Dataset Information

Predicting plant biomass accumulation from image-derived parameters.

ABSTRACT: Background:Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results:In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions:We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species.

SUBMITTER: Chen D

PROVIDER: S-EPMC5827348 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Predicting plant biomass accumulation from image-derived parameters.

Chen Dijun D Shi Rongli R Pape Jean-Michel JM Neumann Kerstin K Arend Daniel D Graner Andreas A Chen Ming M Klukas Christian C

GigaScience 20180201 2

<h4>Background</h4>Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments.<h4>Results</h4>In the present study, we constructed 4 predictive models to examine the ...[more]

PMID: 29346559

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Project description:BackgroundPlant lignocellulosic biomass is an abundant, renewable feedstock for the production of biobased fuels and chemicals. Previously, we showed that iron can act as a co-catalyst to improve the deconstruction of lignocellulosic biomass. However, directly adding iron catalysts into biomass prior to pretreatment is diffusion limited, and increases the cost of biorefinery operations. Recently, we developed a new strategy for expressing iron-storage protein ferritin intracellularly to accumulate iron as a catalyst for the downstream deconstruction of lignocellulosic biomass. In this study, we extend this approach by fusing the heterologous ferritin gene with a signal peptide for secretion into Arabidopsis cell walls (referred to here as FerEX).ResultsThe transgenic Arabidopsis plants. FerEX. accumulated iron under both normal and iron-fertilized growth conditions; under the latter (iron-fertilized) condition, FerEX transgenic plants showed an increase in plant height and dry weight by 12 and 18 %, respectively, compared with the empty vector control plants. The SDS- and native-PAGE separation of cell-wall protein extracts followed by Western blot analyses confirmed the extracellular expression of ferritin in FerEX plants. Meanwhile, Perls' Prussian blue staining and X-ray fluorescence microscopy (XFM) maps revealed iron depositions in both the secondary and compound middle lamellae cell-wall layers, as well as in some of the corner compound middle lamella in FerEX. Remarkably, their harvested biomasses showed enhanced pretreatability and digestibility, releasing, respectively, 21 % more glucose and 34 % more xylose than the empty vector control plants. These values are significantly higher than those of our recently obtained ferritin intracellularly expressed plants.ConclusionsThis study demonstrated that extracellular expression of ferritin in Arabidopsis can produce plants with increased growth and iron accumulation, and reduced thermal and enzymatic recalcitrance. The results are attributed to the intimate colocation of the iron co-catalyst and the cellulose and hemicellulose within the plant cell-wall region, supporting the genetic modification strategy for incorporating conversion catalysts into energy crops prior to harvesting or processing at the biorefinery.

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Dataset Information

Predicting plant biomass accumulation from image-derived parameters.

Publications

Predicting plant biomass accumulation from image-derived parameters.

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