bioRxivBiophysics
Motile curved bacteria are Pareto-optimal
Abstract
Curved-rods are a ubiquitous bacterial phenotype, but the fundamental question of why they are shaped this way remains unanswered. Through in silico experiments, we assessed freely swimming straight- and curved-rod bacteria of a wide diversity of equal-volume shapes parameterized by elongation and curvature, and predicted their performances in tasks likely to strongly influence overall fitness. Performance tradeoffs between these tasks lead to a variety of shapes that are Pareto-optimal, including coccoids, all straight rods, and a range of curvatures. Comparison with an extensive morphological survey of motile curved-rod bacteria indicates that the vast majority of species fall within the Pareto-optimal region of morphospace. This result is consistent with evolutionary tradeoffs between just three tasks: efficient swimming, chemotaxis, and low cell construction cost. We thus reveal the underlying selective pressures driving morphological diversity in a wide-spread component of microbial ecosystems.\n\nSignificance StatementBacteria exhibit a bewildering diversity of morphologies but despite their impact on nearly all aspects of life, they are frequently classified into a few general categories, usually just spheres and rods. Curved-rod bacteria are one simple variation and are widespread, particularly in the ocean. However, why so many species have evolved this shape is unknown. We show that curvature can increase swimming efficiency, revealing a widely-applicable selective advantage. Furthermore, we show that the distribution of cell lengths and curvatures observed across bacteria in nature are predicted by evolutionary tradeoffs between three tasks influenced by shape: efficient swimming, the ability to detect chemical gradients, and reduced cost of cell construction. We therefore reveal shape as an important component of microbial fitness.
- organism: bacteria (other)
- research focus: chromatin & epigenetic regulation
- research focus: development & morphogenesis
- approach: deep learning
- approach: statistical model
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