Bt Research 2025, Vol.16, No.5, 194-203 http://microbescipublisher.com/index.php/bt 201 Melanin-producing Bt strains have stronger resistance to UV rays (Figure 3). This work improves the stability of Bt preparations in the field by editing a secondary metabolic regulatory pathway, indirectly increasing the environmental availability of effective toxins. This shows that gene editing can enhance the actual insecticidal effect of Bt from multiple angles (Zhu et al., 2022). Figure 3 Microscope of B. thuringiensis HD-1 and HD-1-ΔhmgAunder oil lens (100 ×). (A) Crystal observation of strain HD-1. (B) Crystal observation of mutant HD-1-ΔhmgA. Both strains have no distinct difference in spore and crystal formation (Adopted from Zhu et al., 2022) 8 Future Development and Challenges of Comprehensive Genomics 8.1 Multi-omics data integration and network modeling With the accumulation of large amounts of data such as genomics, transcriptomics, proteomics, metabolomics, etc., how to integrate multi-level data to build a systematic Bt regulatory network model is one of the core challenges of future research. A single group can often only reflect one aspect of the regulatory network, while combining multiple groups can depict the overall picture of the network. For example, combining the transcriptome with the proteome can distinguish which gene expression regulation really leads to changes in protein levels and which may be modified by post-translational regulation; and then incorporate metabolomic data to understand how regulatory changes affect the metabolic flux of the end product. The future trend is to develop a "multiomic joint analysis" platform, conduct different omics tests on the same sample, and integrate analysis using systematic biological methods. In Bt research, there are several specific directions for multi-omic integration: spatio-temporal omics, that is, obtaining the omics data of Bt at multiple time points and spatial micro-regions at the same time and establishing a dynamic network model. Conditional omics, that is, obtaining Bt's omics data sets under different environmental conditions, and extracting independent regulatory modules under each condition through machine learning methods such as independent component analysis. 8.2 Application of big data and artificial intelligence in the research on regulating networks Entering the post-genomic era, Bt research is entering a new stage driven by big data and artificial intelligence. High-throughput sequencing and omics technology produces massive amounts of Bt-related data every day, and it is overwhelming from gene sequences to expression profiles. Artificial intelligence (AI), especially machine learning technology, provides a powerful means to extract knowledge from these big data. In the study of Bt gene regulation networks, the role of AI can be reflected in many aspects, and machine learning can be used to infer and optimize network structure. AI technologies such as deep learning can be used to mine regulatory passwords from sequence information. 8.3 Prospects and value of agricultural application of Bt gene regulation network research Taking into account the above developments, the research on Bt gene regulation network has broad prospects and important agricultural application value in the future. At the theoretical level, the gene regulation network that decodes Bt in panoramic form will greatly enrich our understanding of Bacillus molecular biology, which can be one of the models for the regulation of complex life processes of Gram-positive bacteria. At the practical level, these research results will directly serve the improvement and innovation of Bt-related biotechnology. In terms of strain breeding, in the past, screening was mostly blindly selected based on phenotypes. In the future, with a regulatory network model, we can select strains containing specific regulatory characteristics in a targeted manner.
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