Next Article in Journal
Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass
Previous Article in Journal
Landscape Preference Evaluation of Old Residential Neighbourhoods: A Case Study in Shi Jiazhuang, Hebei Province, China
Previous Article in Special Issue
Molecular Traits for Adaptation to Drought and Salt Stress in Birch, Oak and Poplar Species
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 14
Issue 2
10.3390/f14020378
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Codon Usage Profiling of Chloroplast Genome in Juglandaceae

by
Yajun Zeng
1,2,†,
Lianwen Shen
3,†,
Shengqun Chen
2,
Shuang Qu
2 and
Na Hou
2,*
1
College of Forestry, Guizhou University, Guiyang 550025, China
2
Guizhou Academy of Forestry, Guiyang 550005, China
3
College of Forestry, Southwest Forestry University, Kunming 650224, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2023, 14(2), 378; https://doi.org/10.3390/f14020378
Submission received: 4 January 2023 / Revised: 9 February 2023 / Accepted: 10 February 2023 / Published: 13 February 2023
(This article belongs to the Special Issue Applying Molecular Tools to Genetic Diversity and Divergence in Trees)
Browse Figures
Review Reports Versions Notes

Abstract

:
Juglandaceae (walnut) is made up of several economically and ecologically valuable tree species. Chloroplasts, vitally important for plant growth, are also a rich source of genetic and evolutionary information. Both mutational pressure and natural selection are drivers of codon usage pattern variation among genes. Here, we studied the codon usage of Juglandaceae chloroplast genomes in order to further our understanding of the biology and evolution of this plant family. The codon usage patterns associated with the chloroplast genomes of 26 Juglandaceae samples were analyzed. Short CDS sequences (<100 amino acids) and sequencing containing internal stop codons were removed from comparative analyses. The contents of uracil (U) (31.5%–32.0%) and adenine (A) (30.0%–31.2%) of all 26 samples were higher than those of cytosine (C) (17.2%–17.7%) and guanine (G) 19.9%–20.7%. According to the neutrality and correspondence analyses, chloroplast codons tended to exhibit conserved GC content and were primarily altered by natural selection. The parity rule 2 plot analysis revealed that AU were more common than GC at the third-codon position. According to the effective number of codon (ENC) plot analysis, codon preference was driven by natural selection and protein translation, among other factors. This study represents the first examination of the codon usage characteristics of Juglandaceae plants, as revealed through the study of codon bias in 26 Juglandaceae samples.

1. Introduction

Codons are vital for the genetic information transfer process of an organism. Codons are involved in protein translation. Codon preference, or codon usage bias, refers to the phenomenon of different synonymous codons being used more or less frequently within a sequence. The main underlying mechanisms for codon preference are natural selection and mutation pressure [1,2,3]. The pattern of codon usage affects gene expression and function. For example, increased gene expression levels are linked to stronger codon usage preference. By studying the pattern of codon usage, we can better predict the expressional and functional dynamics of unknown genes [4].
The walnut family (Juglandaceae) includes 71 species, in 9 genera, of perennial woody plants across the world [5]. Of these, 7 genera (Platycarya, Engelhardtia, Cyclocarya, Pterocarya, Juglans, Annamocarya, and Carya) and 27 species are present in China. These Chinese species are primarily found south of the Yangtze River, although a few are distributed to the north [6]. The genus Juglans (walnut) is comprised of economically and ecologically important tree species. Walnuts have recently become increasingly popular in southern China due to their unique flavor and nutritional profile. The branches, leaves, exocarp, root bark, inner nut septum, and kernels of Juglandaceae can be used for their medicinal value [5]. Studies have recently highlighted that consumption can improve hyperlipidemia, hyperglycemia, edema, and cancer, among other conditions [7,8,9].
Biologists studying plant molecular evolution have paid increasing attention to chloroplast genomes because they often contain genes with known functions, have a relatively large copy number, and are relatively small in size [10]. Notably, the chloroplast genome exhibits translational dynamics similar to unicellular organisms, with a codon preference similar to that of Escherichia coli [11]. Mutation pressure has been established as a significant driver of variation in codon preference across the chloroplast genome [2]. However, natural selection is also recognized as a significant driver of variation in codon preference in both algal and plant lineages [12]. Because the chloroplast genome is maternally inherited and possesses a considerable amount of genetic information, the chloroplast genome has been widely used to study phylogeny, species classification, and genetic expression in angiosperms [13,14,15]. Furthermore, by analyzing patterns of codon usage and designing exogenous gene expression vectors based on optimal codons, we can theoretically improve heterologous gene expression levels [16]. Although codon preference has been studied extensively in a variety of prokaryotic and eukaryotic model organisms, the phenomenon has received little attention in Juglandaceae. Here, we studied the sequenced chloroplast genomes of 26 Juglandaceae samples in order to evaluate the codon usage patterns of this economically and ecologically valuable group of plants.

2. Materials and Methods

2.1. Materials

We studied 26 Juglandaceae chloroplast genomes, including 16 genomes downloaded from GenBank (https://www.ncbi.nlm.nih.gov/genome (accessed on 15 December 2022) and 10 genomes that were completed and assembled by our laboratory (not yet uploaded to NCBI) (Table 1). The chloroplast genome size of J. regia was found to be identical across samples b through j, although sample k was shorter. Each protein-coding gene was extracted from the 26 chloroplast genomes. For further analyses, genes encoding <100 amino acids, genes without stop codons, and genes with ambiguous bases were removed. The chloroplast genome size of the 26 Juglandaceae samples ranged from 106,349 (J. regia No.k) to 175,313 (Carya kweichowensis) bp. Across the chloroplast genomes, the number of protein-coding genes ranged from 64 (Carya kweichowensis) to 89 (Pterocarya hupehensis, Pterocarya tonkinensis, Cyclocarya paliurus, and Carya hunanensis). After quality control, the number of genes ranged from 49 to 76.

2.2. Codon Usage Bias and Related Index Analysis

For each gene, we determined the guanine–cytosine (GC) composition at the 1st (GC1), 2nd (GC2), and 3rd (GC3) positions of synonymous codon. Traditionally, each gene is assigned a number to quantify Wright’s effective number of codons (ENc) [17]. In this scheme, a gene without codon usage bias would be assigned a maximum value of 62 and a gene with extreme codon usage bias would be assigned a minimum value of 20. Extreme codon usage bias is defined as a case where each amino acid is encoded by only one synonymous codon, and a lack of codon usage bias is defined as the case where each amino acid is encoded by each synonymous codon in equal proportion. Wright’s ENc is commonly used to assess codon usage bias because it requires no reference gene [17]. However, the Wright’s ENc value may be considerably larger than the sense codon number. Based on Wright’s ENc [17], Sun [18] et al. developed a superior Enc, which divides 6-fold codon families into 4- and 2-fold codon families. The value of this updated ENc would therefore range from a minimum value equal to the number of codon families to a maximum value equal to the number of sense codons. Both Sun’s and Wright’s ENc can be considered estimations (estimated ENc) of the “true” ENc, and are calculated from the number of codons present in each gene.

2.3. Evaluation of Relative Synonymous Codon Usage

The relative synonymous codon usage (RSCU) is calculated by dividing the observed codon frequency by the expected codon frequency in the case of a lack of codon usage bias [19]. A calculated RSCU value of >1 indicates positive bias, of <1 indicates negative bias, and =1 indicates a lack of bias. In order to determine codon usage similarity between coding sequences, a cosine similarity index is calculated based on RSCU values [20]. Accordingly, an RSCU value of >1.6 is considered overrepresented and <0.6 is considered underrepresented [21,22].

2.4. Codon Usage Bias and Related Index Analyses

We used CodonW1.4.2 software and CUSP online software [23,24] to analyze the coding sequences (CDSs) of the 26 Juglandaceae plant samples. A standard codon table was utilized to calculate RSCU values. ENC, which has a negative relationship with codon usage bias, was calculated automatically while calculating RSCU in CodonW. Together, both ENC and RSCU were used to holistically evaluate patterns of codon usage.

2.4.1. ENC Plot

ENC values, which range from 20–61, indicate the strength of codon usage preference. For example, codon preference is considered strong for ENC values < 35, and vice versa (ENC values > 35) [25]. An ENC plot compares the expected (expENC) to the observed ENC for each GC3 value. Mutation pressure is considered the sole driver of the identity of third-position bases when ENC values fall along the expENC curve [17]. To draw the ENC plot, we used ENC as the ordinate and GC3 as the abscissa. Using the formula provided by F. Wright [17], we added an ENC standard curve. The closer the coding gene is to the curve, or when it falls above the curve, the more it is affected by mutation pressure. When the gene is located below the curve, or at a farther distance, it is more affected by natural selection [26].

2.4.2. Neutrality Plot

The average GC1 and GC2 (GC12) values were plotted against the GC3 values to generate a neutrality plot for the Juglandaceae chloroplast genome CDSs. Neutrality plots show how the codon usage bias is altered by the mutation–selection equilibrium [27]. In the plots, regression with a slope of 0 suggests the absence of directional mutation pressure or complete selective constraints, whereas a slope of 1 indicates the same mutation module between GC12 and GC3 and that complete neutrality was the main element in the evolutionary process [28].

2.4.3. Parity Rule 2-Bias Plot

The parity rule 2 (PR2)-bias plots were generated according to the PR-2 principle. According to PR2, where mutational pressure and natural selection are absent, nucleotides tend to follow the rules of G = C and A = T (where C + G + T + A = 1) [29]. When G = C and T = A (PR2), the middle of the plot contains the data points with both coordinates = 0.5. Any deviation from such a case would indicate bias caused by natural selection, mutation pressure, or a combination of the two. Any significant deviation exhibited by four-codon amino acids at the third-codon position tends to result from selective pressure rather than mutation pressure. Otherwise, where C + G = T + A at the third-codon position, mutation is considered the primary driver of codon usage preference [29,30].

2.4.4. MILC Analysis

Measure independent of length and composition (MILC), which predicts gene expression, is a useful quantifier of codon usage variation between an the expected and observed codon distribution. The distribution of codons can be determined using either a single gene, a gene group, or the background nucleotide composition [29]. The R package coRdon 1.13.0 was used to analyze each chloroplast genome sequence. The number of occurrences of each codon within each sequence was calculated using default parameters. The MILC value of each sequence was determined for the sample-wide codon usage deviation (https://bioconductor.org/packages/devel/bioc/vignettes/coRdon/ (accessed on 25 January 2023)). Low gene expression is indicated by low MILC values and high gene expression is indicated by high MILC values [31].

3. Results

3.1. Composition of Nucleotides at Different Codon Positions in Juglandaceae

Determining the codon sequence composition of nucleotides is crucial to understand the distribution of codons between genes and associations with gene expression, among other things. The single nucleotide composition, GC frequencies at three positions, and overall composition were studied in all 26 samples. Specifically, we determined the GC content at three positions (GC1, GC2, and GC3) for all 26 samples (Table 2) and observed large differences between the positions. Moreover, GC1, GC2, and GC3 all showed obvious differences among species, but some species were the same (such as J. regia 200 and J. regia 201).
We obtained 1864 coding genes (CDSs) of over 100 bp from the 26 Juglandaceae samples. After measuring the nucleic acid content in the screened genes separately (Table 2), we found that thymine/uracil (T/U) was the most represented (31.5%–32.0%), followed by adenine (A) (30.0%–31.2%), guanine (G) (19.9%–20.7%), and cytosine (C) (17.2%–17.7%) in all the 26 Juglandaceae plants. Across the CDSs of the 26 Juglandaceae samples, all four nucleotides tended to be represented asymmetrically. We also determined the GC content at three CDS codon positions (GC1, GC2, and GC3) (Table 2) and observed the following order: 1st (GC%) > 2nd (GC%) > 3rd (GC%). Overall, the composition at the second position resembled that of the average. According to the correlation analysis (Figure 1), GC1, GC2, GC3, and GC all exhibited highly significant correlations. In particular, GC1 and GC2 were significantly correlated, but GC12 and GC3 were not. These results indicated that codons of Juglandaceae plants prefer to use A/U nucleic acids, and the base composition at the third position differs significantly from those of the first and second positions.

3.2. Codon Usage Bias Patterns in Juglandaceae CDSs

RSCU is a crucial parameter for measuring codon preference. When a codon’s RSCU = 1, that codon’s usage frequency is equal to its synonymous codons. At this point, no preference exists between the two. An RSCU > 1 indicates a high-frequency codon with a strong usage preference. By contrast, an RSCU < 1 indicates weak usage preference. Here, we performed an analysis of RSCU values in order to evaluate any variation in codon usage. Codons with RSCU > 1.5 were considered overrepresented and strongly preferred, codons with RSCU > 1 and <1.5 were considered high-frequency, codons with RSCU > 0.8 and <1 were considered low-frequency, and codons with RSCU < 0.8 were considered underrepresented. Figure 2 shows that the 26 species had an average of approximately 28 codons per species with RSCU > 1. Of these, 15 ended in U, 12 ended in A, 0 ended in C, and 1 ended in G. The highest RSCU values were found for the codon UUA encoding Leu, followed by those for the codon GCU encoding Ala. The Juglandaceae plants had a strong bias for GUU, GUA, and GCU, which were highly preferred. Subsequent to the removal of the three termination codons (UGA, UAG, and UAA), the RSCU of 61 synonymous codons was calculated for each of the 26 Juglandaceae plants. Then, RSCU was used to analyze the codon usage preference in the 26 Juglandaceae samples. The codons CUC, CAC, and AAG had low RSCU values and were avoided. Overexpressed codons, high-frequency codons, and high-expression codons exhibited a preference for codons ending the GC dinucleotide.
We also analyzed the ENC values for 1814 genes of the chloroplast genomes of 26 Juglandaceae samples. The ENC values ranged between 27.68 and 61, indicating the presence of different codon preference trends. Using an ENC value of 35 to discriminate between weak and strong codon usage bias, only three strongly-biased CDSs were identified (ENC < 35). In agreement with the average ENC value (48.86), the majority of CDSs exhibited only weak bias. The occurrence of such high ENC values indicated that nearly all amino acids were used for protein synthesis. Additionally, it appears that genome-wide codon preference is fairly weak in Juglandaceae.

3.3. Optimal Codon Analysis

The RSCU value is often utilized to identify optimal codons [32,33]. Here, we sorted each CDS sequence according to the ENC value. Sequences in the top 5% were defined as high-expression and sequences in the bottom 5% were defined as low-expression. ΔRSCU is equal to the high-expression RSCU value minus the low-expression RSCU value. Significantly positive ΔRSCU values indicate that > 1 codon per amino acid meets this criterion. The most optimal codon is the one with the largest ΔRSCU for a particular amino acid [33]. Optimal codon analysis was performed according to the usage frequency RSCU and ENC values (Figure 3).
Overall, the chloroplast genomes of 26 samples had between 24 and 30 optimal codons. Specifically, C. cathayensis, P. tonkinensis, and P. macroptera had the fewest optimal codons (24), while E. roxburghiana and P. stenoptera had the most optimal codons (30). The third base of the optimal codons was biased toward A and U. The chloroplast genomes of the 26 samples shared six common optimal codons: GGU encoding Gly, AUA encoding Ile, AAU encoding Asn, CCU encoding Pro, CGU encoding Arg, and ACU encoding Thr. The third base of the codon shares the same preference for A and U as the synonymous codons (Figure 2 and Figure 3). This result also illustrates that the optimal codons of the chloroplast genome differed significantly among the 26 Juglandaceae samples (Figure 3), in contrast to the results of the analysis of synonymous codons (Figure 2). The optimal codon usage of J. regia 193 (c) and J. regia 194 (d) are clearly distinguished from those of other 24 Juglandaceae plants (Figure 3). It appears that there are differences even within the same species.

3.4. Factors Affecting Codon Preferences in Juglandaceae CDSs

In general, when the true ENC value of each CDS closely matches the standard curve, base mutation may be the primary driver of codon usage preferences. Where there is deviation from the standard curve, natural selection may be the primary driver of codon usage preference. Upon inspection of the ENC plot (Figure 4A), the ENC value of the Juglandaceae plant CDSs ranged between 24 and 61. For most genes, the actual ENC values deviated from the standard, and the distribution pattern in the figure had nothing to do with the expected value. ENC < 35 below the red line and ENC > 35 above the red line showed that most genes were located above the red line, with only a small number below the red line. Moreover, their GC content was < 50%, which is quite low. This suggests that the codon use pattern in Juglandaceae is largely determined by natural selection as well as the end nucleotides. The codon use pattern of Juglandaceae may also be related to factors such as gene expression level and mutational pressure. Actual ENC values and expENC values are specified in (Schedules SI).
We used the “(ENCexp − ENCobs)/ENCexp” ratio to further examine the deviation between actual ENC values and expENC values. Datasets of 1814 genes were obtained for 26 samples, with the highest ratio (−0.5 to −0.8) and fewer proportions more than negative 0.1 (Figure 4B). The actual ENC value was slightly larger than the expected ENC value, speculating what factors of the expression level and protein length might play a part in the complex codon usage bias in Juglandaceae plants.

3.5. The A/G and C/U Balance Is Disrupted at the Third Position across Juglandaceae CDSs

The third-position frequencies of GC and AU are represented in the PR2-bias plot (Figure 4C). The results showed that neither T3 (U3) and A3 nor C3 and G3 exhibited equal proportions across Juglandaceae samples. G3/C3 was mostly between 0.3 and 0.7, and A3/T3 (U3) was mostly between 0.4 and 0.6. Overall bias for G3/C3 or A3/3 (U3) in Juglandaceae species was low. For comparison, the plot was divided into four quadrants based on (0.5, 05) as the center. The points were primarily distributed in the fourth quadrant, wherein the ratio of G3/GC3 and A3/AU3 was > 0.5. The second quadrant contained the lowest number of points. According to the biocodon usage pattern analysis, AU and GC occurred in pairs at the third position of biological CDSs. However, we found that the frequencies of the third position of pairing between AU and GC were clearly irregular; the results suggest that the Juglandaceae CDSs prefer A and G in the third position.

3.6. The Primary Driver of Codon Usage Bias in Juglandaceae Is Natural Selection

To elucidate the precision of codon usage preference due to natural selection or mutation pressure, we drafted neutral plots using the average content of GC at the first and second (GC12) and third (GC3) positions (Figure 4D). The plot showed that many genes for each species are distributed far from the regression curve. The average correlation coefficient R2 was 0.01 for GC12 and GC3 for the 26 samples. These results revealed a nonsignificant overall correlation between GC12 and GC3, and different degrees of modification were observed between GC12 and GC3 in Juglandaceae. In addition, the average regression slope for 26 species was 0.13. In the straight-line fitting analysis, a regression coefficient close to 1 indicates that mutation mainly affects the codon preference. However, the regression coefficient close to 0 indicated that natural selection mainly affects codon preference. Mutational pressure accounted for 13% of the 26 Juglandaceae samples, whereas natural selection pressure accounted for approximately 87%.

3.7. Gene Expression Is Altered by Codon Usage Bias

Gene expression is reflected by the MILC value, with higher MILC values indicating higher gene expression, and vice versa. Here, we calculated the MILC values of each chloroplast genes. Overall, 6 Juglandaceae samples exhibited a MILC value of 0.54, with the other 20 species exhibiting a MILC value of 0.55. These results suggest that the majority of the chloroplast genes exhibited moderate levels of gene expression.

4. Discussion

Studying the evolutionary phenomenon of codon usage bias can help to clarify evolutionary relationships and improve the efficiency of gene expression in research utilizing genetic transformation [27]. However, patterns of codon usage across plant chloroplast genomes have historically received little attention. More recently, a variety of plant chloroplast genomes have been sequenced, allowing for the comprehensive analysis of codon preferences [34,35,36,37]. These studies have made possible the identification of different plant species, as well as the study of their genealogical geography and evolutionary dynamics. Juglandaceae is a family of plants with edible, medicinal, and cultural value. However, little is known about the codon usage patterns of this plant family. Here, 1814 CDSs were screened from the chloroplast genomes of 26 Juglandaceae plants, and their codon compositions and preferences were analyzed. Our results suggests the following: (1) natural selection is the primary driver of codon preference in Juglandaceae; (2) for approximately 28 codons with RSCU > 1, most codons ended in either A or T (U), and for 18 codons of the 20 amino acids, the optimal codons ended in either A or T (U); (3) the frequency of pairing between AU and GC was clearly disrupted; and (4) the base composition of the first and second positions exhibited a weak correlation with the third position and the AT content was greater than the GC. These results are consistent with those of Liu [38] and Xu et al. [10]. Furthermore, our results are consistent with the notion that plants tend to prefer codons ending in T or A [39]. Here, natural selection may be implicated due to functional constraints on genomic codons GC1 and GC2. Both GC1 and GC2 cannot be changed, because base mutations at these positions would normally result in amino acid changes [40]. The codon usage biases of the 26 Juglandaceae chloroplast genomes were very similar, which was consistent with the report of Sharp et al. [41]. Using optimal codons or codons with RSCU values > 1 would be valuable for optimizing the heterologous expression of Juglandaceae chloroplast genes.
The genomic base composition is determined by the balance between negative and positive mutation pressure on GC base pairs [30]. In general, nonrandom codon bias has been observed across all prokaryotic and eukaryotic coding sequences due to the balance between natural selection and directional mutation pressure [42,43,44]. Across chloroplast genomes, codon preference tends to be biased toward codons ending in T or A, which is reflected in a general AT composition bias [12,45]. Our work revealed that the use of AT (U)/GC nucleotides differed between the first, second, and third positions, and that these compositional differences may have resulted in the emergence of codon usage preferences in Juglandaceae. Interestingly, A and U were more common than expected at the third position. Either due to mutation pressure or natural selection, third-position codons are often directionally substituted. Verifying the drivers of U- or A-ending codon preference may transform our understanding of Juglandaceae evolution. Synonymous codons are different, and base bias at the codon ends may be caused by directed substitutions under the pressure of base mutations and the constraints of natural selection, thus resulting in a biased outcome of terminal bases. Codon bias is relatively weaker in angiosperms [46]. In this study, the ENC plot analysis revealed ENC values higher than 35 for most genes in the Juglandaceae chloroplast genome, indicating a weak bias.
In chloroplast genomes, the composition of nucleotides is the primary driver of variation in codon bias [47]. Because chloroplasts invariably contain a high content of AT, it is suspected that such codon usage bias is related to AT-biased mutation pressure. Here, we determined that the optimal codon of Juglandaceae ended with A/T (U), which is consistent with the results of other studies [12,47]. However, the ENC plot revealed that selection may have acted on the Juglandaceae chloroplast genome because the majority of genes were located both far from and below the expected curve. A few genes were located close to the curve, or only slightly below it, indicating that natural selection pressure regulates the codon preference of the Juglandaceae chloroplast genome. The PR2 bias plot and neutrality plot confirmed that natural selection is the primary driver of the codon usage the Juglandaceae chloroplast genome. Similar to studies in Ficus [36], Zhang et al. [27] found that the codon usage patterns of wheat mitochondria and chloroplasts were mainly affected by the level of translational selection, although the codon usage patterns of nuclear genes were primary driven by base mutations. These results suggest that factors affecting patterns of codon usage are similar in chloroplast and mitochondria, but that the nuclear genome is affected by different factors. We hypothesize that this phenomenon may be related to the conservation of GC content in chloroplast and mitochondrial genomes [27,38,48]. The chloroplast genomes of Theaceae [49] and Fagaceae [50] similarly exhibit a preference for codons ending in A and T. The chloroplast genomes of Juglandaceae were similarly biased toward codons ending in A or T (U), and the majority of codons ending in A or T (U) exhibited higher RSCU values. We also found that GC3 content (average GC3 content: 28.1%–29.35%) was narrowly distributed among the 26 Juglandaceae chloroplast genomes, with no correlation between GC12 and GC3, indicating substantial differences in the base composition of the three positions. We speculate that natural selection may occur in the Juglandaceae chloroplast genome to a certain extent.
It appears that both natural selection and mutation pressure influence codon bias in the Juglandaceae chloroplast genome, although natural selection is the primary driver. By analyzing the ENC and RSCU values, we screened our six optimal codons shared between all 26 Juglandaceae samples. Optimal and rare codons can be utilized to enhance foreign gene expression [46]. We found that the optimal codon usage of J. regia 193 and J. regia 194 was obviously different from that of the other 24 samples, which might distribute to different locations of the samples. To our knowledge, this is the first study describing codon usage bias in Juglandaceae. We found that MILC values of our samples indicated high gene expression. Overall, 6 Juglandaceae samples exhibited a MILC value of 0.54, with the other 20 species exhibiting a MILC value of 0.55. In rice, MILC has been found to range from 0.74 to 0.78, suggesting high levels of chloroplast gene expression [51]. This study represents the first examination of the codon usage characteristics of Juglandaceae plants, as revealed through the study of codon bias in 26 Juglandaceae samples.

5. Conclusions

Our study results suggest that natural selection is the primary driver of patterns of codon usage in Juglandaceae. We identified the best codons for 26 Juglandaceae samples. In addition, we screened for six optimal codons shared by the 26 samples: GGU (Gly), AUA (Ile), AAU (Asn), CCU (Pro), CGU (Arg), and ACU (Thr). Codon usage and codon optimality data provided valuable information for further genetic engineering studies.

Author Contributions

N.H. planned and supervised the project, conceived and designed the experiments, and was involved in funding acquisition; Y.Z. and L.S. wrote the manuscript; S.C. and S.Q. performed data curation and formal analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 31860215, 32070372, and 41471038), the Local Walnut R&D Groups in Guizhou Province ([2019]5643), innovation and application of Guizhou walnut germplasm (Qianlin ke [2022] 17), and Study on the Vitality and Stigma of Guizhou Wuren Walnut Pollen (Qianlin J [2022] No. 05).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We are very grateful to the editor and reviewers’ comments, which is helpful for improving our manuscript during the manuscript preparation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Li, J.; Li, H.; Zhi, J.; Shen, C.; Yang, X.; Xu, J. Codon Usage of Expansin Genes in Populus trichocarpa. Curr. Bioinform. 2017, 12, 452–461. [Google Scholar] [CrossRef]
  2. Morton, B.R. The Role of Context-Dependent Mutations in Generating Compositional and Codon Usage Bias in Grass Chloroplast DNA. J. Mol. Evol. 2003, 56, 616–629. [Google Scholar] [CrossRef]
  3. Quax, T.E.F.; Claassens, N.J.; Söll, D.; van der Oost, J. Codon Bias as a Means to Fine-Tune Gene Expression. Mol. Cell 2015, 59, 149–161. [Google Scholar] [CrossRef] [PubMed]
  4. Rao, Y.; Wu, G.; Wang, Z.; Chai, X.; Nie, Q.; Zhang, X. Mutation Bias is the Driving Force of Codon Usage in the Gallus gallus genome. DNA Res. 2011, 18, 499–512. [Google Scholar] [CrossRef] [PubMed]
  5. Lu, A.M. On the Geographical Distribution of the Juglandaceae. Acta Phytotaxon. Sin. 1982, 20, 257–274. [Google Scholar]
  6. Wu, X.-M. The analysis method and progress in the study of codon bias. Hereditas 2007, 29, 420–426. [Google Scholar] [CrossRef]
  7. Hu, X.J.; Zhao, X.J.; Zhou, F.; Yu, M.R.; Chen, Y.Y.; Li, R.Z.; Jia, Y.H.; Ding, Z.S. The Anti-Tumor Effects of Carya cathayensis Sarg Extracts in Vitro. Chin. Arch. Tradit. Chin. 2007, 25, 369–370. [Google Scholar] [CrossRef]
  8. Li, X.F. Progress of Research on the Chemical Components and Pharmaceutical Action of Walnut Green Husk. Food Sci. Technol. 2007, 4, 241–242. (in Chinese). [Google Scholar] [CrossRef]
  9. Zhang, C.; Ji, Z.H.; Song, C.S. Anti-Tumor Effect F Water Extract from Manchurian Walnut. World Chin. Med. 2010, 5, 210–212. [Google Scholar]
  10. Xu, C.; Cai, X.; Chen, Q.; Zhou, H.; Cai, Y.; Ben, A. Factors Affecting Synonymous Codon Usage Bias in Chloroplast Genome of Oncidium Gower Ramsey. Evol. Bioinform. 2011, 7, EBO.S8092. [Google Scholar] [CrossRef]
  11. Sugiura, M. The chloroplast genome. Plant Mol. Biol. 1992, 19, 149–168. [Google Scholar] [CrossRef] [PubMed]
  12. Morton, B.R. Selection on the codon bias of chloroplast and cyanelle genes in different plant and algal lineages. J. Mol. Evol. 1998, 46, 449–459. [Google Scholar] [CrossRef] [PubMed]
  13. Deng, Y.; Luo, Y.; He, Y.; Qin, X.; Li, C.; Deng, X. Complete Chloroplast Genome of Michelia shiluensis and a Comparative Analysis with Four Magnoliaceae Species. Forests 2020, 11, 267. [Google Scholar] [CrossRef]
  14. Jiang, L.; Li, M.; Zhao, F.; Chu, S.; Zha, L.; Xu, T.; Peng, H.; Zhang, W. Molecular Identification and Taxonomic Implication of Herbal Species in Genus Corydalis (Papaveraceae). Molecules 2018, 23, 1393. [Google Scholar] [CrossRef] [PubMed]
  15. Lu, Q.F.; Luo, W.H.; Huang, Z.H. Codon Usage Bias of Chloroplast Genome from Two Species of Firmiana Marsili. Guihaia 2020, 40, 173–183. [Google Scholar]
  16. Gui, L.Q.; Li, Y.F.; Pan, W.D. Progress of Studies on Chloroplast Genetic Engineering. Biotechnol. Bull. 2012, 25, 1–6. [Google Scholar] [CrossRef]
  17. Wright, F. The ‘effective number of codons’ used in a gene. Gene 1990, 87, 23–29. [Google Scholar] [CrossRef]
  18. Sun, X.; Yang, Q.; Xia, X. An Improved Implementation of Effective Number of Codons (Nc). Mol. Biol. Evol. 2012, 30, 191–196. [Google Scholar] [CrossRef] [PubMed]
  19. Sharp, P.M.; Tuohy, T.M.; Mosurski, K.R. Codon usage in yeast: Cluster analysis clearly differentiates highly and lowly expressed genes. Nucleic Acids Res. 1986, 14, 5125–5143. [Google Scholar] [CrossRef]
  20. Zhou, J.-H.; Zhang, J.; Sun, D.-J.; Ma, Q.; Chen, H.-T.; Ma, L.-N.; Ding, Y.-Z.; Liu, Y.-S. The Distribution of Synonymous Codon Choice in the Translation Initiation Region of Dengue Virus. PLoS ONE 2013, 8, e77239. [Google Scholar] [CrossRef]
  21. Shi, S.-L.; Xia, R.-X. Codon Usage in the Iflaviridae Family is Not Diverse Though the Family Members are Isolated from Diverse Host Taxa. Viruses 2019, 11, 1087. [Google Scholar] [CrossRef] [PubMed]
  22. Wong, E.H.; Smith, D.K.; Rabadan, R.; Peiris, M.; Poon, L.L. Codon usage bias and the evolution of influenza A viruses. Codon Usage Biases of Influenza Virus. BMC Evol. Biol. 2010, 10, 253. [Google Scholar] [CrossRef]
  23. Feng, J.W. Codon Pattern of Papillomavirus (Type I) from Bos Grunniens Based on the Codonw Software. In Proceedings of the 5th International Conference on Mechanics and Mechatronics, Tokyo, Japan, 19–21 July 2018. [Google Scholar] [CrossRef]
  24. Seetharaman, J.; Srinivasan, R. Analysis of Codon Usage: Positional Preference in Various Organisms. Indian J. Biochem. Biophys. 1995, 32, 156–160. [Google Scholar] [CrossRef] [PubMed]
  25. Niu, Y.; Luo, Y.; Wang, C.; Liao, W. Deciphering Codon Usage Patterns in Genome of Cucumis sativus in Comparison with Nine Species of Cucurbitaceae. Agronomy 2021, 11, 2289. [Google Scholar] [CrossRef]
  26. Yang, G.F.; Su, K.L.; Zhao, Y.R.; Sun, J.; Song, Z.B. Analysis of Codon Us-Age in the Chloroplast Genome of Medicago Truncatula. Acta Prataculturae Sin. 2015, 24, 171–179. [Google Scholar]
  27. Zhang, W.-J.; Zhou, J.; Li, Z.-F.; Wang, L.; Gu, X.; Zhong, Y. Comparative Analysis of Codon Usage Patterns Among Mitochondrion, Chloroplast and Nuclear Genes in Triticum aestivum L. J. Integr. Plant Biol. 2007, 49, 246–254. [Google Scholar] [CrossRef]
  28. Guan, D.-L.; Ma, L.-B.; Khan, M.S.; Zhang, X.-X.; Xu, S.-Q.; Xie, J.-Y. Analysis of codon usage patterns in Hirudinaria manillensis reveals a preference for GC-ending codons caused by dominant selection constraints. BMC Genom. 2018, 19, 542. [Google Scholar] [CrossRef]
  29. Sueoka, N. Intrastrand parity rules of DNA base composition and usage biases of synonymous codons. J. Mol. Evol. 1995, 40, 318–325. [Google Scholar] [CrossRef] [PubMed]
  30. Sueoka, N. On the Genetic Basis Of Variation And Heterogeneity of DNA Base Composition. Proc. Natl. Acad. Sci. USA 1962, 48, 582–592. [Google Scholar] [CrossRef]
  31. Dorić, S.; Bilela, L.L. Comparison of Codon Usage in Mitochondrial Genomes of Rhinolophid and Hipposiderid Bats. In Proceedings of the 5th International Symposium on Sustainable Development, Venice, Italy, 15 May 2014. [Google Scholar]
  32. Whittle, C.A.; Extavour, C.G. Codon and Amino Acid Usage Are Shaped by Selection Across Divergent Model Organisms of the Pancrustacea. G3 Genes Genomes Genet. 2015, 5, 2307–2321. [Google Scholar] [CrossRef]
  33. Whittle, C.A.; Sun, Y.; Johannesson, H. Evolution of Synonymous Codon Usage in Neurospora tetrasperma and Neurospora discreta. Genome Biol. Evol. 2011, 3, 332–343. [Google Scholar] [CrossRef]
  34. Li, L.X.; Peng, J.Y.; Wang, D.W.; Duan, A.A. Chloroplast Genome Phylogeny and Codon Preference of Docynia longiunguis. Chin. J. Biotechnol. 2022, 38, 328–342. [Google Scholar] [CrossRef]
  35. Shu, J.X.; Yang, L.; Zhou, T.; Shen, L.W.; Wang, D.W. Analysis of Codon Bias in the Chloroplast Genome of Four Medicinal Plants of Juniperus. Chin. Tradit. Herb. Drugs 2022, 53, 7507–7515. (in Chinese). [Google Scholar] [CrossRef]
  36. Xia, X.; Peng, J.Y.; Wang, D.W.; Zhang, X.L.; Li, J.F. Codon Usage Bias in Chloroplast Genomes of Three Ficus Species. J. Northwest For. Univ. 2022, 37, 88–94. (in Chinese). [Google Scholar] [CrossRef]
  37. Zhou, T.; Yang, L.; Shu, J.X.; Shen, L.W.; Xia, X.; Wang, D.W. Analysis of Codon Bias in the Chloroplast Genome of Three of Michelia spp. Species. J. West China For. Sci. 2022, 51, 91–100. [Google Scholar] [CrossRef]
  38. Liu, Q.; Xue, Q. Comparative studies on codon usage pattern of chloroplasts and their host nuclear genes in four plant species. J. Genet. 2005, 84, 55–62. [Google Scholar] [CrossRef]
  39. Campbell, W.; Gowri, G. Codon Usage in Higher Plants, Green Algae, and Cyanobacteria. Plant Physiol. 1990, 92, 1–11. [Google Scholar] [CrossRef] [PubMed]
  40. Sueoka, N. Directional Mutation Pressure and Neutral Molecular Evolution. Proc. Natl. Acad. Sci. USA 1988, 85, 2653–2657. [Google Scholar] [CrossRef] [PubMed]
  41. Sharp, P.M.; Cowe, E.; Higgins, D.G.; Shields, D.C.; Wolfe, K.H.; Wright, F. Codon usage patterns in Escherichia coli, Bacillus subtilis, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens; a review of the considerable within-species diversity. Nucleic Acids Res. 1988, 16, 8207–8211. [Google Scholar] [CrossRef] [PubMed]
  42. Akashi, H. Gene expression and molecular evolution. Curr. Opin. Genet. Dev. 2001, 11, 660–666. [Google Scholar] [CrossRef]
  43. Duret, L. Evolution of synonymous codon usage in metazoans. Curr. Opin. Genet. Dev. 2002, 12, 640–649. [Google Scholar] [CrossRef]
  44. Sharp, P.M.; Li, W.-H. The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res. 1987, 15, 1281–1295. [Google Scholar] [CrossRef]
  45. Wolfe, K.H.; Morden, C.W.; Palmer, J.D. Function and evolution of a minimal plastid genome from a nonphotosynthetic parasitic plant. Proc. Natl. Acad. Sci. USA 1992, 89, 10648–10652. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Nair, R.R.; Nandhini, M.B.; Monalisha, E.; Murugan, K.; Sethuraman, T.; Nagarajan, S.; Rao, N.S.P.; Ganesh, D. Synonymous codon usage in chloroplast genome of Coffea arabica. Bioinformation 2012, 8, 1096–1104. [Google Scholar] [CrossRef]
  47. Sablok, G.; Nayak, K.C.; Vazquez, F.; Tatarinova, T.V. Synonymous Codon Usage, GC3, and Evolutionary Patterns across Plastomes of Three Pooid Model Species: Emerging Grass Genome Models for Monocots. Mol. Biotechnol. 2011, 49, 116–128. [Google Scholar] [CrossRef]
  48. Liu, Q.; Feng, Y.; Xue, Q. Analysis of factors shaping codon usage in the mitochondrion genome of Oryza sativa. Mitochondrion 2004, 4, 313–320. [Google Scholar] [CrossRef]
  49. Wang, Z.; Cai, Q.; Wang, Y.; Li, M.; Wang, C.; Wang, Z.; Jiao, C.; Xu, C.; Wang, H.; Zhang, Z. Comparative Analysis of Codon Bias in the Chloroplast Genomes of Theaceae Species. Front. Genet. 2022, 13, 824610. [Google Scholar] [CrossRef] [PubMed]
  50. Shi, S.-L.; Liu, Y.-Q.; Xia, R.-X.; Qin, L. Comprehensive Analysis of Codon Usage in Quercus Chloroplast Genome and Focus on psbA Gene. Genes 2022, 13, 2156. [Google Scholar] [CrossRef] [PubMed]
  51. Chakraborty, S.; Yengkhom, S.; Uddin, A. Analysis of codon usage bias of chloroplast genes in Oryza species: Codon usage of chloroplast genes in Oryza species. Planta 2020, 252, 67. [Google Scholar] [CrossRef] [PubMed]
Forests 14 00378 g001 550
Figure 1. The explanation for correlation analysis of GC content at different locations in the figure. * indicates that the correlation has reached a significant level (p < 0.1). ** indicates that the correlation has reached a significant level (p < 0.05); *** indicates that the correlation has reached a very significant level (p < 0.01). The different colored letters in the upper right correspond to the different colored peaks in the middle and the different colored points in the lower left. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z),” respectively.
Figure 1. The explanation for correlation analysis of GC content at different locations in the figure. * indicates that the correlation has reached a significant level (p < 0.1). ** indicates that the correlation has reached a significant level (p < 0.05); *** indicates that the correlation has reached a very significant level (p < 0.01). The different colored letters in the upper right correspond to the different colored peaks in the middle and the different colored points in the lower left. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z),” respectively.
Forests 14 00378 g001
Forests 14 00378 g002 550
Figure 2. Analysis of the relative synonymous codon usage (RSCU) of chloroplast genomes of 26 Juglandaceae plant species. * indicates RSCU = 0.8–1, ** indicates RSCU = 1–1.5, *** indicates RSCU > 0.5, + indicates RSCU = 1. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z),” respectively.
Figure 2. Analysis of the relative synonymous codon usage (RSCU) of chloroplast genomes of 26 Juglandaceae plant species. * indicates RSCU = 0.8–1, ** indicates RSCU = 1–1.5, *** indicates RSCU > 0.5, + indicates RSCU = 1. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z),” respectively.
Forests 14 00378 g002
Forests 14 00378 g003 550
Figure 3. Optimal codon analysis. Forests 14 00378 i001 indicates ΔRSCU < 0, Forests 14 00378 i002 indicates ΔRSCU = 0, Forests 14 00378 i003 indicates 0 < ΔRSCU < 1, Forests 14 00378 i004 indicates 1 ≤ ΔRSCU; 0 < ΔRSCU is the optimal codon. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z)”, respectively.
Figure 3. Optimal codon analysis. Forests 14 00378 i001 indicates ΔRSCU < 0, Forests 14 00378 i002 indicates ΔRSCU = 0, Forests 14 00378 i003 indicates 0 < ΔRSCU < 1, Forests 14 00378 i004 indicates 1 ≤ ΔRSCU; 0 < ΔRSCU is the optimal codon. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z)”, respectively.
Forests 14 00378 g003
Forests 14 00378 g004 550
Figure 4. Analysis of codon preference influencing factors in Juglandaceae. (A) ENC-GC3 plot. The solid red line represents the expected curve when codon usage bias is only affected by mutation pressure. ENC-GC3 plot. The red line represents the expected curve when codon usage bias is only affected by mutation pressure, and the blue line represents the ENC value equal to 35, and the genes distributed below the red line represent strong codon preference, while those above the red line represent weak codon preference. (B) Frequency distributions of the ENC ratio. The dataset displayed a single peak, and the most of genes located into a narrow region of ENC ratios between −0.5 and −0.8. (C) PR2-bias plot using the values of A3/(A3 + T3) against G3/(G3C3). (D) Neutrality plot analysis of GC12 and GC3 contents. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z),” respectively.
Figure 4. Analysis of codon preference influencing factors in Juglandaceae. (A) ENC-GC3 plot. The solid red line represents the expected curve when codon usage bias is only affected by mutation pressure. ENC-GC3 plot. The red line represents the expected curve when codon usage bias is only affected by mutation pressure, and the blue line represents the ENC value equal to 35, and the genes distributed below the red line represent strong codon preference, while those above the red line represent weak codon preference. (B) Frequency distributions of the ENC ratio. The dataset displayed a single peak, and the most of genes located into a narrow region of ENC ratios between −0.5 and −0.8. (C) PR2-bias plot using the values of A3/(A3 + T3) against G3/(G3C3). (D) Neutrality plot analysis of GC12 and GC3 contents. Lowercase letters from a to z indicate “J. regia (a), J. regia 192 (b), J. regia 193 (c), J. regia 194 (d), J. regia 196 (e), J. regia 197 (f), J. regia 198 (g), J. regia 199 (h), J. regia 200 (i), J. regia 201 (j), J. regia 202 (k), J. mandshurica (l), J. hopeiensis (m), P. stenoptera (n), P. macroptera (o), P. hupehensis (p), P. tonkinensis (q), E. hainanensis (r), E. roxburghiana (s), A. sinensis (t), C. paliurus (u), C. kweichowensis (v), C. hunanensis (w), C. illinoensis (x), C. cathayensis (y), and C. tonkinensis (z),” respectively.
Forests 14 00378 g004
Table
Table 1. Chloroplast genomes and the associated information of 26 Juglandaceae species.
Table 1. Chloroplast genomes and the associated information of 26 Juglandaceae species.
GenusSpeciesAccession No.Letter SeriesGenome Size (bp)Protein-Coding Genes
Before ProcessingAfter Processing
JuglansJuglans regiaKT870116.1a160,5378571
Juglans regia 192-b160,3678472
Juglans regia 193-c160,3678472
Juglans regia 194-d160,3678472
Juglans regia 196-e160,3678472
Juglans regia 197-f160,3678472
Juglans regia 198-g160,3678472
Juglans regia 199-h160,3678472
Juglans regia 200-i160,3678472
Juglans regia 201-j160,3678472
Juglans regia 202-k106,3498472
Juglans mandshuricaNC_033892.1l159,7298671
Juglans hopeiensisNC_033894.1m159,7148670
PterocaryaPterocarya stenopteraMN866892.1n160,2128874
Pterocarya macropteraMW194257.1o160,1688867
Pterocarya hupehensisNC_046431.1p159,7708967
Pterocarya tonkinensisNC_046427.1q160,0968965
EngelhardiaEngelhardia hainanensisNC_068233.1r161,5748372
Engelhardia roxburghianaMN652922.1s161,5508372
AnnamocaryaAnnamocarya sinensisMN473449.1t160,0658571
CyclocaryaCyclocarya paliurusNC_034315.1u160,5628968
CaryaCarya kweichowensisNC_040864.1v175,3136449
Carya hunanensisNC_046435.1w160,3978966
Carya illinoensisMH188302.1x160,5858866
Carya cathayensisMN892516.1y160,8258473
Carya tonkin
Ensis		NC_066504.1z160,7158572
Note: “-“ denotes that sequencing has been completed but has not yet been uploaded to the NCBI database.
Table
Table 2. Content of four nucleotides and GC content of CDSs at different positions in 26 Juglandaceae species.
Table 2. Content of four nucleotides and GC content of CDSs at different positions in 26 Juglandaceae species.
SpeciesT/U (%)C (%)A (%)G (%)1st (GC%)2nd (GC%)3rd (GC%)All (GC%)MILC
J. regia31.717.231.12045.6837.6528.2937.210.55
J. regia 19231.717.231.12045.6837.6528.337.210.55
J. regia 19331.717.231.12045.5537.6228.2637.140.55
J. regia 19431.717.231.12045.4337.5629.0837.360.55
J. regia 19631.717.231.12045.6837.6528.3137.210.55
J. regia 19731.917.430.220.546.7738.8628.137.910.55
J. regia 19831.717.231.12045.6837.6528.337.210.55
J. regia 19931.717.231.12045.6837.6528.3137.210.55
J. regia 20031.717.231.12045.6737.6628.337.210.55
J. regia 20131.717.231.12045.6737.6628.337.210.55
J. regia 20231.717.231.12045.6837.6528.337.210.55
J. mandshurica31.717.530.320.546.5738.5928.7137.950.55
J. hopeiensis31.917.53020.646.638.7128.8438.050.55
P. stenoptera31.817.2312045.5737.728.3237.20.55
P. macroptera31.817.530.120.546.5438.4928.837.940.55
P. hupehensis31.817.530.220.546.4938.4428.9637.960.55
P. tonkinensis31.617.73020.74738.7829.3538.380.55
E. hainanensis31.717.231.12045.4337.6228.2237.090.55
E. roxburghiana31.717.231.219.945.4637.5928.2537.10.54
nnamocarya sinensis31.617.33120.145.8137.8428.5937.420.54
C. paliurus31.917.430.220.546.4938.528.837.930.54
C. kweichowensis31.517.33120.146.0737.6528.5537.430.55
C. hunanensis3217.63020.546.3638.5829.1938.040.55
C. illinoensis3217.63020.546.4138.5729.2638.080.54
C. cathayensis31.817.230.920.145.6237.7628.4937.290.54
C. tonkinensis31.717.530.420.446.4538.4128.8637.910.54
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zeng, Y.; Shen, L.; Chen, S.; Qu, S.; Hou, N. Codon Usage Profiling of Chloroplast Genome in Juglandaceae. Forests 2023, 14, 378. https://doi.org/10.3390/f14020378

AMA Style

Zeng Y, Shen L, Chen S, Qu S, Hou N. Codon Usage Profiling of Chloroplast Genome in Juglandaceae. Forests. 2023; 14(2):378. https://doi.org/10.3390/f14020378

Chicago/Turabian Style

Zeng, Yajun, Lianwen Shen, Shengqun Chen, Shuang Qu, and Na Hou. 2023. "Codon Usage Profiling of Chloroplast Genome in Juglandaceae" Forests 14, no. 2: 378. https://doi.org/10.3390/f14020378

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop