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A bstract Gene paralogs are copies of a same gene that appear after gene or full genome duplication. Redundancy generated by gene duplication may release certain evolutionary pressures, allowing one of the copies to access novel gene functions. Here we focused on role of codon usage preferences (CUPrefs) during the evolution of the polypyrimidine tract binding protein ( PTBP ) splicing regulator paralogs. PTBP1-3 show high identity at the amino acid level (up to 80%), but display different nucleotide composition, divergent CUPrefs and distinct tissue-specific expression levels. Phylogenetic inference differentiates the three orthologs and suggests that the three PTBP1-3 lineages predate the basal diversification within vertebrates. We identify a distinct substitution pattern towards GC3-enriching mutations in PTBP1 , with a trend for the use of common codons and for a tissue-wide expression. Genomic context analysis shows that GC3-rich nucleotide composition for PTBP1 s is driven by local mutational processes. In contrast, PTBP2 s are enriched in AT-ending, rare codons, and display tissue-restricted expression. Nucleotide composition and CUPrefs of PTBP2 are only partly driven by local mutational forces, and could have been shaped by selective forces. Interestingly, trends for use of UUG-Leu codon match those of AT-ending codons. Our interpretation is that a combination of mutation and selection has differentially shaped CUPrefs of PTBPs in Vertebrates: GC-enrichment of PTBP1 is linked to the strong and broad tissue-expression, while AT-enrichment of PTBP2 and PTBP3 is linked to rare CUPrefs and specialized spatio-temporal expression. Our model is compatible with a gene subfunctionalisation process by differential expression regulation associated to the evolution of specific CUPrefs.

Additional file1 Fig S1. Violin plots comparing the distribution of the quantification values by gene, amplicon size and storage time including all samples. For each period of time, median values of copies/µL (depicted as Log10) per amplicon either between host and virus or for the two different amplicons within host and within viruses are compared by means of a Wilcoxon Mann–Whitney test. For each comparison, p-values for the null-hypothesis of non-different median values between the corresponding distributions are indicated. Fragments are represented as follows: “Human short” to represent 65 bp tubulin-β gene amplicon; “Human long” to represent 149 bp tubulin-β gene amplicon; “Viral short” to represent 69 bp L1 gene amplicon and “Viral long” to represent 134 bp L1 gene amplicon. Each period of time is represented below fragments.

Background Quality of the nucleic acids extracted from Formalin Fixed Paraffin Embedded (FFPE) samples largely depends on pre-analytic, fixation and storage conditions. We assessed the differential sensitivity of viral and human double stranded DNA (dsDNA) to degradation with storage time. Methods We randomly selected forty-four HPV16-positive invasive cervical cancer (ICC) FFPE samples collected between 1930 and 1935 and between 2000 and 2004. We evaluated through qPCR the amplification within the same sample of two targets of the HPV16 L1 gene (69 bp, 134 bp) compared with two targets of the human tubulin-β gene (65 bp, 149 bp). Results Both viral and human, short and long targets were amplified from all samples stored for 15 years. In samples archived for 85 years, we observed a significant decrease in the ability to amplify longer targets and this difference was larger in human than in viral DNA: longer fragments were nine times (CI 95% 2.6–35.2) less likely to be recovered from human DNA compared with 1.6 times (CI 95% 1.1–2.2) for viral DNA. Conclusions We conclude that human and viral DNA show a differential decay kinetics in FFPE samples. The faster degradation of human DNA should be considered when assessing viral DNA prevalence in long stored samples, as HPV DNA detection remains a key biomarker of viral-associated transformation.

Supplementary data and scripts for Willemsen and Bravo, 2020 "Ecological opportunity, radiation events and genomic innovations shaped the episodic evolutionary history of papillomaviruses". The data set consists of four folders: “alignments”, "RScripts_and_inputData", "startingTrees"and “xml_files”. The “alignments” folder contains the Gblocks filtered and subsequently concatenated E1-E2-L2-L1 alignments used for phylogenetic tree construction. For each data set (RD1, RD2, FD and FDF), alignments are provided in fasta and in nexus format. The"RScripts_and_inputData" folder containsthe R scripts and corresponding input dataused for performingstatistical analyses. This folder contains sub-folders organised by Figure numbers. The"startingTrees" contains the nexus files of the starting trees that were used for phylogenetic reconstruction. The “xml_files” folder contains xml files used for phylogenetic time inference. For each data set, one xml file is given per clock and tree model tested.

Supplementary data and scripts for Willemsen and Bravo, 2020 "Ecological opportunity as a driving force for radiation events and time-dependent evolutionary rates in papillomaviruses". The data set consists of two folders: “alignments” and “xml_files”. The “alignments” folder contains the Gblocks filtered and subsequently concatenated E1-E2-L2-L1 alignments used for phylogenetic tree construction. For each data set (RD1, RD2, FD and FDF), alignments are provided in fasta and in nexus format. The “xml_files” folder contains xml files used for phylogenetic time inference. For each data set, one xml file is given per clock and tree model tested.

Papillomaviruses (PVs) have a wide host range, infecting mammals, birds, turtles, and snakes. The recent discovery of PVs in different fish species allows for a more complete reconstruction of the evolutionary history of the viral family. In this study we combine fossil record estimates and molecular dating to analyze evolutionary events that occurred during PV evolution, as well as to estimate speciation and evolutionary rates. We have used four different data sets to explore and correct for potential biases that particular taxa combinations may introduce in phylogenetic inference. When considering the evolution of speciation rates, we show that these are not constant through time, suggesting the occurrence of evolutionary events like adaptive radiations. We identified four periods along the evolutionary timeline of PVs: (i) a slow increase of PV lineages with an old ancestry that goes parallel to the basal diversification of amniotes; (ii) an initial radiation event of PVs infecting fish, birds, turtles and ancestral mammals; (iii) a secondary radiation event of PVs in parallel to the radiation of placental mammals, that generated a large number of viral lineages; and (iv) a final virus-host co-divergence in parallel to the diversification of PVs within their hosts. When considering the evolution of substitution rates, we show that there are strikingly different trends when stratifying by PV genus, revealing clade-specific events during the evolution of a highly diverse primate PV lineage: the clinically relevant AlphaPVs . We have further explored the effect of heterogeneous taxa representation and the choice of calibration points on the molecular dating of the PV tree. When simultaneously using all calibration points, similar ancestral node ages and overall rates of evolution were obtained with all databases. On the contrary, in our inferences with each calibration point separately, calibrating with younger nodes tended to render higher substitution rates compared to older nodes, also known as the time-dependent rate phenomenon. We discuss that in our case this behavior may be a function of the available host niches and show that a power-law rate decay model performs well to correct for this phenomenon within PVs. Our results provide new insights into the evolutionary history of PVs, where ecological opportunity seems to be the main driving force for the different radiation and key-innovation events we observe and showcases both the potential and limitations of using host-derived calibrations to infer viral speciation times.

Additional file 1: Figure S1. Genome organization of ChPV2; Upstream regulatory region, yellow; early genes, green late genes, blue, potential E4 gene and potential E1^E4 splicing patterns, ochre. Figure S2. Presence and position of putative regulatory elements within URR and early gene region of ChPV1 and ChPV2, E1- binding sites (E1BS) are marked by yellow boxes, E2- binding sites (E2BS) perfectly matching the E2BS consensus sequence are marked by red boxes, E2BS with 1 nucleotide mismatch to the E2BS consensus sequence are marked in orange. nucleotide positions of early genes and regulatory elements are depicted below or above the respective element. Figure S3. Maximum likelihood phylogenetic trees of the concatenated E1E2 and concatenated L2L1 nucleotide alignments of 377 PVs. Clade color codes highlight the different PV groups and recombinant taxa: red, Alpha-OmikronPVs; green, Beta-XiPVs; yellow, Lambda-MuPVs; blue, Delta-ZetaPVs; gray, a yet unclassified clade consisting of PVs infecting birds and turtles; aqua-blue, a yet unclassified clade consisting of PVs infecting Caniformia; orange, known recombinant PVs infecting Cetaceans; and pink, possible recombinant PVs. The position of ChPV2 is indicated with a red arrow. Values on branches correspond to ML bootstrap support values. Figure S4. Maximum likelihood phylogenetic trees of the concatenated E1E2 and concatenated L2L1 nucleotide alignments of 324 PVs. Clade color codes highlight the different PV groups: red, Alpha-OmikronPVs; green, Beta-XiPVs; yellow, Lambda-MuPVs; blue, Delta-ZetaPVs; and gray, a yet unclassified clade consisting of PVs infecting birds and turtles. The position of ChPV2 is indicated with a red arrow. Values on branches correspond to ML bootstrap support values. Figure S5. Maximum likelihood phylogenetic trees of the concatenated E1E2 and concatenated L2L1 amino acid alignments of 324 PVs. Clade color codes highlight the different PV groups: red, Alpha-OmikronPVs; green, Beta-XiPVs; yellow, Lambda-MuPVs; blue, Delta-ZetaPVs; and gray, a yet unclassified clade consisting of PVs infecting birds and turtles. The position of ChPV2 is indicated with a red arrow. Values on branches correspond to ML bootstrap support values. Figure S6. Maximum likelihood phylogenetic tree of the concatenated E1E2L2L1 amino acid alignments of 324 PVs. Clade color codes highlight the different PV groups: red, Alpha-OmikronPVs; green, Beta-XiPVs; yellow, Lambda-MuPVs; blue, Delta-ZetaPVs; and gray, a yet unclassified clade consisting of PVs infecting birds and turtles. The position of ChPV2 is indicated with a red arrow. Values on branches correspond to ML bootstrap support values. Figure S7. Maximum likelihood phylogenetic trees of E6 (A), E7 (B) and the concatenated E1E2L2L1 (C) amino acid alignments of 17 PVs. The taxa highlighted in blue contain both the E6 and E7 oncogenes in their genomes, while the other taxa contain only E7. The position of ChPV2 is indicated with a red arrow. Values on branches correspond to ML bootstrap support values. Figure S8. Macroscopic view of teat papilloma of a Damascus (Shami) goat, taken from (12). Table S1. Genome organization of ChPV2 presenting orf positions, orf length, GC content and amino acid content of encoded potential proteins and their molecular weight. Table S2. Nucleotide (nt) identity of ChPV2 genes with the respective genes of closely related PV types. Table S3. Amino acid (aa) identity of ChPV2 genes with the respective genes of closely related PV types. Table S4. Potential motifs and domains identified in amino acid sequences of ChPV2 proteins. Table S5. Potential regulatory elements identified throughout the ChPV2 genome, elements marked with a * have a mismatch to the published consensus sequence for the respective element. Table S6. List of PV genomes collected from PaVE (pave.niaid.nih.gov) plus ChPV2.