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.

Transcriptional and proteomic profiles of group B Streptococcus type V reveal potential adherence proteins associated with high-level invasion.

Abstract: Group B Streptococcus (GBS) is an opportunistic organism that can harmlessly colonize the human gut, vagina, and rectum but can also cause pneumonia, sepsis, and meningitis in neonates born to colonized mothers. We have shown previously that growth rate and oxygen level regulate the ability of GBS to invade eukaryotic cells in vitro. Herein we extend and expand on these observations to show that GBS type V, an emergent serotype, grown in a chemostat at a cell mass-doubling time (td) of 1.8 h with oxygen invaded human ME-180 cervical epithelial cells in large numbers compared with those grown at the same td without oxygen or at a slower td of 11.0 h. The fact that several GBS type V cell wall-associated and membrane proteins were expressed exclusively under the invasive growth condition prompted an investigation, using genomics and proteomics, of all upregulated genes and proteins. Several proteins with potential roles in adherence were identified, including an undefined surface antigen (SAG1350), a lipoprotein (SAG0971), penicillin-binding protein 2b (SAG0765), glyceraldehyde-3-phosphate dehydrogenase (SAG0823), and an iron-binding protein (SAG1007). Mouse antisera to these five proteins inhibited binding of GBS type V to ME-180 cells by ≥85%. Recombinant undefined surface antigen (SAG1350), lipoprotein (SAG0971), and penicillin-binding protein 2b (SAG0765) each bound to ME-180 cells in a dose-dependent fashion, confirming their ability to act as ligands. Collectively, these data increase the number of potential GBS adherence factors and also suggest a role for these surface-associated proteins in initial pathogenic events.

Mitochondrial aconitase modification, functional inhibition, and evidence for a supramolecular complex of the TCA cycle by the renal toxicant S-(1,1,2,2-tetrafluoroethyl)-L-cysteine.

Abstract: Metabolism of the common industrial gas tetrafluoroethylene in mammals results in the formation of S-(1,1,2,2)-tetrafluoroethyl-l-cysteine (TFEC), which can be bioactivated by a mitochondrial C-S l...

Distribution of codeine and morphine into rat hair after long-term daily dosing with codeine.

Abstract: Hair analysis for drugs of abuse provides a possible long-term measure of drug use not possible with urinalysis. Many drugs and their metabolites have been detected in hair; however, the factors influencing the incorporation of chemicals into hair are poorly understood. An animal model for chemical uptake into hair utilizing controlled drug administration was developed to ascertain if increasing doses of codeine are reflected in the concentrations of codeine and its metabolites found in hair. Male Sprague-Dawley rats were administered codeine at 5, 10, or 20 mg/kg (intraperitoneally; n = 6) daily for 21 days. At various times during and after the dosing protocol, approximately 50 mg of hair was shaved from a different area of the animals' backs and analyzed for codeine and morphine concentrations by ion-trap gas chromatography-mass spectrometry. Peak hair codeine concentrations for the 5-, 10-, and 20-mg/kg groups occurred 20 days after beginning the dosing protocol and were 0.57  0.13, 0.80  0.10, and 1.95  0.35 ng/mg hair, respectively. Morphine peak concentrations occurred at the same time and were 1.08  0.28, 1.21  0.09, and 2.10  0.26 ng/mg hair for the 5-, 10-, and 20-mg/kg groups, respectively. Long-term dosing in the rat resulted in similar or greater hair concentrations of morphine (metabolite) than codeine. The plasma pharmacokinetics of codeine and morphine were also obtained after a single, intraperitoneal codeine administration of 20 mg/kg. An experiment involving washing the rat hair with methanol or phosphate buffer (pH 9.0) did not reduce the concentration of codeine or morphine measured in hair as compared with nonwashed control hair. Data obtained in this study indicate that after controlled administration the incorporation of codeine and its metabolite, morphine, into rat hair occurs in a distinct dose-proportional manner.

Multiplexed Relative Quantitation with Isobaric Tagging Mass Spectrometry Reveals Class I Major Histocompatibility Complex Ligand Dynamics in Response to Doxorubicin

Abstract: MHC-I peptides are intracellular-cleaved peptides, usually 8-11 amino acids in length, which are presented on the cell surface and facilitate CD8+ T cell responses. Despite the appreciation of CD8+ T-cell antitumor immune responses toward improvement in patient outcomes, the MHC-I peptide ligands that facilitate the response are poorly described. Along these same lines, although many therapies have been recognized for their ability to reinvigorate antitumor CD8+ T-cell responses, whether these therapies alter the MHC-I peptide repertoire has not been fully assessed due to the lack of quantitative strategies. We develop a multiplexing platform for screening therapy-induced MHC-I ligands by employing tandem mass tags (TMTs). We applied this approach to measuring responses to doxorubicin, which is known to promote antitumor CD8+ T-cell responses during its therapeutic administration in cancer patients. Using both in vitro and in vivo systems, we show successful relative quantitation of MHC-I ligands using TMT-based multiplexing and demonstrate that doxorubicin induces MHC-I peptide ligands that are largely derived from mitotic progression and cell-cycle proteins. This high-throughput MHC-I ligand discovery approach may enable further explorations to understand how small molecules and other therapies alter MHC-I ligand presentation that may be harnessed for CD8+ T-cell-based immunotherapies.

Extended ubiquitin species are protein-based DUB inhibitors

Abstract: C-terminal extended ubiquitin species, which have been associated with neurodegenerative disorders, were thought to inhibit proteasomes resulting in reduced protein clearance. Biochemical studies now provide evidence that these ubiquitin variants primarily block the activity of the deubiqutinating enzymes.

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.