprisingly, most transgenes restore wild type rhythms despite carrying mutations in several phosphorylation sites. One particular transgene, in which T610 and S613 are mutated to alanine, restores daily rhythmicity, but dramatically lengthens the period to ∼30 hrs. Interestingly, the single S613A mutation extends the period by 2–3 hours, while the single T610A mutation has a minimal effect, suggesting these phospho-residues cooperate to control period length. Conservation of S613 from flies to humans suggests that it possesses a critical clock function, and mutational analysis of residues surrounding T610/S613 implicates the entire region in determining circadian period. Biochemical and immunohistochemical data indicate defects in overall phosphorylation and altered timely degradation of PER carrying the double or single S613A mutation(s). The PER-T610A/S613A mutant also alters CLK phosphorylation and CLK-mediated output. Lastly, we show that a mutation at a previously identified site, S596, is largely epistatic to S613A, suggesting that S613 negatively regulates phosphorylation at S596. Together these data establish functional significance for a new domain of PER, demonstrate that cooperativity between phosphorylation sites maintains PER function, and support a model in which specific phosphorylated regions regulate others to control circadian period.

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.