Using the Teamlet Model to Improve Chronic Care in an Academic Primary Care Practice

Team care can improve management of chronic conditions, but implementing a team approach in an academic primary care clinic presents unique challenges. To implement and evaluate the Teamlet Model, which uses health coaches working with primary care physicians to improve care for patients with diabetes and/or hypertension in an academic practice. Process and outcome measures were compared before and during the intervention in patients seen with the Teamlet Model and in a comparison patient group. First year family medicine residents, medical assistants, health workers, and adult patients with either type 2 diabetes or hypertension in a large public health clinic. Health coaches, in coordination with resident primary care physicians, met with patients before and after clinic visits and called patients between visits. Measurement of body mass index, assessment of smoking status, and formulation of a self-management plan prior to and during the intervention period for patients in the Teamlet Model group. Testing for LDL and HbA1C and the proportion of patients at goal for blood pressure, LDL, and HbA1C in the Teamlet Model and comparison groups in the year prior to and during implementation. Teamlet patients showed improvement in all measures, though improvement was significant only for smoking, BMI, and self-management plan documentation and testing for LDL (p = 0.02), with a trend towards significance for LDL at goal (p = 0.07). Teamlet patients showed a greater, but non-significant, increase in the proportion of patients tested for HbA1C and proportion reaching goal for blood pressure, HgbA1C, and LDL compared to the comparison group patients. The difference for blood pressure was marginally significant (p = 0.06). In contrast, patients in the comparison group were significantly more likely to have had testing for LDL (P = 0.001). The Teamlet Model may improve chronic care in academic primary care practices

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

BMC Public Health

BACKGROUND: Connected health devices and applications (referred to hereafter as "SDApps" - Smart devices and applications) are being portrayed as a new way for prevention, with the promise of accessibility, effectiveness and personalization. Many effectiveness evaluations (experimental designs) with strong internal validity exist. While effectiveness does appear to vary, the mechanisms used by these devices have not yet been thoroughly investigated. This article seeks to unpack this black box, and describes the process of elaboration of an intervention theory for healthy eating and physical activity SDApps. It includes a set of requirements relative to their impact on social health inequalities. METHODS: To build this theory, we drew on theory-driven approaches and in particular on the theory of change (ToC) method. To this end, we developed a cumulative and iterative process combining scientific data from the literature with knowledge from experts (researchers and practitioners) and from patients or users. It was a 3-step process, as follows: 1 - identifying the evidence base; 2 - developing the theory through design intervention and creating realistic expectations, including in our case specific work on social health inequalities (SHIs); 3 - modeling process and outcome. RESULTS: We produced an evidence-based theory according to the ToC model, based on scientific evidence and knowledge from experts and users. It sets out a causal pathway leveraging 11 key mechanisms - theoretical domains - with which 50 behavior change techniques can be used towards 3 ultimate goals: Capacity, Opportunity, Motivation - Behavior (COM-B). Furthermore, the theory specifically integrates requirements relative to the impact on SHIs. CONCLUSIONS: This theory is an aid to SDAapp design and evaluation and it can be used to consider the question of the possible impact of SDApps on the increase in inequalities. Firstly, it enables developers to adopt a more overarching and thorough approach to supporting behavior change, and secondly it encourages comprehensive and contributive evaluations of existing SDApps. Lastly, it allows health inequalities to be fully considered

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