Bacterial Adaptation through Loss of Function

The metabolic capabilities and regulatory networks of bacteria have been optimized by evolution in response to selective pressures present in each species' native ecological niche. In a new environment, however, the same bacteria may grow poorly due to regulatory constraints or biochemical deficiencies. Adaptation to such conditions can proceed through the acquisition of new cellular functionality due to gain of function mutations or via modulation of cellular networks. Using selection experiments on transposon-mutagenized libraries of bacteria, we illustrate that even under conditions of extreme nutrient limitation, substantial adaptation can be achieved solely through loss of function mutations, which rewire the metabolism of the cell without gain of enzymatic or sensory function. A systematic analysis of similar experiments under more than 100 conditions reveals that adaptive loss of function mutations exist for many environmental challenges. Drawing on a wealth of examples from published articles, we detail the range of mechanisms through which loss-of-function mutations can generate such beneficial regulatory changes, without the need for rare, specific mutations to fine-tune enzymatic activities or network connections. The high rate at which loss-of-function mutations occur suggests that null mutations play an underappreciated role in the early stages of adaption of bacterial populations to new environments

Null mutations increase fitness through varied mechanisms.

<p>(<b>A</b>) In a hypothetical cellular network, E1–E4 are enzymes, M1–M5 are metabolites, S is a structural protein, R1–R3 are regulatory proteins, and H is a housekeeping protein that inhibits translation and promotes degradation of some mRNAs. Dotted lines indicate other activities of the indicated proteins. The fitness of cells depends only on the levels of S, M2, and M5. (<b>B,C</b>) Optimal concentrations of S, M2, and M5 in the native environment (<b>B</b>) and a novel environment to which the cells might need to adapt (<b>C</b>). Null mutations adaptive in the novel environment are marked in panel (<b>A</b>) with an orange ‘x’.</p

Identification and characterization of transposon insertion locations that alter fitness in single amino acid media.

<p>(<b>A</b>) A library of ∼500,000 independent transposon insertion mutants <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003617#pgen.1003617-Girgis2" target="_blank">[21]</a> was grown for ∼20 generations in defined media with a single amino acid carbon source. Serial dilutions were used to keep the cultures in exponential phase. To characterize the distribution of transposon insertion locations in the population, DNA adjacent to the transposons was amplified, labeled, and hybridized to a custom ORF microarray. (<b>B</b>) K-means clustering was used to organize the fitness profiles of 809 genes in whose vicinity transposon insertions significantly altered fitness in at least one media (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003617#s4" target="_blank">Materials and Methods</a>). Each row represents a gene; each column contains data from a different biological replicate. Values compare the fraction of mutants with transposon insertions in or near each gene before (<i>f_initial</i>) and after (<i>f_final</i>) growth in single amino acid media. Yellow (blue) indicates an increase (decrease). (<b>C</b>) Shown are functional enrichments and depletions based on Gene Ontology annotations that iPAGE <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003617#pgen.1003617-Goodarzi2" target="_blank">[24]</a> identified in the clusters from (<b>B</b>) and the genes not in any cluster (‘Other’). ECA: enterobacterial common antigen.</p

A regulatory network adapted for an organism's native habitat may perform poorly in a new environment.

<p>The hypothetical organism's fitness (shading) depends only on the concentration of two environmental factors. The area enclosed by the red dotted line indicates the typical range of these parameters in the native environment. ‘X’ indicates the parameter values in a new environment. (<b>A</b>) Fitness of the wild-type organism, which is tuned to be optimal in the native environment. Even if an organism's genome encodes proteins that would confer high fitness in a new environment, its regulatory network might limit the actual fitness achieved. (<b>B</b>) Fitness of a mutated network that might result from a single regulatory null mutation. While not optimal, the mutated network may be advantageous in a new environment by breaking the previous mapping of environment to phenotype. (<b>C</b>) Extended evolution in the new environment will rewire the organism's regulatory network to allow the cell to optimize the use of its genetic resources (even in the absence of new genes).</p

Growth rates of mutants with in-frame deletions.

<p>Average exponential phase doubling times in defined media with (<b>A</b>) alanine, (<b>B</b>) glutamine, or (<b>C</b>) asparagine as the sole carbon source. ‘X’s denote individual measurements. Circles indicate mean doubling times. Of the 48 growth tests performed as a result of the transposon enrichment experiments (24 for alanine, 11 for glutamine, and 13 for asparagine), only the 24 strain/media combinations that grew significantly faster than the parental strain are shown (1-sided Mann-Whitney test, significance cutoff of 5% false discovery rate (FDR) for the entire dataset). Significant q-values are in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003617#pgen.1003617.s011" target="_blank">Table S5</a>. WT: wild-type.</p