Molecular Soil Biology 2025, Vol.16, No.5, 230-240 http://bioscipublisher.com/index.php/msb 236 6.3 Potential in bioengineering nodules for climate-smart agriculture Proteomics provides new molecular targets for root tumor bioengineering and climate-smart agriculture. By regulating root tumor membrane proteins, signal proteins and stress resistance proteins, the adaptability and nitrogen fixation efficiency of root tumors under conditions such as low phosphorus, saline-alkali and drought can be enhanced (Chen et al., 2018; He et al., 2020; Velickoviac et al., 2022; Xing et al., 2022). Proteomics data also provide a basis for synthetic biology and gene editing. In the future, it is possible to construct more efficient and low-carbon nitrogen fixation systems through the engineering modification of root nodules or rhizobia, thereby reducing the use of chemical fertilizers, supporting sustainable agriculture and helping to address climate change (Contador et al., 2020). 7 Challenges and Future Directions 7.1 Limitations of current proteomic methods (coverage, dynamic range, nodule complexity) Proteomic research on soybean root nodules still faces many problems. The protein coverage is limited, the dynamic range is insufficient, and the tissue structure is very complex. High-abundance proteins (such as hemoglobin and Rubisco) often mask low-abundance regulatory proteins, making many key proteins difficult to detect (Krishnan and Natarajan, 2009). The root tumor tissues themselves vary greatly, and most conventional studies are holistic analyses. This is prone to the "dilution effect", causing the response signal to be masked by the non-response cells (Song et al., 2022). Although mass spectrometry and separation techniques have advanced, the coverage and quantitative accuracy of proteomes still need to be further improved (Min et al., 2019; Kelly, 2020; Zhou et al., 2022; Pang et al., 2024). 7.2 Need for single-cell proteomics and spatial proteomics in nodules Single-cell proteomics and spatial proteomics provide new ideas for studying the differences and functional zoning of root tumor cells. However, in plants, single-cell research is still subject to many limitations, such as indigestion of cell walls, too low protein content, and easy sample loss (Kelly, 2020; Yu et al., 2022; Pang et al., 2024; Rhaman et al., 2024). Spatial proteomics (such as MALDI-MSI) has been able to perform spatial imaging of root tumor proteins, revealing the functional differences between the infected area and the cortical area. However, its resolution and protein identification ability are not high enough (Zhou et al., 2022; Mund et al., 2022). 7.3 Integration with multi-omics and machine learning for predictive modeling Future research requires the integration of proteomics with transcriptomics, metabolomics, epigenomics, etc., combined with machine learning and network modeling, to predict root tumor development, nitrogen fixation regulation and stress response (Yan et al., 2022; Rhaman et al., 2024). However, the differences in multi-omics data, the lack of standards and the deficiencies of big data analysis remain the current difficulties (Yu et al., 2022). Machine learning and artificial intelligence methods are expected to improve the efficiency and accuracy of protein function prediction, protein interaction network reconstruction and phenotypic association analysis (Yan et al., 2022; Baysoy et al., 2023; Vandereyken et al., 2023). 7.4 Translational challenges from laboratory findings to field applications The proteomics achievements in the laboratory are not easily applied directly to the field because of the complex environment and the interaction between genotypes and the environment, which makes it difficult for phenotypes to be stably replicated. To truly transform the achievements, it is necessary to validate protein markers in the field, assess environmental adaptability, and establish a high-throughput screening system. Meanwhile, the standardization, sharing and database construction of proteomics data also need to be strengthened, so as to better combine basic research with breeding practice (Hossain et al., 2013; Min et al., 2019; Yan et al., 2022). 8 Conclusion Proteomics of soybean root nodules provides us with new molecular-level information for understanding efficient nitrogen fixation. Researchers have found through systematic analysis that some key proteins related to carbon metabolism, energy supply, signal transduction, antioxidant defense and nutrient transport play an important role
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