Quantification of HTLV-1 clonality and TCR diversity.
Quantification of HTLV-1 clonality and TCR diversity.
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DOI：
10.1371/journal.pcbi.1003646
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发表时间：
2014-06
影响因子：
4.3
通讯作者：
Asquith B
中科院分区：
文献类型：
--
作者：
Laydon DJ;Melamed A;Sim A;Gillet NA;Sim K;Darko S;Kroll JS;Douek DC;Price DA;Bangham CR;Asquith B
免疫学和微生物学多样性的评估对于我们理解感染和免疫反应至关重要。例如，T细胞库的多样性是什么？这些问题通过高通量测序技术部分解决，该技术能够鉴定样品中的免疫学和微生物“物种”。未知物种数量的估计需要从样本多样性来估计种群多样性。在这里，我们测试五个广泛使用的非参数估计，并开发和验证一种新的方法，DivE，估计物种丰富度和分布。我们使用了三个独立的数据集：（i）来自感染人类T淋巴细胞病毒1型的受试者的病毒群体;（ii）T细胞抗原受体克隆型库;（iii）来自婴儿粪便样本的微生物数据。当应用于稀疏曲线没有平台的数据集时，现有的估计量随着样本量的增加而系统地增加。相比之下，DivE一致且准确地估计了所有数据集的多样性。我们确定了限制DivE应用的条件。我们还表明，DivE可以用来准确地估计潜在的人口频率分布。我们开发了一种新方法，比微生物和免疫群体中常用的生物多样性估计值显着更准确。“看不见的物种问题”在生物学中普遍存在，并且经常在种群生态学的原始环境之外遇到。例如，人类逆转录病毒HTLV-1在宿主体内以感染细胞的多个遗传相同克隆形式存在。然而，一个宿主中的克隆数量是未知的;这一知识是了解病毒如何在强烈的宿主免疫反应下存活所必需的。在估计影响适应性免疫的T细胞库的多样性时，问题再次出现。例如，T细胞多样性可能影响病毒攻击的结果。虽然已经有许多尝试来解决看不见的物种问题，但目前在免疫学和微生物学方面还没有达成共识。本研究的目的是确定一种合适的方法来估计免疫和微生物种群中的物种数量。我们发现，我们测试的五个现有估计器在三个数据源（HTLV-1克隆性，T细胞受体和微生物数据）中表现不佳。因此，我们开发了一个新的估计，DivE，显着优于其他估计。准确的多样性量化可以更好地评估老化和感染等因素对免疫力的影响。
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. The “unseen species problem” is ubiquitous in biology and is frequently encountered outside its original setting in population ecology. For example, the human retrovirus HTLV-1 persists within hosts in multiple, genetically identical clones of infected cells. However, the number of clones in one host is unknown; this knowledge is required for an understanding of how the virus survives despite a strong host immune response. The problem arises again in estimating the diversity of the T-cell repertoire, which influences adaptive immunity. For example, the T-cell diversity may influence the outcome of viral challenge. While there have been numerous attempts to address the unseen species problem, there is currently no consensus on how to do so in immunology and microbiology. The aim of this study was to identify a suitable method to estimate the number of species in immunological and microbiological populations. We found that five existing estimators we tested performed poorly across three data sources (HTLV-1 clonality, T cell receptor, and microbial data). We therefore developed a new estimator, DivE, which significantly outperformed the other estimators. Accurate diversity quantification allows better evaluation of the impact on immunity from factors such as ageing and infection.
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影响因子：
56.9
作者：
Arstila, TP;Casrouge, A;Kourilsky, P
通讯作者：
Kourilsky, P
影响因子：
5.4
作者：
Bimber, Benjamin N.;Burwitz, Benjamin J.;O'Connor, David
通讯作者：
O'Connor, David
影响因子：
3.1
作者：
Florins, Arnaud;Gillet, Nicolas;Willems, Luc
通讯作者：
Willems, Luc
影响因子：
2.9
作者：
FAGER, EW
通讯作者：
FAGER, EW
影响因子：
4.8
作者：
BURNHAM, KP;OVERTON, WS
通讯作者：
OVERTON, WS