Data-Driven Models Reveal Mutant Cell Behaviors Important for Myxobacterial Aggregation.

Area of Science:

• Microbiology
• Developmental Biology
• Systems Biology

Background:

• Multicellular self-organization is crucial in biological phenomena, but dissecting the mechanisms of coordinated cell movement remains challenging.
• Genetic mutations can alter cell behavior, yet linking specific behavioral changes to biological significance in multicellular patterns is difficult.
• Myxococcus xanthus provides a model for studying multicellular development, specifically the aggregation into fruiting bodies.

Purpose of the Study:

• To determine which behavioral changes are most critical for multicellular self-organization.
• To investigate how aggregation is restored in csgA and pilC mutants when mixed with wild-type (WT) cells.
• To develop a methodology for connecting gene function to emergent multicellular phenotypes.

Main Methods:

• Utilized cell tracking of fluorescently labeled cells to monitor individual cell movement.
• Employed data-driven agent-based modeling to simulate and analyze cell behavior.
• Quantified specific motility features of mutant and WT cells during aggregation.

Main Results:

• Both csgA and pilC mutants, like WT cells, bias movement towards aggregates and reduce motility within them.
• Several mutant cell behaviors remained uncorrected by WT cells, indicating that perfect behavioral mimicry is not required for aggregation.
• Synergistic interactions between altered behaviors can contribute to robust aggregation.

Conclusions:

• Complete restoration of WT behavior is unnecessary for successful multicellular aggregation in Myxococcus xanthus.
• Quantification of cell behavior and data-driven modeling can identify key motility features driving aggregation.
• This study reveals compensatory mechanisms and synergistic effects in the self-organization of bacterial populations.