International Journal of Aquaculture, 2025, Vol.15, No.4, 165-174 http://www.aquapublisher.com/index.php/ija 171 6 Multi-Level Regulatory Model For Toxin Production 6.1 Gene-environmental interaction model Toxin production is the result of the interaction between genes' intrinsic abilities and external environmental conditions. Existing research proposes a gene-environment interaction regulation model, which believes that the expression of gene clusters is driven by environmental signal input, and gene products (toxins) may also feedback to regulate growth and metabolism. The microcystis toxin mcy gene cluster is affected by the nitrogen regulator NtcA, which reflects the genome's response to exogenous nutritional status; at the same time, MCs are believed to be able to affect the iron metabolism and signaling pathways of microcystis itself (indirectly regulate gene expression), forming a positive and negative feedback circle (Wei et al., 2024). When external conditions change (nutrition, temperature, light intensity, etc.), the gene expression model is quickly adjusted to adapt to the new environment. This type of interaction model emphasizes that simple gene cluster annotation is not enough to predict toxin yield, and environmental variables need to be integrated into the model to more accurately describe the space-time dynamics of toxins. 6.2 Dynamic regulation of toxin synthesis and metabolic energy distribution In algae cells, toxin biosynthesis requires a large amount of energy and precursor substances, so the dynamic regulation of toxin synthesis is closely related to the distribution of cell metabolic energy. Research shows that when cell growth rate slows down (such as entering a stable period), the immediate yield of toxins can remain unchanged or relatively increased, resulting in an increase in cell toxicity (Salvador et al., 2016). This is because during the growth restricted phase, excess energy is redistributed to toxin biosynthesis. On the contrary, during the rapid growth period, energy is mainly used for cell reproduction, and the amount of newly synthesized toxins is low. This energy distribution model illustrates the relationship between growth kinetics and toxin concentration, and also provides a theoretical basis for understanding the laws of toxin accumulation at different growth stages. A dynamic metabolic model constructed incorporating multiomics data can be used to simulate how algae regulate energy flow to balance growth and toxin production under different environmental conditions. 6.3 Comprehensive regulatory network revealed by multiomics data In recent years, the joint analysis of multipleomics (genome, transcriptome, proteome, metabolomic, epigenetic group, etc.) has made breakthroughs in algatoxin research. By integrating these data, a complete toxin synthesis regulatory network map can be drawn. Single-cell sequencing technology can identify gene modules with the greatest differences in expression between toxic and avirulent strains; proteome-metabolomic analysis reveals changes in toxin precursor supply and transporters; epilogue data supplement the modification information at the transcriptional regulation level. Combining these high-dimensional data can establish a hierarchical network model from gene-transcription-protein-metabolism, identify key regulatory nodes and pathways, and provide multi-scale explanations for predicting toxin production. This type of comprehensive regulatory network research is becoming a hot topic in the future, and it is expected to describe the full dynamics of toxin production at the single cell level (Erwin et al., 2023; Li et al., 2024). 7 Technological Progress in Monitoring and Predicting Algae Toxin Production 7.1 Real-time monitoring method based on molecular markers With the analysis of toxin synthesis gene clusters, real-time monitoring based on gene markers has become an effective means to quickly detect the poison-producing populations in algae flowers. Traditional ecological monitoring methods are difficult to quickly distinguish between toxin-producing and non-toxin-producing algae strains, while molecular technologies such as PCR and qPCR can detect the abundance of toxin synthetic genes (such as mcyE, anaC, sxtA, etc.) to evaluate the potential toxicity risks in real time. In addition, high-throughput sequencing technologies (such as 18S/16S sequencing or functional gene-based metagenomics) can quickly obtain microbial community structure and toxin gene diversity in water bodies, providing a basis for early warning. In recent years, environmental DNA (eDNA) detection methods have also been developed, which can directly amplify and quantify the scattered DNA in water samples without distinguishing between cells or DNA regions,
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