Synthetic Biology Parts, Devices and Applications

2.4 Current Challenges 25

In both TRMR and T^2 RMR, relative fitness was calculated by determining the
ratio of the final allele frequency after selection to the initial allele frequency. Any
allele that increases in frequency after selection is likely to confer tolerance to the
selective condition. While both TRMR and T^2 RMR are successful at identifying
alleles responsible for a desired phenotype, T^2 RMR may be able to identify alleles
that improve fitness under weak selective pressure that the original TRMR would
not be able to identify. For example, T^2 RMR does significantly better than TRMR
at discriminating between LB and MOPS growth media (Figure 2.5). In all cases,
to confirm a fitness advantage, it is advisable to analyze the growth of cells con-
taining each individual allele that is enriched during selection and compare it
with wild-type cells.

2.4 Current Challenges

TRMR and T^2 RMR are novel and powerful techniques that allow for the modifi-
cation and tracking of thousands of genes in a single step. However, there are
some challenges that need to be considered when performing TRMR or T^2 RMR.

0.0

0.5

1.0

1.5

2.0

0.0

0.5

1.0

1.5

2.0

Pearson dissimilarity (1 −

r) between

MOPS and LB samples

TRMR

‘‘do

wn

’’

TRMR

‘‘up’

’

(^2) T
RMR ‘‘off’’
(^2) T
RMR ‘‘low’’
(^2) T
RMR ‘‘intermediate’’
(^2) T
RMR ‘‘high’’
Decreased expression Increased expression
Figure 2.5 T^2 RMR has significantly increased ability to discriminate between MOPS minimal
medium and LB-rich medium. The Pearson dissimilarity (0 indicates perfectly linearly
correlated, and 2 indicates negatively correlated) between MOPS and LB samples for each
library type is shown. * indicates p < 0.05 Benjamini−Hochberg corrected significance.
Adapted with permission from Freed et al. 2015 [22]. Copyright 2015 American Chemical
Society.