Divergent Codon Usage in Rice (Oryza sativa) and Its Blast Pathogen Magnaporthe oryzae Reveals Species-Specific Translational Selection and Host–Pathogen Co-evolution

Divergent Codon Usage in Rice (Oryza sativa) and Its Blast Pathogen Magnaporthe oryzae Reveals Species-Specific Translational Selection and Host–Pathogen Co-evolution | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Divergent Codon Usage in Rice ( Oryza sativa ) and Its Blast Pathogen Magnaporthe oryzae Reveals Species-Specific Translational Selection and Host–Pathogen Co-evolution Yathu Krishna Y K This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8576147/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Natural selection for translational precision and efficiency as well as mutational pressure both affect codon use bias (CUB), which is the non-random use of synonymous codons. Key information about the co-evolutionary dynamics of host-pathogen systems can be obtained by analyzing CUB. Using a range of statistical and computational techniques, we carried out a thorough comparison of codon use between rice ( Oryza sativa ) and its fungal blast pathogen, Magnaporthe oryzae . Coding sequences (CDS) between the pathogen and host were clearly separated, showing different codon preferences, according to principal component analysis (PCA) and correspondence analysis (CA). Permutational multivariate analysis of variance (PERMANOVA) showed significant divergence in codon usage profiles (pseudo-F = 8.94, p = 0.001), whereas silhouette analysis showed significant species-specific clumping. Quantitative measurements were used to identify biologically meaningful differences: Although the effective number of codons (ENC) did not differ significantly between species, the codon adaptation index (CAI), which was calculated using rice as the reference, was much higher in rice CDS than in M. oryzae CDS. This implies that fungal genes may function under distinct translational restrictions and are less tailored for the host's translational machinery. Analysis of neutrality plots showed that natural selection shapes codon usage in both organisms more strongly than mutational drift. All of these findings point to different translational selection in rice and the fungus that causes it. While M. oryzae has a unique codon usage pattern, which most likely reflects evolutionary adaptation to its pathogenic lifestyle, rice genes seem to be optimized for efficiency under their own codon preference regime. The significance of translational control in host-pathogen interactions is highlighted by this difference. Epigenetics & Genomics Evolutionary Biology Bioinformatics Computational Biology Plant Molecular Biology and Genetics Codon usage bias Oryza sativa Magnaporthe oryzae translational selection codon adaptation index effective number of codons host–pathogen interaction Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction All areas of life exhibit codon usage bias (CUB), which is the preferential use of specific synonymous codons [ 1 , 2 ]. Natural selection, genetic drift, and mutational pressure interact intricately to produce this bias [ 3 , 4 ]. Different species, tissues, and functional gene categories exhibit varying degrees and patterns of CUB, which provide important information on molecular evolution, translational optimization, and genomic organization [ 5 , 6 ]. Strong frameworks for measuring the degree and biological ramifications of codon preference are offered by quantitative measures like the Effective Number of Codons (ENC) [ 7 ] and the Codon Adaptation Index (CAI) [ 8 ], which frequently disclose adaptive mechanisms and evolutionary restrictions. Examining host-pathogen systems is especially fascinating because of their continuous evolutionary "arms race." While infections may either diverge from host preferences to avoid immune surveillance or converge towards them to effectively take over the host's translational machinery [ 10 , 11 ], hosts frequently adopt codon usage profiles tailored for their own translational efficiency and stress responses [ 9 ]. The relative contributions of natural selection and mutation pressure to CUB can be distinguished with the use of neutrality plot analysis, which contrasts the GC content at synonymous third codon sites (GC3) with that at first and second positions (GC12) [ 12 , 13 ]. In host-pathogen systems, where co-evolutionary dynamics can amplify selection pressures, these methods are particularly instructive [ 14 ]. Magnaporthe oryzae, a filamentous ascomycete fungus, is the source of blast disease, which poses a serious danger to rice (Oryza sativa), a vital global staple crop [ 15 , 16 ]. This illness is regarded as one of the most harmful plant diseases in the world [ 17 ]. Effector proteins, host resistance (R) genes, and defense signaling pathways have been the main subjects of earlier studies on the rice–M. oryzae relationship [ 18 , 19 ]. On the other hand, less research has been done on how codon use affects host-pathogen adaptation. In fungi, codon use can be associated with pathogenicity, gene expression, and genome architecture [ 20 , 21 ], whereas in rice, CUB has been connected to the optimization of photosynthetic and stress-responsive genes [ 22 ]. According to comparative genomic research, CUB in plants and their pathogens is not just a result of mutational restrictions, but rather of adaptive evolution [ 23 ]. However, there is yet no thorough, genome-wide comparison of M. oryzae and rice's codon use. In order to assess the factors influencing synonymous codon choice, this study uses multivariate analyses, bias indicators (ENC, CAI), and neutrality plots to give a systematic examination of CUB in O. sativa and M. oryzae. Significant differences between host and pathogen CUB are revealed by our research, shedding light on their evolutionary histories and offering implications for translational optimization, pathogen adaptation, and next crop enhancement tactics. 2 Materials and Methods 2.1 Data Collection Coding sequences (CDS) for the rice plant Oryza sativa and the rice blast fungus Magnaporthe oryzae were obtained directly through NCBI Entrez using Biopython's Entrez.esearch and Entrez.efetch, with a 200 sequence limit per species. A random subset of 200 CDS per species was selected to ensure balanced computational comparison. Sequences were parsed in-memory without the need for external files or manual downloads. 2.2 Codon Usage Computation We used a sliding window (3-nt frame) extraction over the whole CDS to calculate codon counts per CDS. A DataFrame (df_all) was created by aggregating the frequencies of the 61 sense codons per CDS, with rows representing each CDS and a column denoting species identity ("Rice" or "Fungus"). 2.3 Multivariate Analyses Principal Component Analysis (PCA): Used to display species clustering on normalized codon frequency vectors per CDS using PCA from scikit-learn. Correspondence Analysis (CA): To determine the ordination of rice vs. fungal CDS in CA dimensions, the Prince program was used in conjunction with the codon frequency table. PERMANOVA: To check for substantial species-level divergence in codon usage, a permutational multivariate analysis of variance was conducted on the Euclidean distance matrix of standardized codon frequencies using scikit-bio's permanova, 999 permutations (pseudo-F and p-value provided). Silhouette analysis: To evaluate the quality of the clustering, the silhouette score was calculated after the codon frequency data were grouped using K-means, k = 2. 2.4 Codon Bias Indices Wright's technique was used to approximate the Effective Number of Codons (ENC) per CDS. Codons were categorized by synonymous families, and we calculated the homozygosity of codon use within each family. The ENC estimate was then produced in accordance with this [ 7 ] [ 24 ]. Using rice codon use as a reference (mean frequencies across rice CDS), the Codon Adaptation Index (CAI) is calculated for each CDS. Relative adaptiveness weights and a geometric mean are then used to produce CAI scores [ 8 ] [ 25 ]. 2.5 Neutrality Plot (Selection vs Mutation) We determined the GC content at the first, second, and third codon positions (GC1, GC2, and GC3) for every CDS. Plotting GC12 (mean of GC1 and GC2) against GC3 was done. Slope, intercept, R2, and p-value were obtained via linear regression (using scipy.stats.linregress), which shed light on how natural selection and mutational pressure are balanced. [ 12 ] [ 26 ]. 2.6 Statistical Comparisons Chi-square test: Using scipy.stats.chi2_contingency, the aggregated codon counts between rice and fungus (contingency table of 61 codon counts × 2 species) were examined. Per-codon tests: To identify the most divergent codons, the counts of each codon were examined separately using chi-square analysis. KL Divergence: To quantify the distributional difference between the rice and fungal codon frequency distributions, the Kullback-Leibler divergence was calculated. Welch's t-test (unequal variances) was used to compare the ENC and CAI distributions amongst species, which were summarized (mean, median, SD, range) in order to determine statistical significance. 2.7 Visualization Heatmap: Seaborn was used to illustrate the codon frequency discrepancies (mean frequencies and differences between rice and fungus). heatmap. Histograms: Using seaborn.histplot with species as the hue, we plotted the ENC and CAI distributions and added overlays for kernel density estimates. Scatter plots: Matplotlib and Seaborn were used to plot PCA, CA, and neutrality plots (GC12 vs. GC3). 2.8 Software and Packages All analyses were performed in Python (version 3.10) within Google Colab using the following libraries: Biopython (Entrez, SeqIO), pandas, numpy, scipy, scikit-learn, scikit-bio, prince, matplotlib, seaborn. 3 Results 3.1 Codon Usage Frequencies in Rice and Magnaporthe oryzae We examined 200 coding sequences (CDS) from the rice blast fungus (Magnaporthe oryzae) and rice (Oryza sativa). Some codons (e.g., ACC, GTC, AAC, GCC, TTT, AAA, GCG, GTG, GAG, CTC; p < 0.05) had significantly different frequencies, but a global chi-square test of the aggregated codon counts revealed no significant difference (Chi-square = 3.92, p = 1.0, df = 63). This suggests that while specific, biologically significant preferences vary among species, average codon usage is often comparable on a global scale. 3.2 Multivariate Analyses Reveal Species-Specific Codon Patterns Correspondence analysis (CA) revealed a partial separation of fungal and rice CDS along the first two dimensions (Fig. 1 ), suggesting distinct, species-specific patterns of codon usage. This visual separation was statistically substantiated by PERMANOVA, which confirmed significant divergence in the total codon usage profiles across the species (pseudo-F = 8.937, p = 0.001). The silhouette analysis, which assesses CDS clustering based on codon frequencies, revealed a tiny but discernible difference between the two groups with a modest score of 0.283. 3.3 Codon Bias Indices (ENC and CAI) In comparison to rice (86.60 ± 9.86), M. oryzae had a marginally higher Effective Number of Codons (ENC) (mean ± SD: 89.04 ± 17.19). Statistical significance was not achieved by this difference (t = -1.7416, p = 0.0826) (Fig. 2 ). Although M. oryzae's large standard deviation reflects significant gene diversity, a greater ENC indicates a significantly less codon bias in the fungus. Conversely, rice CDS had a considerably higher Codon Adaptation Index (CAI) (0.8515 ± 0.015) than fungal CDS (0.8426 ± 0.020) (t = 4.9634, p = 1.06 × 10⁻⁶) (Fig. 3 ), which was determined using rice codon frequencies as the reference. This indicates that the genes of rice are more suited to the translational optimization regime of the host. 3.4 Neutrality Plot and Divergence Metrics Overall rice and fungal codon use patterns showed a modest Kullback-Leibler (KL) divergence (0.0194), indicating that although the changes are significant, they are not very noticeable. A regression slope of 0.105 (p = 1.15 × 10⁻⁴) was obtained from neutrality plot analysis (GC12 vs. GC3) (Fig. 4 ). This small slope suggests that in both species, natural selection has a greater impact on codon usage than mutational pressure. 4 Discussion Different patterns of codon usage in Magnaporthe oryzae and rice are shown by our genome-wide research, which reflects different translational selection pressures. Species-specific codon preferences were consistently shown by multivariate analyses, suggesting that distinct evolutionary paths had influenced the translational landscapes of the pathogen and host. Moderate clustering and the significant PERMANOVA result support biologically significant divergence. The difference between the ENC and CAI results is a significant discovery. Compared to rice, M. oryzae appears to have a slightly weaker or more varied codon bias throughout its genome, as indicated by the non-significant trend towards a greater ENC. The fungus's far lower CAI values, however, make it abundantly evident that its genes are not tailored for the host's preferred codons. This suggests that M. oryzae has its own selection regime, which may optimize translation for its own gene expression programs linked to development in a hostile environment and pathogenicity [ 20 , 21 ]. Rice CDS have strong adaptations for effective translation in their native cellular environment, as evidenced by their high CAI values. The analysis of neutrality plots reveals a clear reason for these patterns: the dominating force influencing codon usage in both organisms is selection rather than mutation. This is consistent with research in other plant-pathogen systems where a crucial adaptation mechanism is translational optimization [ 23 ]. The slight but steady variations in codon usage (KL divergence = 0.0194) most likely reflect co-evolutionary pressures, where translational efficiency might impact the rates of fungal effector and host defense protein production, hence influencing the outcome of infection [ 19 ]. To sum up, the different CUB between M. oryzae and rice emphasizes the importance of codon use as a layer in the host-pathogen arms race that is often overlooked. The virus has developed a unique codon preference strategy, maybe to enhance its virulence, whereas rice is tailored for its own translational environment. Novel disease management techniques, such creating synthetic resistance genes that are fully optimized for host translation or engineering rice genes with suboptimal codons for pathogen effectors to decrease their production, could be influenced by an understanding of these translational subtleties. 5 Conclusion Our comparative genomic research shows that Magnaporthe oryzae and rice are under different translational selection pressures, which is probably due to their shared evolutionary past. While the fungal pathogen shows a unique, species-specific codon preference pattern with little adaptability to the host's translational machinery, rice genes show high adaptation to their own codon usage regime. In both organisms, the main force behind codon usage is natural selection rather than mutational drift. According to our results, translational efficiency is a key factor in this evolutionary struggle, offering a fresh viewpoint on the molecular adaptations underlying host-pathogen interactions. Further research examining CUB in certain functional gene categories, such host immune receptors and pathogen effectors, may provide even more profound understandings with useful crop protection applications. References Sharp PM, Li WH (1987) The codon adaptation index—a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15(3):1281–1295 Bulmer M (1991) The selection–mutation–drift theory of synonymous codon usage. 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Int J Mol Sci 24(2):1098 Rao Y, Wu G, Wang Z, Chai X, Nie Q, Zhang X (2011) Mutation bias is the driving force of codon usage in the Gallus gallus genome. DNA Res 18(6):499–512 Wright F (1990) The ‘effective number of codons’ used in a gene. Gene 87(1):23–29 Sharp PM, Li WH (1987) The codon Adaptation Index—a measure of directional synonymous codon usage bias. Nucleic Acids Res 15(3):1281–1295 Sueoka N (1988) Directional mutation pressure and neutral molecular evolution. Proc Natl Acad Sci USA 85(8):2653–2657 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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4","display":"","copyAsset":false,"role":"figure","size":52745,"visible":true,"origin":"","legend":"\u003cp\u003eNeutrality Plot\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8576147/v1/05163b7be0c32c53b6e19457.png"},{"id":100382514,"identity":"a847695d-7b81-4930-a189-16f6fe0d929f","added_by":"auto","created_at":"2026-01-16 10:43:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":653891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8576147/v1/d9d07ea6-5e95-4d79-b44c-00038ee0586e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDivergent Codon Usage in Rice (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eOryza sativa\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) and Its Blast Pathogen \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMagnaporthe oryzae\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Reveals Species-Specific Translational Selection and Host–Pathogen Co-evolution\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAll areas of life exhibit codon usage bias (CUB), which is the preferential use of specific synonymous codons [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Natural selection, genetic drift, and mutational pressure interact intricately to produce this bias [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Different species, tissues, and functional gene categories exhibit varying degrees and patterns of CUB, which provide important information on molecular evolution, translational optimization, and genomic organization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Strong frameworks for measuring the degree and biological ramifications of codon preference are offered by quantitative measures like the Effective Number of Codons (ENC) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and the Codon Adaptation Index (CAI) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], which frequently disclose adaptive mechanisms and evolutionary restrictions.\u003c/p\u003e \u003cp\u003eExamining host-pathogen systems is especially fascinating because of their continuous evolutionary \"arms race.\" While infections may either diverge from host preferences to avoid immune surveillance or converge towards them to effectively take over the host's translational machinery [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], hosts frequently adopt codon usage profiles tailored for their own translational efficiency and stress responses [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The relative contributions of natural selection and mutation pressure to CUB can be distinguished with the use of neutrality plot analysis, which contrasts the GC content at synonymous third codon sites (GC3) with that at first and second positions (GC12) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In host-pathogen systems, where co-evolutionary dynamics can amplify selection pressures, these methods are particularly instructive [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMagnaporthe oryzae, a filamentous ascomycete fungus, is the source of blast disease, which poses a serious danger to rice (Oryza sativa), a vital global staple crop [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This illness is regarded as one of the most harmful plant diseases in the world [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Effector proteins, host resistance (R) genes, and defense signaling pathways have been the main subjects of earlier studies on the rice\u0026ndash;M. oryzae relationship [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. On the other hand, less research has been done on how codon use affects host-pathogen adaptation. In fungi, codon use can be associated with pathogenicity, gene expression, and genome architecture [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], whereas in rice, CUB has been connected to the optimization of photosynthetic and stress-responsive genes [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to comparative genomic research, CUB in plants and their pathogens is not just a result of mutational restrictions, but rather of adaptive evolution [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, there is yet no thorough, genome-wide comparison of M. oryzae and rice's codon use. In order to assess the factors influencing synonymous codon choice, this study uses multivariate analyses, bias indicators (ENC, CAI), and neutrality plots to give a systematic examination of CUB in O. sativa and M. oryzae. Significant differences between host and pathogen CUB are revealed by our research, shedding light on their evolutionary histories and offering implications for translational optimization, pathogen adaptation, and next crop enhancement tactics.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection\u003c/h2\u003e \u003cp\u003eCoding sequences (CDS) for the rice plant \u003cem\u003eOryza sativa\u003c/em\u003e and the rice blast fungus \u003cem\u003eMagnaporthe oryzae\u003c/em\u003e were obtained directly through NCBI Entrez using Biopython's Entrez.esearch and Entrez.efetch, with a 200 sequence limit per species. A random subset of 200 CDS per species was selected to ensure balanced computational comparison. Sequences were parsed in-memory without the need for external files or manual downloads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Codon Usage Computation\u003c/h2\u003e \u003cp\u003eWe used a sliding window (3-nt frame) extraction over the whole CDS to calculate codon counts per CDS. A DataFrame (df_all) was created by aggregating the frequencies of the 61 sense codons per CDS, with rows representing each CDS and a column denoting species identity (\"Rice\" or \"Fungus\").\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Multivariate Analyses\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePrincipal Component Analysis (PCA): Used to display species clustering on normalized codon frequency vectors per CDS using PCA from scikit-learn.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCorrespondence Analysis (CA): To determine the ordination of rice vs. fungal CDS in CA dimensions, the Prince program was used in conjunction with the codon frequency table.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePERMANOVA: To check for substantial species-level divergence in codon usage, a permutational multivariate analysis of variance was conducted on the Euclidean distance matrix of standardized codon frequencies using scikit-bio's permanova, 999 permutations (pseudo-F and p-value provided).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSilhouette analysis: To evaluate the quality of the clustering, the silhouette score was calculated after the codon frequency data were grouped using K-means, k\u0026thinsp;=\u0026thinsp;2.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Codon Bias Indices\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWright's technique was used to approximate the Effective Number of Codons (ENC) per CDS. Codons were categorized by synonymous families, and we calculated the homozygosity of codon use within each family. The ENC estimate was then produced in accordance with this [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUsing rice codon use as a reference (mean frequencies across rice CDS), the Codon Adaptation Index (CAI) is calculated for each CDS. Relative adaptiveness weights and a geometric mean are then used to produce CAI scores [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Neutrality Plot (Selection vs Mutation)\u003c/h2\u003e \u003cp\u003eWe determined the GC content at the first, second, and third codon positions (GC1, GC2, and GC3) for every CDS. Plotting GC12 (mean of GC1 and GC2) against GC3 was done. Slope, intercept, R2, and p-value were obtained via linear regression (using scipy.stats.linregress), which shed light on how natural selection and mutational pressure are balanced. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Comparisons\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChi-square test: Using scipy.stats.chi2_contingency, the aggregated codon counts between rice and fungus (contingency table of 61 codon counts \u0026times; 2 species) were examined.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePer-codon tests: To identify the most divergent codons, the counts of each codon were examined separately using chi-square analysis.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eKL Divergence: To quantify the distributional difference between the rice and fungal codon frequency distributions, the Kullback-Leibler divergence was calculated.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWelch's t-test (unequal variances) was used to compare the ENC and CAI distributions amongst species, which were summarized (mean, median, SD, range) in order to determine statistical significance.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Visualization\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHeatmap: Seaborn was used to illustrate the codon frequency discrepancies (mean frequencies and differences between rice and fungus). heatmap.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHistograms: Using seaborn.histplot with species as the hue, we plotted the ENC and CAI distributions and added overlays for kernel density estimates.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eScatter plots: Matplotlib and Seaborn were used to plot PCA, CA, and neutrality plots (GC12 vs. GC3).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Software and Packages\u003c/h2\u003e \u003cp\u003eAll analyses were performed in Python (version 3.10) within Google Colab using the following libraries: Biopython (Entrez, SeqIO), pandas, numpy, scipy, scikit-learn, scikit-bio, prince, matplotlib, seaborn.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Codon Usage Frequencies in Rice and Magnaporthe oryzae\u003c/h2\u003e \u003cp\u003eWe examined 200 coding sequences (CDS) from the rice blast fungus (Magnaporthe oryzae) and rice (Oryza sativa). Some codons (e.g., ACC, GTC, AAC, GCC, TTT, AAA, GCG, GTG, GAG, CTC; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) had significantly different frequencies, but a global chi-square test of the aggregated codon counts revealed no significant difference (Chi-square\u0026thinsp;=\u0026thinsp;3.92, p\u0026thinsp;=\u0026thinsp;1.0, df\u0026thinsp;=\u0026thinsp;63). This suggests that while specific, biologically significant preferences vary among species, average codon usage is often comparable on a global scale.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multivariate Analyses Reveal Species-Specific Codon Patterns\u003c/h2\u003e \u003cp\u003eCorrespondence analysis (CA) revealed a partial separation of fungal and rice CDS along the first two dimensions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting distinct, species-specific patterns of codon usage. This visual separation was statistically substantiated by PERMANOVA, which confirmed significant divergence in the total codon usage profiles across the species (pseudo-F\u0026thinsp;=\u0026thinsp;8.937, p\u0026thinsp;=\u0026thinsp;0.001). The silhouette analysis, which assesses CDS clustering based on codon frequencies, revealed a tiny but discernible difference between the two groups with a modest score of 0.283.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Codon Bias Indices (ENC and CAI)\u003c/h2\u003e \u003cp\u003eIn comparison to rice (86.60\u0026thinsp;\u0026plusmn;\u0026thinsp;9.86), M. oryzae had a marginally higher Effective Number of Codons (ENC) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 89.04\u0026thinsp;\u0026plusmn;\u0026thinsp;17.19). Statistical significance was not achieved by this difference (t = -1.7416, p\u0026thinsp;=\u0026thinsp;0.0826) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although M. oryzae's large standard deviation reflects significant gene diversity, a greater ENC indicates a significantly less codon bias in the fungus. Conversely, rice CDS had a considerably higher Codon Adaptation Index (CAI) (0.8515\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015) than fungal CDS (0.8426\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020) (t\u0026thinsp;=\u0026thinsp;4.9634, p\u0026thinsp;=\u0026thinsp;1.06 \u0026times; 10⁻⁶) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which was determined using rice codon frequencies as the reference. This indicates that the genes of rice are more suited to the translational optimization regime of the host.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Neutrality Plot and Divergence Metrics\u003c/h2\u003e \u003cp\u003eOverall rice and fungal codon use patterns showed a modest Kullback-Leibler (KL) divergence (0.0194), indicating that although the changes are significant, they are not very noticeable. A regression slope of 0.105 (p\u0026thinsp;=\u0026thinsp;1.15 \u0026times; 10⁻⁴) was obtained from neutrality plot analysis (GC12 vs. GC3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This small slope suggests that in both species, natural selection has a greater impact on codon usage than mutational pressure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eDifferent patterns of codon usage in Magnaporthe oryzae and rice are shown by our genome-wide research, which reflects different translational selection pressures. Species-specific codon preferences were consistently shown by multivariate analyses, suggesting that distinct evolutionary paths had influenced the translational landscapes of the pathogen and host. Moderate clustering and the significant PERMANOVA result support biologically significant divergence.\u003c/p\u003e \u003cp\u003eThe difference between the ENC and CAI results is a significant discovery. Compared to rice, M. oryzae appears to have a slightly weaker or more varied codon bias throughout its genome, as indicated by the non-significant trend towards a greater ENC. The fungus's far lower CAI values, however, make it abundantly evident that its genes are not tailored for the host's preferred codons. This suggests that M. oryzae has its own selection regime, which may optimize translation for its own gene expression programs linked to development in a hostile environment and pathogenicity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Rice CDS have strong adaptations for effective translation in their native cellular environment, as evidenced by their high CAI values.\u003c/p\u003e \u003cp\u003eThe analysis of neutrality plots reveals a clear reason for these patterns: the dominating force influencing codon usage in both organisms is selection rather than mutation. This is consistent with research in other plant-pathogen systems where a crucial adaptation mechanism is translational optimization [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The slight but steady variations in codon usage (KL divergence\u0026thinsp;=\u0026thinsp;0.0194) most likely reflect co-evolutionary pressures, where translational efficiency might impact the rates of fungal effector and host defense protein production, hence influencing the outcome of infection [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To sum up, the different CUB between M. oryzae and rice emphasizes the importance of codon use as a layer in the host-pathogen arms race that is often overlooked. The virus has developed a unique codon preference strategy, maybe to enhance its virulence, whereas rice is tailored for its own translational environment. Novel disease management techniques, such creating synthetic resistance genes that are fully optimized for host translation or engineering rice genes with suboptimal codons for pathogen effectors to decrease their production, could be influenced by an understanding of these translational subtleties.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur comparative genomic research shows that Magnaporthe oryzae and rice are under different translational selection pressures, which is probably due to their shared evolutionary past. While the fungal pathogen shows a unique, species-specific codon preference pattern with little adaptability to the host's translational machinery, rice genes show high adaptation to their own codon usage regime. In both organisms, the main force behind codon usage is natural selection rather than mutational drift. According to our results, translational efficiency is a key factor in this evolutionary struggle, offering a fresh viewpoint on the molecular adaptations underlying host-pathogen interactions. Further research examining CUB in certain functional gene categories, such host immune receptors and pathogen effectors, may provide even more profound understandings with useful crop protection applications.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSharp PM, Li WH (1987) The codon adaptation index\u0026mdash;a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15(3):1281\u0026ndash;1295\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulmer M (1991) The selection\u0026ndash;mutation\u0026ndash;drift theory of synonymous codon usage. 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PLoS ONE 9(3):e90905\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhury MN, Chakraborty S, Gupta S, Deb A (2018) Comparative genome-wide analysis of codon usage of different bacterial species infecting \u003cem\u003eOryza sativa\u003c/em\u003e. J Cell Biochem 119(11):9346\u0026ndash;9356\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSueoka N (1988) Directional mutation pressure and neutral molecular evolution. Proc Natl Acad Sci USA 85(8):2653\u0026ndash;2657\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Shi Y, Deng H, Gu T, Xu J (2014) Mutation and selection pressure on synonymous codon usage in human enterovirus. Infect Genet Evol 28:367\u0026ndash;372\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngellotti MC, Bhuiyan SB, Chen G, Wan XF (2007) CodonO: codon usage bias analysis within and across genomes. Nucleic Acids Res ;35(Web Server issue):W132\u0026ndash;W136\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkamnioti P, Gurr SJ (2009) Against the grain: safeguarding rice from rice blast disease. Trends Biotechnol 27(3):141\u0026ndash;150\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDean R, Van Kan JA, Pretorius ZA et al (2012) The Top 10 fungal pathogens in molecular plant pathology. Mol Plant Pathol 13(4):414\u0026ndash;430\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato H (2001) Rice blast disease. Pest Sci 26(2):47\u0026ndash;55\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValent B, Khang CH (2010) Recent advances in rice blast effector research. Curr Opin Plant Biol 13(4):434\u0026ndash;441\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu W, Wang GL (2016) Plant innate immunity in rice: a defense against pathogen infection. 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DNA Res 18(6):499\u0026ndash;512\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWright F (1990) The \u0026lsquo;effective number of codons\u0026rsquo; used in a gene. Gene 87(1):23\u0026ndash;29\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharp PM, Li WH (1987) The codon Adaptation Index\u0026mdash;a measure of directional synonymous codon usage bias. Nucleic Acids Res 15(3):1281\u0026ndash;1295\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSueoka N (1988) Directional mutation pressure and neutral molecular evolution. Proc Natl Acad Sci USA 85(8):2653\u0026ndash;2657\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Codon usage bias, Oryza sativa, Magnaporthe oryzae, translational selection, codon adaptation index, effective number of codons, host–pathogen interaction","lastPublishedDoi":"10.21203/rs.3.rs-8576147/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8576147/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNatural selection for translational precision and efficiency as well as mutational pressure both affect codon use bias (CUB), which is the non-random use of synonymous codons. Key information about the co-evolutionary dynamics of host-pathogen systems can be obtained by analyzing CUB. Using a range of statistical and computational techniques, we carried out a thorough comparison of codon use between rice (\u003cem\u003eOryza sativa\u003c/em\u003e) and its fungal blast pathogen, \u003cem\u003eMagnaporthe oryzae\u003c/em\u003e. Coding sequences (CDS) between the pathogen and host were clearly separated, showing different codon preferences, according to principal component analysis (PCA) and correspondence analysis (CA). Permutational multivariate analysis of variance (PERMANOVA) showed significant divergence in codon usage profiles (pseudo-F\u0026thinsp;=\u0026thinsp;8.94, p\u0026thinsp;=\u0026thinsp;0.001), whereas silhouette analysis showed significant species-specific clumping. Quantitative measurements were used to identify biologically meaningful differences: Although the effective number of codons (ENC) did not differ significantly between species, the codon adaptation index (CAI), which was calculated using rice as the reference, was much higher in rice CDS than in M. oryzae CDS. This implies that fungal genes may function under distinct translational restrictions and are less tailored for the host's translational machinery. Analysis of neutrality plots showed that natural selection shapes codon usage in both organisms more strongly than mutational drift. All of these findings point to different translational selection in rice and the fungus that causes it. While \u003cem\u003eM. oryzae\u003c/em\u003e has a unique codon usage pattern, which most likely reflects evolutionary adaptation to its pathogenic lifestyle, rice genes seem to be optimized for efficiency under their own codon preference regime. The significance of translational control in host-pathogen interactions is highlighted by this difference.\u003c/p\u003e","manuscriptTitle":"Divergent Codon Usage in Rice (Oryza sativa) and Its Blast Pathogen Magnaporthe oryzae Reveals Species-Specific Translational Selection and Host–Pathogen Co-evolution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 12:32:54","doi":"10.21203/rs.3.rs-8576147/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34560ef6-8af1-4919-8b71-fdb8bc850648","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60952739,"name":"Epigenetics \u0026 Genomics"},{"id":60952740,"name":"Evolutionary Biology"},{"id":60952741,"name":"Bioinformatics"},{"id":60952742,"name":"Computational Biology"},{"id":60952743,"name":"Plant Molecular Biology and Genetics"}],"tags":[],"updatedAt":"2026-01-13T12:32:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 12:32:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8576147","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8576147","identity":"rs-8576147","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}