Once the foundation of life on Earth, they have now become “hidden bombs” in water bodies. Cyanobacteria, as ancient prokaryotes, not only support aquatic ecosystems through photosynthesis but can also form harmful blooms when overproliferated, releasing toxins that threaten drinking water and ecological security.
Most existing algal bloom warning systems are based on “post-event monitoring”, relying on indicators such as chlorophyll and phycocyanin to issue alerts only after blooms have occurred. Machine learning predictions are limited by regional data and are difficult to generalize, while molecular detection methods cannot simultaneously cover multiple species due to primer specificity. More importantly, these methods fail to mechanistically explain why certain cyanobacteria still dominate and even produce toxins in phosphorus-deficient waters.
Phosphorus is the most critical “limiting element” for cyanobacterial growth. Previous studies have shown that phosphorus limitation alters algal species composition and can even promote the proliferation of certain non-nitrogen-fixing cyanobacteria, leading to toxin production. Cyanobacteria have also developed adaptive mechanisms, such as high-affinity phosphate transport systems, and even “streamlined genomes” to enhance survival efficiency. But how small a genome must be to indicate lower risk? What are the underlying metabolic mechanisms?
The research team collected metagenomic data from a large-scale water transfer canal with long-term phosphorus levels below 0.02 mg/L and reconstructed 317 cyanobacterial genomes. They discovered a clear “genome size–ecological function” differentiation pattern: cyanobacteria with genomes smaller than 3 Mbp (megabase pairs) are “streamlined types”. They dominate in phosphorus-deficient environments, excel in phosphorus uptake, light capture, and carbon fixation, rarely produce toxins, and act as “low-risk workers” in the system. In contrast, those with genomes larger than 3 Mbp are “complex types”. They carry toxin genes and buoyancy regulation genes, which under certain conditions can trigger blooms and release harmful substances such as microcystins, making them “high-risk groups”.
Moreover, these small-genome cyanobacteria exhibit distinct seasonal succession: Synechococcus dominates in spring, while Cyanobium takes over in autumn, reflecting niche differentiation driven by temperature and light variations.
Based on this discovery, the study proposes using “~3 Mbp” as a threshold to establish a genome size-oriented proxy indicator for cyanobacterial risk early warning. If the proportion of cyanobacterial genomes larger than 3 Mbp increases, the system could initiate preventive measures in advance, achieving a critical shift from “responding after blooms occur” to “preventing blooms before they happen”.
This research not only reveals the genomic evolutionary strategies of cyanobacteria in adversity but also advances algal risk monitoring from reactive to preventive approaches. It provides a replicable paradigm for sustainable algal control in global artificial freshwater networks.