Enhancing Cotton Fiber Elongation and Cellulose Synthesis by Manipulating Fructokinase Activity

Author(s):  
David Granot ◽  
Scott Holaday ◽  
Randy D. Allen

a. Objectives (a) Identification and characterization of the cotton fiber FRKs; (b) Generating transgenic cotton plants overproducing either substrate inhibited tomato FRK or tomato FRK without substrate inhibition; (c) Generating transgenic cotton plants with RNAi suppression of fiber expressed FRKs; (d) Generating Arabidopsis plants that over express FRK1, FRK2, or both genes, as additional means to assess the contribution of FRK to cellulose synthesis and biomass production.   b. Background to the topic: Cellulose synthesis and fiber elongation are dependent on sugar metabolism. Previous results suggested that FRKs (fructokinase enzymes that specifically phosphorylate fructose) are major players in sugar metabolism and cellulose synthesis. We therefore hypothesized that increasing fructose phosphorylation may enhance fiber elongation and cellulose synthesis in cotton plants. Accordinlgy, the objectives of this research were:   c. Major conclusions and achievements: Two cotton FRKs expressed in fibers, GhFRK2 and GhFRK3, were cloned and characterized. We found that GhFRK2 enzyme is located in the cytosol and GhFRK3 is located within plastids. Both enzymes enable growth on fructose (but not on glucose) of hexose kinase deficient yeast strain, confirming the fructokinase activity of the cloned genes. RNAi constructs with each gene were prepared and sent to the US collaborator to generate cotton plants with RNAi suppression of these genes.   To examine the effect of FRKs using Arabidopsis plants we generated transgenic plants expressing either LeFRK1 or LeFRK2 at high level. No visible phenotype has been observed. Yet, plants expressing both genes simultaneously are being created and will be tested.   To test our hypothesis that increasing fructose phosphorylation may enhance fiber cellulose synthesis, we generated twenty independent transgenic cotton plant lines overexpressing Lycopersicon (Le) FRK1. Transgene expression was high in leaves and moderate in developing fiber, but enhanced FRK activity in fibers was inconsistent between experiments. Some lines exhibited a 9-11% enhancement of fiber length or strength, but only one line tested had consistent improvement in fiber strength that correlated with elevated FRK activity in the fibers. However, in one experiment, seed cotton mass was improved in all transgenic lines and correlated with enhanced FRK activity in fibers. When greenhouse plants were subjected to severe drought during flowering and boll development, no genotypic differences in fiber quality were noted. Seed cotton mass was improved for two transgenic lines but did not correlate with fiber FRK activity. We conclude that LeFRK1 over-expression in fibers has only a small effect on fiber quality, and any positive effects depend on optimum conditions. The improvement in productivity for greenhouse plants may have been due to better structural development of the water-conducting tissue (xylem) of the stem, since stem diameters were larger for some lines and the activity of FRK in the outer xylem greater than observed for wild-type plants. We are testing this idea and developing other transgenic cotton plants to understand the roles of FRK in fiber and xylem development. We see the potential to develop a cotton plant with improved stem strength and productivity under drought for windy, semi-arid regions where cotton is grown.   d. Implications, scientific and agricultural: FRKs are probably bottle neck enzymes for biomass and wood synthesis and their increased expression has the potential to enhance wood and biomass production, not only in cotton plants but also in other feed and energy renewable plants.

Plant Science ◽  
2021 ◽  
pp. 111168
Author(s):  
Yanjun Guo ◽  
Feng Chen ◽  
Jinwen Luo ◽  
Mengfei Qiao ◽  
Wei Zeng ◽  
...  

2021 ◽  
Vol 22 (24) ◽  
pp. 13200
Author(s):  
Yinxuan Xue ◽  
Siyan Li ◽  
Deyu Miao ◽  
Sai Huang ◽  
Bin Guo ◽  
...  

Cellulose synthesis is a complex process in plant cells that is important for wood processing, pulping, and papermaking. Cellulose synthesis begins with the glycosylation of sitosterol by sitosterol glycosyltransferase (SGT) to produce sitosterol-glucoside (SG), which acts as the guiding primer for cellulose production. However, the biological functions of SGTs in Populus tomentosa (P. tomentosa) remain largely unknown. Two full-length PtSGT genes (PtSGT1 and PtSGT4) were previously isolated from P. tomentosa and characterized. In the present study, CRISPR/Cas9 gene-editing technology was used to construct PtSGT1-sgRNA and PtSGT4-sgRNA expression vectors, which were genetically transformed into P. tomentosa using the Agrobacterium-mediated method to obtain transgenic lines. Nucleic acid and amino acid sequencing analysis revealed both base insertions and deletions, in addition to reading frame shifts and early termination of translation in the transgenic lines. Sugar metabolism analysis indicated that sucrose and fructose were significantly downregulated in stems and leaves of mutant PtSGT1-1 and PtSGT4-1. Glucose levels did not change significantly in roots and stems of PtSGT1-1 mutants; however, glucose was significantly upregulated in stems and downregulated in leaves of the PtSGT4-1 mutants. Dissection of the plants revealed disordered and loosely arranged xylem cells in the PtSGT4-1 mutant, which were larger and thinner than those of the wild-type. This work will enhance our understanding of cellulose synthesis in the cell walls of woody plants.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ravinderdeep SINGH BRAR ◽  
Avneesh KUMAR ◽  
Simranjeet KAUR ◽  
Sandip SAHA ◽  
Anuj KUMAR ◽  
...  

Abstract Cotton production substantiated a crucial part in the escalating economic development of many countries. To realize the increasing global demand for cotton, the emphasis should be laid on to improve cotton fiber growth and production. The bioengineered transgenic cotton proved expedient in resolving inadequacies of conventional cotton, but still required improvements to encounter heightened demand of textile industries. One possible solution pertaining to this has been provided by nanoscience in the form of metal or metal oxide nanoparticles. These metal oxide nanoparticles have easy access to the various parts of cotton plants through its transportation system, and thus significantly influence several parameters relative to the growth and production of cotton fiber. This review summarizes the distribution and accumulation of metal oxide nanoparticles in cotton plant and its impact on different plant growth-promoting factors, which resulted in the improved cotton yields. Graphical abstract Metal/metal-oxide nanoparticles have easy access to the various parts of cotton plant through its transportation system, and thus significantly influence its growth parameters, and hence the production of cotton fiber. This review summarizes the distribution and accumulation of metal oxide nanoparticles in cotton plants, and its impact on different plant growth promoting factors.


Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1019
Author(s):  
Honglan Yang ◽  
Tohir A. Bozorov ◽  
Xiaoping Chen ◽  
Dawei Zhang ◽  
Jiancheng Wang ◽  
...  

Water scarcity is the major limiting factor for oasis-desert agricultural production of cotton. It is necessary to improve cotton for drought tolerance and minimize drought-related crop losses, and the transgenic approach is efficient for cotton improvement. In order to evaluate the value of ScALDH21 transgenic cotton (G.hirsutum L.), it was tested in the main cotton region of south Xinjiang, in an environment of extreme drought around the desert. Transgenic cotton, overexpressing aldehyde dehydrogenase gene (ScALDH21) from the desiccation-tolerant moss Syntrichia caninervis in cotton variety Xin Nong Mian 1, was field-tested under six treatments based on three irrigation schedules and two irrigation levels (full (FI) and deficit (DI) irrigation) as follows: root zone model-simulated forecast irrigation (F) (FFI and FDI), soil moisture sensor-based irrigation (S) (SFI and SDI), and flood irrigation based on experience estimates (E) (EFI and EDI) to evaluate growth and yield performances. The results revealed that plant height and leaf area increased significantly in ScALDH21-transgenic cotton genotypes under all treatments. Physiological parameters such as chlorophyll content, net photosynthesis rate, and instantaneous water use efficiency were not significantly highly in transgenic lines compared to non-transgenic plants (NT). However, transgenic lines showed significantly improved yield and superior fiber quality than NT plants regardless of irrigation. The results demonstrate that ScALDH21-transgenic lines were excellent compared to NT plants under different water deficiency conditions. The study also provides guidelines for optimal irrigation protocol and minimum water requirements for the use of the ScALDH21-transgenic cotton lines in arid zones.


2019 ◽  
Vol 37 ◽  
Author(s):  
W.D. MATTE ◽  
S.D. CAVALIERI ◽  
C.S. PEREIRA ◽  
F.S. IKEDA ◽  
W.B. COSTA

ABSTRACT: The application of alternative herbicides to replace glyphosate can affect the succession cropping due to the persistence in the soil. The aim of this work was to evaluate the residual activity of diclosulam applied to a pre-emergence soybean crop on a cotton plant grown in succession. The present study used a randomized complete block design with five replicates and seven doses of diclosulam (0, 2.19, 4.38, 8.75, 17.5, 35 and 70 g a.i. ha-1). The cotton was sown 112 days after application of the herbicide, with accumulated rainfall of 637 mm during the soybean cycle. Variables related to photosynthetic characteristics, phytointoxication, growth, components of production and productivity were evaluated in both crops. Diclosulam did not affect the soybean cultivar M7739 IPRO. The residual activity of diclosulam (35 g ha-1) on cotton caused phytointoxication at a rate of 5% at 14, 20 and 27 days after sowing (DAS). However, the components of production, productivity and the cotton fiber quality were not affected up to 70 g ha-1 of diclosulam.


2021 ◽  
Vol 13 (14) ◽  
pp. 2822
Author(s):  
Zhe Lin ◽  
Wenxuan Guo

An accurate stand count is a prerequisite to determining the emergence rate, assessing seedling vigor, and facilitating site-specific management for optimal crop production. Traditional manual counting methods in stand assessment are labor intensive and time consuming for large-scale breeding programs or production field operations. This study aimed to apply two deep learning models, the MobileNet and CenterNet, to detect and count cotton plants at the seedling stage with unmanned aerial system (UAS) images. These models were trained with two datasets containing 400 and 900 images with variations in plant size and soil background brightness. The performance of these models was assessed with two testing datasets of different dimensions, testing dataset 1 with 300 by 400 pixels and testing dataset 2 with 250 by 1200 pixels. The model validation results showed that the mean average precision (mAP) and average recall (AR) were 79% and 73% for the CenterNet model, and 86% and 72% for the MobileNet model with 900 training images. The accuracy of cotton plant detection and counting was higher with testing dataset 1 for both CenterNet and MobileNet models. The results showed that the CenterNet model had a better overall performance for cotton plant detection and counting with 900 training images. The results also indicated that more training images are required when applying object detection models on images with different dimensions from training datasets. The mean absolute percentage error (MAPE), coefficient of determination (R2), and the root mean squared error (RMSE) values of the cotton plant counting were 0.07%, 0.98 and 0.37, respectively, with testing dataset 1 for the CenterNet model with 900 training images. Both MobileNet and CenterNet models have the potential to accurately and timely detect and count cotton plants based on high-resolution UAS images at the seedling stage. This study provides valuable information for selecting the right deep learning tools and the appropriate number of training images for object detection projects in agricultural applications.


2014 ◽  
Vol 33 (2) ◽  
pp. 167-177 ◽  
Author(s):  
Guoxin Shen ◽  
Jia Wei ◽  
Xiaoyun Qiu ◽  
Rongbin Hu ◽  
Sundaram Kuppu ◽  
...  

iScience ◽  
2021 ◽  
Vol 24 (7) ◽  
pp. 102737
Author(s):  
Liping Zhu ◽  
Lingling Dou ◽  
Haihong Shang ◽  
Hongbin Li ◽  
Jianing Yu ◽  
...  

iScience ◽  
2021 ◽  
pp. 102199
Author(s):  
Liping Zhu ◽  
Lingling Dou ◽  
Haihong Shang ◽  
Hongbin Li ◽  
Jianing Yu ◽  
...  

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