Comparison between qPCR and RNA-seq reveals challenges of quantifying HLA expression.
Comparison between qPCR and RNA-seq reveals challenges of quantifying HLA expression.
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DOI：
10.1007/s00251-023-01296-7
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发表时间：
2023-06
期刊：
影响因子：
3.2
通讯作者：
Meyer, Diogo
中科院分区：
文献类型：
--
作者：
Aguiar, Vitor R. C.;Castelli, Erick C.;Single, Richard M.;Bashirova, Arman;Ramsuran, Veron;Kulkarni, Smita;Augusto, Danillo G.;Martin, Maureen P.;Gutierrez-Arcelus, Maria;Carrington, Mary;Meyer, Diogo
人类白细胞抗原（HLA）I类和II类基因座是先天性和获得性免疫的基本要素。它们的功能包括抗原呈递给T细胞，导致细胞和体液免疫应答，以及调节NK细胞。它们对疾病结果的特殊影响现已通过全基因组关联研究清楚地表明。编码肽结合沟的外显子一直是确定HLA对疾病易感性/发病机制的影响的主要焦点。然而，HLA表达水平也与疾病结果有关，为HLA的极端多样性增加了另一个维度，影响个体间免疫应答的变异性。为了估计HLA表达，免疫遗传学研究传统上依赖于定量PCR（qPCR）。由于HLA基因的极端多态性，替代高通量技术如RNA-seq的采用受到技术问题的阻碍。然而，最近，已经开发了多种生物信息学方法来从RNA-seq数据准确估计HLA表达。这为在大型数据集中量化HLA表达提供了一个令人兴奋的机会，但也带来了关于RNA-seq结果是否与qPCR结果相当的问题。在这项研究中，我们分析了匹配的一组个体的HLA I类基因的三类表达数据：（a）RNA-seq，（B）qPCR和（c）细胞表面HLA-C表达。我们观察到HLA-A、-B和-C的qPCR和RNA-seq表达估计值之间存在中度相关性（0.2 ≤ rho ≤ 0.53）。我们讨论了在比较不同分子表型或使用不同技术的定量时需要考虑的技术和生物学因素。在线版本包含补充材料，可通过10.1007/s 00251 -023-01296-7获得。
Human leukocyte antigen (HLA) class I and II loci are essential elements of innate and acquired immunity. Their functions include antigen presentation to T cells leading to cellular and humoral immune responses, and modulation of NK cells. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. The exons encoding the peptide-binding groove have been the main focus for determining HLA effects on disease susceptibility/pathogenesis. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on quantitative PCR (qPCR). Adoption of alternative high-throughput technologies such as RNA-seq has been hampered by technical issues due to the extreme polymorphism at HLA genes. Recently, however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. This opens an exciting opportunity to quantify HLA expression in large datasets but also brings questions on whether RNA-seq results are comparable to those by qPCR. In this study, we analyze three classes of expression data for HLA class I genes for a matched set of individuals: (a) RNA-seq, (b) qPCR, and (c) cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C (0.2 ≤ rho ≤ 0.53). We discuss technical and biological factors which need to be accounted for when comparing quantifications for different molecular phenotypes or using different techniques. The online version contains supplementary material available at 10.1007/s00251-023-01296-7.
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影响因子：
3.7
作者：
Alcina A;Abad-Grau Mdel M;Fedetz M;Izquierdo G;Lucas M;Fernández O;Ndagire D;Catalá-Rabasa A;Ruiz A;Gayán J;Delgado C;Arnal C;Matesanz F
通讯作者：
Matesanz F
影响因子：
3.7
作者：
通讯作者：
--
影响因子：
6.7
作者：
Bachtel ND;Umviligihozo G;Pickering S;Mota TM;Liang H;Del Prete GQ;Chatterjee P;Lee GQ;Thomas R;Brockman MA;Neil S;Carrington M;Bwana B;Bangsberg DR;Martin JN;Kallas EG;Donini CS;Cerqueira NB;O'Doherty UT;Hahn BH;Jones RB;Brumme ZL;Nixon DF;Apps R
通讯作者：
Apps R
DOI：
10.1073/pnas.2208525120
发表时间：
2023-01-03
影响因子：
11.1
作者：
通讯作者：
--
DOI：
10.4049/jimmunol.1403234
发表时间：
2015-04-15
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Apps R;Meng Z;Del Prete GQ;Lifson JD;Zhou M;Carrington M
通讯作者：
Carrington M