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.

Quantitative proteomics identifies NCOA4 as the cargo receptor mediating ferritinophagy

Abstract: Autophagy, the process by which proteins and organelles are sequestered in double-membrane structures called autophagosomes and delivered to lysosomes for degradation, is critical in diseases such as cancer and neurodegeneration. Much of our understanding of this process has emerged from analysis of bulk cytoplasmic autophagy, but our understanding of how specific cargo, including organelles, proteins or intracellular pathogens, are targeted for selective autophagy is limited. Here we use quantitative proteomics to identify a cohort of novel and known autophagosome-enriched proteins in human cells, including cargo receptors. Like known cargo receptors, nuclear receptor coactivator 4 (NCOA4) was highly enriched in autophagosomes, and associated with ATG8 proteins that recruit cargo-receptor complexes into autophagosomes. Unbiased identification of NCOA4-associated proteins revealed ferritin heavy and light chains, components of an iron-filled cage structure that protects cells from reactive iron species but is degraded via autophagy to release iron through an unknown mechanism. We found that delivery of ferritin to lysosomes required NCOA4, and an inability of NCOA4-deficient cells to degrade ferritin led to decreased bioavailable intracellular iron. This work identifies NCOA4 as a selective cargo receptor for autophagic turnover of ferritin (ferritinophagy), which is critical for iron homeostasis, and provides a resource for further dissection of autophagosomal cargo-receptor connectivity.

MultiNotch MS3 Enables Accurate, Sensitive, and Multiplexed Detection of Differential Expression across Cancer Cell Line Proteomes

Abstract: Multiplexed quantitation via isobaric chemical tags (e.g., tandem mass tags (TMT) and isobaric tags for relative and absolute quantitation (iTRAQ)) has the potential to revolutionize quantitative proteomics. However, until recently the utility of these tags was questionable due to reporter ion ratio distortion resulting from fragmentation of coisolated interfering species. These interfering signals can be negated through additional gas-phase manipulations (e.g., MS/MS/MS (MS3) and proton-transfer reactions (PTR)). These methods, however, have a significant sensitivity penalty. Using isolation waveforms with multiple frequency notches (i.e., synchronous precursor selection, SPS), we coisolated and cofragmented multiple MS2 fragment ions, thereby increasing the number of reporter ions in the MS3 spectrum 10-fold over the standard MS3 method (i.e., MultiNotch MS3). By increasing the reporter ion signals, this method improves the dynamic range of reporter ion quantitation, reduces reporter ion signal variance...

Comprehensive proteomic analysis of the human spliceosome.

Abstract: The precise excision of introns from pre-messenger RNA is performed by the spliceosome, a macromolecular machine containing five small nuclear RNAs and numerous proteins. Much has been learned about the protein components of the spliceosome from analysis of individual purified small nuclear ribonucleoproteins and salt-stable spliceosome 'core' particles. However, the complete set of proteins that constitutes intact functional spliceosomes has yet to be identified. Here we use maltose-binding protein affinity chromatography to isolate spliceosomes in highly purified and functional form. Using nanoscale microcapillary liquid chromatography tandem mass spectrometry, we identify approximately 145 distinct spliceosomal proteins, making the spliceosome the most complex cellular machine so far characterized. Our spliceosomes comprise all previously known splicing factors and 58 newly identified components. The spliceosome contains at least 30 proteins with known or putative roles in gene expression steps other than splicing. This complexity may be required not only for splicing multi-intronic metazoan pre-messenger RNAs, but also for mediating the extensive coupling between splicing and other steps in gene expression.

MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics

Abstract: Quantitative mass spectrometry-based proteomics is highly versatile but not easily multiplexed. Isobaric labeling strategies allow mass spectrometry-based multiplexed proteome quantification; however, ratio distortion owing to protein quantification interference is a common effect. We present a two-proteome model (mixture of human and yeast proteins) in a sixplex isobaric labeling system to fully document the interference effect, and we report that applying triple-stage mass spectrometry (MS3) almost completely eliminates interference.

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.