bioRxivEvolutionary BiologyPreprint
Complex multicellularity linked with expanded chemical arsenals in microbes
Abstract
Single-celled microorganisms evolving to form cooperative multicellular units represents a key innovation in the history of life. Multicellularity is required for cell differentiation and, as such, is critical to the emergence of biological complexity. Here, we propose and find support for our hypothesis that multicellularity facilitated massive expansions in the biosynthetic potential to produce specialized metabolites across microbial life. Our systematic characterization of biosynthetic gene clusters (BGCs) across bacteria confirms that most taxa have limited predicted potential to produce natural products. In contrast with primarily unicellular lineages, we show that the origins of biosynthetic expansion in Actinomycetota, Cyanobacteriota, and Myxococcota coincide with independent origins of multicellular structures such as mycelia, filaments, and fruiting bodies. Further, we reveal that the two primary origins of expansions in specialized metabolism for fungi, corresponding to the Pezizomycotina and Agaricomycetes, similarly coincide with the emergence of complex multicellularity and fruiting body formation as well as mechanisms for self-identification during haplontic life cycles. Interestingly, all five of the biosynthetically talented multicellular microbial lineages are concomitantly enriched in carbohydrate utilization enzymes, suggesting that ancient chemical innovations have been further shaped by catabolic processes. Intraspecific cooperation shaping the evolution of specialized metabolism further informs our understanding of the major transitions in life and will help guide efforts to discover new antimicrobial compounds to counter the emergence of antibiotic resistance.
- organism: bacteria (other)
- organism: fungi (other)
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: chromatin & epigenetic regulation
- research focus: gene regulatory network inference
- research focus: clustering
- approach: statistical model
- Citations
- 0
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