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.

DPP9 directly sequesters the NLRP1 C-terminus to repress inflammasome activation

Abstract: NLRP1 is a cytosolic inflammasome sensor that mediates activation of caspase-1, which in turn induces cytokine maturation and pyroptotic cell death1-6. Gain-of-function NLPR1 mutations cause skin inflammatory diseases including carcinoma, keratosis, and papillomatosis7-14. NLRP1 contains a unique function-to-find domain (FIIND) that autoproteolyzes into noncovalently associated subdomains15-18. Proteasomal degradation of the autoinhibitory N-terminal fragment (NT) activates NLRP1 by releasing the inflammatory C-terminal fragment (CT)19,20. Cytosolic dipeptidyl peptidases 8 and 9 (DPP8/9) interact with NLRP1, and small-molecule DPP8/9 inhibitors activate NLRP1 by poorly characterized mechanisms11,19,21. Here, we report cryo-EM structures of the human NLRP1-DPP9 complex, alone and in complex with the DPP8/9 inhibitor Val-boroPro (VbP). Surprisingly, the NLRP1-DPP9 complex is a ternary complex comprised of DPP9, one intact FIIND of a non-degraded full-length NLRP1 (NLRP1-FL) and one NLRP1-CT freed by NT degradation. The N-terminus of the NLRP1-CT unfolds and inserts into the DPP9 active site but is not cleaved by DPP9, and this binding is disrupted by VbP. Structure-based mutagenesis reveals that the binding of NLRP1-CT to DPP9 requires NLRP1-FL and vice versa, and inflammasome activation by ectopic NLRP1-CT expression is rescued by co-expressing autoproteolysis-deficient NLRP1-FL. Collectively, these data indicate that DPP9 functions as a “bomb-diffuser” to prevent NLRP1-CTs from inducing inflammation during homeostatic protein turnover.

Spatial snapshots of amyloid precursor protein intramembrane processing via early endosome proteomics

Abstract: Abstract Degradation and recycling of plasma membrane proteins occurs via the endolysosomal system, wherein endosomes bud into the cytosol from the plasma membrane and subsequently mature into degradative lysosomal compartments. While methods have been developed for rapid selective capture of lysosomes (Lyso-IP), analogous methods for isolation of early endosome intermediates are lacking. Here, we develop an approach for rapid isolation of early/sorting endosomes through affinity capture of the early endosome-associated protein EEA1 (Endo-IP) and provide proteomic and lipidomic snapshots of EEA1-positive endosomes in action. We identify recycling, regulatory and membrane fusion complexes, as well as candidate cargo, providing a proteomic landscape of early/sorting endosomes. To demonstrate the utility of the method, we combined Endo- and Lyso-IP with multiplexed targeted proteomics to provide a spatial digital snapshot of amyloid precursor protein (APP) processing by β and γ-Secretases, which produce amyloidogenic Aβ species, and quantify small molecule modulation of Secretase action on endosomes. We anticipate that the Endo-IP approach will facilitate systematic interrogation of processes that are coordinated on EEA1-positive endosomes.

Translocation of polyubiquitinated protein substrates by the hexameric Cdc48 ATPase

Abstract: The hexameric Cdc48 ATPase (p97 or VCP in mammals) cooperates with its cofactor Ufd1/Npl4 to extract polyubiquitinated proteins from membranes or macromolecular complexes for degradation by the proteasome. Here, we clarify how the Cdc48 complex unfolds its substrates and translocates polypeptides with branchpoints. The Cdc48 complex recognizes primarily polyubiquitin chains, rather than the attached substrate. Cdc48 and Ufd1/Npl4 cooperatively bind the polyubiquitin chain, resulting in the unfolding of one ubiquitin molecule (initiator). Next, the ATPase pulls on the initiator ubiquitin and moves all ubiquitin molecules linked to its C-terminus through the central pore of the hexameric double-ring, causing transient ubiquitin unfolding. When the ATPase reaches the isopeptide bond of the substrate, it can translocate and unfold both N- and C-terminal segments. Ubiquitins linked to the branchpoint of the initiator dissociate from Ufd1/Npl4 and move outside the central pore, resulting in the release of unfolded, polyubiquitinated substrate from Cdc48.

Dual DNA and protein tagging of open chromatin unveils dynamics of epigenomic landscapes in leukemia.

Abstract: The architecture of chromatin regulates eukaryotic cell states by controlling transcription factor access to sites of gene regulation. Here we describe a dual transposase-peroxidase approach, integrative DNA and protein tagging (iDAPT), which detects both DNA (iDAPT-seq) and protein (iDAPT-MS) associated with accessible regions of chromatin. In addition to direct identification of bound transcription factors, iDAPT enables the inference of their gene regulatory networks, protein interactors and regulation of chromatin accessibility. We applied iDAPT to profile the epigenomic consequences of granulocytic differentiation of acute promyelocytic leukemia, yielding previously undescribed mechanistic insights. Our findings demonstrate the power of iDAPT as a platform for studying the dynamic epigenomic landscapes and their transcription factor components associated with biological phenomena and disease.

Liver RBFOX2 regulates cholesterol homeostasis via Scarb1 alternative splicing in mice

Abstract: RNA alternative splicing (AS) expands the regulatory potential of eukaryotic genomes. The mechanisms regulating liver-specific AS profiles and their contribution to liver function are poorly understood. Here, we identify a key role for the splicing factor RNA-binding Fox protein 2 (RBFOX2) in maintaining cholesterol homeostasis in a lipogenic environment in the liver. Using enhanced individual-nucleotide-resolution ultra-violet cross-linking and immunoprecipitation, we identify physiologically relevant targets of RBFOX2 in mouse liver, including the scavenger receptor class B type I (Scarb1). RBFOX2 function is decreased in the liver in diet-induced obesity, causing a Scarb1 isoform switch and alteration of hepatocyte lipid homeostasis. Our findings demonstrate that specific AS programmes actively maintain liver physiology, and underlie the lipotoxic effects of obesogenic diets when dysregulated. Splice-switching oligonucleotides targeting this network alleviate obesity-induced inflammation in the liver and promote an anti-atherogenic lipoprotein profile in the blood, underscoring the potential of isoform-specific RNA therapeutics for treating metabolism-associated diseases.

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.