**"The stars might lie but the numbers never do." Mary Chapin Carpenter**

- some particularly robust cells in the population might adjust to the harsh environment (i.e., adaption);
- exposure to the toxic conditions might produce rare, resistant mutations in the population of bacteria (i.e., induced mutations); or
- spontaneous mutants might have ocurred in the bacterial population prior to exposure to the toxic conditions, yielding resistant progeny cells (i.e., spontaneous mutations).

It is easy to distinguish cells that have simply adapted to the toxic conditions from resistant mutants. Adaption is a transient property that depends upon continued exposure to the toxic conditions. Thus, if the cells are transferred from the toxic conditions and grown for many generations under favorable growth conditions they would de-adapt, so most of the cells would fail to thrive if subsequently transferred back into the toxic growth conditions.

In contrast, distinguishing between induced vs spontaneous mutations is much more difficult. For many years, most microbiologists believed that mutations in bacteria were induced by exposure to a particular environment. (At the time Salvador Luria said that "bacteriology is the last stronghold of Lamarckism".)

The first rigorous evidence that mutations in bacteria followed the
same Darwinian principles as in eukaryotic cells came from a clever study
by Luria and Delbruck [Luria and Delbruck. 1943. Genetics 28: 491-511]. They studied mutations that made *E. coli* resistant to phage T1. Phage T1 interacts with specific receptors on the surface of *E.
coli*, enters the cell, and subsequently kills the cell. Thus, when
*E. coli* is spread on a plate with 10^{10} phage T1, most of
the cells are killed. However, rare T1 resistant (Ton^{R})
colonies can arise due to mutations in *E. coli* that alter the T1
receptor in the cell wall. Luria noted that the two theories of mutation
made different statistical predictions. If the Ton^{R} mutations
were induced by exposure to phage T1, then every population of cells would
be expected to have an equal probability of developing resistance and
hence a nearly equal number of Ton^{R} colonies would be produced
from different cultures. For example, if there was a 10^{-8}
probability that exposure to phage T1 would induce a Ton^{R}
mutant, then approximately 10 colonies would arise on each plate spread
with 10^{9} bacteria. In contrast, if Ton^{R} mutations
were due to random, spontaneous mutations that occured sometime during the
growth of the culture **prior** to exposure to phage T1, then the
number of Ton^{R} colonies would vary widely between each
different culture. For example, although there is an equal probability
(say 10^{-8}) that a Ton^{R} mutant would arise per cell
division, the number of resistant bacteria in each culture would depend
upon whether the mutation occured during one of the first cell divisions
or one of the last cell divisions. The figure below shows a cartoon of
the alternative predictions. (Ton^{S} cells are indicated in white
and Ton^{R} cells are indicated in black. The shaded area
indicates when the cells were exposed to phage T1.

In either of these two cases, if multiple samples from a single culture of bacteria were plated on phage T1, each of the resulting plates should yield approximately the same number of colonies. However, the two possibilities can be distinguished mathematically by comparing the mean and variance of the number of the number of mutants in each culture:

where m = Number of mutants per culture and n = number of cultures. If approximately the same number of resistant mutants are obtained on each plate as with multiple samples from a single culture or as predicted by the directed-mutagenesis hypothesis, the mean should be approximately equal to the variance. In contrast, if there is large variation in the number of mutants per plate, the mean will be much less than the variance. The results obtained (see below) indicated that mutation to Ton^{R} is a random event.

where: P _{x}= probability that a target will have exactly x hitsh = average number of hits per target (For a non-mathematical analogy, see the cartoon drawn to illustrate the poisson distribution by one of the participants in the Cold Spring Harbor Laboratory Phage course over 50 years ago.)

The simplest way of determining P_{x} is to determine the frequency of zero events and plug this value into the Poisson equation:

Thus, for example a fluctuation test where 11 of 20 tubes had no mutants:

Once the value for *h* has been determined from the number of tubes with 0 mutants, this value can then be used to calculate P_{x} for the other number of mutants. The theoretical values calculated from this prediction of randomness can then be compared with the observed values.

Note that this simple solution is not accurate if less than 10% of the tubes have no mutants or more than 70% of the tubes have no mutants. (A more complex equation is necessary to accurately estimate higher mutation frequencies -- for a thorough mathematical explanation see [Lea and Coulson. 1949. J. Genetics 49: 264-285].)

The mutation rate is the number of mutations per cell division. Because the cell population is so large, the number of cell divisions is approximately equal to the number of cells in the population (N).

In the example shown above, if h was determined from a fluctuation test with 10^{7} cells per tube, then the mutation rate would be:

[Some mathematical problems associated with calculating mutation rates by this approach, and alternative approachs are described in the listed references by Foster (2000) and Rosche and Foster (2000).]

To determine the frequency of Str^{R}mutants a fluctuation test was done with 50 tubes each containing 10^{8}cells and 42 of the tubes contained no mutants. Use the Luria-Delbruck calculation to determine the mutation rate to Str^{R}.

ANSWER:First calculate the average number of hits per cellh = -ln (42/50) = -ln(0.84) = 0.17

Then divide the average number of hits per cell by the number of cells in the populationa = h / N = 0.17 / 10^{8}= 1.7 x 10^{-9}To determine the frequency of putP mutants a fluctuation test was done using 20 tubes with a final concentration of 10

^{7}bacteria each. From each tube 0.1 ml of culture was plated on medium that selects for putP mutants. Seventeen of the tubes yielded putP mutants but 3 of the tubes yielded no mutants. Based upon these results, use the Luria-Delbruck calculation to determine the mutation rate to putP^{-}.

ANSWER:First calculate the average number of hits per cellh = -ln (3/20) = 1.9

Then divide the average number of hits per cell by the number of cells in the populationa = h / N = 1.9 / 10^{7}= 1.9 x 10^{-7}Suggest two reasons why the rate of mutation to Str

^{R}is so much less than the rate of mutation to Pro^{-}.

ANSWER: One reasonable explanation is that any mutation that disrupts any of the proline biosynthetic genes would result in a Pro^{-}phenotype, but only very specific base substitutions in ribosomal genes result in streptomycin resistance (i.e., Str resistance is a smaller target size for mutations) -- this is the actual reason. A second potential reason could be that there are redundant genes that encode the wild-type Str sensitive phenotype and the Str resistant mutant phenotype is recessive to the wild-type.To get a feeling for the probability of random events, try the simple Poisson Distribution program at http://www.math.csusb.edu/faculty/stanton/m262/poisson_distribution/Poisson.html.

- Brock, T. 1990. The emergence of bacterial genetics, pp. 58-63. Cold Spring Harbor Press, NY.
- Foster, P. 2000. Sorting out mutation rates. Proc. Natl. Acad. Sci. USA 96: 7617-7618.
- Luria, S. 1966. Mutations of bacteria and bacteriophage. In J. Cairns, G. Stent, and J. Watson (eds.), Phage and the Origins of Molecular Biology. Cold Spring Harbor Laboratory Press, NY, pp. 173-179.
- Rosche, W., and P. Foster. 2000. Determining mutation rates in bacterial populations. Methods 20: 4-17.
- Roth, J., and D. Andersson. 2004. Amplification-mutagenesis-how growth under selection contributes to the origin of genetic diversity and explains the phenomenon of adaptive mutation. Res Microbiol. 155: 342-351.

Please send comments, suggestions, or questions to smaloy@sciences.sdsu.edu

Last modified July 10, 2004