MétaCan
Menu
Back to cohort
Record W4235088431 · doi:10.1074/mcp.s800708-mcp200

Session 3

2009· article· en· W4235088431 on OpenAlexaboutno aff

Bibliographic record

VenueMolecular & Cellular Proteomics · 2009
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)Computer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

3.1 Quantitative Analysis of Proteome Localisation and Dynamics A. Lamond Wellcome Trust Centre for Gene Regulation and Expression, MSI/WTB Complex, University of Dundee, Dundee, Scotland, United Kingdom We are studying the functional organization of mammalian cell nuclei using a dual strategy that combines mass spectrometry (MS) based proteomics with live cell fluorescence imaging (see www.LamondLab.com). This applies two distinct but complementary quantitative techniques to analyse the same biological problem, providing a rigorous approach where potential artifacts or limitations of one method are avoided in the complementary approach and vice versa. The quantitative proteomic methods involve metabolic labeling of cellular proteins in cultured cell lines with the amino acids lysine and arginine containing heavy isotopes such as (13)C and (15)N. The quantitative imaging experiments, including time-lapse microscopy, FRAP, FLIP, FLIM and FLIM-FRET, are performed on mammalian cell lines stably expressing one or more fluorescent protein-tagged reporters. Both the proteomics and microscopy methods are used to study the same stable cell lines, allowing a direct comparison the resulting data from both techniques. We have used this dual strategy to characterize in detail the molecular composition of nucleoli under different metabolic and growth conditions and at specific stages of cell cycle progression (see http://lamondlab.com/nopdb/). We have developed a MS-based proteomics strategy to perform quantitative analyses of subcellular protein localization - “spatial proteomics” - including the analysis of protein turnover rates in separate cell compartments. This provides a new approach for annotating the spatial organization of the proteome and for measuring how this changes in response to inhibitors and different cell growth conditions. We have also developed quantitative MS-based approaches for identifying specific protein- protein interactions. These strategies provide a general approach for characterizing the composition, dynamic properties and interactions of either cell organelles or multi-protein complexes. 3.2 Post-Translational Adenosine Monophosphate (AMP) Modification of Proteins C. A. Worby(1,2,3,9), S. Mattoo(4,9), R. P. Kruger(5), L. B. Corbeil(6,7), A. Koller(8), Juan C. Mendez(6), B. Zekarias(6), C. Lazar(1,2,3), and Jack E. Dixon(1,2,3,4) Departments of (1)Pharmacology, (2)Cellular and Molecular Medicine, (3)Chemistry and Biochemistry, and (4)Howard Hughes Medical Institute, University of California, San Diego, La Jolla, CA; (5)Department of Biological Chemistry, University of Michigan, Ann Arbor, MI; (6)Department of Pathology, University of California, San Diego Medical Center, San Diego, CA; (7)Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA; (8)Department of Pathology, Stony Brook University, Stony Brook, NY Eukaryotic cells have devised different strategies to regulate signaling pathways. The best known modification is phosphorylation, which attaches a phosphate group to serine, threonine or tyrosine residues in proteins, thereby regulating their activities. Here, we describe a new modification: the addition of adenosine monophosphate (AMP) on tyrosine residues. AMP addition to Rho GTPases by the Fic domain containing secreted surface antigen IbpA of the respiratory pathogen Histophilus somni leads to cytoskeletal collapse in host cells (1). Specifically, incubation of purified Rho GTPases (RhoA, Rac1 and Cdc42) with GST-tagged and purified Fic domain of IbpA in the presence of α(32)P-ATP, but not γ(32)P-ATP, allows transfer of the (32)P-label to RhoA, Rac1 or Cdc42, thus indicating the addition of AMP versus a phosphorylation event. Unlike VopS, another Fic domain containing protein from V. parahemolyticus, that modifies AMP on threonine residues (2), mass spectrometric analysis of IbpA-treated Rho GTPases show that the IbpA Fic domain adds an AMP to a conserved tyrosine residue in the switch I region of Rho GTPases. In addition, we show that the only human protein containing a Fic domain, HYPE (Huntingtin Yeast-interacting Protein E), also has the ability to add AMP to tyrosine residues in Rho GTPases in vitro. Thus, we identify Fic domain containing proteins as a new class of enzymes that mediate not just bacterial pathogenesis, but also a previously unrecognized eukaryotic post-translational modification that may regulate key signaling events. Interestingly, threonine and tyrosine AMP modified peptides behave similarly in the mass spectrometer as threonine and tyrosine phosphorylated peptides: whereas threonine modified peptides undergo neutral loss of AMP (plus 18Da) on fragments upon activation of the peptide, peptides fragments modified with AMP on tyrosine mostly stay intact, partially losing adenine as well as adenosine. In addition, AMP-Tyr modified peptides are only identifiable in an ion-trap CID fragmentation experiment, as fragmentation in an HCD cell (or CID in a QSTAR) will lead to a strong signal for adenine and only weak fragmentation peaks of the peptide backbone. References 1. Worby, C. A., Mattoo, S., Kruger, R. P., Corbeil, L. B., Koller, A., Mendez, J. C., Zekarias, B., Lazar, C., and Dixon, J. E. (2009) The fic domain: regulation of cell signaling by adenylylation. Mol. Cell 34(1), 93–103. 2. Yarbrough, M. L., Li, Y., Kinch, L. N., Grishin, N. V., Ball, H. L., and Orth, K. (2009) AMPylation of Rho GTPases by Vibrio VopS disrupts effector binding and downstream signaling. Science 323(5911), 269–272. 3.3 Dissecting the Structure of the Human Spliceosome by Looking at Its Pieces P. Coltri(1), J. Ilagan(1), R. J. Chalkley(2), A. L. Burlingame(2), and M. S. Jurica(1) (1)Department of Molecular, Cell and Developmental Biology and Center for Molecular Biology of RNA, University of California, Santa Cruz, CA; (2)Mass Spectrometry Facility, Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA Pre-mRNA splicing is the removal of the non-coding introns that interrupt most gene transcripts and serves an essential step in eukaryotic gene expression. The cellular machinery responsible for splicing, termed the spliceosome, is a large protein/RNA macromolecular complex comprised of five structural RNAs and over 100 individual polypeptides. The human complex assembles and functions via a progression of structural intermediates that are not yet fully characterized. The dynamics and complexity of the spliceosome have long posed challenges to detailed biochemical and structural studies that will provide insight into the spliceosome's molecular mechanisms. In particular, isolating distinct conformations of this moving target in the amounts needed for standard biochemical and structural analyses is not simple. We made a key advance in this regard with our development of a substrate-based affinity method to isolate human spliceosomes arrested midway through splicing catalysis (C-complex). Initial mass spectrometry analysis of this complex identified over 200 proteins, ∼100 of which were specific to splicing. Using cryo-electron microscopy (cryo-EM) and single particle reconstruction techniques, we solved the structure of C-complex spliceosomes to 30 Å resolution. This model represents an important first step in visualizing the structure of the spliceosome. However, before we can more fully interpret the model in functional terms we must answer questions regarding which components of the spliceosome are visualized/represented in our model and where they are located in the structure. Currently, we are finding ways to take the spliceosome apart and then examining the resultant pieces. Mass spectrometry analysis is critical for defining the protein composition of the pieces, enabling us to define interactions that underpin the spliceosomes architecture. We have examined the contribution of exon sequences in the composition and structure of C-complex and are now looking at the proteins that tightly associate with the intron vs. the region of the upstream exon poised for ligation. In addition to these studies, we have made progress in using chemical modification in conjunction with mass spectrometry to identify regions of proteins that are located at the surface of the spliceosome. This work will allow us to begin localizing these proteins' positions in the complex. By combining the results of these studies with our structural investigation of the spliceosome, we are on the path to assembling a more detailed model of this critical cellular machine. 3.4 Protein Complexes and Functional Pathways in S. cerevisiae and E. coli M. Babu(1), G. Butland(3), J. J. Diaz-Mejia(1,4), P. Hu(1), S. Pu(5), G. Moreno-Hagelsieb(4), S. C. Janga(1), S. Wodak(2,5), A. Emili(1,2), and J. Greenblatt(1,2) (1)Banting and Best Department of Medical Research, (2)Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada, (3)Life Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA, (4)Department of Biology, Wilfrid Laurier University, Waterloo, ON, Canada, (5)Hospital for Sick Children, Toronto, ON, Canada We have used TAP-tagging and affinity-purification to sort the soluble proteins of S. cerevisiae into complexes. We combined this with systematic synthetic genetic interaction analysis for non-essential gene deletion mutants and essential gene hypomorphs, using the synthetic genetic array (SGA) approach, for genes related to nuclear processes. More recently, we have extended the yeast protein interaction network by focusing on the predicted yeast membrane proteins, purifying each protein three times in the presence of different detergents. We are testing the co-functionality of proteins in various membrane-associated protein complexes by comparing our protein complex data with synthetic genetic interaction data and assessments of the effects of the various proteins in a com

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.246
Teacher spread0.239 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueMolecular & Cellular ProteomicsSame topicAdvanced Proteomics Techniques and ApplicationsFrench-language works237,207