Antibacterial and Anticancer Properties of Anthraquinone Derivatives from Streptomyces kunmingensis BH111, an Isolate from Beehive.

<b>Background and

Objective

</b> The persistent global threats of antimicrobial resistance and cancer necessitate the discovery of novel chemical entities, a search where actinomycetes from unique environments are critical. To discover new therapeutics, this study investigated actinomycetes from the <i>Apis florea</i> beehive environment, aiming to identify a promising strain and structurally characterize its antibacterial and anticancer metabolites. <b>Materials and

Methods

</b> Actinomycetes were isolated from five <i>Apis florea</i> beehives. Strain identification was performed using morphological, chemotaxonomic and dual-gene sequencing (16S rRNA and <i>gyr</i>B). Metabolites were purified and biological activity was assessed using MIC/MBC (antibacterial) and MTT cytotoxicity assays. The mechanism was explored via molecular docking against Topoisomerase II, complemented by ADMET prediction. <b>

Results

</b> The most active isolate, BH111, was identified as <i>Streptomyces kunmingensis</i>. It produced two anthraquinone derivatives: Resistomycin (Compound <b>1</b>) and tetracenomycin D (Compound <b>2</b>). These compounds demonstrated selective, potent antibacterial activity against Gram-positive strains (MIC and MBC values ranged from 16 to 32 and 128 to 256 μg/mL, respectively) and significant cytotoxicity against tested cancer lines (IC<sub>50</sub> values ranged from 10.72 to 27.57 μg/mL). Docking results supported Topoisomerase II inhibition. However, low selectivity indices and ADMET flags (CYP450, hepatotoxicity) were noted. <b>

Conclusion

</b> <i>Streptomyces kunmingensis</i> BH111 is the first strain of this species reported to produce resistomycin and tetracenomycin D from a beehive source. While these anthraquinone derivatives are promising drug scaffolds due to their potent antibacterial and topoisomerase-inhibiting anticancer activities, significant therapeutic optimization is required to enhance selectivity and mitigate predicted toxicological risks.