Quantification of HTLV-1 Clonality and TCR Diversity

被引:56
作者
Laydon, Daniel J. [1 ]
Melamed, Anat [1 ]
Sim, Aaron [2 ]
Gillet, Nicolas A. [1 ,3 ]
Sim, Kathleen [4 ]
Darko, Sam [5 ]
Kroll, J. Simon [4 ]
Douek, Daniel C. [5 ]
Price, David A. [5 ,6 ]
Bangham, Charles R. M. [1 ]
Asquith, Becca [1 ]
机构
[1] Imperial Coll Sch Med, Wright Fleming Inst, Immunol Sect, London, England
[2] Univ London Imperial Coll Sci Technol & Med, Ctr Integrat Syst Biol & Bioinformat, London, England
[3] Univ Liege, Dept Mol & Cellular Epigenet, Liege, Belgium
[4] Imperial Coll Sch Med, Wright Fleming Inst, Paediat Sect, London, England
[5] NIH, Vaccine Res Ctr, Bethesda, MD 20892 USA
[6] Cardiff Univ, Sch Med, Inst Infect & Immun, Cardiff CF10 3AX, S Glam, Wales
基金
英国惠康基金; 英国医学研究理事会; 英国工程与自然科学研究理事会; 美国国家卫生研究院;
关键词
T-CELL REPERTOIRE; SPECIES RICHNESS ESTIMATORS; POPULATION-SIZE; NONPARAMETRIC-ESTIMATION; RECEPTOR DIVERSITY; MODEL SELECTION; NUMBER; INFECTION; VACCINATION; MICROBIOTA;
D O I
10.1371/journal.pcbi.1003646
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Estimation of immunological and microbiological diversity is vital to our understanding of infection and the immune response. For instance, what is the diversity of the T cell repertoire? These questions are partially addressed by high-throughput sequencing techniques that enable identification of immunological and microbiological "species" in a sample. Estimators of the number of unseen species are needed to estimate population diversity from sample diversity. Here we test five widely used non-parametric estimators, and develop and validate a novel method, DivE, to estimate species richness and distribution. We used three independent datasets: (i) viral populations from subjects infected with human T-lymphotropic virus type 1; (ii) T cell antigen receptor clonotype repertoires; and (iii) microbial data from infant faecal samples. When applied to datasets with rarefaction curves that did not plateau, existing estimators systematically increased with sample size. In contrast, DivE consistently and accurately estimated diversity for all datasets. We identify conditions that limit the application of DivE. We also show that DivE can be used to accurately estimate the underlying population frequency distribution. We have developed a novel method that is significantly more accurate than commonly used biodiversity estimators in microbiological and immunological populations.
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页数:13
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