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Article

Deciphering Codon Usage Patterns in the Mitochondrial Genome of the Oryza Species

by
Yuyang Zhang
1,†,
Yunqi Ma
1,†,
Huanxi Yu
2,
Yu Han
3,4,* and
Tao Yu
5,*
1
The National-Local Joint Engineering Laboratory of High Efficiency and Superior-Quality Cultivation and Fruit Deep Processing Technology on Characteristic Fruit Trees, College of Horticulture and Forestry Sciences, Tarim University, Alar 843300, China
2
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People’s Republic of China, Nanjing 210008, China
3
State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-Products, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
4
Institute of Virology and Biotechnology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
5
Key Laboratory of Surveillance and Management of Invasive Alien Species in Guizhou Education Department, Guiyang University, Guiyang 550005, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(11), 2722; https://doi.org/10.3390/agronomy14112722
Submission received: 21 July 2024 / Revised: 7 November 2024 / Accepted: 12 November 2024 / Published: 18 November 2024
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

Rice (Oryza) is a genus in the Gramineae family, which has grown widely all over the world and is a staple food source for people’s survival. The genetic information of rice has garnered significant attention in recent years, prompting numerous researchers to conduct extensive investigations in this field. But rice mitochondrial codon usage patterns have received little attention. The present study systematically analyzed the codon usage patterns and sources of variance in the mitochondrial genome sequences of five rice species by the CodonW and R software programs. Our results revealed that the GC content of codons in rice mitochondrial genome genes was determined to be 43.60%. Notably, the individual codon positions exhibited distinct GC contents: 48.00% for position 1, 42.65% for position 2, and 40.16% for position 3. These findings suggest the preference of the rice mitochondrial genome for codons ending in A or U. A weak codon bias was observed, with the effective number of codons (ENC) varying between 40.02 and 61.00, with an average value of 54.34. Subsequently, we identified 25 identical high-frequency codons in five rice mitochondrial genomes, with 11 codons ending in A and 12 codons ending in U. The regression lines in the neutrality plot exhibited slopes of less than 0.5 in five rice species, indicating a predominant role of natural selection, while mutation pressure remained relatively insignificant. In the PR2-plot analysis, most of the genes were located in the right half of the plot, indicating that the third base of the synonymous codon was preferred to end in G than C. Additionally, the ENC plot and ENC ratio analysis unveiled that codon preferences in the rice mitochondrial genome were predominantly influenced by natural selection rather than mutational pressure. The analysis of correspondence revealed distinct variations in the codon usage pattern across five rice mitochondrial genomes. Based on the RSCU values of species, a cluster tree was inconsistent with the mitochondrial genetic data, indicating that RSCU data could not be used as a basis for classification at the species level in the Oryza genus. These results will help decide the specific types of natural selection pressures influencing codon usage and improve the expression of exogenous genes in rice mitochondrial genomes by optimizing their codons.

1. Introduction

The codon plays a pivotal role in the transmission of genetic information and is an indispensable step in biological processes [1,2]. The accurate identification of codons encoding different amino acids is crucial for ensuring the precise expression of genetic information [3]. Apart from methionine (Met) and tryptophan (Trp), most amino acids are encoded by multiple synonymous codons [4]. The synonymous codon usage (SCU) bias in various plant genomes follows a non-random pattern known as SCU bias [5]. Numerous research studies have demonstrated that SCU bias is associated with various biological factors, including the size of the genome, gene length, amino acid composition, protein local structure, codon context, biased gene conversion, frequency and patterns of mutations, and GC-rich compositions [6,7,8,9,10]. The coding sequences within a genome serve as fundamental blueprints for generating gene products that provide valuable insights into the organism’s evolution and functionality [11]. Exogenous sequence optimization based on the codon usage pattern of the mitochondrial genome facilitates enhancing the efficiency of carrier construction for heterologous genes, thereby improving gene expression and translation efficiency in subsequent studies on genetic development and species protection through genetic engineering [12]. Therefore, comprehensive genomic investigations on codon bias patterns are crucial for understanding how the significant derived forces have shaped their evolutionary trajectory in genome biology.
Mitochondria function as cellular powerhouses, providing the essential energy for intracellular biosynthesis and degradation processes [13,14]. Mitochondria play a vital role in aerobic respiration and are intricately involved in various vital cellular activities, including intracellular differentiation, apoptosis, growth, and division [15]. Consequently, the mitochondrial genome is indispensable for investigating plant phylogeny and key cellular processes [16]. This genetic information is also of great significance for the study of species evolution, species identification, and genetic transformation [17].
Rice is the staple for half of the global population and holds significant importance as a model organism in the research of monocots and crops [18,19]. With the rapid advancements in sequencing technology and bioinformatics analysis methods, an abundance of rice genome resources have become accessible [19,20]. These genomic resources can provide a comprehensive and profound understanding of the genetic evolution process of the rice genome. Although there are a few publications on the nuclear and chloroplast genome codon usage bias (CUB) of various rice varieties, research on codon bias in the mitochondrial genome is still scarce [7,21]. Studying codon usage in rice mitochondrial genomes is essential for understanding mitochondrial gene expression, optimizing genetic engineering, gaining evolutionary insights, and addressing rice diseases. This is particularly important when introducing foreign genes for crop improvement.
In this study, we comprehensively analyzed the protein-coding sequences of the mitochondrial genomes of five rice varieties and discovered their codon usage characteristics and influencing factors. Additionally, we evaluated the impact of natural selection, mutational pressure, and other variables on codon usage patterns. This study provides valuable insights into molecular breeding and genetic modification of rice varieties and contributes to a deeper understanding of codon usage bias in the rice mitochondrial genome.

2. Materials and Methods

2.1. Data Collection

The protein-coding CDS sequences of five rice mitochondrial genomes were obtained and downloaded from the National Center for Biotechnology Information (NCBI). To ensure data quality, we applied the established criteria for filtering protein-coding sequences: (1) presence of initiation codon ATG at the beginning and termination codon at the end and (2) gene length exceeding 300 bp. The filtering process was conducted used in-house-developed custom Python scripts.

2.2. Analysis of Nucleotide Composition and Codon Bias Indices

The guanine plus cytosine (G + C) content at the first, second, and third positions of synonymous codons (referred to as GC1s, GC2s, and GC3s) was analyzed for each gene. To quantify codon usage bias, we employed the effective number of codons (ENC). The ENC assigns a numerical value to reflect the preference level of specific codons in a gene. A gene with extreme bias would have a minimum ENC value of 20 (indicating that each amino acid was encoded by only one synonymous codon), while a gene without any bias would have a maximum ENC value of 61 (suggesting equal usage of all possible codons for every amino acid) [22,23].

2.3. Neutrality Plot and ENC Plot Analysis

The neutrality plot (GC12-GC3) was employed to evaluate and characterize the patterns of codon usage across three codon positions [24]. GC12 represents the average of GC1 and GC2. A regression plot with a slope of 0 indicates no influence from directional mutation pressure (complete selective constraints), while a slope of 1 suggests that the mutation module between GC12 and GC3 remains consistent, highlighting complete neutrality as the primary driving force in evolution [25].
The ENC-plot (ENC-GC3s) served as a comprehensive approach to determine whether gene codon usage is influenced by mutation and selection. The anticipated ENC values were plotted against the corresponding GC3s values, calculated using Equation (1), where F represents the frequency of estimated GC3s. When actual ENC values closely align with or fall within proximity of the standard GC3s curve, it signifies that codon bias is primarily determined by G + C mutation bias alone. Conversely, when values deviate significantly below the standard curve, it indicates the presence of other factors, such as selection effects [26].
ENC = 2 + F + 29 F 2 + ( 1 − F ) 2

2.4. PR2-Bias Plot and Correlation Analysis

The PR2-bias plot analysis compared A3/(A3 + U3) to G3/(G3 + C3). The central point of the plot, where A equals T and C equals G, represents a codon with no usage bias [27]. Other vectors originating from this point indicate both the magnitude and direction of gene bias. The five rice species were used in the correlation analysis according to their relative synonymous codon usage (RSCU) values, calculated by the R software, version 4.2.3.

2.5. Phylogenetic Analysis

We conducted a comparative analysis of the phylogenetic relationships among different rice species using two methods: codon usage-based and mitochondrial gene sequence-based. To determine the relationship tree between these species, we utilized the R software and employed Euclidean clustering based on RSCU values for five rice species and two Zea species. Additionally, we constructed phylogenetic trees for these species using combined mitochondrial gene datasets. Individual mitochondrial genes were aligned using MAFFT v7.037 [28], and then concatenated into a combined mitochondrial gene set using PhyloSuite. The best-fit model of partitioning schemes and evolution for the combined mitochondrial gene set was determined using Partition Finder 2.1.1 [29]. Subsequently, we employed MrBayes v3.2.6 [30] to construct the phylogenetic tree through Bayesian inference (BI), running simultaneously for a total of 2 × 106 generations, with sampling every 100 generations. We considered stationarity to be achieved when the estimated sample size exceeded 100 and approached a potential scale reduction factor close to 1.0. To ensure accuracy, we discarded the initial burn-in phase comprising the first 25% of samples while calculating the Bayesian posterior probabilities (BPP) in a majority-rule consensus tree at a threshold of 50%.

3. Results

3.1. Analysis of Codon Preference-Related Indices

The compositional features and codon preference of the mitochondrial genome is usually reflected by its GC content (Table 1). The average GC content of the five rice mitochondrial genome genes was determined to be 43.60%. Notably, the distribution of the GC contents across the three different positions of codons was non-uniform, with significantly higher values observed at the first and second positions compared to the third position (Figure 1), indicating a preference for A or U usage at the third position within rice mitochondrial genome genes. The GC content at codon positions GC1, GC2, and GC3 exhibited averages of 48.00%, 42.65%, and 40.16%, respectively. The ENC values for the five rice mitochondrial genome genes varied between 40.02 and 61.00, with an average value of 54.34, surpassing the threshold value of 35 that signified a subtle preference for particular codons in the coding region of the rice mitochondrial genome. The results of our correlation analysis demonstrated statistically significant positive associations among pairs such as T3s–A3s, as well as among combinations involving T3s with other factors; nevertheless, we did not observe correlations involving the codon bias index (CBI)-Aromo, GC3s-Aromo (Figure S1). We initially examined the correlation coefficients between GC12 and GC3 in the five rice species by calculating a single GC12/GC3 value used in the mitochondrial genome. The results presented in Table 2 reveal no significant association between GC12 and GC3, suggesting that codon usage bias in these rice species was primarily influenced by translation selection.

3.2. Pattern of Codon Usage

To explore the rice mitochondrial genome heterogeneity in codon usage, we generated a plot (Figure 2) illustrating the RSCU values for each codon. Codons with RSCU values exceeding 1.6 were identified as being over-represented, while those with RSCU values below 0.6 were considered under-represented. Our analysis revealed no codon to be over-represented in the five rice mitochondrial genomes, whereas six codons, ACG (Oryza coarctata, O. rufipogon, and O. sativa Indica), CAC (O. coarctata and O. sativa Indica), CAG (O. coarctata and O. sativa Indica), CGC (O. sativa Indica), GGC (O. coarctata), and UAC (O. coarctata and O. sativa Indica), exhibited under-representation throughout the rice mitochondrial genome (Figure 2 and Table 3). The RSCU analysis revealed that 25 codons had an RSCU greater than 1 in the five rice mitochondrial genomes. There were 11 codons ending in A (AAA, AGA, CAA, CCA, CGA, GAA, GCA, GGA, UAA, UCA, and UUA) and 12 codons ending in U (AAU, ACU, AUU, CAU, CCU, CUU, GAU, GCU, GUU, UAU, UCU, and UUU). This indicated that the rice mitochondrial genome codons prefer to end with A or U.

3.3. Neutral Plot Analysis

To investigate the impact of mutation pressure on codon usage bias, we calculated the GC content for each position within the codons in the rice mitochondrial genome genes. Subsequently, we analyzed the distribution of the genes on a scatter plot illustrating GC3 and GC12 (Figure 3). In five rice species, an insignificant positive correlation was observed between GC3 and GC12, with a regression line slope between 0.00562 and 0.0681, indicating that approximately 99.9% could be attributed to mutation pressure. The findings suggested that natural selection was the primary determinant of codon usage bias (CUB) in rice species’ mitochondrial genes, while the influence of mutation pressure was limited.

3.4. PR2-Bias Plot Analysis

The PR2-bias plots depicted in Figure 4 demonstrate the distribution patterns of mitochondrial genes within the five rice species. It was evident that a majority of the genes were concentrated in the right regions along the x-axis, indicating a consistent distribution across all mitochondrial genomes. However, when considering the y-axis, almost equal numbers of genes were distributed on both sides. These findings suggest an imbalance in codon usage between G and C at the third base position due to factors such as selection, rather than solely mutation influencing codon usage patterns within these rice species.

3.5. ENC Plot Analysis

The results of the ENC plot analysis for the five rice samples are presented in Figure 5. The diagrams of all samples exhibited a high degree of similarity, with the majority of data points clustered towards the lower section of the standard curve, indicating a strong genetic conservation among all mitochondrial genomes. In conclusion, the observed SCU bias in the five rice mitochondrial genomes was mainly driven by selection pressure.
The frequency distribution of expected ENC (ENCexp) and the ratio of ENCexp to observed ENC (ENCcobs) for the five rice mitochondrial genome genes are presented in Figure 6, with similar distribution patterns observed in the ratios, indicating a high level of genetic conservation across the mitochondrial genome. These findings align with those depicted in Figure 5. Furthermore, 45.95–60.98% of the genes exhibited ENC ratios within the range of −0 to 0.1. This observation suggests that these specific genes did not demonstrate significant deviations from their expected ENC values and that their codon preferences were primarily influenced by mutational pressure.

3.6. Application of RSCU

Figure 7A shows that the frequency of RSCU was similar in different rice mitochondrial genomes. The RSCU quantitative calculation similarity was based on Pearson correlation coefficients, which demonstrate the correlations among different rice species and indicate their level of closeness. The highest value observed was 0.99 between O. coarctata vs. O. sativa Indica and O. rufipogon vs. O. sativa Indica, indicating a strong relationship between these two species. On the other hand, O. minuta and O. sativa Japonica exhibited the lowest similarity value at 0.84 (Figure 7B). We analyzed the relationship between these five species in rice based on their respective RSCU values at the mitochondrial genomic level. The results revealed the Oryza genus and Zea genus were classified by mitochondrial genetic data and RSCU. However, in terms of intra-genus clustering, the two sets of data showed inconsistent results, indicating that RSCU data could not be used as a basis for species classification at the species level (Figure 8).

4. Discussion

CUB is a pivotal determinant in gene regulation and molecular evolution, observed across diverse organisms including prokaryotes and eukaryotes. Also, the extent of preference for synonymous codons varies among species [31]. It is imperative to investigate the genomic-level characteristics of CUB and analyze the evolutionary pressures that have contributed to its formation for comprehensive genome biology studies [32], since rice has been domesticated for thousands of years, leading to a significant shift in selective pressures compared to its wild ancestors. Humans have selected for desirable traits like yield, grain size, and disease resistance, influencing the evolution of specific genes [33]. These genes, under strong selection, might display distinct codon usage patterns compared to genes less affected by domestication [34]. And different rice varieties have adapted to specific geographic locations and environmental conditions. These adaptations can affect codon usage patterns, as certain codons might be favored in particular environments [34]. The third base of the codon, when undergoing synonymous mutation, does not alter the amino acid type [35]. Consequently, it experiences reduced selection pressure and is often employed as a significant indicator for assessing codon bias [26]. In the rice mitochondrial genomes, there were higher prevalences of GC1 and GC2 codons compared to GC3 codons. This observation suggests an uneven distribution of bases across the three positions within the codons and indicates a preference for A/U-ending codons in the rice mitochondrial genomes. Similar AT-rich codon usage patterns have been identified in various plant mitochondrial genomes such as P. patens, Z. mays, T. aestivum, and A. thaliana [36,37]. This resemblance may be attributed to the shared origin of plant mitochondrial genomes, and further underscores the consistent and stable nature of codon usage patterns in plant mitochondrial genomes over time.
Various theories have been proposed to elucidate the origin of the codon usage bias. Two widely accepted primary factors influencing codon usage bias are mutation pressure and natural selection [38]. As indicated by the GC12/GC3 analysis, the codon usage bias in these rice species was translation selection. However, rice species have undergone significant evolutionary divergence, leading to substantial genetic differences. As a result, GC content can vary significantly among different species, even within the same genus [39]. Using a single value fails to account for these evolutionary distances. The consideration should not be limited to GC12/GC3 alone, but rather, a comprehensive analysis of multiple factors is necessary. The analysis of neutral plot and ENC plot indicated that mutation pressure has a minor impact on shaping codon bias, while natural selection appears to be the dominant force (Figure 3 and Figure 5). In the ENC plot, deviations from the expected ENC curve can be caused by a combination of factors, including selection pressure, gene-specific characteristics, methodological issues, and other factors [40], such as when genes with specific functions might be constrained by certain codons, leading to deviations from the expected curve. For example, genes encoding proteins with specific structural or functional properties might have a limited number of codons available for use, and genes that have been duplicated might show deviations due to the relaxation of selection on one of the copies, leading to an increase in synonymous substitutions and a lower ENC [41]. Moreover, comparing the regression slopes revealed that pseudogene sequences were more susceptible to mutations compared to protein coding sequences. Therefore, throughout evolution, natural selection may exert a greater influence on the codons present in protein coding sequences. However, several other factors associated with codon preference such as gene length, RNA structure, and environmental stress require further exploration [42,43,44].
The traditional methods employed for studying species classification and evolutionary relationships encompass the examination of morphology, karyotype analysis, isozyme analysis, and molecular markers [45,46,47]. In addition to these approaches, clustering based on RSCU values can be utilized to elucidate the evolutionary relationships by representing their CUB characteristics. Our findings based on CUB are inconsistent with the genetic tree, indicating that the RSCU-based cluster analysis failed to accurately reflect their evolutionary relationship. Previous works have suggested that RSCU cluster analysis can have different results, for example, RSCU cluster analysis could reflect the evolutionary relationship among melons but showed no sensitivity in cotton species research [48,49]. While RSCU values can provide valuable insights into codon usage patterns, their use in phylogenetic analysis should be approached with caution, such as when RSCU values reflect both selection pressure and genetic drift. It is difficult to disentangle these forces, making it challenging to infer phylogenetic relationships based on codon usage patterns. RSCU values can fluctuate within a lineage due to factors like gene expression levels, environmental conditions, and developmental stages. This variability can create misleading phylogenetic signals. We speculate that while RSCU-based cluster analysis has been proven effective at higher taxonomic levels, such as at the family or genus level, it may not be as sensitive when applied at the individual species level due to the consistent CUB patterns of related species.
Understanding the codon usage patterns in rice mitochondria is essential for unlocking the potential of this organelle for gene expression and engineering. Codon usage bias in rice mitochondria can help identify the most efficient codons for translation. By understanding the codons that are most efficiently translated, researchers can design guide RNAs and Cas9 proteins that are optimized for mitochondrial gene editing [50]. This knowledge can help improve our understanding of mitochondrial gene regulation, enhancing the efficiency of transgene expression, facilitating targeted gene editing, and contributing to the development of novel strategies for combating mitochondrial diseases.

5. Conclusions

The codon usage pattern in the mitochondrial genome of five rice species was comprehensively analyzed. The results revealed that the GC content of codons in the rice mitochondrial genome genes was determined to be 43.60%. It was found that the individual codon positions exhibited distinct GC contents: 48.00% for position 1, 42.65% for position 2, and 40.16% for position 3, and an extensive investigation was conducted on the factors influencing SCU bias. The presence of a preference for A/U-ending bases suggests a weak SCU bias in the rice mitochondrial genome. In addition to significant effects from mutation pressure, it is likely that natural selection also plays a part in driving genetic evolution. These findings are the first to provide novel insights into SCU patterns and offer valuable insights into potential evolutionary forces shaping the rice mitochondrial genome. This study provides a valuable foundation for understanding CUB in rice mitochondria; however, this study has some limitations. Future researchers could deepen their insights by utilizing genomic data from both mitochondrial and nuclear genomes, which could provide a more holistic view of codon usage bias. This approach would allow for comparisons between mitochondrial and nuclear codon preferences, potentially revealing insights into the evolutionary pressures acting on different genomic compartments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14112722/s1, Figure S1. Correlation analysis of the main indices influencing codon bias in the five rice species’ mitochondrial genome. Table S1. The five rice species’ mitochondrial genome information used in this study.

Author Contributions

Y.Z., Writing—original draft, Writing—review and editing, Project administration, Funding acquisition. Y.M., Writing—original draft, Writing—review and editing, Methodology. H.Y., Data curation, Formal analysis. Y.H., Resources, Software, Visualization. T.Y., Supervision, Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financially supported by the Key Areas of Science and Technology Research Plan (2023AB004-04) to Y.Y.Z., The Open Project of the National and Local Joint Engineering Laboratory of High Efficiency and Superior-Quality Cultivation and Fruit Deep Processing Technology of Characteristic Fruit Trees in South Xinjiang (EF202201) to Y.Y.Z.

Data Availability Statement

The original contributions presented in the study are included in the article and Supplementary Materials, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The GC content variation in different codon positions (1st, 2nd, 3rd, and all (overall mitochondrial genome)) and ENC value.
Figure 1. The GC content variation in different codon positions (1st, 2nd, 3rd, and all (overall mitochondrial genome)) and ENC value.
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Figure 2. RSCU of the codons in the mitochondrial genome of the five rice species.
Figure 2. RSCU of the codons in the mitochondrial genome of the five rice species.
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Figure 3. Neutrality plot analysis of the GC12 and GC3 values in the rice mitochondrial genome.
Figure 3. Neutrality plot analysis of the GC12 and GC3 values in the rice mitochondrial genome.
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Figure 4. Parity (PR2) plot analysis of codons [A3/(A3 + T3)] and [G3/(G3 + C3)] in five rice mitochondrial genomes.
Figure 4. Parity (PR2) plot analysis of codons [A3/(A3 + T3)] and [G3/(G3 + C3)] in five rice mitochondrial genomes.
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Figure 5. ENC plot of the five rice mitochondrial genomes.
Figure 5. ENC plot of the five rice mitochondrial genomes.
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Figure 6. Distribution of (expected ENC (ENCexp)-observed ENC (ENCcobs))/ENCexp of the five rice mitochondrial genomes.
Figure 6. Distribution of (expected ENC (ENCexp)-observed ENC (ENCcobs))/ENCexp of the five rice mitochondrial genomes.
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Figure 7. (A)The heat map of RSCU based on the five rice mitochondrial genomes. (B) Correlation analysis of RSCU based on the five rice mitochondrial genomes. Note: Correlation was significant at the 0.0001 level (***).
Figure 7. (A)The heat map of RSCU based on the five rice mitochondrial genomes. (B) Correlation analysis of RSCU based on the five rice mitochondrial genomes. Note: Correlation was significant at the 0.0001 level (***).
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Figure 8. (A) Clustering tree of the five rice species according to RSUC values for 59 synonymous codons. (B) Phylogenetic relationships among the five rice species based on mitochondrial genes.
Figure 8. (A) Clustering tree of the five rice species according to RSUC values for 59 synonymous codons. (B) Phylogenetic relationships among the five rice species based on mitochondrial genes.
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Table 1. The composition parameter values of codon usage in the five rice mitochondrial genomes.
Table 1. The composition parameter values of codon usage in the five rice mitochondrial genomes.
SpeciesT3sA3sG3sC3sGC3sGCENC
Oryza coarctata0.402 0.231 0.362 0.226 0.362 0.431 53.521
Oryza minuta0.379 0.241 0.359 0.247 0.386 0.441 55.120
Oryza rufipogon0.386 0.239 0.360 0.242 0.380 0.44054.853
Oryza sativa Indica0.394 0.234 0.361 0.233 0.370 0.437 54.111
Oryza sativa Japonica0.387 0.242 0.353 0.242 0.383 0.439 54.283
Table 2. The correlation coefficients between GC12 and GC3 in the five rice mitochondrial genomes.
Table 2. The correlation coefficients between GC12 and GC3 in the five rice mitochondrial genomes.
GC1GC2GC12
GC20.257 **
GC120.805 **0.780 **
GC3−0.0640.185 **0.072
Note: Correlation was significant at the 0.05 level (*). Correlation was significant at the 0.01 level (**).
Table 3. Comparative analysis of synonymous codon usage in the mitochondrial genome of the five rice species.
Table 3. Comparative analysis of synonymous codon usage in the mitochondrial genome of the five rice species.
SpeciesRSCU > 1.0The Highest Frequency CodonsThe Lowest Frequency Codons
Oryza coarctata30GCU (1.53)CAG (0.49)
Oryza minuta29UAA (1.38)CUG (0.60)
Oryza rufipogon31AGA (1.47)ACG (0.58)
Oryza sativa Indica30AGA (1.52)CAC (0.53)
Oryza sativa Japonica27AGA (1.52)CUG (0.58)
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Zhang, Y.; Ma, Y.; Yu, H.; Han, Y.; Yu, T. Deciphering Codon Usage Patterns in the Mitochondrial Genome of the Oryza Species. Agronomy 2024, 14, 2722. https://doi.org/10.3390/agronomy14112722

AMA Style

Zhang Y, Ma Y, Yu H, Han Y, Yu T. Deciphering Codon Usage Patterns in the Mitochondrial Genome of the Oryza Species. Agronomy. 2024; 14(11):2722. https://doi.org/10.3390/agronomy14112722

Chicago/Turabian Style

Zhang, Yuyang, Yunqi Ma, Huanxi Yu, Yu Han, and Tao Yu. 2024. "Deciphering Codon Usage Patterns in the Mitochondrial Genome of the Oryza Species" Agronomy 14, no. 11: 2722. https://doi.org/10.3390/agronomy14112722

APA Style

Zhang, Y., Ma, Y., Yu, H., Han, Y., & Yu, T. (2024). Deciphering Codon Usage Patterns in the Mitochondrial Genome of the Oryza Species. Agronomy, 14(11), 2722. https://doi.org/10.3390/agronomy14112722

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