The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
Metabolic Fluxes in an Illuminated Arabidopsis Rosette
Open accessAbstract
Photosynthesis is the basis for life, and its optimization is a key biotechnological aim given the problems of population explosion and environmental deterioration. We describe a method to resolve intracellular fluxes in intact Arabidopsis thaliana rosettes based on time-dependent labeling patterns in the metabolome. Plants photosynthesizing under limiting irradiance and ambient CO2 in a custom-built chamber were transferred into a 13CO2-enriched environment. The isotope labeling patterns of 40 metabolites were obtained using liquid or gas chromatography coupled to mass spectrometry. Labeling kinetics revealed striking differences between metabolites. At a qualitative level, they matched expectations in terms of pathway topology and stoichiometry, but some unexpected features point to the complexity of subcellular and cellular compartmentation. To achieve quantitative insights, the data set was used for estimating fluxes in the framework of kinetic flux profiling. We benchmarked flux estimates to four classically determined flux signatures of photosynthesis and assessed the robustness of the estimates with respect to different features of the underlying metabolic model and the time-resolved data set.
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
Cites
Gas chromatography mass spectrometry–based metabolite profiling in plants
Lisec, J., Schauer, N., Kopka, J., Willmitzer, L., & Fernie, A. R. (2006) · Nature Protocols · doi:10.1038/nprot.2006.59 · Cited by 2,170
Self-citationMetabolite profiling for plant functional genomics
Fiehn, O., Kopka, J., Dörmann, P., Altmann, T., Trethewey, R. N., & Willmitzer, L. (2000) · Nature Biotechnology · doi:10.1038/81137 · Cited by 2,127
GMD@CSB.DB: the Golm Metabolome Database
Kopka, J., Schauer, N., Krueger, S., Birkemeyer, C., Usadel, B., Bergmuller, E., Dormann, P., Weckwerth, W., Gibon, Y., Stitt, M., et al. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti236 · Cited by 1,382
Self-citationBoyes, D. C., Zayed, A. M., Ascenzi, R., McCaskill, A. J., Hoffman, N. E., Davis, K. R., & Görlach, J. (2001) · The Plant Cell · doi:10.1105/tpc.010011 · Cited by 1,266
Roessner, U., Luedemann, A., Brust, D., Fiehn, O., Linke, T., Willmitzer, L., & Fernie, A. R. (2001) · The Plant Cell · doi:10.1105/tpc.13.1.11 · Cited by 1,017
Self-citationNot just a circle: flux modes in the plant TCA cycle
Sweetlove, L. J., Beard, K. F. M., Nunes-Nesi, A., Fernie, A. R., & Ratcliffe, R. G. (2010) · Trends in Plant Science · doi:10.1016/j.tplants.2010.05.006 · Cited by 874
Self-citationMetabolomics for Functional Genomics, Systems Biology, and Biotechnology
Saito, K., & Matsuda, F. (2010) · Annual Review of Plant Biology · doi:10.1146/annurev.arplant.043008.092035 · Cited by 744
CO2 CONCENTRATING MECHANISMS IN PHOTOSYNTHETIC MICROORGANISMS
Kaplan, A., & Reinhold, L. (1999) · Annual Review of Plant Physiology and Plant Molecular Biology · doi:10.1146/annurev.arplant.50.1.539 · Cited by 693
Amor, Y., Haigler, C. H., Johnson, S., Wainscott, M., & Delmer, D. P. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.20.9353 · Cited by 654
Photorespiration: players, partners and origin
Bauwe, H., Hagemann, M., & Fernie, A. R. (2010) · Trends in Plant Science · doi:10.1016/j.tplants.2010.03.006 · Cited by 652
Self-citation
Cited by
Flood adaptive traits and processes: an overview
Voesenek, L. A. C. J., & Bailey‐Serres, J. (2015) · New Phytologist · doi:10.1111/nph.13209 · Cited by 837
Ascorbic acid metabolism and functions: A comparison of plants and mammals
Smirnoff, N. (2018) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2018.03.033 · Cited by 667
Trehalose metabolism in plants
Lunn, J. E., Delorge, I., Figueroa, C. M., Van Dijck, P., & Stitt, M. (2014) · The Plant Journal · doi:10.1111/tpj.12509 · Cited by 623
Self-citationFormation of starch in plant cells
Pfister, B., & Zeeman, S. C. (2016) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-016-2250-x · Cited by 423
Plant Metabolic Modeling: Achieving New Insight into Metabolism and Metabolic Engineering
Baghalian, K., Hajirezaei, M.-R., & Schreiber, F. (2014) · The Plant Cell · doi:10.1105/tpc.114.130328 · Cited by 63
Lindén, P., Keech, O., Stenlund, H., Gardeström, P., & Moritz, T. (2016) · Journal of Experimental Botany · doi:10.1093/jxb/erw030 · Cited by 57
Advances in metabolic flux analysis toward genome-scale profiling of higher organisms
Basler, G., Fernie, A. R., & Nikoloski, Z. (2018) · Bioscience Reports · doi:10.1042/bsr20170224 · Cited by 41
Self-citationMathematical models of plant metabolism
Shi, H., & Schwender, J. (2016) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2015.10.008 · Cited by 21
Insights into the spatial and temporal organisation of plant metabolism from network flux analysis
Moreira, T. B., Lima, J. M., Coca, G. C., & Williams, T. C. R. (2019) · Theoretical and Experimental Plant Physiology · doi:10.1007/s40626-018-0132-3 · Cited by 13
Metadata
- Authors
- Szecowka, M., Heise, R., Tohge, T., Nunes-Nesi, A., Vosloh, D., Huege, J., Feil, R., Lunn, J., Nikoloski, Z., Stitt, M., Fernie, A. R., & Arrivault, S.
- Venue
- The Plant Cell
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-02-01
- Volume
- 25
- Issue
- 2
- Pages
- 694-714
- ISSN
- 1040-4651 (ISSN-L)
- 1532-298X
- OpenAlex
- W2128927770
- Cited by
- 347
- References
- 89
- FWCI
- 12.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
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.
