Advances in Computational Biology Methods and Applications for Nutritional Metabolomics of Vegetable Crops
DOI:
https://doi.org/10.54097/nwmy4m55Keywords:
Vegetable crops; Nutritional metabolomics; Computational biology; Machine learning; Systems biology.Abstract
As an essential component of systems biology, metabolomics provides novel technological approaches and theoretical frameworks for studying nutritional quality in vegetable crops. This review summarizes recent advances in computational biology methods for vegetable crop nutritional metabolomics, including key technologies such as data preprocessing, statistical analysis, machine learning, network analysis, and pathway enrichment analysis. We focus on the applications of these methods in vegetable nutritional composition analysis, quality evaluation, stress response mechanism elucidation, and breeding-assisted selection. The current challenges are analyzed, including data standardization, method standardization, and multi-omics data integration. Future development trends are discussed to provide references for vegetable crop nutritional metabolomics research.
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[1] Fiehn O. 2002. Metabolomics-the link between genotypes and phenotypes. Plant Molecular Biology, 48(1-2): 155-171.
[2] Saito K, Matsuda F. 2010. Metabolomics for functional genomics, systems biology, and biotechnology. Annual Review of Plant Biology, 61: 463-489.
[3] Fernie AR, Schauer N. 2009. Metabolomics-assisted breeding: a viable option for crop improvement? Trends in Genetics, 25(1): 39-48.
[4] Fukushima A, Kusano M. 2013. Recent progress in the development of metabolome databases for plant systems biology. Frontiers in Plant Science, 4: 73.
[5] Smith CA, Want EJ, O'Maille G, et al. 2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78(3): 779-787.
[6] Pluskal T, Castillo S, Villar-Briones A, et al. 2010. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics, 11: 395.
[7] Tsugawa H, Cajka T, Kind T, et al. 2015. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nature Methods, 12(6): 523-526.
[8] Wishart DS, Feunang YD, Marcu A, et al. 2018. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Research, 46(D1): D608-D617.
[9] Dunn WB, Broadhurst D, Begley P, et al. 2011. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6(7): 1060-1083.
[10] Dieterle F, Ross A, Schlotterbeck G, et al. 2006. Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical Chemistry, 78(13): 4281-4290.
[11] van den Berg RA, Hoefsloot HC, Westerhuis JA, et al. 2006. Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics, 7: 142.
[12] Johnson WE, Li C, Rabinovic A. 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1): 118-127.
[13] Keurentjes JJ, Fu J, de Vos CH, et al. 2006. The genetics of plant metabolism. Nature Genetics, 38(7): 842-849.
[14] Tohge T, Fernie AR. 2010. Combining genetic diversity, informatics and metabolomics to facilitate annotation of plant gene function. Nature Protocols, 5(6): 1210-1227.
[15] Yamamoto H, Fujimori T, Sato H, et al. 2014. Statistical hypothesis testing of factor loading in principal component analysis and its application to metabolite set enrichment analysis. BMC Bioinformatics, 15: 51.
[16] Pomyen Y, Wanichthanarak K, Poungsombat P, et al. 2020. Deep metabolome: applications of deep learning in metabolomics. Computational and Structural Biotechnology Journal, 18: 2818-2825.
[17] Wen B, Mei Z, Zeng C, et al. 2017. metaX: a flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics, 18(1): 183.
[18] Kopka J, Schauer N, Krueger S, et al. 2005. GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics, 21(8): 1635-1638.
[19] Saito K, Hirai MY, Yonekura-Sakakibara K. 2008. Decoding genes with coexpression networks and metabolomics - 'majority report by precogs'. Trends in Plant Science, 13(1): 36-43.
[20] Keurentjes JJ, Fu J, Terpstra IR, et al. 2007. Regulatory network construction in Arabidopsis by using genome-wide gene expression quantitative trait loci. Proceedings of the National Academy of Sciences, 104(5): 1708-1713.
[21] Krumsiek J, Suhre K, Illig T, et al. 2011. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Systems Biology, 5: 21.
[22] Langfelder P, Horvath S. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9: 559.
[23] Kanehisa M, Goto S, Sato Y, et al. 2014. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Research, 42(D1): D199-D205.
[24] Xia J, Sinelnikov IV, Han B, et al. 2015. MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Research, 43(W1): W251-W257.
[25] Schauer N, Fernie AR. 2006. Plant metabolomics: towards biological function and mechanism. Trends in Plant Science, 11(10): 508-516.
[26] Fiehn O, Kopka J, Dörmann P, et al. 2000. Metabolite profiling for plant functional genomics. Nature Biotechnology, 18(11): 1157-1161.
[27] Roessner U, Luedemann A, Brust D, et al. 2001. Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. The Plant Cell, 13(1): 11-29.
[28] Roessner U, Wagner C, Kopka J, et al. 2000. Simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. The Plant Journal, 23(1): 131-142.
[29] Sumner LW, Mendes P, Dixon RA. 2003. Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry, 62(6): 817-836.
[30] Hall RD. 2006. Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytologist, 169(3): 453-468.
[31] Kusano M, Redestig H, Hirai T, et al. 2011. Covering chemical diversity of genetically-modified tomatoes using metabolomics for objective substantial equivalence assessment. PLoS One, 6(2): e16989.
[32] Kusano M, Fukushima A, Kobayashi M, et al. 2007. Application of a metabolomic method combining one-dimensional and two-dimensional gas chromatography-time-of-flight/mass spectrometry to metabolic phenotyping of natural variants in rice. Journal of Chromatography B, 855(1): 71-79.
[33] Schauer N, Semel Y, Roessner U, et al. 2006. Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotechnology, 24(4): 447-454.
[34] Tohge T, Nishiyama Y, Hirai MY, et al. 2005. Functional genomics by integrated analysis of metabolome and transcriptome of Arabidopsis plants over-expressing an MYB transcription factor. The Plant Journal, 42(2): 218-235.
[35] Liu CW, Murray JD. 2016. The role of flavonoids in nodulation host-range specificity: an update. Plants, 5(3): 33.
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