The DOI matches its Crossref record.
Arabidopsis cell wall proteome defined using multidimensional protein identification technology
Abstract
With the completion of the sequencing of the Arabidopsis genome and the recent advances in proteomic technology, the identification of proteins from highly complex mixtures is now possible. Rather than using gel electrophoresis and peptide mass fingerprinting, we have used multidimensional protein identification technology (MudPIT) to analyse the ‘tightly‐bound’ proteome for purified cell walls from Arabidospis cell suspension cultures. Using bioinformatics for the prediction of signal peptides for targeting to the secretory pathway and for the absence of ER retention signal, 89 proteins were selected as potential extracellular proteins. Only 33% of these were identified in previous proteomic analyses of Arabidopsis cell walls. A functional classification revealed that a large proportion of the proteins were enzymes, notably carbohydrate active enzymes, peroxidases and proteases. Comparison of all the published proteomic analyses for the Arabidopsis cell wall identified 268 non‐redundant genes encoding wall proteins. Sixty of these (22%) were derived from our analysis of tightly‐bound wall proteins.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Wang, J., Ding, Y., Wang, J., Hillmer, S., Miao, Y., Lo, S. W., Wang, X., Robinson, D. G., & Jiang, L. (2011) · The Plant Cell · doi:10.1105/tpc.110.080697 · Cited by 357
Methods for samples preparation in proteomic research
Bodzon-Kulakowska, A., Bierczynska-Krzysik, A., Dylag, T., Drabik, A., Suder, P., Noga, M., Jarzebinska, J., & Silberring, J. (2007) · Journal of Chromatography B · doi:10.1016/j.jchromb.2006.10.040 · Cited by 254
Metadata
- Authors
- Bayer, E. M., Bottrill, A. R., Walshaw, J., Vigouroux, M., Naldrett, M. J., Thomas, C. L., & Maule, A. J.
- Venue
- PROTEOMICS
- Publisher
- Wiley
- Published
- 2006-01
- Volume
- 6
- Issue
- 1
- Pages
- 301-311
- ISSN
- 1615-9853 (ISSN-L)
- 1615-9861
- OpenAlex
- W2068830600
- Cited by
- 244
- References
- 0
- FWCI
- 6.22
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Arabidopsis cell wall proteome defined using multidimensional protein identification technology. https://app.cochat.ai/papers/10.1002/pmic.200500046
- The paper
- Bayer, E. M., Bottrill, A. R., Walshaw, J., Vigouroux, M., Naldrett, M. J., Thomas, C. L., & Maule, A. J. (2006). Arabidopsis cell wall proteome defined using multidimensional protein identification technology. PROTEOMICS. https://doi.org/10.1002/pmic.200500046
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.