Phenotypic plasticity as a long-term memory easing readaptations to ancestral environments
Plasticidade fenotípica como memória de longo prazo, facilitando a readaptação a ambientes ancestrais
See all authors and affiliations
Abstract
Plasticidade fenotípica refere-se a alterações fenotípicas induzidas pelo ambiente sem mutação e está presente em todos os organismos. O papel da plasticidade fenotípica nas adaptações orgânicas para novos ambientes atraiu muita atenção, mas seu papel nas readaptações para ambientes ancestrais é pouco estudado. Para abordar essa questão, usamos a abordagem de transplante recíproco para investigar os transcriptomas multissetoriais de galinhas adaptadas ao platô tibetano e à planície adjacente. Embora muitas mudanças transcriptômicas genéticas tenham ocorrido na adaptação direta às terras altas, as mudanças plásticas transformam amplamente os transcriptomas no estado preferido quando as galinhas tibetanas são trazidas de volta às terras baixas. A mesma tendência se aplica à eclodibilidade dos ovos, um componente essencial da aptidão dos frangos. Essas descobertas, juntamente com padrões altamente semelhantes em experimentos comparáveis de guppies e Escherichia coli, demonstram que os organismos geralmente “lembram” seus ambientes ancestrais via plasticidade fenotípica e revelam um mecanismo pelo qual a experiência passada afeta a evolução futura.
INTRODUCTION
Organismal
adaptation to a new environment often consists of two steps. The first
step involves plastic phenotypic changes without mutational input, while
the second step involves genetic phenotypic changes resulting from the
fixations of mutations advantageous in the new environment. It is
important to clarify the relationship between these two steps of
phenotypic changes for a comprehensive understanding of environmental
adaptation. In particular, discussions have focused on the question of
whether plastic changes serve as stepping-stones to genetic adaptations (1–6).
While earlier studies on a small number of morphological,
physiological, or behavioral traits did not reveal a general trend,
recent experimental evolution studies have shown that plastic gene
expression changes tend to be reversed rather than reinforced by
subsequent genetic changes, casting doubts on the contribution of
phenotypic plasticity to environmental adaptations (7–12).
Nevertheless, because these recent studies examined organismal
adaptations to novel environments only, it remains possible that the
role of phenotypic plasticity is much greater in readaptations to
ancestral environments. Using gene expression levels as focal traits, we
explore this possibility by investigating cases of domestication,
experimental evolution, and/or natural evolution of animals and
bacteria. We report that phenotypic plasticity generally serves as a
long-term memory that could ease organismal readaptations to their
ancestral environments.
RESULTS
Lowland and Tibetan chickens
Studies
of animal and plant domestication have been important in evolutionary
biology because of the availability of historical records of evolution
and many drastically selected phenotypic traits (13–16). Chickens were domesticated from the red jungle fowl in South and Southeast Asia at least 4000 to 4500 years ago (15) and were brought to the Tibetan Plateau by ~1200 years ago (17). Tibetan chickens are now well adapted to their high-altitude environment. For instance, they have more red blood cells (18), a smaller mean blood cell volume (18), and a higher efficiency of oxygen dissociation from hemoglobin in embryos (19) when compared with lowland chickens.
Changes in gene expression and regulation play important roles in environmental adaptations (20), including high-altitude adaptations of humans and other animals (21).
To study transcriptomic changes in chicken’s adaptation from the
lowland to the highland, we performed mRNA sequencing (RNA-seq) of five
tissues (brain, heart, liver, lung, and muscle) from lowland chickens
raised in Ya’an (670 m above sea level, Fig. 1A; square 1 in Fig. 1B; “lowland” hereinafter) and Tibetan chickens raised in A’ba (3300 m, Fig. 1A; circle 3 in Fig. 1B;
“highland” hereinafter), localities on the opposite sides of the east
edge of the Tibet Plateau in Sichuan Province, China. In addition, we
conducted reciprocal transplant experiments to investigate whether any
detected gene expression difference is a plastic response to a different
environment or has genetic basis. Specifically, we hatched and raised
lowland chickens in the highland (square 2 in Fig. 1B) and Tibetan chickens in the lowland (circle 4 in Fig. 1B),
followed by transcriptome profiling of the same five tissues. In the
forward adaptation of lowland chickens to the highland, the three
samples marked 1, 2, and 3 in Fig. 1B respectively represent the original adapted stage in the lowland (O, Fig. 1C), the plastic stage in the highland (P, Fig. 1C), and the adapted stage in the highland (A, Fig. 1C).
Phenotypic differences between stages O and P are plastic, whereas
those between P and A are genetic. When studying the potential reverse
adaptation of Tibetan chickens to the ancestral lowland environment, we
can view the samples marked 3, 4, and 1 in Fig. 1B as representatives of the original stage in the highland (O, Fig. 1D), plastic stage in the lowland (P, Fig. 1D), and adapted stage in the lowland (A, Fig. 1D).
Hence, our experiment allows comparing the relative roles of genetic
and plastic phenotypic changes in the adaptation to a new environment
and potential readaptation to an ancestral environment. Each of the four
samples included eight to nine chickens for each tissue except for the
muscle, which had five to six chickens (table S1).
(A)
Map showing the locations and altitudes of the two experimental sites
in Sichuan Province, China. Adapted with permission from (33). (B)
Design of the reciprocal transplant experiment, in which each of the
two breeds are phenotyped in its native environment and the other
breed’s native environment. Different symbols indicate different breeds,
and different colors indicate different environments where chickens are
hatched, reared, and phenotyped. The four samples of chickens
phenotyped are numbered. The diagram shows the scenario in which the
gene expression level is higher in sample 3 than in sample 1, but the
same principle applies if the opposite is true. (C)
Samples 1, 2, and 3 in (B) respectively represent the original (O),
plastic (P), and adapted (A) stages during the forward adaptation from
the lowland to highland. (D) Samples 3, 4, and 1 in (B)
respectively represent the O, P, and A stages during the reverse
adaptation from the highland to the lowland. (E) Venn
diagram showing the number (and percentage) of differentially expressed
genes (DEGs) in each tissue between samples 1 and 3 in (B). (F)
Numbers of DEGs in each tissue that are plastic-change-only (PO) or
genetic-change-needed (GN) during the forward (F) or reverse (R)
adaptation. (G) Number of DEGs that are PO divided by
the number of DEGs that are GN in F or R adaptation in each tissue and
all tissues combined. P values are based on a G test of independence.
After mapping RNA-seq reads (data files S1 and
S2), we first identified differentially expressed genes (DEGs) between
samples 1 and 3 in Fig. 1B
[false discovery rate (FDR) < 5%; see Materials and Methods], which
inform us about gene expression differences between lowland and Tibetan
chickens in their respective native environments. We found 0.3, 0.9,
1.5, 4.6, and 3.1% of annotated genes as DEGs in the brain, heart,
liver, lung, and muscle, respectively (Fig. 1E).
We found that the DEGs are most significantly enriched with gene
ontology terms of (i) metabolic processes of organonitrogen compound,
phosphate-containing compound, amino acids, and protein; (ii) regulation
of hydrolase [especially guanosine triphosphatase (GTPase)] activity;
(iii) regulation of protein phosphorylation; (iv) regulation of protein
secretion; (v) actin cytoskeleton organization and phagocytosis; (vi)
stress response, intracellular signal transduction, and apoptosis; (vii)
immune processes, lymphocyte differentiation, and leukocyte cell-cell
adhesion; and (viii) negative regulation of p38 mitogen-activated
protein kinases cascade (data file S3).
For each DEG, we
evaluated the relative importance of plastic and genetic changes in its
expression difference by the following steps. First, we considered the
forward adaptation (Fig. 1C) and defined the expression levels at stages O, P, and A as EO, EP, and EA, respectively. Second, if EO and EP differ significantly but EP and EA are not significantly different, then the difference between EO and EA can be entirely explained by a plastic change so is considered “plastic-change-only” (PO). Third, if EP and EA
differ significantly, then the expression variation of the gene is
“genetic-change-needed” (GN). Note that these two categories are
mutually exclusive, but they together do not include all DEGs because of
the situation where neither EO nor EA differs significantly from EP despite a significant difference between EO and EA. Fourth, we similarly classified DEGs to the categories of PO and GN in the reverse adaptation to the lowland (Fig. 1D).
Compared
with the forward adaptation, the reverse adaptation shows fewer GN
genes but more PO genes in four of the five tissues (Fig. 1F). The ratio (RPO/GN)
of the number of PO genes to that of GN genes in the reverse adaptation
is 2.9 to 106 times the ratio in the forward adaptation (P < 10−8, G test of independence; Fig. 1G) in these four cases. The exception is the brain, which has the fewest DEGs among the five tissues. For the brain, RPO/GN in the reverse adaptation is 1.06 times that in the forward adaptation, which is not significantly different from 1 (P = 0.9; Fig. 1G). When all five tissues are considered together, RPO/GN in the reverse adaptation is 5.3 times that in the forward adaptation (P < 10−129; Fig. 1G). The elevation in RPO/GN during the reverse adaptation is general rather than specific to certain gene ontology terms (fig. S1).
Because
at least some gene expression changes are expected to affect fitness,
the overall preponderance of GN expression changes in the forward
adaptation and that of PO expression changes in the reverse adaptation
prompted us to investigate whether similar trends exist for life-history
traits that are crucial to fitness. We found that Tibetan chicken eggs
have a drastically higher hatchability than lowland chicken eggs when
both are incubated in the highland (Fig. 2A),
indicating GN in hatchability during the forward adaptation to the
highland. When incubated in the lowland, however, Tibetan and lowland
chicken eggs show no significant difference in hatchability (Fig. 2A).
This observation, together with a significant plastic change in
hatchability of Tibetan chicken eggs between the two localities (Fig. 2A),
indicates PO in hatchability during the potential reverse adaptation to
the lowland. Hence, the variation of egg hatchability also follows the
trends observed from the transcriptomic data. We further respectively
examined the survival rate of chicken embryos in the first (Fig. 2B), second (Fig. 2C), and third (Fig. 2D)
week of hatching, representing temporal components of egg hatchability.
Overall, similar trends were observed between these traits and the
final hatchability, with minor variations, apparently due to the
relatively large standard errors of the survival rate estimates that
render some statistical tests underpowered (Fig. 2).
(A) Fraction of fertilized eggs that are hatched. (B)
Survival rate of chicken embryos in the first week of hatching,
measured by the number of viable embryos on day 7 divided by the
corresponding number on day 0. (C) Survival rate of
chicken embryos in the second week of hatching, measured by the number
of viable embryos on day 14 divided by the corresponding number on day
7. (D) Survival rate of chicken embryos in the third
week of hatching, measured by the number of viable embryos on day 21
divided by the corresponding number on day 14. Breeds are indicated by
symbols, while environments are indicated by colors. Error bars show one
standard error based on the binomial distribution. P values determined by a G test of independence are shown as follows: ns, P > 0.06; o, 0.05 < P < 0.06; *, 0.01 < P < 0.05; ** 0.001 < P < 0.01; and ***, P < 0.001.
Other organisms
Notably,
studies of potential reverse adaptations of sparrows and deer mice from
high-altitude environments to their ancestral low-altitude habitats
also suggested a prominent role of plastic transcriptomic changes (22, 23).
However, because these earlier studies did not transplant organisms
from low to high elevations, they could not compare the roles of
plasticity between forward and reverse adaptations. To examine the
generality of our findings, especially outside altitude adaptations and
vertebrates, we analyzed recently released transcriptomic data from a
large study of Escherichia coli experimental evolution (24). Specifically, E. coli
cells were first adapted to a benign environment (M9 medium) for ~90
generations, followed by 350 to 800 generations of adaptation to one of
11 harsh environments (M9 medium supplemented with 1 of 11 stressors).
Cells adapted to each harsh environment were then single-cloned.
Transcriptomic data were collected for the benign environment–adapted
strain in the benign environment and each harsh environment, as well as
each stress-adapted strain in the benign environment and its respective
harsh environment. These data allowed investigating plastic and genetic
expression changes in 11 forward adaptations to different stresses and
11 potential reverse adaptations to the benign environment, in the same
manner as in Fig. 1B.
We found that in all 11 pairs of forward and reverse adaptations, more
genes exhibit GN changes in the forward than reverse adaptations (P < 5 × 10−4, one-tailed binomial test; Fig. 3A), and more genes exhibit PO changes in the reverse than forward adaptations (P < 5 × 10−4; Fig. 3B). Combined across the 11 experiments, RPO/GN is 0.63 in forward adaptations but 1.77 in reverse adaptations (Fig. 3C).
The experimental design is analogous to that in Fig. 1B. (A)
Numbers of DEGs that are GN in the forward adaptation from the benign
environment to one of the harsh environments and the reverse adaptation
from each harsh environment to the benign environment. Each line
represents the result of one pair of forward and reverse adaptations. (B) Number of DEGs that are PO in F and R adaptations. (C)
Number of DEGs that are PO divided by the number of DEGs that are GN in
F and R adaptations for each harsh environment and all 11 environments
combined. P values are based on a G test of
independence. The benign environment is the M9 medium with glucose (5
g/liter), whereas the harsh environments numbered 1 to 11 respectively
contain cobalt chloride (16 μM), sodium carbonate (32.5 mM),
methylglyoxal (350 μM), cetylpyridinium chloride (4.8 μM), crotonate (50
mM), n-butanol (1.25%), methacrylate (8.75 mM), potassium chloride (210 mM), l-lactate (40 mM), sodium chloride (400 mM), and l-malate (30 mM).
In the chicken and E. coli cases, the
new environments are harsher than the ancestral environments. To examine
whether this relationship is necessary for our observations, we took
advantage of a study of one natural and two human-introduced populations
of Trinidadian guppies (Poecilia reticulata) that independently adapted from high- to low-predation streams (10).
Here, the new environment is not harsher than the ancestral
environment. In each case, we analyzed the published brain
transcriptomes of the four groups of guppies from reciprocal transplant
experiments (10), as in Fig. 1B. We found that RPO/GN
in the reverse adaptation is larger than that in the forward adaptation
in all three cases, including two cases where the difference is
statistically significant (Table 1). Thus, our finding holds regardless of the relative stress of the ancestral and new environments.
Table 1 Numbers
of DEGs that are PO or GN in adaptations of guppies from high- to
low-predation streams and the potential reverse adaptations.
Independent evolution occurred in a natural population (natural) and two human-introduced populations (Intro1 and Intro2).
View this table:
DISCUSSION
In
this work, we observed a tendency that, compared with genetic
phenotypic changes, plastic phenotypic changes play a more prominent
role when organisms return to ancestral environments than when they
adapt to new environments. This trend appears quite general, because the
pattern we initially observed in chickens also exists in guppies and E. coli.
The same trend holds in all types of adaptations analyzed, varying from
an environmental change in predation to elevation, nutrient, and toxin,
regardless of whether the ancestral environment is less or more
stressful than the new environment. The observation holds not only for
gene expression levels but for life-history traits such as egg
hatchability. Why does returning to ancestral environments yield a
higher RPO/GN than adaptations to new environments? Below, we evaluate several potential explanations.
First,
although a plastic change occurs without mutation, an organism’s
ability to make a plastic change has genetic basis. Hence, natural
selection can drive the spread of genotypes capable of making certain
plastic changes that are beneficial. Consider, for example, the scenario
that chickens are frequently exposed to both lowland and highland
environments for many generations or the scenario that a strong
bidirectional gene flow exists between the lowland and highland
populations. It is likely that gene regulatory programs will emerge that
turn the chicken transcriptome to the highland-preferred state when in
the highland and to the lowland-preferred state when in the lowland,
provided that mutations creating such regulations exist and do not have
harmful pleiotropic effects (25, 26).
This model, however, predicts similar numbers of PO changes in the two
environments, so it cannot explain the asymmetry in the relative
prevalence of PO changes observed. Furthermore, it is clear, at least in
the E. coli and guppy experimental evolution, that no repeated environmental swaps or gene flows occurred.
Second,
when a population faces a new environment for a relatively short time,
ancestral alleles that are fitter in the ancestral environment may not
have been completely replaced by new alleles that are fitter in the new
environment. Consequently, the reverse adaptation of this population to
the ancestral environment can use standing genetic variants and need not
wait for new beneficial mutations (27, 28).
But this model is inapplicable in the present study because of the
difference in experimental design. For example, regardless of the
underlying genetic architecture, lowland-preferred genotypes should be
rare among Tibetan chickens such that it is extremely improbable for all
of the Tibetan chickens hatched, raised, and transcriptomically
profiled in the lowland to be of such genotypes. The same can be said
for the guppies and E. coli.
Because the above
two simple models cannot explain our finding, we now consider the
possibility that our observation reflects a property of the genetic
mechanism by which adaptation to a new environment occurs. For example,
let us assume that lowland chickens express a particular gene at a basal
level when in the lowland (Fig. 4A)
and that the preferred expression level of the gene is higher in the
highland. However, the preferred higher expression cannot be realized by
plasticity alone (Fig. 4B),
because the realization requires the evolutionary acquisition of a
regulatory motif that binds to a highland-specific transcriptional
activator (Fig. 4C).
However, when highland-adapted chickens are moved back to the lowland,
the highland-specific transcriptional activator is turned off, causing
the expression of the gene to return to the basal level without the need
for any genetic change (Fig. 4D).
In other words, the reaction norm of highland-adapted chickens becomes
different from that of the lowland-adapted chickens, as a byproduct of
the adaptation to the highland. The same model involving a
transcriptional repressor instead of activator applies if the preferred
expression level is lower in the highland than in the lowland. Thus,
under this model, a forward adaptation to a new environment requires
genetic changes that are de novo or standing in the population, whereas
returning to the ancestral environment needs only plastic changes, the
asymmetry being caused by the establishment of a basal expression
program before the emergence of a condition-specific transcriptional
regulation in the new environment. More broadly speaking, our
observations are explainable by the presence of a sizable fraction of
mutations acquired in new environments that have phenotypic effects in
the new but not ancestral environments. We propose that this model
reflects a general genetic logic of environmental adaptations, and
future studies should test this proposition.
(A) Lowland expression of a hypothetical gene in lowland-adapted chickens. (B) Highland expression of the gene in lowland-adapted chickens. (C) Highland expression of the gene in highland-adapted chickens. (D)
Lowland expression of the gene in highland-adapted chickens. The gray
rectangle shows a regulatory motif that binds to a basal transcription
factor (gray oval) to allow transcription at the basal level. Green
diamonds indicate the mRNA molecules produced. The orange rectangle
shows a regulatory motif that binds to a highland-specific transcription
factor (orange oval) to increase the transcription level. From (A) to
(B), a plastic change is unable to raise the expression level because of
the lack of the orange motif, whereas from (C) to (D), a plastic change
can lower the gene expression level because of the turnoff of the
highland-specific transcription factor in the lowland. A similar model
in which the highland-specific transcription factor is a repressor can
explain should the optimal gene expression be lower in the highland than
in the lowland.
Our study has several caveats worth discussing.
First, in interpreting the transplant experiments, we ignored the
possibility of maternal effects and environmental effects before the
transplant. If these factors are the causes of the asymmetric
observations from the reciprocal transplants, then these factors must be
different between the lowland and Tibetan chickens (and the relevant
populations of guppies and E. coli), which is highly unlikely.
Second, while the forward adaptations actually took place in the cases
analyzed, the reverse adaptations are inferred under the assumption that
the optimal phenotype in an environment is the same for conspecifics of
different genetic backgrounds. Although this assumption may not hold
for every trait, previous studies suggested that it is true for
expression traits relevant to the environment (26, 29).
Furthermore, plasticity renders Tibetan chickens as adapted to the
lowland as are lowland chickens at least in terms of egg hatchability,
suggesting that plasticity eases readaptations to ancestral
environments. Third, not every gene expression change is adaptive, nor
does our analysis assume so. Hence, a higher RPO/GN
in reverse than forward adaptations means that reverse adaptations are
accompanied by relatively more plastic expression changes than forward
adaptations. Because at least some expression changes are likely
adaptive, it is plausible that plastic expression changes contribute
more to reverse than forward adaptations. Note that, although there is
no one-to-one correspondence between genetic phenotypic changes and the
underlying genetic changes, everything else being equal, our results do
suggest that the number of underlying genetic changes for the observed
phenotypic changes is greater in the forward than reverse adaptations.
Fourth, while it is clear that Tibetan chickens originated from lowland
chickens, the specific lowland breed analyzed here may not represent the
lowland sister lineage of Tibetan chickens. That is, other than a
relatively low altitude, Ya’an may not represent the ancestral
environment of Tibetan chickens. This potential environmental mismatch
should make our conclusion about chickens conservative, because
potentially only the fraction of genes related to the altitude
adaptation show an increase in RPO/GN in the reverse
adaptation compared with the forward adaptation. Potential
environmental mismatches do not exist for the bacteria and guppies
analyzed.
Our findings raise two critical questions.
First, do the patterns observed depend on how long ago the population
left the ancestral environment? The human-introduced guppies had been in
the new environment for only three to four generations, while the E. coli
experiment had hundreds of generations in the new environments and the
Tibetan chickens have been on the highland for at least 2400
generations. Under the model proposed, the plastic change in the
ancestral environment (Fig. 4D)
is expected regardless of the time since the population left the
ancestral environment, as long as the adaptation to the new environment
has been established. Second, how many ancestral environments will
organisms “remember”? Do they remember only the immediate past or
multiple previous environments? Clearly, more studies are needed to
answer these questions.
In summary, our work uncovers a
phenomenon conserved from bacteria to vertebrates that organisms
remember their ancestral environments in the form of phenotypic
plasticity. Although this observation does not mean that no genetic
adaptation is needed when organisms return to their ancestral
environment, it does suggest that fewer genetic phenotypic changes are
required for readaptations to ancestral environments than adaptations to
novel environments. Our finding contributes to the recent debate on the
relative roles of plastic and genetic changes in adaptation and reveals
the importance of considering whether the environment is changing to a
novel or ancestral one. Our results demonstrate the impact of
evolutionary histories on future evolutionary dynamics and are therefore
a manifestation of historical contingency. It remains to be seen
whether this phenomenon can be used to infer ancestral environments of
organisms.
MATERIALS AND METHODS
Chicken tissue sample preparation and RNA-seq
We
hatched and reared Tibetan chickens and lowland chickens (Pengxian
yellow chicken breed) in Ya’an, as well as Tibetan chickens and lowland
chickens in A’ba. For each of the four groups of chickens, we collected
five tissues (brain, liver, lung, heart, and muscle) from five to nine
healthy males of similar body weights at the age of 120 days (table S1).
Total RNA was isolated from these tissues by the standard TRIzol method
(Invitrogen). RNA sequencing was performed on an Illumina HiSeq 2000 or
NovaSeq 6000 sequencing system, and a total of 1384.50 Gb of sequences
from 165 libraries were generated. Read mapping was performed using
TopHat2 (30)
against the reference genome Galgal5.0 from Ensembl release 92. The raw
reads were submitted to the National Center for Biotechnology
Information (NCBI) and National Genomics Data Center (NGDC) (https://bigd.big.ac.cn/) with the reference number of GSE85318 and PRJCA001948, respectively.
Analysis of chicken transcriptomes
We used DESeq2 (31)
to identify DEGs using the cutoff of an FDR of 0.05. In the forward
adaptation, the expression difference of a DEG is considered PO if the
expression levels from samples 1 and 2 in Fig. 1B
are significantly different but those from samples 2 and 3 are not
significantly different based on DESeq2 (FDR cutoff of 0.05 considering
tests of all DEGs). In the forward adaptation, the expression difference
of a DEG is considered GN if the expression levels from samples 2 and 3
are significantly different. In the reverse adaptation, the expression
difference of a DEG is considered PO if the expression levels from
samples 3 and 4 are significantly different but those from samples 4 and
1 are not significantly different. In the reverse adaptation, the
expression difference of a DEG is considered GN if the expression levels
from samples 4 and 1 are significantly different.
Phenotyping of chicken life-history traits
Eggs
from Tibetan chickens and lowland chickens were collected daily from
day 2 after the first insemination to day 6 after the last insemination.
In total, we collected 466 eggs from Tibetan chickens and 534 eggs from
lowland chickens in A’ba. We also collected 576 eggs from Tibetan
chickens and 512 eggs from lowland chickens in Ya’an. All eggs were
incubated at 37.8°C with a relative humidity of 60% in an automatic
incubator (Changsha Photosynthetic Solar Energy Co. Ltd.). Embryonic
mortality was determined using an egg candler. This animal handling
experiment was approved by the Institutional Animal Care and Use
Committee of Sichuan Agricultural University under permit number
YCS-B20100804.
When comparing the hatching performance of different samples (Fig. 2A), we performed a G
test of independence using a 2 × 2 contingency table, where the columns
are different samples and the rows are the number of eggs hatched or
not hatched. When comparing the hatching performance in the second week (Fig. 2C) and in the third week (Fig. 2D), only embryos surviving the previous week were considered.
Analysis of E. coli transcriptomes
Horinouchi et al. (24) performed experimental evolution of E. coli
first in the M9 medium with glucose (5 g/liter) for ~90 generations and
then in 11 different harsh environments for 350 to 800 generations. In
each harsh environment, five replicate evolution experiments were
conducted with the same starting strain. Upon the completion of the
experimental evolution, single clones were picked and transcriptomically
profiled. One microarray transcriptome was generated for each of the
five replicates from each of the 11 harsh environments in its adapted
environment (corresponding to sample 3 in Fig. 1B). In addition, three replicate transcriptomes of the ancestral strain in the M9 medium (corresponding to sample 1 in Fig. 1B), one transcriptome from each replicate of each of the 11 adaptations in the M9 medium (sample 4 in Fig. 1B), and one replicate of the ancestral strain in each harsh environment (sample 2 in Fig. 1B) were generated. All transcriptome data were from table S2 in the paper (24).
For each harsh environment, we first tested the equality of gene expression level between sample 1 and sample 3 by a two-sample t
test. Genes with FDR < 0.05 in this test were referred to as DEGs.
We categorized DEGs to PO and GN on the basis of the same criteria used
in the chicken data analysis. Note that before any two-sample t test, we performed an F test to examine the equality of variance. When the nominal P value of the F test is smaller than 0.05, we conducted a two-sample t test assuming unequal variances; otherwise, we conducted a two-sample t test assuming equal variances.
Analysis of guppy transcriptomes
Ghalambor et al. (10) performed experimental evolution of Trinidadian guppies (P. reticulata)
previously adapted to the streams with high numbers of cichlid
predators [high-predation (HP) environment] in two different
cichlid-free streams [low-predation (LP) environment]. After 1 year of
experimental evolution (approximately three to four generations), fish
were collected from each introduced population (Intro1 and Intro2). In
addition, fish originating from an HP environment and naturally adapted
to an LP environment were collected. In the laboratory, breeds of
Intro1, Intro2, and the natural LP population were respectively reared
in an LP environment (corresponding to sample 3 in Fig. 1B) and an HP environment (corresponding to sample 4 in Fig. 1B).
Breeds of the ancestral population sampled from the HP stream were also
respectively reared in the LP (corresponding to sample 2 in Fig. 1B) and HP environments (corresponding to sample 1 in Fig. 1B).
Brain RNAs from these reared fish were extracted, sequenced, and
quantified. Numbers of fish individuals meeting quality filters are four
for Intro1 and Intro2 reared in each environment, three for natural LP
reared in HP environment, two for natural LP reared in LP environment,
and five for the ancestral HP reared in each environment. The numbers of
mapped reads for 37,493 genes were provided by the authors. DESeq2 was
used to identify DEGs at FDR < 0.05. For each of Intro1, Intro2, and
natural LP populations, we first tested the equality of gene expression
level between sample 1 and sample 3, as in Fig. 1B. We categorized DEGs into PO and GN on the basis of the same criteria used in the chicken data analysis.
Software for statistical analysis
The t test and F
test were respectively performed using the function “ttest2” and
“vartest2” in MATLAB R2019b. FDR adjustment was performed by the
function “mafdr” in MATLAB R2019b or the function “p.adjust” with the
“BH” method in R. The G test of independence was performed using the function “G.test” from the package “RVAideMemoire” in R.
The enrichment test of gene ontology terms was performed by the web server g:Profiler (32)
using the default setting. Specifically, the tool g:GOSt applies a
hypergeometric test for each gene ontology term with its default
multiple testing correction tool g:SCS. The test was performed in
November 2019, when g:Profiler had been updated to Ensembl 98. Only the
genes with at least one annotation were considered in the reference
background.
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/21/eaba3388/DC1
This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
REFERENCES AND NOTES
Acknowledgments: We thank W. Qian, J. -R. Yang, and members of the Zhang laboratory for valuable comments. Funding:
This work was supported by U.S. NIH research grant GM103232 to J.Z. and
Sichuan Provincial Department of Science and Technology Program
2015JQO023 to D.L. Author contributions: W.-C.H., D.L.,
Q.Z., and J.Z. designed the research. W.-C.H. and D.L. performed the
experiments and analyzed the data. W.-C.H. and J.Z. wrote the paper with
input from all authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The raw sequencing reads were submitted to NCBI (https://ncbi.nlm.nih.gov/) and NGDC (https://bigd.big.ac.cn/)
with the reference number of GSE85318 and PRJCA001948, respectively.
All data needed to evaluate the conclusions in the paper are present in
the paper and/or the Supplementary Materials. Additional data related to
this paper may be requested from the authors.
- Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
Nenhum comentário:
Postar um comentário
Observação: somente um membro deste blog pode postar um comentário.