Back

Structural Elucidation and Revised Biosynthetic Pathway of the Membrane Vesicle-Associated Antifungal Compound AFC

Tavares, I.; Chen, Y.-C.; Agnoli, K.; Berger, M.; Sieber, S.; Gademann, K.; Eberl, L.

2025-10-30 biochemistry
10.1101/2025.10.28.685075 bioRxiv
Show abstract

Bacterial membrane vesicles (MVs) serve as delivery vehicles for hydrophobic and membrane-associated secondary metabolites, enhancing their solubility, stability, and bioactivity. Here, we show that the antifungal compound AFC-BC11 (AFC), produced by Burkholderia cenocepacia K56-2, is selectively packaged into and released via MVs. Using HR-MS/MS, NMR, and stable isotope feeding experiments, we determined the chemical structure of AFC and analyzed its biosynthesis. Our results confirm that the structure largely matches the recent report by Zhong et al., with a key difference: the double bond in the fatty acid moiety is positioned between C11 and C12. We provide compelling evidence that this constitution reflects the direct incorporation of cis-vaccenic acid, the most abundant fatty acid in B. cenocepacia, rather than a tailoring modification. Comparative analysis of afcU, afcF, and afcS mutants suggests a biosynthetic pathway involving {omega}-modification of cis-vaccenic acid, revising previous proposals of citric acid conjugation to myristic acid and opening avenues for acyl chain engineering. Together, these findings establish AFC as an MV-associated antifungal metabolite, provide a refined structural and biosynthetic model, and highlight the role of MVs in the dispersal of hydrophobic bioactive compounds. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/685075v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1f4d736org.highwire.dtl.DTLVardef@15d2683org.highwire.dtl.DTLVardef@8b891corg.highwire.dtl.DTLVardef@d6b693_HPS_FORMAT_FIGEXP M_FIG C_FIG

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.