scholarly journals Genotypic variation in root morphology, cotton subtending leaf physiology and fiber quality against nitrogen

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Iqbal ASIF ◽  
Qiang DONG ◽  
Xiangru WANG ◽  
Huiping GUI ◽  
Hengheng ZHANG ◽  
...  

Abstract Background Nitrogen (N) is important for improving various morphological and physiological processes of cotton but their contribution to fiber quality is still lacking. Aims The current study aimed to explore the relationship between root morphology, subtending leaf physiology, and fiber quality of contrasting N-efficient cotton genotypes in response to N. Methods We analyzed the above parameters of CCRI 69 (N-efficient) and Xinluzao-30 (XLZ-30, N-inefficient) under control (2.5 mmol·L−1) and high N (5 mmol·L−1) conditions. Results The results showed that root morphological traits were increased in CCRI-69 under control conditions than high N. Subtending leaf morphology, chlorophyll and carotenoid contents, free amino acids, and soluble proteins were higher under high N as compared with the control. However, soluble sugars, fructose, sucrose contents, and sucrose phosphate synthase were higher under control conditions than high N across the growth stages. Irrespective of the N conditions, all morphological and physiological traits of cotton subtending leaf were higher in CCRI-69 than XLZ-30. Except for fiber uniformity, fiber quality traits like fiber length, strength, micronaire, and elongation were improved under control conditions than high N. Between the genotypes, CCRI-69 had significantly higher fiber length, strength, micronaire, and elongation as compared with XLZ-30. Strong positive correlations were found between root morphology, soluble sugars, sucrose content, and sucrose phosphate synthase activity with fiber quality traits, respectively. Conclusions These findings suggest that CCRI-69 performed better in terms of growth and fiber quality under relatively low N condition, which will help to reduce fertilizer use, the cost of production, and environmental pollution.

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaohui Song ◽  
Guozhong Zhu ◽  
Sen Hou ◽  
Yamei Ren ◽  
Muhammad Waqas Amjid ◽  
...  

Fiber length, fiber strength, and fiber micronaire are the main fiber quality parameters in cotton. Thus, mining the elite and stable loci/alleles related to fiber quality traits and elucidating the relationship between the two may accelerate genetic improvement of fiber quality in cotton. Here, genome-wide association analysis (GWAS) was performed for fiber quality parameters based on phenotypic data, and 56,010 high-quality single nucleotide polymorphisms (SNPs) using 242 upland cotton accessions under 12 field environments were obtained. Phenotypic analysis exhibited that fiber length (FL) had a positive correlation with fiber strength (FS) and had a negative correlation with fiber micronaire (Mic). Genetic analysis also indicated that FL, FS, and Mic had high heritability of more than 80%. A total of 67 stable quantitative trait loci (QTLs) were identified through GWAS analysis, including 31 for FL, 21 for FS, and 22 for Mic. Of them, three pairs homologous QTLs were detected between A and D subgenomes, and seven co-located QTLs with two fiber quality parameters were found. Compared with the reported QTLs, 34 co-located with previous studies, and 33 were newly revealed. Integrated with transcriptome analysis, we selected 256, 244, and 149 candidate genes for FL, FS, and Mic, respectively. Gene Ontology (GO) analysis showed that most of the genes located in QTLs interval of the three fiber quality traits were involved in sugar biosynthesis, sugar metabolism, microtubule, and cytoskeleton organization, which played crucial roles in fiber development. Through correlation analysis between haplotypes and phenotypes, three genes (GH_A05G1494, GH_D11G3097, and GH_A05G1082) predominately expressed in fiber development stages were indicated to be potentially responsible for FL, FS, and Mic, respectively. The GH_A05G1494 encoded a protein containing SGS-domain, which is related to tubulin-binding and ubiquitin-protein ligase binding. The GH_D11G3097 encoded 20S proteasome beta subunit G1, and was involved in the ubiquitin-dependent protein catabolic process. The GH_A05G1082 encoded RAN binding protein 1 with a molecular function of GTPase activator activity. These results provide new insights and candidate loci/genes for the improvement of fiber quality in cotton.


Author(s):  
An-hui Guo ◽  
Ying Su ◽  
Yi Huang ◽  
Yu-mei Wang ◽  
Hu-shuai Nie ◽  
...  

Abstract Key message QTL for fiber quality traits under salt stress discerned candidate genes controlling fatty acid metabolism. Abstract Salinity stress seriously affects plant growth and limits agricultural productivity of crop plants. To dissect the genetic basis of response to salinity stress, a recombinant inbred line population was developed to compare fiber quality in upland cotton (Gossypium hirsutum L.) under salt stress and normal conditions. Based on three datasets of (1) salt stress, (2) normal growth, and (3) the difference value between salt stress and normal conditions, 51, 70, and 53 QTL were mapped, respectively. Three QTL for fiber length (FL) (qFL-Chr1-1, qFL-Chr5-5, and qFL-Chr24-4) were detected under both salt and normal conditions and explained 4.26%, 9.38%, and 3.87% of average phenotypic variation, respectively. Seven genes within intervals of two stable QTL (qFL-Chr1-1 and qFL-Chr5-5) were highly expressed in lines with extreme long fiber. A total of 35 QTL clusters comprised of 107 QTL were located on 18 chromosomes and exhibited pleiotropic effects. Thereinto, two clusters were responsible for improving five fiber quality traits, and 6 influenced FL and fiber strength (FS). The QTL with positive effect for fiber length exhibited active effects on fatty acid synthesis and elongation, but the ones with negative effect played passive roles on fatty acid degradation under salt stress.


1984 ◽  
Vol 34 (3) ◽  
pp. 247-252 ◽  
Author(s):  
Thomas W. Rufty ◽  
Steven C. Huber ◽  
Phillip S. Kerr

PLoS ONE ◽  
2017 ◽  
Vol 12 (10) ◽  
pp. e0186650 ◽  
Author(s):  
Juan Wang ◽  
Junjie Du ◽  
Xiaopeng Mu ◽  
Pengfei Wang

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