Plant Gene and Trait 2025, Vol.16, No.3, 92-103 http://genbreedpublisher.com/index.php/pgt 100 customizing promoters or regulatory elements are aimed at making gene expression more controllable and efficient (Bashor and Collins, 2018; Huang et al., 2021). Now, methods such as combinatorial DNA assembly and transplastomics have also been introduced, which can make prototype plant construction faster (Jackson et al., 2021). However, if you want to successfully transfer the C4 mechanism into C3 crops, it is not as simple as changing the engine, because C4 needs to coordinate anatomical structure and cell-specific expression, which must be systematically understood (Schuler et al., 2016). In the final analysis, synthetic biology is like opening a toolbox for “reprogramming plants”. Although the technology is complex, the idea is actually one: let the plant operate efficiently in the way you set it (Kassaw et al., 2018). 9.3 Importance of multi-environment field validation and systems biology No matter how well it is described in the book or how beautiful the laboratory data is, once it is exposed to heat, rain, or wind in the field, many effects will become unstable. Therefore, whether it is traditional genetic engineering or synthetic biology design, the step of “multi-environment verification” cannot be avoided in the end. Especially under different combinations of water, temperature, and soil nutrients, how genes and the environment interact and how resources are allocated, once these factors are superimposed, plant performance becomes difficult to predict (Liao et al., 2017; Sickle et al., 2020). To understand these, field yield measurement alone is not enough, and systems biology must keep up - after the integration of omics data, the model can be closer to the real growth dynamics (Cui, 2021). And those “good traits” that have been verified can remain stable in different locations and years. This is the premise for the real implementation of smart breeding. 10 Conclusion The high photosynthetic efficiency of sugarcane is largely due to its C4 photosynthetic pathway. But the operation of this pathway is far from being as simple as it seems. It involves a whole set of intricate genetic regulatory networks. Recent transcriptome and small RNA studies have actually revealed a little “unpopular” discovery - in many cases, miRNA does not directly control C4 genes, but “indirectly intervenes” through some transcription factors such as the GRAS family. These regulatory relationships further affect chlorophyll synthesis, carbon fixation, and a series of metabolic processes, which will eventually be reflected in the strength of photosynthesis. After analyzing the transcriptome and metabolome data together, researchers have unearthed thousands of genes and metabolites related to carbon fixation, sugar metabolism, and stress resistance. These achievements are not a simple accumulation of data, but a “gene map of excellent sugarcane varieties” for us. In other words, if you want to breed good varieties, you have a direction to start with. In terms of breeding methods, methods such as MAS, GS, and genetic engineering are no longer unfamiliar. Genomic selection is particularly worth mentioning. It can significantly speed up the breeding process and improve the accuracy of high-quality clone screening, especially with the support of high-throughput phenotyping methods. Of course, the polyploid genome of sugarcane does make things a bit tricky, but now gene editing technology is becoming more and more mature, and CRISPR is no longer just a “showmanship” in the laboratory, but can really move to the “core position” of regulating photosynthesis, sugar accumulation and stress resistance genes. It can be said that by combining omics data with modern breeding tools, we have taken a key step towards new high-yield and stress-resistant sugarcane varieties. In the future, if we want to grow sugarcane stably and produce high yields, we cannot do without the support of these genetic and biotechnologies. Especially in the face of climate uncertainty, resource constraints and other challenges, precision editing, systematic omics analysis and intelligent breeding strategies are becoming the way to deal with it. Of course, no matter how good the laboratory data is, it has to return to the field for testing. Only by continuing to study the photosynthetic regulatory network in depth, combined with large-scale phenotyping technology and multi-environment testing, can we truly transform these “potential genes” into real yields and resistance. In the long run, these innovations will not only help us meet the world’s growing demand for sugar and bioenergy, but also drive agricultural production towards a more sustainable direction.
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