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

White matter hyperintensities mediate gray matter volume and processing speed relationship in cognitively unimpaired participants

White matter hyperintensities (WMH) have been extensively associated with cognitive impairment and reductions in gray matter volume (GMv) independently. This study explored whether WMH lesion volume mediates the relationship between cerebral patterns of GMv and cognition in 521 (mean age 57.7 years) cognitively unimpaired middle‐aged individuals. Episodic memory (EM) was measured with the Memory Binding Test and executive functions (EF) using five WAIS‐IV subtests. WMH were automatically determined from T2 and FLAIR sequences and characterized using diffusion‐weighted imaging (DWI) parameters. WMH volume was entered as a mediator in a voxel‐wise mediation analysis relating GMv and cognitive performance (with both EM and EF composites and the individual tests independently). The mediation model was corrected by age, sex, education, number of Apolipoprotein E (APOE)‐ε4 alleles and total intracranial volume. We found that even at very low levels of WMH burden in the cohort (median volume of 3.2 mL), higher WMH lesion volume was significantly associated with a widespread pattern of lower GMv in temporal, frontal, and cerebellar areas. WMH mediated the relationship between GMv and EF, mainly driven by processing speed, but not EM. DWI parameters in these lesions were compatible with incipient demyelination and axonal loss. These findings lead to the reflection on the relevance of the control of cardiovascular risk factors in middle‐aged individuals as a valuable preventive strategy to reduce or delay cognitive decline

Multitracer model for staging cortical amyloid deposition using PET imaging

Objective: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. Methods:Three thousand twenty-seven individuals (1,763 cognitively unimpaired [CU], 658 impaired, 467 with Alzheimer disease [AD] dementia, 111 with non-AD dementia, and 28 with missing diagnosis) from 6 cohorts (European Medical Information Framework for AD, Alzheimer's and Family, Alzheimer's Biomarkers in Daily Practice, Amsterdam Dementia Cohort, Open Access Series of Imaging Studies [OASIS]-3, Alzheimer's Disease Neuroimaging Initiative [ADNI]) who underwent amyloid PET were retrospectively included; 1,049 individuals had follow-up scans. With application of dataset-specific cutoffs to global standard uptake value ratio (SUVr) values from 27 regions, single-tracer and pooled multitracer regional rankings were constructed from the frequency of abnormality across 400 CU individuals (100 per tracer). The pooled multitracer ranking was used to create a staging model consisting of 4 clusters of regions because it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables, and longitudinal cognitive decline were investigated. Results:SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices and then the associative, temporal, and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of individuals in stage 0 to 4, respectively. Baseline stage predicted decline in Mini-Mental State Examination (MMSE) score (ADNI: n = 867, F = 67.37, p 3,000 individuals across cohorts and radiotracers and detects preglobal amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive individuals

Description of a European memory clinic cohort undergoing amyloid‐PET: The AMYPAD Diagnostic and Patient Management Study

International audienceIntroduction: AMYPAD Diagnostic and Patient Management Study (DPMS) aims to investigate the clinical utility and cost-effectiveness of amyloid-PET in Europe. Here we present participants' baseline features and discuss the representativeness of the cohort.Methods: Participants with subjective cognitive decline plus (SCD+), mild cognitive impairment (MCI), or dementia were recruited in eight European memory clinics from April 16, 2018, to October 30, 2020, and randomized into three arms: ARM1, early amyloid-PET; ARM2, late amyloid-PET; and ARM3, free-choice.Results: A total of 840 participants (244 SCD+, 341 MCI, and 255 dementia) were enrolled. Sociodemographic/clinical features did not differ significantly among recruiting memory clinics or with previously reported cohorts. The randomization assigned 35% of participants to ARM1, 32% to ARM2, and 33% to ARM3; cognitive stages were distributed equally across the arms.Discussion: The features of AMYPAD-DPMS participants are as expected for a memory clinic population. This ensures the generalizability of future study results

Description of a European memory clinic cohort undergoing amyloid-PET: The AMYPAD Diagnostic and Patient Management Study

Introduction AMYPAD Diagnostic and Patient Management Study (DPMS) aims to investigate the clinical utility and cost-effectiveness of amyloid-PET in Europe. Here we present participants' baseline features and discuss the representativeness of the cohort. Methods Participants with subjective cognitive decline plus (SCD+), mild cognitive impairment (MCI), or dementia were recruited in eight European memory clinics from April 16, 2018, to October 30, 2020, and randomized into three arms: ARM1, early amyloid-PET; ARM2, late amyloid-PET; and ARM3, free-choice. Results A total of 840 participants (244 SCD+, 341 MCI, and 255 dementia) were enrolled. Sociodemographic/clinical features did not differ significantly among recruiting memory clinics or with previously reported cohorts. The randomization assigned 35% of participants to ARM1, 32% to ARM2, and 33% to ARM3; cognitive stages were distributed equally across the arms. Discussion The features of AMYPAD-DPMS participants are as expected for a memory clinic population. This ensures the generalizability of future study results

Clinical Effect of Early vs Late Amyloid Positron Emission Tomography in Memory Clinic Patients

International audienceImportance Amyloid positron emission tomography (PET) allows the direct assessment of amyloid deposition, one of the main hallmarks of Alzheimer disease. However, this technique is currently not widely reimbursed because of the lack of appropriately designed studies demonstrating its clinical effect. Objective To assess the clinical effect of amyloid PET in memory clinic patients. Design, Setting, and Participants The AMYPAD-DPMS is a prospective randomized clinical trial in 8 European memory clinics. Participants were allocated (using a minimization method) to 3 study groups based on the performance of amyloid PET: arm 1, early in the diagnostic workup (within 1 month); arm 2, late in the diagnostic workup (after a mean [SD] 8 [2] months); or arm 3, if and when the managing physician chose. Participants were patients with subjective cognitive decline plus (SCD+; SCD plus clinical features increasing the likelihood of preclinical Alzheimer disease), mild cognitive impairment (MCI), or dementia; they were assessed at baseline and after 3 months. Recruitment took place between April 16, 2018, and October 30, 2020. Data analysis was performed from July 2022 to January 2023. Intervention Amyloid PET. Main Outcome and Measure The main outcome was the difference between arm 1 and arm 2 in the proportion of participants receiving an etiological diagnosis with a very high confidence (ie, ≥90% on a 50%-100% visual numeric scale) after 3 months. Results A total of 844 participants were screened, and 840 were enrolled (291 in arm 1, 271 in arm 2, 278 in arm 3). Baseline and 3-month visit data were available for 272 participants in arm 1 and 260 in arm 2 (median [IQR] age: 71 [65-77] and 71 [65-77] years; 150/272 male [55%] and 135/260 male [52%]; 122/272 female [45%] and 125/260 female [48%]; median [IQR] education: 12 [10-15] and 13 [10-16] years, respectively). After 3 months, 109 of 272 participants (40%) in arm 1 had a diagnosis with very high confidence vs 30 of 260 (11%) in arm 2 ( P < .001). This was consistent across cognitive stages (SCD+: 25/84 [30%] vs 5/78 [6%]; P < .001; MCI: 45/108 [42%] vs 9/102 [9%]; P < .001; dementia: 39/80 [49%] vs 16/80 [20%]; P < .001). Conclusion and Relevance In this study, early amyloid PET allowed memory clinic patients to receive an etiological diagnosis with very high confidence after only 3 months compared with patients who had not undergone amyloid PET. These findings support the implementation of amyloid PET early in the diagnostic workup of memory clinic patients. Trial Registration EudraCT Number: 2017-002527-2