Evidence map›Paper›PMID 27462319›Full record

ArticleFrontiers in plant science2016

Non-targeted Metabolomics in Diverse Sorghum Breeding Lines Indicates Primary and Secondary Metabolite Profiles Are Associated with Plant Biomass Accumulation and Photosynthesis.

Marie F Turner, Adam L Heuberger, Jay S Kirkwood, Carl C Collins, Edward J Wolfrum, Corey D Broeckling, Jessica E Prenni, Courtney E Jahn

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
7.2field-weighted citation impact, top 3% 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

36 citing papers in PubMed, 111 citations in OpenAlex.

  1. Article
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  7. Plant Metabolomics: Current Initiatives and Future Prospects.Current issues in molecular biology · 2023
    Review
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  19. Drought-Induced Root Pressure inFrontiers in plant science · 2021
    Article
  20. 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

8 authors at 2 institutions in 1 country.

Marie F TurnerDepartment of Bioagricultural Sciences and Pest Management, Colorado State University Fort Collins, CO, USA.
Adam L HeubergerDepartment of Horticulture and Landscape Architecture, Colorado State University Fort Collins, CO, USA.
Jay S KirkwoodDepartment of Bioagricultural Sciences and Pest Management, Colorado State UniversityFort Collins, CO, USA; Proteomics and Metabolomics Facility, Colorado State UniversityFort Collins, CO, USA.
Carl C CollinsDepartment of Bioagricultural Sciences and Pest Management, Colorado State University Fort Collins, CO, USA.
Edward J WolfrumNational Renewable Energy Laboratory, National Bioenergy Center Golden, CO, USA.
Corey D BroecklingProteomics and Metabolomics Facility, Colorado State University Fort Collins, CO, USA.
Jessica E PrenniProteomics and Metabolomics Facility, Colorado State University Fort Collins, CO, USA.
Courtney E JahnDepartment of Bioagricultural Sciences and Pest Management, Colorado State University Fort Collins, CO, USA.
Colorado State University · USNational Renewable Energy Laboratory · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolomics is an emerging method to improve our understanding of how genetic diversity affects phenotypic variation in plants. Recent studies have demonstrated that genotype has a major influence on biochemical variation in several types of plant tissues, however, the association between metabolic variation and variation in morphological and physiological traits is largely unknown. Sorghum bicolor (L.) is an important food and fuel crop with extensive genetic and phenotypic variation. Sorghum lines have been bred for differing phenotypes beneficial for production of grain (food), stem sugar (food, fuel), and cellulosic biomass (forage, fuel), and these varying phenotypes are the end products of innate metabolic programming which determines how carbon is allocated during plant growth and development. Further, sorghum has been adapted among highly diverse environments. Because of this geographic and phenotypic variation, the sorghum metabolome is expected to be highly divergent; however, metabolite variation in sorghum has not been characterized. Here, we utilize a phenotypically diverse panel of sorghum breeding lines to identify associations between leaf metabolites and morpho-physiological traits. The panel (11 lines) exhibited significant variation for 21 morpho-physiological traits, as well as broader trends in variation by sorghum type (grain vs. biomass types). Variation was also observed for cell wall constituents (glucan, xylan, lignin, ash). Non-targeted metabolomics analysis of leaf tissue showed that 956 of 1181 metabolites varied among the lines (81%, ANOVA, FDR adjusted p < 0.05). Both univariate and multivariate analyses determined relationships between metabolites and morpho-physiological traits, and 384 metabolites correlated with at least one trait (32%, p < 0.05), including many secondary metabolites such as glycosylated flavonoids and chlorogenic acids. The use of metabolomics to explain relationships between two or more morpho-physiological traits was explored and showed chlorogenic and shikimic acid to be associated with photosynthesis, early plant growth and final biomass measures in sorghum. Taken together, this study demonstrates the integration of metabolomics with morpho-physiological datasets to elucidate links between plant metabolism, growth, and architecture.

Indexed as

biomasschlorogenic acidGC-MSLC-MSmetabolomicsphotosynthesisshikimic acidSorghum bicolor

Identifiers

PMID27462319
PMCPMC4939745
OpenAlexW2438696876

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.