甘蔗属种及其近缘属种蔗茅的全基因组密码子偏好性分析

doi:10.13560/j.cnki.biotech.bull.1985.2023-0982

摘要/Abstract

摘要：

【目的】 为解析甘蔗基因组的密码子使用特征，提高异源基因在甘蔗中的表达效率。【方法】 以已发布的甘蔗属种（热带种LA-purple、割手密NP-X和AP85-441）及其近缘属种蔗茅（Yunnan2009-3）基因组为数据，利用Python、CodonW1.4.2进行密码子偏好性分析，同时通过中性绘图、ENC-plot、PR2-plot等分析探讨密码子偏好性形成的影响因素，并结合转录组测序数据分析密码子偏好性参数与基因表达水平的相关性。最后,基于RSCU均值与7个主要模式生物种（玉米、高粱、水稻、拟南芥、烟草、大肠杆菌、酿酒酵母）的密码子使用模式进行比较分析。【结果】 显示热带种、割手密和蔗茅的基因组都富含GC，平均GC含量为56.3%，且GC3>GC1>GC2, 倾向于使用以G/C结尾的密码子, 平均ENC值为48.45，偏好性较低。中性绘图、ENC-plot和PR2-plot分析表明它们的密码子偏好性受到自然选择、突变压力等多种因素的共同影响，其中自然选择占主导作用。相关性分析表明密码子偏好性参数与基因实际的转录表达水平存在显著相关性，但相关性不强。根据RSCU和∆RSCU值，确定了13个最优密码子，均以C或G结尾，密码子使用特性在全基因组和染色体组水平上无差异。通过比较发现，甘蔗的核苷酸组成及密码子偏好性与玉米、高粱和水稻较为相似，而与拟南芥、烟草、大肠杆菌和酵母具有显著差异。【结论】 甘蔗热带种、割手密和蔗茅的密码子偏好性高度相似，其形成受自然选择和突变因素的影响。此外，对甘蔗优异基因功能异源验证时可优先选择玉米、水稻和高粱作为异源表达系统。

关键词: 甘蔗属, 蔗茅, 基因组, 密码子使用偏好性, 最优密码子

Abstract:

【Objective】 To clarify the codon usage characteristics and improve expression efficiency of exogenous genes in sugarcane. 【Method】 Codon usage bias were analyzed by Python and CodonW1.4.2 tools based on their genomes data of three species including Saccharum officinarum (LA-purple), S. sponaneum (NP-X and AP85-441), and Erianthus fulvus (Yunnan2009-3). Neutrality-plot, ENC-plot, and Parity Rule 2（PR2）-plot analysis were also used to explore the possible factors that affect the formation of codon usage bias. Simultaneously, correlation analysis between gene expressions and codon usage bias parameters was carryd out based on transcriptome data. Finally, a comparison analysis of codon usage patterns were performed between sugarcane and the other seven main model organisms, namely, Zea mays, Sorghum bicolor, Oryza sativa, Arabidopsis thaliana, Nicotiana tabacum, Escherichia coli, and Saccharomyces cerevisiae based on the average RSCU value of three species. 【Result】 S. officinarum, S. sponaneum, and E. fulvus were rich in GC base pairs, the average GC content was 56.3%, and GC3 was the highest, followed by GC1 and GC2, indicating that sugarcane preferred to use synonymous codons ending with a C/G base. The average ENC value was 48.45, suggesting that sugarcane had a lower codon usage bias. According to the results of neutral-plot, ENC-plot, and PR2-plot analysis, the codon usage bias of four genomes was affected by natural selection, mutation pressure, and so on, and the natural selection dominated in them. Correlation analysis showed that codon preference parameters were significantly correlated with the actual transcription expressions of genes, but the correlation was not strong. Based on the RSCU and ∆RSCU values, thirteen identical optimal codons were confirmed, and all of them were ended with C/G base pairs. The codon usage characteristics were not different at genome-wide and chromosome level. In addition, the codon usage bias of sugarcane was similar to that of Z. mays, S. bicolor, and O. sativa, while it had a significant difference with A. thaliana, N. tabacum, E. coli, and S. cerevisiae. 【Conclusion】 The codon usage bias among S. officinarum, S.sponaneum and E. fulvus was highly similar, and its formation was affected by natural selection and mutation pressure factors. Meanwhile, Z. mays, O. sativa and S. bicolor could be used for elite gene function research on sugarcane as a exogenous expression system on account of their codon usage bias patterns.

Key words: Saccharum, Erianthus fulvus, genome, codon usage bias, optimal codons

田春艳, 李旭娟, 李纯佳, 毛钧, 刘新龙. 甘蔗属种及其近缘属种蔗茅的全基因组密码子偏好性分析[J]. 生物技术通报, 2024, 40(3): 202-214.

TIAN Chun-yan, LI Xu-juan, LI Chun-jia, MAO Jun, LIU Xin-long. Genome-wide Analysis of Codon Usage Bias in Saccharum Species and Its Phylogenetically Related Species Erianthus fulvus[J]. Biotechnology Bulletin, 2024, 40(3): 202-214.

图/表 10

参考文献 40

[1]	Ikemura T. Codon usage and tRNA content in unicellular and multicellular organisms[J]. Mol Biol Evol, 1985, 2(1): 13-34. doi: 10.1093/oxfordjournals.molbev.a040335 pmid: 3916708
[2]	Bulmer M. The selection-mutation-drift theory of synonymous codon usage[J]. Genetics, 1991, 129(3): 897-907. doi: 10.1093/genetics/129.3.897 pmid: 1752426
[3]	Hershberg R, Petrov DA. Selection on codon bias[J]. Annu Rev Genet, 2008, 42: 287-299. doi: 10.1146/annurev.genet.42.110807.091442 pmid: 18983258
[4]	张乐, 金龙国, 罗玲, 等. 大豆基因组和转录组的核基因密码子使用偏好性分析[J]. 作物学报, 2011, 37(6): 965-974.
	Zhang L, Jin LG, Luo L, et al. Analysis of nuclear gene codon bias on soybean genome and transcriptome[J]. Acta Agron Sin, 2011, 37(6): 965-974.
[5]	李亚麒, 黄家雄, 娄予强, 等. 小粒咖啡铁皮卡叶绿体基因组密码子偏好性分析[J]. 西北林学院学报, 2023, 38(2): 92-99.
	Li YQ, Huang JX, Lou YQ, et al. Codon usage bias of the chloroplast genome in Coffea arabica‘Typica’[J]. J Northwest For Univ, 2023, 38(2): 92-99.
[6]	Tao P, Dai L, Luo MC, et al. Analysis of synonymous codon usage in classical swine fever virus[J]. Virus Genes, 2009, 38(1): 104-112. doi: 10.1007/s11262-008-0296-z pmid: 18958611
[7]	Das S, Paul S, Dutta C. Synonymous codon usage in adenoviruses: influence of mutation, selection and protein hydropathy[J]. Virus Res, 2006, 117(2): 227-236. pmid: 16307819
[8]	Yadav MK, Swati D. Comparative genome analysis of six malarial parasites using codon usage bias based tools[J]. Bioinformation, 2012, 8(24): 1230-1239. doi: 10.6026/97320630081230 pmid: 23275725
[9]	赖瑞联, 冯新, 陈瑾, 等. 橄榄查尔酮异构酶基因CHI的密码子偏好模式[J]. 应用与环境生物学报, 2017, 23(5): 945-951.
	Lai RL, Feng X, Chen J, et al. Codon usage pattern of chalcone isomerase gene(CHI)in Canarium album(Lour.) Raeusch[J]. Chin J Appl Environ Biol, 2017, 23(5): 945-951.
[10]	寇莹莹, 宋英今, 杨少辉, 等. 植酸酶phyA基因的密码子优化及其在大豆中的表达[J]. 作物学报, 2016, 42(12): 1798-1804. doi: 10.3724/SP.J.1006.2016.01798
	Kou YY, Song YJ, Yang SH, et al. Codon optimization and expression of phyA gene in soybean(Glycine max merr.)[J]. Acta Agron Sin, 2016, 42(12): 1798-1804. doi: 10.3724/SP.J.1006.2016.01798 URL
[11]	周宗梁, 林智敏, 耿丽丽, 等. 水稻中cry1Ah1基因密码子优化方案的比较[J]. 生物工程学报, 2012, 28(10): 1184-1194.
	Zhou ZL, Lin ZM, Geng LL, et al. Comparison of codon optimizations of cry1Ah1 gene in rice[J]. Chin J Biotechnol, 2012, 28(10): 1184-1194.
[12]	陈惠, 赵海霞, 王红宁, 等. 植酸酶基因中稀有密码子的改造提高其在毕赤酵母中的表达量[J]. 中国生物化学与分子生物学报, 2005, 21(2): 170-174.
	Chen H, Zhao HX, Wang HN, et al. Increasing expression level of phytase gene(phyA)in Pichia pastoris by changing rare codons[J]. Chin J Biochem Mol Biol, 2005, 21(2): 170-174.
[13]	Menzella HG. Comparison of two codon optimization strategies to enhance recombinant protein production in Escherichia coli[J]. Microb Cell Fact, 2011, 10: 15. doi: 10.1186/1475-2859-10-15 pmid: 21371320
[14]	Jabeen R, Khan MS, Zafar Y, et al. Codon optimization of cry1Ab gene for hyper expression in plant organelles[J]. Mol Biol Rep, 2010, 37(2): 1011-1017. doi: 10.1007/s11033-009-9802-1 pmid: 19757171
[15]	Hirai H, Kashima Y, Hayashi K, et al. Efficient expression of laccase gene from white-rot fungus Schizophyllum commune in a transgenic tobacco plant[J]. FEMS Microbiol Lett, 2008, 286(1): 130-135. doi: 10.1111/fml.2008.286.issue-1 URL
[16]	D'Hont A. Unraveling the genome structure of polyploids using FISH and GISH; examples of sugarcane and banana[J]. Cytogenet Genome Res, 2005, 109(1-3): 27-33. doi: 10.1159/000082378 pmid: 15753555
[17]	Zhang JS, Zhang XT, Tang HB, et al. Allele-defined genome of the autopolyploid sugarcane Saccharum spontaneum L[J]. Nat Genet, 2018, 50(11): 1565-1573. doi: 10.1038/s41588-018-0237-2
[18]	Zhang Q, Qi YY, Pan HR, et al. Genomic insights into the recent chromosome reduction of autopolyploid sugarcane Saccharum spontaneum[J]. Nat Genet, 2022, 54(6): 885-896. doi: 10.1038/s41588-022-01084-1 pmid: 35654976
[19]	Wang TY, Wang BY, Hua XT, et al. A complete gap-free diploid genome in Saccharum complex and the genomic footprints of evolution in the highly polyploid Saccharum genus[J]. Nat Plants, 2023, 9(4): 554-571. doi: 10.1038/s41477-023-01378-0
[20]	Zhang WJ, Zhou J, Li ZF, et al. Comparative analysis of codon usage patterns among mitochondrion, chloroplast and nuclear genes in Triticum aestivum L[J]. J Integr Plant Biol, 2007, 49(2): 246-254. doi: 10.1111/jipb.2007.49.issue-2 URL
[21]	冯展, 江媛, 郑燕, 等. 肉苁蓉属植物叶绿体基因组密码子偏好性分析[J]. 中草药, 2023, 54(5): 1540-1550.
	Feng Z, Jiang Y, Zheng Y, et al. Codon use bias analysis of chloroplast genome of Cistanche[J]. Chin Tradit Herb Drugs, 2023, 54(5): 1540-1550.
[22]	Wright F. The ‘effective number of codons’ used in a gene[J]. Gene, 1990, 87(1): 23-29. doi: 10.1016/0378-1119(90)90491-9 pmid: 2110097
[23]	Jiang Y, Deng F, Wang HL, et al. An extensive analysis on the global codon usage pattern of baculoviruses[J]. Arch Virol, 2008, 153(12): 2273-2282. doi: 10.1007/s00705-008-0260-1 pmid: 19030954
[24]	张以忠, 曾文艺, 邓琳琼, 等. 甘蓝S-位点基因SRK、SLG和SP11/SCR密码子偏好性分析[J]. 作物学报, 2022, 48(5): 1152-1168. doi: 10.3724/SP.J.1006.2022.14003
	Zhang YZ, Zeng WY, Deng LQ, et al. Codon usage bias analysis of S-locus genes SRK, SLG, and SP11/SCR in Brassica oleracea[J]. Acta Agron Sin, 2022, 48(5): 1152-1168. doi: 10.3724/SP.J.1006.2022.14003 URL
[25]	金刚, 覃旭, 龙凌云, 等. 剑麻叶绿体基因组编码序列密码子的使用特征[J]. 福建农林大学学报: 自然科学版, 2018, 47(6): 705-710.
	Jin G, Qin X, Long LY, et al. Characteristics of codon usage in the chloroplast protein-coding genes of Agave hybrid No.11648[J]. J Fujian Agric For Univ Nat Sci Ed, 2018, 47(6): 705-710.
[26]	雷佳欣, 张丽娟, 高丹丹, 等. 文冠果基因组密码子偏好性分析[J]. 西北林学院学报, 2023, 38(4): 104-110.
	Lei JX, Zhang LJ, Gao DD, et al. Codon usage bias of Xanthoceras sorbifolium genome[J]. J Northwest For Univ, 2023, 38(4): 104-110.
[27]	赖瑞联, 林玉玲, 钟春水, 等. 龙眼生长素受体基因TIR1密码子偏好性分析[J]. 园艺学报, 2016, 43(4): 771-780. doi: 10.16420/j.issn.0513-353x.2015-0662
	Lai RL, Lin YL, Zhong CS, et al. Analysis of codon bias of auxin receptor gene TIR1 in Dimocarpus longan[J]. Acta Hortic Sin, 2016, 43(4): 771-780.
[28]	Liu QP, Feng Y, Zhao XA, et al. Synonymous codon usage bias in Oryza sativa[J]. Plant Sci, 2004, 167(1): 101-105. doi: 10.1016/j.plantsci.2004.03.003 URL
[29]	刘汉梅, 何瑞, 张怀渝, 等. 玉米同义密码子偏爱性分析[J]. 农业生物技术学报, 2010, 18(3): 456-461.
	Liu HM, He R, Zhang HY, et al. Analysis of synonymous codon bias in maize[J]. J Agric Biotechnol, 2010, 18(3): 456-461.
[30]	张晓峰, 薛庆中. 水稻和拟南芥NBS-LRR基因家族同义密码子使用偏好的比较[J]. 作物学报, 2005, 31(5): 596-602.
	Zhang XF, Xue QZ. Synonymous codon bias of NBS-LRR gene family in rice and Arabidopsis[J]. Acta Agron Sin, 2005, 31(5): 596-602.
[31]	Kawabe A, Miyashita NT. Patterns of codon usage bias in three dicot and four monocot plant species[J]. Genes Genet Syst, 2003, 78(5): 343-352. doi: 10.1266/ggs.78.343 pmid: 14676425
[32]	Comeron JM, Aguadé M. An evaluation of measures of synonymous codon usage bias[J]. J Mol Evol, 1998, 47(3): 268-274. doi: 10.1007/pl00006384 pmid: 9732453
[33]	唐玉娟, 赵英, 黄国弟, 等. 芒果叶绿体基因组密码子使用偏好性分析[J]. 热带作物学报, 2021, 42(8): 2143-2150. doi: 10.3969/j.issn.1000-2561.2021.08.004
	Tang YJ, Zhao Y, Huang GD, et al. Analysis on codon usage bias of chloroplast genes from mango[J]. Chin J Trop Crops, 2021, 42(8): 2143-2150.
[34]	杨祥燕, 蔡元保, 谭秦亮, 等. 菠萝叶绿体基因组密码子偏好性分析[J]. 热带作物学报, 2022, 43(3): 439-446. doi: 10.3969/j.issn.1000-2561.2022.03.001
	Yang XY, Cai YB, Tan QL, et al. Analysis of codon usage bias in the chloroplast genome of Ananas comosus[J]. Chin J Trop Crops, 2022, 43(3): 439-446.
[35]	Wen Y, Zou ZL, Li HS, et al. Analysis of codon usage patterns in Morus notabilis based on genome and transcriptome data[J]. Genome, 2017, 60(6): 473-484. doi: 10.1139/gen-2016-0129 URL
[36]	王占军, 丁亮, 蔡倩文, 等. 3种木薯全基因组的密码子偏好性模式与变异来源比较[J]. 应用与环境生物学报, 2021, 27(4): 1013-1021.
	Wang ZJ, Ding L, Cai QW, et al. Comparison of codon preference patterns and variation sources in Manihot esculenta Crantz genomes[J]. Chin J Appl Environ Biol, 2021, 27(4): 1013-1021.
[37]	毛立彦, 黄秋伟, 龙凌云, 等. 7种睡莲属植物叶绿体基因组密码子偏好性分析[J]. 西北林学院学报, 2022, 37(2): 98-107.
	Mao LY, Huang QW, Long LY, et al. Comparative analysis of codon usage bias in chloroplast genomes of seven Nymphaea species[J]. J Northwest For Univ, 2022, 37(2): 98-107.
[38]	冯瑞云, 梅超, 王慧杰, 等. 籽粒苋叶绿体基因组密码子偏好性分析[J]. 中国草地学报, 2019, 41(4): 8-15.
	Feng RY, Mei C, Wang HJ, et al. Analysis of codon usage in the chloroplast genome of grain amaranth(Amaranthus hypochondriacus L.)[J]. Chin J Grassland, 2019, 41(4): 8-15.
[39]	原晓龙, 李云琴, 张劲峰, 等. 乳油木叶绿体基因组密码子偏好性分析[J]. 分子植物育种, 2020, 18(17): 5658-5664.
	Yuan XL, Li YQ, Zhang JF, et al. Codon usage bias analysis of chloroplast genome in Vitellaria paradoxa[J]. Mol Plant Breed, 2020, 18(17): 5658-5664.
[40]	Tang DF, Wei F, Cai ZQ, et al. Analysis of codon usage bias and evolution in the chloroplast genome of Mesona chinensis Benth[J]. Dev Genes Evol, 2021, 231(1-2): 1-9. doi: 10.1007/s00427-020-00670-9

核苷酸组成和ENC Nucleotide composition and ENC	材料 Materials				平均值 Mean
核苷酸组成和ENC Nucleotide composition and ENC	LA-purple	NP-X	AP85-441	Yunnan2009-3	平均值 Mean
A3s/%	15.61±8.47	15.65±8.14	14.91±8.01	16.41±8.38	15.65
T3s/%	20.19±11.13	19.95±10.52	19.00±10.41	21.14±10.95	20.07
G3s/%	31.24±8.23	31.11±7.78	31.95±7.95	30.42±7.84	31.18
C3s/%	32.97±12.91	33.28±12.31	34.14±12.13	32.04±12.66	33.11
GC/%	56.21±0.10	56.42±0.09	57.53±0.09	55.02±0.09	56.30
GC1/%	58.39±7.29	58.53±6.76	59.29±6.80	57.67±6.86	58.47
GC2/%	45.73±7.79	46.05±7.34	46.87±7.57	44.67±7.06	45.83
GC3/%	64.21±18.91	64.39±18.02	66.09±17.78	62.46±18.68	64.29
GC3s/%	62.89±0.20	63.10±0.19	64.91±0.18	61.06±0.19	62.99
ENC	47.97±9.11	48.74±8.93	48.19±9.26	48.89±8.82	48.45

核苷酸组成和ENC Nucleotide composition and ENC	材料 Materials				平均值 Mean
核苷酸组成和ENC Nucleotide composition and ENC	LA-purple	NP-X	AP85-441	Yunnan2009-3	平均值 Mean
A3s/%	15.61±8.47	15.65±8.14	14.91±8.01	16.41±8.38	15.65
T3s/%	20.19±11.13	19.95±10.52	19.00±10.41	21.14±10.95	20.07
G3s/%	31.24±8.23	31.11±7.78	31.95±7.95	30.42±7.84	31.18
C3s/%	32.97±12.91	33.28±12.31	34.14±12.13	32.04±12.66	33.11
GC/%	56.21±0.10	56.42±0.09	57.53±0.09	55.02±0.09	56.30
GC1/%	58.39±7.29	58.53±6.76	59.29±6.80	57.67±6.86	58.47
GC2/%	45.73±7.79	46.05±7.34	46.87±7.57	44.67±7.06	45.83
GC3/%	64.21±18.91	64.39±18.02	66.09±17.78	62.46±18.68	64.29
GC3s/%	62.89±0.20	63.10±0.19	64.91±0.18	61.06±0.19	62.99
ENC	47.97±9.11	48.74±8.93	48.19±9.26	48.89±8.82	48.45

ENC值 ENC value	LA-purple		NP-X		AP85-441		Yunnan2009-3
ENC值 ENC value	数目	百分比/%	数目	百分比/%	数目	百分比/%	数目	百分比/%
ENC≤35	34 381	14.15	15 880	12.10	8 642	14.23	4 290	12.05
35<ENC≤45	46 920	19.32	24 450	18.64	11 548	19.07	5 955	16.72
ENC˃45	161 603	66.53	90 849	69.26	40 414	66.70	25 371	71.23

ENC值 ENC value	LA-purple		NP-X		AP85-441		Yunnan2009-3
ENC值 ENC value	数目	百分比/%	数目	百分比/%	数目	百分比/%	数目	百分比/%
ENC≤35	34 381	14.15	15 880	12.10	8 642	14.23	4 290	12.05
35<ENC≤45	46 920	19.32	24 450	18.64	11 548	19.07	5 955	16.72
ENC˃45	161 603	66.53	90 849	69.26	40 414	66.70	25 371	71.23

氨基酸 Amino acid	密码子 Codon	LA-purple				NP-X				AP85-441				Yunnan2009-3
氨基酸 Amino acid	密码子 Codon	GW	HEB	LEB	∆RSCU	GW	HEB	LEB	∆RSCU	GW	HEB	LEB	∆RSCU	GW	HEB	LEB	∆RSCU
Phe	UUU	0.76	0.05	0.95	-0.90	0.73	0.06	0.95	-0.89	0.69	0.19	0.93	-0.74	0.78	0.06	0.96	-0.90
	UUC**	1.24	1.95	1.05	0.90	1.27	1.94	1.05	0.89	1.31	1.81	1.07	0.74	1.22	1.94	1.04	0.90
Leu	UUA	0.39	0.01	0.53	-0.52	0.37	0.01	0.52	-0.51	0.33	0.04	0.51	-0.47	0.41	0.01	0.53	-0.52
	UUG	0.94	0.14	1.11	-0.97	0.92	0.16	1.11	-0.95	0.86	0.08	1.10	-1.02	0.97	0.15	1.12	-0.97
	CUU	1.04	0.13	1.27	-1.14	1.03	0.15	1.24	-1.09	1.00	1.30	1.26	0.04	1.08	0.14	1.26	-1.12
	CUC**	1.59	2.99	1.20	1.79	1.60	2.95	1.21	1.74	1.72	2.09	1.23	0.86	1.51	2.94	1.19	1.75
	CUA	0.50	0.07	0.66	-0.59	0.50	0.08	0.67	-0.59	0.46	0.88	0.65	0.23	0.54	0.08	0.67	-0.59
	CUG**	1.54	2.65	1.24	1.41	1.58	2.65	1.24	1.41	1.64	1.61	1.26	0.35	1.49	2.67	1.22	1.45
Ile	AUU	1.04	0.10	1.17	-1.07	1.01	0.11	1.16	-1.05	0.97	0.31	1.17	-0.86	1.05	0.12	1.17	-1.05
	AUC**	1.34	2.81	1.04	1.77	1.35	2.78	1.04	1.74	1.43	2.48	1.05	1.43	1.29	2.78	1.03	1.75
	AUA	0.63	0.09	0.78	-0.69	0.64	0.10	0.79	-0.69	0.60	0.21	0.78	-0.57	0.66	0.10	0.80	-0.70
Val	GUU	0.95	0.09	1.23	-1.14	0.94	0.1	1.21	-1.11	0.90	0.78	1.22	-0.44	1.00	0.09	1.23	-1.14
	GUC	1.15	1.69	0.97	0.72	1.17	1.69	0.99	0.70	1.22	1.48	0.99	0.49	1.12	1.67	0.97	0.70
	GUA	0.41	0.04	0.58	-0.54	0.41	0.05	0.58	-0.53	0.38	0.53	0.57	-0.04	0.44	0.05	0.59	-0.54
	GUG**	1.48	2.18	1.22	0.96	1.48	2.17	1.22	0.95	1.50	1.21	1.01	0.20	1.44	2.19	1.21	0.98
Ser	UCU	1.03	0.15	1.17	-1.02	1.00	0.16	1.16	-1.00	0.96	0.58	1.14	-0.56	1.06	0.16	1.17	-1.01
	UCC**	1.25	2.24	1.06	1.18	1.24	2.18	1.07	1.11	1.32	1.85	1.08	0.77	1.19	2.17	1.03	1.14
	UCA	0.99	0.13	1.18	-1.05	0.99	0.14	1.17	-1.03	0.94	0.33	1.16	-0.83	1.04	0.14	1.20	-1.06
	UCG	0.80	1.65	0.68	0.97	0.81	1.64	0.69	0.95	0.87	1.21	0.70	0.51	0.76	1.66	0.68	0.98
	AGU	0.72	0.07	0.85	-0.78	0.70	0.08	0.85	-0.77	0.64	0.30	0.84	-0.54	0.75	0.08	0.87	-0.79
	AGC**	1.22	1.76	1.05	0.71	1.25	1.80	1.07	0.73	1.27	1.73	1.07	0.66	1.20	1.79	1.05	0.74
Pro	CCU	0.98	0.20	1.15	-0.95	0.96	0.21	1.13	-0.92	0.92	1.20	1.14	0.06	1.02	0.21	1.17	-0.96
	CCC	0.88	1.38	0.80	0.58	0.88	1.36	0.81	0.55	0.92	0.95	0.82	0.13	0.85	1.36	0.79	0.57
	CCA	1.03	0.20	1.20	-1.00	1.03	0.22	1.20	-0.98	0.96	0.70	1.18	-0.48	1.08	0.21	1.22	-1.01
	CCG	1.11	2.22	0.85	1.37	1.12	2.21	0.85	1.36	1.20	1.14	0.86	0.28	1.05	2.22	0.82	1.40
Thr	ACU	0.95	0.10	1.13	-1.03	0.91	0.11	1.12	-1.01	0.87	0.38	1.12	-0.74	0.97	0.11	1.14	-1.03
	ACC	1.20	1.91	1.00	0.91	1.20	1.87	1.00	0.87	1.26	1.72	1.01	0.71	1.17	1.87	0.98	0.89
	ACA	1.00	0.11	1.22	-1.11	1.01	0.12	1.21	-1.09	0.94	0.33	1.21	-0.88	1.06	0.12	1.24	-1.12
	ACG	0.85	1.88	0.65	1.23	0.88	1.89	0.67	1.22	0.93	1.58	0.67	0.91	0.81	1.90	0.65	1.25
Ala	GCU	0.92	0.15	1.16	-1.01	0.90	0.16	1.14	-0.98	0.85	1.21	1.14	0.07	0.95	0.16	1.17	-1.01
	GCC**	1.27	1.91	1.02	0.89	1.28	1.90	1.03	0.87	1.33	1.14	1.03	0.11	1.24	1.88	1.02	0.86
	GCA	0.81	0.14	1.06	-0.92	0.81	0.15	1.05	-0.90	0.75	0.66	1.04	-0.38	0.86	0.14	1.06	-0.92
	GCG	1.00	1.80	0.76	1.04	1.01	1.79	0.78	1.01	1.07	1.00	0.79	0.21	0.95	1.81	0.75	1.06
Tyr	UAU	0.81	0.05	1.00	-0.95	0.77	0.05	1.01	-0.96	0.72	0.2	0.99	-0.79	0.83	0.05	1.02	-0.97
	UAC**	1.19	1.95	1.00	0.95	1.23	1.95	0.99	0.96	1.28	1.80	1.01	0.79	1.17	1.95	0.98	0.97
His	CAU	0.93	0.12	1.09	-0.97	0.90	0.13	1.08	-0.95	0.84	0.95	1.07	-0.12	0.96	0.13	1.10	-0.97
	CAC	1.07	1.88	0.91	0.97	1.10	1.87	0.92	0.95	1.16	1.05	0.93	0.12	1.04	1.87	0.90	0.97
Gln	CAA	0.75	0.10	0.88	-0.78	0.73	0.11	0.88	-0.77	0.67	1.19	0.86	0.33	0.79	0.11	0.90	-0.79
	CAG*	1.25	1.90	1.12	0.78	1.27	1.89	1.12	0.77	1.33	0.81	1.14	-0.33	1.21	1.89	1.10	0.79
Asn	AAU	0.92	0.09	1.06	-0.97	0.90	0.10	1.06	-0.96	0.86	0.61	1.04	-0.43	0.95	0.11	1.06	-0.95
	AAC	1.08	1.91	0.94	0.97	1.10	1.90	0.94	0.96	1.14	1.39	0.96	0.43	1.05	1.89	0.94	0.95
Lys	AAA	0.65	0.09	0.79	-0.70	0.66	0.09	0.80	-0.71	0.62	0.25	0.79	-0.54	0.68	0.09	0.80	-0.71
	AAG**	1.35	1.91	1.21	0.70	1.34	1.91	1.2	0.71	1.38	1.75	1.21	0.54	1.32	1.91	1.20	0.71
Asp	GAU	0.96	0.12	1.15	-1.03	0.94	0.12	1.14	-1.02	0.89	0.77	1.14	-0.37	0.99	0.12	1.15	-1.03
	GAC	1.04	1.88	0.85	1.03	1.06	1.88	0.86	1.02	1.11	1.23	0.86	0.37	1.01	1.88	0.85	1.03
Glu	GAA	0.73	0.10	0.87	-0.77	0.73	0.12	0.88	-0.76	0.68	0.90	0.87	0.03	0.77	0.11	0.88	-0.77
	GAG*	1.27	1.90	1.13	0.77	1.27	1.88	1.12	0.76	1.32	1.10	1.13	-0.03	1.23	1.89	1.12	0.77
Cys	UGU	0.70	0.06	0.87	-0.81	0.67	0.06	0.86	-0.80	0.62	0.42	0.84	-0.42	0.72	0.06	0.87	-0.81
	UGC**	1.30	1.94	1.13	0.81	1.33	1.94	1.14	0.80	1.38	1.58	1.16	0.42	1.28	1.94	1.13	0.81
Arg	CGU	0.63	0.17	0.77	-0.60	0.62	0.18	0.77	-0.59	0.59	1.15	0.76	0.39	0.62	0.18	0.77	-0.59
	CGC**	1.42	2.86	1.10	1.76	1.43	2.79	1.11	1.68	1.53	1.92	1.11	0.81	1.35	2.78	1.10	1.68
	CGA	0.45	0.11	0.63	-0.52	0.46	0.12	0.64	-0.52	0.45	1.28	0.64	0.64	0.46	0.12	0.63	-0.51
	CGG	1.06	1.82	0.94	0.88	1.09	1.82	0.96	0.86	1.17	1.44	0.98	0.46	1.02	1.84	0.94	0.90
	AGA	1.00	0.09	1.19	-1.10	0.96	0.18	0.77	-0.59	0.87	0.06	1.15	-1.09	1.07	0.10	1.20	-1.10
	AGG	1.44	0.95	1.38	-0.43	1.45	2.79	1.11	1.68	1.39	0.15	1.35	-1.20	1.48	0.98	1.37	-0.39
Gly	GGU	0.83	0.17	1.02	-0.85	0.81	0.17	1.00	-0.83	0.77	0.67	0.98	-0.31	0.86	0.16	1.02	-0.86
	GGC**	1.52	2.67	1.10	1.57	1.53	2.65	1.13	1.52	1.59	1.57	1.13	0.44	1.47	2.65	1.10	1.55
	GGA	0.79	0.19	1.01	-0.82	0.79	0.20	1.02	-0.82	0.75	1.03	1.01	0.02	0.82	0.19	1.02	-0.83
	GGG	0.86	0.98	0.87	0.11	0.87	0.98	0.86	0.12	0.89	0.73	0.88	-0.15	0.86	0.99	0.86	0.13
Trp	UGG	1.00	1.00	1.00	0.00	1.00	1.00	1.00	0.00	1.00	1.00	1.00	0.00	1.00	1.00	1.00	0.00
Met	AUG	1.00	1.00	1.00	0.00	1.00	1.00	1.00	0.00	1.00	1.00	1.00	0.00	1.00	1.00	1.00	0.00
Ter	UAA	0.93	0.38	0.74	-0.36	0.66	0.42	0.73	-0.31	0.66	0.54	0.73	-0.19	0.67	0.38	0.73	-0.35
	UAG	0.93	0.90	0.90	0.00	0.93	0.92	0.91	0.01	0.92	0.50	0.89	-0.39	0.94	0.91	0.91	0.00
	UGA	1.42	1.00	1.00	0.00	1.41	1.00	1.00	0.00	1.43	1.96	1.38	0.58	1.39	1.70	1.35	0.35