Synonymous nucleotide changes drive papillomavirus evolution

Cooling perspectives on the risk of pathogenic viruses from thawing permafrost

Climate change is inducing wide-scale permafrost thaw in the Arctic and subarctic, triggering concerns that long-dormant pathogens could reemerge from the thawing ground and initiate epidemics or pandemics. Viruses, as opposed to bacterial pathogens, garner particular interest because outbreaks cannot be controlled with antibiotics, though the effects can be mitigated by vaccines and newer antiviral drugs. To evaluate the potential hazards posed by viral pathogens emerging from thawing permafrost, we review information from a diverse range of disciplines. This includes efforts to recover infectious virus from human remains, studies on disease occurrence in polar animal populations, investigations into viral persistence and infectivity in permafrost, and assessments of human exposure to the enormous viral diversity present in the environment. Based on currently available knowledge, we conclude that the risk posed by viruses from thawing permafrost is no greater than viruses in other environments such as temperate soils and aquatic systems.

Analysis of codon usage bias of exonuclease genes in invertebrate iridescent viruses

Invertebrate iridescent viruses (IIVs) are double-stranded DNA viruses that belong to the Iridoviridae family. IIVs result diseases that vary in severity from subclinical to lethal in invertebrate hosts. Codon usage bias (CUB) analysis is a versatile method for comprehending the genetic and evolutionary aspects of species. In this study, we analyzed the CUB in 10 invertebrate iridescent viruses exonuclease genes by calculating and comparing the nucleotide contents, effective number of codons (ENC), codon adaptation index (CAI), relative synonymous codon usage (RSCU), and others. The results revealed that IIVs exonuclease genes are rich in A/T. The ENC analysis displayed a low codon usage bias in IIVs exonuclease genes. ENC-plot, neutrality plot, and parity rule 2 plot demonstrated that besides mutational pressure, other factors like natural selection, dinucleotide content, and aromaticity also contributed to CUB. The findings could enhance our understanding of the evolution of IIVs exonuclease genes.

Improved detection of low-frequency within-host variants from deep sequencing: A case study with human papillomavirus

High-coverage sequencing allows the study of variants occurring at low frequencies within samples, but is susceptible to false-positives caused by sequencing error. Ion Torrent has a very low single nucleotide variant (SNV) error rate and has been employed for the majority of human papillomavirus (HPV) whole genome sequences. However, benchmarking of intrahost SNVs (iSNVs) has been challenging, partly due to limitations imposed by the HPV life cycle. We address this problem by deep sequencing three replicates for each of 31 samples of HPV type 18 (HPV18). Errors, defined as iSNVs observed in only one of three replicates, are dominated by C→T (G→A) changes, independently of trinucleotide context. True iSNVs, defined as those observed in all three replicates, instead show a more diverse SNV type distribution, with particularly elevated C→T rates in CCG context (CCG→CTG; CGG→CAG) and C→A rates in ACG context (ACG→AAG; CGT→CTT). Characterization of true iSNVs allowed us to develop two methods for detecting true variants: (1) VCFgenie, a dynamic binomial filtering tool which uses each variant's allele count and coverage instead of fixed frequency cut-offs; and (2) a machine learning binary classifier which trains eXtreme Gradient Boosting models on variant features such as quality and trinucleotide context. Each approach outperforms fixed-cut-off filtering of iSNVs, and performance is enhanced when both are used together. Our results provide improved methods for identifying true iSNVs in within-host applications across sequencing platforms, specifically using HPV18 as a case study.

Distinguishing Genetic Drift from Selection in Papillomavirus Evolution

Pervasive purifying selection on non-synonymous substitutions is a hallmark of papillomavirus genome history, but the role of selection on and the drift of non-coding DNA motifs on HPV diversification is poorly understood. In this study, more than a thousand complete genomes representing Alphapapillomavirus types, lineages, and SNP variants were examined phylogenetically and interrogated for the number and position of non-coding DNA sequence motifs using Principal Components Analyses, Ancestral State Reconstructions, and Phylogenetic Independent Contrasts. For anciently diverged Alphapapillomavirus types, composition of the four nucleotides (A, C, G, T), codon usage, trimer usage, and 13 established non-coding DNA sequence motifs revealed phylogenetic clusters consistent with genetic drift. Ancestral state reconstruction and Phylogenetic Independent Contrasts revealed ancient genome alterations, particularly for the CpG and APOBEC3 motifs. Each evolutionary analytical method we performed supports the unanticipated conclusion that genetic drift and different evolutionary drivers have structured Alphapapillomavirus genomes in distinct ways during successive epochs, even extending to differences in more recently formed variant lineages.

Human papillomavirus genomics: Understanding carcinogenicity

Human papillomavirus (HPV) causes virtually all cervical cancers and many cancers at other anatomical sites in both men and women. However, only 12 of 448 known HPV types are currently classified as carcinogens, and even the most carcinogenic type - HPV16 - only rarely leads to cancer. HPV is therefore necessary but insufficient for cervical cancer, with other contributing factors including host and viral genetics. Over the last decade, HPV whole genome sequencing has established that even fine-scale within-type HPV variation influences precancer/cancer risks, and that these risks vary by histology and host race/ethnicity. In this review, we place these findings in the context of the HPV life cycle and evolution at various levels of viral diversity: between-type, within-type, and within-host. We also discuss key concepts necessary for interpreting HPV genomic data, including features of the viral genome; events leading to carcinogenesis; the role of APOBEC3 in HPV infection and evolution; and methodologies that use deep (high-coverage) sequencing to characterize within-host variation, as opposed to relying on a single representative (consensus) sequence. Given the continued high burden of HPV-associated cancers, understanding HPV carcinogenicity remains important for better understanding, preventing, and treating cancers attributable to infection.