Evidence map›Paper›PMID 25798134›Full record

ArticleFrontiers in microbiology2015

Mining proteomic data to expose protein modifications in Methanosarcina mazei strain Gö1.

Deborah R Leon, A Jimmy Ytterberg, Pinmanee Boontheung, Unmi Kim, Joseph A Loo, Robert P Gunsalus, Rachel R Ogorzalek Loo

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in microbiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Emerging facets of prokaryotic glycosylation.FEMS microbiology reviews · 2017
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Deborah R LeonDepartment of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA.
A Jimmy YtterbergDepartment of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA.
Pinmanee BoontheungDepartment of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA.
Unmi KimMicrobiology, Immunology, and Molecular Genetics, University of California, Los Angeles Los Angeles, CA, USA.
Joseph A LooDepartment of Chemistry and Biochemistry, University of California, Los Angeles Los Angeles, CA, USA ; Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, USA ; UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles Los Angeles, CA, USA.
Robert P GunsalusMicrobiology, Immunology, and Molecular Genetics, University of California, Los Angeles Los Angeles, CA, USA ; UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles Los Angeles, CA, USA.
Rachel R Ogorzalek LooDepartment of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, USA ; UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles Los Angeles, CA, USA.
University of California, Los Angeles · US

Funding

UCLA NIGMS T32 Program AssessmentT32GM007185 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI TORRES, JORGE · 1985 to 2020
$21.7M
Impact of Non-canonical Decoding on the ProteomeR01GM085402 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LOO, JOSEPH A, OGORZALEK LOO, RACHEL R. · 2008 to 2011
$1.2M
NIGMS NIH HHS R01 GM085402NIGMS NIH HHS T32 GM007185
6 · The paper itself

Abstract

Proteomic tools identify constituents of complex mixtures, often delivering long lists of identified proteins. The high-throughput methods excel at matching tandem mass spectrometry data to spectra predicted from sequence databases. Unassigned mass spectra are ignored, but could, in principle, provide valuable information on unanticipated modifications and improve protein annotations while consuming limited quantities of material. Strategies to "mine" information from these discards are presented, along with discussion of features that, when present, provide strong support for modifications. In this study we mined LC-MS/MS datasets of proteolytically-digested concanavalin A pull down fractions from Methanosarcina mazei Gö1 cell lysates. Analyses identified 154 proteins. Many of the observed proteins displayed post-translationally modified forms, including O-formylated and methyl-esterified segments that appear biologically relevant (i.e., not artifacts of sample handling). Interesting cleavages and modifications (e.g., S-cyanylation and trimethylation) were observed near catalytic sites of methanogenesis enzymes. Of 31 Methanosarcina protein N-termini recovered by concanavalin A binding or from a previous study, only M. mazei S-layer protein MM1976 and its M. acetivorans C2A orthologue, MA0829, underwent signal peptide excision. Experimental results contrast with predictions from algorithms SignalP 3.0 and Exprot, which were found to over-predict the presence of signal peptides. Proteins MM0002, MM0716, MM1364, and MM1976 were found to be glycosylated, and employing chromatography tailored specifically for glycopeptides will likely reveal more. This study supplements limited, existing experimental datasets of mature archaeal N-termini, including presence or absence of signal peptides, translation initiation sites, and other processing. Methanosarcina surface and membrane proteins are richly modified.

Indexed as

archaeal surface proteinsconcanavalin Amembrane proteinsMethanosarcina mazeiprokaryotic glycosylationS-layers

Identifiers

PMID25798134
PMCPMC4350412
OpenAlexW1986120101

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.