Mitochondrial Genome Characteristics and Comparative Genomic Analysis of Spartina alterniflora
1
Zhejiang Academy of Forestry, Hangzhou 310023, China
2
Research Centre for Zhejiang Wetland, Hangzhou 310023, China
*
Author to whom correspondence should be addressed.
Curr. Issues Mol. Biol. 2025, 47(2), 107; https://doi.org/10.3390/cimb47020107
Submission received: 28 November 2024
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Revised: 6 February 2025
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Accepted: 7 February 2025
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Published: 8 February 2025
(This article belongs to the Special Issue Functional Genomics and Comparative Genomics Analysis in Plants, 2nd Edition)
Abstract
:The mitochondrial genome of Spartina alterniflora, an invasive species with significant ecological and economic impacts, was analyzed to provide a theoretical basis for understanding its phylogenetic relationships and molecular biology. Mitochondrial genome sequences of S. alterniflora and 23 related species from NCBI were utilized for bioinformatics and comparative genomic analyses. A sliding window analysis identified three genes (rps2, atp9, and nad6) as potential DNA barcodes for species identification. Intracellular gene transfer (IGT) events between mitochondrial and chloroplast genome were detected, highlighting the dynamic nature of genomic evolution. A selective pressure analysis revealed that most protein-coding genes (PCGs) underwent purifying selection (Ka/Ks < 1), while the nad2 and ccmB genes showed signs of positive selection pressure (Ka/Ks > 1), indicating their role in adaptation. A phylogenetic analysis demonstrated a close relationship between S. alterniflora and Eleusine indica, supported by a collinearity analysis, which suggests environmental convergence. This study provides novel insights into the structural and evolutionary characteristics of the S. alterniflora mitochondrial genome, offering valuable genomic resources for future research on invasive species management and evolutionary biology.
1. Introduction
Spartina alterniflora Loisel. (synonyms: S. maritima var. Alterniflora, S. stricta var. Alterniflora, etc.) is a perennial grass of the genus Spartina in the Poaceae family. Native to the Atlantic and Gulf coasts of the Americas, this species holds ecological and economical significance. Historically, it has been utilized for coastal stabilization, wetland restoration, and soil erosion control due to its robust root system and rapid growth. Additionally, S. alterniflora contributes to carbon sequestration in coastal ecosystems, with its dense biomass acting as a significant carbon sink [1]. However, its invasive proliferation in non-native regions, such as China’s coastal zones, has overshadowed these benefits, posing a significant threat to ecosystems, biodiversity maintenance, and the ecological security of most coastal marsh wetlands [2,3]. Consequently, it has been included in the initial catalog of alien invasive species by the National Environmental Protection Agency of China (https://www.mee.gov.cn/gkml/zj/wj/200910/t20091022_172155.htm/, accessed on 11 April 2024).
Mitochondria are essential organelles in eukaryotic cells, playing a central role in oxidative metabolism, energy synthesis, and physiological processes, such as cellular signal transduction, cell division, differentiation, and apoptosis regulation [4,5]. Plant mitochondrial genomes exhibit remarkable structural diversity and complexity, including polycyclic chromosomes, linear branches, and radial structures [6]. Their sizes ranging from approximately 66 kb to 11.7 Mb and often exist as dynamic, multipartite structures due to frequent recombination mediated by repeat sequences [7,8]. However, the difficulty associated with assembly and high costs of sequencing pose significant challenges to research on plant mitochondria [9,10,11]. With advancement in genomics and sequencing technologies, particularly third-generation sequencing technologies, an increasing number of mitochondrial genomes have been successfully assembled. Species such as Oryza sativa L., Triticum aestivum L., and Lolium perenne L., all belonging to the Poaceae family, have been reported successively. Researchers have systematically investigated research on sequence characteristics, genome structure, and phylogenetic relationships, among other aspects [12,13,14]. For invasive plants like S. alterniflora, while the organellar genomes have been sequenced, existing reports have only provided basic information, such as the total mitochondrial genome length (566,328 bp), G + C content, and structural annotation [15,16].
Biological invasions drive environmental changes, potentially endangering local biodiversity, human health, and agricultural and forestry economies [17]. In recent years, mitochondrial genomics has emerged as a valuable research tool, particularly the study of mitochondrial genome dynamics, which provides new insights into the genetic factors underlying invasion success and adaptive evolution [18]. Currently, an in-depth analysis of the mitochondrial genome characteristics and comparative genomics of S. alterniflora has not been conducted. Numerous research aspects, including the origin of organellar genomes, the evolution of nuclear–cytoplasmic interactions, structural complexity, and recombination events remain unknown. Therefore, conducting mitochondrial genome research on S. alterniflora can enrich the mitochondrial genome information of plants in the Poaceae family and provide essential foundational information for molecular systematics and molecular ecology research on S. alterniflora. In this study, we retrieved the mitochondrial genome sequences of S. alterniflora and its 23 related species from public databases. Meanwhile, we conducted a comprehensive analysis regarding nucleotide diversity, migration sequences, Ka/Ks analysis, and comparative genomics. The goal is to explore genomic structural and functional information to deepen our understanding of this species.
2. Materials and Methods
2.1. Data Source
To elucidate the evolutionary status of the S. alterniflora mitochondrial genome, mitochondrial genome sequences of this species and 23 related plants were retrieved from the NCBI Organelle Genome Resources database (http://www.ncbi.nlm.nih.gov/genome/organelle/ accessed on 22 April 2024). The selection criteria were guided by phylogenetic proximity, data availability as well as ecological and functional diversity. A systematic phylogenetic reconstruction was performed using mitochondrial genome data from a single S. alterniflora specimen (GenBank accession number: MT471321) publicly available on NCBI, alongside data from 23 related plants. The final dataset included 21 Poales species (20 from the Poaceae family and one from the Cyperaceae family) and an outgroup consisting of three species (one from the Asparagaceae family and two from the Arecaceae family).
2.2. Nucleotide Diversity Analysis
2.3. Intergenomic Sequence Transfers Analysis
The chloroplast genome sequence (GenBank accession number: MT311317) of S. alterniflora were download from the NCBI organelle genome database (https://www.ncbi.nlm.nih.gov/genome/organelle/ accessed on 22 April 2024). The BLAST software was utilized to search for homologous sequences between the chloroplast and mitochondrial genomes, with the filtering criteria set at a match identity of ≥70% and an E-value of ≤1 × 10−5. To visually represent the transfer of genomic fragments between the chloroplast and mitochondrial genomes, we used the circos ver. 0.69-5 software (http://circos.ca/software/download/ accessed on 25 April 2024) for visualization.
2.4. Ka/Ks Analysis
The ratio of non-synonymous substitutions (Ka) to synonymous substitutions (Ks) was analyzed based on 31 PCGs in the S. alterniflora mitochondrial genome, with pairwise comparisons between all species. Gene sequence alignment was conducted using the MAFFT ver. 7.427 software [19], and the Ka/Ks values for each gene were calculated using the KaKs_Calculator ver. 2.0 software [21] with the MLWL model. Finally, a box plot summarizing the Ka/Ks values for each gene was generated.
2.5. Phylogenetic Tree Construction
The mitochondrial genomes of 24 representative plants from 18 genera of 4 families in 3 orders were downloaded from NCBI. The coding sequences (CDS) were selected to construct a maximum likelihood phylogenetic tree. The sequences between species were aligned using MAFFT ver. 7.427 software (–auto mode) [19], and the aligned sequences were concatenated. The optimal nucleotide substitution model was determined using jModelTest ver. 2.1.10 software [22]. The GTRGAMMA model was employed in RAxML ver. 8.2.10 software [23] with a Bootstrap value of 1000 to evaluate the maximum likelihood method for building the phylogenetic tree.
2.6. Synteny Analysis
The genome sequences were aligned using MUMmer (ver. 4.0.0 beta2) software [24] with the maxmatch parameter to generate dot-plot plots. The x-axis in each box represents the assembled sequence, while the y-axis represents the other sequences. The red lines inside the boxes indicate forward matches, and the blue lines indicate reverse complementary matches.
3. Results
3.1. Nucleotide Diversity of the S. alterniflora mt Genome
Nucleotide diversity (Pi) can be used to evaluate the variation of nucleic acid sequences among different species. Therefore, selecting regions with higher variation can serve as potential molecular markers. The total number of mutations in 36 genes ranges from 0 to 399, with corresponding Pi values between 0.000 and 0.245, most of which are less than 0.100. Among these, the rRNA gene coding for ribosomal RNA, rrn5, has the lowest variability (Pi = 0.000). In contrast, the variable gene coding for the ribosomal small subunit, rps2, has the highest variability (Pi = 0.245). The core genes, atp9 (coding for ATP synthase) with a Pi of 0.070 and nad6 (coding for NADH dehydrogenase) with a Pi of 0.060, also exhibit slightly higher variability and can be considered as candidate DNA barcoding markers for further research on phylogeny and population genetics of the species (Figure 1). These findings suggest that rps2, atp9, and nad6 may serve as candidates for DNA barcoding, enabling rapid and accurate identification of S. alterniflora in mixed populations, which is critical for monitoring and controlling its invasive spread.
3.2. Intergenomic Sequence Transfers of S. alterniflora
Through a sequence similarity analysis, homologous fragments between the mitochondrial and chloroplast genomes of S. alterniflora were detected, revealing extensive interorganellar sequence transfer events. A total of 46 homologous fragments were identified (Figure 2; Table 1), with lengths ranging from 32 to 3203 bp, of which 8 exceed 1000 bp. The total length of the homologous sequences is 28,860 bp, with 28,475 bp located in the chloroplast genome’s repeat regions and 14,962 bp in the mitochondrial genome’s repeat regions. There are 5 protein-coding genes (PCGs) (ndhJ, psaB, rpl2, rpl23, and rps7) and 12 transfer RNA (tRNA) genes (trnM-CAT, trnH-GTG, trnF-GAA, trnR-TCT, trnS-GGA, trnP-TGG, trnC-GCA, trnW-CCA, trnN-GTT, trnN-GTT, and trnM-CAT) found to be completely located within the homologous sequences between the mitochondrial genome and nuclear genome of S. alterniflora. These homologous fragments are referred to as mitochondrial plastid DNAs (MTPTs), signifying chloroplasts to mitochondria transfer [25]. The CP-derived sequences contributed substantially to mitochondrial genomic diversity, consistent with prior studies highlighting species-specific variation in MTPT length and composition. However, the mechanisms driving sequence migration between the genomes and the expression of genes within the migrated sequences remain unknown, warranting further investigation.
3.3. Selective Pressure Analysis
Calculating the mean Ka/Ks can assess the selective pressure in the evolutionary dynamics of protein-coding genes (PCGs) among related species. In the case of neutral selection, Ka/Ks = 1. When Ka/Ks > 1, it indicates positive selection, while Ka/Ks < 1 indicates purifying selection [26]. By comparing the 31 PCGs in the mitochondrial genome of S. alterniflora with those of related species, we observed that the Ka/Ks values ranged from 0.097 to 1.473. Among these, nad2 (1.473 ± 0.818) and ccmB (1.090 ± 0.470) have mean Ka/Ks values greater than 1, indicating that they are under positive selection, while the remaining 28 PCGs have mean values less than 1, suggesting they are subject to purifying selection (Figure 3).
3.4. Phylogenetic Inference
The phylogenetic tree demonstrates a significant divergence between the outgroup and the Poaceae, with a support value of 100%. The 14 taxonomic units within the Poaceae are well clustered. The target species, S. alterniflora, forms a distinct subtree with Eleusine indica (L.) Gaertn., belonging to the genus Eleusine, and is sister to a small clade comprising six genera: Zea, Tripsacum, Coix, Sorghum, Eremochloa, and Chrysopogon. Additionally, Cyperus esculentus L. from the genus Cyperus is positioned at the base of the Poaceae, characterized by the longest branch length, thereby strongly supporting the separation between the Cyperaceae and Poaceae families (Figure 4).
3.5. Sequence Collinearity
In the Poaceae, mitochondrial gene sequence synteny analysis was randomly conducted with Sorghum bicolor (L.) Moench, Triticum aestivum L., E. indica, Chrysopogon zizanioides (L.) Roberty, Agrostis stolonifera L., and Eremochloa ophiuroides (Munro) Hack. alongside S. alterniflora. The results of the dot-plot analysis showed that among all pairwise comparisons of the plants, the homologous sequences between S. alterniflora and E. indica were the longest, accounting for 56.25% and 57.60% of their respective chloroplast genomes. This indicates the highest similarity and a close genetic relationship between the two species (Figure 5).
4. Discussion
During the evolution of higher plants, there has been extensive exchange between the mitochondrial and chloroplast genomes, a process known as intracellular gene transfer (IGT) [11,27]. It is hypothesized that IGT occurred as early as the endosymbiotic events that led to the evolution of eukaryotic chloroplasts. This gene transfer process facilitates information exchange and gene recombination between genomes, contributing to the emergence of new genomic structures and functions, thereby enhancing species’ adaptation to varying environmental conditions and survival pressures [28]. IGT is widely regarded as one of the most important driving forces of species evolution [25]. As the number of complete mitochondrial genomes increases, more instances of IGT have been discovered. In this study, approximately 19.15% (25,959 bp) of MTPTs were found in the chloroplast genome, whereas only 3.56% (20,140 bp) of MTPTs were present in the mitochondria, indicating that transfer from chloroplasts is more common than from mitochondria. Sequence transfers between genomes has been ongoing, primarily involving the transfer of non-functional DNA [29]. Although many organelle-derived sequences are inactive or non-functional, there are exceptions, some organelle genes transferred to the host cell nucleus can enhance the host cell’s ability to regulate organelle gene expression [30]. Additionally, tRNA genes are transferred more frequently than PCGs, similar to findings in Ilex metabaptista Loes. ex Diels [31] and Punica granatum L. [32], indicating that tRNA genes are more conserved in mitochondrial genomes and play an irreplaceable role in mitochondria. In summary, studying IGT is crucial for tracing ancient recombination events and structural variations in plant mitochondrial genomes. However, current explanations of its mechanisms remain at the hypothesis stage. Future research could focus on how the organelle transfer processes in S. alterniflora contribute to environmental stress tolerance.
Through a selection pressure analysis, we can gain a deeper understanding of the roles and evolutionary processes of protein-coding genes, providing important clues to unravel the mechanisms underlying species adaptive evolution [31]. The Ka/Ks statistical results indicate that most variant genes have undergone purifying selection, consistent with studies on species such as Suaeda glauca (Bunge) Bunge [33], Bupleurum chinense DC. [34], and Cymbidium ensifolium (L.) Sw. [35], suggesting that PCGs in the mitochondrial genome are conserved. In contrast, the nad2 and ccmB genes showed signs of positive selection pressure, suggesting their role in adaptation. These findings are consistent with previous studies on other invasive species, indicating that positive selection of energy metabolism-related genes may be a common strategy for successful invasion. The nad2 gene, also known as mitochondrial NADH dehydrogenase subunit 2, encodes a subunit of NADH dehydrogenase complex I in mitochondria. Its role is to transfer electrons to coenzyme Q within the mitochondrial respiratory chain, thereby generating energy to support cellular metabolism and survival. Studies have shown that mutations or adaptive evolution in nad2 can enhance the efficiency of oxidative phosphorylation, particularly under environmental stressors such as hypoxia or salinity [36]. The ccmB gene encodes a component of the mitochondrial respiratory chain complex, which regulates electron transfer in the electron transport chain and ultimately promotes ATP production, playing a crucial role in maintaining plant respiration and energy generation [37]. Therefore, it is inferred that these two energy metabolism-related genes underwent positive selection during the evolution of S. alterniflora to adapt to complex coastal environmental changes, thus sustaining the species’ strong invasive capability and reproductive success. However, further experiments are needed to validate the functions of these key genes through gene annotation and enrichment analyses.
With the ongoing development of high-throughput sequencing technology, the study of phylogenetic relationships has progressively shifted to the genomic level. The topological results of the phylogenetic clustering based on the mitochondrial genome in this study are largely consistent with the classification established by the Angiosperm Phylogeny Group (APG), indicating that utilization information obtained from mitochondrial genomes for plant phylogenetic research is feasible. Wang et al. [16] constructed a maximum likelihood (ML) tree based on 15 mitochondrial genes and suggested that the closest relationship exists between S. alterniflora and S. bicolor. Building on this foundation, our study expanded the dataset to include 24 sequenced mitochondrial genomes and similarly employed the ML method for phylogenetic reconstruction. The phylogenetic analysis demonstrated a close evolutionary relationship between S. alterniflora and E. indica, which was further supported by the results of synteny visualized through a dot-plot analysis. This finding is particularly interesting, as it suggests a shared ancestry or convergent evolution under similar environmental pressures (e.g., coastal/ruderal habitats). Both species thrive in disturbed or marginal habitats (e.g., coastal zones for S. alterniflora and ruderal environments for E. indica). However, due to the lack of sufficiently representative mitochondrial genomes for the genus Spartina, it is necessary to obtain additional mitochondrial genomes in the future to better address phylogenetic and evolutionary biology issues concerning this species.
5. Conclusions
This study conducted bioinformatics and comparative genomic analyses of S. alterniflora and related species based on publicly available mitochondrial genome sequences. The analyses included nucleotide polymorphism, intracellular gene transfer, Ka/Ks ratios, and a synteny analysis. Additionally, a phylogenetic reconstruction of the coding region sequences from 24 plant mitochondrial genomes further validated the phylogenetic position of S. alterniflora. The comprehensive analysis of the structure and function of the mitochondrial genome of S. alterniflora, along with the identification of potential molecular markers, contributes to a deeper understanding of the genomic evolution of this invasive species. These findings not only provide valuable genomic resources for future research on invasive species management but also offer potential targets for the development of novel control strategies. Future studies should focus on the functional validation of the identified candidate genes and their role in the invasive success of S. alterniflora.
Author Contributions
H.Z. collected data and wrote the original draft. C.Y. and H.L. reviewed and edited the manuscript. C.Y. and H.L. acquired funding. All authors have read and agreed to the published version of the manuscript.
Funding
This study was funded by “Leading Goose” R&D Program of Zhejiang (grant No. 2024C02002); Zhejiang Forestry Science and Technology Project (grant No. 2022SY06).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The sequences of complete mitogenomes of S. alterniflora can be available in NCBI (accession number: MT311317).
Acknowledgments
We thank Genepioneer Biotechnologies Co., Ltd. (Nanjing, China) for providing technical assistance.
Conflicts of Interest
The authors declare no conflicts of interest.
References
- Zhang, D.; Hu, Y.; Liu, M.; Chang, Y.; Yan, X.; Bu, R.; Zhao, D.; Li, Z. Introduction and spread of an exotic plant, Spartina alterniflora, along coastal marshes of China. Wetlands 2017, 37, 1181–1193. [Google Scholar] [CrossRef]
- Cui, L.; Berger, U.; Cao, M.; Zhang, Y.; He, J.; Pan, L.; Jiang, J. Conservation and restoration of mangroves in response to invasion of Spartina alterniflora based on the MaxEnt model: A case study in China. Forests 2023, 14, 1220. [Google Scholar] [CrossRef]
- Zheng, J.; Wei, H.; Chen, R.; Liu, J.; Wang, L.; Gu, W. Invasive trends of Spartina Alterniflora in the southeastern Coast of China and potential distributional impacts on mangrove forests. Plants 2023, 12, 1923. [Google Scholar] [CrossRef] [PubMed]
- Hammani, K.; Giegé, P. RNA metabolism in plant mitochondria. Trends Plant Sci. 2014, 19, 380–389. [Google Scholar] [CrossRef] [PubMed]
- Madreiter-Sokolowski, C.T.; Ramadani-Muja, J.; Ziomek, G.; Burgstaller, S.; Bischof, H.; Koshenov, Z.; Gottschalk, B.; Malli, R.; Graier, W.F. Tracking intra-and inter-organelle signaling of mitochondria. FEBS J. 2019, 286, 4378–4401. [Google Scholar] [CrossRef]
- Kong, J.; Wang, J.; Nie, L.; Tembrock, L.R.; Zou, C.; Kan, S.; Ma, X.; Wendel, J.F.; Wu, Z. Evolutionary dynamics of mitochondrial genomes and intracellular transfers among diploid and allopolyploid cotton species. BMC Biol. 2025, 23, 9. [Google Scholar] [CrossRef] [PubMed]
- Skippington, E.; Barkman, J.; Rice, D.W.; Palmer, J.D. Miniaturized mitogenome of the parasitic plant Viscum scurruloideum is extremely divergent and dynamic and has lost all nad genes. Proc. Natl. Acad. Sci. USA 2015, 112, E3515–E3524. [Google Scholar] [CrossRef] [PubMed]
- Putintseva, Y.A.; Bondar, E.I.; Simonov, E.P.; Sharov, V.V.; Oreshkova, N.V.; Kuzmin, D.A.; Konstantinov, Y.M.; Shmakov, V.N.; Belkov, V.I.; Sadovsky, M.G.; et al. Siberian larch (Larix sibirica Ledeb.) mitochondrial genome assembled using both short and long nucleotide sequence reads is currently the largest known mitogenome. BMC Genom. 2020, 21, 654. [Google Scholar] [CrossRef]
- Jackman, S.D.; Coombe, L.; Warren, R.L.; Kirk, H.; Trinh, E.; MacLeod, T.; Pleasance, S.; Pandoh, P.; Zhao, Y.; Coope, R.J.; et al. Complete mitochondrial genome of a gymnosperm, Sitka spruce (Picea sitchensis), indicates a complex physical structure. Genome Biol. Evol. 2020, 12, 1174–1179. [Google Scholar] [CrossRef] [PubMed]
- Zhang, S.; Wang, J.; He, W.; Kan, S.; Liao, X.; Jordan, D.R.; Mace, E.S.; Tao, Y.; Cruickshank, A.W.; Klein, R.; et al. Variation in mitogenome structural conformation in wild and cultivated lineages of sorghum corresponds with domestication history and plastome evolution. BMC Plant Biol. 2023, 23, 91. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Kan, S.; Liao, X.; Zhou, J.; Tembrock, L.R.; Daniell, H.; Jin, S.; Wu, Z. Plant organellar genomes: Much done, much more to do. Trends Plant Sci. 2024, 29, 754–769. [Google Scholar] [CrossRef]
- Notsu, Y.; Masood, S.; Nishikawa, T.; Kubo, N.; Akiduki, G.; Nakazono, M.; Hirai, A.; Kadowaki, K. The complete sequence of the rice (Oryza sativa L.) mitochondrial genome: Frequent DNA sequence acquisition and loss during the evolution of flowering plants. Mol. Genet. Genom. 2002, 268, 434–445. [Google Scholar] [CrossRef]
- Cui, P.; Liu, H.; Lin, Q.; Ding, F.; Zhou, G.; Hu, S.; Liu, D.; Yang, D.; Zhan, K.; Zhang, A.; et al. A complete mitochondrial genome of wheat (Triticum aestivum cv. Chinese Yumai), and fast evolving mitochondrial genes in higher plants. J. Genet. 2009, 88, 299–307. [Google Scholar] [CrossRef] [PubMed]
- Islam, M.S.; Studer, B.; Byrne, S.L.; Farrell, J.D.; Panitz, F.; Bendixen, C.; Møller, I.M.; Asp, T. The genome and transcriptome of perennial ryegrass mitochondria. BMC Genom. 2013, 14, 202. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Y.; Wang, K.; He, Y.; Wang, Y.; Qu, C.; Miao, J. The complete chloroplast genome of Spartina alterniflora. Mitochondrial DNA B 2020, 5, 2440–2441. [Google Scholar] [CrossRef]
- Wang, Y.; Xie, W.; Cao, J.; He, Y.; Zhao, Y.; Qu, C.; Miao, J. The complete mitochondrial genome of Sporobolus alterniflorus (loisel.) PM Peterson & Saarela (Poaceae) and phylogenetic analysis. Mitochondrial DNA B 2021, 6, 1303–1305. [Google Scholar]
- Matheson, P.; McGaughran, A. Genomic data is missing for many highly invasive species, restricting our preparedness for escalating incursion rates. Sci. Rep. 2022, 12, 13987. [Google Scholar] [CrossRef]
- Li, C.; Wang, B.; Ji, Y.; Huang, L.; Wang, X.; Zhao, W.; Wang, H.; Yao, Y. Mitochondrial genome provides species-specific targets for the rapid detection of early invasive populations of Hylurgus ligniperda in China. BMC Genom. 2024, 25, 90. [Google Scholar] [CrossRef]
- Katoh, K.; Rozewicki, J.; Yamada, K.D. MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. 2019, 20, 1160–1166. [Google Scholar] [CrossRef]
- Librado, P.; Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 2009, 25, 1451–1452. [Google Scholar] [CrossRef]
- Wang, D.; Zhang, Y.; Zhang, Z.; Zhu, J.; Yu, J. KaKs_Calculator 2.0: A toolkit incorporating gamma-series methods and sliding window strategies. Genom. Proteom. Bioinf. 2010, 8, 77–80. [Google Scholar] [CrossRef]
- Darriba, D.; Taboada, G.L.; Doalla, R.; Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 2012, 9, 772. [Google Scholar] [CrossRef]
- Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014, 30, 1312–1313. [Google Scholar] [CrossRef]
- Marçais, G.; Delcher, A.L.; Phillippy, A.M.; Coston, R.; Salzberg, S.L.; Zimin, A. MUMmer4: A fast and versatile genome alignment system. PLoS Comput. Biol. 2018, 14, e1005944. [Google Scholar] [CrossRef] [PubMed]
- Wang, X.C.; Chen, H.; Yang, D.; Liu, C. Diversity of mitochondrial plastid DNAs (MTPTs) in seed plants. Mitochondrial DNA A 2018, 29, 635–642. [Google Scholar] [CrossRef]
- Hurst, L.D. The Ka/Ks ratio: Diagnosing the form of sequence evolution. Trends Genet. 2002, 18, 486–487. [Google Scholar] [CrossRef]
- Yang, J.; Park, S.; Gil, H.Y.; Pak, J.H.; Kim, S.C. Characterization and dynamics of intracellular gene transfer in plastid genomes of Viola (Violaceae) and order Malpighiales. Front. Plant Sci. 2021, 12, 678580. [Google Scholar] [CrossRef]
- Filip, E.; Skuza, L. Horizontal gene transfer involving chloroplasts. Int. J. Mol. Sci. 2021, 22, 4484. [Google Scholar] [CrossRef] [PubMed]
- Leister, D.; Kleine, T. Role of intercompartmental DNA transfer in producing genetic diversity. Int. Rev. Cell Mol. Biol. 2011, 291, 73–114. [Google Scholar]
- Martin, W. Gene transfer from organelles to the nucleus: Frequent and in big chunks. Proc. Natl. Acad. Sci. USA 2003, 100, 8612–8614. [Google Scholar] [CrossRef]
- Zhou, P.; Zhang, Q.; Li, F.; Huang, J.; Zhang, M. Assembly and comparative analysis of the complete mitochondrial genome of Ilex metabaptista (Aquifoliaceae), a Chinese endemic species with a narrow distribution. BMC Plant Biol. 2023, 23, 393. [Google Scholar] [CrossRef] [PubMed]
- Lu, G.; Zhang, K.; Que, Y.; Li, Y. Assembly and analysis of the first complete mitochondrial genome of Punica granatum and the gene transfer from chloroplast genome. Front. Plant Sci. 2023, 14, 1132551. [Google Scholar] [CrossRef] [PubMed]
- Cheng, Y.; He, X.; Priyadarshani, S.V.G.N.; Wang, Y.; Ye, L.; Shi, C.; Ye, K.; Zhou, Q.; Luo, Z.; Deng, F.; et al. Assembly and comparative analysis of the complete mitochondrial genome of Suaeda glauca. BMC Genom. 2021, 22, 167. [Google Scholar] [CrossRef]
- Qiao, Y.; Zhang, X.; Li, Z.; Song, Y.; Sun, Z. Assembly and comparative analysis of the complete mitochondrial genome of Bupleurum chinense DC. BMC Genom. 2022, 23, 664. [Google Scholar] [CrossRef] [PubMed]
- Shen, B.; Shen, A.; Liu, L.; Tan, Y.; Li, S.; Tan, Z. Assembly and comparative analysis of the complete multichromosomal mitochondrial genome of Cymbidium ensifolium, an orchid of high economic and ornamental value. BMC Plant Biol. 2024, 24, 255. [Google Scholar] [CrossRef]
- Jethva, J.; Lichtenauer, S.; Schmidt-Schippers, R.; Steffen-Heins, A.; Poschet, G.; Wirtz, M.; van Dongen, J.T.; Eirich, J.; Finkemeier, I.; Bilger, W.; et al. Mitochondrial alternative NADH dehydrogenases NDA1 and NDA2 promote survival of reoxygenation stress in Arabidopsis by safeguarding photosynthesis and limiting ROS generation. New Phytol. 2023, 238, 96–112. [Google Scholar] [CrossRef]
- Faivre-Nitschke, S.E.; Nazoa, P.; Gualberto, J.M.; Grienenberger, J.M.; Bonnard, G. Wheat mitochondria ccmB encodes the membrane domain of a putative ABC transporter involved in cytochrome c biogenesis. BBA-Gene Struct. Expr. 2001, 1519, 199–208. [Google Scholar] [CrossRef]
Figure 1.
Sliding window analysis for the nucleotide diversity (Pi) of S. alterniflora.
Figure 2.
Distribution of homologous fragments between mitochondria and chloroplasts in S. alterniflora.
Figure 2.
Distribution of homologous fragments between mitochondria and chloroplasts in S. alterniflora.
Figure 3.
Boxplots of the pairwise Ka/Ks values among every shared mitochondrial gene of S. alterniflora.
Figure 3.
Boxplots of the pairwise Ka/Ks values among every shared mitochondrial gene of S. alterniflora.
Figure 4.
The maximum likelihood phylogenetic tree was constructed based on CDS sequences for 24 species. Numbers at each node represent bootstrap support values. The accession number in blue color following the species name corresponds to the GenBank accession number.
Figure 4.
The maximum likelihood phylogenetic tree was constructed based on CDS sequences for 24 species. Numbers at each node represent bootstrap support values. The accession number in blue color following the species name corresponds to the GenBank accession number.
Figure 5.
A collinearity analysis of S. alterniflora compared to six typical species in Poaceae. The horizontal coordinate in each box indicates the assembled sequence, the vertical coordinate indicates the other sequences, the value in parentheses is the proportion of homologous sequences to the total genome, the red line in the box indicates the forward alignment, and the blue line indicates the reverse complementary alignment.
Figure 5.
A collinearity analysis of S. alterniflora compared to six typical species in Poaceae. The horizontal coordinate in each box indicates the assembled sequence, the vertical coordinate indicates the other sequences, the value in parentheses is the proportion of homologous sequences to the total genome, the red line in the box indicates the forward alignment, and the blue line indicates the reverse complementary alignment.
Table 1.
Homologous fragments between mitochondria and chloroplasts in S. alterniflora.
| No. | Aligened Length (bp) | Sequence Identity (%) | Mismatches | Gap Opens | Chloroplast Genome (CP) | Mitochondrial Genome (MT) | Contained Genes | ||
|---|---|---|---|---|---|---|---|---|---|
| Start | End | Start | End | ||||||
| 1 | 3203 | 99.657 | 6 | 1 | 80,973 | 84,170 | 196,986 | 193,784 | trnM-CAT; trnH-GTG |
| 2 | 3203 | 99.657 | 6 | 1 | 132,219 | 135,416 | 193,784 | 196,986 | trnM-CAT; trnH-GTG |
| 3 | 3007 | 99.734 | 2 | 2 | 37,454 | 40,460 | 258,992 | 261,992 | |
| 4 | 1709 | 99.649 | 2 | 1 | 126,698 | 128,406 | 565,996 | 564,292 | |
| 5 | 1709 | 99.649 | 2 | 1 | 87,983 | 89,691 | 564,292 | 565,996 | |
| 6 | 2166 | 86.888 | 147 | 58 | 48,194 | 50,273 | 357,558 | 355,444 | trnF-GAA |
| 7 | 1297 | 98.227 | 20 | 2 | 35,467 | 36,761 | 203,241 | 201,946 | trnR-TCT |
| 8 | 1034 | 87.041 | 84 | 23 | 51,282 | 52,304 | 201,390 | 200,396 | |
| 9 | 585 | 99.658 | 1 | 1 | 95,399 | 95,982 | 389,831 | 389,247 | |
| 10 | 585 | 99.658 | 1 | 1 | 120,407 | 120,990 | 389,247 | 389,831 | |
| 11 | 825 | 84.848 | 70 | 23 | 44,149 | 44,951 | 359,302 | 358,511 | |
| 12 | 522 | 93.678 | 19 | 4 | 622 | 1137 | 85,462 | 85,975 | |
| 13 | 486 | 93.621 | 27 | 2 | 36,772 | 37,253 | 201,889 | 201,404 | |
| 14 | 532 | 87.030 | 51 | 7 | 110,463 | 110,982 | 592 | 67 | |
| 15 | 532 | 87.030 | 51 | 7 | 110,463 | 110,982 | 543,296 | 542,771 | |
| 16 | 453 | 87.417 | 41 | 10 | 5324 | 5768 | 101,801 | 102,245 | |
| 17 | 258 | 99.225 | 1 | 1 | 56,844 | 57,100 | 194,757 | 195,014 | |
| 18 | 410 | 81.463 | 36 | 17 | 45,103 | 45,487 | 358,312 | 357,918 | trnS-GGA |
| 19 | 238 | 89.916 | 11 | 8 | 64,331 | 64,567 | 436,134 | 436,359 | trnP-TGG |
| 20 | 889 | 73.566 | 180 | 41 | 123,201 | 124,064 | 73,559 | 72,701 | rrnS (partical:43.63%) |
| 21 | 889 | 73.566 | 180 | 41 | 92,325 | 93,188 | 72,701 | 73,559 | rrnS (partical:43.63%) |
| 22 | 889 | 73.566 | 180 | 41 | 92,325 | 93,188 | 463,240 | 462,382 | rrnS (partical:43.63%) |
| 23 | 889 | 73.566 | 180 | 41 | 123,201 | 124,064 | 462,382 | 463,240 | rrnS (partical:43.63%) |
| 24 | 155 | 97.419 | 3 | 1 | 41,618 | 41,772 | 258,998 | 258,845 | |
| 25 | 148 | 97.973 | 3 | 0 | 23,448 | 23,595 | 324,214 | 324,067 | |
| 26 | 226 | 86.726 | 29 | 1 | 97,053 | 97,277 | 327,985 | 327,760 | |
| 27 | 226 | 86.726 | 29 | 1 | 119,112 | 119,336 | 327,760 | 327,985 | |
| 28 | 145 | 93.793 | 8 | 1 | 45,141 | 45,284 | 40,212 | 40,356 | trnS-GGA (partical:86.52%) |
| 29 | 182 | 87.363 | 11 | 6 | 121,318 | 121,499 | 212,946 | 212,777 | |
| 30 | 182 | 87.363 | 11 | 6 | 94,890 | 95,071 | 212,777 | 212,946 | |
| 31 | 152 | 87.500 | 10 | 6 | 18,689 | 18,839 | 213,562 | 213,705 | trnC-GCA |
| 32 | 93 | 97.849 | 1 | 1 | 117,548 | 117,640 | 450,821 | 450,730 | |
| 33 | 93 | 97.849 | 1 | 1 | 98,749 | 98,841 | 450,730 | 450,821 | |
| 34 | 123 | 87.805 | 13 | 2 | 64,133 | 64,254 | 435,907 | 436,028 | trnW-CCA |
| 35 | 81 | 97.531 | 2 | 0 | 99,799 | 99,879 | 188,235 | 188,155 | trnN-GTT |
| 36 | 81 | 97.531 | 2 | 0 | 116,510 | 116,590 | 188,155 | 188,235 | trnN-GTT |
| 37 | 76 | 92.105 | 5 | 1 | 52,299 | 52,373 | 381,683 | 381,608 | trnM-CAT |
| 38 | 97 | 82.474 | 17 | 0 | 96,206 | 96,302 | 248,985 | 248,889 | rrnL (partical:2.73%) |
| 39 | 97 | 82.474 | 17 | 0 | 120,087 | 120,183 | 248,889 | 248,985 | rrnL (partical:2.73%) |
| 40 | 97 | 82.474 | 17 | 0 | 96,206 | 96,302 | 504,491 | 504,395 | |
| 41 | 97 | 82.474 | 17 | 0 | 120,087 | 120,183 | 504,395 | 504,491 | |
| 42 | 63 | 88.889 | 5 | 2 | 12,949 | 13,011 | 201,726 | 201,666 | |
| 43 | 32 | 100 | 0 | 0 | 99,400 | 99,431 | 162,713 | 162,682 | |
| 44 | 32 | 100 | 0 | 0 | 116,958 | 116,989 | 162,682 | 162,713 | |
| 45 | 35 | 97.143 | 0 | 1 | 103,130 | 103,163 | 48,940 | 48,974 | |
| 46 | 37 | 94.595 | 1 | 1 | 8068 | 8104 | 358,125 | 358,160 | trnS-GGA (partical:41.38%) |
| Total | 28,860 | ||||||||
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Zhu, H.; Yue, C.; Li, H. Mitochondrial Genome Characteristics and Comparative Genomic Analysis of Spartina alterniflora. Curr. Issues Mol. Biol. 2025, 47, 107. https://doi.org/10.3390/cimb47020107
AMA Style
Zhu H, Yue C, Li H. Mitochondrial Genome Characteristics and Comparative Genomic Analysis of Spartina alterniflora. Current Issues in Molecular Biology. 2025; 47(2):107. https://doi.org/10.3390/cimb47020107
Chicago/Turabian StyleZhu, Hong, Chunlei Yue, and Hepeng Li. 2025. "Mitochondrial Genome Characteristics and Comparative Genomic Analysis of Spartina alterniflora" Current Issues in Molecular Biology 47, no. 2: 107. https://doi.org/10.3390/cimb47020107
APA StyleZhu, H., Yue, C., & Li, H. (2025). Mitochondrial Genome Characteristics and Comparative Genomic Analysis of Spartina alterniflora. Current Issues in Molecular Biology, 47(2), 107. https://doi.org/10.3390/cimb47020107
