Strain selection during artificial evolution
The strain selection process was tracked using strain-specific
metabarcoding of a locus with high allelic richness of 110 unique
alleles in the 59 strains. At the start of the experiment, individual
strains contributed between 1.9 to 6.3% cells of the start population
based on microscopic counts. Both alleles of all strains were also
detected initially (Fig. 4), although some were observed at up to
three-fold higher or lower relative abundance compared with microscopic
cell counts. Out of 669 strain observations across all samples, only 22
were of a strain in the wrong population, likely because of sequencing
errors in key SNP positions or PCR chimeras, suggesting a FDR of 0.065.
These false positive observations amounted to a negligible amount of
total strain observations (on average 0.02% [SD: 0.05] per sample)
but could affect absent/present scoring and underestimate the number of
extinct strains. After 42 days, a single strain generally dominated
30-99% of the total strain abundance and replicates typically delivered
the same dominant strain, but different strains were selected for in the
copper and control treatment (Fig. 4).
The metabarcoding revealed that, as expected for the mining population,
the tolerant VG1-2_81 and VG1-2_74 strains became the most abundant
ones in all copper selection replicates, with a joint final abundance of
37 to 99% (Fig S5). VG1-2_81 was initially highly competitive in the
control conditions but VG1-2_103 eventually outcompeted it and other
strains in this treatment, making up 22-90% of all amplicons in all
five replicates on day 42 (Table 1). VG1-2_103 was also the second
fastest-growing strain and statistically indistinguishable from the
other two when grown as a mono-clonal culture (Fig. S2).
The selection outcome of the reference population deviated more from
expectations based on mono-clonal traits (Fig. 5). In the control,
GP2-4_40, a strain with an observed average growth rate of only 1.32 ±
0.11 day-1, dominated all five replicates after 42
days of selection (37-90%, Fig. 4), with several more strains retained
at 1-25% abundance (Table 1, Fig S4). In the copper treatment,
GP2-4_27, which had a below-average EC50 (7.8 µM Cu; Fig. S6),
outcompeted everyone else in four out of five replicates (93-97%, Fig.
3) and was thus identified as the strain responsible for the plastic
tolerance developing in this population (Fig. 2 and 3). In the last
replicate, GP2-4_57, a strain that was extinct in the other four, as
well as in all the controls, became dominant (78%, Fig. S5).
Importantly, like all strains at the beginning of the experiment,
GP2-4_57 was heterozygous for the barcoding locus Sm_C12W1 , but
all 9,099 amplicons observed at day 42 were from only one of its two
alleles (Fig. S6), indicating a loss of heterozygosity, which could be
explained through inbreeding. Furthermore, this bottle replicate had its
own evolutionary trajectory and developed higher copper tolerance (EC50
11.2 µM Cu, versus 9.45-10.8) but slower growth rate (46 generations
versus 54-59) than the GP2-4_27 dominated replicates (Table 1).
The metabarcoded relative abundances of strains were used to disentangle
individual growth rates during co-cultivation. The barcoded copper
tolerance traits were not correlated with other mono-clonal strain
traits like cellular surface-to-volume ratios, Fv/Fm, or growth rate
(Fig. S7 and S8). Several strains had already gone extinct after nine
days, especially in the Mining population and copper treatment, where
nine out of 30 were lost (Fig. 5). The remaining strains’ growth rates
in the reference population correlated poorly against what was predicted
from mono-clonal observations, with R2 of 0.003,p =0.8 (copper), and 0.07, p =0.2 (control, without the
outlier-strain GP2-4_42). Correlations were higher in the Mining
experiments with R2 of 0.39, p =0.002 (copper)
and 0.24, p =0.006 (control, without outlier-strain VG1-2_63),
but it was still difficult to distinguish growth rates between many
strains with confidence (Fig. 1, S1, and 5). Importantly the precision
of the metabarcode-derived growth rates was, on average, three times
higher for the barcoded growth rates (95% conf. +/- 0.038
day-1) compared with the mono-clonally estimated rates
(+/- 0.11 day-1), showing that this approach has much
higher chance of detecting subtle strain differences in fitness.
Furthermore, the evolutionary trajectories observed via metabarcoding on
day nine generally persisted for the final 33 days of the evolution
experiment (Fig. 4, 5, S4, and S5). Consequently, a short selection
experiment of pooled populations of strains, combined with observations
of strain abundance using intraspecific metabarcoding, appears to be a
robust approach to estimate both fitness of individual strains and the
evolutionary potential of phytoplankton populations.