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.

Translation elongation factor 2 depletion by siRNA in mouse liver leads to mTOR-independent translational upregulation of ribosomal protein genes.

Abstract: Due to breakthroughs in RNAi and genome editing methods in the past decade, it is now easier than ever to study fine details of protein synthesis in animal models. However, most of our understanding of translation comes from unicellular organisms and cultured mammalian cells. In this study, we demonstrate the feasibility of perturbing protein synthesis in a mouse liver by targeting translation elongation factor 2 (eEF2) with RNAi. We were able to achieve over 90% knockdown efficacy and maintain it for 2 weeks effectively slowing down the rate of translation elongation. As the total protein yield declined, both proteomics and ribosome profiling assays showed robust translational upregulation of ribosomal proteins relative to other proteins. Although all these genes bear the TOP regulatory motif, the branch of the mTOR pathway responsible for translation regulation was not activated. Paradoxically, coordinated translational upregulation of ribosomal proteins only occurred in the liver but not in murine cell culture. Thus, the upregulation of ribosomal transcripts likely occurred via passive mTOR-independent mechanisms. Impaired elongation sequesters ribosomes on mRNA and creates a shortage of free ribosomes. This leads to preferential translation of transcripts with high initiation rates such as ribosomal proteins. Furthermore, severe eEF2 shortage reduces the negative impact of positively charged amino acids frequent in ribosomal proteins on ribosome progression.

Mapping Angiotensin II Type 1 Receptor-Biased Signaling Using Proximity Labeling and Proteomics Identifies Diverse Actions of Biased Agonists.

Abstract: Angiotensin II type 1 receptors (AT1Rs) are one of the most widely studied G-protein-coupled receptors. To fully appreciate the diversity in cellular signaling profiles activated by AT1R transducer-biased ligands, we utilized peroxidase-catalyzed proximity labeling to capture proteins in close proximity to AT1Rs in response to six different ligands: angiotensin II (full agonist), S1I8 (partial agonist), TRV055 and TRV056 (G-protein-biased agonists), and TRV026 and TRV027 (β-arrestin-biased agonists) at 90 s, 10 min, and 60 min after stimulation (ProteomeXchange Identifier PXD023814). We systematically analyzed the kinetics of AT1R trafficking and determined that distinct ligands lead AT1R to different cellular compartments for downstream signaling activation and receptor degradation/recycling. Distinct proximity labeling of proteins from a number of functional classes, including GTPases, adaptor proteins, and kinases, was activated by different ligands suggesting unique signaling and physiological roles of the AT1R. Ligands within the same class, that is, either G-protein-biased or β-arrestin-biased, shared high similarity in their labeling profiles. A comparison between ligand classes revealed distinct signaling activation such as greater labeling by G-protein-biased ligands on ESCRT-0 complex proteins that act as the sorting machinery for ubiquitinated proteins. Our study provides a comprehensive analysis of AT1R receptor-trafficking kinetics and signaling activation profiles induced by distinct classes of ligands.

Filamin C Cardiomyopathy Variants Cause Protein and Lysosome Accumulation

Abstract: Rationale: Dominant heterozygous variants in Filamin C (FLNC) cause diverse cardiomyopathies, though the underlying molecular mechanisms remain poorly understood. Objective: We aimed to define the molecular mechanisms by which FLNC variants altered human cardiomyocyte gene and protein expression, sarcomere structure, and contractile performance. Methods and Results: Using CRISPR/Cas9, we introduced FLNC variants into human cardiomyocytes derived from induced pluripotent stem cells (hiPSC-CMs). We compared isogenic hiPSC-CMs with normal (WT), ablated expression (FLNC-/-) or haploinsufficiency (FLNC+/-) that causes DCM. We also studied a heterozygous in-frame deletion (FLNC+/∆7aa) which did not affect FLNC expression but caused aggregate formation, similar to FLNC variants associated with hypertrophic cardiomyopathy (HCM). FLNC-/- hiPSC-CMs demonstrated profound sarcomere misassembly and reduced contractility. While sarcomere formation and function were unaffected in FLNC+/- and FLNC+/∆7aa hiPSC-CMs, these heterozygous variants caused increases in lysosome content, enhancement of autophagic flux, and accumulation of FLNC-binding partners and Z-disc proteins. Conclusions: FLNC expression is required for sarcomere organization and physiologic function. Variants that produce misfolded FLNC proteins cause the accumulation of FLNC and FLNC binding partners which leads to increased lysosome expression and activation of autophagic pathways. Surprisingly, similar pathways were activated in FLNC haploinsufficient hiPSC-CMs, likely initiated by the loss of stoichiometric FLNC protein interactions and impaired turnover of proteins at the Z-disc. These results indicate that both FLNC haploinsufficient variants and variants that produce misfolded FLNC protein cause disease by similar proteotoxic mechanisms, and indicate the therapeutic potential for augmenting protein degradative pathways to treat a wide range of FLNC-related cardiomyopathies.

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.