ISSN 0006-2979, Biochemistry (Moscow), 2025, Vol. 90, No. 11, pp. 1454-1467 © Pleiades Publishing, Ltd., 2025.
Russian Text © The Author(s), 2025, published in Biokhimiya, 2025, Vol. 90, No. 11, pp. 1544-1560.
1454
REVIEW
Laboratory Evolution:
Molecular–Genetic Basis and Phenotypic Plasticity
Yakov E. Dunaevsky
1,a
*, Olga A. Kudryavtseva
2
, and Mikhail A. Belozersky
1
1
Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, 119991 Moscow, Russia
2
Faculty of Biology, Lomonosov Moscow State University, 119991 Moscow, Russia
a
e-mail: dun@belozersky.msu.ru
Received May 21, 2025
Revised August 19, 2025
Accepted September 9, 2025
Abstract— Adaptive laboratory evolution (ALE) is aimed at elucidating the molecular basis of adaptation and
is widely employed as a tool for gaining deeper insight into genetic and/or metabolic pathways underlying
evolutionary processes. One of the primary goals of experimental evolution is to predict mutations represent-
ing the key driving forces of adaptation. The use of whole-genome resequencing enables easy identification
of mutations that arise during ALE, and consequently, biochemical alterations that occur in the experimental
lineages. ALE has also proven highly relevant in practical applications, as it provides an innovative approach
to the construction of evolved microbial strains with desirable performance, such as rapid growth, stress
resistance, efficient utilization of diverse substrates, and production of compounds with a high added value
(amino acids, ethanol, aromatic compounds, lipids, etc.). In this review, we analyzed the results of studies
focused on the demonstration and explanation of relationships between mutations and resulting phenotypic
and biochemical changes, as well as discussed a potential of microorganisms as model systems for ALE ex-
periments and testing of various evolutionary hypotheses. We also described achievements reached by using
ALE strategies, as well as the still unresolved issues and methodological limitations of this approach.
DOI: 10.1134/S0006297925601583
Keywords: adaptation, laboratory evolution, metabolic engineering, microorganisms, causal mutations
* To whom correspondence should be addressed.
INTRODUCTION
Understanding the mechanisms by which organ-
isms respond to global crises, such as climate and
ecosystem changes, spread of invasive species, emer-
gence of multidrug-resistant pathogens, and a growing
demand for food, has become progressively important
for predicting evolution and interpreting its dynamics.
In nature, evolution can be driven by various factors,
including environmental shifts or isolation of small
populations. However, assessing the predictability of
evolution solely from the observational studies of nat-
ural populations is rather challenging, as the evolu-
tionary history cannot be experimentally replicated.
To overcome these limitations, scientists have turned
to laboratory evolution experiments, primarily using
microbial populations [1], since they provide simple
model systems in which multiple replicate populations
can be propagated for hundreds or thousands of gen-
erations, allowing precise quantification of mutation
frequencies over time under controlled and manip-
ulable environmental conditions. This approach can
contribute much to the insight into the mechanisms
of adaptation and genome evolution. Hence, experi-
mental laboratory evolution studies aim to observe
living organisms in controlled scenarios that promote
evolutionary changes and enable investigation of the
molecular basis of adaptation. Although controlled
laboratory environment does not always fully repre-
sent natural ecological conditions, adaptive laboratory
evolution (ALE) studies have proven highly success-
ful in substantiating evolutionary theories based on
actual molecular and mechanistic evolutionary mod-
els [2, 3]. By combining phenotypic characterization
and genome sequencing of the evolved experimental
lineages, it has become possible to observe evolution
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in real time and to address the question of whether
certain phenotypic and genotypic results can be pre-
dicted. In a stable environment, the rate of fitness im-
provement slows down as populations become more
adapted [4]. However, improvement in fitness never
ceases completely, even it becomes markedly slower.
If we assume that the representatives of different gen-
erations enter into direct competition, younger gener-
ations might exhibit higher adaptive fitness or possess
greater resources, which gives them a competitive ad-
vantage. However, such hypothesis requires to take
into account multiple factors, including environmen-
tal conditions, available resources, and specific abili-
ties characteristic of each generation [2, 5]. Adaptive
traits resulting from mutations acquired in the pro-
cess of laboratory evolution may manifest themselves
in various ways, e.g., as increased tolerance to specific
stressors, ability to utilize non-natural substrates, or
more efficient metabolism of conventional substrates
[6-11]. At the same time, the rate of molecular evolu-
tion (i.e., mutation fixation) tends to remain relatively
constant [12-14].
Experimental studies of microbial evolution have
a long-standing history. The concept of a continuous
evolutionary experiment achievable by using stan-
dard laboratory methods can be traced back to the
19th century, following the publication of Charles
Darwin’s works. A contemporary of Darwin, the Rev-
erend Dr. William Dallinger, who was also a micro-
biologist, conducted experiments on the unicellular
flagellated eukaryotes Cytomonads [15]. Over seven
years, he had succeeded in gradually adapting three
Cytomonads species to a temperature increase from
16°C (60°F) to 78°C (155°F). The initial cultures felt
comfortable at 65°F but perished at 140°F. This work
provided the first demonstration that microbial pop-
ulations can undergo significant adaptive changes
under laboratory conditions within a relatively short
period of time. The number of studies on ALE has
significantly increased over the past decade [16].
At the current stage of biology development, a keen
interest in laboratory evolution has become a result
of development of relatively inexpensive next-genera-
tion sequencing technologies, which enable detailed
comparison between the experimental and ancestral
lineages. Whole-genome resequencing of experimental
and ancestral lineages has revealed that phenotypic
adaptation is accompanied by a continuous process of
mutation accumulation, extensive genetic parallelism,
and a pronounced historical contingency [2, 3, 17].
In many cases, the effects of de novo mutations in-
ferred from bioinformatic analysis, can be validated
using molecular methods.
Mutations range from single-nucleotide poly-
morphisms and small indels (insertions or deletions)
in genes encoding specific enzymes or transporters
that facilitate utilization of new substrates or more
efficient uptake and channeling of substrates into
the central metabolism) to regulatory mutations that
restore the metabolic network balance by disabling
nonessential growth functions and releasing resources
for processes directly related to growth (e.g., substrate
uptake) [9, 10, 18-21].
This goal of our review was to demonstrate the
relationship between mutations fixed in the course
of ALE experiments and corresponding phenotypic
and biochemical changes, as well as the potential of
microorganisms as model systems in laboratory evo-
lution experiments aimed at investigating adaptation
processes, estimating evolutionary parameters, and
testing various evolutionary hypotheses. It is import-
ant to mention the already obtained results not only
demonstrate the capacity and versatility of the ALE
approach, but also demonstrate its current limita-
tions, as well as open questions that required further
investigation.
ADVANTAGES OF BACTERIA AND FUNGI
AS MODEL ORGANISMS IN ALE
Microorganisms are well-suited for the ALE ex-
periments for several reasons: their short generation
times allow to study the evolutionary dynamics across
many generations within a relatively short time pe-
riod; they can easily sustain large population sizes;
many bacterial species can be stored in a dormant
state for further investigation, etc. This enables re-
searchers to “go back in time” and directly compare
evolved strains with their ancestors [22]. Most exper-
imental evolution studies use unicellular organisms,
such as Escherichia coli, due to their rapid growth,
short reproduction time, small genome size, ability to
adapt to environments with certain nutrients and/or
stressors, and their status as model organisms, which
provides an appropriate body of knowledge for inter-
preting experimental results. Importantly, most micro-
bial cells have simple nutrient requirements and are
easy to cultivate under laboratory conditions. A high
cell division rate and a relatively small genome size
of unicellular organisms also make it feasible to se-
quence multiple clones or microbial population sam-
ples from different time points at a low cost, thus pro-
viding high resolution for detecting genetic changes
associated with observed evolutionary processes. In a
typical microbial evolution experiment, cells are inoc-
ulated into a medium and allowed to grow until the
culture reaches a high population density. Next, they
are transferred to a fresh medium or diluted to enable
further growth and division. This cycle can continue
indefinitely, and over successive generations, natural
selection results in the adaptation to the laboratory
DUNAEVSKY et al.1456
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environment. Establishment of conditions exceeding
those providing the optimum performance of a strain,
will stimulate adaptation. The range of environmental
parameters that can be used for driving the selection
are very broad; as long as a chosen condition imposes
selective pressure that determines differential surviv-
al within a population, adaptation will occur.
E. coli, which has a generation time of only about
20 min, is easy to cultivate with a high yield, and its
gene expression in this bacterium can be efficiently
controlled, making E. coli an undoubtedly convenient
model organism for experimental evolution [9, 23-25].
However, bacteria are highly sensitive to low pH and
presence of sulfur dioxide in the medium, as well
as lack of the ability to perform post-translational
modifications. Hence, the range of questions that
can be explored in these microorganisms is limited
by their relative simplicity. Eukaryotes, such as fungi
or protists, with their more complex gene regulation,
morphology, and physiology, provide opportunities to
address a much broader spectrum of evolutionary
questions [26].
Fungi occupy a unique niche among eukaryotic
systems. Because of the short generation times, com-
pact genomes, and sexual life cycles, they represent
a valuable and still largely underutilized resource for
advancing experimental evolution research. Fungi are
convenient subjects for ALE experiments for several
reasons. They are generally characterized by simple
and rapid life cycles and can produce hundreds to
thousands of generations in a relatively short time
period, and their large effective population sizes are
comparable to those of bacteria and reflect the sizes
of natural population. Fungi exhibit high tolerance to
acidic environments; they have a larger cell size, can
perform post-transcriptional protein modifications
(such as glycosylation), and possess effective stress
adaptation mechanisms. Many fungal species grow on
agar media and are cryotolerant. Fungi can reproduce
both as haploid and diploid organisms, sexually and
asexually. They are easy to cross, and the progeny
can be easily separated from the parental cells. Most
original population can be limited to a single cell to
provide genetic uniformity at the beginning of the
experiment. Therefore, fungi have simple genomes
and fast cell cycles typical of other microorganisms,
but with the eukaryotic complexity unattainable in
bacterial systems [27]. In this regard, fungi are per-
fect model organisms. Furthermore, they have a sim-
ple morphology, are easy to experiment on, inhabit
distinct and well-characterized ecological niches, and
show remarkable diversity, ranging from saprotrophs
to pathogens, mutualists, and even predators [28].
Sexual reproduction is widespread in nature, and
almost all organisms engage in some form of genet-
ic exchange. However, most experimental evolution
studies have been focused on asexual systems, leaving
the role of recombination in the adaptation largely
unexplored. Fungi can serve as the optimal model sys-
tems for addressing this question. Direct comparison
of the adaptation rates and mutations evolved in sex-
ual and asexual populations of Saccharomyces cerevi-
siae has shown that sex not only promoted adaptation
but also enabled natural selection to more efficiently
separate beneficial mutations from deleterious ones.
Sexual reproduction combines advantageous muta-
tions from different lineages in the same genome,
reduces clonal interference, and restores beneficial
mutations from deleterious backgrounds [27, 29].
When comparing adaptation rates to a new environ-
ment over 3000 mitotic generations, it was demon-
strated that in Aspergillus nidulans, somatic growth
involves mitotic recombination at an extremely high
rate, which reinforces adaptation to the new environ-
mental conditions [30]. Another potential advantage
of recombination is resolution of clonal interference.
In populations lacking recombination, different ben-
eficial mutations cannot be combined within a single
genetic background, whereas in sexual populations,
all measured advantageous mutations had been fixed
[29, 31].
Fungal systems used in experimental evolution
studies include unicellular yeasts of the Saccharomy-
ces, Schizosaccharomyces, and Candida genera, fila-
mentous basidiomycetes (Schizophyllum and Ustilago),
and ascomycetes (Neurospora and Aspergillus). Re-
cently, new species, such as the halotolerant Hortaea
werneckii black yeast [32] and short-lived filamentous
ascomycete Podospora anserina have been added to
the models used in ALE studies [26, 33]. It should be
noted that filamentous fungi remain underrepresent-
ed in this field, as the development of genetic en-
gineering tools for these organisms is hindered by
the high complexity of their genomes and associated
metabolic and cellular processes. Challenges include
slow rates of mycelium growth, low production yields,
suboptimal accumulation of alternative products, and
difficulties in purification. Nevertheless, several in-
teresting experiments have been carried out in Pen-
icillium commune and Penicillium sp. [34], as well
as in Mortierella elongata co-cultured with the alga
Nannochloropsis oceanica [35], although specific ge-
netic mechanisms controlling the observed evolution
of traits in Penicillium and Mortierella have not yet
been identified.
SHORT-TERM ALE EXPERIMENTS
The main goal of short-term ALE experiments is
to predict which mutations are “significant” drivers
of adaptation. Using this methodology, it was shown
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that gene loss can enhance an organism’s ability to
evolve and adapt by providing alternative evolution-
ary pathways. Although gene deletions often lead to
an immediate reduction in fitness, many mutants rap-
idly acquire suppressor mutations that restore fitness,
and some even surpass the fitness levels of similar-
ly adapted wild-type cells [36]. The observed high
frequency in the loss-of-function mutations suggests
that these mutations play a major role in bacterial
adaptation to new environments and may provide
substantial fitness advantages under various stress-
ful conditions. In most short-term ALE experiments
(74%), the frequency of nonsense mutations exceeded
that of missense mutations, and this increased rate
of gene-inactivating nonsense mutations was due to
their selective advantage (i.e., beneficial effect). The
authors concluded that the loss-of-function mutations
that escape purifying selection are major drivers of
adaptation during short-term evolution [37].
ALE has been extensively used to explore the
genetic and biochemical basis of bacterial and fun-
gal adaptation. Whole-genome resequencing enables
easy identification of mutations that arise during ALE
and, consequently, biochemical changes occurring in
experimental lineages. For instance, short-term ALE
was successfully applied to improve carotenoid pro-
duction in an engineered S. cerevisiae strain using
periodic hydrogen peroxide shock cycles, resulting
in a threefold increase in the carotenoid yield. Sub-
sequent transcriptomic analysis aimed at elucidating
molecular mechanisms underlying this improvement
revealed activation of genes involved in lipid metab-
olism and mevalonate biosynthesis pathway in hyper-
producing strains [38]. The tolerance of Rhodococcus
opacus to phenol, a model product of lignin degrada-
tion, was achieved through serial cultivation in the
media containing phenol as the sole carbon source,
followed by screening for fast-growing mutants. After
40 passages, some strains exhibited enhanced phenol
tolerance. Whole-genome sequencing of the experi-
mental strains to identify genomic alterations that had
appeared during adaptive evolution, combined with
comparative transcriptomics to detect transcriptional
changes, proved to be effective for revealing the tol-
erance mechanisms and identifying promising candi-
date genes to facilitate future metabolic engineering
of Rhodococcus. The adapted strains demonstrated
higher phenol consumption rates and approximately
twofold increase in the lipid production from phenol
compared to the wild-type strain. Due to the consis-
tent identification of single-nucleotide polymorphisms
in two transporter/permease genes, it was suggest-
ed that the adapted strains had altered transport of
phenol or related compounds. Genes involved in the
phenol conversion to catechol were among the most
upregulated when the strains were grown on phe-
nolvs. glucose, indicating that phenol-to-catechol con-
version may represent a rate-limiting factor for the
growth on phenol [39]. It was discovered that through
~350 generations of laboratory evolution, the early
evolutionary response (tolerance) of E. coli cells to
the antibiotic trimethoprim included derepression of
signaling through the Mg
2+
-sensitive two-component
PhoPQ system achieved via inactivation of the neg-
ative feedback regulator MgrB. It was suggested that
mutations in the mgrB gene precede and promote the
development of antibiotic resistance in bacteria [40].
Several studies have investigated the early stages
of evolution of multicellularity from unicellular an-
cestors using experimentally evolved yeasts exhibit-
ing a “snowflake” phenotype (formation of clusters by
yeast cells). These yeasts have acquired the ability to
cluster through inactivation of the trans-acting tran-
scription factor ACE2 [41], which regulates expression
of enzymes required for the separation of mother and
daughter cells after mitosis [42]. As a result, daughter
cells remain attached to the mother cells, producing
a branched, snowflake-like phenotype. ALE was used
to directly study the evolution of early multicellulari-
ty, including transition from the cell-level selection to
the cluster-level selection, as well as the development
of cellular differentiation. Because cell clusters settle
faster in liquid media than individual cells, primitive
multicellular forms of S. cerevisiae were selected us-
ing gravity, which allowed straightforward selection
of genotypes that formed the clusters. After 60 trans-
fers of settled cells into fresh medium, all selected
populations exhibited approximately spherical, snow-
flake-like phenotypes consisting of multiple adherent
cells. Since “snowflake” yeasts were formed due to the
adhesion between the mother and daughter cells after
cell division, they exhibited a high genetic uniformity
within the clusters, thus reducing potential conflicts
of interest among constituent cells. The snowflake
phenotype was stable: after 35 transfers without grav-
itational selection, populations derived from day 30
of the first evolutionary experiment showed no inva-
sion by any unicellular strains. Cell differentiation is a
characteristic feature of multicellular organisms, and
some cells were required to compromise their repro-
ductive potential for the efficient reproduction of the
whole cluster. Presumably, the evolution of cellular
differentiation has occurred due to apoptosis, which
snowflake yeasts underwent to generate more nu-
merous, smaller (and thus faster-growing) propagules
compared with the parental cluster. In this experimen-
tal system, most cells remained viable and capable of
reproduction, while some underwent apoptosis, which
is functionally analogous to the germ-soma differenti-
ation, when cells specialize in the reproductive and
nonreproductive roles [43, 44]. Apoptotic cells func-
tioned as the breaking points within multicellular
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clusters, allowing snowflake yeasts to produce more
propagules from a given number of cells. Seven-day-
old yeasts formed relatively small clusters with a low
level of apoptosis, whereas 60-day-old yeasts formed
larger clusters with elevated apoptosis [45]. This high-
er rate of apoptosis observed in the late-stage yeast
isolates could not be explained by diffusion gradi-
ents caused by multicellularity but rather appeared
to evolve in parallel with the increase in the cluster
size, possibly because larger clusters tended to con-
tain a higher proportion of aged cells, which under-
went apoptosis at a higher rate [46].
Directed evolution of approximately 800 gener-
ations of snowflake yeast combined with mathemat-
ical modeling provided insights into the influence of
transition to aerobic metabolism on multicellularity
[47]. Although oxygen provides substantial metabolic
advantages, the shift from anaerobic to microaerobic
conditions constrains the evolution of larger body
sizes because at low concentrations, oxygen cannot
diffuse deeply into the tissues. As the availability of
oxygen in the environment increases, larger organ-
isms gain selective advantage, as deeper oxygen pen-
etration alleviates a compromise between the growth
rate and body size [47].
Pineau et al. [48] observed the development of
two distinct phenotypes – small and large snowflake
yeasts – from a single clonal ancestor. Their coex-
istence resulted from the ecological specialization:
small snowflakes evolved as the growth-rate special-
ists, whereas genotypes forming 16-48 times larger
clusters evolved as survival specialists. The com-
promise between the size and resource competition
maintained their long-term coexistence. This oxygen
availability-based compromise was essential for main-
taining the diversity, as the coexistence was disrupted
when extra oxygen was supplied and has never de-
veloped in the mixotrophic and anaerobic populations
during ALE. The authors suggested that a simple com-
promise between the growth and survival, established
by different extent of oxygen diffusion through the
bodies of varying sizes, may promote and maintain
ecological diversity in emerging multicellular lineag-
es [48].
Most microbial evolution experiments, as well
as the studies in multicellular eukaryotes, such as
Caenorhabditis elegans and Drosophila melanogaster,
include no more than 1000 generations of adaptation
to a new environment [1]. This makes them suit-
able for studying the early dynamics of adaptation,
when a population encounters a novel environment
for the first time and rapidly accumulates beneficial
mutations in response to the new challenge. Howev-
er, many researchers have questioned how far such
results can be extrapolated. Will evolutionary dynam-
ics remain the same over longer periods of time?
Can it change qualitatively after thousands of gener-
ations of adaptation to laboratory conditions? These
issues motivated the development of long-term ALE
experiments.
LONG-TERM EVOLUTION EXPERIMENTS
The experiment initiated by Richard Lenski has
become a universally recognized standard in the field.
The critical factor that determined the success of this
work was the choice of one of the simplest, most
convenient, and best-studied laboratory organisms –
E. coli bacterium. The experiment began in February
1988 and has now been running for 37 years, allowing
the author to draw certain conclusions and answer a
number of frequently asked questions [49]. This work
has become known as the long-term evolution experi-
ment (LTEE). The simplicity of E. coli cultivation, the
use of 12 parallel experimental lineages grown under
identical conditions, and the employment of periodic
batch culture ensuring indefinite continuation of the
experiment, together with a rigorous protocol involv-
ing daily transfers of 1% of each population, cryo-
preservation of samples every 75 days, application of
modern genomic analysis techniques, and ability to
branch off new experiments at any time – all these
factors have contributed to the LTEE success and al-
lowed numerous co-authors to obtain experimental
evidence supporting key evolutionary hypotheses [50-
52]. During the first 26,000 generations, the results
followed the classical theory – random mutations oc-
curred, with beneficial ones accumulating and dele-
terious ones being purged. After 26,000 generations,
however, the mutation rate rose sharply due to alter-
ations in the mutator gene responsible for the DNA
error correction (any gene involved in DNA replica-
tion or repair may serve as such if slightly impaired).
Overall, this effect is deleterious, since most muta-
tions are harmful, but it also increases the probability
of rare beneficial mutations. This becomes especially
important once all high-probability beneficial muta-
tions had already occurred. The mutator gene and
the beneficial mutation are inherited together due
to their physical linkage on the same chromosome,
a process known as “hitchhiking.” Thus, selection for
the beneficial mutation simultaneously maintains the
mutator gene (via linked inheritance). Some obtained
results were entirely unexpected [13].
Despite a considerable time investment, the need
for continuous and laborious monitoring of cells,
strict aseptic conditions to prevent contamination,
and a requirement for direct relationship between
the desired function and benefits to the organism,
Lenski’s work demonstrated that extending the du-
ration of evolutionary experiments can indeed lead
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toadditional valuable outcomes. Thus, only a few de-
cades ago, it had been believed that the optimal ad-
aptation to specified environmental conditions, such
as those of LTEE, would be achieved by a bacterial
population within only a few thousand generations
[31]. However, reality proved otherwise: even after
60,000 generations (according to the Lenski’s experi-
ment), E. coli populations continued to adapt, and this
was observed in all parallel lineages [13]. In LTEE,
the rate of fitness increase in E. coli follows a power
law, suggesting that there is no optimal adaptation
that could be achieved by an evolving population [31].
Another example is the ability to utilize citrate
from the growth medium under aerobic conditions,
which unexpectedly emerged in one experimental
E. coli lineage. This ability had been formed gradual-
ly during many preceding generations and manifested
itself after ~31,000 generations [53]. The evolution of
the cit
+
phenotype is particularly significant because
the inability to metabolize citrate aerobically is a
defining characteristic of E. coli as a species. Recent
studies have shown that even after 2500 generations
of adaptive evolution, the cit
+
trait causes the death
of a large fraction of cells cultivated aerobically on
citrate as the only carbon source, thus highlighting
an inherent incompatibility between the aerobic ci-
trate metabolism and stable E. coli physiology [54].
The effect of the key mutation enabling citrate trans-
port into a cell under aerobic conditions, i.e., cit
+
phenotype, was found to depend on other “potentiat-
ing” mutations. These mutations did not affect citrate
utilization directly, but instead provided genetic or
physiological basis enabling manifestation of the key
mutation, a process known as potentiation. Such ad-
ditional mutations may be neutral or even beneficial
for other functions (pre-adaptation), with a side effect
in a form of ability for citrate utilization. This demon-
strates that complex phenotypic evolutionary changes
often require interactions among multiple mutations
and “preparatory” alterations in the genetic back-
ground. The cit
+
phenotype (actualization stage) could
be obtained only through mutation accumulation in a
course of many bacterial generations. In other words,
this trait is unlikely to develop during a short-term
experiment [31]. Further evolutionary process pro-
vided increase in the citrate utilization efficiency due
to novel optimization mutations (refinement stage).
The authors suggested that the development of highly
novel traits trough complex evolutionary trajectories
may be akin to the processes occurring during spe-
ciation. Notably, none of the remaining 11 lines had
evolved a similar ability even after 75,000 genera-
tions [2].
The studies on citrate utilization have elucidated
the nature of observed limitations: the appearance
of the key mutation depended on prior, random evo-
lutionary changes. This explains why the new func-
tion emerged only after 31,000 generations and in
only one of 12 replicate populations [2, 53].
Following Lenski, other researchers have adopt-
ed the term LTEE for their own long-term projects.
Although none of them has been at the timescale of
the Lenski’s experiment, several investigations have
exceeded most previous studies in duration, en-
compassing over a thousand generations. For exam-
ple, Behringer et al. [55] had maintained 100 E. coli
populations over 10,000 generations in tubes under
conditions allowing both spatial and nutritional spe-
cialization. They observed repeated evolution of bio-
film-forming phenotypes and stable coexistence of
subpopulations and analyzed possible reasons for
the stable coexistence of multiple dominant haplo-
types over thousands of generations. They also iden-
tified a substantial number of parallel mutations
among replicate populations. Fisher et al. [56] had
maintained laboratory populations of budding yeast
S. cerevisiae for 4000 generations and found that,
similarly to E. coli, these populations acquired fit-
ness along predictable trajectories characterized by
the declining adaptivity. One of the most intriguing
results of laboratory evolution experiments initiated
with haploid yeast populations was the emergence of
diploid lineages via whole-genome duplication. Track-
ing recurrent genome duplication across 46 haploid
yeast populations evolving over 4000 generations re-
vealed that autodiploids had been fixed already by
generation 1000 in all 46 populations. Whole-genome
duplication led to a decline in the adaptation rate,
indicating a compromise between immediate fitness
improvement and long-term adaptivity. The presence
of ploidy-enriched targets of selection and structural
variants showed that autodiploids can access adaptive
pathways unavailable to haploids. In the same exper-
iment, analysis of the relationship between ploidy and
adaptation demonstrated that, overall, diploids adapt-
ed more slowly than haploids [57]. It was suggested
that the slower adaptation of diploids observed in
the evolution experiments in yeast grown on differ-
ent media was caused by the reduced efficiency of
selection for recessive or partially recessive beneficial
mutations in diploid genomes [58, 59].
Johnson et al. [1] reported the results of another
LTEE-similar study, in which 205 budding yeast pop-
ulations (divided into haploid and diploid groups)
have evolved under three different sets of laboratory
conditions. The authors described the first 10,000 gen-
erations of yeast. They found that several aspects of
evolution in this system were broadly consistent with
findings from the LTEE and other long-term evolution
studies. For example, the dynamics of fitness improve-
ment was largely reproducible across replicate lineages
and demonstrated a decline in adaptivity over time,
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even while the rate of molecular evolution remained
relatively constant. Evolution in diploid populations
involved both fixation of heterozygous mutations and
frequent occurrences of heterozygosity loss. However,
there were key differences from the E. coli experi-
ments, such as no evidence was found for a sponta-
neous emergence of stably coexisting lineages or for
widespread evolution of mutator phenotypes resulting
on sharp elevation of mutation rates, which could be
due to a shorter time frame of the experiment, as well
as a reduced indirect selection of mutators.
Johnson and Desai [60] developed an innovative
yeast experimental evolution platform enabling inser-
tion of barcoded deleterious mutations into the ge-
nome to measure their individual effects on fitness.
This system made it possible to examine whether
these mutations exert different effects on evolving lin-
eages as these lineages continue to adapt over time.
By applying the barcode-based mutagenesis system to
analyze the adaptation effects of 91 specific mutations
over 8000-10,000 generations of yeast in two constant
media, the authors were able to describe how overall
mutational tolerance of insertional mutations (defined
based on the mean effect of this type of mutations),
as well as adaptation effects of individual mutations,
have changed throughout the course of evolution [60].
Analysis of the relationship between the organ-
ism size, metabolism, and demography in 12 E. coli
populations evolving for more than 60,000 gener-
ations and diverging from a common ancestor [61]
demonstrated that although experimental E. coli lin-
eages that had evolved toward larger cell sizes ex-
hibited relatively slower metabolism, they grew faster
than the smaller cells. They achieve this growth-rate
advantage by reducing relative costs associated with
producing larger cells. Doubling cell size in experi-
mental lineages resulted in only a ~30% metabolic in-
crease. This observation is consistent with the results
of other LTEE studies showing that evolved cells are
larger, more efficient, and contribute to higher max-
imal biomass yields than their ancestors. It is worth
noting that in the LTEE, larger evolved cells possessed
slightly smaller genomes than smaller ancestral cells
[14], which reduced relative and even absolute costs
of genome replication. Most importantly, evolved cells
have undergone significant adjustment of their gene
regulatory networks to the LTEE environment, there-
by reducing costly expression of unneeded transcripts
and proteins [18, 19].
Experimental evolution studies have revealed a
remarkable capacity of bacteria for a relatively rap-
id adaptation, which is often driven by mutations
in central housekeeping genes responsible for fun-
damental cellular functions [62]. The most striking
example of this trend involves mutations occurring
in genes encoding the core enzyme RNA polymerase.
It has been well established that the widespread use
of antibiotics promotes accumulation and dissemina-
tion of resistant bacteria. However, resistance can also
arise in the absence of antibiotic exposure. Antibiotic
resistance mutations that arise in regulatory house-
keeping genes, particularly, in RNA polymerase genes,
may also exert adaptive effects independently of an-
tibiotic presence, because they modify these genes in
a way that eliminates the susceptibility to antibiotics.
Many antibiotic resistance mutations are deleterious
in antibiotic-free environments. Nevertheless, muta-
tions that substantially alter an essential housekeep-
ing gene may frequently carry additional fitness ben-
efits unrelated to the antibiotic resistance. Adaptive
mutations arising within antibiotic-targeted genes are
often antagonistically pleiotropic (i.e., adaptive under
certain conditions but deleterious under others). Such
antibiotic-independent adaptive effects of resistance
mutations can considerably alter the dynamics of the
emergence and spread of antibiotic resistance. Under-
standing evolutionary pathways aimed at the resis-
tance evolution in pathogenic bacteria is essential for
developing effective strategies to combat infections.
It is important to emphasize that although these
experiments have provided fundamental insights into
evolutionary processes in controlled environments,
evolution in nature occurs under much more complex
conditions and may differ from the laboratory one.
Under laboratory conditions, adaptation develops in
response to simple and strong selective pressure; it
also often results from mutations that are extremely
unlikely to occur in nature. Cohen and Hershberg[63]
studied two genes encoding E. coli RNA polymerase
and found that under laboratory conditions, muta-
tions occurred in highly conserved regions that evolve
slowly in the wild and remain extremely conserved
in their sequence, structure, and function from bac-
teria to humans. To determine whether this pattern
is broadly applicable, the researchers examined 19
enzymes bearing adaptive mutations associated with
the resource depletion and exposure to antibiotics.
As with RNA polymerase, the loci of “laboratory” mu-
tations were found to be highly conserved, frequently
located within specific protein domains and in a clos-
er proximity to the active site. Therefore, the dynam-
ics of laboratory evolution differs markedly from that
observed in nature. Selective pressure encountered in
more natural environments is likely to be much more
complex than the simple and strong pressure creat-
ed under laboratory conditions, as in nature, multiple
different factors exert conflicting pressures simultane-
ously and/or selective pressure alters over time. Con-
sequently, adaptations that readily arise in laboratory
experiments cannot be implemented as easily in the
wild due to their pleiotropic effect. Moreover, posi-
tions that change effortlessly under a well-defined
MOLECULAR–GENETIC BASIS OF LABORATORY EVOLUTION 1461
BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025
selection in a laboratory are often among the least
variable sites in nature. These observations suggest
that mutations that are highly adaptive under a spe-
cific selective pressure may not contribute significant-
ly to the overall adaptation in most natural environ-
ments or, if they do arise, are transient in natural
bacterial populations [63].
P. anserina is a model ascomycete fungus that
demonstrates pronounced phenotypic aging when
grown on solid media, but exhibits an unlimited
lifespan in a submerged culture. To investigate the
genetic basis of adaptation to a submerged culture,
P. anserina was established as a novel model system
for LTEE. When grown in a submerged culture, most
ascomycetes cease sexual and asexual reproduction
and, instead, propagate vegetatively. Depending on
particular cultivation conditions, the mycelium either
grows dispersed or forms a spherical structure known
as granuloma. In P. anserina populations, transition to
long-term submerged cultivation invariably results in
the same morphophysiological changes. These chang-
es arise early in the experiment and are primarily
attributed to epigenetic modifications that alter gene
expression. The transition to an indefinite lifespan pro-
ceeds through three stages. Following transition to a
submerged culture, non-adapted cells forms spherical
cortical granules of darker color due to melanin syn-
thesis. The granules then become smaller and begin
to lose their dark pigmentation. Finally, in acclimat-
ed cultures, the mycelium becomes lightly pigmented
and uniform, partly forming fluffy granules of varying
sizes and partly remaining diffuse and structureless.
Two wild P. anserina strains were used to es-
tablish eight independent experimental populations,
which were propagated by serial passaging and main-
tained in the dark in a standard synthetic medium for
8 years. With time, the number of single-nucleotide
polymorphisms has linearly increased. Evolution in
the eight experimental populations frequently pro-
ceeded in parallel, with the same genes and proteins
experiencing emergence of the same mutations up
to seven times. Six proteins associated with fungal
growth and development had evolved in more than
one population; notably, in seven out of eight popula-
tions, new alleles had been fixed in the FadA protein
α-subunit gene, with only four amino acid sites affect-
ed, thus representing a unique parallelism in experi-
mental evolution. Some of the six proteins undergoing
parallel changes participate in the same pathways,
and all proteins appeared to be associated with fungal
growth and development, likely promoting vegetative
growth and inhibiting sexual reproduction at a very
early stage. Evolutionary parallelism at the protein
function level was also observed for several tran-
scription factors, suggesting selection leading to the
optimization of a broad range of cellular processes
under experimental conditions. Parallel evolution at
the gene and pathway levels, excessive nonsense and
missense substitutions, and increased conservation of
proteins and their fragments affected by mutations
suggest that many observed fixed mutations were
adaptive and driven by positive selection [26, 33].
Laboratory evolution experiments have provided
substantial insight into the relationship between the
rates of genomic evolution and organism adaptation,
genetic basis of fitness improvement under constant
environmental conditions [12, 14, 64], morphological
evolution of cells [64, 65], ecological specialization
[53, 66], consequences of historical contingency and
emergence of novel functions [2, 67], second-order
evolutionary effects [68], and forces maintaining di-
versity [69]. They have enabled quantitative under-
standing of the correlation between the size, stabili-
ty, and evolutionary potential of populations [70] and
demonstrated that the most influential mutations are
generally grouped into two classes – those that affect
specific functions (e.g., in rate-limiting enzymes) and
those that impair global transcriptional patterns (e.g.,
in RNA polymerase) [71]. The use of 12 E. coli lineag-
es in the LTEE has enabled investigation of changes
in the genotypes and phenotypes occurring in par-
allel in independently evolving bacterial populations
[64, 68, 72]. The ALE technology has also been used
to systematically define evolutionary mechanisms at
the microbial metabolism and gene regulation levels
[73]. Experimental evolution continues to expand its
applications in biotechnology, in particular, for strain
optimization in the production of novel compounds,
increase of product titers, and enhancement of toler-
ance to adverse environmental conditions [74].
Initially employed to elucidate evolutionary
mechanisms, ALE is now an important component
of biotechnology-focused engineering strategies [75].
Being not limited by the requirements for the a priori
understanding of genetic basis of target phenotypes,
ALE has significantly expanded the potential for en-
gineering non-model microbes, including those high-
ly resistant to engineering. The molecular basis of
adaptation, previously elusive, is becoming increas-
ingly obvious due to advances in the high-through-
put next-generation sequencing technologies. Table 1
summarizes selected ALE studies that have provid-
ed key insights into evolutionary processes, enabled
widespread ALE application in metabolic engineering,
and revealed molecular mechanisms underlying this
strategy.
CONCLUSION
Evolution in a test tube has become a widely
applied methodology in modern microbiology and
DUNAEVSKY et al.1462
BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025
Table 1. Representative examples of results achieved using the ALE strategy
Species Novel information obtained due to ALE implementation References
S. cerevisiae,
E. coli
on clonal interference and frequency dependence [76, 77]
S. cerevisiae
on the evolutionary pressure and molecular mechanisms
leading to the selection of multicellular forms of life
[42]
Candida albicans
on the dynamics and mechanisms of mutation
development
[78]
S. cerevisiae
on the role of chromosomal rearrangements
in the reversible cellular adaptation to a changing
environment
[79]
E. coli
on the changing balance of forces driving genome
evolution in populations adapting to a new environment,
and genetic basis of enhanced fitness
[14]
E. coli
on the consequences of historical contingencies
in laboratory evolution and emergence of a new function
(aerobic citrate utilization or metabolism of the non-
natural carbon source 1,2-propanediol)
[2, 67]
E. coli
on ecological specialization (high mutation rates
as a driving force of specialization)
[66]
E. coli,
Gluconobacter oxydans,
Torulaspora delbrueckii,
S. cerevisiae
on the molecular basis of microbial strain optimization
through the enhanced utilization of substrate (glycerol,
xylose, mannitol) or production of commercially valuable
compounds (nonanedioic acid, ethanol, valencene)
[9, 10, 80, 81]
E. coli,
Propionibacterium acidipropionici,
Leuconostoc mesenteroides,
Lactococcus lactis,
S. cerevisiae
on the molecular basis of microbial strain optimization
through the enhanced stress tolerance (to isobutyl
acetate, propionic acid, lactate, heat and osmotic shock,
selenium, and furfural)
[82-87]
molecular biology. Some researchers use this ap-
proach to collect experimental data that can verify
various evolutionary hypotheses and elucidate mo-
lecular basis of observed phenotypic changes. Others
implement the methodological scheme of evolution-
ary experiments for applied studies aimed at selecting
more productive and stress-resistant microbial strains
required for modern industry [88]. At times, these di-
rections intersect in an unexpected way. For example,
S.cerevisiae strains exhibiting a distinctive snowflake
phenotype had been originally obtained to address a
solely fundamental question – to serve as a model for
studying the emergence of multicellularity in eukary-
otes [41]. Later, ALE was successfully applied to three
non-flocculent industrial brewing strains of S. cerevi-
siae to obtain microorganisms with the aggregative
(snowflake) phenotype, thereby demonstrating the use
of ALE for improving the sedimentation properties
of non-flocculent brewing strains [89].
To conclude, we have to emphasized three ma-
jor issues. First, most ALE experiments are relatively
short in duration – ranging from several weeks to a
few months [16]. However, there are also long-term,
multi-year projects that provide a much deeper un-
derstanding of evolutionary mechanisms and adapta-
tion dynamics. Despite all their complexity, such stud-
ies reveal unique data on the sequence of mutations
and molecular pathways underlying adaptive pheno-
types – information that cannot be fully captured in
short-term experiments [31]. One of the key advan-
tages of laboratory evolution is its ability to explore
the relationship between an increase in fitness and
underlying mutations in evolved populations, at the
same time solving the issue of interactions among sin-
gle mutations. Compared with rational metabolic en-
gineering, which directly introduces exogenous genes
or disables endogenous ones, ALE cannot consider-
ably alter metabolism within a short period of time.
MOLECULAR–GENETIC BASIS OF LABORATORY EVOLUTION 1463
BIOCHEMISTRY (Moscow) Vol. 90 No. 11 2025
However, the use of a rationally engineered strain as
a starting point of evolution can substantially short-
en the evolutionary process, particularly, when the
acquisition of complex phenotypes is required [88].
Second, another important aspect is predictabil-
ity of evolutionary trajectories. According to the re-
search data, evolution may indeed be limited by a
specific sequence of events, the so-called evolution-
ary pathway, in which certain mutations are more
probable or advantageous at early stages than the
others [27]. Nonetheless, it remains difficult to pre-
dict a full course of such trajectories, as they depend
on numerous factors, including random mutations,
selective forces, and interactions between mutations.
Some studies demonstrate that early mutations can
strongly influence subsequent evolutionary stages,
providing the basis for predictive models; however,
such approaches still require further development
and validation.
Third, despite a significant progress achieved us-
ing ALE for both selection and investigation of com-
plex phenotypes, several limitations remain, including
those related to the population size, restricted time
frames, simplified nature of laboratory environments,
and possible misinterpretation of the roles of fixed
mutations and selective forces [27, 90]. Therefore,
when interpreting the results of experimental evolu-
tion, it is important to account for these limitations.
Any prediction should be probabilistic and supported
by additional experimental evidence.
Abbreviations
ALE adaptive laboratory evolution
LTEE long-term evolution experiment
Contributions
Ya.E.D. wrote the manuscript; O.A.K. collected and sys-
tematized the data; M.A.B. edited the manuscript.
Funding
This study was carried out as a part of the State As-
signment to the Lomonosov Moscow State University.
Ethics approval and consent to participate
This work does not contain any studies involving hu-
man and animal subjects.
Conflict of interest
The authors of this work declare that they have no
conflicts of interest.
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