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Organellar genome comparisons of Sargassum polycystum and S. plagiophyllum (Fucales, Phaeophyceae) with other Sargassum species

Abstract

Background

Sargassum polycystum C. Agardh and Sargassum plagiophyllum C. Agardh are inhabitants of tropical coastal areas, their populations are negatively influenced by global warming and marine environment changes. The mitochondrial and chloroplast genomes of these species have not been sequenced.

Results

The mitochondrial genomes of S. polycystum and S. plagiophyllum were 34,825 bp and 34,862 bp, respectively, and their corresponding chloroplast genomes were 124,493 bp and 124,536 bp, respectively. The mitochondrial and chloroplast genomes of these species share conserved synteny, sequence regions and gene number when compared with the organellar genomes of other Sargassum species. Based on sequence analysis of 35 protein-coding genes, we deduced that S. polycystum and S. plagiophyllum were closely related with S. ilicifolium; these species diverged approximately 0.3 million years ago (Ma; 0.1–0.53 Ma) during the Pleistocene period (0.01–2.59 Ma). Rates of synonymous and non-synonymous substitutions in the mitochondrial genome of the Sargassum genus were 3 times higher than those in the chloroplast genome. In the mitochondrial genome, rpl5, rpl31 and rps11 had the highest synonymous substitution rates. In the chloroplast genome, psaE, rpl14 and rpl27 had the highest synonymous substitution rates.

Conclusions

Phylogenetic analysis confirms the close relationship between the two sequenced species and S. ilicifolium. Both synonymous and non-synonymous substitution rates show significant divergence between the group of mitochondrial genomes versus the group of chloroplast genomes. The deciphering of complete mitochondrial and chloroplast genomes is significant as it advances our understanding of the evolutionary and phylogenetic relationships between species of brown seaweeds.

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Background

Sargassum polycystum C. Agardh and S. plagiophyllum C. Agardh (Fucales, Phaeophyceae) are large canopy-forming seaweeds that grow in the intertidal and shallow subtidal zones of the tropical Indo-West Pacific, with the former having the widest distribution [1, 2]. With global warming and changes of the marine environment, seaweed forests and ecosystems have recently begun to rapidly diminish [3]. Generally, Sargassum species are economically and ecologically important in tropical and temperate coastal areas [2] as they can form seabed forests that play a critical role in the ecology of tropical marine environment [4, 5]. Sargassum provides resources for seaweeds fertilizers, alginate, cosmetics, etc., and exhibit antibacterial, antiviral, anti-oxidant [6], and antifungal activities [7].

Deciphering of complete mitochondrial and chloroplast genomes are prerequisites for understanding the evolutionary and phylogenetic background of brown seaweeds [8, 9]. Both mitochondria and chloroplasts are semi-autonomous organelles that preserve their own genetic information [10]. Mitochondria are the main site for respiration and energy supply, whereas chloroplasts are the site of photosynthesis [11]. Generally, organellar genomes reflect the evolutionary history of the algal nuclear genome [12, 13]. Nevertheless, limited information on the organellar genomes of the tropical Sargassum species hinders our understanding of their evolutionary history. Sargassum polycystum and S. plagiophyllum are morphologically distinct; they are considered two different species that occur in sympatry across the Sargassum subgenus Sargassum section Polycystae [14]. Nevertheless, molecular phylogenetic analyses far did not resolve the complexities of the two morphologically different species [15, 16], reflecting their very recent divergence.

Through the sequencing of complete chloroplast and mitochondrial genomes of S. polycystum and S. plagiophyllum, and by comparing them with those of other Sargassum species, we detected variable regions that may be targets for future phylogenetic analysis.

Results

Mitochondrial genome characteristics

The mitochondrial genomes (Fig. 1) of S. polycystum and S. plagiophyllum comprised 34,825 and 34,862 bp, respectively, and were within the size range of previously sequenced mitochondrial genomes (Table 1) of other Sargassum species [17] and Stramenopiles [18, 19]. The mitochondrial genome of S. polycystum and S. plagiophyllum contained 25 tRNA (transfer RNA) genes, 3 rRNA (ribosomal RNA) genes, 37 protein-coding genes (PCGs). In S. plagiophyllum, there was an intergenic region located between rrn5 and rns. In S. plagiophyllum, only ORF (open reading frame) 129, rpl16, rps3, rps19, rpl2 and tatC were transcribed from the light strand (L-strand) (clockwise direction in Fig. 1), and the other genes from the complementary heavy strand (H-strand) (anticlockwise in Fig. 1). In S. polycystum, the genes of rpl16, rps3, rps19, rpl2 and tatC were transcribed from the L-strand, and the other genes from the complementary H-strand. In these two Sargassum species, the protein-coding regions analogously accounted for 77.50% of the total mitochondrial genome, whereas the non-PCGs regions only accounted for 22.50%. The total GC content (35.71% in S. polycystum and 35.70% in S. plagiophyllum), the size, gene content and architecture of the mitochondrial genomes were similar to those in other Sargassum species (Table 1).

Fig. 1
figure 1

The mitochondrial genome maps of S. polycystum and S. plagiophyllum. The annotated genes are colored according to the functional categories. Genes on the inside are transcribed in the clock-wise direction, whereas genes on the outside are transcribed in the anticlockwise direction. The ring of bar graphs on the inner circle shows the GC content in dark gray

Table 1 Genomes features of mitochondria in nine species of the Sargassum

Chloroplast genome characteristics

The chloroplast genomes of S. polycystum and S. plagiophyllum were 124,493 bp and 124,536 bp, respectively, whereas the size of the chloroplast genome in the other Sargassum species (namely S. horneri, S. thunbergii, S. fusiforme, S. confusum and S. muticum) (Table 2) ranged from 124,068 to 124,592 bp. The two assembled chloroplast genomes each had a total of 172 genes, which included 139 PCGs, 27 tRNA genes, and 6 rRNA genes (Fig. 2). The S. polycystum chloroplast genome had one 73,674 bp large single-copy (LSC) region, one 39,967 bp small single-copy (SSC) region and a pair of 5426 bp inverted repeats (i.e., IRa and IRb) regions. The chloroplast genome size of S. plagiophyllum was 43 bp larger than that of S. polycystum, because it had one 73,693 bp LSC region, one 39,971 bp SSC region, and two IR regions of 5436 bp each. Compared to the chloroplast genomes in other Sargassum species, the chloroplast genome of S. plagiophyllum lost the trnL-UAA gene and S. polycystum lost the trnK-UUU gene. The rnl gene was 1215 bp in S. polycystum and 1698 bp in S. plagiophyllum. The chloroplast genomes of S. polycystum and S. plagiophyllum were otherwise identical in their structure, gene count, and gene content [see Additional files 1 and 2], except for the before mentioned missing trnL-UAA and trnK-UUU genes.

Table 2 Genomes features of chloroplasts in seven species of the Sargassum
Fig. 2
figure 2

The chloroplast genome maps of S. polycystum and S. plagiophyllum. The annotated genes are colored according to the functional categories. Genes on the inside are transcribed in the clock-wise direction, whereas genes on the outside are transcribed in the anticlockwise direction. The ring of bar graphs on the inner circle shows the GC content in dark gray

In addition, there were no introns observed in the tRNA regions of the chloroplast genomes of S. plagiophyllum and S. polycystum, and only one intron was observed in the five other Sargassum species compared here. The features of the chloroplast genomes of these two Sargassum species and five other Sargassum species were summarized in Table 2.

IR boundary analysis

Although the chloroplast genomes of the Sargassum species studied were highly conserved, structural variations were prevalent in the IR boundary region (Fig. 3). In S. polycystum and S. plagiophyllum, the cbbx gene spanned the junction between the LSC and IRb regions; this gene extended 721 bp into the LSC and 185 bp into the IRb regions. At the junction of the SSC-IRb (JSB) regions, the rrn5 gene was located in the IRb region, 181 bp (S. polycystum and S. plagiophyllum) from the junction; the ycf19 gene was located in the SSC region, 416 bp (for S. polycystum) and 406 bp (for S. plagiophyllum) from the junction. The rpl21 gene spanned the junction of the SSC-IRa (JSA) regions, with most of the gene (230 bp for S. polycystum and 229 bp for S. plagiophyllum) encoded in the SSC region and the remaining (19 bp for S. polycystum and 89 bp for S. plagiophyllum) in the IRa region. The junction of the LSC-IRa (JLA) regions was spanned by the ycf37 gene, 4 bp of which was in the IRa region and 524 bp of which was in the LSC region in both S. polycystum and S. plagiophyllum; this deviated slightly in other species. The IRa, IRb, LSC and SSC regions of S. plagiophyllum were 10 bp, 10 bp, 19 bp and 4 bp longer than those in S. polycystum, respectively.

Fig. 3
figure 3

Comparison of Inverted repeat (IR), Large single-copy (LSC) and Small single-copy (SSC) boundary regions in seven Sargassum species. JLB: Junction of LSC-IRb, JSB: Junction of SSC-IRb, JSA: Junction of SSC-IRa, JLA: Junction of LSC-IRa

To determine the differences in the chloroplast genomes of Sargassum species, we compared the IR/LSC/SSC boundary regions with those in five other Sargassum species. We found that they all share the same IR/LSC/SSC regions, with only minor differences in the length of each region (Fig. 3). The IR regions of S. horneri, with the shortest chloroplast genome, were 234 bp and 254 bp shorter than S. polycystum and S. plagiophyllum respectively. Remarkably, the rpl21 gene in S. polycystum was much shorter compared to the other species and overlapped only by 19 bp with IRa, whereas in the other species it overlapped with IRa by 87–89 bp.

Substitution rate estimations

We compared the non-synonymous (dN) and synonymous (dS) substitution rates of the sequences of 35 mitochondrial PCGs, excluding ORF129 and ORF41, [see Additional file 3] and 114 chloroplast PCGs [see Additional file 4] in S. polycystum and S. plagiophyllum, as well as in five other Sargassum species (namely S. horneri, S. thunbergii, S. fusiforme, S. confusum, and S. muticum). In the 35 mitochondrial PCGs, rps11 and tatC showed the highest dN (0.0996 and 0.0936, respectively) and moderate dS (0.6769 and 0.4832, respectively) values. Atp9 exhibited the lowest dN values with 0.0028. Nad4L and nad5 had identical dS value (0.4899) and dN value (0.0029), which reflected the similar degree of selection acting upon these two genes among these Sargassum species. The dN/dS ratios were different for the 35 PCGs in the mitochondrial genome [see Additional file 5]; the low dN/dS ratios of nad4L and nad5 (dN/dS = 0.0069) implied their slow evolutionary rate. The higher dN/dS ratio in tatC (0.2175) reflected the unique selection process (dN/dS < 0.25) of the 35 PCGs in the mitochondrial genome. Moreover, we verified that the dN/dS ratios of five genes (namely petJ, rpl21, psaD, rpoC2 and thiS) in the chloroplast genomes were greater than one and that the dN/dS ratios of the remaining genes were lower than one. In the 114 chloroplast PCGs [see Additional file 6], rpoC2 and thiS exhibited the highest dN/dS ratios with 3.40 and 3.48, respectively, whereas psbH, psbJ, psbT, rpl29, atpH and petD exhibited the lowest and identical dN/dS ratios of 0.001. This implied that these genes were under various degrees of selective pressure.

The dS values of the mitochondrial genes varied from 0.1999 to 0.6769 and those of the chloroplast genes varied from 0.0196 to 0.2615 (Fig. 4). The dN values of the mitochondrial genes varied from 0.0028 to 0.0996 and those of the chloroplast genes varied from 0.0001 to 0.118. Compared with the chloroplast genes, most mitochondrial genes had higher dS and dN values. The average dN and dS values of the mitochondrial PCGs are approximately 3.6 and 3.2 times higher than those of the chloroplast PCGs, respectively. The non-synonymous substitution rates of most PCGs were 0.00–0.02, which accounted for 89.5% of all PCGs from the chloroplast genome.

Fig. 4
figure 4

The difference in synonymous and non-synonymous divergence rates between chloroplast and mitochondrial protein-coding genes. The alignments of 114 chloroplast genes (orange) and 35 mitochondrial genes (yellow) from seven Sargassum species (five previous reported and two newly sequenced) were used to estimate the divergence values

The average dN/dS ratios of the mitochondrial genes were lower than that of the chloroplast genes (0.08 for the mitochondrion and 0.16 for the chloroplast). The dN/dS ratios of the two organellar genomes from the seven selected Sargassum species were less than 0.25.

Phylogenetic analysis and divergence time of brown algae

A 26,360 bp nucleotide alignment was obtained by concatenating 35 shared PCGs from the mitochondrial genomes of thirteen Sargassum species, additionally Turbinaria ornata (Sargassaceae) and Fucus vesiculosus (Fucaceae) were selected as outgroups. This alignment was used to infer phylogenetic relationships between Sargassum species using Maximum Likelihood (ML) and Bayesian Inference (BI) approaches. The ML and BI tree were identical in topology; the ML bootstrap support percentages and BI posterior probabilities were shown in Fig. 5. The Sargassum clade had maximum support, with infrageneric relationships all having high to maximum support (ML bootstrap ≥94%, BI posterior probability = 1). All Sargassum subgenera and sections were monophyletic. The Polycystae clade (highlighted in Fig. 5), which consisted of the two species sequenced in this study, was sister to the Ilicifolia clade, which only had a single representative in the tree (i.e., S. ilicifolium).

Fig. 5
figure 5

Phylogenetic tree of brown algae constructed using the 35 shared mitochondrial protein-coding genes. The numbers near each node are Maximum likelihood bootstrap support values based on 1000 ultrafast bootstrap replicates in IQ-tree and Bayesian inference posterior probabilities

We also reconstructed a time tree of Sargassum species with divergence times using five mitochondrial genes (cox1, cox3, nad1, nad4 and atp9) and three chloroplast genes (rbcL, psbA and atpB), with F. vesiculosus as the outgroup (Fig. 6). According to this chronogram S. polycystum and S. plagiophyllum diverged approximately 0.3 Ma (0.1–0.53 Ma), which corresponded with the Pleistocene period (0.01–2.6 Ma). The subgenera Sargassum and Bactrophycus diverged between 8.2–11.6 Ma (Miocene). The Fucaceae (F. vesiculosus) and Sargassaceae (Sargassum) diverged between 18.2–39.6 Ma (95% HPD), which corresponded to a geological period spanning early Miocene-Oligocene-late Eocene.

Fig. 6
figure 6

Divergence time estimations of the genus Sargassum (Sargassaceae) based on time-calibrated molecular clock analysis. Fucus vesiculosus (Fucaceae) is used as the outgroup. Violet bars represent 95% highest posterior density (HPD) intervals of node ages (minimum and maximum values given in square brackets)

Discussion

Although the number of rRNA genes and PCGs was identical across the five Sargassum species, the trnL-UAA and trnK-UUU tRNA genes were lost in the chloroplast genomes of S. plagiophyllum and S. polycystum, respectively. Plastid gene loss has been observed in Dictyochophyceae [20], Eustigmatophyceae [21] and Synurophyceae [22], indicating that gene loss was widespread in Stramenopiles, and these genes were lost during evolution.

The chloroplast genome of S. plagiophyllum was 43 bp longer than that of S. polycystum. We suspected that this was caused by expansion of the IR regions, which caused the enlargement of the spacing regions between the trnA-TGC and rnl genes. In S. plagiophyllum, IRa and IRb were each an extra 10 bp longer, the LSC region had an extra 19 bp, and the SSC region had an extra 4 bp. The different degrees of IR regions expansion from the chloroplast quadripartite genomes may be related to species diversification.

The dN/dS ratio can be used to estimate the type and degree of selective pressure acting on a protein-coding gene [23]. In our comparison of the organellar genomes data, both synonymous and non-synonymous substitution rates for the mitochondria-encoded genes were more than 3 times higher than those for the chloroplast-encoded genes. The mitochondrial genes in brown algae exhibited a higher mutation rate than the chloroplast genes [24]. Moreover, both nad4L and nad5 in the mitochondrial genome were involved in the synthesis of Nicotinamide adenine dinucleotide dehydrogenase. They showed similar dN/dS (=0.0069) values, which was possibly driven by their involvement in the same or similar functions. The dN/dS values (dN/dS > 1) of five genes (namely petJ, rpl21, psaD, rpoC2 and thiS) in the chloroplast genome were considerably higher than the other genes from this organelle, demonstrating that they were under positive selection.

Phylogenetically, S. plagiophyllum and S. polycystum were sister-species and exhibited very limited genetic divergence and a relatively recent divergence time (0.1–0.53 Ma, 95% HPD). The two species retained their morphological distinctness, yet Stankovic et al. [1] reported that sympatric populations of the two species in Phuket did grow adjacent to each other; S. plagiophyllum grows in the low intertidal zones, and S. polycystum grows in deeper area of low intertidal zones. In the present study, S. plagiophyllum and S. polycystum were the closest related sister-taxa with the least genetic divergence. While S. fusiforme and S. horneri, which grow in moderate seawater temperature conditions [25], were observed to be the most divergent sister-taxa (5.86–8.83 Ma, 95% HPD). Comparatively S. plagiophyllum and S. polycystum, which grow in tropical temperature conditions [26], are the most closely related clades (diverging 0.1–0.53 Ma, 95% HPD). Decoding of the organellar genomes of S. plagiophyllum and S. polycystum could enhanced our understanding of the evolution of the Sargassum genus.

Conclusions

We firstly reported for the first time the mitochondrial and chloroplast genomes of S. plagiophyllum and S. polycystum, describing their genome size and structure. Phylogenetic analysis confimed their close relationship to each other, and to S. ilicifolium in the sister-section Ilicifolia. Both synonymous and non-synonymous substitution rates reflected significant differences in the selective pressure acting on the mitochondrial and chloroplast genomes. Our study improves our understanding of the phylogenetic and evolutionary history of Sargassum.

Methods

Algal sample collection and DNA extraction

Sargassum polycystum and S. plagiophyllum samples were collected from Koh Kut, Trat, Thailand (11°36′34″N 102°35′47″E, Gulf of Thailand) and Penang, Malaysia (5°28′56″N 100°15′18″E, Strait of Malacca), respectively, in 2019 and stored in silica. After sample collection, the total algal DNA was extracted from young blades using the FastPure Plant DNA Isolation Mini Kit (Vazyme Biotech Co., Ltd., Nanjing, China) according to the manufacturer’s instructions. The extracted DNA was subsequently purified according to the DNA Purification Kit (Vazyme Biotech Co., Ltd., Nanjing, China) instructions.

Genome sequencing, assembly, and annotation

To obtain a full-length mitochondrial and chloroplast sequence, we used the Illumina strategy: For each species of mitochondrion and chloroplast, 1 μg of purified DNA was used to construct a paired-end library. DNA was purified using the TIANgel Midi Purification Kit (Tiangen Biotech Co., Ltd., Beijing, China) following manufacturer’s instructions; purified DNA was subsequently sonicated to ~ 450-bp insert sequences using the Covaris M220 system with the parameter to 450-bp. Sequencing libraries were constructed using the NEBNext Ultra™ DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, England) following the manufacturer’s instructions. The constructed libraries were sequenced using Illumina NovaSeq 6000 (Biozeron, Shanghai, China). We quality-controlled and trimmed low-quality raw sequences using Trimmomatic-0.39 [27] with the parameters “SLIDINGWINDOW: 4:15 MINLEN: 75”. All the clean reads were used to generate de novo assemblies using SOAP denovo v2.04 [28]. Potential contigs were extracted by aligning against the protein-coding genes from the plant chloroplast database (http://ftp.ncbi.nih.gov/refseq/release/plastid/) and the mitogenome database (http://ftp.ncbi.nlm.nih.gov/refseq/release/mitochondrion/) with BLAST v 2.8.1+ [29]. GapCloser v1.12 [28] was employed to fill the gaps in the scaffolds. Finally, the mitochondrial and chloroplast genomes of S. polycystum and S. plagiophyllum were obtained.

We used the online Dual Organellar GenoMe Annotator tool with default parameters [30] for annotating PCGs and ORFs. The cloverleaf structures of tRNA were predicted using tRNAscan-SE v1.23 with default parameters [31], and rRNA genes were predicted using RNAmmer v1.2 [32]. The circular physical maps of mitochondrial and chloroplast genomes were generated using OGDRAW v1.3.1 [33]. MEGA 7.0 [34] was used to align the sequences and determine the base composition. Functional annotations were performed using sequence-similarity Blast searches with a typical cut-off E-value of 10− 5 [35] against several publicly available protein databases, i.e., NCBI non-redundant (Nr) protein database, Swiss-Prot, Clusters of Orthologous Groups (COGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The mitochondria and chloroplast genomes of the two Sargassum species were deposited into the GenBank database, with the accession numbers of MW485977 and MW485983 (respectively) for S. polycystum, MW485978 and MW485981 for S. plagiophyllum.

Chloroplast genome boundary region analysis

To investigate the contraction and expansion of inverted repeat (IR) boundary regions in the chloroplast genomes of Sargassum, we downloaded the chloroplast genome data of S. horneri, S. thunbergii, S. fusiforme, S. confusum and S. muticum from the GenBank database (Table 2) and compared and visualized the IR boundary positions and their adjacent genes using the IR scope online program [36]. The genome length, GC content, and gene length of chloroplast genome data of all Sargassum species were compared using MEGA v7.0 and BioEdit software [37].

Substitution rate estimation

We compared sequence divergence rates of genes between the mitochondrial and chloroplast genomes of S. polycystum and S. plagiophyllum, and with other studied Sargassum species (namely S. horneri, S. thunbergii, S. fusiforme, S. confusum and S. muticum). The non-synonymous (dN) and synonymous (dS) substitution rates for the 35 mitochondrial PCGs and 114 chloroplast PCGs were analyzed. Codon alignments for each PCG were performed with MEGA and the identified conserved blocks of multiple sequence alignments were extracted using Gblocks v0.91b with default parameters [38]. The sequence data were aligned and converted into PML format using DAMBE5 [39]. Subsequently, dN, dS, and the dN/dS ratios were calculated using the CodeML program from the Phylogenetic Analysis by Maximum Likelihood (PAML) package v4.8 [40]. The model was run with the following settings in the codeml.ctl files: runmode = − 2 and CodonFreq = 2. To ensure the accuracy and reliability of the data, synonymous substitution values > 5 were discarded from the subsequent analysis [41]. Bar plots and scatter plots of the values of dN, dS and dN/dS were generated for comparing mutation rates and screening of the selected genes.

Phylogenetic analysis and divergence time estimation

Phylogenetic relationships were analyzed on the basis of the 35 mitochondrial PCGs (rps2–4, rps7, rps8, rps10–14, rps19; rpl2, rpl5, rpl6, rpl14, rpl16, rpl31; nad1–7, nad9, nad11; cob; cox1–3; atp6, atp8, atp9; and tatC) for a total of 15 species in the Fucales. Turbinaria ornata (Sargassaceae) and Fucus vesiculosus (Fucaceae) (GenBank accessions: KM501562 and NC_007683) were used as outgroup taxa. The nucleotide sequences of each gene were aligned using the default setting of ClustalW v2.0 [42]. FASTA [43] was applied to concatenate these sequences. Using IQ-TREEv1.6.11 [44] and MrBayes v3.2 [45], we reconstructed maximum likelihood (ML) and Bayesian inference (BI) trees, respectively. One optimal fit model selection of amino acid replacement was performed using IQ-TREEv1.6.11 [46]. Based on the Bayesian information criterion, GTR + F + I + G4 (I = 0.4387, G = 1.113) was chosen as the optimized model for ML methods. The node support values were estimated in IQ-tree based on 1000 ultrafast bootstrap replicates with other parameters set as default. For the BI tree, Modeltest v3.7 [47] was used for identifying the best-fit substitution model for the concatenated dataset (GTR + G + I, I = 0.4378, G = 1.1099) under the Akaike information criterion. The Markov chain Monte Carlo (MCMC) algorithm parameters were set to: 1 × 106 generations running with four chains (one cold chain and three heated chains) with a tree sampling frequency of every 1000 generations. The results of the first 25% runs were discarded. We used FigTree v1.4.4 (https://tree.bio.ed.ac.uk/softw are/figtree/) to visualize the derived BI and ML trees.

To build the divergence time estimate tree, five mitochondrial genes (cox1, cox3, nad1, nad4 and atp9) and three chloroplast genes (rbcL, psbA and atpB) from ten Sargassum species (representing two subgenera and seven sections) and Fucus vesiculosus were concatenated and aligned. The multi-gene alignment was partitioned into 1st, 2nd and 3rd codon positions using DAMBE5 [39]. The ML trees were reconstructed using PhyML v.3.1 under the optimal model of TIM3 + F + I + G4 with 100 bootstrap replicates. MCMCTree of PAML v4.8 [48] was used to estimate the divergence times between ten Sargassum species by using the approximate likelihood calculations and two fossil calibrations. We set a minimum age of 13 Ma for the divergence time of Sargassaceae and Fucaceae [49, 50] and the minimum age of the genus Sargassum at 6.7 (3.4–11) Ma based on the divergence time estimate of the Sargassacean genera Turbinaria and Sargassum in Yip et al. [14]. The overall substitution rates of the ML tree acquired through BASEML in PML were measured before rgene-gamma calculations. The gradient and Hessian of the branch lengths were estimated by BASEML using the TIM3 + F + I + G4 substitution model at the maximum likelihood estimates [51]. The gradient was calculated using a different method, and the Hessian parameter was adopted for calculating the estimated scores [52, 53]. We used the program MCMCTree to estimate the divergence times under both the independent rates model (clock = 2 in the mcmctree.ctl control file) and the nucleotide substitution model (TIM3 + F + I + G4). The substitution rate per time unit (0.080406), rgene_gamma (1 12.5) and sigma2_gamma (1 4.5) were used as parameters. To determine whether convergence had been achieved, two independent MCMC chains were run with 5 × 106 steps after discarding 104 generations as burn-in.

Availability of data and materials

All datasets supporting the conclusions of this article are included within the article and its additional files. The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request, and the GenBank accession numbers of the mitochondrial genomes of S. plagiophyllum and S. polycystum are MW485978 and MW485977, respectively, and the chloroplast genomes of S. plagiophyllum and S. polycystum are MW485981 and MW485983, respectively. The raw reads of S. plagiophyllum and S. polycystum organelle genomes have been deposited in the NCBI Sequence Read Archive under the BioProject numbers PRJNA851243 and PRJNA851213, respectively, Sequence Read Archive accession number of S. plagiophyllum and S. polycystum are SAMN29219088 and SAMN29214723 respectively.

Abbreviations

Ma:

megaannus (Million Years Ago)

ORFs:

Open Reading Frames

PCGs:

Protein-coding genes

LSC:

Large single-copy

SSC:

Small single-copy

IR:

Inverted repeat

JLB:

Junction of LSC-IRb

JSB:

Junction of SSC-IRb

JSA:

Junction of SSC-IRa

JLA:

Junction of LSC-IRa

dN:

Non-synonymous substitution

dS:

Synonymous substitution

HPD:

Highest posterior density

ML:

Maximum likelihood

BI:

Bayesian inference

tRNA:

Transfer RNA

rRNA:

Ribosomal RNA

MCMC:

Markov chain Monte Carlo

References

  1. Stankovic M, Draisma SGA, Pongparadon S, Wichachucherd B, Noiraksar T, Hu Z-M. Predicting macroalgal species distribution along the Thai-Malay peninsula. Estuar Coast Shelf S. 2022;267:107760. https://doi.org/10.1016/j.ecss.2022.107760.

    CAS  Article  Google Scholar 

  2. Guiry MD, Guiry GM. Algae Base. World-wide electronic publication. Galway: National University of Ireland; 2022. http://www.algaebase.org. Searched on 5 May 2022

    Google Scholar 

  3. Fraga S. Global climate change and harmful algae. Forth European Phycological Congress EPC4; 2007. https://doi.org/10.13140/2.1.1768.5280.

    Book  Google Scholar 

  4. Duggins DO, Simenstad CA, Estes JA. Magnification of secondary production by kelp detritus in coastal marine ecosystems. Science. 1989;245(4914):170–3. https://doi.org/10.1126/science.245.4914.170.

    CAS  Article  PubMed  Google Scholar 

  5. Teagle H, Hawkins SJ, Moore PJ, Smale DA. The role of kelp species as biogenic habitat formers in coastal marine ecosystems. J Exp Mar Biol Ecol. 2017;492:81–98. https://doi.org/10.1016/j.jembe.2017.01.017.

    Article  Google Scholar 

  6. Asma C, Khaled N, Hiba M. Antioxidant and antifungal activities of Padina pavonica and Sargassum vulg are from the Lebanese Mediterranean coast. Adv Environ Biol. 2012;6(1):42–8.

    Google Scholar 

  7. Costa LS, Fidelis GP, Telles CBS, Dantas-Santos N, Camara RBG, Cordeiro SL, et al. Antioxidant and antiproliferative activities of heterofucans from the seaweed Sargassum filipendula. Mar Drugs. 2011;9(6):952–66. https://doi.org/10.3390/md9060952.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Curole JP, Kocher TD. Mitogenomics digging deeper with complete mitochondrial genomes. Trends Ecol Evol. 1999;14(10):394–8. https://doi.org/10.1016/S0169-5347(99)01660-2.

    CAS  Article  PubMed  Google Scholar 

  9. Liu F, Pan J, Zhang Z, Moejes FW. Organelle genomes of Sargassum confusum (Fucales, Phaeophyceae): mtDNA vs cpDNA. J Appl Phycol. 2018;30:2715–22. https://doi.org/10.1007/s10811-018-1461-y.

    CAS  Article  Google Scholar 

  10. John F, Allen. Why chloroplasts and mitochondria retain their own genomes and genetic systems: colocation for redox regulation of gene expression. PNAS. 2015;112(33):10231–8. https://doi.org/10.1073/pnas.1500012112.

    CAS  Article  Google Scholar 

  11. Odintsova MS, Yurina NP. Plastid genomes of higher plants and algae: structure and functions. Mol Biol. 2003;37(5):768–83. https://doi.org/10.1023/A:1026020623631.

    CAS  Article  Google Scholar 

  12. Olmstead RG, Palmer JD. Chloroplast DNA systematics: a review of methods and data analysis. Am J Bot. 1994;81(9):1205–24. https://doi.org/10.2307/2445483.

    CAS  Article  Google Scholar 

  13. Smith DR, Keeling PJ. Mitochondrial and plastid genome architecture: reoccurring themes, but significant differences at the extremes. PNAS. 2015;112:10177–84. https://doi.org/10.1016/j.ympev.2012.06.017.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Yip ZT, Quek RZB, Huang D. Historical biogeography of the widespread macroalga Sargassum (Fucales, Phaeophyceae). J Phycol. 2020;56:300–9. https://doi.org/10.1111/jpy.12945.

    Article  PubMed  Google Scholar 

  15. Kantachumpoo A, Uwai S, Noiraksar T, Komatsu T. Systematics of marine brown alga Sargassum from Thailand: a preliminary study based on morphological data and nuclear ribosomal internal transcribed spacer 2 (ITS2) sequences. Ocean Sci J. 2015;50(2):251–62. https://doi.org/10.1007/s12601-015-0022-4.

    CAS  Article  Google Scholar 

  16. Yip ZT, Quek RZB, Low JKY, Wilson B, Bauman AG, Chou LM, et al. Diversity and phylogeny of Sargassum (Fucales, Phaeophyceae) in Singapore. Phytotaxa. 2018;369(3):200–10. https://doi.org/10.11646/phytotaxa.369.3.3.

    Article  Google Scholar 

  17. Liu F, Li X, Che Z. Mitochondrial genome sequences uncover evolutionary relationships of two Sargassum subgenera, Bactrophycus and Sargassum. J Appl Phycol. 2017;29:3261–70. https://doi.org/10.1007/s10811-017-1143-1.

    CAS  Article  Google Scholar 

  18. Smith DR. Updating our view of organelle genome nucleotide landscape. Front Genet. 2012;3:175. https://doi.org/10.3389/fgene.2012.00175.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Liu F, Bi Y, Chen W, Zhang Y, Moejes FW. Understanding the evolution of mitochondrial genomes in Phaeophyceae inferred from mitogenomes of Ishige okamurae (Ishigeales) and Dictyopteris divaricata (Dictyotales). J Mol Evol. 2019;87:16–26. https://doi.org/10.1007/s00239-018-9881-5.

    CAS  Article  PubMed  Google Scholar 

  20. Han KY, Maciszewski K, Graf L, Yang JH, Andersen RA, Karnkowska A, et al. Dictyochophyceae plastid genomes reveal unusual variability in their organization. J Phycol. 2019;55(5):1166–80. https://doi.org/10.1111/jpy.12904.

    CAS  Article  PubMed  Google Scholar 

  21. Tereza E, Tatiana Y, Fawley KP, Amaral R, Strnad H, Santos LMA, et al. Plastid genomes and proteins illuminate the evolution of eustigmatophyte algae and their bacterial endosymbionts. Genome Biol Evol. 2019;11:362–79. https://doi.org/10.1093/gbe/evz004.

    CAS  Article  Google Scholar 

  22. Kim JI, Shin H, Skaloud P, Jung J, Yoon HS, Archibald JM, et al. Comparative plastid genomes of Synurophyceae: inverted repeat dynamics and gene content variation. BMC Evol Biol. 2019;19:20–33. https://doi.org/10.1186/s12862-018-1316-9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Samuel S, Bringloe TT, Soto GM, Hayley D, Graham SW, Martone PT. Genomic rearrangements and sequence evolution across brown algal organelles. Genome Biol Evol. 2021;7:7. https://doi.org/10.1093/gbe/evab124.

    CAS  Article  Google Scholar 

  24. Drouin G, Daoud H, Xia JN. Relative rates of synonymous substitutions in the mitochondrial, chloroplast and nuclear genomes of seed plants. Mol Phylogenet Evol. 2008;49:827–31. https://doi.org/10.1016/j.ympev.2008.09.009.

    CAS  Article  PubMed  Google Scholar 

  25. Tanaka T, Yoshimitsu S, Imayoshi Y, Ishiga Y, Terada R. Distribution and characteristics of seaweed/seagrass community in Kagoshima Bay, Kagoshima prefecture, Japan. Nippon Suisan Gakk. 2013;79(1):20–30. https://doi.org/10.2331/suisan.79.20.

    Article  Google Scholar 

  26. Dang X, Nb V, Tran TTV, Le NH. Effect of storage time on phlorotannin content and antioxidant activity of six Sargassum species from Nhatrang Bay, Vietnam. J Appl Phycol. 2016;28:567–72. https://doi.org/10.1007/s10811-015-0600-y.

    CAS  Article  Google Scholar 

  27. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20. https://doi.org/10.1093/bioinformatics/btu170.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Luo RB, Liu BH, Xie YL, Li ZY, Huang WH. SOAPdenovo2:an empirically improved memory-efficient short-read de novo assembler. GigaScience. 2012;1:1–18. https://doi.org/10.1186/2047-217X-1-18.

    Article  Google Scholar 

  29. Altschul SF. BLAST v 2.8.1+: basic local alignment search tool (BLAST). J Mol Biol. 2012;215(3):403–10. https://doi.org/10.1016/S0022-2836(05)80360-2.

    Article  Google Scholar 

  30. Wyman SK, Jansen RK, Boore JL. Automatic annotation of organellar genomes with DOGMA. Bioinformatics. 2004;20(17):3252–5. https://doi.org/10.1093/bioinformatics/bth352.

    CAS  Article  PubMed  Google Scholar 

  31. Schattner P, Brooks AN, Lowe TM. The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs. Nucleic Acids Res. 2005;33:686–9. https://doi.org/10.1093/nar/gki366.

    CAS  Article  Google Scholar 

  32. Lagesen K, Hallin P, Rødland EA, Stærfeldt H, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35:3100–8. https://doi.org/10.1093/nar/gkm160.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Greiner S, Lehwark P, Bock R. Organellar genome DRAW (OGDRAW) version 1.3.1: expanded toolkit for the graphical visualization of organellar genomes. Nucleic Acids Res. 2019;47:W59–64. https://doi.org/10.1093/nar/gkz238.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Sudhir K, Glen S, Koichiro T. MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol. 2016;33:1870–4. https://doi.org/10.1093/molbev/msw054.

    CAS  Article  Google Scholar 

  35. Chakraborty A, Sarkar D, Satya P, Karmakar PG, Singh NK. Pathways associated with lignin biosynthesis in lignomaniac jute fibres. Mol Gen Genomics. 2015;290(4):1523–42. https://doi.org/10.1007/s00438-015-1013-y.

    CAS  Article  Google Scholar 

  36. Ali A, Jaakko H, Peter P. IRscope: an online program to visualize the junction sites of chloroplast genomes. Bioinformatics. 2018;34(17):3030–1. https://doi.org/10.1093/bioinformatics/bty220.

    CAS  Article  Google Scholar 

  37. Hall TA. BioEdit: a user-friendly biological sequence alignment program for windows 95/98/NT. Nucleic Acids Symp Ser. 1999;41(41):95–8. https://doi.org/10.1021/bk-1999-0734.ch008.

    CAS  Article  Google Scholar 

  38. Talavera G, Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol. 2007;56(4):564–77. https://doi.org/10.1080/10635150701472164.

    CAS  Article  PubMed  Google Scholar 

  39. Xia X. DAMBE5: a comprehensive software package for data analysis in molecular biology and evolution. Mol Biol Evol. 2013;7:1720–8. https://doi.org/10.1093/molbev/mst064.

    CAS  Article  Google Scholar 

  40. Yang Z. PAML 4: a program package for phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–91. https://doi.org/10.1093/molbev/msm088.

    CAS  Article  PubMed  Google Scholar 

  41. Hu Y, Xing W, Hu Z, Liu G. Phylogenetic analysis and substitution rate estimation of colonial Volvocine algae based on mitochondrial genome. Genes. 2020;11(1):115. https://doi.org/10.3390/genes11010115.

    CAS  Article  PubMed Central  Google Scholar 

  42. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, Mcwilliam H, et al. Clustal W and Clustal X version 2.0. Bioinformatics. 2013;23:2947–8. https://doi.org/10.1093/bioinformatics/btm404.

    CAS  Article  Google Scholar 

  43. Stothard P. The sequence manipulation suite: JavaScript programs for analyzing and formatting protein and DNA sequences. Biotechniques. 2000;28(6):1102–4. https://doi.org/10.1016/s1389-0344(00)00056-3.

    CAS  Article  PubMed  Google Scholar 

  44. Lam-Tung N, Schmidt HA, Arndt VH, Quang MB. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;1:268–74. https://doi.org/10.1093/molbev/msu300.

    CAS  Article  Google Scholar 

  45. Huelsenbeck JP, Ronquist F. MrBAYES: Bayesian inference of phylogeny. Bioinformatics. 2001;17:754–5. https://doi.org/10.1093/bioinformatics/17.8.754.

    CAS  Article  PubMed  Google Scholar 

  46. Subha K, Bui QM, Thomas KFW, Arndt von H, Lars SJ. ModelFinder: fast model selection foraccurate phylogenetic estimates. Nat Methods. 2017;14:587–9. https://doi.org/10.1038/nmeth.4285.

    CAS  Article  Google Scholar 

  47. Posada D, Crandall KA. Modeltest: testing the model of DNA substitution. Bioinformatics. 1998;14:817–8. https://doi.org/10.1093/bioinformatics/14.9.817.

    CAS  Article  PubMed  Google Scholar 

  48. Dos Reis M, Gunnell GF, Barba-Montoya J, Wilkins A, Yang Z, Yoder AD. Using phylogenomic data to explore the effects of relaxed clocks and calibration strategies on divergence time estimation: primates as a test case. Syst Biol. 2018;67(4):594–615. https://doi.org/10.1093/sysbio/syy001.

    Article  Google Scholar 

  49. 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(2):659–74. https://doi.org/10.1016/j.ympev.2010.04.020.

    CAS  Article  PubMed  Google Scholar 

  50. Starko S, Gomez MS, Darby H, Demes KW, Kawai H, Yotsukura N, et al. A comprehensive kelp phylogeny sheds light on the evolution of an ecosystem. Mol Phylogenet Evol. 2019;136:138–50. https://doi.org/10.1016/j.ympev.2019.04.012.

    Article  PubMed  Google Scholar 

  51. Dos Reis M, Yang ZH. Approximate likelihood calculation on a phylogeny for Bayesian estimation of divergence times. Mol Biol Evol. 2011;28:2161–72. https://doi.org/10.1093/molbev/msr045.

    CAS  Article  PubMed  Google Scholar 

  52. Porter J. Efficiency of covariance matrix estimators for maximum likelihood estimation. J Bus Econ Stat. 2002;20:431–40. https://doi.org/10.1198/073500102288618559.

    Article  Google Scholar 

  53. Seo TK, Kishino H, Thorne JL. Estimating absolute rates of synonymous and nonsynonymous nucleotide substitution in order to characterize natural selection and date species divergences. Mol Biol Evol. 2004;21(7):1201–13. https://doi.org/10.1093/molbev/msh088.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgments

We are grateful to the support of the Strategic Priority Research Program of Chinese Academy of Sciences (XDB42030203), National Natural Science Foundation of China (31900279), Shandong Provincial Natural Science Foundation (ZR2019BC024), and Thailand Research Fund (RDG6130002). The authors would like to thank TopEdit (www.topeditsci.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB42030203) to DLD, National Natural Science Foundation of China (31900279) and Shandong Provincial Natural Science Foundation (ZR2019BC024) to JZ, Thailand Research Fund (RDG6130002) to SGAD.

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Contributions

Shuangshuang Zhang, Yanshuo Liang and Jie Zhang were responsible for experiments, data analysis, interpretation, and manuscript preparation. Stefano G. A. Draisma collected the samples and revised the manuscript. Delin Duan gave contribution to the experimental design and editing of the final manuscript. All authors read and approved the final manuscript.

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Correspondence to Delin Duan.

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Supplementary Information

Additional file 1: Table S1.

The chloroplast genome coding genes of Sargassum polycystum.

Additional file 2: Table S2.

The chloroplast genome coding genes of Sargassum plagiophyllum.

Additional file 3: Table S3.

The dN/dS ratio, dN and dS values of 35 mitochondrial PCGs from 7 brown algae.

Additional file 4: Table S4.

The dN/dS ratio, dN and dS values of 114 chloroplast genes from 7 brown algae.

Additional file 5: Fig. S1.

The dN/dS of mitochondrial genes (n = 35) estimated from the Sargassum species. The ratio of non-synonymous and synonymous sequence divergence to the 7 species of brown algae.

Additional file 6: Fig. S2.

The dN/dS for chloroplast genes (n = 114) estimated to the Sargassum species. Shown is the ratio of non-synonymous and synonymous sequence divergence to the 7 species with scales in top and bottom panel.

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Zhang, S., Liang, Y., Zhang, J. et al. Organellar genome comparisons of Sargassum polycystum and S. plagiophyllum (Fucales, Phaeophyceae) with other Sargassum species. BMC Genomics 23, 629 (2022). https://doi.org/10.1186/s12864-022-08862-5

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Keywords

  • Sargassum polycystum
  • Sargassum plagiophyllum
  • Mitochondrial genome
  • Chloroplast genome
  • Phylogeny