Molecular Pathogens, 2025, Vol.16, No.1, 19-26 http://microbescipublisher.com/index.php/mp 24 rotation, the microbiome in the soil will be healthier and have stronger disease resistance. This shows that rational arrangement of planting order is also a good way to improve soil disease prevention (Peralta et al., 2018; Zhang et al., 2024). 6.3 Lessons learned and future directions from case studies Through these studies, scientists have summarized some experience and also seen future research directions. First of all, the effect of biological control agents is largely related to the composition of microorganisms. What bacterial species are more or the combination of good bacteria will affect the results. Crop rotation methods such as planting and the variety selected will also affect the combination of these microorganisms. Secondly, the effects seen in laboratories or greenhouses may not necessarily be the same when you go to the field. Therefore, if you want to understand the performance of microorganisms in the actual environment, you have to do more field experiments. Future research can focus on two aspects: one is to find out the genetic markers that can help wheat attract good bacteria; the other is to study how wheat shows disease resistance in different types of soils (Ossowicki et al., 2020). These explorations are particularly important for formulating stable, green and easy-to-use wheat disease prevention strategies in the future. 7 Future Prospects in Wheat Microbiome Research 7.1 Integration of multi-omics approaches for microbiome analysis Now, scientists are beginning to combine different research methods, such as the metatranscriptome, genome and metabolomicome. These methods can help us gain a more comprehensive understanding of the microbiome around wheat roots and see how they help wheat prevent diseases. For example, the metatranscriptome can compare the gene expression of microorganisms in diseased soil and common soil, and find which bacteria and which genes have inhibitory effects on the disease (Hayden et al., 2018). This approach can also help us understand the complex relationship between wheat and microorganisms, and guide us to develop more effective biocontrol methods (Schlatter et al., 2017). 7.2 Role of artificial intelligence in microbiome studies Artificial intelligence (AI) and machine learning are now also used to study the microbiome. Because when studying these microorganisms, a lot of complex data will be generated. Artificial intelligence can help us find some rules from them, which cannot be discovered by traditional methods. For example, AI can predict which microbial combinations are beneficial to wheat health, and can also help us develop smarter breeding plans and select more disease-resistant varieties (Hu et al., 2016; Dilla-Ermita et al., 2021). It can also help us optimize the management methods in the fields, so that wheat can grow better and have fewer diseases. 7.3 Advances in microbiome-based disease prediction models Now, some studies have begun to use microbial data to model disease prediction. These models can tell us which plots may soon have a disease outbreak and also predict how serious the disease is. For example, some studies have found that if there are certain bacteria in the soil (like Pseudomonas), the degree of disease in wheat will be reduced. Then these bacteria can be used as “indicators” for prediction. In addition, if we add environmental factors and wheat variety information into the model, the prediction results can be made more accurate and more useful (Ridout and Newcombe, 2016; Peralta et al., 2018). Acknowledgments Thanks to the reviewers for their valuable feedback, which has helped improve the the manuscript. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Andargie Y.E., Lee G.D., Jeong M., Tagele S.B., and Shin J.H., 2023, Deciphering key factors in pathogen-suppressive microbiome assembly in the rhizosphere, Frontiers in Plant Science, 14: 1301698. https://doi.org/10.3389/fpls.2023.1301698
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