The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Metabolite profiles of maize leaves in drought, heat and combined stress field trials reveal the relationship between metabolism and grain yield
Open accessAbstract
The development of abiotic stress-resistant cultivars is of premium importance for the agriculture of developing countries. Further progress in maize (Zea mays) performance under stresses is expected by combining marker-assisted breeding with metabolite markers. In order to dissect metabolic responses and to identify promising metabolite marker candidates, metabolite profiles of maize leaves were analyzed and compared with grain yield in field trials. Plants were grown under well-watered conditions (control) or exposed to drought, heat, and both stresses simultaneously. Trials were conducted in 2010 and 2011 using 10 tropical hybrids selected to exhibit diverse abiotic stress tolerance. Drought stress evoked the accumulation of many amino acids, including isoleucine, valine, threonine, and 4-aminobutanoate, which has been commonly reported in both field and greenhouse experiments in many plant species. Two photorespiratory amino acids, glycine and serine, and myoinositol also accumulated under drought. The combination of drought and heat evoked relatively few specific responses, and most of the metabolic changes were predictable from the sum of the responses to individual stresses. Statistical analysis revealed significant correlation between levels of glycine and myoinositol and grain yield under drought. Levels of myoinositol in control conditions were also related to grain yield under drought. Furthermore, multiple linear regression models very well explained the variation of grain yield via the combination of several metabolites. These results indicate the importance of photorespiration and raffinose family oligosaccharide metabolism in grain yield under drought and suggest single or multiple metabolites as potential metabolic markers for the breeding of abiotic stress-tolerant maize.
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
Photosynthesis under drought and salt stress: regulation mechanisms from whole plant to cell.
Chaves, M. M., Flexas, J., & Pinheiro, C. (2009) · Annals of Botany · doi:10.1093/aob/mcn125 · Cited by 4,181
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-citationAbiotic and biotic stress combinations
Suzuki, N., Rivero, R. M., Shulaev, V., Blumwald, E., & Mittler, R. (2014) · New Phytologist · doi:10.1111/nph.12797 · Cited by 2,153
Field high-throughput phenotyping: the new crop breeding frontier
Araus, J. L., & Cairns, J. E. (2014) · Trends in Plant Science · doi:10.1016/j.tplants.2013.09.008 · Cited by 1,817
Self-citationRizhsky, L., Liang, H., Shuman, J., Shulaev, V., Davletova, S., & Mittler, R. (2004) · Plant Physiology · doi:10.1104/pp.103.033431 · Cited by 1,729
Metabolic implications of stress-induced proline accumulation in plants
Hare, P. D., & Cress, W. A. (1997) · Plant Growth Regulation · doi:10.1023/a:1005703923347 · Cited by 1,397
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-citationNonlinear heat effects on African maize as evidenced by historical yield trials
Lobell, D. B., Bänziger, M., Magorokosho, C., & Vivek, B. (2011) · Nature Climate Change · doi:10.1038/nclimate1043 · Cited by 1,206
Genomic selection in plant breeding: from theory to practice
Jannink, J.-L., Lorenz, A. J., & Iwata, H. (2010) · Briefings in Functional Genomics · doi:10.1093/bfgp/elq001 · Cited by 1,206
pcaMethods—a bioconductor package providing PCA methods for incomplete data
Stacklies, W., Redestig, H., Scholz, M., Walther, D., & Selbig, J. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm069 · Cited by 1,151
Cited by
Plant adaptations to the combination of drought and high temperatures
Zandalinas, S. I., Mittler, R., Balfagón, D., Arbona, V., & Gómez‐Cadenas, A. (2018) · Physiologia Plantarum · doi:10.1111/ppl.12540 · Cited by 1,179
Metabolomics for Plant Improvement: Status and Prospects
Kumar, R., Bohra, A., Pandey, A. K., Pandey, M. K., & Kumar, A. (2017) · Frontiers in Plant Science · doi:10.3389/fpls.2017.01302 · Cited by 328
Plant responses to climate change: metabolic changes under combined abiotic stresses
Zandalinas, S. I., Balfagón, D., Gómez-Cadenas, A., & Mittler, R. (2022) · Journal of Experimental Botany · doi:10.1093/jxb/erac073 · Cited by 299
Khan, N., Bano, A., Rahman, M. A., Rathinasabapathi, B., & Babar, M. A. (2019) · Plant, Cell & Environment · doi:10.1111/pce.13195 · Cited by 265
Metabolomics: A Way Forward for Crop Improvement
Razzaq, A., Sadia, B., Raza, A., Khalid Hameed, M., & Saleem, F. (2019) · Metabolites · doi:10.3390/metabo9120303 · Cited by 253
Metadata
- Authors
- Obata, T., Witt, S., Lisec, J., Palacios-Rojas, N., Florez-Sarasa, I., Araus, J. L., Cairns, J. E., Yousfi, S., & Fernie, A. R.
- Venue
- Plant Physiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-09-30
- Volume
- 169
- Issue
- 4
- Pages
- pp.01164.2015
- ISSN
- 0032-0889 (ISSN-L)
- 1532-2548
- OpenAlex
- W2118643445
- Cited by
- 332
- References
- 98
- FWCI
- 25.56
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Metabolite profiles of maize leaves in drought, heat and combined stress field trials reveal the relationship between metabolism and grain yield. https://app.cochat.ai/papers/10.1104/pp.15.01164
- The paper
- Obata, T., Witt, S., Lisec, J., Palacios-Rojas, N., Florez-Sarasa, I., Araus, J. L., Cairns, J. E., Yousfi, S., & Fernie, A. R. (2015). Metabolite profiles of maize leaves in drought, heat and combined stress field trials reveal the relationship between metabolism and grain yield. Plant Physiology. https://doi.org/10.1104/pp.15.01164
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.