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Record W7060057525

Session 2

2017· article· en· W7060057525 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed Central · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsTSG101Work (physics)ParkinLafora diseaseProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

3.1 FLASH TALKS Eight poster presenters will be invited to give “flash” talks – a short presentation on their work using a maximum of 3 slides to give a 5minute presentation followed by 2minutes of Q&A. Registrants who are presenting a poster will be selected to speak by the co-chairs of the Symposium based on their research topic, results. Posters should be set up Sunday afternoon for this consideration. Selected presenters will be notified Monday morning. “Flash” talks will be given at the beginning of Session I on Tuesday. 3.2 Quantifying Ubiquitin Signaling for Mitophagy Wade Harper, Alban Ordureau, Jin-mi Heo, Joao A. Paulo, Nathan Harper, Steve Gygi Harvard Medical School, Boston, MA, USA Targeting of damaged mitochondria for mitophagy involves ubiquitylation of a number of mitochondrial outer membrane (MOM) proteins by the PARKIN ubiquitin ligase upon activation by PINK1. PARKIN recruitment to the MOM as well as UB chain assembly relies on phosphorylation of both PARKIN on S65 and UB chains on S65, which binds and further activates PARKIN UB chain synthesis in a feed-forward mechanism. The assembly of UB chains on the MOM then promotes recruitment of ubiquitin-binding autophagy adaptors such as OPTN in a process that is amplified by the associated TBK1 kinase. A major goal is to develop a quantitative framework for understanding the links between ubiquitin chain assembly, primary substrate ubiquitylation, and selective autophagy. We have developed a quantitative proteomics system for monitoring the kinetics and stoichiometry of primarily ubiquitylation on numerous MOM proteins simultaneously with measurement of UB chain synthesis and UB S65 phosphorylation. Mitochondrial substrates for PARKIN differ in total abundance by 2 orders of magnitude, which has major implications for underlying mechanisms of mitophagy, and a small number of substrates carry the majority of the ubiquitin molecules in response to depolarization. In parallel, we have used APEX2-based proximity labeling to examine proteins located nearby autophagy receptors on damaged mitochondria and to also examine the spatial localization of autophagy adaptors by electron microscopy. This is beginning to allow the development of a spatial model for mitophagy. The majority of studies in this area have focused on the use of a HeLa cell model to study mitophagy. In order to understand this pathway in neurons, we have initiated an analysis in gene-edited ES cells that are subsequently converted to cortical or dopaminergic neurons. We find that some aspects of the pathway in neurons are distinct from that in the standard HeLa model, thereby providing a new paradigm for studying PARKIN-dependent mitophagy. 3.3 Spatial proteomics and transcriptomics Alice Y. Ting Stanford University, Stanford, CA, USA In biology as in real estate, location is a cardinal organizing principle of gene control and function. Where proteins and RNA are located within the cell can dictate their translation, folding, editing, degradation, post-translational modifications, and function. The classical technique for mapping subcellular organization is biochemical fractionation followed by MS proteomics or RNA-seq. However, fractionation can yield significant false positives due to contamination by membranes and components of other organelles. Furthermore, many subcellular regions of interest are impossible to purify. Our lab has developed an alternative approach, based on in-cell enzyme-catalyzed proximity biotinylation of endogenous proteins and RNA. The enzyme, an engineered mutant of ascorbate peroxidase (APEX2) is genetically targeted to a subcellular region of interest. After 1 minute biotinylation, tagged proteins or RNA are enriched by streptavidin, and identified by MS or RNA-seq. My talk will describe recent developments in the improvement and extension of proximity biotinylation methods, and cover applications of the methodology to biological questions related to the ER and mitochondria of mammalian cells. 3.4 High-density proximity interactome mapping at steady-state reveals the subcellular organization of mRNA associated granules and bodies Ji-Young Youn(1), Wade H. Dunham(1), Seo-Jung Hong(1), James D. R. Knight(1), Anne-Claude Gingras(1,2) (1)Lunenfeld-Tanenbaum Research Institute, Toronto ON, Canada; (2)Department of Molecular Genetics, University of Toronto, Canada The intricate post-transcriptional regulation steps necessary to determine mRNA fate are compartmentalized inside the cells. Compartmentalization often involves liquid-liquid phase separation into distinct RNA-associated bodies and granules. For example, in the cytosol, p-bodies serve largely for mRNA degradation, while stress granules are implicated in mRNA storage during stress and in triage. To understand the specificity and spatial organization of the mRNA biogenesis and degradation machinery, we systematically performed in vivo biotinylation coupled to mass spectrometry (BioID) on 139 baits associated with RNA biology, 40% of which linked to p-bodies and stress granules. We reveal largely pre-existing contacts between the stress granule proteins in the absence of stress that are only modestly affected following stress treatment. The high-density of our network enabled organizing the RNA-associated machinery based on the correlated profiles of the 1792 detected endogenous preys (about half of which previously reported to bind RNA). This revealed subcellular localization of the preys, but also often definition of protein complexes and identification of potentially new components, notably for the CCR4-NOT deadenylase machinery. Our dataset also provided definition and validation of groups of proteins associated with stress granules and p-bodies, and detection of the connections that they establish to different cellular processes. Lastly, we uncovered a set of core stress granule proteins, three of which (UBAP2L, CSDE1 and PRRC2A) were found critical for the proper formation of microscopically-visible stress granules upon arsenite treatment. Taken together, our dataset provides an important resource to illuminate the organization of the RNA processing machinery. 3.5 Hybrid mass spectrometry approaches targeting cellular signaling Albert J. R. Heck Utrecht University, Utrecht, The Netherlands Around for more than a century mass spectrometry is blooming more than ever, and applied in nearly all aspects of the natural and life sciences. In the last two decades, mass spectrometry has become routine for the high-throughput analysis of peptides and their post-translational modifications. In this talk I will highlight some of these hybrid mass spectrometry approaches targeting cellular signaling. In the first part of the talk I will briefly discuss how alternative enzyme and fragmentation techniques help us to further disentangle the proteome, and especially peptides being modified by (multiple) PTMs. The hybrid fragmentation technique EThcD provides much better ion score and unambiguous assignments of localization sites. We also used it to enhance our understanding of the immunopeptidome, whereby I will specially focus on presented HLA peptides bearing post-translational modifications, such as phosphorylation, and OGlcNAcylation and those formed by proteasome induced splicing. In the second part I will focus on the application of native MS and top-down proteomics for monitoring two of the most important PTMs in cells; phosphorylation and O-GlcNAcylation. The interplay between these two modifications has been shown to play a critical role in cancer and neurodegenerative diseases. Native MS was used to monitor the reaction kinetics of O-GlcNAcylation and phosphorylation on the collapse mediator response protein, CRMP2. The data highlights the criticality of the phosphosite location in determining CRMP2 O-GlcNAcylation rate. Indeed, phosphorylation on the P-3 residue with respect to the O-GlcNAcylation site dramatically decreases the O-GlcNAcylation rate compared with its unphosphorylated counterpart, revealing extensive cross-talk. The data reveal the power of MS in not only monitoring O-GlcNAcylation and phosphorylation, but also the dynamic interplay that exists between these two modifications. 4.1 Role of mediator in transcription control Philip Robinson(1), Michael J. Trnka(2), Riccardo Pellarin(3), David A. Bushnell(1), Charles Greenberg(2), Ralph E. Davis(1), Pierre-Jean Mattei(1), Andrej Sali(2), A. L. Burlingame(2), Roger D. Kornberg(1) (1)Stanford University Medical School, Stanford, CA, USA; (2)University of California, San Francisco, San Francisco, CA, USA; (3)Institut Pasteur, Paris, France The Mediator complex, an essential 21-subunit transcriptional coactivator, is a core component of the Eukaryotic transcription machinery. It assembles with RNA Polymerase II and a group of general transcription factors in a large pre-initiation complex (PIC) at the gene promoter where it functions both as a conduit of regulatory information from cellular signaling pathways as well as playing a general role in the mechanism of gene activation. An integrative structural approach, comprising X-ray crystallography, cross-linking with mass spectrometry, molecular modelling, biophysical measurements and cryo-electron microscopy, has been used to investigate the structure of Mediator and its interaction with the core transcriptional apparatus. Key findings will be presented including the complex of Mediator bound to the conserved C-terminal domain of RNA Pol II, the molecular architecture of Mediator and the recently determined cryo-EM structure of the full 2.5MDa, 52-component, Mediator-RNA polymerase II preinitiation complex. These structures provide a number of key insights into the mechanism of eukaryotic transcriptional initiation that will be discussed. 4.2 Chemical crosslinking based integrative structural biology of the Mediator complex and higher order transcription complexes Michael J. Trnka(1), Philip Robin

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.189
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0070.003
Open science0.0020.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.8110.657

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.251
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
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