Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane
Abstract 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...
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| Language: | English |
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BMC
2022-07-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-022-08768-2 |
| _version_ | 1811287427486056448 |
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| author | Ao-Mei Li Zhong-Liang Chen Cui-Xian Qin Zi-Tong Li Fen Liao Ming-Qiao Wang Prakash Lakshmanan Yang-Rui Li Miao Wang You-Qiang Pan Dong-Liang Huang |
| author_facet | Ao-Mei Li Zhong-Liang Chen Cui-Xian Qin Zi-Tong Li Fen Liao Ming-Qiao Wang Prakash Lakshmanan Yang-Rui Li Miao Wang You-Qiang Pan Dong-Liang Huang |
| author_sort | Ao-Mei Li |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T03:17:13Z |
| format | Article |
| id | doaj.art-27281566c39b4f0eb050cf89ae45691c |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-13T03:17:13Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-27281566c39b4f0eb050cf89ae45691c2022-12-22T03:04:51ZengBMCBMC Genomics1471-21642022-07-0123111410.1186/s12864-022-08768-2Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcaneAo-Mei Li0Zhong-Liang Chen1Cui-Xian Qin2Zi-Tong Li3Fen Liao4Ming-Qiao Wang5Prakash Lakshmanan6Yang-Rui Li7Miao Wang8You-Qiang Pan9Dong-Liang Huang10Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesMelbourne Integrative Genomics and School of Mathematics and Statistics, the University of MelbourneKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesAbmartKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesKey Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Guangxi Key Laboratory of Sugarcane Genetic Improvement /Sugarcane Research Institute, Guangxi Academy of Agricultural SciencesAbstract 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.https://doi.org/10.1186/s12864-022-08768-2SugarcaneProteomicsDifferentially abundant proteinStatistical approachSucrose accumulation |
| spellingShingle | Ao-Mei Li Zhong-Liang Chen Cui-Xian Qin Zi-Tong Li Fen Liao Ming-Qiao Wang Prakash Lakshmanan Yang-Rui Li Miao Wang You-Qiang Pan Dong-Liang Huang Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane BMC Genomics Sugarcane Proteomics Differentially abundant protein Statistical approach Sucrose accumulation |
| title | Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane |
| title_full | Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane |
| title_fullStr | Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane |
| title_full_unstemmed | Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane |
| title_short | Proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane |
| title_sort | proteomics data analysis using multiple statistical approaches identified proteins and metabolic networks associated with sucrose accumulation in sugarcane |
| topic | Sugarcane Proteomics Differentially abundant protein Statistical approach Sucrose accumulation |
| url | https://doi.org/10.1186/s12864-022-08768-2 |
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