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R E S E A R C H A R T I C L E Open Access
Gene regulatory response to hyposalinity in
the brown seaweed Fucus vesiculosus
Luca Rugiu
1*
, Marina Panova
1
, Ricardo Tomás Pereyra
1
and Veijo Jormalainen
2
Abstract
Background: Rockweeds are among the most important foundation species of temperate rocky littoral shores. In
the Baltic Sea, the rockweed Fucus vesiculosus is distributed along a decreasing salinity gradient from the North
Atlantic entrance to the low-salinity regions in the north-eastern margins, thus, demonstrating a remarkable
tolerance to hyposalinity. The underlying mechanisms for this tolerance are still poorly understood. Here, we
exposed F. vesiculosus from two range-margin populations to the hyposaline (2.5 PSU - practical salinity unit)
conditions that are projected to occur in the region by the end of this century as a result of climate change. We
used transcriptome analysis (RNA-seq) to determine the gene expression patterns associated with hyposalinity
acclimation, and examined the variation in these patterns between the sampled populations.
Results: Hyposalinity induced different responses in the two populations: in one, only 26 genes were differentially
expressed between salinity treatments, while the other population demonstrated up- or downregulation in 3072
genes. In the latter population, the projected future hyposalinity induced an acute response in terms of antioxidant
production. Genes associated with membrane composition and structure were also heavily involved, with the
upregulation of fatty acid and actin production, and the downregulation of ion channels and alginate pathways.
Changes in gene expression patterns clearly indicated an inhibition of the photosynthetic machinery, with a
consequent downregulation of carbohydrate production. Simultaneously, energy consumption increased, as
revealed by the upregulation of genes associated with respiration and ATP synthesis. Overall, the genes that
demonstrated the largest increase in expression were ribosomal proteins involved in translation pathways. The
fixation rate of SNP:s was higher within genes responding to hyposalinity than elsewhere in the transcriptome.
Conclusions: The high fixation rate in the genes coding for salinity acclimation mechanisms implies strong
selection for them. The among-population differentiation that we observed in the transcriptomic response to
hyposalinity stress suggests that populations of F. vesiculosus may differ in their tolerance to future desalination,
possibly as a result of local adaptation to salinity conditions within the Baltic Sea. These results emphasise the
importance of considering interspecific genetic variation when evaluating the consequences of environmental
change.
Keywords: Fucus, Hyposalinity, Climate change, Transcriptomic, Genetic variation
Background
Foundation species influence the structure and function
of the ecosystems in which they live by providing
physical habitat and resources for associated communi-
ties [1]. In temperate rocky shores, brown rockweeds are
a foundation species for littoral communities and
contribute to ecosystem function through biomass
accumulation, the transfer of energy and matter to
higher trophic levels, and by controlling environmental
conditions such as hydrodynamic forces and sedimenta-
tion [2]. Although rockweeds are adapted to life in the
highly variable littoral environment, their reproduction,
growth, and survival are vulnerable to variability in en-
vironmental factors such as temperature, salinity, eu-
trophication, and pH [3–6]. Climate change is expected
to modify all these factors, with the extent of the per-
turbation varying from one region to another. In general,
our current knowledge of the tolerance of marine
© The Author(s). 2020 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: luca.rugiu@gu.se
1
Department of Marine Sciences –Tjärnö, University of Gothenburg, SE 452
96 Strömstad, Sweden
Full list of author information is available at the end of the article
Rugiu et al. BMC Genomics (2020) 21:42
https://doi.org/10.1186/s12864-020-6470-y
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macroalgae to future climate-change conditions is lim-
ited to a few factors, mainly temperature and acidifica-
tion [7,8], but also salinity [9–11]. In the Baltic Sea, a
major change induced by climate change will be desalin-
ation [12], which will shift the surface water salinity gra-
dient southward and challenge the persistence of marine
macroalgae at the low-salinity end of the gradient.
Although seaweeds are well able to tolerate short-term
fluctuations in salinity [13], their growth may be inhib-
ited when they are exposed to low salinity for extended
periods [14–17] or when they are faced with additional
stressors close to their lower salinity limit [18]. One of
the main effects of salinity stress on seaweed physiology
is the formation of reactive oxygen species (ROS), which
are synthesised in response to different stressors and
may lead to cellular damage by causing oxidative stress
[19]. The production of antioxidant compounds is one
of the most important and common components of the
stress response to ROS [20,21]. Hyposalinity stress in
macroalgae may also lead to the inhibition of photosyn-
thesis [22], with potential negative repercussions for the
balance between photosynthetic activity and respiration,
which plays a central role in algal physiology. These sal-
inity responses, however, are inconsistent among sea-
weeds, with some algae showing short-term patterns of
increased respiration and either inhibition or enhance-
ment of photosynthetic activity (reviewed in Karsten
et al. [6]). Other responses to changes in salinity include
the activation of mechanisms to maintain constant cell
turgor through managing the concentration of osmolytes
such as inorganic ions and organic compounds in the
cytoplasm and the vacuoles [22]. In seaweed cells, ion
concentration is controlled by actively importing ions
from a hyposaline environment or excreting them in a
hypersaline environment via ion channels and ion trans-
porters [23]. As the flux of inorganic ions may result in
metabolic oxidative damage to intracellular components,
this osmotic strategy is combined with the accumulation
of organic osmolytes [24], which, in seaweeds, are often
carbohydrate by-products of photosynthetic activity [22].
In the brackish water of the Baltic Sea, Fucus vesiculo-
sus is the dominant brown seaweed and the major foun-
dation species in rocky littoral shores [25]. The species
has a broad tolerance range to salinity, with populations
stretching from the highly saline waters of the Baltic Sea
entrance (24 PSU) to the relatively hyposaline waters (2
PSU) of the northern and eastern margins of the Baltic
Sea and the White Sea. Low salinity limits the species’
distribution most probably due to the low tolerance
threshold of the gametes [26]. However, Baltic popula-
tions of F. vesiculosus have evolved a broader tolerance
to low salinity than their Atlantic counterparts [27]. Fur-
thermore, both Atlantic and Baltic Sea populations are
locally adapted to salinity; a reciprocal transplant
experiment in a common garden found that populations
grew better in their local salinity compared to the
foreign salinity (24 and 4 PSU, respectively [28];). Des-
pite such adaptation the Baltic Sea salinity gradient re-
mains as a major factor affecting size, morphology and
chemical contents of F. vesiculosus [29].
The physiological mechanisms underpinning the
osmoregulatory abilities of F. vesiculosus are still un-
known. Without this information, it is difficult to predict
how this species may react to the further decrease in sal-
inity that has been projected for the Baltic Sea as a result
of climate change. Here, we studied the process of hypo-
salinity acclimation by quantifying differences in gene
expression between algae in current ambient salinity and
those in future hyposaline conditions, which were pre-
dicted by a recent climate model [12]. In order to deter-
mine the functions of differentially expressed genes,
given that no reference transcriptome was previously
available for the species, we assembled de novo and an-
notated a full transcriptome library for F. vesiculosus. Be-
cause the species shows both strong genetic structuring
[30] and geographic variation in salinity tolerance [10],
we also assessed possible among-population variation in
gene expression by including algal specimens from two
geographic localities.
Results
Transcriptome assembly and annotation
After the quality filtering, we retained 88.7% of read
pairs (22.6 million), or 52.7 to 95.7% per sample (mean ±
SE; Current condition: Rauma = 17.8 ± 1.5; Parainen =
15 ± 3.2, Future condition: Rauma = 15.3 ± 1.8, Parai-
nen = 17.5 ± 0.7, Table 1). De novo assembly with the de-
fault Trinity parameters produced 295,013 genes and
382,992 transcripts (Table 2). Filtering with TransRate
and TransDecoder reduced the size of the assembly to
33,487 genes and 58,943 transcripts (Table 2). According
to the BUSCO assessment, our assembly is 89.4%
complete, i.e. it contains 271 complete single-copy and
163 complete duplicated reference eukaryotic genes,
while 19 genes are fragmented and 13 are missing. Of
24,486 E. siliculosus reference proteins, 43% had a
Conditional Reciprocal Best Blast in our F. vesiculosus
assembly.
In the assembly, 16,195 genes (48%) could be assigned
one or more GO mapping terms. In total 57,220 annota-
tions were assigned with mean GO level = 6.69. In the
GO classification by Biological Process, the largest group
was related to translation, similar to what was found in
F. vesiculosus transcriptome analysis by Martins et al.
[31]. Otherwise, the Fucus transcriptome represents a
wide range of biological processes, none of them being
particularly dominating (Fig. 1a). In the GO classification
by Molecular Function, the largest groups were related
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to the binding of organic cyclic compounds and hetero-
cyclic compounds, followed by ion binding (Fig. 1b).
Finally, in the GO classification by Cellular Component,
the transcripts representing all main cell components,
the largest groups related to cytoplasm (Fig. 1c).
KEGG metabolic pathways provide another way to
summarize functional content of the expressed genes. In
our assembly, the ten most highly represented molecular
functions (as defined by the number of sequences
mapped to pathway) were purine and thiamine
metabolism, biosynthesis of antibiotics, aminobenzoate
degradation, pyrimidine metabolism, glycolysis and
glucogenesis, carbon fixation in photosynthetic organ-
isms, phenylpropanoid biosynthesis, drug metabolism
and amino sugar, nucleotide sugar metabolism (see
Additional file 1for a list of total 133 pathways found in
the assembly).
Variation in gene expression between populations in
response to hyposalinity
We were able to map 34–46% of reads per sample to the
final transcriptome assembly, for a total of 33,487 genes
and 58,943 isoforms (Tables 1,2). A paired t-test
showed that there was no significant difference in map-
ping success between treatments (t = 1.21, p= 0.273). Of
all the genes, 32,345 were expressed at the level of at
least one transcript per million in at least one sample in
the expression matrix.
Principal components suggested that the algal popula-
tions differed in their response to hyposalinity (Fig. 2a, b
and c). The analysis of similarity using the most variable
genes, which detected differences in gene expression be-
tween populations (R = 0.783, P= 0.001) and between
salinity treatments (R = 0.51, P< 0.01), confirmed the
pattern: we observed a similar pattern of gene expression
in both populations in present conditions and in the
Rauma population in future conditions, while the Parai-
nen population had a unique gene expression pattern
when exposed to hyposalinity (Fig. 2a, b and c). Because
of the differences between populations, we ran the
downstream analysis separately for each population.
We found a very weak response to hyposalinity in
algae from the Rauma population: when we applied the
criteria FDR < 0.05 and |log
2
FC| > 1, only six genes were
upregulated and 20 were downregulated (Fig. 3a). All of
these genes were also differentially expressed in the
Parainen population in response to hyposalinity, and two
genes in particular had very similar responses in both
populations: a glucose/sorbonose dehydrogenase and a
thiosulfate sulfurtransferase (Additional file 1, and
Additional file 2). In contrast with the weak response in
the Rauma population, the expression response to hypo-
salinity was very pronounced in the Parainen population:
a total of 3072 genes were differentially expressed be-
tween the salinity conditions. Among these, 1633 genes
Table 1 The number of reads (total million read pairs), the quality score (Q30), and the % of read pairs successfully mapped to the
final transcriptome assembly in the gene expression analysis for each of the samples sequenced
Sample Population Climate condition Reads (M) row data Reads (M) filtered > = Q30 % Reads mapped
R1 Rauma Future 15.68 14.3 89.9 34.2%
R1 Rauma Current 23.43 21.05 91.9 46.5%
R3 Rauma Future 24.02 20.82 89.8 48.5%
R3 Rauma Current 15.53 14.14 89.1 41.03%
R7 Rauma Future 13.71 12.46 90.1 38.7%
R7 Rauma Current 17.68 16.92 91.0 42.5%
R9 Rauma Future 14.87 13.63 89.7 41.1%
R9 Rauma Current 20.7 18.99 91.5 44.8%
S1 Parainen Future 17.62 16.38 89.1 37.9%
S1 Parainen Current 11.98 11.08 89.7 39%
S8 Parainen Future 20.21 18.88 89.5 37.8%
S8 Parainen Current 24.16 21.34 91.7 44.6%
S9 Parainen Future 18.58 17.34 89.9 46.5%
S9 Parainen Current 23.30 12.28 90.5 46%
Table 2 Summary statistics for the transcriptome assembly. The
evaluation of the assembly is shown both for the original
assembly and after filtering with TransRate and TransDecoder
Transcriptome assembly Original assembly Filtered assembly
Total # genes 295,013 33,487
Total # of transcripts 382,992 58,943
N50 transcript size, bp 854 1206
Average transcript length, bp 581.5 912
Total assembled bases 222,713,028 53,753,658
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were upregulated and 1439 were downregulated in hypo-
saline conditions (FDR < 0.05, |log
2
FC| > 1, Fig. 3b).
Annotation of the highly responsive genes
To identify the mechanisms behind the stress response,
we annotated the genes in the Parainen population that
had the largest expression changes in response to
hyposalinity (i.e. |log
2
FC| > 2). This yielded 399 and 396
up- and down-regulated genes, respectively. We grouped
the most-relevant genes according to their putative
functions: the response to oxidative stress (Table 3), mem-
brane/cytoskeleton composition and transport (Table 4),
and energy production and conversion (Table 5).
Hyposalinity induced the upregulation of at least 32
genes directly involved in defensive responses to oxida-
tive stress (Table 3, Fig. 4). Among these, we found
genes coding for enzymes that are used as defence
against cell damage from free radicals, such as glutathi-
one reductase, superoxide dismutase, disulfide
isomerase, nucleoredoxin-like protein, and vanadium-
dependent bromoperoxidase 2. Seven of these genes
have an important role in maintaining the osmotic bal-
ance of the cell. Finally, three of the upregulated genes
encoded heat-shock proteins. Instead, only four genes
related to oxidative stress were downregulated. Among
these was xanthine dehydrogenase, whose function in-
cludes the replacement of monounsaturated fatty acids
that are turned into polyunsaturated fatty acids by oxy-
gen radicals.
Hyposalinity triggered the differential gene expression
of at least 32 genes that control the composition of the
membrane and cytoskeleton. Among these, mannuronan
Fig. 1 Annotation summary of de novo transcriptome assembly of F. vesiculosus: GO categories for biological processes (a), molecular functions
(b), and cellular components (c). Numbers in brackets indicate the number of genes belonging to each group
Rugiu et al. BMC Genomics (2020) 21:42 Page 4 of 17
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C-5-epimerases were both down- and up-regulated. The
upregulated acetyl-COA carboxylase and actin protein
directly affect cytoskeletoncomposition as they, together
with ATP binding transporters such as ATPase and ATP
translocase, are responsible for the stability of the mem-
brane. Nine genes containing an ankyrin domain were
also upregulated; these coded for proteins with functions
ranging from ion transport to the transmembrane transit
of ions and molecules. Instead, other cytoskeleton com-
ponents, such as tubulin proteins, were downregulated,
as were genes that control ion channels, such as voltage-
gated ion channels and the ABC transporter.
Hyposalinity triggered the upregulation of several
genes involved in energy production and conversion,
whose functions were implicated in the regulation of
photosynthesis, ATP synthesis, and respiration (Table 5,
Fig. 4). Among the 34 genes that regulate photosyn-
thesis, 23 coded for fucoxanthin, a xanthophyll pigment
that shades the photosystems from high irradiance. Five
more genes coded for proteins that protect and repair
photosystems I and II from oxidative stress, and five
others for proteins that regulate the transfer of energy in
the reaction centre of the photosystems. The regulation
of the ATP cycle was represented by six genes
Fig. 2 Principal component plot of F. vesiculosus samples based on their gene expression profiles. The identity of each sample is indicated by the
code next to the dot/triangle representing it. Genes were grouped using PCA (Principal Component Analysis) based on the pairwise distances
between the populations and treatments that were in turn based on a) and b) the normalised read count from all genes as a proxy for the
biological coefficient of variation, and c) the normalised read count from the differentially regulated genes (P< 0.05, absolute value of the fold
change > 2)
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implicated in ATP synthesis, and two genes involved in
the respiratory chain in mitochondria, both coding for
cytochrome b-c1. A total of 11 genes that participate in
the metabolism of carbohydrates and proteins, including
the decomposition of glucan and glucose, were downreg-
ulated in response to hyposalinity. The activation of en-
zymes that regulate glycogen metabolism was inhibited
through the downregulation of at least five protein ki-
nases (CAMK). Furthermore, the synthesis of glutamate,
an amino-acid precursor of glutathione, was reduced
through the downregulation of glutamate cysteine ligase,
ionotropic glutamate receptor, and glutaredoxin.
In addition to these functional groups, hyposalinity
triggered expression changes in several protein families
with very broad functions, such as ribosomal proteins.
For example, 55 upregulated genes code for ribosomal
subunits 40S and 60S, which act in DNA repair and
protein translation. For other genes the exact function in
brown algae is unknown. The complete list of genes
whose expression was affected by hyposalinity in the
Parainen population can be found in Additional file 1.
Comparison of hyposalinity response in Fucus to other
brown algae
Dittami et al. [32] reported 161 annotated genes
responding to hyposalinity stress in Ectocarpus. The ma-
jority of these DE genes (134 out of 161) were found
among DE genes in Fucus, resulting in 78 unique hits
(Additional file 3). These hits include genes belonging to
the biological processes influenced under hyposalinity
conditions in Ectocarpus, such as amino acid metabol-
ism, photosynthesis, transport, carbohydrate metabolism,
protein turnover, general stress response, and regulation
of transcription and translation. Similar search was
Fig. 3 Volcano plots showing genes differentially expressed between salinity treatments and the magnitude of the expression change for Rauma
(a) and Parainen (b) populations. Each point represents one of 33.487 genes. The x-axis shows the log
2
fold change and the y-axis shows log
2
p-
value, adjusted for multiple comparisons. Differentially expressed genes at adjusted p-value < 0.05 and absolute log
2
fold-change > 1 are
indicated in red
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performed for 230 unknown Ectocarpus genes respond-
ing to hyposalinity stress. Of them, 48 genes showed se-
quence similarity to differentially regulated genes in
Fucus. Among those, 30 genes have some functional in-
formation in Fucus (Additional file 3), while 18 genes re-
main unknown. In Sargassum, Qian et al. [33] reported
34 proteins involved in hyposalinity response, of them,
25 showed similarity to Fucus DE genes, resulting in 17
unique hits. These hits included genes involved in
photosynthesis, carbohydrate metabolism, energy metab-
olism, cytoskeleton and protein folding (Additional
file 3). In summary, we found considerable consistency
among the brown algal species in their gene responses
to hyposalinity, but also some taxon specificity.
Genotypes of the individuals in the experiment and fixed
differences between the populations
After filtering, we retained 260,571 bi-allelic SNPs. Both
in the PCA and the clustering analyses 6 of 7 individuals
were well separated along while two individuals from
Rauma remained close, suggesting that they may be
clones of the same genotype (Additional file 4). We also
observed the separation by population along the first
PCA axis and the clustering by population in the NJ-tree
(Additional file 5). We found a total of 10,241 sites fixed
for different alleles in the two populations. In both pop-
ulations, the numbers of differentially expressed genes
and the genes with fixed differences were highly
dependent (contingency table tests; Parainen: G
2
= 935,
DF = 1, p< 0.001; Rauma: G
2
= 6.1, DF =, p< 0.05). We
found that in the Parainen population28.0% of non-DE
genes contained fixed sites, with on average 2.16 ± 0.05
fixed SNPs (mean ± C. I.) per gene. In Rauma, 30.6% of
non-DE genes had such sites, with on average 2.15 ±
0.04 (mean ± C. I.) fixed SNPs per gene. Among DE
genes from Parainen sample 1715 (55.9%) genes
Table 3 Genes involved in the oxidative stress response that
were differentially expressed in future vs. present salinity
conditions in F. vesiculosus
Regulatory
response to
future salinity
Gene annotation # of
genes
GO term Biological
process
up glutathione reductase 1 GO:0045454 cell
redox homeostasis
disulfide isomerase 4 GO:0004362
nucleoredoxin-like 2 GO:0045454
ribulose-1,5-bisphosphate
carboxylase/oxygenase
1 GO:0055114
superoxide dismutase 2 GO:0019430
vanadium-dependent
bromoperoxidase 2
2 GO:0055114
14–3-3 protein 7 GO:0055114
hydroxyphenylpyruvate
dioxygenase
4 GO:0055114
copper oxydase 1 GO:0016491
glyceraldehyde-3-phosphate
dehydrogenase
3 GO:0055114
methylmalonate-
semialdehyde
dehydrogenase
1 GO:0055114
pyruvate dehydrogenase 1 GO:0055114
heat shock proteins 3 GO:0006950
down phenylacetate-CoA
oxygenase
1 GO:0016491
stearoyl-CoA desaturase 1 GO:0016491
xanthine dehydrogenase 1 GO:0016614
NAD(P)/FAD-dependent
oxidoreductase
1 GO:0055114
Table 4 Genes involved in membrane/cytoskeleton composition and transport that were differentially expressed in future vs.
present salinity conditions in F. vesiculosus
Regulatory response to
future salinity
Category Gene annotation # of genes GO term Biological process
up membrane and cytoskeleton composition mannuroan C-5-epimerase 2 GO:0016021
acetyl-COA carboxylase 1 GO:0003989
actin 2 GO:0006972
up transmembrane transport ATPase 1 GO:0042626
ADP/ATP translocase 1 GO:0046902
ankyrin 9 GO:0006357
down membrane and cytoskeleton composition mannuronan C-5-epimerase 9 GO:0016021
tubulin proteins (α,β) 17 GO:0005200 GO:0007010
down transmembrane transport cation diffusion Facilitator 1 GO:0008324
voltage-gated Ion Channel 2 GO:0006813
ABC transporter 2 GO:0055085
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contained fixed sites a, with an average 2.2 ±
0.11(mean ± C. I.) SNPs per gene. For Rauma, 14 (53.8%)
DE genes contained the fixed sites with 2.4 ± 1.5 fixed
SNPs (mean ± C. I.). Thus, the proportion of genes with
the fixed differences in the two populations were higher
in DE genes than in non-DE genes, while the average
number of fixed sites per gene seems to be similar in DE
and non-DE genes.
Discussion
De novo assembly of Fucus vesiculosus transcriptome
In this study we produced a de novo transcriptome, con-
taining 33,487 assembled “genes”. It is reasonably
complete, i.e. contains 89% of the conserved eukaryotic
genes. Despite the filtering, this assembly is still somewhat
redundant, as suggested by found duplicated BUSCO-
orthologs, and likely overestimates the number of genes.
Of the Ectocarpus reference genes, 43% appeared to have
one-to-one orthologs in our F. vesiculosus assembly, which
may be expected given a large evolutionary distance be-
tween Fucales and Ectocarpales [34].
The most common gene functions in the characterized
transcriptome were either core eukaryotic metabolic
pathways (e.g. purine, thiamine and pyrimidine metabol-
ism and glycolysis), or pathways characteristic for plants
and algae, such as carbon fixation or metabolism of
amino sugars and nucleotide sugars (sugar donor for
various glycans, and also connected to fructose and
mannose metabolism). Another highly represented glo-
bal pathway was biosynthesis of antibiotics, which
includes biosynthesis of carbohydrates, aromatic amino
acids (shikimate pathway) and secondary metabolites
and is represented by many genes in e.g. green algae.
Finding the “Drug metabolism –other enzymes”path-
way among most highly represented may appear some-
what unexpected. Fucus genes belong to the part of the
map referring to degradation of anti-cancer and im-
munosuppressive agents: irinotecan, fluorouracil, isonia-
zid, azathioprine and 6-mercaptipurine. Interestingly,
sulfated polysaccharides from F. vesiculosus (fucoidan)
and other algae are increasingly used in cancer therapy
together with these drugs to mitigate their toxic side ef-
fects, and even themselves are suggested to have antitu-
mor effects. Finally, another unexpected finding was
several genes from phenylpropanoid biosynthesis path-
way, leading to lignin biosynthesis in higher plants
(Embryophyta), i.e. cellobiase, cinnamyl alcohol dehydro-
genase, caffeic acid O-methyltransferase and lactoperoxi-
dase. For long time lignin was thought to be a key
innovation in higher plants, but recently was also found
in the closest relative of higher plants, streptophyte algae
[35], and also in red algae [36]. Genes from the lignin
biosynthesis pathway were also found in genomes or
transcriptomes of diatoms and other member of
Stramenopiles [37], but it is unclear whether this
group can perform the final step of lignin polymer
biosynthesis. In light of this, it is interesting that in
Fucus transcriptome we found lactoperoxidase, re-
sponsible for the last steps of lignin synthesis from
coniferyl alcohol or p-coumaryl alcohol.
Table 5 Genes involved in energy production and conversion that were differentially expressed in future vs. present salinity
conditions in F. vesiculosus
Regulatory response
to future salinity
Category Gene annotation # of genes GO term Biological
process
up photosynthesis regulation cell division protein FtsH 1 GO:0010205
fucoxanthin proteins 23 GO:0031409
light harvesting 5 GO:0009768
photosystem II reaction center protein 2 GO:0016682
photosystem I P700 apoprotein A2 1 GO:0022900
oxygen-evolving enhancer protein 2 GO:0042549
up ATP synthesis ATP synthase 6 GO:0045261
up respiration cytochrome b-c1 complex 2 GO:0005750
down metabolism of carbohydrate beta-glucanase, GH16 family 1 GO:0005975
glucose dehydrogenase 2 GO:0047936
UTP-glucose-1-phosphate uridylyltransferase 1 GO:0006011
glutamine-fructose-6-phosphate transaminase 2 GO:0006002
CAMK protein kinase 5 GO:0035556
down Metabolism of amino acids glutamate cysteine ligase 1 GO:0006750
glutamate receptor ionotropic, kainate 2-like 1 GO:0035235
glutaredoxin 1 GO:0045454
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Among-population variation in hyposalinity tolerance
Here, we show that the physiological acclimation to
hyposalinity differed between the two populations of F.
vesiculosus. The Rauma population showed hardly any
expression response, while in the Parainen population
more than 3000 genes responded to the change in salin-
ity. We emphasise that our hyposalinity manipulation
took place gradually and the algae had several days to
acclimate to the slow decrease in salinity. In nature, sal-
inity fluctuates during rainy periods, when river runoff is
directed at rockweed stands, or when freshwater runoff
spreads under ice. Because the salinity change in this
experiment was so gradual, we do not believe that the
expression response in our experiment represents acute
hyposalinity shock, i.e. a direct stress response that
would be expected if an alga is suddenly exposed to
hyposaline conditions. Instead, we interpret the expres-
sion changes as representing the acclimation process, i.e.
the adaptive plasticity that allows algae to sustain their
functions across variable conditions, with the among-
population differences that we observed indicating the
presence of geographic differentiation among popula-
tions in their acclimation ability. Differentiation in
tolerance to abiotic stressors has been documented
previously for Baltic F. vesiculosus: a study of algal
performance in expected future conditions of hyposali-
nity and warming found different responses in popula-
tions from the Bothnian Sea with respect to those from
the Archipelago Sea [10]. The among-population vari-
ation found here may arise as a consequence of
population-level adaptation to local salinity. Indeed, long
term yearly measurements (from 1900 to 2005) indicate
that Rauma and Parainen differ for the mean salinity but
most importantly for its variation [Rauma: mean = 5.25,
max = 6.2, min. = 1; Parainen: mean = 6.06, max = 6.4,
min. = 5.3, 59]. This difference in both the mean and
variation of salinity could be biologically highly relevant
because of its implications for the populations’salinity
tolerance limits. Thus, the Rauma population may be
better adapted to low salinity as it experiences higher
Fig. 4 Schematic depiction of the main proteins involved into the differentially expressed genes as response to hyposalinity and the main
processes involved inside a Fucus cell. Each cell component is represented only once due to space availability and its name coded as follows:
(C = Cytosol, Ch = Chloroplast, Cs = Cytoskeleton, CW = Cell Wall, IC = Ion Channel, M = Mitochondrion, R = Ribosome, N = Nucleus)
Rugiu et al. BMC Genomics (2020) 21:42 Page 9 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
variation throughout the year than the Parainen popula-
tion, and it is perhaps better adapted to more extreme
fluctuations in salinity.
However, differences between the Rauma and Parainen
populations were not created by the constitutive upregu-
lation of genes involved in the acclimation process, as
has been suggested for some resilient coral species (so-
called “constitutive frontloading gene expression”[38];),
because the populations did not differ in their gene ex-
pression patterns under ambient conditions. Instead, the
possible mechanism could be a local adaptation, i.e. gen-
etic changes of some other traits that no longer respond
plastically to hyposalinity in Rauma. Local adaptation to
salinity conditions may be particularly likely in the Baltic
Sea because of the presence of a strong salinity gradient.
There is already evidence for regional adaptation in
Baltic Sea populations with respect to those in the North
Atlantic [28], but data at a smaller geographic scale
within the Baltic Sea are lacking. However, several char-
acteristics of F. vesiculosus –the strong genetic structur-
ing of populations [30], very limited gamete dispersal
ability [26], and the potential for gene flow through
floating dispersal [39]–generate promising conditions
for the evolution and maintenance of local adaptations.
In the present study we found that the proportion of
genes fixed for different alleles in the two populations
was higher in DE genes compared to non-DE genes,
which suggests local adaptation. Conducting a genomic
scan of these and other local populations of Fucus in the
Baltic will be a focus of future research.
Mechanisms of hyposalinity acclimation
Our study describes for the first time differential expres-
sion of genes in response to hyposalinity in the brown
seaweed F. vesiculosus, based on the performance of the
Parainen population. In general, we found large overlap
between genes differentially regulated in hyposalinity in
Fucus and in the two other studied brown alga: Ectocar-
pus and Sargassum [40,41] They are involved in such
biological processes as photosynthesis and energy pro-
duction; cytoskeleton and membrane transport; and
stress response. These are discussed in details below.
Of particular interest are the genes related to the oxi-
dative stress response, which defends cells from oxida-
tive bursts that occur due to an excess of ROS. Among
the upregulated genes found here, we observed
antioxidant enzymes such as superoxide dismutase and
glutathione reductase, which are a commonly found
components of the stress response [21]. In addition, we
found genes coding for disulfide isomerase and
nucleoredoxin-like proteins, which are involved in the
production of thioredoxins, low-molecular weight pro-
teins involved in the regulation of enzymatic redox reac-
tions in the chloroplast [32,42]. Another antioxidant,
vanadium-dependent bromoperoxidase 2, plays an im-
portant role in the salinity tolerance of brown algae by
contributing to cell strengthening [43]. This stress gene
has been previously identified in protoplasts of L. digi-
tata, and it is involved into the synthesis of halo-ganic
compounds possibly linked to defences against patho-
gens and scavenging H
2
O
2
[44]. This compound also
contributes to the production of secondary compounds
such as phlorotannins [40], which are known to increase
as a response to biotic and abiotic stress [45,46]. Certain
heat shock proteins (HSPs) were also upregulated. In
many organisms as well as in brown and red algae [40,
47], the upregulation of HSPs is linked to abiotic shifts
because these proteins stabilise other proteins and cell
membrane structure, thereby improving cellular
homeostasis [48]. Interestingly, we also detected the
downregulation of genes related to the ROS scavenging
mechanism. Among these was a gene coding for
xanthine dehydrogenase, an enzyme that controls the
metabolism of purines and pyrimidines [33] and which
is upregulated as a response to desiccation in Arabidop-
sis spp.[49]. Since both desiccation and salinity affect
intracellular osmotic potential and turgor, the downreg-
ulation of these genes here may indicate an attempt to
remove excess intracellular water due to hyposalinity.
Changes in membrane and cytoskeleton structure
Gene expression changes indicated the importance of
membrane and cytoskeleton modifications as a response
to hyposalinity. Specifically, the change in regulation of
genes encoding mannuronan-C-5-epimerases indicates
that alginates have a key role in hyposalinity acclimation.
Alginates are the main polysaccharide components of
the cell walls of brown algae, and they can form almost
half of algal dry weight [50]. These polysaccharides are
important in acclimation because they separate the cell
from the surrounding environment, which is likely to be
especially important when the osmotic environment be-
comes challenging (reviewed in [43]). The upregulation
of alginate-related genes as a response to hyposalinity
has also been reported in other brown algae [41].
The upregulation of acetyl-COA carboxylase has im-
plications for the biosynthesis of fatty acids [51], which
are important membrane components. Fatty acids can
become the targets of ROS, which exchange a hydrogen
with them and thus compromise their stability [45].
Since hyposalinity generates oxidative stress, increased
production of fatty acids may be needed to maintain the
membrane stability. In addition, most of the genes in
this functional group were linked with actin and tubulin
production, and their expression was inversely affected,
with the former being upregulated and the latter down-
regulated. A similar pattern was described in the brown
alga S. fusiforme [41]. Both actin and tubulin play
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Content courtesy of Springer Nature, terms of use apply. Rights reserved.
important roles in determining the strength of the
cytoskeleton and cell volume [52]. The production of
ROS is known to induce reactions such as oxidative
modification, acetylation, and phosphorylation in tubulin
polymers in the cytoskeleton in response to stress such
as a change in water salinity [53]. Our result likewise in-
dicates that changes in actin and tubulin production are
involved in hyposalinity acclimation, possibly because of
the oxidative stress involved.
The regulation of membrane-bound ion channels bal-
ances the amount of ions transported in and out of cells
[54]. Here, certain transportation-related genes were up-
regulated, such as ATPase which is known to respond to
hyposalinity (reviewed by [6,47]). Several genes coding
for ankyrin proteins were also upregulated. Proteins with
ankyrin motifs serve many different biological functions,
including controlling vesicular trafficking [55], which is
involved in the hyposalinity response [40]. In seaweeds,
the concentration of intracellular ions is also controlled
by ion-selective carriers that are activated by the mem-
brane potential [23]. We found that genes coding for
voltage-gated ion channels, which regulate the mem-
brane permeability for sodium and potassium [56], were
downregulated in hyposaline conditions. Downregulation
of these genes may help to balance intracellular ion
composition under conditions of osmotic imbalance.
Furthermore, we also found downregulation in two ABC
transporter-related genes. ABC transporters detoxify the
cell through the excretion of toxic compounds and regu-
lation of intracellular ion concentration [57]. Our results
suggest that this transporter may have a function in
hyposalinity acclimation in F. vesiculosus, as has been
suggested to be the case in some seaweeds [58].
Energy production and conversion
Photosynthetic activity is fundamentally important for
seaweeds, but can become inhibited under stressful con-
ditions such as hypo/hypersalinity, high irradiance, and
herbivory [40,59–61]. Our study showed that hyposaline
conditions caused the upregulation of genes that control
the production of fucoxanthin and other light-harvesting
proteins that bind to photosynthetic pigments. Fucoxan-
thin is among the most abundant carotenoids in sea-
weeds and is responsible for their brownish colouration
and the protection of the photosystemic apparatus from
excess of light [62]. Inhibition of the photosystem in
stressful conditions may provide several benefits. First,
since the production of photosynthetic proteins requires
energy, photoinhibition may make available additional
resources for osmotic adjustments,thus reducing the
overall cost of acclimation. This strategy was proposed
to explain the downregulation of chloropyll a/c binding
proteins of E. siliculosus acclimating to hypo- and hyper-
salinity as well as Chaetoceros neogracile acclimating to
thermal stress [63]. A reduction in primary metabolism,
such as photosynthetic activity, may be a way to save en-
ergy [40]. In addition, the photosynthetic machinery
usually absorbs more photons than necessary, and then
dissipates the excess by rearranging photosynthetic pig-
ments (e.g., from a single chlorophyll to a triple chloro-
phyll). This chlorophyll reacts with oxygen, generating
ROS, and thus, oxidative stress. Such reduction in the
electrons reaching the reaction centres would translate
into a lower need for the synthesis of new ROS [64].
When F. vesiculosus was exposed to hyposalinity, en-
ergy production was enhanced via the upregulation of
genes involved in respiration and ATP synthesis. The
balance between respiration and photosynthesis plays a
crucial role in algal physiology, as it determines growth
and shapes competition for irradiance and resources
[58]. An imbalance between these two processes, caused
by abiotic or biotic stressors, may lead to a decrease or
cessation in growth or, in extreme cases, death [65].
Respiration enables the storage of biochemical energy as
ATP, so respiration and ATP metabolism are strongly
correlated. The upregulation of respiration that we ob-
served as a response to hyposalinity likely serves to pro-
duce the energy required for osmotic adjustments.
Compared to respiration and ATP synthesis, we de-
tected the opposite pattern for carbohydrate metabolism,
with six genes downregulated. A similar response was
also reported in E. siliculosus as a response to hyposali-
nity [40]. Carbohydrate synthesis in autotrophs is highly
dependent on photosynthetic activity, so it is probable
that this decrease in carbohydrate metabolism is a con-
sequence of the photoinhibition caused by hyposalinity
stress. It is also worth noting that carbohydrates can be
used for osmotic adjustments by means of their accumu-
lation in vacuoles or excretion through the cell mem-
brane [22]. In brown algae, a previous study indicated
that the concentration of mannitol, the main carbon
storage compounds in brown algae, varies according to
the sea water salinity, and it is recognised as part of the
osmotic adjustments in the algal cell [66,67]. Previous
studies have shown that Baltic F. vesiculosus differs from
its Atlantic counterpart by having lower photosynthetic
activity, higher respiration rate [68], and lower concen-
trations of mannitol, the main carbohydrate used for en-
ergy storage [53,55,56]. Our results may indicate that
the differential expression of genes involved in the above
processes may be attributed to its adaptation to the
brackish water of the Baltic Sea, and, furthermore, that
these modifications may be among the first and perhaps
most important adjustments behind hyposalinity accli-
mation in this species.
Hyposalinity hindered glutathione synthesis by sup-
pressing the expression of early steps of the glutamate
pathway. In seaweeds, glutathione synthesis is important
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Content courtesy of Springer Nature, terms of use apply. Rights reserved.
in preventing oxidative stress [54], and therefore, the
downregulation observed here appears counter-intuitive.
However, the oxido-reduction response in seaweeds does
not rely on glutathione as much as it does in terrestrial
plants, and seaweeds are able to detoxify cells via the ex-
cretion of ROS or the use of other enzymes to reduce
ROS into oxygen and water [69]. It is therefore possible
that F. vesiculosus relies more on other detoxification
mechanisms than on glutathione.
Conclusions
Our study supports the existence of geographic variation
in tolerance to hyposalinity in F. vesiculosus.Weshowed
that physiological acclimation to the projected hyposali-
nity differed strikingly between algal populations, which
suggests the presence of genetic variation among popula-
tions in the regulation of gene expression. This highlights
the importance of considering intraspecific genetic diver-
sity and variance in tolerance to environmental changes
when predicting organismal responses to climate change.
In the population that showed a substantial acclimation
response, we observed that acclimation to hyposalinity
involved major adjustments to most of the main metabolic
activities. Among these were inhibition of photosynthetic
activity, increased metabolism, changes in the membrane
composition and structure, and a pronounced anti-
oxidative response. Taken together, these results reveal for
the first time the genetic mechanisms behind the regula-
tion of osmotic activity, and provide evidence for selection
for and possibly local adaptation of genes coding for salin-
ity acclimation responses in this species.
Methods
Sample collection
We collected F. vesiculosus from Parainen (N 60°13′
10.5″, E 22°05′52.3″) on the 11th of May 2016 and from
Rauma (N 61°0.5′17.5″, E 21°18′11.1″) on the 10th of
May 2016 from a depth of 0.5–1.5 m. No permission for
the collection was needed according to the Finnish na-
tional guidelines. From each population, we sampled ten
individuals, with at least 10 m between samples. Herein,
one individual is defined as all apical tips of a thallus
growing from a single stem attached to a holdfast. We
measured ambient salinity and temperature in both loca-
tions (Parainen: 5 PSU, 12.5 °C; Rauma: 5.3 PSU, 13 °C).
We stored algae in coolers between wet paper tissues for
transportation to the University of Turku. There, we
carefully rinsed the algae with freshwater to remove as-
sociated grazers and epiphytes and we maintained them
in their native salinity and temperature until the experi-
ment started. Samples were left unsterilized to avoid re-
moval of the micro-epibionts such as associated bacteria,
as this could influence the algal physiology, thus contrib-
uting to the algal acclimation capability [4,70,71].
Conditions for gene expression and sample preparation
We examined the effect of hyposalinity on gene expres-
sion by exposing algae to current (5 PSU) and predicted
future salinity (2.5 PSU) conditions in an indoor aquar-
ium experiment. The current condition reflected the
salinity and temperature conditions recorded during the
sampling. The condition used for the hyposalinity stress
was obtained from the model RCAO-ECHAM-A2-REF
developed in Meier and Eilola [12]. According to this
model, the average salinity of the coastal areas around
our sampling sites will drop in the upcoming future
(2070–2099). We used two separate aquarium racks to
expose the algae to the two different salinities. Each
aquarium rack consisted of a bottom tank (~ 300 L) and
three 24-L aquaria. Seawater was pumped from the
bottom tank to the aquaria, from where it flowed back
into the bottom tank. Seawater was cleaned first by an
acrylic filtration unit (SCHURAN Jetskim 120) that was
equipped with a mechanical and biological filter, then by
a protein skimmer, and finally by UV radiation. Each
bottom tank was equipped with a chiller/heater to regu-
late the water temperature (10 °C for both aquarium
racks). We obtained the salinity for future climate condi-
tions by diluting seawater with distilled water. To ensure
ample nutrient availability, we added an enriched
seawater medium, composed of micro- (trace metals and
vitamins) and macro-nutrients (phosphate and nitrogen),
to the bottom reservoirs [72]. Macronutrients were
added to mimic the in situ surface concentrations
present in the Archipelago Sea from September to April
(SYKE, Finnish Environment Institute).
Individual thalli were split into two similar-sized ra-
mets, one of which was randomly distributed in an
aquarium in current salinity conditions and the other in
an aquarium in future salinity conditions. In order to
prevent the ramets from floating, a small ceramic weight
was attached to each. Initially both racks were estab-
lished at 5 PSU (current salinity). In the future-salinity
aquaria, we decreased the salinity to 2.5 PSU slowly over
the course of 3 days, then maintained this new salinity
for 24 h. We then sampled thalli from both salinities by
cutting the apical tips, wrapping them individually with
aluminium foil, and flash-freezing them in liquid nitro-
gen. By lowering the salinity slowly over 3 days and
keeping the algae in 2.5 PSU for 24 h, we ensured that
we were not measuring immediate stress effects, but ra-
ther, changes in gene expression that took place during
the acclimation process. Samples were subsequently
stored at −80 °C until RNA extraction.
RNA extraction, sequencing, and pre-processing
Total RNA was extracted using a modified protocol
from Pearson et al. [73]; we started with freeze-dried tis-
sue and added an initial acetone wash step as in Panova
Rugiu et al. BMC Genomics (2020) 21:42 Page 12 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
et al. [74]. We treated the RNA with RNAse-free
DNAse-I according to the manufacturer’s instructions
(Qiagen) to remove any contaminating DNA. The con-
centration and quality of RNA was assessed using the
2100 Bioanalyser (Agilent). Of the 20 samples, 14 passed
the RNA quality-control threshold, three of them from
Rauma population and four from Parainen, each of them
replicated in both conditions (Table 1). These samples
were sent to the National Genomics Infrastructure
(NGI) facility in Stockholm for library preparation and
sequencing (Table 1). The libraries were generated with
the Illumina TruSeq Stranded mRNA sample prepar-
ation protocol with poly-A selection and average insert
sizes of 369–476 bp. The indexed libraries were pooled
in equimolar amounts and sequenced in one lane of an
Illumina HiSeq 2500 apparatus in High Output V4, PE
2 × 125 bp mode. The sequencing generated between
11.98 and 24.16 million raw reads per sample (Table 1).
Raw reads from this study are deposited in the NCBI
SRA database under accession numbers SRP144722.
Quality control of the raw reads was performed using
FASTQC v 0.11.5 software (www.bioinformatics.babra
ham.ac.uk/projects/fastqc). We quality-filtered the reads
in all the samples with Trimmomatic software v. 0.32
[75]. First, bases from the beginning and the end of each
read that fell below a quality score of three were
trimmed. Second, we used a sliding-window approach
with a window size of four bp to remove bases with an
average quality score below 15. Adapter sequences were
removed using Cutadapt v. 1.9.1 [76] software, and reads
shorter than 50 nt were discarded. After trimming, the
reads were again visualised using FASTQC.
De novo transcriptome assembly and annotation
Using all cleaned reads, we produced a de novo tran-
scriptome assembly with the Trinity assembler v. 2.3.2
[77]. We applied in silico read normalisation with max-
imum coverage set to 30. In assembly, k-mer size was
set to 30 and the remaining Trinity parameters were
kept as default. Assembly statistics were calculated using
the TrinityStats.pl script and the completeness of the
assembly was assessed with BUSCO (Benchmarking
Universal Single-Copy Orthologs) v. 1.22 [78] against
the “eukaryota_odb9”reference set. TransRate v. 1.0.1
[79] was used to evaluate the assembly and remove tran-
scripts that were not supported by read mapping. Tran-
scripts identified as “good”by TransRate were further
analysed with TransDecoder v. 2.0.1 [80] to predict the
likely coding regions. Assembly statistics and complete-
ness of the filtered assembly were calculated as above
and compared to the original assembly. We also com-
pared this assembly to a reference set of 24,486 proteins
of the brown algae Ectocarpus siliculosus, the genome of
which has been sequenced (http://bioinformatics.psb.
ugent.be/orcae/overview/EctsiV2), using Conditional Re-
ciprocal Best Blast algorithm within TransRate [79].
For annotation we retained one isoform per gene, the
one with the highest read support as identified with the
Trinity utility “filter_low_expr_transcripts.pl”and option
--highest_iso_only. Annotation of this final transcrip-
tome assembly were performed within the Blast2GO
pipeline [81] and included Blastx comparison of se-
quences to the NCBI nr protein database, GO (Gene
Ontology) mapping, InterProScan search and merging of
BLAST and InterProScan annotations, applying the de-
fault parameters in Blast2GO. Finally, the transcripts
were mapped to KEGG pathways (http://www.genome.
jp/kegg/pathway.html).
Genotyping of individuals
Fucus vesiculosus can reproduce asexually and clonal in-
dividuals may constitute a large proportion in some
Baltic populations [82,83]. The subject populations of
this study have also been found harbouring clonality
[84] and to ensure that the individuals in our experiment
represent different genotypes, we performed
transcriptome-wide genotyping of SNPs (Single Nucleo-
tide Polymorphisms). Cleaned reads were mapped to the
transcriptome assembly using bowtie2 with the default
settings [85] and bam files for the same individual from
the future and present conditions were merged. From
bam files, a bcf file was generated with samtools mpileup
[86], discarding positions with base quality and/or
mapping quality < 20. The genotypes were called with
bcftools call –c option (https://samtools.github.io/
bcftools/bcftools.html) and filtered for min read depth =
8 with vcfutils.pl varFilter. Non-biallelic SNPs were dis-
carded and Principal Component Analysis (PCA) based
on SNP allele frequencies was done using R package
“adegenet”[87]. Finally, clustering of the individuals was
done using Nei’s genetic distance and Neighbor-Joining
algorithm in R package “poppr”[88,89]. Differences in
the hyposalinity response between the two populations
can result from different genetic background, including
allelic variation in the important genes. While the lim-
ited number of individuals per population used in the
experiment precluded a genome scan, we tested the hy-
pothesis that genes, involved in hyposalinity response
show higher genetic divergence between the two studied
populations than other genes by comparing the number
of SNPs that appear to be fixed for different alleles in
the two populations. Bam files, described above, were fil-
tered for min depth = 8 using vcfutils.pl and non-
reference SNPs were identified with --non-ref-af option
in vcftools [90], applying minimum frequency of non-
reference alleles = 0.99. Subsequently, we calculated
number of fixed SNPs for each gene, and compared
average values between differentially regulated and non-
Rugiu et al. BMC Genomics (2020) 21:42 Page 13 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
regulated genes. We used a G
2
test to check whether the
amount of DE and non-DE genes was dependent on the
amount of fixed SNPs separately for each population
using the R package DescTools [91].
Gene expression analysis
We estimated the transcript abundances based on the
pseudo-alignment method kallisto [92] implemented
within the Trinity utilities “align_and_estimate_abundan-
ce.pl”followed by “abundance_estimates_to_matrix.pl”.
These data were used to generate a matrix of gene
expression values TPM (transcripts per million), normal-
ised across the samples, which was then used for down-
stream analyses. We identified groups of expression
profiles using principal components based on the bio-
logical coefficient of variation (read count) between
library pairs. This was performed with the R/Bioconduc-
tor package “DESeq 2”[93]; the R package “ggplot2”v.
2.2.1 was used to plot the results of the principal compo-
nents (PCA).
Since the clustering of samples on the plot suggested
differences in response between the two populations, we
identified the genes with the largest changes in expres-
sion. Starting from raw counts, we estimated the library
sizes and converted raw counts into expression magni-
tude (variance/raw means of read counts
2
). Then, we de-
termined the genes that varied in read count from the
95% IC, which highlighted 799 significant outliers.
We estimated the amount of variance in gene expression
among populations by ANOSIM (analysis of similarity),
which tested distances between populations, salinity condi-
tions, and the interaction population ×salinity) imple-
mented in the R package “vegan”v. 2.0.3 [94]. This was
performed with 999 permutations based on the 799 most-
variable genes (according to normalised fold changes). This
test showed a significant population ×salinity interaction
(see Results); consequently, we performed the analysis of
differentially expressed genes with “DESeq 2”separately for
each population. We used a generalised linear model
(GLM) with a negative binomial distribution to test the dif-
ference in gene expression among salinity treatments for
each gene using Wald statistics. We set salinity as a fixed
factor (two levels: present and future), nested within indi-
vidual, and read counts for each gene as the response vari-
able. We corrected the results for multiple testing using the
Benjamini-Hochberg procedure, set the false discovery rate
(FDR)atasignificancethresholdofα<0.05,andappliedan
absolute log
2
fold change (FC) cut-off of > 1 (corresponding
to expression changes greater than two-fold).
Comparison with genes involved in hyposalinity response
in other brown algae
To date, two studies have looked at the gene expression
response to low salinity in two species of brown algae:
Ectocarpus siliculosus [32] and Sargassum fusiforme [47].
Differentially regulated genes reported in these studies
were compared to differentially expressed (DE) genes in
Fucus. We retrieved corresponding protein and/or uni-
genes sequences from https://bioinformatics.psb.ugent.
be/gdb/ectocarpus/Archive/ for Ectocarpus and from
NCBI for Sargassum, and compared them to Fucus DE
genes by tblastn and tblastx with e-value =1e-3.
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s12864-020-6470-y.
Additional file 1. list of genes differentially expressed due to
hyposalinity for Parainen population (FDR < 0.05 and |log
2
FC| > 1).
Additional file 2. list of genes differentially expressed due to
hyposalinity for Rauma population (FDR < 0.05 and |log
2
FC| > 1).
Additional file 3 list of DE genes found by the present research and
previously described in Ectocarpus and Sargassum in response to
hyposalinity.
Additional file 4. PCAs obtained by plotting the allele frequencies in all
SNPs plotting the axes a) 1 and 2, b) 3 and 4, c) 1 and 5.
Additional file 5 Neighbor Joining tree for Fucus populations studied in
the present research. The standard genetic distance of Nei [95] was
used.)
Abbreviations
ANOSIM: Analysis of similarity; BUSCO: Benchmarking Universal Single-Copy
Orthologs; FDR: False discovery rate; GLM: Generalised linear model;
GO: Gene ontology; NGI: National Genomics Infrastructure; PSU: Practical
salinity units; RNA-seq: RNA-sequencing; SNP: Single nucleotide
polymorphism; TPM: Transcripts per million
Acknowledgments
We are grateful to Mikael Elfving and Essi Kiiskinen for helping with the
setup and sampling of the experiment. Mario Lewis, Meri Lindqvist, and Katja
Salminen are acknowledged for their support during the lab work and with
the use of the Bioanalyser. The authors would like to acknowledge support
from the Science for Life Laboratory, the National Genomics Infrastructure
(NGI), and Uppmax in providing assistance with massively parallel
sequencing and the computational infrastructure. This study has benefitted
from facilities of the Finnish Marine Research Infrastructure network
(FINMARI). Images of organelles (Fig. 4) designed by Freepik (https://www.
freepik.com/).
Authors’contributions
All Authors contributed to the study design. LR sampled the material in the
field, run the experiment and extracted the RNA. LR and MP analysed the
sequencing data and performed the bioinformatics analyses. LR led the
manuscript writing and all authors contributed to editing the final version.
All authors read and approved the final manuscript.
Funding
This study was funded by the BONUS BAMBI Project and was supported by
BONUS (Art 185), funded jointly by the European Union’s Seventh
Programme for Research, Technological Development, and Demonstration
and the Academy of Finland (grant decision number 273623). Open access
funding provided by University of Gothenburg.
Availability of data and materials
Scripts and information for setting up the analysis can be obtained from the
authors upon request. The raw data supporting the conclusions of this article
are available in the NCBI SRA database under accession numbers SRP144722.
Ethics approval and consent to participate
Not applicable.
Rugiu et al. BMC Genomics (2020) 21:42 Page 14 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests. The funding
bodies played no role in the design of the study and collection, analysis, and
interpretation of data and in writing the manuscript.
Author details
1
Department of Marine Sciences –Tjärnö, University of Gothenburg, SE 452
96 Strömstad, Sweden.
2
Department of Biology, University of Turku,
FIN-20014 Turku, Finland.
Received: 12 November 2019 Accepted: 8 January 2020
References
1. Dayton PK. Experimental evaluation of ecological dominance in a rocky
intertidal algal community. Ecol Monogr. 2008;45:137–59.
2. Duffy JE, Hay ME. Strong impacts of grazing amphipods on the organization
of a benthic community. Ecol Monogr. 2000;70:237–63.
3. Harley CDG, Anderson KM, Demes KW, Jorve JP, Kordas RL, Coyle TA.
Effects of climate change on global seaweed communities. J Phycol.
2012;48:1064–78.
4. Wahl M, Molis M, Hobday AJ, Dudgeon S, Neumann R, Steinberg P,
Campbell AH, Marzinelli E, Connell S. The responses of brown macroalgae
to environmental change from local to global scales: direct versus
ecologically mediated effects. Perspect Phycol. 2015;2:11–29.
5. Davison IR, Pearson GA. Stress tolerance in intertidal seaweeds. J Phycol.
1996;32:197–211.
6. Karsten U. Seaweed Acclimation to salinity and desiccation stress. Seaweed
Biol. Berlin: Springer; 2012. p. 87–107.
7. Harley CDG, Hughes AR, Hultgren KM, Miner BG, Sorte CJB, Thornber CS,
Rodriguez LF, Tomanek L, Williams SL. The impacts of climate change in
coastal marine systems. Ecol Lett. 2006;9:228–41.
8. Takolander A, Cabeza M, Leskinen E. Climate change can cause
complex responses in Baltic Sea macroalgae: a systematic review. J Sea
Res. 2017;123:16–29.
9. Takolander A, Leskinen E, Cabeza M. Synergistic effects of extreme
temperature and low salinity on foundational macroalga Fucus vesiculosus in
the northern Baltic Sea. J Exp Mar Bio Ecol. 2017;495:110–8.
10. Rugiu L, Manninen I, Rothäusler E, Jormalainen V. Tolerance and potential
for adaptation of a Baltic Sea rockweed under predicted climate change
conditions. Mar Environ Res. 2018;134:76–84.
11. Rothäusler E, Rugiu L, Jormalainen V. Forecast climate change
conditions sustain growth and physiology but hamper reproduction in
range-margin populations of a foundation rockweed species. Mar
Environ Res. 2018;141:1–9.
12. Meier HEM, Eilola K. Future projections of ecological patterns in the Baltic
Sea. Oceanografi. 2011;207:1–28.
13. Rothäusler E, Sjöroos J, Heye K, Jormalainen V, Hurd C. Genetic variation in
photosynthetic performance and tolerance to osmotic stress (desiccation,
freezing, hyposalinity) in the rocky littoral foundation species Fucus
vesiculosus (Fucales, Phaeophyceae). J Phycol. 2016;52:877–87.
14. Bäck S. Morphological variation of northern Baltic Fucus vesiculosus along
the exposure gradient. Ann Bot Fennici. 1993;30:275–83.
15. Bäck S, Ruuskanen A. Distribution and maximum growth depth of Fucus
vesiculosus along the Gulf of Finland. Mar Biol. 2000;136:303–7.
16. Kalvas A, Kautsky L. Morphological variation in Fucus vesiculosus populations
along temperature and salinity gradients in Iceland. J Mar Biol Assoc. 1998;
78:985–1001.
17. Waern M. Rocky -shore algae in the Öregrund archipelago. Acta Phytogeogr
Suec. 1952;30:1–298.
18. Ruuskanen A, Bäck S, Reitalu T. A comparison of two cartographic exposure
methods using Fucus vesiculosus as an indicator. Mar Biol. 1999;134:139–45.
19. Mittler R, Vanderauwera S, Gollery M, Van Breusegem F. Reactive oxygen
gene network of plants. Trends Plant Sci. 2004;9:490–8.
20. Apel K, Hirt H. Reactive oxygen species: metabolism, oxidative stress, and
signaling transduction. Annu Rev Plant Biol. 2004;55:373–99.
21. Mallick N, Mohn FH. Reactive oxygen species: response of algal cells. J Plant
Physiol. 2000;157:183–93.
22. Kirst GO. Salinity tolerance of eukaryotic marine algae. Annu. Rev. plant
Physiol. Plant Mol. Biol. 1989;41:21–53.
23. Gimmler H. Primary sodium plasma membrane ATPases in salt-tolerant
algae: facts and fictions. J Exp Bot. 2000;51:1171–8.
24. Yancey PH. Organic osmolytes as compatible, metabolic and counteracting
cytoprotectants in high osmolarity and other stresses. J Exp Biol. 2005;208:
2819–30.
25. Kalvas A, Kautsky L. Geographical variation in Fucus vesiculosus morphology
in the Baltic and north seas. Eur J Phycol. 1993;28:85–91.
26. Serrão EA, Kautsky L, Brawley SH. Distributional success of the marine
seaweed Fucus vesiculosus L . in the brackish Baltic Sea correlates with
osmotic capabilities of Baltic gametes. Oecologia. 1996;107:1–12.
27. Bäck S, Collins JC, Russell G. Effects of salinity on growth of Baltic and
Atlantic Fucus vesiculosus. Br Phycol J. 1992;27:39–47.
28. Johansson D, Pereyra RT, RafajlovićM, Johannesson K. Reciprocal transplants
support a plasticity-first scenario during colonisation of a large hyposaline
basin by a marine macro alga. BMC Ecol. 2017;17:14.
29. Barboza FR, Kotta J, Weinberger F, Jormalainen V, Kraufvelin P, Molis M,
Schubert H, Pavia H, Nylund GM, Kautsky L, Schagerström E, Rickert E, Saha
M, Frierdiksen S, Martin G, Torn K, Ruuskanen A, Wahl M. Geographic
variation in fitness-related traits of the bladderwrack Fucus vesiculosus along
the Baltic Sea-North Sea salinity gradient. Ecol Evol. 2019;9:9225–38.
30. Tatarenkov A, Jonsson RB, Kautsky L, Johannesson K. Genetic structure in
populations of Fucus vesiculosus (Phaeophyceae) over spatial scales from 10
m to 800 km. J Phycol. 2007;43:675–85.
31. Martins MJF, Mota CF, Pearson GA. Sex-biased gene expression in the
brown alga Fucus vesiculosus. BMC Genomics. 2013;14:294.
32. Funato Y, Michiue T, Asashima M, Miki H. The thioredoxin-related redox-
regulating protein nucleoredoxin inhibits wnt–β-catenin signalling through
dishevelled. Nat Cell Biol. 2006;8:501–8.
33. Hille R, Nishino T. Xantliine oxidase and xanthine dehydrogenase. FASEB J.
1995;9:995–1003.
34. Silberfeld T, Leigh JW, Verbruggen H, Cruaud C, de Reviers B, Rousseau F. A
multi-locus time-calibrated phylogeny of the brown algae (Heterokonta,
Ochrophyta, Phaeophyceae): investigating the evolutionary nature of the
“brown algal crown radiation.”. Mol Phylogenet Evol. 2010;56:659–74.
35. De Vries J, De Vries S, Slamovits CH, Rose LE, Archibald JM. How
embryophytic is the biosynthesis of phenylpropanoids and their derivatives
in streptophyte algae? Plant Cell Physiol. 2017;58:934–45.
36. Martone PT, Estevez JM, Lu F, Ruel K, Denny MW, Somerville C, Ralph J.
Discovery of lignin in seaweed reveals convergent evolution of cell-wall
architecture. Curr Biol. 2009;19:169–75.
37. Labeeuw L, Martone PT, Boucher Y, Case RJ. Ancient origin of the
biosynthesis of lignin precursors. Biol Direct. 2015;10:1–21.
38. Barshis DJ, Ladner JT, Oliver TA, Seneca FO, Traylor-Knowles N, Palumbi SR.
Genomic basis for coral resilience to climate change. Proc Natl Acad Sci.
2013;110:1387–92.
39. Rothäusler E, Corell H, Jormalainen V. Abundance and dispersal trajectories
of floating Fucus vesiculosus in the northern Baltic Sea. Limnol Oceanogr.
2015;60:2173–84.
40. Dittami SM, Scornet D, Petit JL, Ségurens B, Da Silva C, Corre E, Dondrup M,
Glatting K-H, König R, Sterck L, Rouzé P, Van de Peer Y, Cock JM, Boyen C,
Tonon T. Global expression analysis of the brown alga Ectocarpus siliculosus
(Phaeophyceae) reveals large-scale reprogramming of the transcriptome in
response to abiotic stress. Genome Biol. 2009;10:R66.
41. Qian WG, Li N, Lin LD, Xu T, Zhang X, Wang LH, Zou H-X, Wu M-J, Yan X-F.
Parallel analysis of proteins in brown seaweed Sargassum fusiforme
responding to hyposalinity stress. Aquaculture. 2016;465:189–97.
42. Arnér ESJ, Holmgren A. Physiological functions of thioredoxin and
thioredoxin reductase. Eur J Biochem. 2000;267:6102–9.
43. Meslet-Cladiere L, Delage L, Leroux CJ-J, Goulitquer S, Leblanc C, Creis E,
Gall EA, Stiger-Pouvreau V, Czjzek M, Potin P. Structure/function analysis of a
type III polyketide synthase in the brown alga Ectocarpus siliculosus reveals a
biochemical pathway in phlorotannin monomer biosynthesis. Plant Cell.
2013;25:3089–103.
44. Roeder V, Collén J, Rousvoal S, Corre E, Leblanc C, Boyen C. Identification of
stress gene transcripts in Laminaria digitata (Phaeophyceae) protoplast
cultures by expressed sequence tag analysis. J Phycol. 2005;41:1227–35.
45. Zubia M, Robledo D, Freile-Pelegrin Y. Antioxidant activities in tropical
marine macroalgae from the Yucatan peninsula. Mexico J Appl Phycol.
2007;19:449–58.
Rugiu et al. BMC Genomics (2020) 21:42 Page 15 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
46. Haavisto F, Koivikko R, Jormalainen V. Defensive role of macroalgal
phlorotannins: benefits and trade-offs under natural herbivory. Mar Ecol
Prog Ser. 2017;566:79–90.
47. Collén J, Guisle-Marsollier I, Léger JJ, Boyen C. Response of the
transcriptome of the intertidal red seaweed Chondrus crispus to controlled
and natural stresses. New Phytol. 2007;176:45–55.
48. Wang W, Vinocur B, Shoseyov O, Altman A. Role of plant heat-shock
proteins and molecular chaperones in the abiotic stress response. Trends
Plant Sci. 2004;9:244–52.
49. Yesbergenova Z, Yang G, Oron E, Soffer D, Fluhr R, Sagi M. The plant Mo-
hydroxylases aldehyde oxidase and xanthine dehydrogenase have distinct
reactive oxygen species signatures and are induced by drought and
abscisic acid. Plant J. 2005;42:862–76.
50. Deniaud-Bouët E, Kervarec N, Michel G, Tonon T, Kloareg B, Hervé C.
Chemical and enzymatic fractionation of cell walls from Fucales: insights
into the structure of the extracellular matrix of brown algae. Ann Bot. 2014;
114:1203–16.
51. Coleman RA, Lee DP. Enzymes of triacylglycerol synthesis and their
regulation. Prog Lipid Res. 2004;43:134–76.
52. Bagniewska-Zadworna A. The root microtubule cytoskeleton and cell cycle
analysis through desiccation of Brassica napus seedlings. Protoplasma. 2008;
233:177–85.
53. Livanos P, Galatis B, Apostolakos P. The interplay between ROS and tubulin
cytoskeleton in plants. Plant Signal Behav. 2014;9:e28069.
54. Wiencke C, Bischof K. Seaweed biology. Ecological studies 219; 2012.
55. Mosavi L, Cammett T. The ankyrin repeat as molecular architecture for
protein recognition. Protein Sci. 2004;13:1435–48.
56. Amtmann A, Beilby MJ. The role of ion channels in plant salt tolerance.
Ion Channels Plant Stress Responses. Berlin, Heidelberg: Springer; 2010.
p. 23–46.
57. Schulz B, Kolukisaoglu HÜ. Genomics of plant ABC transporters: the
alphabet of photosynthetic life forms or just holes in membranes? FEBS
Lett. 2006;580:1010–6.
58. Kumar M, Kumari P, Reddy CRK, Jha B. Sea plants. San Diego: BS:ABR
Elsevier; 2014.
59. Bilgin DD, Zavala JA, Zhu J, Clough SJ, Ort DR, Delucia EH. Biotic stress
globally downregulates photosynthesis genes. Plant Cell Environ. 2010;
33:1597–613.
60. Flöthe CR, Molis M, Kruse I, Weinberger F, John U. Herbivore-induced
defence response in the brown seaweed Fucus vesiculosus (
Phaeophyceae ): temporal pattern and gene expression. Eur J Phycol.
2014;49:356–69.
61. Heinrich S, Valentin K, Frickenhaus S, John U, Wiencke C. Transcriptomic
analysis of acclimation to temperature and light stress in Saccharina
latissima (Phaeophyceae). PLoS One. 2012;7:e44342.
62. Peng J, Yuan JP, Wu CF, Wang JH. Fucoxanthin, a marine carotenoid
present in brown seaweeds and diatoms: metabolism and bioactivities
relevant to human health. Mar Drugs. 2011;9:1806–28.
63. Hwang YS, Jung G, Jin ES. Transcriptome analysis of acclimatory responses
to thermal stress in Antarctic algae. Biochem Biophys Res Commun. 2008;
367:635–41.
64. Niyogi KK. Safety valves for photosynthesis. Curr Opin Plant Biol. 2000;3:
455–60.
65. Amsler CD. Algal chemical ecology. Berlin: Springer. Algal Chem. Ecol; 2008.
66. Reed RH, Davison IR, Chudek JA, Foster R. The osmotic role of mannitol in
the Phaeophyta: an appraisal. Phycologia. 1985;24:35–47.
67. Rousvoal S, Groisillier A, Dittami SM, Michel G, Boyen C, Tonon T. Mannitol-
1-phosphate dehydrogenase activity in Ectocarpus siliculosus, a key role for
mannitol synthesis in brown algae. Planta. 2011;233:261–73.
68. Nygård CA, Ekelund NGA. Photosynthesis and UV-B tolerance of the marine
alga Fucus vesiculosus at different sea water salinities. J Appl Phycol. 2006;18:
461–7.
69. Collén J, Pedersén M, Collén J. Production, scavenging and toxicity of
hydrogen peroxide in the green seaweed Ulva rigida.EurJPhycol.
1996;31:265–71.
70. Croft MT, Lawrence AD, Raux-Deery E, Warren MJ, Smith AG. Algae acquire
vitamin B12 through a symbiotic relationship with bacteria. Nature. 2005;
438:90–9.
71. Tsavkelova EA, Klimova SY, Cherdyntseva TA, Netrusov AI. Microbial
producers of plant growth stimulators and their practical use: a review. Appl
Biochem Microbiol. 2006;42:117–26.
72. Guillard RRL, Ryther JH. Studies of marine planktonic diatoms. I.
Cyclotella nana Hustedt, and Detonula confervacea (Cleve). Can. J.
Microbiol. 1962;8:229–40.
73. Pearson G, Lago-leston A, Valente M, Serrão E. Simple and rapid RNA
extraction from freeze-dried tissue of brown algae and seagrasses. Eur J
Phycol. 2006;41:97–104.
74. Panova M, Aronsson H, Cameron RA, Dahl P, Godhe A, Lind U, Ortega-
Martinez O, Pereyra R, Tesson SVM, Wrange A-L, Blomberg A,
Johannesson K. DNA extraction protocols for whole-genome
sequencing in marine organisms. In: Bourlat S. (eds) Marine Genomics.
Methods in Molecular Biology, vol 1452. New York: Humana Press; 2016.
75. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina
sequence data. Bioinformatics. 2014;30:2114–20.
76. Martin M. Cutadapt removes adapter sequences from high-throughput
sequencing reads. EMBnet.j. 2011;17:10.
77. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I,
AdiconisX,LinF,RaychowdhuryR,QiandongZ,ChenZ,MauceliE,
Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C,
Lindblad-toth K, Friedman N, Regev A. Trinity: reconstructing a full-
length transcriptome without a genome from RNA-Seq data. Nat.
Biotechnol. 2013;29:644–52.
78. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM.
BUSCO: assessing genome assembly and annotation completeness with
single-copy orthologs. Bioinformatics. 2015;31:3210–2.
79. Smith-Unna R, Boursnell C, Patro R, Hibberd JM, Kelly S. TransRate:
reference-free quality assessment of de novo transcriptome assemblies.
Genome Res. 2016;26:1134–44.
80. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J,
Couger MB, Eccles D, Li B, Lieber M, MacManes MD, Ott M, Orvis J, Pochet
N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R,
Leduc RD, Friedman N, Regev A. De novo transcript sequence
reconstruction from RNA-Seq: reference generation and analysis with trinity.
Nat Protoc. 2014;8:1494.
81. Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M. Blast2GO: a
universal tool for annotation, visualization and analysis in functional
genomics research. Bioinformatics. 2005;21:3674–6.
82. Tatarenkov A, Bergström L, Jönsson RB, Serrão EA, Kautsky L, Johannesson K.
Intriguing asexual life in marginal populations of the brown seaweed Fucus
vesiculosus. Mol Ecol. 2005;14:647–51.
83. Pereyra RT, Huenchuñir C, Johansson D, Forslund H, Kautsky L, Jonsson PR,
et al. Parallel speciation or long-distance dispersal? Lessons from seaweeds
(Fucus) in the Baltic Sea. J Evol Biol. 2013;26:1727–37.
84. Ardehed A, Johansson D, Sundqvist L, Schagerstrm E, Zagrodzka Z,
Kovaltchouk NA, Bergström L, Kautsky L, Rafajlovic PR, Johannesson K.
Divergence within and among seaweed siblings (Fucus vesiculosus and F.
radicans) in the Baltic Sea. PLoS One. 2016;11:1–16.
85. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient
alignment of short DNA sequences to the human genome. Genome Biol.
2009;10:R25.
86. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G,
Abecasis G. Durbin R and 1000 genome project data processing
subgroup. The sequence alignment/map format and SAMtools.
Bioinformatics. 2009;25:2078–9.
87. Jombart T, Ahmed I. Adegenet 1.3-1: new tools for the analysis of genome-
wide SNP data. Bioinformatics. 2011;27:3070–1.
88. Kamvar ZN, Brooks JC, Grünwald NJ. Novel R tools for analysis of
genome-wide population genetic data with emphasis on clonality.
Front Genet. 2015;6:1–10.
89. Nei M. Genetic distance between populations. Am Nat. 1972;106:283–92.
90. Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA,
Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G. Durbin and 1000
genome project data processing subgroup. The variant call format and
VCFtools. Bioinformatics. 2011;27:2156–8.
91. Signorell A. DescTools: Tools for descriptive statistics. R package version 0.
99.20. CRAN. 2017;297–309.
92. Bray N, Pimentel H, Melsted P, Pachter L. Near-optimal RNA-Seq
quantification. Nat Biotechnol. 2016;34:525–7.
93. Love MI, Huber W, Anders S. Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21.
94. Oksanen J, Kindt R, Legendre P, O’Hara B, Henry S. The vegan
package; 2007.
Rugiu et al. BMC Genomics (2020) 21:42 Page 16 of 17
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
95. Nei M. Estimation of average heterozygosity and genetic distance from a
small number of individuals. Genetics. 1978;89:583–90.
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