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.

Proteomic and transcriptomic profiling reveal different aspects of aging in the kidney

Abstract: The kidney is an excellent model for studying organ aging. Kidney function shows steady decline with age and is easy to assay using urine or blood samples. However, little is known about the molecular changes that take place in the kidney during the aging process. In order to better understand the molecular changes that occur with age, we measured mRNA and protein levels in 188 genetically diverse mice at ages 6, 12, and 18 months. We observed distinctive change in mRNA and protein levels as a function of age. Changes in both mRNA and protein are associated with increased immune infiltration and decreases in mitochondrial function. Proteins show a greater extent of change and reveal changes in a wide array of biological processes including unique, organ-specific features of aging in kidney. Most importantly, we observed functionally important age-related changes in protein that occur in the absence of corresponding changes in mRNA. Our findings suggest that mRNA profiling alone provides an incomplete picture of molecular aging in the kidney and that examination of changes in proteins is essential to understand aging processes that are not transcriptionally regulated.

Proteomic profiling of yeast heterochromatin connects direct physical and genetic interactions.

Abstract: Heterochromatin domains are stably repressed chromatin structures composed of a core assembly of silencing proteins that condense adjacent nucleosomes. The minimal heterochromatin structure can serve as a platform for recruitment of complementary regulatory factors. We find that a reconstituted budding yeast heterochromatin domain can act as a platform to recruit multiple factors that play a role in regulating heterochromatin function. We uncover the direct interaction between the SIR heterochromatin complex and a chromosomal boundary protein that restricts the spread of heterochromatin. We find that the SIR complex relieves a mechanism of auto-inhibition within the boundary protein Yta7, allowing the Yta7 bromodomain to engage chromatin. Our results suggest that budding yeast shares with other eukaryotes the ability to establish complex heterochromatin domains that coordinate multiple mechanisms of silencing regulation through physical interactions.

Multi-omics analysis identifies drivers of protein phosphorylation

Abstract: Phosphorylation of proteins is a key step in the regulation of many cellular processes including activation of enzymes and signaling cascades. The abundance of a phosphorylated peptide (phosphopeptide) is determined by the abundance of its parent protein and the proportion of target sites that are phosphorylated.We quantified phosphopeptides, proteins, and transcripts in heart, liver, and kidney tissue samples of mice from 58 strains of the Collaborative Cross strain panel. We mapped ~700 phosphorylation quantitative trait loci (phQTL) across the three tissues and applied genetic mediation analysis to identify causal drivers of phosphorylation. We identified kinases, phosphatases, cytokines, and other factors, including both known and potentially novel interactions between target proteins and genes that regulate site-specific phosphorylation. Our analysis highlights multiple targets of pyruvate dehydrogenase kinase 1 (PDK1), a regulator of mitochondrial function that shows reduced activity in the NZO/HILtJ mouse, a polygenic model of obesity and type 2 diabetes.Together, this integrative multi-omics analysis in genetically diverse CC strains provides a powerful tool to identify regulators of protein phosphorylation. The data generated in this study provides a resource for further exploration.

Msp1/ATAD1 restores mitochondrial function in Zellweger Spectrum Disease

Abstract: Peroxisomal Biogenesis Disorders (PBDs) are a class of inherited metabolic disorders with profound neurological and other phenotypes. The most severe PBDs are caused by mutations in peroxin genes, which result in nonfunctional peroxisomes typically through impaired protein import. In order to better understand the molecular causes of Zellweger Spectrum Disease (ZSD) - the most severe PBDs -, we investigated the fate of peroxisomal mRNAs and proteins in ZSD model systems. We found that loss of peroxisomal import has no effect on peroxin mRNA expression or translational efficiency. Instead, peroxin proteins -still produced at high levels- aberrantly accumulate on the mitochondrial membrane, impairing respiration and ATP generation. Finally, we rescued mitochondrial function in fibroblasts derived from human patients with ZSD by overexpressing ATAD1, an AAA-ATPase that functions in mitochondrial quality control. These findings might provide a new focus of PBD therapies in supporting quality control pathways that protect mitochondrial function.

Rapid quantitative analysis for protein or protein function in complex mixture

Abstract: PROBLEM TO BE SOLVED: To provide a method and a reagent used in a proteome analysis capable of conquering a limitation intrinsic to a conventional art. SOLUTION: The present invention provides an automated LC/MS/MS system provided with (a) an autosampler fluid-connected to a capillary HPLC, (b) an electro-spray ionization triplet quadrupole MS/MS device fluid-connected to the capillary HPLC, and (c) a device control and data analytical system connected electrically to the autosampler, the capillary HPLC, and the MS/MS device. COPYRIGHT: (C)2006,JPO&NCIPI

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.