Abstract

It is well-documented that codon usage biases affect gene translational efficiency; however, it is less known if viruses share their host’s codon usage motifs. We determined that human-infecting viruses share similar codon usage biases as proteins that are expressed in tissues the viruses infect. By performing 7,052,621 pairwise comparisons of genes from humans versus genes from 113 viruses that infect humans, we determined which codon usage motifs were most highly correlated. We found that 16 viruses averaged a significant correlation in codon usage with over 500 human genes per viral gene, 58 viruses were highly correlated with an average of at least 100 human genes per viral gene, and 37 viruses were significantly correlated with an average of at least one human gene per viral gene at an alpha level of 7.09 × 10^-9 (0.05 alpha / 7,052,621 comparisons). Only two viruses were not highly correlated with an average of one human gene per viral gene. While relatively few of the interactions were previously documented, the high statistical correlations suggest that researchers may be able to determine which tissues a virus is most likely to infect by analyzing codon usage biases.

Key words

codon usage bias, host, human, virus, virus-host interactions

Introduction

Amino acids are encoded by DNA triplets known as codons; however, since there are only 20 canonical amino acids and 64 possible codons, multiple codons encode a single amino acid [1]. The majority of amino acids are encoded by 2-6 different codons. Despite multiple codons encoding a single amino acid, codon usage is not random in most species [2-5]. Various species, including many plant species, E. coli and Drosophila, also maintain DNA triplet preferences, or codon usage biases, over time in both intronic and exonic regions [6-8].

It is generally accepted that non-random mutations occur more frequently at the third position in the codon, and codon bias persists through selection [9,10]. Numerous biological factors create evolutionary pressure to use certain codons. First, an incomplete set of transfer RNAs (tRNAs) or unequal expression of tRNA anticodons within a tissue or species creates pressure for codons with complementary tRNAs available. Second, translational speed may either increase or decrease depending on the codon used, creating pressure to select codons for which translational efficiency matches the needs of the tissue/cell (i.e. suboptimal codons might be preferential to some species for increased translational efficiency, while in other instances suboptimal codons might decrease translational efficiency) [10,11]. Finally, codon usage bias primarily affects the translation of a gene and is a main determinant of gene expression [12].

Recently, significant correlations for codon usage preferences between RNA viruses (e.g. SBV and KV) and their host, the honeybee, were reported [13]. They proposed that such similarities resulted from co-evolution, which typically occurs in a leapfrog fashion (i.e. as the host evolves to combat the parasite, the parasite evolves to adapt to the new conditions).

We aimed to determine whether the same relationship exists between human and viral genes expressed in tissues targeted by the virus. We analyzed 19,482 human proteins, and compared their codon usage biases against 113 viruses that infect human hosts. We found significant correlations for many viral and human proteins, and where tissue information was available, the top correlated human protein was frequently highly expressed in the tissue type targeted by the virus.

Materials and methods

Data collection and cleaning

We used gene annotations from the General Feature Format (GFF) and GFF3 files from the National Center for Biotechnology Information (NCBI) to extract the reference viral and human sequences [14-16]. Since the reference genome is intended to most accurately represent an average individual in a species, we downloaded all reference sequence data, including the corresponding gene annotations, from NCBI. Similar to the methods used by [17], when multiple isoforms were annotated, the longest isoform was always chosen as the representative isoform for that gene, and we removed all genes with any annotated translational exceptions (e.g., translational, unclassified transcription discrepancy, suspected errors, etc.). These filters had only a minor effect on our data because they eliminated less than 5% of the total sequences. All 19,482 sequence accession numbers can be found in the NCBI database by downloading the complete genome annotations for Homo sapiens; the accession numbers for each virus and their highest correlating genes are located in Table S1.

Virus Accession Number	Virus Protein Name	Pearson’s R Correlation Value	P-value	Highest Correlating Protein Accession Number	Protein Common Name
NC_000883	NS1	0.764596741	1.94E-13	NP_002763.2	TMPRSS15
NC_000898	U90	0.931483267	6.40E-29	NP_112561.2	TEX15
NC_001348	ICP4	0.798569441	2.68E-15	NP_787081.2	FAM181B
NC_001352	E1	0.725454272	1.20E-11	NP_037485.2	TMOD4
NC_001354	Pos: 951-2795	0.804857764	1.11E-15	NP_001273387.1	USP7
NC_001355	E1	0.798328333	2.77E-15	NP_940841.1	KBTBD3
NC_001356	E1	0.903438527	1.74E-24	NP_001138663.1	FAM200B
NC_001357	E1	0.805278655	1.05E-15	NP_940841.1	KBTBD3
NC_001405	L1	0.865302979	2.94E-20	NP_001073990.2	RASSF10
NC_001430	Pos: 727-7311	0.837550489	6.34E-18	NP_000123.1	F8
NC_001436	Pr55	0.752880597	7.22E-13	NP_001092872.1	CCNK
NC_001454	L3	0.792140958	6.41E-15	NP_612426.1	KTI12
NC_001457	Pos: 5345-6895	0.859158203	1.06E-19	NP_061854.1	DNAJC10
NC_001458	Pos: 822-2678	0.847795937	9.88E-19	NP_001273176.1	RALGPS2
NC_001460	E1B	0.806525776	8.74E-16	NP_001116801.1	ZBTB1
NC_001472	Pos: 742-7290	0.800822126	1.96E-15	NP_005224.2	EPHA3
NC_001488	Pos: 807-2108	0.748225962	1.19E-12	NP_001073882.3	NOBOX
NC_001490	Pos: 629-7168	0.891321462	5.65E-23	NP_002175.2	IL6ST
NC_001526	L1	0.807134439	8.00E-16	NP_942089.1	MAP4K5
NC_001531	Pos: 961-2781	0.852165343	4.29E-19	NP_079114.3	THNSL1
NC_001576	Pos: 791-2836	0.785723092	1.48E-14	NP_899059.1	RAB27A
NC_001583	Pos: 878-2794	0.787008282	1.26E-14	NP_940841.1	KBTBD3
NC_001586	Pos: 850-2778	0.799660538	2.31E-15	NP_940841.1	KBTBD3
NC_001587	Pos: 5430-7016	0.749586507	1.03E-12	NP_057654.2	ERGIC2
NC_001591	E1	0.845045382	1.65E-18	NP_078787.2	HAUS3
NC_001593	L1	0.744112558	1.84E-12	NP_001167579.1	ZBED6
NC_001595	Pos: 5798-7315	0.770647823	9.56E-14	NP_001273644.1	AGTPBP1
NC_001596	E1	0.844374112	1.86E-18	NP_940841.1	KBTBD3
NC_001612	Pos: 751-7332	0.842341207	2.70E-18	NP_001116105.1	CPS1
NC_001617	Pos: 619-7113	0.86771873	1.74E-20	NP_002175.2	IL6ST
NC_001664	IE1	0.893813269	2.86E-23	NP_653091.3	CASC5
NC_001676	Pos:828-2729	0.787967453	1.11E-14	NP_940841.1	KBTBD3
NC_001690	E1	0.855417316	2.26E-19	NP_001092688.1	RAD51AP2
NC_001691	E1	0.876751214	2.23E-21	NP_940841.1	KBTBD3
NC_001693	E1	0.894934035	2.10E-23	NP_940841.1	KBTBD3
NC_001716	IE1	0.927833476	3.03E-28	NP_001073973.2	RBM44
NC_001722	Pos: 1103-2668	0.737893765	3.50E-12	NP_002408.3	MKI67
NC_001781	L	0.876166171	2.56E-21	NP_065982.1	KIAA1586
NC_001796	Pos: 8646-15347	0.903986563	1.47E-24	NP_065982.1	KIAA1586
NC_001798	UL39	0.904920752	1.10E-24	NP_036567.2	SHC2
NC_001802	Pr55	0.78047161	2.89E-14	NP_001093866.1	C2orf73
NC_001806	UL30	0.90801467	4.15E-25	NP_055778.2	SBNO2
NC_001897	Pos: 703-7242	0.890389641	7.26E-23	NP_001017975.3	HFM1
NC_001943	Pos: 86-4380	0.830734096	2.04E-17	NP_114161.3	SPATA16
NC_002645	Pos: 293-12550	0.774229507	6.22E-14	NP_000099.2	DLD
NC_003266	L4	0.898683268	7.19E-24	NP_009115.2	NISCH
NC_003443	L	0.839684044	4.35E-18	NP_004645.2	USP9Y
NC_003461	L	0.866879002	2.09E-20	NP_065982.1	KIAA1586
NC_004104	E1	0.68207836	5.44E-10	NP_899059.1	RAB27A
NC_004148	L	0.867913209	1.67E-20	NP_065982.1	KIAA1586
NC_004295	VP1	0.773099678	7.13E-14	NP_114414.2	EIF2A
NC_004500	E1	0.880929983	8.18E-22	NP_004645.2	USP9Y
NC_005134	E1	0.851299523	5.07E-19	NP_001138663.1	FAM200B
NC_005147	Pos: 21507-22343	0.820880135	1.01E-16	NP_064506.3	UGGT2
NC_005831	Pos: 287-20475	0.750091303	9.77E-13	NP_037471.2	ALG6
NC_006273	IE1	0.87654333	2.35E-21	NP_055478.2	KDM4A
NC_006577	Pos: 22942-27012	0.756094354	5.07E-13	NP_852607.3	LRRC70
NC_007018	ORF2	0.774104535	6.31E-14	NP_005112.2	MED13
NC_007026	Pos: 828-2486	0.704735964	8.08E-11	NP_001024.1	RRM1
NC_007027	Pos: 94-1698	0.746908872	1.37E-12	NP_002717.3	PREP
NC_007455	VP1	0.768556356	1.22E-13	NP_803875.2	PKHD1L1
NC_007605	BALF5	0.934931283	1.36E-29	NP_620124.1	RHOT2
NC_008188	E1	0.85042555	6.00E-19	NP_940841.1	KBTBD3
NC_008189	E1	0.781785258	2.45E-14	NP_000305.3	PTEN
NC_009333	ORF75	0.91780911	1.47E-26	NP_002891.1	RBP3
NC_009334	BALF5	0.935906758	8.64E-30	NP_620124.1	RHOT2
NC_009996	Pos: 616-7050	0.834124398	1.15E-17	NP_004939.1	DSC1
NC_010329	E1	0.908048024	4.10E-25	NP_940841.1	KBTBD3
NC_010810	Pos: 956-7837	0.825974666	4.46E-17	NP_004939.1	DSC1
NC_010956	L4	0.884411516	3.44E-22	NP_009115.2	NISCH
NC_011202	L1	0.825443151	4.86E-17	NP_787072.2	EXOC8
NC_011203	L4	0.84556954	1.50E-18	NP_009115.2	NISCH
NC_011800	Pos: 1892-2533	0.744847797	1.71E-12	NP_056526.3	GLTSCR1
NC_012042	VP1	0.776501461	4.72E-14	NP_005424.1	YES1
NC_012213	E1	0.843291298	2.27E-18	NP_001138663.1	FAM200B
NC_012485	E1	0.883809966	4.00E-22	NP_940841.1	KBTBD3
NC_012486	E1	0.902494945	2.32E-24	NP_001138663.1	FAM200B
NC_012564	VP1	0.783191043	2.05E-14	NP_002899.1	REL
NC_012729	NS2	0.805392124	1.03E-15	NP_001073932.1	DYNC2H1
NC_012798	Pos: 139-6480	0.82589364	4.52E-17	NP_057190.2	SCFD1
NC_012801	Pos: 750-7124	0.824196863	5.94E-17	NP_001191195.1	GABRA4
NC_012802	Pos: 748-7128	0.834958942	9.94E-18	NP_001161829.1	PLA2G7
NC_012950	Pos: 21445-22281	0.818600204	1.44E-16	NP_064506.3	UGGT2
NC_012959	Pos: 22707-24845	0.842896843	2.44E-18	NP_009115.2	NISCH
NC_012986	Pos: 719-7831	0.755617438	5.35E-13	NP_004215.2	GPR50
NC_013035	E1	0.900330229	4.44E-24	NP_940841.1	KBTBD3
NC_014185	E1	0.928268261	2.53E-28	NP_940841.1	KBTBD3
NC_014952	E1	0.879231256	1.24E-21	NP_940841.1	KBTBD3
NC_014953	E1	0.904630266	1.21E-24	NP_940841.1	KBTBD3
NC_014954	E1	0.895597619	1.74E-23	NP_940841.1	KBTBD3
NC_014955	E1	0.905343727	9.67E-25	NP_940841.1	KBTBD3
NC_014956	E1	0.903382032	1.77E-24	NP_940841.1	KBTBD3
NC_015150	Pos: c5026-4790, c4437-2632	0.897789054	9.32E-24	NP_060862.3	C4orf21
NC_015630	Pos: 381-1076	0.54440122	3.32E-06	NP_689786.2	RASEF
NC_016157	Pos: 817-2640	0.919910732	6.78E-27	NP_940841.1	KBTBD3
NC_017993	Pos: 805-2610	0.859261423	1.04E-19	NP_940841.1	KBTBD3
NC_017994	E1	0.868341489	1.52E-20	NP_940841.1	KBTBD3
NC_017995	Pos: 714-2546	0.883104334	4.77E-22	NP_001138663.1	FAM200B
NC_017996	Pos: 717-2534	0.881915256	6.42E-22	NP_940841.1	KBTBD3
NC_017997	Pos; 703-2502	0.825068761	5.16E-17	NP_112561.2	TEX15
NC_019023	E1	0.864842857	3.24E-20	NP_940841.1	KBTBD3
NC_019843	orf1ab	0.777846368	4.00E-14	NP_079265.2	PGAP1
NC_020890	large T antigen	0.894050364	2.68E-23	NP_001017975.3	HFM1
NC_021483	E1	0.858044662	1.34E-19	NP_001092688.1	RAD51AP2
NC_021568	Pos: 279-13433, 13433-21514	0.731131014	6.89E-12	NP_066012.1	METTL14
NC_021928	Pos: c5033-4821, c4421-2508	0.8986358	7.29E-24	NP_065982.1	KIAA1586
NC_022095	L1	0.818922205	1.37E-16	NP_001273644.1	AGTPBP1
NC_022518	Pos: 6451-8550	0.801092218	1.89E-15	NP_001121143.1	LIFR
NC_022892	E1	0.856537918	1.81E-19	NP_065982.1	KIAA1586
NC_023874	Pos: 161-997	0.720321018	1.95E-11	NP_060146.2	GIN1
NC_023891	E1	0.88293482	4.98E-22	NP_940841.1	KBTBD3
NC_023984	Pos: 1362-7727	0.837884709	5.98E-18	NP_036434.1	LPHN2
NC_024694	Pos: 1 - 1113	0.676628221	8.40E-10	NP_054860.1	CNTNAP2

Table S1. A comprehensive list of the 113 viruses with their highest correlating protein, accompanied by the Pearson’s r correlation and the respective p-value. Bolded rows were found to be insignificant. Unnamed viral proteins are designated by their position numbers in the following format— Pos: start position-stop position.

Codon usage correlation values

To determine if there was a correlation between human and viral codon usage biases, we performed a Pearson’s r correlation test with discrete codon usage counts by comparing total codon usage counts in human and viral coding sequences (CDS). We used Pearson’s r because it uses a product-moment correlation coefficient that is used to determine the correlation between two variables with different units or different magnitudes [18]. Since gene lengths can vary greatly between genes, and genes do not contain all codons, the assumptions for most statistical tools would not be adequately met using the raw data. Furthermore, the high number of zero codon usage counts in some genes meant that a percentage comparison of codon usages using a traditional t-test was unfeasible, even with a transformation. We chose an implementation of Pearson’s r from the package SciPy in Python version 2.7 because Pearson’s r is robust to variations in sequence sizes as well as zero values. Using Pearson’s r, we graphed a linear regression and calculated the R² coefficients of determination and p-values by plotting the discrete codon counts from each gene within each virus against each human gene. Next, we ranked the correlation of codon usage between viral and human genes from highest to lowest. We corrected for multiple tests using a Bonferroni correction; the significance threshold used was 7.09 × 10^-9 (0.05/7,052,621 total comparisons). We obtained the highest correlations when the viral and human protein codon usage motifs were most similar.

Human tissue comparisons

We determined which proteins were expressed in each human tissue by querying each highly correlated human protein against the Human Protein Atlas [19,20]. We checked the top correlating human proteins for each virus (113 total proteins) to determine in which tissues they were most highly expressed. While many proteins were expressed in low levels throughout the body, we were most concerned with high expression areas, and only the high expression areas were compared in this study.

Results

Of the 113 viruses analyzed, we found that on average, each viral gene in 16 viruses was significantly correlated with more than 500 human proteins (Table S2). Of the remaining 97 viruses, 58 were significantly correlated with at least 100 human proteins per viral gene, and 37 were significantly correlated with at least one human gene per viral gene on average at a p-value <7.09 × 10^-9. Only two viruses, Human papillomavirus type 90 (NC_004104) and Human gyrovirus type 1 (NC_015630) were not significantly correlated with the codon usage of at least one human gene per viral gene, on average.

Virus Accession Number	Virus Name	Virus Protein Name	Protein Accession Number	Protein Name	Correlation %	P-value
NC_009334	Human herpesvirus 4	BALF5	NP_620124.1	RHOT2	93.6	8.64E-30
NC_007605	Human herpesvirus 4 (wild type)	BALF5	NP_620124.1	RHOT2	93.5	1.36E-29
NC_000898	Human herpesvirus 6B	U90	NP_112561.2	TEX15	93.1	6.40E-29
NC_014185	Human papillomavirus 121	E1	NP_940841.1	KBTBD3	92.8	2.53E-28
NC_001716	Human herpesvirus 7	IE1	NP_001073973.2	RBM44	92.8	3.03E-28
NC_016157	Human papillomavirus 126	Pos: 817-2640	NP_940841.1	KBTBD3	92.0	6.78E-27
NC_009333	Human herpesvirus 8	ORF75	NP_002891.1	RBP3	91.8	1.47E-26
NC_010329	Human papillomavirus 88	E1	NP_940841.1	KBTBD3	90.8	4.10E-25
NC_001806	Human herpesvirus 1	UL30	NP_055778.2	SBNO2	90.8	4.15E-25
NC_014955	Human papillomavirus 132	E1	NP_940841.1	KBTBD3	90.5	9.67E-25

Table 1. Here we report the top-ten codon usage bias correlations (Pearson’s r values) between a virus and a human protein with their respective p-values (all under 10-25), demonstrating that viruses and proteins in their host (humans) share high codon biases. Unnamed viral proteins are designated by their position numbers in the following format— Pos: start position-stop position.

Virus Accession Number	Number of Genes in Virus	Number of Highly Correlating Genes in Humans	Number of Highly Correlating Human Proteins per Viral Protein
NC_015630	3	0	0
NC_004104	7	4	0.57
NC_012986	1	1	1
NC_001436	6	7	1.17
NC_024694	4	13	3.25
NC_001488	6	27	4.5
NC_011800	6	28	4.67
NC_007026	2	15	7.5
NC_005831	6	47	7.83
NC_001722	9	91	10.11
NC_001352	7	91	13
NC_023874	2	32	16
NC_001595	6	104	17.33
NC_001357	8	152	19
NC_001454	34	655	19.26
NC_006577	8	165	20.63
NC_021568	2	50	25
NC_001576	7	221	31.57
NC_001587	6	219	36.5
NC_001348	73	2843	38.95
NC_001593	7	331	47.29
NC_000883	6	317	52.83
NC_019843	11	582	52.91
NC_001355	9	478	53.11
NC_001460	36	1950	54.17
NC_001583	6	328	54.67
NC_001676	7	391	55.86
NC_001526	8	456	57
NC_008189	6	353	58.83
NC_001802	10	629	62.9
NC_002645	8	613	76.63
NC_001586	6	517	86.17
NC_015150	5	435	87
NC_007027	1	93	93
NC_011202	38	3637	95.71
NC_007455	4	392	98
NC_001781	11	1079	98.09
NC_017997	7	691	98.71
NC_001354	11	1096	99.64
NC_012950	12	1268	105.67
NC_005147	9	970	107.78
NC_012042	4	438	109.5
NC_004500	7	787	112.43
NC_013035	7	837	119.57
NC_008188	6	720	120
NC_004295	6	747	124.5
NC_022095	6	750	125
NC_012564	4	555	138.75
NC_004148	9	1314	146
NC_001405	38	5628	148.11
NC_000898	104	15694	150.9
NC_012485	7	1083	154.71
NC_006273	169	26217	155.13
NC_001664	88	13960	158.64
NC_012213	5	801	160.2
NC_003461	10	1706	170.6
NC_003266	38	7275	191.45
NC_001798	77	14790	192.08
NC_022892	6	1160	193.33
NC_010956	38	7500	197.37
NC_017993	7	1382	197.43
NC_001690	7	1464	209.14
NC_021483	7	1467	209.57
NC_001596	7	1470	210
NC_014953	7	1498	214
NC_012959	36	7762	215.61
NC_001591	6	1327	221.17
NC_014952	7	1601	228.71
NC_011203	39	9069	232.54
NC_001531	8	1903	237.88
NC_012729	5	1212	242.4
NC_003443	7	1720	245.71
NC_020890	5	1235	247
NC_010329	7	1744	249.14
NC_012486	7	1768	252.57
NC_001691	7	1771	253
NC_023891	7	1843	263.29
NC_001356	7	1844	263.43
NC_021928	7	1879	268.43
NC_005134	7	1893	270.43
NC_014956	7	1894	270.57
NC_001796	8	2167	270.88
NC_016157	7	1969	281.29
NC_001457	7	1980	282.86
NC_014954	7	1981	283
NC_014955	7	2051	293
NC_017994	7	2061	294.43
NC_014185	7	2076	296.57
NC_009333	86	26437	307.41
NC_001458	7	2182	311.71
NC_001693	7	2316	330.86
NC_001806	77	26054	338.36
NC_019023	6	2070	345
NC_017996	7	2500	357.14
NC_007018	2	769	384.5
NC_001716	86	33651	391.29
NC_017995	7	2784	397.71
NC_001943	2	1088	544
NC_022518	1	592	592
NC_001472	1	753	753
NC_007605	95	85227	897.13
NC_009334	80	82905	1036.31
NC_001612	1	1133	1133
NC_009996	1	1157	1157
NC_001617	1	1193	1193
NC_010810	1	1223	1223
NC_012802	1	1408	1408
NC_001490	1	1423	1423
NC_012798	1	1437	1437
NC_023984	1	1453	1453
NC_012801	1	1482	1482
NC_001430	1	1720	1720
NC_001897	1	1918	1918
Average	15.74	4161.41	303.36
Total	1779	470239	34279.52

Table S2. A comprehensive list of the 113 viruses with the number of genes in the virus, the number of highly correlating human genes, and the number of highly correlating human proteins per viral protein. Viruses are ordered in accending order based on the number of highly correlating human genes per viral gene.

The viruses listed in Table 1 have the highest Pearson r correlation values of all comparisons made, with their codon usages strongly correlating to their host codon usages (p-value<10^-25). Four of the top 10 correlations in Table 1 belong to the group of 16 viruses that strongly correlate to over 500 human proteins per viral gene on average, and the rest of them belong to the group of 58 with significant correlations with at least 100 human genes significantly correlating to each viral gene, on average. Overall, the average correlation of the 113 viruses with the top hit from each virus was 83.1%, meaning about 83% of the codon usage bias in the virus also existed in the human host protein. Each viral protein strongly correlated to an average of 303 human genes.

To demonstrate the strong correlations in codon usage bias, we plotted codon usage for several representative viral proteins compared to the human protein with the strongest correlation (Figure 1).

<p><strong>Figure 1. Codon counts. </strong>Four of the highest correlating virus-protein pairs found in Table 1 are displayed. We plotted codon counts for the viral protein (X-axis) against the human protein&rsquo;s codon counts (Y-axis). Each graph has 64 points, each representing a codon. Points near the top right are used at a higher rate than points near the bottom left. The line represents the result of a best-fit linear model, indicating that there is a strong correlation--as protein codon usage increases, so does the codon usage count of the respective virus. Residual plots of the linear regression were also analyzed and appear to fit the assumptions of the model. (A) displays RHOT2 vs HHV-4 (correlation of 93.6%), (B) shows TEX15 vs HHV-6B (correlation of 93.1%), (C) shows KBTBD3 vs HPV-121 (correlation of 92.8%), and (D) displays RBM44 vs HHV-7 (correlation of 92.8%). See Table 1 for more information on these pairs.</p>

Finally, we analyzed the correlations of codon usage biases for human proteins expressed in tissues infected by a specific virus. With the exception of sexually transmitted diseases (STDs), tissue information was incomplete for many viruses, and further exacerbating this problem is that many human proteins expressed in a specific tissue were also expressed in many other tissues. We report all known tissue information in Table S3, and in Table 2 list representative viruses with their highest correlating protein and affected tissues.

Discussion

The high number of proteins significantly correlated with each virus suggests that humans and human-host viruses share similar codon usage biases. For example, each of the 80 Human herpesvirus 4 (HHV-4, NC_009334) genes significantly correlated with 1 to 10,012 human genes with a median of 8,290 highly correlated human genes and an average of 1,036 highly correlated human genes. HHV-4 was previously identified as having a similar codon usage bias to its host cells [21,22], which may provide insights into the efficient proliferation of HHV-4, since it can more readily utilize host tRNA machinery in the tissue types it infects. Indeed, HHV-4 (commonly known as mononucleosis or “the kissing disease”) is one of the most common viruses known to infect humans, with almost 90% of adults having antibodies suggesting previous HHV-4 infection [22]. Herpesviruses overtake host translational machinery through virion host shutoff (vhs), which limits the expression of host mRNA [23], and through the degradation of host mitochondrial DNA [24], although some herpesvirus strains act differently [25]. Our data suggest that herpesvirus is able to co-opt the translational apparatus of the infected cell by closely matching codon usage biases. The virus is able to use existing tRNAs in the cell, which are not being used by the cell due to vhs.

Furthermore, viruses such as HPV-90 (NC_004104) and Human gyrovirus 1 (NC_015630) with fewer correlating proteins typically occur less frequently in human populations. Although limited data exist for the prevalence of HPV-90 in the general population, in general it presents a very low risk to the general population [26,27]. Human gyrovirus 1, which is identical to the Chicken Anemia Virus, is relatively rare and the effects of the virus still remain largely unknown, although it may affect the apoptosis pathway [28,29].

Human-host viruses appear to target tissues where the correlating human protein also has high expression. Although many viruses analyzed were not clearly annotated as infecting a particular human tissue, the viruses with documented tissue interactions were always highly correlated with a protein that was highly expressed in that tissue. For instance, HPV-128 correlates most with the human protein TIGD4, which is mainly expressed in the genitalia. In addition, other STDs were strongly correlated with proteins that were also mainly expressed in genitalia (Table 2, Table S3). We note that viruses tend to share the same codon usage biases as at least one protein that is highly expressed in the disease targeted area, further emphasizing our conclusion that viral and host codon usage biases are highly correlated.

Accession Number	Virus Name	Virus Protein	Correlating Human Protein	Protein’s Expression Location
NC_004500	HPV 92	E1	MSH4	Testis
NC_022095	HPV 179	L1	HLTF	Testis
NC_014952	HPV 128	E1	TIGD4	Testis, vagina
NC_001691	HPV 50	E1	TEX15	Testis
NC_001405	HPV 18	L1	MRC2	Soft tissue, testis, endometrium
NC_001354	HPV 41	USP7	SLC12A2	Digestive tract, breast, placenta
NC_000898	HHV 6	U90	ELTD1	Gallbladder, breast, smooth muscle
NC_019023	HPV 166	E1	OTOGL	Cervix, testis
NC_009334	HHV 4	BALF5	SPTB	Epididymis
NC_010329	HPV 88	E1	RAD51AP2	Seminal Vesicle, Fallopian Tube
NC_004500	HPV 92	E1	USP9Y	Prostate

Table 2. A selection of viral proteins and their top correlating human proteins, along with the human protein’s documented area of expression. These results show that viral codon usage biases highly correlate with the codon usage biases of human proteins that are found within tissues that the viruses are known to promote symptomatic issues.

Virus Accession Number	Highest Correlating Human Protein Accession Number	Region(s) Where Human Protein is Most Highly Expressed
NC_000883	NP_002763.2	Stomach glandular cells
NC_000898	NP_112561.2	Testis, urinary tract, and brain
NC_001348	NP_787081.2	Myocytes in heart muscle, lateral ventricle, cerebral cortex,
		hippocampus
NC_001352	NP_037485.2	Myocytes in skeletal muscle, and glandular cells in the stomach.
NC_001354	NP_001273387.1	Liver, pancreas, digestive tract, male reproductive system, endocrine
NC_001355	NP_940841.1	Skeletal muscle, smooth muscle, epidermal cells, hepatocytes in liver
NC_001356	NP_001138663.1	GI-tract, gallbladder, and the blood and immune system
NC_001357	NP_940841.1	Smooth muscle cells
NC_001405	NP_001073990.2	Stomach, kidney, fallopian tube,
NC_001430	NP_000123.1	Adipocytes of soft tissue, placenta, tubule cells in the kidney
NC_001436	NP_001092872.1	Hematopoietic cells in bone marrow, glandular cells in the stomach
NC_001454	NP_612426.1	Glandular cells of the GI tract, urinary tract cells, adrenal glands
NC_001457	NP_061854.1	Glandular cells of the epididymis and the endometrium
NC_001458	NP_001273176.1	Testis.
NC_001460	NP_001116801.1	Kidney, testis, stomach, esophagus, vagina, skin, lung, and heart
NC_001472	NP_005224.2	Low expression everywhere
NC_001488	NP_001073882.3	No information found
NC_001490	NP_002175.2	Stomach cells, prostate, kidney, liver, pancreas, heart muscle
NC_001526	NP_942089.1	Female reproductive system
NC_001531	NP_079114.3	Stomach
NC_001576	NP_899059.1	Stomach and rectum
NC_001583	NP_940841.1	Smooth muscle cells
NC_001586	NP_940841.1	Smooth muscle cells
NC_001587	NP_057654.2	Heart muscle cells, and some GI-tract cells.
NC_001591	NP_078787.2	Stomach
NC_001593	NP_001167579.1	GI-tract and female reproductive system
NC_001595	NP_001273644.1	Testis
NC_001596	NP_940841.1	Smooth muscle cells
NC_001612	NP_001116105.1	Stomach and liver
NC_001617	NP_002175.2	Stomach cells, prostate, kidney, liver, pancreas, heart muscle
NC_001664	NP_653091.3	Testis
NC_001676	NP_940841.1	Smooth muscle cells
NC_001690	NP_001092688.1	Male reproductive system
NC_001691	NP_940841.1	Smooth muscle cells
NC_001693	NP_940841.1	Smooth muscle cells
NC_001716	NP_001073973.2	Testis
NC_001722	NP_002408.3	Blood, immune system
NC_001781	NP_065982.1	Seminal vesicle in men, and the breast in women
NC_001796	NP_065982.1	Seminal vesicle in men, and the breast in women
NC_001798	NP_036567.2	Varied expression everywhere
NC_001802	NP_001093866.1	Male reproductive system and GI-tract
NC_001806	NP_055778.2	Liver cells, skeletal muscle, cerebral cortex, endocrine glands, lung
NC_001897	NP_001017975.3	Lung cells and skeletal muscles
NC_001943	NP_114161.3	Testis and cerebellum
NC_002645 NC_003266	NP_000099.2 NP_009115.2	Nearly everywhere, except skin
		Skin, gallbladder, cerebellum, heart muscle, adrenal gland, bronchus
NC_003443	NP_004645.2	Prostate
NC_003461	NP_065982.1	Seminal vesicle in men, and the breast in women
NC_004104	NP_899059.1	Stomach and rectum
NC_004148	NP_065982.1	Seminal vesicle in men, and the breast in women
NC_004295	NP_114414.2	Skin
NC_004500	NP_004645.2	Prostate
NC_005134	NP_001138663.1	GI-tract, gallbladder, and the blood and immune system
NC_005147	NP_064506.3	Testis and the brain
NC_005831	NP_037471.2	Both male and female reproductive systems
NC_006273	NP_055478.2	Stomach, testis, and brain
NC_006577	NP_852607.3	Hippocampus, heart muscle, parathyroid gland
NC_007018	NP_005112.2	Bone marrow, and testis
NC_007026	NP_001024.1	Testis, lymph nodes, and lateral ventricles
NC_007027	NP_002717.3	GI-tract, and endometrium in women
NC_007455	NP_803875.2	Spleen and bone marrow
NC_007605	NP_620124.1	Stomach, placenta, skeletal muscle, and cerebral cortex
NC_008188	NP_940841.1	Smooth muscle cells
NC_008189	NP_000305.3	Cerebral cortex
NC_009333	NP_002891.1	No information found
NC_009334	NP_620124.1	Stomach, placenta, skeletal muscle, and cerebral cortex
NC_009996	NP_004939.1	highest expression in the skin keratinocytes
NC_010329	NP_940841.1	Smooth muscle cells
NC_010810	NP_004939.1	Skin keratinocytes
NC_010956	NP_009115.2	Skin, gallbladder, cerebellum, heart muscle, adrenal gland, bronchus
NC_011202	NP_787072.2	Adrenal gland, cerebellum, stomach, and placenta
NC_011203	NP_009115.2	Skin, gallbladder, cerebellum, heart muscle, adrenal gland, bronchus
NC_011800	NP_056526.3	Medium/high expression everywhere
NC_012042	NP_005424.1	Testis, stomach, and placenta
NC_012213	NP_001138663.1	GI-tract, gallbladder, blood and immune system
NC_012485	NP_940841.1	Smooth muscle cells
NC_012486	NP_001138663.1	GI-tract, gallbladder, blood and immune system
NC_012564	NP_002899.1	Blood, immune system, women reproductive system, and GI-tract
NC_012729	NP_001073932.1	GI-tract
NC_012798	NP_057190.2	Pancreas, testis, kidney, and placenta
NC_012801	NP_001191195.1	Cerebral cortex
NC_012802	NP_001161829.1	Appendix, prostate, placenta, lymph node, and spleen
NC_012950	NP_064506.3	Testis and the brain
NC_012959	NP_009115.2	Skin, gallbladder, cerebellum, heart muscle, adrenal gland, bronchus
NC_012986	NP_004215.2	Kidney and smooth muscle tissue
NC_013035	NP_940841.1	Smooth muscle cells
NC_014185	NP_940841.1	Smooth muscle cells
NC_014952	NP_940841.1	Smooth muscle cells
NC_014953	NP_940841.1	Smooth muscle cells
NC_014954	NP_940841.1	Smooth muscle cells
NC_014955	NP_940841.1	Smooth muscle cells
NC_014956	NP_940841.1	Smooth muscle cells
NC_015150	NP_060862.3	No information available
NC_015630	NP_689786.2	GI-tract and urinary tract
NC_016157	NP_940841.1	Smooth muscle cells
NC_017993	NP_940841.1	Smooth muscle cells
NC_017994	NP_940841.1	Smooth muscle cells
NC_017995	NP_001138663.1	GI-tract, gallbladder, blood and immune system
NC_017996	NP_940841.1	Smooth muscle cells
NC_017997	NP_112561.2	Low expression everywhere
NC_019023	NP_940841.1	Smooth muscle cells
NC_019843	NP_079265.2	Testis, placenta and parathyroid gland
NC_020890	NP_001017975.3	Lung cells and skeletal muscles
NC_021483	NP_001092688.1	Stomach, male reproductive system, and skin
NC_021568 NC_021928	NP_066012.1 NP_065982.1	Testis and stomach
		Seminal vesicle in men, and the breast in women
NC_022095	NP_001273644.1	Testis
NC_022518	NP_001121143.1	Male reproductive tissue and in the heart
NC_022892	NP_065982.1	Seminal vesicle in men, and the breast in women
NC_023874	NP_060146.2	Tonsil, stomach, and pancreas
NC_023891	NP_940841.1	Smooth muscle cells
NC_023984	NP_036434.1	Skeletal and smooth muscle, tonsils, small intestine, colon
NC_024694	NP_054860.1	Cerebral cortex

Table S3. A comprehensive list of where the highest correlating human protein with respect to a human-infecting virus is most highly expressed.

2021 Copyright OAT. All rights reserv

Highly expressed genes have codon biases that utilize highly abundant tRNAs in order for optimal translational and transcriptional speed [12,13,30-33]. The Human Adenovirus E (NP_009115.2), which causes respiratory illness, has an 89.9% codon usage correlation with the NISCH gene, which is mainly expressed in the bronchus. Since NISCH is highly expressed in the tissues that the adenovirus normally infects, the virus is able to take advantage of its codon usage bias similarities with the host proteins to rapidly proliferate and infect additional hosts.

There are other possibilities for the observed shared codon usage biases. For example, co-evolution may have contributed to the appearance of such strong codon bias correlations, in which the host and the virus evolve at similar rates in order to either combat or maintain parasitic infection [34]. Since viruses have smaller genomes, they can selectively evolve more rapidly toward being similar to a preferred host.

While co-evolution and the abundance of optimal tRNAs are thought to allow greater viral spread, determining the exact cause of this correlation remains unexplored. Our extensive analysis of codon usage determined that a strong correlation in codon usage bias exists between human-host viruses and proteins expressed in the human tissues that they infect. Future research should focus on the causes of these correlations.

Authorship and contributorship

JM and PR conceived the idea. JM oversaw all aspects of the project. AH developed the comparison algorithms and ran the comparisons. CM and SW conducted literature searches and wrote sections of the paper. JM and PR were primarily responsible for editing the manuscript. PR mentored the project.

Acknowledgements

We also appreciate Mark Ebbert and Samantha Jensen who provided expert suggestions for the project flow and design.

Funding information

We appreciate the contributions of Brigham Young University and the Fulton Supercomputing Laboratory in supporting our research.

Competing interests

The authors declare that they have no competing interests.

Availability of data and material

All data are freely available from the NCBI database at ftp://ftp.ncbi.nlm.nih.gov/

References

1. Crick FH (1968) The origin of the genetic code. J Mol Biol 38: 367-379. [Crossref]
2. Ikemura T (1985) Codon usage and tRNA content in unicellular and multicellular organisms. Mol Biol Evol 2: 13-34. [Crossref]
3. Sharp PM, Li WH (1986) An evolutionary perspective on synonymous codon usage in unicellular organisms. J Mol Evol 24: 28-38. [Crossref]
4. Gutman GA, Hatfield GW (1989) Nonrandom utilization of codon pairs in Escherichia coli. Proc Natl Acad Sci U S A 86: 3699-3703. [Crossref]
5. Zhang YM, Shao ZQ, Yang LT, Sun XQ, Mao YF, et al. (2013) Non-random arrangement of synonymous codons in archaea coding sequences. Genomics 101: 362-367. [Crossref]
6. Akashi H, Goel P, John A (2007) Ancestral inference and the study of codon bias evolution: implications for molecular evolutionary analyses of the Drosophila melanogaster subgroup. PLoS One 2: e1065. [Crossref]
7. Yang Z, Nielsen R (2008) Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage. Mol Biol Evol 25: 568-579. [Crossref]
8. Xu W, Xing T, Zhao M, Yin X, Xia G, et al. (2015) Synonymous codon usage bias in plant mitochondrial genes is associated with intron number and mirrors species evolution. PLoS One 10: e0131508.
9. Hershberg R, Petrov DA (2008) Selection on codon bias. Annu Rev Genet 42: 287-299. [Crossref]
10. Quax TE, Claassens NJ, Söll D, van der Oost J (2015) Codon Bias as a Means to Fine-Tune Gene Expression. Mol Cell 59: 149-161. [Crossref]
11. Xu Y, Ma P, Shah P, Rokas A, Liu Y, et al. (2013) Non-optimal codon usage is a mechanism to achieve circadian clock conditionality. Nature 495: 116-120. [Crossref]
12. Zhou Z, Dang Y, Zhou M, Li L, Yu CH, et al. (2016) Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc Natl Acad Sci U S A 113: E6117-6117E6125. [Crossref]
13. Chantawannakul P, Cutler RW (2008) Convergent host-parasite codon usage between honeybee and bee associated viral genomes. J Invertebr Pathol 98: 206-210. [Crossref]
14. Pruitt KD, Brown GR, Hiatt SM, Thibaud-Nissen F, Astashyn A, et al. (2014) RefSeq: an update on mammalian reference sequences. Nucleic Acids Res 42: D756-D763. [Crossref]
15. Tatusova T, Ciufo S, Fedorov B, O'Neill K, Tolstoy I (2014) RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res 42: D553-D559. [Crossref]
16. Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, et al. (2007) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 35: D5-D12. [Crossref]
17. Camiolo S, Melito S, Porceddu A (2015) New insights into the interplay between codon bias determinants in plants. DNA Res 22: 461-470. [Crossref]
18. Häne BG, Jäger K, Drexler HG (1993) The Pearson product-moment correlation coefficient is better suited for identification of DNA fingerprint profiles than band matching algorithms. Electrophoresis 14: 967-972. [Crossref]
19. Uhlén M, Björling E, Agaton C, Szigyarto CA, Amini B, et al. (2005) A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol Cell Proteomics 4: 1920-1932. [Crossref]
20. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, et al. (2015) Proteomics. Tissue-based map of the human proteome. Science, 347, 1260419. [Crossref]
21. Roychoudhury S, Mukherjee D (2010) A detailed comparative analysis on the overall codon usage pattern in herpesviruses. Virus Res 148: 31-43. [Crossref]
22. Virgin HW, Wherry EJ, Ahmed R (2009) Redefining chronic viral infection. Cell 138: 30-50. [Crossref]
23. Smiley JR (2004) Herpes simplex virus virion host shutoff protein: immune evasion mediated by a viral RNase? J Virol 78: 1063-1068. [Crossref]
24. Saffran HA, Pare JM, Corcoran JA, Weller SK, Smiley JR (2007) Herpes simplex virus eliminates host mitochondrial DNA. EMBO Rep 8: 188-193. [Crossref]
25. Duguay BA, Saffran HA, Ponomarev A, Duley SA, Eaton HE, et al. (2014) Elimination of mitochondrial DNA is not required for herpes simplex virus 1 replication. J Virol 88: 2967-2976. [Crossref]
26. Schmitt M, Depuydt C, Benoy I, Bogers J, Antoine J, et al. (2013) Prevalence and viral load of 51 genital human papillomavirus types and three subtypes. Int J Cancer 132: 2395-2403. [Crossref]
27. Quiroga-Garza G, Zhou H, Mody DR, Schwartz MR, Ge Y (2013) Unexpected high prevalence of HPV 90 infection in an underserved population: is it really a low-risk genotype? Arch Pathol Lab Med 137: 1569-1573. [Crossref]
28. Sauvage V, Cheval J, Foulongne V, Gouilh MA, Pariente K, et al. (2011) Identification of the first human gyrovirus, a virus related to chicken anemia virus. J Virol 85: 7948-7950. [Crossref]
29. Chaabane W, Cieślar-Pobuda A, El-Gazzah M, Jain MV, Rzeszowska-Wolny J, et al. (2014) Human-gyrovirus-Apoptin triggers mitochondrial death pathway--Nur77 is required for apoptosis triggering. Neoplasia 16: 679-693. [Crossref]
30. Grosjean H, Fiers W (1982) Preferential codon usage in prokaryotic genes: the optimal codon-anticodon interaction energy and the selective codon usage in efficiently expressed genes. Gene 18: 199-209. [Crossref]
31. Morton BR (1998) Selection on the codon bias of chloroplast and cyanelle genes in different plant and algal lineages. J Mol Evol 46: 449-459. [Crossref]
32. Morton BR, So BG (2000) Codon usage in plastid genes is correlated with context, position within the gene, and amino acid content. J Mol Evol 50: 184-193. [Crossref]
33. Merkl R (2003) A survey of codon and amino acid frequency bias in microbial genomes focusing on translational efficiency. J Mol Evol 57: 453-466. [Crossref]
34. Parrish CR, Holmes EC, Morens DM, Park EC, Burke DS, et al. (2008) Cross-species virus transmission and the emergence of new epidemic diseases. Microbiol Mol Biol Rev 72: 457-470. [Crossref]