RNA Biol

The HIV-1 Vif protein plays an essential role in the regulation of the infectivity of HIV-1 virion and in vivo pathogenesis. Vif neutralizes the human DNA-editing enzyme APOBEC3 protein, an antiretroviral cellular factor from the innate immune system, allowing the virus to escape the host defence system. It was shown that Vif is packaged into viral particles through specific interactions with the viral genomic RNA. Conserved and structured sequences from the 5'-noncoding region, such as the Tat-responsive element (TAR) or the genomic RNA dimerization initiation site (DIS), are primary binding sites for Vif. In the present study we used isothermal titration calorimetry to investigate sequence and structure determinants important for Vif binding to short viral RNA corresponding to TAR and DIS stem-loops. We showed that Vif specifically binds TAR and DIS in the low nanomolar range. In addition, Vif primarily binds the TAR UCU bulge, but not the apical loop. Determinants for Vif binding to the DIS loop-loop complex are likely more complex and involve the self-complementary loop together with the upper part of the stem. These results suggest that Tat-TAR inhibitors or DIS small molecule binders might be also effective to disturb Vif-TAR and Vif-DIS binding in order to reduce Vif packaging into virions

Degradation of p53 by Human Alphapapillomavirus E6 Proteins Shows a Stronger Correlation with Phylogeny than Oncogenicity

Human Papillomavirus (HPV) E6 induced p53 degradation is thought to be an essential activity by which high-risk human Alphapapillomaviruses (alpha-HPVs) contribute to cervical cancer development. However, most of our understanding is derived from the comparison of HPV16 and HPV11. These two viruses are relatively distinct viruses, making the extrapolation of these results difficult. In the present study, we expand the tested strains (types) to include members of all known HPV species groups within the Alphapapillomavirus genus.We report the biochemical activity of E6 proteins from 27 HPV types representing all alpha-HPV species groups to degrade p53 in human cells. Expression of E6 from all HPV types epidemiologically classified as group 1 carcinogens significantly reduced p53 levels. However, several types not associated with cancer (e.g., HPV53, HPV70 and HPV71) were equally active in degrading p53. HPV types within species groups alpha 5, 6, 7, 9 and 11 share a most recent common ancestor (MRCA) and all contain E6 ORFs that degrade p53. A unique exception, HPV71 E6 ORF that degraded p53 was outside this clade and is one of the most prevalent HPV types infecting the cervix in a population-based study of 10,000 women. Alignment of E6 ORFs identified an amino acid site that was highly correlated with the biochemical ability to degrade p53. Alteration of this amino acid in HPV71 E6 abrogated its ability to degrade p53, while alteration of this site in HPV71-related HPV90 and HPV106 E6s enhanced their capacity to degrade p53.These data suggest that the alpha-HPV E6 proteins' ability to degrade p53 is an evolved phenotype inherited from a most recent common ancestor of the high-risk species that does not always segregate with carcinogenicity. In addition, we identified an amino-acid residue strongly correlated with viral p53 degrading potential

Cellular binding partners of the human papillomavirus E6 protein

The high-risk strains of human papillomavirus (HR-HPV) are known to be causative agents of cervical cancer and have recently also been implicated in cancers of the oropharynx. E6 is a potent oncogene of HR-HPVs, and its role in the progression to malignancy has been and continues to be explored. E6 is known to interact with and subsequently inactivate numerous cellular proteins pivotal in the mediation of apoptosis, transcription of tumor suppressor genes, maintenance of epithelial organization, and control of cell proliferation. Binding of E6 to these proteins cumulatively contributes to the oncogenic potential of HPV. This paper provides an overview of these cellular protein partners of HR-E6, the motifs known to mediate oncoprotein binding, and the agents that have the potential to interfere with E6 expression and activity and thus prevent the subsequent progression to oncogenesis

The HPV E6 oncoprotein targets histone methyltransferases for modulating specific gene transcription

Expression of viral proteins causes important epigenetic changes leading to abnormal cell growth. Whether viral proteins directly target histone methyltransferases (HMTs), a key family enzyme for epigenetic regulation, and modulate their enzymatic activities remains elusive. Here we show that the E6 proteins of both low-risk and high-risk human papillomavirus (HPV) interact with three coactivator HMTs, CARM1, PRMT1 and SET7, and downregulate their enzymatic activities in vitro and in HPV-transformed HeLa cells. Furthermore, these three HMTs are required for E6 to attenuate p53 transactivation function. Mechanistically, E6 hampers CARM1- and PRMT1-catalyzed histone methylation at p53-responsive promoters, and suppresses the binding of p53 to chromatinized DNA independently of E6-mediated p53 degradation. p53 pre-methylated at lysine-372 (p53K372 mono-methylation) by SET7 protects p53 from E6-induced degradation. Consistently, E6 downregulates p53K372 mono-methylation and thus reduces p53 protein stability. As a result of the E6-mediated inhibition of HMT activity, expression of p53 downstream genes is suppressed. Together, our results not only reveal a clever approach for the virus to interfere with p53 function, but also demonstrate the modulation of HMT activity as a novel mechanism of epigenetic regulation by a viral oncoprotein

Effect of the COVID-19 pandemic on surgery for indeterminate thyroid nodules (THYCOVID): a retrospective, international, multicentre, cross-sectional study

Background Since its outbreak in early 2020, the COVID-19 pandemic has diverted resources from non-urgent and elective procedures, leading to diagnosis and treatment delays, with an increased number of neoplasms at advanced stages worldwide. The aims of this study were to quantify the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic; and to evaluate whether delays in surgery led to an increased occurrence of aggressive tumours.Methods In this retrospective, international, cross-sectional study, centres were invited to participate in June 22, 2022; each centre joining the study was asked to provide data from medical records on all surgical thyroidectomies consecutively performed from Jan 1, 2019, to Dec 31, 2021. Patients with indeterminate thyroid nodules were divided into three groups according to when they underwent surgery: from Jan 1, 2019, to Feb 29, 2020 (global prepandemic phase), from March 1, 2020, to May 31, 2021 (pandemic escalation phase), and from June 1 to Dec 31, 2021 (pandemic decrease phase). The main outcomes were, for each phase, the number of surgeries for indeterminate thyroid nodules, and in patients with a postoperative diagnosis of thyroid cancers, the occurrence of tumours larger than 10 mm, extrathyroidal extension, lymph node metastases, vascular invasion, distant metastases, and tumours at high risk of structural disease recurrence. Univariate analysis was used to compare the probability of aggressive thyroid features between the first and third study phases. The study was registered on ClinicalTrials.gov, NCT05178186.Findings Data from 157 centres (n=49 countries) on 87 467 patients who underwent surgery for benign and malignant thyroid disease were collected, of whom 22 974 patients (18 052 [78 center dot 6%] female patients and 4922 [21 center dot 4%] male patients) received surgery for indeterminate thyroid nodules. We observed a significant reduction in surgery for indeterminate thyroid nodules during the pandemic escalation phase (median monthly surgeries per centre, 1 center dot 4 [IQR 0 center dot 6-3 center dot 4]) compared with the prepandemic phase (2 center dot 0 [0 center dot 9-3 center dot 7]; p<0 center dot 0001) and pandemic decrease phase (2 center dot 3 [1 center dot 0-5 center dot 0]; p<0 center dot 0001). Compared with the prepandemic phase, in the pandemic decrease phase we observed an increased occurrence of thyroid tumours larger than 10 mm (2554 [69 center dot 0%] of 3704 vs 1515 [71 center dot 5%] of 2119; OR 1 center dot 1 [95% CI 1 center dot 0-1 center dot 3]; p=0 center dot 042), lymph node metastases (343 [9 center dot 3%] vs 264 [12 center dot 5%]; OR 1 center dot 4 [1 center dot 2-1 center dot 7]; p=0 center dot 0001), and tumours at high risk of structural disease recurrence (203 [5 center dot 7%] of 3584 vs 155 [7 center dot 7%] of 2006; OR 1 center dot 4 [1 center dot 1-1 center dot 7]; p=0 center dot 0039).Interpretation Our study suggests that the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic period could have led to an increased occurrence of aggressive thyroid tumours. However, other compelling hypotheses, including increased selection of patients with aggressive malignancies during this period, should be considered. We suggest that surgery for indeterminate thyroid nodules should no longer be postponed even in future instances of pandemic escalation.Funding None.Copyright (c) 2023 Published by Elsevier Ltd. All rights reserved

Assessing parallel gene histories in viral genomes

Background: The increasing abundance of sequence data has exacerbated a long known problem: gene trees and species trees for the same terminal taxa are often incongruent. Indeed, genes within a genome have not all followed the same evolutionary path due to events such as incomplete lineage sorting, horizontal gene transfer, gene duplication and deletion, or recombination. Considering conflicts between gene trees as an obstacle, numerous methods have been developed to deal with these incongruences and to reconstruct consensus evolutionary histories of species despite the heterogeneity in the history of their genes. However, inconsistencies can also be seen as a source of information about the specific evolutionary processes that have shaped genomes. Results: The goal of the approach here proposed is to exploit this conflicting information: we have compiled eleven variables describing phylogenetic relationships and evolutionary pressures and submitted them to dimensionality reduction techniques to identify genes with similar evolutionary histories. To illustrate the applicability of the method, we have chosen two viral datasets, namely papillomaviruses and Turnip mosaic virus (TuMV) isolates, largely dissimilar in genome, evolutionary distance and biology. Our method pinpoints viral genes with common evolutionary patterns. In the case of papillomaviruses, gene clusters match well our knowledge on viral biology and life cycle, illustrating the potential of our approach. For the less known TuMV, our results trigger new hypotheses about viral evolution and gene interaction. Conclusions: The approach here presented allows turning phylogenetic inconsistencies into evolutionary information, detecting gene assemblies with similar histories, and could be a powerful tool for comparative pathogenomics.IGB was funded by the disappeared Spanish Ministry for Science and Innovation (CGL2010-16713). Work in Valencia was supported by grant BFU2012-30805 from the Spanish Ministry of Economy and Competitiveness (MINECO) to SFE. BMC is the recipient of an IDIBELL PhD fellowship.Mengual-Chuliá, B.; Bedhomme, S.; Lafforgue, G.; Elena Fito, SF.; Bravo, IG. (2016). Assessing parallel gene histories in viral genomes. BMC Evolutionary Biology. 16:1-15. https://doi.org/10.1186/s12862-016-0605-4S11516Hess J, Goldman N. Addressing inter-gene heterogeneity in maximum likelihood phylogenomic analysis: Yeasts revisited. PLoS ONE. 2011;6:e22783.Salichos L, Rokas A. Inferring ancient divergences requires genes with strong phylogenetic signals. Nature. 2013;497:327–31.Zhong B, Liu L, Yan Z, Penny D. Origin of land plants using the multispecies coalescent model. Trends Plant Sci. 2013;18:492–5.Song S, Liu L, Edwards SV, Wu S. Resolving conflict in eutherian mammal phylogeny using phylogenomics and the multispecies coalescent model. Proc Natl Acad Sci U S A. 2012;109:14942–7.Nichols R. Gene trees and species trees are not the same. Trends Ecol Evol. 2001;16:358–64.Maddison WP. Gene trees in species trees. Syst Biol. 1997;46:523–36.Suh A, Smeds L, Ellegren H. The dynamics of incomplete lineage sorting across the ancient adaptive radiation of neoavian birds. PLoS Biol. 2015;13:e1002224.McBreen K, Lockhart PJ. Reconstructing reticulate evolutionary histories of plants. Trends Plant Sci. 2006;11:398–404.Dagan T, Martin W. The tree of one percent. Genome Biol. 2006;7:118.Beiko RG, Harlow TJ, Ragan MA. Highways of gene sharing in prokaryotes. Proc Natl Acad Sci U S A. 2005;102:14332–7.Cotton JA, Page RD. Going nuclear: Gene family evolution and vertebrate phylogeny reconciled. Proc Biol Sci. 2002;269:1555–61.Kuhner MK, Yamato J. Practical performance of tree comparison metrics. Syst Biol. 2015;64:205–14.Brochier C, Bapteste E, Moreira D, Philippe H. Eubacterial phylogeny based on translational apparatus proteins. Trends Genet. 2002;18:1–5.Bapteste E, Susko E, Leigh J, MacLeod D, Charlebois RL, Doolittle WF. Do orthologous gene phylogenies really support tree-thinking? BMC Evol Biol. 2005;5:33.Leigh JW, Susko E, Baumgartner M, Roger AJ. Testing congruence in phylogenomic analysis. Syst Biol. 2008;57:104–15.Leigh JW, Schliep K, Lopez P, Bapteste E. Let them fall where they may: Congruence analysis in massive phylogenetically messy data sets. Mol Biol Evol. 2011;28:2773–85.de Vienne DM, Ollier S, Aguileta G. Phylo-mcoa: A fast and efficient method to detect outlier genes and species in phylogenomics using multiple co-inertia analysis. Mol Biol Evol. 2012;29:1587–98.Wang S, Luo X, Wei W, Zheng Y, Dou Y, Cai X. Calculation of evolutionary correlation between individual genes and full-length genome: A method useful for choosing phylogenetic markers for molecular epidemiology. PLoS ONE. 2013;8:e81106.Salichos L, Stamatakis A, Rokas A. Novel information theory-based measures for quantifying incongruence among phylogenetic trees. Mol Biol Evol. 2014;31:1261–71.Weyenberg G, Huggins PM, Schardl CL, Howe DK, Yoshida R. Kdetrees: Non-parametric estimation of phylogenetic tree distributions. Bioinformatics. 2014;30:2280–7.de Queiroz A. For consensus (sometimes). Syst Biol. 1993;42:368–72.Miyamoto MM, Fitch WM. Testing the covarion hypothesis of molecular evolution. Mol Biol Evol. 1995;12:503–13.Sanderson MJ, Purvis A, Henze C. Phylogenetic supertrees: Assembling the trees of life. Trends Ecol Evol. 1998;13:105–9.Bininda-Emonds ORP. Phylogenetic supertrees: Combining information to reveal the tree of life. Comput Biol. Dordrecht (The Netherlands): Kluwer Academic Publishers; 2004.Creevey CJ, Fitzpatrick DA, Philip GK, Kinsella RJ, O’Connell MJ, Pentony MM, et al. Does a tree-like phylogeny only exist at the tips in the prokaryotes? Proc Biol Sci. 2004;271:2551–8.Pisani D, Cotton JA, McInerney JO. Supertrees disentangle the chimerical origin of eukaryotic genomes. Mol Biol Evol. 2007;24:1752–60.Ane C, Larget B, Baum DA, Smith SD, Rokas A. Bayesian estimation of concordance among gene trees. Mol Biol Evol. 2007;24:412–26.Gordon AD. A measure of the agreement between rankings. Biometrika. 1979;66:7–15.de Vienne DM, Giraud T, Martin OC. A congruence index for testing topological similarity between trees. Bioinformatics. 2007;23:3119–24.Suchard MA, Weiss RE, Sinsheimer JS, Dorman KS, Patel M, McCabe ERB. Evolutionary similarity among genes. J Am Stat Assoc. 2003;98:653–62.Edwards SV, Liu L, Pearl DK. High-resolution species trees without concatenation. Proc Natl Acad Sci U S A. 2007;104:5936–41.Liu L, Pearl DK. Species trees from gene trees: Reconstructing bayesian posterior distributions of a species phylogeny using estimated gene tree distributions. Syst Biol. 2007;56:504–14.Liu L, Pearl DK, Brumfield RT, Edwards SV. Estimating species trees using multiple-allele DNA sequence data. Evolution. 2008;62:2080–91.Levasseur C, Lapointe FJ. War and peace in phylogenetics: A rejoinder on total evidence and consensus. Syst Biol. 2001;50:881–91.de Queiroz A, Gatesy J. The supermatrix approach to systematics. Trends Ecol Evol. 2007;22:34–41.Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006;23:254–67.Layeghifard M, Peres-Neto PR, Makarenkov V. Inferring explicit weighted consensus networks to represent alternative evolutionary histories. BMC Evol Biol. 2013;13:274.Stockham C, Wang LS, Warnow T. Statistically based postprocessing of phylogenetic analysis by clustering. Bioinformatics. 2002;18 Suppl 1:S285–93.Bonnard C, Berry V, Lartillot N. Multipolar consensus for phylogenetic trees. Syst Biol. 2006;55:837–43.Guenoche A. Multiple consensus trees: A method to separate divergent genes. BMC Bioinformatics. 2013;14:46.Duggal R, Cuconati A, Gromeier M, Wimmer E. Genetic recombination of poliovirus in a cell-free system. Proc Natl Acad Sci U S A. 1997;94:13786–91.Reiter J, Perez-Vilaro G, Scheller N, Mina LB, Diez J, Meyerhans A. Hepatitis c virus rna recombination in cell culture. J Hepatol. 2011;55:777–83.Desbiez C, Lecoq H. Evidence for multiple intraspecific recombinants in natural populations of watermelon mosaic virus (wmv, potyvirus). Arch Virol. 2008;153:1749–54.Larsen RC, Miklas PN, Druffel KL, Wyatt SD. Nl-3 k strain is a stable and naturally occurring interspecific recombinant derived from bean common mosaic necrosis virus and bean common mosaic virus. Phytopathology. 2005;95:1037–42.Valli A, Lopez-Moya JJ, Garcia JA. Recombination and gene duplication in the evolutionary diversification of p1 proteins in the family potyviridae. J Gen Virol. 2007;88:1016–28.Gottschling M, Bravo IG, Schulz E, Bracho MA, Deaville R, Jepson PD, et al. Modular organizations of novel cetacean papillomaviruses. Mol Phylogenet Evol. 2011;59:34–42.Woolford L, Rector A, Van Ranst M, Ducki A, Bennett MD, Nicholls PK, et al. A novel virus detected in papillomas and carcinomas of the endangered western barred bandicoot (perameles bougainville) exhibits genomic features of both the papillomaviridae and polyomaviridae. J Virol. 2007;81:13280–90.Chen X, Zhang Q, He C, Zhang L, Li J, Zhang W, et al. Recombination and natural selection in hepatitis e virus genotypes. J Med Virol. 2012;84:1396–407.Cadar D, Csagola A, Kiss T, Tuboly T. Capsid protein evolution and comparative phylogeny of novel porcine parvoviruses. Mol Phylogenet Evol. 2013;66:243–53.Smith LM, McWhorter AR, Shellam GR, Redwood AJ. The genome of murine cytomegalovirus is shaped by purifying selection and extensive recombination. Virology. 2013;435:258–68.Münk C, Willemsen A, Bravo IG. An ancient history of gene duplications, fusions and losses in the evolution of apobec3 mutators in mammals. BMC Evol Biol. 2012;12:71.Daugherty MD, Malik HS. Rules of engagement: Molecular insights from host-virus arms races. Annu Rev Genet. 2012;46:677–700.Edgar RC. Muscle: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol. 2000;17:540–52.Stamatakis A, Ludwig T, Meier H. Raxml-iii: A fast program for maximum likelihood-based inference of large phylogenetic trees. Bioinformatics. 2005;21:456–63.Soria-Carrasco V, Talavera G, Igea J, Castresana J. The k tree score: Quantification of differences in the relative branch length and topology of phylogenetic trees. Bioinformatics. 2007;23:2954–6.Stern A, Doron-Faigenboim A, Erez E, Martz E, Bacharach E, Pupko T. Selecton 2007: Advanced models for detecting positive and purifying selection using a bayesian inference approach. Nucleic Acids Res. 2007;35:W506–11.Doron-Faigenboim A, Pupko T. A combined empirical and mechanistic codon model. Mol Biol Evol. 2007;24:388–97.Swanson WJ, Nielsen R, Yang Q. Pervasive adaptive evolution in mammalian fertilization proteins. Mol Biol Evol. 2003;20:18–20.Shukla DD, Ward CW, Brunt AA. The potyviridae. Wallingford (UK): CABI; 1994.Chung BY, Miller WA, Atkins JF, Firth AE. An overlapping essential gene in the potyviridae. Proc Natl Acad Sci U S A. 2008;105:5897–902.Tan Z, Wada Y, Chen J, Ohshima K. Inter- and intralineage recombinants are common in natural populations of turnip mosaic virus. J Gen Virol. 2004;85:2683–96.Bravo IG, de Sanjose S, Gottschling M. The clinical importance of understanding the evolution of papillomaviruses. Trends Microbiol. 2010;18:432–8.Klingelhutz AJ, Roman A. Cellular transformation by human papillomaviruses: Lessons learned by comparing high- and low-risk viruses. Virology. 2012;424:77–98.Bravo IG, Alonso A. Mucosal human papillomaviruses encode four different e5 proteins whose chemistry and phylogeny correlate with malignant or benign growth. J Virol. 2004;78:13613–26.Garcia-Vallve S, Alonso A, Bravo IG. Papillomaviruses: Different genes have different histories. Trends Microbiol. 2005;13:514–21.Bravo IG, Felez-Sanchez M. Papillomaviruses: Viral evolution, cancer and evolutionary medicine. Evol Med Public Health. 2015;2015:32–51.Aleman-Verdaguer ME, Goudou-Urbino C, Dubern J, Beachy RN, Fauquet C. Analysis of the sequence diversity of the p1, hc, p3, nib and cp genomic regions of several yam mosaic potyvirus isolates: Implications for the intraspecies molecular diversity of potyviruses. J Gen Virol. 1997;78(Pt 6):1253–64.Sakai J, Mori M, Morishita T, Tanaka M, Hanada K, Usugi T, et al. Complete nucleotide sequence and genome organization of sweet potato feathery mottle virus (s strain) genomic rna: The large coding region of the p1 gene. Arch Virol. 1997;142:1553–62.Tordo VM, Chachulska AM, Fakhfakh H, Le Romancer M, Robaglia C, Astier-Manifacier S. Sequence polymorphism in the 5’ntr and in the p1 coding region of potato virus y genomic rna. J Gen Virol. 1995;76(Pt 4):939–49.Verchot J, Carrington JC. Evidence that the potyvirus p1 proteinase functions in trans as an accessory factor for genome amplification. J Virol. 1995;69:3668–74.Salvador B, Saenz P, Yanguez E, Quiot JB, Quiot L, Delgadillo MO, et al. Host-specific effect of p1 exchange between two potyviruses. Mol Plant Pathol. 2008;9:147–55.Desbiez C, Lecoq H. The nucleotide sequence of watermelon mosaic virus (wmv, potyvirus) reveals interspecific recombination between two related potyviruses in the 5’ part of the genome. Arch Virol. 2004;149:1619–32.Majer E, Salvador Z, Zwart MP, Willemsen A, Elena SF, Daros JA. Relocation of the nib gene in the tobacco etch potyvirus genome. J Virol. 2014;88:4586–90.Pasin F, Simon-Mateo C, Garcia JA. The hypervariable amino-terminus of p1 protease modulates potyviral replication and host defense responses. PLoS Pathog. 2014;10:e1003985.Lopez-Lastra M, Rivas A, Barria MI. Protein synthesis in eukaryotes: The growing biological relevance of cap-independent translation initiation. Biol Res. 2005;38:121–46.Kang ST, Wang HC, Yang YT, Kou GH, Lo CF. The DNA virus white spot syndrome virus uses an internal ribosome entry site for translation of the highly expressed nonstructural protein icp35. J Virol. 2013;87:13263–78.Dolja VV, Haldeman-Cahill R, Montgomery AE, Vandenbosch KA, Carrington JC. Capsid protein determinants involved in cell-to-cell and long distance movement of tobacco etch potyvirus. Virology. 1995;206:1007–16.Carrington JC, Jensen PE, Schaad MC. Genetic evidence for an essential role for potyvirus ci protein in cell-to-cell movement. Plant J. 1998;14:393–400.Wei T, Zhang C, Hong J, Xiong R, Kasschau KD, Zhou X, et al. Formation of complexes at plasmodesmata for potyvirus intercellular movement is mediated by the viral protein p3n-pipo. PLoS Pathog. 2010;6:e1000962.Felez-Sanchez M, Trosemeier JH, Bedhomme S, Gonzalez-Bravo MI, Kamp C, Bravo IG. Cancer, warts, or asymptomatic infections: Clinical presentation matches codon usage preferences in human papillomaviruses. Genome Biol Evol. 2015;7:2117–35.Doorbar J, Gallimore PH. Identification of proteins encoded by the l1 and l2 open reading frames of human papillomavirus 1a. J Virol. 1987;61:2793–9.Hughes FJ, Romanos MA. E1 protein of human papillomavirus is a DNA helicase/atpase. Nucleic Acids Res. 1993;21:5817–23.Sarafi TR, McBride AA. Domains of the bpv-1 e1 replication protein required for origin-specific DNA binding and interaction with the e2 transactivator. Virology. 1995;211:385–96.Chen G, Stenlund A. Characterization of the DNA-binding domain of the bovine papillomavirus replication initiator e1. J Virol. 1998;72:2567–76.McBride AA. Replication and partitioning of papillomavirus genomes. Adv Virus Res. 2008;72:155–205.McBride A, Myers G. The e2 proteins: An update. In: Laboratory HPLAN. Los Alamos: Myers, G., and coworkers; 1997. p. III54–99.Kirnbauer R, Booy F, Cheng N, Lowy DR, Schiller JT. Papillomavirus l1 major capsid protein self-assembles into virus-like particles that are highly immunogenic. Proc Natl Acad Sci U S A. 1992;89:12180–4.Penrose KJ, McBride AA. Proteasome-mediated degradation of the papillomavirus e2-ta protein is regulated by phosphorylation and can modulate viral genome copy number. J Virol. 2000;74:6031–8.Poddar A, Reed SC, McPhillips MG, Spindler JE, McBride AA. The human papillomavirus type 8 e2 tethering protein targets the ribosomal DNA loci of host mitotic chromosomes. J Virol. 2009;83:640–50.Lai MC, Teh BH, Tarn WY. A human papillomavirus e2 transcriptional activator. The interactions with cellular splicing factors and potential function in pre-mrna processing. J Biol Chem. 1999;274:11832–41.Zou N, Lin BY, Duan F, Lee KY, Jin G, Guan R, et al. The hinge of the human papillomavirus type 11 e2 protein contains major determinants for nuclear localization and nuclear matrix association. J Virol. 2000;74:3761–70.Steger G, Schnabel C, Schmidt HM. The hinge region of the human papillomavirus type 8 e2 protein activates the human p21(waf1/cip1) promoter via interaction with sp1. J Gen Virol. 2002;83:503–10.Hughes AL, Hughes MA. Patterns of nucleotide difference in overlapping and non-overlapping reading frames of papillomavirus genomes. Virus Res. 2005;113:81–8.Ahola H, Bergman P, Strom AC, Moreno-Lopez J, Pettersson U. Organization and expression of the transforming region from the european elk papillomavirus (eepv). Gene. 1986;50:195–205.Chen Z, Schiffman M, Herrero R, Desalle R, Burk RD. Human papillomavirus (hpv) types 101 and 103 isolated from cervicovaginal cells lack an e6 open reading frame (orf) and are related to gamma-papillomaviruses. Virology. 2007;360:447–53.Nobre RJ, Herraez-Hernandez E, Fei JW, Langbein L, Kaden S, Grone HJ, et al. E7 oncoprotein of novel human papillomavirus type 108 lacking the e6 gene induces dysplasia in organotypic keratinocyte cultures. J Virol. 2009;83:2907–16.Stevens H, Rector A, Bertelsen MF, Leifsson PS, Van Ranst M. Novel papillomavirus isolated from the oral mucosa of a polar bear does not cluster with other papillomaviruses of carnivores. Vet Microbiol. 2008;129:108–16.Stevens H, Rector A, Van Der Kroght K, Van Ranst M. Isolation and cloning of two variant papillomaviruses from domestic pigs: Sus scrofa papillomaviruses type 1 variants a and b. J Gen Virol. 2008;89:2475–81.Dyson N, Howley PM, Munger K, Harlow E. The human papilloma virus-16 e7 oncoprotein is able to bind to the retinoblastoma gene product. Science. 1989;243:934–7.Werness BA, Levine AJ, Howley PM. Association of human papillomavirus types 16 and 18 e6 proteins with p53. Science. 1990;248:76–9.Huibregtse JM, Scheffner M, Howley PM. A cellular protein mediates association of p53 with the e6 oncoprotein of human papillomavirus types 16 or 18. EMBO J. 1991;10:4129–35.Hartley KA, Alexander KA. Human tata binding protein inhibits human papillomavirus type 11 DNA replication by antagonizing e1-e2 protein complex formation on the viral origin of replication. J Virol. 2002;76:5014–23.Ilves I, Kadaja M, Ustav M. Two separate replication modes of the bovine papillomavirus bpv1 origin of replication that have different sensitivity to p53. Virus Res. 2003;96:75–84.Narahari J, Fisk JC, Melendy T, Roman A. Interactions of the cellular ccaat displacement protein and human papillomavirus e2 protein with the viral origin of replication can regulate DNA replication. Virology. 2006;350:302–11.Barrow-Laing L, Chen W, Roman A. Low- and high-risk human papillomavirus e7 proteins regulate p130 differently. Virology. 2010;400:233–9.White EA, Sowa ME, Tan MJ, Jeudy S, Hayes SD, Santha S, et al. Systematic identification of interactions between host cell proteins and e7 oncoproteins from diverse human papillomaviruses. Proc Natl Acad Sci U S A. 2012;109:E260–7.Nomine Y, Masson M, Charbonnier S, Zanier K, Ristriani T, Deryckere F, et al. Structural and functional analysis of e6 oncoprotein: Insights in the molecular pathways of human papillomavirus-mediated pathogenesis. Mol Cell. 2006;21:665–78.Zanier K, ould M’hamed ould Sidi A, Boulade-Ladame C, Rybin V, Chappelle A, Atkinson A, et al. Solution structure analysis of the hpv16 e6 oncoprotein reveals a self-association mechanism required for e6-mediated degradation of p53. Structure. 2012;20:604–17.Briddon RW, Patil BL, Bagewadi B, Nawaz-ul-Rehman MS, Fauquet CM. Distinct evolutionary histories of the DNA-a and DNA-b components of bipartite begomoviruses. BMC Evol Biol. 2010;10:97.Chen JM, Sun YX, Chen JW, Liu S, Yu JM, Shen CJ, et al. Panorama phylogenetic diversity and distribution of type a influenza viruses based on their six internal gene sequences. J Virol. 2009;6:137

Technical evaluation of optical remote detection instruments: DOAS and LIDAR (in French)

International audienceThe new generation of optical remote detection instruments such as FTIR, DOAS and LIDAR is issued from various technical developments during the last years. They offer new perspectives in the determination of the atmospheric characteristics since they allow measuring simultaneously several pollutants over long distances. The integration of these instruments into an experimental site or air quality control network involves the evaluation of their potentiality and the determination of their metrology performances. In this work, we focus on the estimate of DOAS and LIDA