Chance and pleiotropy dominate genetic diversity in complex bacterial environments

Lianet Noda-García, Dan Davidi, Elisa Korenblum, Assaf Elazar, Ekaterina Putintseva, Asaph Aharoni, Dan S. Tawfik*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


How does environmental complexity affect the evolution of single genes? Here, we measured the effects of a set of Bacillus subtilis glutamate dehydrogenase mutants across 19 different environments—from phenotypically homogeneous single-cell populations in liquid media to heterogeneous biofilms, plant roots and soil populations. The effects of individual gene mutations on organismal fitness were highly reproducible in liquid cultures. However, 84% of the tested alleles showed opposing fitness effects under different growth conditions (sign environmental pleiotropy). In colony biofilms and soil samples, different alleles dominated in parallel replica experiments. Accordingly, we found that in these heterogeneous cell populations the fate of mutations was dictated by a combination of selection and drift. The latter relates to programmed prophage excisions that occurred during biofilm development. Overall, for each condition, a wide range of glutamate dehydrogenase mutations persisted and sometimes fixated as a result of the combined action of selection, pleiotropy and chance. However, over longer periods and in multiple environments, nearly all of this diversity would be lost—across all the environments and conditions that we tested, the wild type was the fittest allele.

Original languageAmerican English
Pages (from-to)1221-1230
Number of pages10
JournalNature Microbiology
Issue number7
StatePublished - 1 Jul 2019
Externally publishedYes

Bibliographical note

Funding Information:
L.N.-G. was supported by CONACYT grant no. 203740 and the Martin Kushner Fellowship at the Weizmann Institute of Science. D.S.T. is the Nella and Leon Benoziyo Professor of Biochemistry. Financial support from the Kahn Centre for Systems Biology at the Weizmann Institute of Science is gratefully acknowledged. We thank R. Milo, S. Fleishman, Z. Livneh and F. Kondrashov for their support and critical advice and E. Segev and A. de Visser for their critical and insightful comments on the manuscript. We appreciate the help of M. Hershko with script development for data processing and of Y. Bar-On and S. Gleizer with the analysis of genomic sequences. We are grateful to R. Rotkopf from Weizmann Life Sciences Core Facilities for his guidance on the statistical analysis. We are thankful for the services provided by the Crown Genomics Institute of the Nancy and Stephen Grand Israel National Centre for Personalized Medicine, Weizmann Institute of Science.

Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature Limited.


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