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 deficiency causes brown fat respiratory chain depletion and sensitizes mitochondria to calcium overload-induced dysfunction.

Abstract: Brown adipose tissue (BAT) mitochondria exhibit high oxidative capacity and abundant expression of both electron transport chain components and uncoupling protein 1 (UCP1). UCP1 dissipates the mitochondrial proton motive force (Δp) generated by the respiratory chain and increases thermogenesis. Here we find that in mice genetically lacking UCP1, cold-induced activation of metabolism triggers innate immune signaling and markers of cell death in BAT. Moreover, global proteomic analysis reveals that this cascade induced by UCP1 deletion is associated with a dramatic reduction in electron transport chain abundance. UCP1-deficient BAT mitochondria exhibit reduced mitochondrial calcium buffering capacity and are highly sensitive to mitochondrial permeability transition induced by reactive oxygen species (ROS) and calcium overload. This dysfunction depends on ROS production by reverse electron transport through mitochondrial complex I, and can be rescued by inhibition of electron transfer through complex I or pharmacologic depletion of ROS levels. Our findings indicate that the interscapular BAT of Ucp1 knockout mice exhibits mitochondrial disruptions that extend well beyond the deletion of UCP1 itself. This finding should be carefully considered when using this mouse model to examine the role of UCP1 in physiology.

Accurate multiplexed proteomics at the MS2 level using the complement reporter ion cluster.

Abstract: Isobaric labeling strategies, such as isobaric tags for relative and absolute quantitation (iTRAQ) or tandem mass tags (TMT), have promised to dramatically increase the power of quantitative proteomics. However, when applied to complex mixtures, both the accuracy and precision are undermined by interfering peptide ions that coisolate and cofragment with the target peptide. Additional gas-phase isolation steps, such as proton-transfer ion-ion reactions (PTR) or higher-order MS3 scans, can almost completely eliminate this problem. Unfortunately, these methods come at the expense of decreased acquisition speed and sensitivity. Here we present a method that allows accurate quantification of TMT-labeled peptides at the MS2 level without additional ion purification. Quantification is based on the fragment ion cluster that carries most of the TMT mass balance. In contrast to the use of low m/z reporter ions, the localization of these complement TMT (TMT(C)) ions in the spectrum is precursor-specific; coeluting peptides do not generally affect the measurement of the TMT(C) ion cluster of interest. Unlike the PTR or MS3 strategies, this method can be implemented on a wide range of high-resolution mass spectrometers like the quadrupole Orbitrap instruments (QExactive). A current limitation of the method is that the efficiency of TMT(C) ion formation is affected by both peptide sequence and peptide ion charge state; we discuss potential routes to overcome this problem. Finally, we show that the complement reporter ion approach allows parallelization of multiplexed quantification and therefore holds the potential to multiply the number of distinct peptides that can be quantified in a given time frame.

The tumor suppressor CYLD regulates entry into mitosis

Abstract: Mutations in the cylindromatosis (CYLD) gene cause benign tumors of skin appendages, referred to as cylindromas. The CYLD gene encodes a deubiquitinating enzyme that removes Lys-63-linked ubiquitin chains from I kappa B kinase signaling components and thereby inhibits NF-kappaB pathway activation. The dysregulation of NF-kappaB activity has been proposed to promote cell transformation in part by increasing apoptosis resistance, but it is not clear whether this is CYLD's only or predominant tumor-suppressing function. Here, we show that CYLD is also required for timely entry into mitosis. Consistent with a cell-cycle regulatory function, CYLD localizes to microtubules in interphase and the midbody during telophase, and its protein levels decrease as cells exit from mitosis. We identified the protein kinase Plk1 as a potential target of CYLD in the regulation of mitotic entry, based on their physical interaction and similar loss-of-function and overexpression phenotypes. Our findings raise the possibility that, as with other genes regulating tumorigenesis, CYLD has not only tumor-suppressing (apoptosis regulation) but also tumor-promoting activities (enhancer of mitotic entry). We propose that this additional function of CYLD could provide an explanation for the benign nature of most cylindroma lesions.

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.