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Current IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Comparative Analysis of Mitochondrial and Chloroplast Genomes in Alfalfa (Medicago sativa L.)

W
Wenxuan Xu1
H
Huafeng Ding1
Y
Yingtong Mu1
C
Cuiping Gao1,2
Y
Yan Gao3
Q
Qi Wang1
F
Fengling Shi1,2,*
1College of Grassland Science, Inner Mongolia Agricultural University, Hohhot, 010010, China.
2Key Laboratory of Grassland Resources, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, 010010, China.
3Duolun Caimushan Autonomous Region-level Nature Reserve Management Station Xilingol, 027300, China.

  • Submitted28-06-2025|

  • Accepted24-11-2025|

  • First Online 09-12-2025|

  • doi 10.18805/LRF-882

ABSTRACT

Background: Codon usage bias is a widespread phenomenon in many organisms, including higher plants and plays an important role in regulating gene expression efficiency and accuracy. As an important forage species with high nutritional value, Medicago sativa (alfalfa) has been extensively utilized not only in traditional breeding but also in molecular genetic improvement. Understanding the characteristics of codon preference in its organelle genomes can provide valuable guidance for optimizing gene design and enhancing the expression of exogenous genes in alfalfa or related species.

Methods: A total of 30 mitochondrial and 51 chloroplast high-quality coding sequences were obtained from the NCBI database. Codon usage patterns and preferences were analyzed using CodonW, R, MEGA and Excel.

Result: The effective number of codons (ENC) in mitochondria ranged from 44.73 to 61.0, with an average GC content of 0.42 and GC content at the third codon position (GC3s) of 0.37. In chloroplasts, the ENC ranged from 38.90 to 57.76, with an average GC content of 0.37 and GC3s of 0.27. Analyses using ENC-plot, neutrality plot and PR2 bias analysis suggested that codon usage bias in Medicago sativa is influenced by both natural selection and mutation pressure. Twelve optimal codons were identified in mitochondria (mostly ending in ‘A’ and ‘U’) and fourteen in chloroplasts, also showing a preference for ‘A/U’-ending codons.

INTRODUCTION

Mitochondria and chloroplasts are essential semiautonomous organelles in plant cells, each possessing distinct genomes and performing complementary physiological functions. The mitochondrial genome drives oxidative phosphorylation and energy metabolism, generating adenosine triphosphate (ATP) and maintaining redox homeostasis (Stefano et al., 2015; Wang et al., 2016). The chloroplast genome governs photosynthesis and carbon assimilation, converting light energy into chemical energy that sustains plant growth (Adamiec et al., 2024). These two organelles not only underpin bioenergetic functions but also reflect ancient endosymbiotic origins and coevolution with the nuclear genome. Comparative analyses of organellar genomes reveal mechanisms of plant evolution, gene transfer and adaptation. Plant mitochondrial genomes are typically large and structurally dynamic, exhibiting frequent recombination, subgenomic circles and horizontal gene transfer (Kozik et al., 2019; Gualberto et al., 2014). In contrast, chloroplast genomes are more conserved and usually circular, encoding photosynthetic proteins but occasionally showing lineage-specific rearrangements and loss of the large inverted repeat (IR) region in some legumes (Palmer, 1985; Zhang et al., 2024). Inter-organelle DNA exchange and differential selection pressures contribute to plant adaptation under environmental stress (Liu et al., 2019; Morley et al., 2017).
       
Medicago sativa L. (alfalfa) is a perennial forage legume of high agronomic and ecological importance, providing protein-rich feed and improving soil fertility via nitrogen fixation (Avci et al., 2018). Despite its significance, few studies have examined evolutionary relationships between its mitochondrial and chloroplast genomes. The M. sativa chloroplast genome exhibits a typical IR-lacking structure (~125 kb) with variations in noncoding regions and adaptive signals in stress-related genes (Zhang et al., 2024; Jia et al., 2025). The mitochondrial genome shows high plasticity and extensive sequence exchange with the chloroplast genome in Medicago and related legumes (He et al., 2023; Jo et al., 2024). Organelle-level molecular adaptation has gained increasing attention in forage legumes. Codon-usage studies in M. ruthenica and M. varia revealed distinct synonymous codon preferences in genes linked to stress response and energy metabolism (Hao et al., 2025; Wang et al., 2020). DNA barcoding and transcriptomic approaches further distinguish Medicago species and identify functional loci within organellar genomes that contribute to adaptability and breeding improvement (Yang et al., 2023). These findings indicate that organelle genome variation drives evolutionary diversification and provides valuable cytoplasmic resources for enhancing stress tolerance, seed vigor and photosynthetic efficiency in legumes. Mitochondrial and chloroplast genomes also differ in codon-usage bias, nucleotide composition and synonymous substitution rates, all of which affect gene expression efficiency and adaptive evolution (Yang et al., 2024; Wang et al., 2020). Comparative investigation of these features can clarify organelle-specific selection mechanisms that shape energy metabolism and photosynthesis. Furthermore, organelle genomic variation offers cytoplasmic markers useful for breeding, since their maternal inheritance influences hybrid vigor, fertility restoration and stress resilience (Cauz-Santos et al., 2025; Zhang et al., 2025).
               
A systematic comparative analysis of M. sativa mitochondrial and chloroplast genomes is therefore essential for understanding organelle evolution, molecular adaptation and functional divergence. Integrating structural annotation, codon-usage bias, repeat-sequence characterization and selection-pressure analysis can elucidate evolutionary relationships and provide a theoretical basis for cytoplasmic inheritance and genetic improvement in alfalfa. Such insights will advance understanding of plant organelle evolution and support molecular breeding strategies to enhance stress tolerance, productivity and sustainability in forage crops.

MATERIALS AND METHODS

Materials for testing
 
The experiment was conducted in the Key Laboratory of Grassland Resources of Inner Mongolia Agricultural University from March to June 2025. Complete mitochondrial genome of Medicago sativa (GenBank accession number NC_068105.1) and chloroplast genome (GenBank accession number MZ983396.1) download in NCBI database. A total of 33 protein-coding gene sequences were obtained from the mitochondrial genome and 77 were obtained from the chloroplast genome. In order to analyze the codon bias more accurately, the gene coding sequences less than 300 bp were first removed and then the gene coding sequences with the start codon of ATG and the stop codons of TAA, TAG and TGA were selected. Finally, 30 mitochondrial genome sequences and 51 chloroplast genome sequences were obtained for subsequent data analysis (Yang et al., 2015).
 
Relative synonymous codon usage degree
 
Relative Synonymous Codon Usage Degree (RSCU) measures the ratio of a synonymous codon’s observed usage in a gene to its expected average usage. It helps detect shifts in the usage pattern of all such codons within a gene (Grantham et al., 1980).
 
Relative codon fitness
 
Relative codon fitness is often evaluated through the codon adaptation index (CAI), a widely-adopted geometric approach. The CAI quantifies the relative adaptation levels of individual codons. This method has found extensive applications across diverse biological fields (Sharp et al., 1987).
 
ENC plot analysis of codon usage bias
 
The effective number of codons (ENC) measures the deviation of codon usage from randomness and indicates the bias in synonymous codon usage. High-expression genes, having fewer rare codons, show strong biases and lower ENC values; low-expression genes with weak biases have higher ENC values (Wright, 1990).
 
PR2-plot plot analysis
 
PR2 - plot analysis focuses on codon usage biases. It aims to precisely detect mutational imbalances between A/T and C/G at the third codon position. Analyzing A/T and C/G frequencies at this position reveals if mutational biases or natural selection shapes codon usage (Parvathy et al., 2022).
 
Neutral mapping analysis
 
correlates GC12 (first and second positions) with GC3 (third position) to infer evolutionary forces. A strong correlation suggests mutation-driven bias, while a weak or absent correlation indicates selection dominance (Sharp and Li, 1987; Sueoka et al., 1999).
 
Optimal codon analysis
 
Optimal codon analysis: Frequency of optimal codons (FOP), which is defined as the codons most frequently utilized in the highly expressed genes of a species. FOP is species - specific and the determination of optimal codons typically relies on a set of gene sequences and their corresponding expression data (dos Reis et al., 2003).
 
Analysis of evolutionary selection pressure
 
The nonsynonymous (Ka) and synonymous (Ks) substitution rates were analyzed using Medicago sativa mitochondrial and chloroplast protein-coding genes as references. Pairwise comparisons among species were conducted with MEGA11 (Kumar et al., 2016) for sequence alignment and DnaSP v5.10 (Rozas et al., 2017) to calculate Ka/Ks values using the MLWL method. The resulting Ka/Ks values for each gene were then summarized and visualized as a box plot.
 
Analysis of organelle fragment exchange
 
Horizontal gene transfer (HGT) between mitochondria and chloroplasts is frequent in higher plants, where 5-10% of mitochondrial sequences may occur in chloroplast genomes (Smith, 2011). Homologous regions between the mitochondrial and chloroplast genomes of Medicago sativa were identified and visualized using TBtools to reveal organelle-level genetic exchange and evolutionary patterns.
 
Data processing
 
CodonW0R0EMBOSS0MEGA and other software tools were used for basic codon characterization, codon bias analysis neutral mapping analysis, etc. in R. The codon usage preference of individual genes was also analyzed.

RESULTS AND DISCUSSION

Genome structure and characteristics of Medicago sativa
 
The mitochondrial genome of Medicago sativa spans 290,285 bp, encoding 55 genes-18 tRNAs, 3 rRNAs and 34 protein-coding genes-with a GC content of 45.34% (Fig 1). The chloroplast genome measures 125,637 bp, comprising 111 genes-30 tRNAs, 4 rRNAs and 77 protein-coding genes (Table 1) -and a GC content of 33.82%.GC content influences genome stability and gene expression (Du et al., 2018; Zhou et al., 2014). In M. sativa, mitochondrial coding sequences averaged 42.47% GC (GC = 47.95% > GC = 42.21% > GCƒ = 37.02%), while chloroplast sequences averaged 37.15% GC (GC = 46.38% >GC = 38.54% > GCƒ = 26.54%).

Fig 1: Circular gene map of the mitochondrial mitochondrial and chloroplasts in Medicago sativa.



Table 1: Detailed characteristics of Medicago sativa mitochondrial and chloroplast genome.


       
The lower GCƒ values in both organelles indicate weak codon-usage bias and an AT-rich preference, typical of plant genomes (Zhao et al., 2021). Overall, the nucleotide composition of M. sativa organellar genomes reflects structural and evolutionary features that may affect molecular breeding and transgene design.
 
GC content analysis
 
CodonW (Fig 2) analyzed 30 mitochondrial protein-coding genes of Medicago sativa, revealing a mean GC content of 42.47% (33.90-51.80%) and GC3 of 37.00% (26.01-57.40%). Similarly, 51 chloroplast genes showed an average GC content of 37.25% (30.40-43.70%) and GC3 of 26.54% (19.78-33.46%). GC content affects genome stability and expression because G-C pairs form three hydrogen bonds, enhancing structural and thermal stability (Du et al., 2018; Zhou et al., 2014). Both organelles exhibited the typical GC1 > GC2 > GC3 pattern-mitochondria: 47.95%, 42.21%, 37.02%; chloroplasts: 46.38%, 38.54%, 26.54% indicating an AT-rich bias at the third position and weak codon usage bias (Table 2). This conserved A/U-ending preference, consistent with Arabidopsis and soybean (Zhao et al., 2021), reflects the combined effects of translational selection and mutational pressure in M. sativa organelles.

Fig 2: Mitochondrial and chloroplasts GC content.



Table 2: Codon preference parameters of mitochondrial and chloroplast genome coding genes in Medicago sativa.


 
Neutral plot analysis
 
In Medicago sativa mitochondria,GC3 ranged from 26.01-57.40% and GC12 from 37.89-50.39%. The regression y = -0.0275x + 0.468 (R² = 0.0023) showed no correlation between GC12 and GC3, indicating nonuniform base composition and strong selective influence on codon bias.For chloroplast genes (Fig 3), GC3 varied from 19.78-33.46% and GC12 from 32.17-53.24%, with y = 0.1375x + 0.3881 (R² = 0.0075) revealing a weak positive correlation, suggesting joint effects of selection and mutation pressure. Following Sharp and Li (1987), low GC12-GC3 correlations indicate selection dominance. Thus, in both organelles, codon bias is mainly shaped by selection rather than mutation, consistent with translational efficiency models (Sueoka, 1999; Bhattacharyya et al., 2019). Similar selection-driven patterns occur in Arabidopsis, maize and soybean (Smith et al., 2011; Wang et al., 2020).

Fig 3: Neutral graph analysis of mitochondrial and chloroplasts codons.


 
Relative codon adaptation
 
The codon adaptation index (CAI) measures how closely gene codon usage matches that of highly expressed genes, reflecting expression efficiency (Sharp and Li, 1987). In Medicago sativa mitochondria, CAI values ranged from 0.123 to 0.234 (Fig 4), indicating low expression potential. CAI correlated positively with ENC and GC content, suggesting codon bias is mainly shaped by nucleotide composition rather than translational selection.In chloroplasts, CAI values ranged from 0.119 to 0.305 (Fig 5), showing low–moderate expression potential. Here, CAI correlated negatively with ENC and positively with GC content, implying greater influence of gene expression.Thus, mitochondrial codon usage is composition-driven, whereas chloroplast codon usage reflects both compositional and translational selection. These trends agree with broader plant organelle patterns, where CAI effectively predicts expression potential (dos Reis et al., 2003).

Fig 4: Relative adaptability of mitochondrial codons.



Fig 5: Relative adaptability of chloroplast codons.


 
ENC plot analysis of codon usage bias
 
The effective number of codons (ENC) quantifies codon usage bias, where values near 20 indicate strong bias and values close to 61 suggest random codon use (Wright, 1990).
       
In Medicago sativa mitochondria, ENC values ranged from 44.73-61.00, all above 28, revealing weak codon bias.
       
The ENC-GC3 plot (Fig 6) showed most genes close to the expected curve, suggesting that mutational pressure predominates but natural selection also contributes.

Fig 6: Association analysis mitochondrial and chloroplasts between ENC and GC3.


       
Similarly, chloroplast genes displayed ENC values of 38.90-57.76, indicating weak bias. The ENC-GC3 relationship mirrored the mitochondrial pattern, implying joint influences of mutation and selection on codon preference.
       
These findings align with neutral and PR2-plot analyses, indicating dual constraints: background nucleotide composition shaped by mutation and fine-tuning by translational selection for expression efficiency. Comparable trends were observed in other species-soybean (Gualberto et al., 2014) and maize-where selection intensified in highly expressed or domestication-related genes.
 
PR2-plot bias analysis
 
The PR2 (Parity Rule 2) plot assesses nucleotide asymmetry (A vs. T, G vs. C) at the third codon position, where (0.5, 0.5) indicates no bias (Parvathy et al., 2022).

In Medicago sativa mitochondria (Fig 7), cytosine occurred less often than guanine and thymine more than adenine, with most genes below the midline (y<0.5), showing preference for G/T-ending codons-implying effects of both mutation and selection. Similarly, chloroplast genes  favored G and T at the third position, also clustering below the center.

Fig 7: Mitochondrial and chloroplasts PR2-plot bias analysis.


       
Combined with ENC and neutrality analyses, these findings suggest that mutational bias, natural selection and organelle-specific evolution jointly shape codon usage in M. sativa, consistent with reports for Miscanthus and Arachis (Sheng et al., 2021; Yang et al., 2023; Shen et al., 2025).

Relative synonymous codon usage analysis
 
Relative synonymous codon usage (RSCU) quantifies the frequency of a codon relative to its expected occurrence under equal usage. Values greater than 1 indicate codon preference, while those below 1 indicate avoidance.
       
In the Medicago sativa mitochondrial genome, 32 codons had RSCU>1, with 71.88% ending in A or U-particularly favoring U-ending codons-revealing a strong AU-rich bias (Fig 8). Similarly, 31 codons in the chloroplast genome showed RSCU>1 and 96.55% of these ended in A or U, confirming an AT-rich trend (Fig 9). This pattern corresponds to the low GC3 content and suggests that both translational selection and mutational pressure shape codon usage. Comparable A/U-ending preferences have been reported in Arachis and Miscanthus chloroplast genomes, especially in photosynthesis-and stress-related genes (Yang et al., 2023; Sheng et al., 2021). Such compositional and selective influences ensure efficient translation and evolutionary stability.

Fig 8: RSCU analysis of amino acids in Medicago sativa mitochondrial genome.



Fig 9: RSCU analysis of amino acids in the chloroplast genome of Medicago sativa.


       
Overall, codon usage in M. sativa is clearly nonrandom, reflecting the combined effects of nucleotide composition and evolutionary selection, consistent with patterns observed in other higher plants.
 
Optimal codon analysis
 
Optimal codons, defined as those most frequent in highly expressed genes, reflect translational efficiency and tRNA abundance (dos Reis et al., 2003). In the Medicago sativa mitochondrial genome (Table 3), 13 optimal codons were identified (ΔRSCU≥0.08, RSCU≥1), six ending with U and five with A, showing a clear A/U-ending bias consistent with its AT-rich composition. Similarly, 14 optimal codons were detected in the chloroplast genome, mainly A-or U-ending, again indicating preference for A/U codons.

Table 3: Relative frequency of synonymous codons used in Medicago sativa mitochondria and chloroplasts.


       
This pattern agrees with previous findings that organelle genomes favor codons matching abundant tRNAs for efficient translation (Zhao et al., 2021). Comparable A/U-ending preferences have also been reported in Arachis and Miscanthus chloroplasts (Yang et al., 2023; Sheng et al., 2021). Such organelle-specific codon usage provides valuable references for transgene optimization and synthetic biology applications in M. sativa and related species.
 
Analysis of evolutionary selection pressure
 
The Ka/Ks ratio indicates selective pressure on protein-coding genes: values >1, <1 and ≈1 represent positive, purifying and neutral selection, respectively (Nei and Gojobori, 1986). Pairwise Ka/Ks analyses of Medicago sativa mitochondrial and chloroplast genes were conducted using MEGA11 (Kumar et al., 2016) and DnaSP v5.10 (Rozas et al., 2017). Mitochondrial genes showed a mean Ka/Ks of 0.52, indicating predominant purifying selection, though several pairs (e.g., ccmB/rps14, atp8/rps3, rps4/rps14) exhibited Ka/Ks>1, suggesting adaptive evolution in respiration-related genes (Table 4). Chloroplast genes had a mean Ka/Ks of 0.70, also under purifying selection, with some pairs (petD/rps3, rpl14/rpl16, rps11/rps3) showing signs of positive selection in photosynthetic or ribosomal functions (Table 5).

Table 4: Mitochondria and chloroplast codon Ka Ka analysis.



Table 5: Ka Ka analysis of 22 pairs of repeat gene pairs in chloroplasts codon.


       
These patterns align with previous reports that photosynthetic genes frequently experience adaptive evolution, whereas core metabolic genes remain conserved (Yang, 2007; Sloan et al., 2017), reflecting a balance between conservation and adaptation in M. sativa organelles.
 
Analysis of organelle fragment exchange
 
Horizontal gene transfer (HGT) between mitochondria and chloroplasts is common in plants. In Medicago sativa, multiple collinear regions between mitochondrial and chloroplast genes indicate inter-organelle exchange. Several tRNA genes (e.g., trnW-CCA, trnN-GUU, trnD-GUC) were shared between both genomes (Fig 10), suggesting ancient transfer events and functional conservation (Smith, 2011; Morley et al., 2017). Similarly, collinearity between mitochondrial 18S/26S rRNAs and chloroplast 16S/23S rRNAs implies coordinated ribosomal evolution supporting translational compatibility. Fragments of nad7 also aligned with chloroplast 23S rRNA, reflecting co-evolution between respiration and translation systems (Gualberto and Newton, 2017). Overall, HGT between the two organelles in M. sativa likely promotes genome stability, redundancy and adaptive flexibility through mutation, recombination and gene transfer (Hao and Palmer, 2009; Rice et al., 2013).

Fig 10: Analysis of organelle fragment exchange.

CONCLUSION

This study comparatively analyzed the mitochondrial and chloroplast genomes of Medicago sativa, focusing on codon usage bias, nucleotide composition and selection pressure. Results showed that codon usage in both organelles is mainly shaped by natural selection and mutation pressure, with a preference for A/U-ending codons and generally AT-rich composition, especially at the third codon position.Thirteen and fourteen optimal codons were identified in the mitochondrial and chloroplast genomes, respectively, most ending in A or U, indicating translational selection. Ka/Ks analysis revealed that most genes are under purifying selection, while a few show positive selection, suggesting adaptive evolution. Gene fragment exchange between organelles further reflects their evolutionary interaction.Overall, the findings clarify organellar genome evolution in M. sativa and provide guidance for transgenic improvement-organelle-specific codon optimization could enhance gene expression and support breeding of more resilient, high-yield cultivars.

ACKNOWLEDGEMENT

The present study was supported by the Major Demonstration Program for Seed Industry Science and Technology Innovation of the Inner Mongolia Autonomous Region (Grant No. 2022JBGS0040) and Key Laboratory of Grassland Germplasm Innovation and Sustainable Utilization of Grassland Resources in Inner Mongolia Autonomous Region-Research on High-Quality herbage breeding and High-Yield, Stress-Resistant Cultivation Techniques(2025KYPT0033).
 
Disclaimers

The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article.

REFERENCES


  1. Adamiec, M. and Luciñski, R. (2024). The roles of RNA modifications in regulating chloroplast performance and photosynthesis efficiency. International Journal of Molecular Sciences. 25(22): 11912.

  2. Avci, M., Hatipoglu, R., Cinar, S. et al. (2018). Assessment of yield and quality characteristics of alfalfa (Medicago sativa L.) cultivars with different fall dormancy rating. Legume Research. 41(3): 369-373. doi: 10.18805/LR-364.

  3. Bhattacharyya, D., Uddin, A., Das, S. and Chakraborty, S. (2019). Mutation pressure and natural selection on codon usage in chloroplast genes of two species in Pisum L. (Fabaceae:  Faboideae). Mitochondrial DNA. Part A, DNA Mapping, Sequencing and Analysis. 30(4): 664-673.

  4. Cauz-Santos, L.A., da Costa, Z.P., Sader, M.A., van den Berg, C. and Vieira, M.L. (2025). Chloroplast genomic insights into adaptive evolution and rapid radiation in the genus Passiflora (Passifloraceae). BMC Plant Biology. 25. 192. https://doi.org/10.1186/s12870-025-06210-9.

  5. dos Reis, M., Wernisch, L. and Savva, R. (2003). Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome. Nucleic Acids Research. 31(23): 6976-6985.

  6. Du, M.Z., Zhang, C., Wang, H., Liu, S., Wei, W. and Guo, F.B. (2018). The GC content as a main factor shaping the amino acid usage during bacterial evolution process. Frontiers in Microbiology. 9: 2948.

  7. Grantham, R., Gautier, C., Gouy, M., Mercier, R. and Pavé, A. (1980). Codon catalog usage and the genome hypothesis. Nucleic  Acids Research. 8(1): r49-r62.

  8. Gualberto, J.M. and Newton, K.J. (2017). Plant mitochondrial genomes: Dynamics and mechanisms of mutation. Annual Review of Plant Biology. 68: 225-252.

  9. Gualberto, J.M., Mileshina, D., Wallet, C., Niazi, A.K., Weber-Lotfi, F. and Dietrich, A. (2014). The plant mitochondrial genome: Dynamics and maintenance. Biochimie. 100: 107-120.

  10. Hao, J., Liang, Y., Wang, T. and Su, Y. (2025). Correlations of gene expression, codon usage bias and evolutionary rates of the mitochondrial genome show tissue differentiation in Ophioglossum vulgatum. BMC Plant Biology. 25(1): 134.

  11. Hao, W. and Palmer, J.D. (2009). Fine-scale mergers of chloroplast and mitochondrial genes create functional, transcompartmentally chimeric mitochondrial genes. Proceedings of the National Academy of Sciences of the United States of America. 106(39): 16728-16733.

  12. He, X., Zhang, X., Deng, Y., Yang, R., Yu, L.X., Jia, S. and Zhang, T. (2023). Structural reorganization in two alfalfa mitochondrial genome assemblies and mitochondrial evolution in Medicago  species. International Journal of Molecular Sciences. 24(24): 17334. 

  13. Jia, M., Yixin, M., Zhao, Y., Jinhui, S., Fengling, S., Lan, Y. (2025). Cold resistance evaluation of alfalfa during the germination and seedling stages and analysis of its relationship with fall dormancy. Legume Research. 48(10): 1656-1662. doi: 10.18805/LRF-875.

  14. Jo, S., Park, M., Yusupov, Z., Tojibaev, K.S., Kenicer, G.J., Choi, S. and Paik, J.H. (2024). Intracellular gene transfer (IGT) events from the mitochondrial genome to the plastid genome of the subtribe ferulinae drude (Apiaceae) and their implications. BMC Plant Biology. 24(1): 1172. 

  15. Kozik, A., Rowan, B.A., Lavelle, D., Berke, L., Schranz, M.E., Michelmore, R.W. and Christensen, A.C. (2019). The alternative reality of plant mitochondrial DNA: One ring does not rule them all. PLoS Genetics. 15(8): e1008373.

  16. Kumar, S., Stecher, G. and Tamura, K. (2016). MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Molecular Biology and Evolution. 33(7): 1870- 1874.

  17. Liu, L. and Li, J. (2019). Communications between the endoplasmic reticulum and other organelles during abiotic stress response in plants. Frontiers in Plant Science. 10: 749.

  18. Morley, S.A. and Nielsen, B.L. (2017). Plant mitochondrial DNA. Frontiers in Bioscience (Landmark edition). 22(6): 1023-1032.

  19. Nei, M. and Gojobori, T. (1986). Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Molecular Biology and Evolution. 3(5): 418-426.

  20. Palmer, J.D. (1985). Comparative organization of chloroplast genomes. Annual Review of Genetics. 19: 325-354.

  21. Parvathy, S.T., Udayasuriyan, V. and Bhadana, V. (2022). Codon usage bias. Molecular Biology Reports. 49(1): 539-565. 

  22. Rice, D.W., Alverson, A.J., Richardson, A.O., Young, G.J., Sanchez- Puerta, M.V., Munzinger, J., Barry, K., Boore, J.L., Zhang, Y., dePamphilis, C.W., Knox, E.B. and Palmer, J.D. (2013). Horizontal transfer of entire genomes via mitochondrial fusion in the angiosperm Amborella. Science (New York, N.Y.). 342(6165): 1468-1473.

  23. Rozas, J., Ferrer-Mata, A., Sánchez-DelBarrio, J.C. et al. (2017). DnaSP 6: DNA sequence polymorphism analysis of large data sets. Molecular Biology and Evolution. 34(12): 3299- 3302.

  24. Sharp, P.M. and Li, W.H. (1987). The codon adaptation index: A measure of directional synonymous codon usage bias and its potential applications. Nucleic Acids Research. 15(3): 1281-1295.

  25. Shen, Y., Qi, L., Yang, L., Lu, X., Liu, J. and Wang, J. (2025). Natural selection as the primary driver of codon usage bias in the mitochondrial genomes of three Medicago  species. Genes. 16(6): 673.

  26. Sheng, J., She, X., Liu, X. et al. (2021). Comparative analysis of codon usage patterns in chloroplast genomes of five Miscanthus species and related species. Peer J. 9: e12173.

  27. Sloan, D.B., Havird, J.C. and Sharbrough, J. (2017). The on-again, off-again relationship between mitochondrial genomes and species boundaries. Molecular Ecology. 26(8): 2212-2236.

  28. Smith, D.R. (2011). Extending the limited transfer window hypothesis to inter-organelle DNA migration. Genome Biology and Evolution. 3: 743-748.

  29. Stefano, G.B., Snyder, C. and Kream, R.M. (2015). Mitochondria, chloroplasts in animal and plant cells: Significance of conformational matching. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research. 21: 2073-2078.

  30. Sueoka, N. (1999). Two aspects of DNA base composition: G+C content and translation-coupled deviation from intra- strand rule of A = T and G = C. Journal of Molecular Evolution. 49(1): 49-62.

  31. Wang, D., Khurshid, M., Sun, Z.M., Tang, Y.X., Zhou, M.L. and Wu, Y.M. (2016). Genetic engineering of alfalfa (Medicago sativa L.). Protein and Peptide Letters. 23(5): 495-502.

  32. Wang, Z., Xu, B., Li, B., Zhou, Q., Wang, G., Jiang, X., Wang, C., and Xu, Z. (2020). Comparative analysis of codon usage patterns in chloroplast genomes of six Euphorbiaceae  species. Peer J. 8: e8251.

  33. Wright, F. (1990). The ‘effective number of codons’ used in a gene. Gene. 87(1):  23-29.

  34. Yang, G.F., Su, K.L., Zhao, Y.R., Song, Z.B. and Sun, J. (2015). Analysis of codon usage in the chloroplast genome of Medicago truncatula. Acta Prataculturae Sinica. 24(12): 171-179.

  35. Yang, S., Li, G. and Li, H. (2023). Molecular characterizations of genes in chloroplast genomes of the genus Arachis L. (Fabaceae) based on codon usage divergence. Plos One. 18(3): e0281843.

  36. Yang, X., Wang, Y., Gong, W. and Li, Y. (2024). Comparative analysis of the codon usage pattern in the chloroplast genomes of gnetales species. International Journal of Molecular Sciences. 25(19): 10622.

  37. Yang, Z. (2007). PAML 4: Phylogenetic analysis by maximum likelihood. Molecular Biology and Evolution. 24(8): 1586- 1591.

  38. Zhang, S., Chang, S., Lin, X. et al. (2025). Comparative genomics uncovers organellar genome structural divergence in caryophyllales and reveals widespread non-coding transcription in Bougainvillea glabra organellar. BMC Genomics. 26. 722. 

  39. Zhang, T., Chen, X., Yan, W., Li, M., Huang, W., Liu, Q., Li, Y., Guo, C. and Shu, Y. (2024). Comparative analysis of chloroplast pan-genomes and transcriptomics reveals cold adaptation in Medicago sativa. International Journal of Molecular Sciences. 25(3): 1776.

  40. Zhao, Y., Zeng, W., Li, W. and Bi, Y. (2021). Complete chloroplast genome sequence of the drought- and heat-resistant Chinese alfalfa landrace, Medicago sativa ‘Deqin’. Mitochondrial DNA Part B: Resources. 6(4): 1488-1489.

  41. Zhou, H.Q., Ning, L.W., Zhang, H.X. and Guo, F.B. (2014). Analysis of the relationship between genomic GC content and patterns of base usage, codon usage and amino acid usage in prokaryotes: Similar GC content adopts similar compositional frequencies regardless of phylogenetic lineages. Plos One. 9(9): e107319.
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    Comparative Analysis of Mitochondrial and Chloroplast Genomes in Alfalfa (Medicago sativa L.)

    W
    Wenxuan Xu1
    H
    Huafeng Ding1
    Y
    Yingtong Mu1
    C
    Cuiping Gao1,2
    Y
    Yan Gao3
    Q
    Qi Wang1
    F
    Fengling Shi1,2,*
    1College of Grassland Science, Inner Mongolia Agricultural University, Hohhot, 010010, China.
    2Key Laboratory of Grassland Resources, Ministry of Education, Inner Mongolia Agricultural University, Hohhot, 010010, China.
    3Duolun Caimushan Autonomous Region-level Nature Reserve Management Station Xilingol, 027300, China.

    • Submitted28-06-2025|

    • Accepted24-11-2025|

    • First Online 09-12-2025|

    • doi 10.18805/LRF-882

    ABSTRACT

    Background: Codon usage bias is a widespread phenomenon in many organisms, including higher plants and plays an important role in regulating gene expression efficiency and accuracy. As an important forage species with high nutritional value, Medicago sativa (alfalfa) has been extensively utilized not only in traditional breeding but also in molecular genetic improvement. Understanding the characteristics of codon preference in its organelle genomes can provide valuable guidance for optimizing gene design and enhancing the expression of exogenous genes in alfalfa or related species.

    Methods: A total of 30 mitochondrial and 51 chloroplast high-quality coding sequences were obtained from the NCBI database. Codon usage patterns and preferences were analyzed using CodonW, R, MEGA and Excel.

    Result: The effective number of codons (ENC) in mitochondria ranged from 44.73 to 61.0, with an average GC content of 0.42 and GC content at the third codon position (GC3s) of 0.37. In chloroplasts, the ENC ranged from 38.90 to 57.76, with an average GC content of 0.37 and GC3s of 0.27. Analyses using ENC-plot, neutrality plot and PR2 bias analysis suggested that codon usage bias in Medicago sativa is influenced by both natural selection and mutation pressure. Twelve optimal codons were identified in mitochondria (mostly ending in ‘A’ and ‘U’) and fourteen in chloroplasts, also showing a preference for ‘A/U’-ending codons.

    INTRODUCTION

    Mitochondria and chloroplasts are essential semiautonomous organelles in plant cells, each possessing distinct genomes and performing complementary physiological functions. The mitochondrial genome drives oxidative phosphorylation and energy metabolism, generating adenosine triphosphate (ATP) and maintaining redox homeostasis (Stefano et al., 2015; Wang et al., 2016). The chloroplast genome governs photosynthesis and carbon assimilation, converting light energy into chemical energy that sustains plant growth (Adamiec et al., 2024). These two organelles not only underpin bioenergetic functions but also reflect ancient endosymbiotic origins and coevolution with the nuclear genome. Comparative analyses of organellar genomes reveal mechanisms of plant evolution, gene transfer and adaptation. Plant mitochondrial genomes are typically large and structurally dynamic, exhibiting frequent recombination, subgenomic circles and horizontal gene transfer (Kozik et al., 2019; Gualberto et al., 2014). In contrast, chloroplast genomes are more conserved and usually circular, encoding photosynthetic proteins but occasionally showing lineage-specific rearrangements and loss of the large inverted repeat (IR) region in some legumes (Palmer, 1985; Zhang et al., 2024). Inter-organelle DNA exchange and differential selection pressures contribute to plant adaptation under environmental stress (Liu et al., 2019; Morley et al., 2017).
           
    Medicago sativa     L. (alfalfa) is a perennial forage legume of high agronomic and ecological importance, providing protein-rich feed and improving soil fertility via nitrogen fixation (Avci et al., 2018). Despite its significance, few studies have examined evolutionary relationships between its mitochondrial and chloroplast genomes. The M. sativa chloroplast genome exhibits a typical IR-lacking structure (~125 kb) with variations in noncoding regions and adaptive signals in stress-related genes (Zhang et al., 2024; Jia et al., 2025). The mitochondrial genome shows high plasticity and extensive sequence exchange with the chloroplast genome in Medicago and related legumes (He et al., 2023; Jo et al., 2024). Organelle-level molecular adaptation has gained increasing attention in forage legumes. Codon-usage studies in M. ruthenica and M. varia revealed distinct synonymous codon preferences in genes linked to stress response and energy metabolism (Hao et al., 2025; Wang et al., 2020). DNA barcoding and transcriptomic approaches further distinguish Medicago species and identify functional loci within organellar genomes that contribute to adaptability and breeding improvement (Yang et al., 2023). These findings indicate that organelle genome variation drives evolutionary diversification and provides valuable cytoplasmic resources for enhancing stress tolerance, seed vigor and photosynthetic efficiency in legumes. Mitochondrial and chloroplast genomes also differ in codon-usage bias, nucleotide composition and synonymous substitution rates, all of which affect gene expression efficiency and adaptive evolution (Yang et al., 2024; Wang et al., 2020). Comparative investigation of these features can clarify organelle-specific selection mechanisms that shape energy metabolism and photosynthesis. Furthermore, organelle genomic variation offers cytoplasmic markers useful for breeding, since their maternal inheritance influences hybrid vigor, fertility restoration and stress resilience (Cauz-Santos et al., 2025; Zhang et al., 2025).
                   
    A systematic comparative analysis of M. sativa mitochondrial and chloroplast genomes is therefore essential for understanding organelle evolution, molecular adaptation and functional divergence. Integrating structural annotation, codon-usage bias, repeat-sequence characterization and selection-pressure analysis can elucidate evolutionary relationships and provide a theoretical basis for cytoplasmic inheritance and genetic improvement in alfalfa. Such insights will advance understanding of plant organelle evolution and support molecular breeding strategies to enhance stress tolerance, productivity and sustainability in forage crops.

    MATERIALS AND METHODS

    Materials for testing
     
    The experiment was conducted in the Key Laboratory of Grassland Resources of Inner Mongolia Agricultural University from March to June 2025. Complete mitochondrial genome of Medicago sativa (GenBank accession number NC_068105.1) and chloroplast genome (GenBank accession number MZ983396.1) download in NCBI database. A total of 33 protein-coding gene sequences were obtained from the mitochondrial genome and 77 were obtained from the chloroplast genome. In order to analyze the codon bias more accurately, the gene coding sequences less than 300 bp were first removed and then the gene coding sequences with the start codon of ATG and the stop codons of TAA, TAG and TGA were selected. Finally, 30 mitochondrial genome sequences and 51 chloroplast genome sequences were obtained for subsequent data analysis (Yang et al., 2015).
     
    Relative synonymous codon usage degree
     
    Relative Synonymous Codon Usage Degree (RSCU) measures the ratio of a synonymous codon’s observed usage in a gene to its expected average usage. It helps detect shifts in the usage pattern of all such codons within a gene (Grantham et al., 1980).
     
    Relative codon fitness
     
    Relative codon fitness is often evaluated through the codon adaptation index (CAI), a widely-adopted geometric approach. The CAI quantifies the relative adaptation levels of individual codons. This method has found extensive applications across diverse biological fields (Sharp et al., 1987).
     
    ENC plot analysis of codon usage bias
     
    The effective number of codons (ENC) measures the deviation of codon usage from randomness and indicates the bias in synonymous codon usage. High-expression genes, having fewer rare codons, show strong biases and lower ENC values; low-expression genes with weak biases have higher ENC values (Wright, 1990).
     
    PR2-plot plot analysis
     
    PR2 - plot analysis focuses on codon usage biases. It aims to precisely detect mutational imbalances between A/T and C/G at the third codon position. Analyzing A/T and C/G frequencies at this position reveals if mutational biases or natural selection shapes codon usage (Parvathy et al., 2022).
     
    Neutral mapping analysis
     
    correlates GC12 (first and second positions) with GC3 (third position) to infer evolutionary forces. A strong correlation suggests mutation-driven bias, while a weak or absent correlation indicates selection dominance (Sharp and Li, 1987; Sueoka et al., 1999).
     
    Optimal codon analysis
     
    Optimal codon analysis: Frequency of optimal codons (FOP), which is defined as the codons most frequently utilized in the highly expressed genes of a species. FOP is species - specific and the determination of optimal codons typically relies on a set of gene sequences and their corresponding expression data (dos Reis et al., 2003).
     
    Analysis of evolutionary selection pressure
     
    The nonsynonymous (Ka) and synonymous (Ks) substitution rates were analyzed using Medicago sativa mitochondrial and chloroplast protein-coding genes as references. Pairwise comparisons among species were conducted with MEGA11 (Kumar et al., 2016) for sequence alignment and DnaSP v5.10 (Rozas et al., 2017) to calculate Ka/Ks values using the MLWL method. The resulting Ka/Ks values for each gene were then summarized and visualized as a box plot.
     
    Analysis of organelle fragment exchange
     
    Horizontal gene transfer (HGT) between mitochondria and chloroplasts is frequent in higher plants, where 5-10% of mitochondrial sequences may occur in chloroplast genomes (Smith, 2011). Homologous regions between the mitochondrial and chloroplast genomes of Medicago sativa were identified and visualized using TBtools to reveal organelle-level genetic exchange and evolutionary patterns.
     
    Data processing
     
    CodonW0R0EMBOSS0MEGA and other software tools were used for basic codon characterization, codon bias analysis neutral mapping analysis, etc. in R. The codon usage preference of individual genes was also analyzed.

    RESULTS AND DISCUSSION

    Genome structure and characteristics of Medicago sativa
     
    The mitochondrial genome of Medicago sativa spans 290,285 bp, encoding 55 genes-18 tRNAs, 3 rRNAs and 34 protein-coding genes-with a GC content of 45.34% (Fig 1). The chloroplast genome measures 125,637 bp, comprising 111 genes-30 tRNAs, 4 rRNAs and 77 protein-coding genes (Table 1) -and a GC content of 33.82%.GC content influences genome stability and gene expression (Du et al., 2018; Zhou et al., 2014). In M. sativa, mitochondrial coding sequences averaged 42.47% GC (GC = 47.95% > GC = 42.21% > GCƒ = 37.02%), while chloroplast sequences averaged 37.15% GC (GC = 46.38% >GC = 38.54% > GCƒ = 26.54%).

    Fig 1: Circular gene map of the mitochondrial mitochondrial and chloroplasts in Medicago sativa.



    Table 1: Detailed characteristics of Medicago sativa mitochondrial and chloroplast genome.


           
    The lower GCƒ values in both organelles indicate weak codon-usage bias and an AT-rich preference, typical of plant genomes (Zhao et al., 2021). Overall, the nucleotide composition of M. sativa organellar genomes reflects structural and evolutionary features that may affect molecular breeding and transgene design.
     
    GC content analysis
     
    CodonW (Fig 2) analyzed 30 mitochondrial protein-coding genes of Medicago sativa, revealing a mean GC content of 42.47% (33.90-51.80%) and GC3 of 37.00% (26.01-57.40%). Similarly, 51 chloroplast genes showed an average GC content of 37.25% (30.40-43.70%) and GC3 of 26.54% (19.78-33.46%). GC content affects genome stability and expression because G-C pairs form three hydrogen bonds, enhancing structural and thermal stability (Du et al., 2018; Zhou et al., 2014). Both organelles exhibited the typical GC1 > GC2 > GC3 pattern-mitochondria: 47.95%, 42.21%, 37.02%; chloroplasts: 46.38%, 38.54%, 26.54% indicating an AT-rich bias at the third position and weak codon usage bias (Table 2). This conserved A/U-ending preference, consistent with Arabidopsis and soybean (Zhao et al., 2021), reflects the combined effects of translational selection and mutational pressure in M. sativa organelles.

    Fig 2: Mitochondrial and chloroplasts GC content.



    Table 2: Codon preference parameters of mitochondrial and chloroplast genome coding genes in Medicago sativa.


     
    Neutral plot analysis
     
    In Medicago sativa mitochondria,GC3 ranged from 26.01-57.40% and GC12 from 37.89-50.39%. The regression y = -0.0275x + 0.468 (R² = 0.0023) showed no correlation between GC12 and GC3, indicating nonuniform base composition and strong selective influence on codon bias.For chloroplast genes (Fig 3), GC3 varied from 19.78-33.46% and GC12 from 32.17-53.24%, with y = 0.1375x + 0.3881 (R² = 0.0075) revealing a weak positive correlation, suggesting joint effects of selection and mutation pressure. Following Sharp and Li (1987), low GC12-GC3 correlations indicate selection dominance. Thus, in both organelles, codon bias is mainly shaped by selection rather than mutation, consistent with translational efficiency models (Sueoka, 1999; Bhattacharyya et al., 2019). Similar selection-driven patterns occur in Arabidopsis, maize and soybean (Smith et al., 2011; Wang et al., 2020).

    Fig 3: Neutral graph analysis of mitochondrial and chloroplasts codons.


     
    Relative codon adaptation
     
    The codon adaptation index (CAI) measures how closely gene codon usage matches that of highly expressed genes, reflecting expression efficiency (Sharp and Li, 1987). In Medicago sativa mitochondria, CAI values ranged from 0.123 to 0.234 (Fig 4), indicating low expression potential. CAI correlated positively with ENC and GC content, suggesting codon bias is mainly shaped by nucleotide composition rather than translational selection.In chloroplasts, CAI values ranged from 0.119 to 0.305 (Fig 5), showing low–moderate expression potential. Here, CAI correlated negatively with ENC and positively with GC content, implying greater influence of gene expression.Thus, mitochondrial codon usage is composition-driven, whereas chloroplast codon usage reflects both compositional and translational selection. These trends agree with broader plant organelle patterns, where CAI effectively predicts expression potential (dos Reis et al., 2003).

    Fig 4: Relative adaptability of mitochondrial codons.



    Fig 5: Relative adaptability of chloroplast codons.


     
    ENC plot analysis of codon usage bias
     
    The effective number of codons (ENC) quantifies codon usage bias, where values near 20 indicate strong bias and values close to 61 suggest random codon use (Wright, 1990).
           
    In Medicago sativa mitochondria, ENC values ranged from 44.73-61.00, all above 28, revealing weak codon bias.
           
    The ENC-GC3 plot (Fig 6) showed most genes close to the expected curve, suggesting that mutational pressure predominates but natural selection also contributes.

    Fig 6: Association analysis mitochondrial and chloroplasts between ENC and GC3.


           
    Similarly, chloroplast genes displayed ENC values of 38.90-57.76, indicating weak bias. The ENC-GC3 relationship mirrored the mitochondrial pattern, implying joint influences of mutation and selection on codon preference.
           
    These findings align with neutral and PR2-plot analyses, indicating dual constraints: background nucleotide composition shaped by mutation and fine-tuning by translational selection for expression efficiency. Comparable trends were observed in other species-soybean (Gualberto et al., 2014) and maize-where selection intensified in highly expressed or domestication-related genes.
     
    PR2-plot bias analysis
     
    The PR2 (Parity Rule 2) plot assesses nucleotide asymmetry (A vs. T, G vs. C) at the third codon position, where (0.5, 0.5) indicates no bias (Parvathy et al., 2022).

    In Medicago sativa mitochondria (Fig 7), cytosine occurred less often than guanine and thymine more than adenine, with most genes below the midline (y<0.5), showing preference for G/T-ending codons-implying effects of both mutation and selection. Similarly, chloroplast genes  favored G and T at the third position, also clustering below the center.

    Fig 7: Mitochondrial and chloroplasts PR2-plot bias analysis.


           
    Combined with ENC and neutrality analyses, these findings suggest that mutational bias, natural selection and organelle-specific evolution jointly shape codon usage in M. sativa, consistent with reports for Miscanthus and Arachis (Sheng et al., 2021; Yang et al., 2023; Shen et al., 2025).

    Relative synonymous codon usage analysis
     
    Relative synonymous codon usage (RSCU) quantifies the frequency of a codon relative to its expected occurrence under equal usage. Values greater than 1 indicate codon preference, while those below 1 indicate avoidance.
           
    In the Medicago sativa mitochondrial genome, 32 codons had RSCU>1, with 71.88% ending in A or U-particularly favoring U-ending codons-revealing a strong AU-rich bias (Fig 8). Similarly, 31 codons in the chloroplast genome showed RSCU>1 and 96.55% of these ended in A or U, confirming an AT-rich trend (Fig 9). This pattern corresponds to the low GC3 content and suggests that both translational selection and mutational pressure shape codon usage. Comparable A/U-ending preferences have been reported in Arachis and Miscanthus chloroplast genomes, especially in photosynthesis-and stress-related genes (Yang et al., 2023; Sheng et al., 2021). Such compositional and selective influences ensure efficient translation and evolutionary stability.

    Fig 8: RSCU analysis of amino acids in Medicago sativa mitochondrial genome.



    Fig 9: RSCU analysis of amino acids in the chloroplast genome of Medicago sativa.


           
    Overall, codon usage in M. sativa is clearly nonrandom, reflecting the combined effects of nucleotide composition and evolutionary selection, consistent with patterns observed in other higher plants.
     
    Optimal codon analysis
     
    Optimal codons, defined as those most frequent in highly expressed genes, reflect translational efficiency and tRNA abundance (dos Reis et al., 2003). In the Medicago sativa mitochondrial genome (Table 3), 13 optimal codons were identified (ΔRSCU≥0.08, RSCU≥1), six ending with U and five with A, showing a clear A/U-ending bias consistent with its AT-rich composition. Similarly, 14 optimal codons were detected in the chloroplast genome, mainly A-or U-ending, again indicating preference for A/U codons.

    Table 3: Relative frequency of synonymous codons used in Medicago sativa mitochondria and chloroplasts.


           
    This pattern agrees with previous findings that organelle genomes favor codons matching abundant tRNAs for efficient translation (Zhao et al., 2021). Comparable A/U-ending preferences have also been reported in Arachis and Miscanthus chloroplasts (Yang et al., 2023; Sheng et al., 2021). Such organelle-specific codon usage provides valuable references for transgene optimization and synthetic biology applications in M. sativa and related species.
     
    Analysis of evolutionary selection pressure
     
    The Ka/Ks ratio indicates selective pressure on protein-coding genes: values >1, <1 and ≈1 represent positive, purifying and neutral selection, respectively (Nei and Gojobori, 1986). Pairwise Ka/Ks analyses of Medicago sativa mitochondrial and chloroplast genes were conducted using MEGA11 (Kumar et al., 2016) and DnaSP v5.10 (Rozas et al., 2017). Mitochondrial genes showed a mean Ka/Ks of 0.52, indicating predominant purifying selection, though several pairs (e.g., ccmB/rps14, atp8/rps3, rps4/rps14) exhibited Ka/Ks>1, suggesting adaptive evolution in respiration-related genes (Table 4). Chloroplast genes had a mean Ka/Ks of 0.70, also under purifying selection, with some pairs (petD/rps3, rpl14/rpl16, rps11/rps3) showing signs of positive selection in photosynthetic or ribosomal functions (Table 5).

    Table 4: Mitochondria and chloroplast codon Ka Ka analysis.



    Table 5: Ka Ka analysis of 22 pairs of repeat gene pairs in chloroplasts codon.


           
    These patterns align with previous reports that photosynthetic genes frequently experience adaptive evolution, whereas core metabolic genes remain conserved (Yang, 2007; Sloan et al., 2017), reflecting a balance between conservation and adaptation in M. sativa organelles.
     
    Analysis of organelle fragment exchange
     
    Horizontal gene transfer (HGT) between mitochondria and chloroplasts is common in plants. In Medicago sativa, multiple collinear regions between mitochondrial and chloroplast genes indicate inter-organelle exchange. Several tRNA genes (e.g., trnW-CCA, trnN-GUU, trnD-GUC) were shared between both genomes (Fig 10), suggesting ancient transfer events and functional conservation (Smith, 2011; Morley et al., 2017). Similarly, collinearity between mitochondrial 18S/26S rRNAs and chloroplast 16S/23S rRNAs implies coordinated ribosomal evolution supporting translational compatibility. Fragments of nad7 also aligned with chloroplast 23S rRNA, reflecting co-evolution between respiration and translation systems (Gualberto and Newton, 2017). Overall, HGT between the two organelles in M. sativa likely promotes genome stability, redundancy and adaptive flexibility through mutation, recombination and gene transfer (Hao and Palmer, 2009; Rice et al., 2013).

    Fig 10: Analysis of organelle fragment exchange.

    CONCLUSION

    This study comparatively analyzed the mitochondrial and chloroplast genomes of Medicago sativa, focusing on codon usage bias, nucleotide composition and selection pressure. Results showed that codon usage in both organelles is mainly shaped by natural selection and mutation pressure, with a preference for A/U-ending codons and generally AT-rich composition, especially at the third codon position.Thirteen and fourteen optimal codons were identified in the mitochondrial and chloroplast genomes, respectively, most ending in A or U, indicating translational selection. Ka/Ks analysis revealed that most genes are under purifying selection, while a few show positive selection, suggesting adaptive evolution. Gene fragment exchange between organelles further reflects their evolutionary interaction.Overall, the findings clarify organellar genome evolution in M. sativa and provide guidance for transgenic improvement-organelle-specific codon optimization could enhance gene expression and support breeding of more resilient, high-yield cultivars.

    ACKNOWLEDGEMENT

    The present study was supported by the Major Demonstration Program for Seed Industry Science and Technology Innovation of the Inner Mongolia Autonomous Region (Grant No. 2022JBGS0040) and Key Laboratory of Grassland Germplasm Innovation and Sustainable Utilization of Grassland Resources in Inner Mongolia Autonomous Region-Research on High-Quality herbage breeding and High-Yield, Stress-Resistant Cultivation Techniques(2025KYPT0033).
     
    Disclaimers

    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect losses resulting from the use of this content.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article.

    REFERENCES


    1. Adamiec, M. and Luciñski, R. (2024). The roles of RNA modifications in regulating chloroplast performance and photosynthesis efficiency. International Journal of Molecular Sciences. 25(22): 11912.

    2. Avci, M., Hatipoglu, R., Cinar, S. et al. (2018). Assessment of yield and quality characteristics of alfalfa (Medicago sativa L.) cultivars with different fall dormancy rating. Legume Research. 41(3): 369-373. doi: 10.18805/LR-364.

    3. Bhattacharyya, D., Uddin, A., Das, S. and Chakraborty, S. (2019). Mutation pressure and natural selection on codon usage in chloroplast genes of two species in Pisum L. (Fabaceae:  Faboideae). Mitochondrial DNA. Part A, DNA Mapping, Sequencing and Analysis. 30(4): 664-673.

    4. Cauz-Santos, L.A., da Costa, Z.P., Sader, M.A., van den Berg, C. and Vieira, M.L. (2025). Chloroplast genomic insights into adaptive evolution and rapid radiation in the genus Passiflora (Passifloraceae). BMC Plant Biology. 25. 192. https://doi.org/10.1186/s12870-025-06210-9.

    5. dos Reis, M., Wernisch, L. and Savva, R. (2003). Unexpected correlations between gene expression and codon usage bias from microarray data for the whole Escherichia coli K-12 genome. Nucleic Acids Research. 31(23): 6976-6985.

    6. Du, M.Z., Zhang, C., Wang, H., Liu, S., Wei, W. and Guo, F.B. (2018). The GC content as a main factor shaping the amino acid usage during bacterial evolution process. Frontiers in Microbiology. 9: 2948.

    7. Grantham, R., Gautier, C., Gouy, M., Mercier, R. and Pavé, A. (1980). Codon catalog usage and the genome hypothesis. Nucleic  Acids Research. 8(1): r49-r62.

    8. Gualberto, J.M. and Newton, K.J. (2017). Plant mitochondrial genomes: Dynamics and mechanisms of mutation. Annual Review of Plant Biology. 68: 225-252.

    9. Gualberto, J.M., Mileshina, D., Wallet, C., Niazi, A.K., Weber-Lotfi, F. and Dietrich, A. (2014). The plant mitochondrial genome: Dynamics and maintenance. Biochimie. 100: 107-120.

    10. Hao, J., Liang, Y., Wang, T. and Su, Y. (2025). Correlations of gene expression, codon usage bias and evolutionary rates of the mitochondrial genome show tissue differentiation in Ophioglossum vulgatum. BMC Plant Biology. 25(1): 134.

    11. Hao, W. and Palmer, J.D. (2009). Fine-scale mergers of chloroplast and mitochondrial genes create functional, transcompartmentally chimeric mitochondrial genes. Proceedings of the National Academy of Sciences of the United States of America. 106(39): 16728-16733.

    12. He, X., Zhang, X., Deng, Y., Yang, R., Yu, L.X., Jia, S. and Zhang, T. (2023). Structural reorganization in two alfalfa mitochondrial genome assemblies and mitochondrial evolution in Medicago  species. International Journal of Molecular Sciences. 24(24): 17334. 

    13. Jia, M., Yixin, M., Zhao, Y., Jinhui, S., Fengling, S., Lan, Y. (2025). Cold resistance evaluation of alfalfa during the germination and seedling stages and analysis of its relationship with fall dormancy. Legume Research. 48(10): 1656-1662. doi: 10.18805/LRF-875.

    14. Jo, S., Park, M., Yusupov, Z., Tojibaev, K.S., Kenicer, G.J., Choi, S. and Paik, J.H. (2024). Intracellular gene transfer (IGT) events from the mitochondrial genome to the plastid genome of the subtribe ferulinae drude (Apiaceae) and their implications. BMC Plant Biology. 24(1): 1172. 

    15. Kozik, A., Rowan, B.A., Lavelle, D., Berke, L., Schranz, M.E., Michelmore, R.W. and Christensen, A.C. (2019). The alternative reality of plant mitochondrial DNA: One ring does not rule them all. PLoS Genetics. 15(8): e1008373.

    16. Kumar, S., Stecher, G. and Tamura, K. (2016). MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Molecular Biology and Evolution. 33(7): 1870- 1874.

    17. Liu, L. and Li, J. (2019). Communications between the endoplasmic reticulum and other organelles during abiotic stress response in plants. Frontiers in Plant Science. 10: 749.

    18. Morley, S.A. and Nielsen, B.L. (2017). Plant mitochondrial DNA. Frontiers in Bioscience (Landmark edition). 22(6): 1023-1032.

    19. Nei, M. and Gojobori, T. (1986). Simple methods for estimating the numbers of synonymous and nonsynonymous nucleotide substitutions. Molecular Biology and Evolution. 3(5): 418-426.

    20. Palmer, J.D. (1985). Comparative organization of chloroplast genomes. Annual Review of Genetics. 19: 325-354.

    21. Parvathy, S.T., Udayasuriyan, V. and Bhadana, V. (2022). Codon usage bias. Molecular Biology Reports. 49(1): 539-565. 

    22. Rice, D.W., Alverson, A.J., Richardson, A.O., Young, G.J., Sanchez- Puerta, M.V., Munzinger, J., Barry, K., Boore, J.L., Zhang, Y., dePamphilis, C.W., Knox, E.B. and Palmer, J.D. (2013). Horizontal transfer of entire genomes via mitochondrial fusion in the angiosperm Amborella. Science (New York, N.Y.). 342(6165): 1468-1473.

    23. Rozas, J., Ferrer-Mata, A., Sánchez-DelBarrio, J.C. et al. (2017). DnaSP 6: DNA sequence polymorphism analysis of large data sets. Molecular Biology and Evolution. 34(12): 3299- 3302.

    24. Sharp, P.M. and Li, W.H. (1987). The codon adaptation index: A measure of directional synonymous codon usage bias and its potential applications. Nucleic Acids Research. 15(3): 1281-1295.

    25. Shen, Y., Qi, L., Yang, L., Lu, X., Liu, J. and Wang, J. (2025). Natural selection as the primary driver of codon usage bias in the mitochondrial genomes of three Medicago  species. Genes. 16(6): 673.

    26. Sheng, J., She, X., Liu, X. et al. (2021). Comparative analysis of codon usage patterns in chloroplast genomes of five Miscanthus species and related species. Peer J. 9: e12173.

    27. Sloan, D.B., Havird, J.C. and Sharbrough, J. (2017). The on-again, off-again relationship between mitochondrial genomes and species boundaries. Molecular Ecology. 26(8): 2212-2236.

    28. Smith, D.R. (2011). Extending the limited transfer window hypothesis to inter-organelle DNA migration. Genome Biology and Evolution. 3: 743-748.

    29. Stefano, G.B., Snyder, C. and Kream, R.M. (2015). Mitochondria, chloroplasts in animal and plant cells: Significance of conformational matching. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research. 21: 2073-2078.

    30. Sueoka, N. (1999). Two aspects of DNA base composition: G+C content and translation-coupled deviation from intra- strand rule of A = T and G = C. Journal of Molecular Evolution. 49(1): 49-62.

    31. Wang, D., Khurshid, M., Sun, Z.M., Tang, Y.X., Zhou, M.L. and Wu, Y.M. (2016). Genetic engineering of alfalfa (Medicago sativa L.). Protein and Peptide Letters. 23(5): 495-502.

    32. Wang, Z., Xu, B., Li, B., Zhou, Q., Wang, G., Jiang, X., Wang, C., and Xu, Z. (2020). Comparative analysis of codon usage patterns in chloroplast genomes of six Euphorbiaceae  species. Peer J. 8: e8251.

    33. Wright, F. (1990). The ‘effective number of codons’ used in a gene. Gene. 87(1):  23-29.

    34. Yang, G.F., Su, K.L., Zhao, Y.R., Song, Z.B. and Sun, J. (2015). Analysis of codon usage in the chloroplast genome of Medicago truncatula. Acta Prataculturae Sinica. 24(12): 171-179.

    35. Yang, S., Li, G. and Li, H. (2023). Molecular characterizations of genes in chloroplast genomes of the genus Arachis L. (Fabaceae) based on codon usage divergence. Plos One. 18(3): e0281843.

    36. Yang, X., Wang, Y., Gong, W. and Li, Y. (2024). Comparative analysis of the codon usage pattern in the chloroplast genomes of gnetales species. International Journal of Molecular Sciences. 25(19): 10622.

    37. Yang, Z. (2007). PAML 4: Phylogenetic analysis by maximum likelihood. Molecular Biology and Evolution. 24(8): 1586- 1591.

    38. Zhang, S., Chang, S., Lin, X. et al. (2025). Comparative genomics uncovers organellar genome structural divergence in caryophyllales and reveals widespread non-coding transcription in Bougainvillea glabra organellar. BMC Genomics. 26. 722. 

    39. Zhang, T., Chen, X., Yan, W., Li, M., Huang, W., Liu, Q., Li, Y., Guo, C. and Shu, Y. (2024). Comparative analysis of chloroplast pan-genomes and transcriptomics reveals cold adaptation in Medicago sativa. International Journal of Molecular Sciences. 25(3): 1776.

    40. Zhao, Y., Zeng, W., Li, W. and Bi, Y. (2021). Complete chloroplast genome sequence of the drought- and heat-resistant Chinese alfalfa landrace, Medicago sativa ‘Deqin’. Mitochondrial DNA Part B: Resources. 6(4): 1488-1489.

    41. Zhou, H.Q., Ning, L.W., Zhang, H.X. and Guo, F.B. (2014). Analysis of the relationship between genomic GC content and patterns of base usage, codon usage and amino acid usage in prokaryotes: Similar GC content adopts similar compositional frequencies regardless of phylogenetic lineages. Plos One. 9(9): e107319.
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