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Human viruses have codon usage biases that match highly expressed proteins in the tissues they infect

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 x (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. Correspondence to: Perry G. Ridge, Department of Biology, Brigham Young University, Provo, Utah 84602, USA, E-mail: perry.ridge@byu.edu


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][3][4][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][7][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.

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][15][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.

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 R 2 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. 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.

The viruses listed in
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).
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     Table 1 for more information on these pairs.  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.
Highly expressed genes have codon biases that utilize highly abundant tRNAs in order for optimal translational and transcriptional speed [12,13,[30][31][32][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.