Evidence map›Paper›PMID 36707444›Full record

ArticleImmunogenetics2023

Comparison between qPCR and RNA-seq reveals challenges of quantifying HLA expression.

Vitor R C Aguiar, Erick C Castelli, Richard M Single, Arman Bashirova, Veron Ramsuran, Smita Kulkarni, Danillo G Augusto, Maureen P Martin, Maria Gutierrez-Arcelus, Mary Carrington and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in Immunogenetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
7.2field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 28 citations in OpenAlex.

  1. Methodological strategies for mappingWorld journal of methodology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 9 institutions in 3 countries.

Vitor R C AguiarDepartment of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP, Brazil. vitor@ib.usp.br.ORCID 0000-0002-4137-9518
Erick C CastelliMolecular Genetics and Bioinformatics Laboratory, Experimental Research Unit, School of Medicine, São Paulo State University, Botucatu, SP, Brazil.
Richard M SingleDepartment of Mathematics and Statistics, University of Vermont, Burlington, VT, USA.
Arman BashirovaBasic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Veron RamsuranBasic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Smita KulkarniBasic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Danillo G AugustoBasic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Maureen P MartinBasic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Maria Gutierrez-ArcelusDivision of Immunology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
Mary CarringtonBasic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, MD, USA.
Diogo MeyerDepartment of Genetics and Evolutionary Biology, Institute of Biosciences, University of São Paulo, São Paulo, SP, Brazil. diogo@ib.usp.br.
Broad Institute · USFrederick National Laboratory for Cancer Research · USCentre for the AIDS Programme of Research in South Africa · ZARagon Institute of MGH, MIT and Harvard · USTexas Biomedical Research Institute · USUniversidade de São Paulo · BRUniversidade Estadual Paulista (Unesp) · BRUniversity of North Carolina at Charlotte · USUniversity of Vermont · US

Funding

Molecular genetics and population studies of the KIR and HLA gene complexesZIABC010792 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CARRINGTON, MARY N. · 2009 to 2025
$7.8M
Theoretical Population Genetics SupplementR01GM075091 · NIGMS · UNIVERSITY OF WASHINGTON · PI WEIR, BRUCE S. · 2006 to 2020
$4.7M
Role of cellular long non-coding RNAs in HIV replication and disease outcomeR01AI157850 · NIAID · TEXAS BIOMEDICAL RESEARCH INSTITUTE · PI KULKARNI, SMITA · 2022 to 2025
$2.0M
HIV- induced long non-coding RNAs in viral replication and immune responseR56AI150371 · NIAID · TEXAS BIOMEDICAL RESEARCH INSTITUTE · PI KULKARNI, SMITA · 2020 to 2020
$490k
CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001ENIAID NIH HHS R01 AI157850NIAID NIH HHS R56 AI150371NIGMS NIH HHS R01 GM075091NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

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.

Indexed as

Genome-Wide Association StudyHistocompatibility Antigens Class IHLA-C AntigensHumansPolymerase Chain ReactionRNA-SeqHistocompatibility Antigens Class IHLA-C AntigensExpressionHLAPCRRNA-seq

Identifiers

PMID36707444
PMCPMC9883133
OpenAlexW4318321652

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.