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Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane

文献类型： 外文期刊

作者： Li, Ao-Mei 1 ; Chen, Zhong-Liang 1 ; Qin, Cui-Xian 1 ; Li, Zi-Tong 2 ; Liao, Fen 1 ; Wang, Ming-Qiao 4 ; Lakshmanan, Prakash 1 ; Li, Yang-Rui 1 ; Wang, Miao 1 ; Pan, You-Qiang 1 ; Huang, Dong-Liang 1 ;

作者机构： 1.Guangxi Acad Agr Sci, Sugarcane Res Inst, Guangxi Key Lab Sugarcane Genet Improvement,Minis, Key Lab Sugarcane Biotechnol & Genet Improvement, Nanning 530007, Peoples R China

2.Univ Melbourne, Melbourne Integrat Genom, Parkville, Vic 3010, Australia

3.Univ Melbourne, Sch Math & Stat, Parkville, Vic 3010, Australia

4.Abmart, Shanghai 200033, Peoples R China

5.Southwest Univ, Coll Resources & Environm, Interdisciplinary Res Ctr Agr Green Dev Yangtze R, Chongqing 400716, Peoples R China

6.Univ Queensland, Queensland Alliance Agr & Food Innovat, St Lucia, Qld 4067, Australia

关键词： Sugarcane; Proteomics; Differentially abundant protein; Statistical approach; Sucrose accumulation

期刊名称：BMC GENOMICS （ 影响因子：4.547； 五年影响因子：4.931 ）

ISSN： 1471-2164

年卷期： 2022 年 23 卷 1 期

页码：

收录情况： SCI

摘要： Background Sugarcane is the most important sugar crop, contributing > 80% of global sugar production. High sucrose content is a key target of sugarcane breeding, yet sucrose improvement in sugarcane remains extremely slow for decades. Molecular breeding has the potential to break through the genetic bottleneck of sucrose improvement. Dissecting the molecular mechanism(s) and identifying the key genetic elements controlling sucrose accumulation will accelerate sucrose improvement by molecular breeding. In our previous work, a proteomics dataset based on 12 independent samples from high- and low-sugar genotypes treated with ethephon or water was established. However, in that study, employing conventional analysis, only 25 proteins involved in sugar metabolism were identified . Results In this work, the proteomics dataset used in our previous study was reanalyzed by three different statistical approaches, which include a logistic marginal regression, a penalized multiple logistic regression named Elastic net, as well as a Bayesian multiple logistic regression method named Stochastic search variable selection (SSVS) to identify more sugar metabolism-associated proteins. A total of 507 differentially abundant proteins (DAPs) were identified from this dataset, with 5 of them were validated by western blot. Among the DAPs, 49 proteins were found to participate in sugar metabolism-related processes including photosynthesis, carbon fixation as well as carbon, amino sugar, nucleotide sugar, starch and sucrose metabolism. Based on our studies, a putative network of key proteins regulating sucrose accumulation in sugarcane is proposed, with glucose-6-phosphate isomerase, 2-phospho-D-glycerate hydrolyase, malate dehydrogenase and phospho-glycerate kinase, as hub proteins. Conclusions The sugar metabolism-related proteins identified in this work are potential candidates for sucrose improvement by molecular breeding. Further, this work provides an alternative solution for omics data processing.

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