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Motor-independent retraction of type IV pili is governed by an inherent property of the pilus filament.

Jennifer L ChlebekRémi DeniseLisa CraigAnkur B Dalia
Published in: Proceedings of the National Academy of Sciences of the United States of America (2021)
Type IV pili (T4P) are dynamic surface appendages that promote virulence, biofilm formation, horizontal gene transfer, and motility in diverse bacterial species. Pilus dynamic activity is best characterized in T4P that use distinct ATPase motors for pilus extension and retraction. Many T4P systems, however, lack a dedicated retraction motor, and the mechanism underlying this motor-independent retraction remains a mystery. Using the Vibrio cholerae competence pilus as a model system, we identify mutations in the major pilin gene that enhance motor-independent retraction. These mutants likely diminish pilin-pilin interactions within the filament to produce less-stable pili. One mutation adds a bulky residue to α1C, a universally conserved feature of T4P. We found that inserting a bulky residue into α1C of the retraction motor-dependent Acinetobacter baylyi competence T4P enhances motor-independent retraction. Conversely, removing bulky residues from α1C of the retraction motor-independent, V. cholerae toxin-coregulated T4P stabilizes the filament and diminishes pilus retraction. Furthermore, alignment of pilins from the broader type IV filament (T4F) family indicated that retraction motor-independent T4P, gram-positive Com pili, and type II secretion systems generally encode larger residues within α1C oriented toward the pilus core compared to retraction motor-dependent T4P. Together, our data demonstrate that motor-independent retraction relies, in part, on the inherent instability of the pilus filament, which may be a conserved feature of diverse T4Fs. This provides evidence for a long-standing yet previously untested model in which pili retract in the absence of a motor by spontaneous depolymerization.
Keyphrases
  • biofilm formation
  • staphylococcus aureus
  • escherichia coli
  • pseudomonas aeruginosa
  • machine learning
  • gene expression
  • electronic health record
  • candida albicans
  • deep learning
  • antimicrobial resistance
  • drug resistant