Evidence map›Paper›PMID 41851630›Full record

ArticleBMC infectious diseases2026

Evaluation of a HIV-1 drug resistance genotyping method based on high-throughput sequencing (HTS).

Jialu Li, Yang Xu, Yue Tang, Jingjing Hao, Chang Song, Wansi Mao, Tang Li, Xiang Li, Dong Wang, Lu Lin and 7 more

Abstract readEvaluation Study
In one paragraph

Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Article
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

17 authors.

Jialu Li *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Yang Xu *MGI Tech, Shenzhen, 518083, China.
Yue Tang *College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China.
Jingjing HaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Chang SongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Wansi MaoMGI Tech, Shenzhen, 518083, China.
Tang LiMGI Tech, Shenzhen, 518083, China.
Xiang LiMGI Tech, Shenzhen, 518083, China.
Dong WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Lu LinMGI Tech, Shenzhen, 518083, China.
Jianwei YangMGI Tech, Shenzhen, 518083, China.
Yuanyuan WuMGI Tech, Shenzhen, 518083, China.
Yuhua RuanNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Yi FengNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
Yicong WangMGI Tech, Shenzhen, 518083, China. wangyicong@mgi-tech.com.ORCID http://orcid.org/0000-0003-2230-3718
Hui XingNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China. xingh@chinaaids.cn.ORCID http://orcid.org/0000-0002-3067-0908
Lingjie LiaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China. liaolj@chinaaids.cn.ORCID http://orcid.org/0000-0003-3880-4744

Funding

Ministry of Science and Technology of the People's Republic of China 2022YFC2305201
6 · The paper itself

Abstract

backgroundHigh-throughput sequencing (HTS) enables the detection of low-frequency HIV-1 drug resistance mutations (DRMs) that are often missed by conventional Sanger sequencing. However, the clinical implementation of HTS remains limited, and standardized evaluations of emerging platforms are still lacking. This study aims to evaluate the performance of the MGI HTS platform in detecting HIV-1 DRMs compared to Sanger sequencing.

methodsPlasma samples were collected from treatment-naïve and treatment-experienced individuals living with HIV. HIV-1 pol gene fragments, encompassing the protease (PR), reverse transcriptase (RT), and integrase (IN) regions, were amplified using a one-step reverse-transcription polymerase chain reaction (RT-PCR) protocol. Paired sequencing was performed on both the MGI DNBSEQ platform and Sanger platform. Sequences were analyzed for drug resistance mutations using the Stanford HIVdb algorithm. Detection rates and mutation frequencies were compared between methods.

resultsMGI HTS demonstrated higher overall amplification success, especially for the integrase region in samples with viral loads between 1,000 and 10,000 copies/mL. It also identified a greater number of DRMs, including low-frequency variants below the Sanger detection threshold (typically < 20.0% variant frequency). Among cases with discordant results, MGI detected additional resistance-associated mutations in both NNRTI and NRTI classes, potentially impacting treatment decisions.

conclusionsThis study highlights the superior performance of the MGI HTS platform over traditional Sanger sequencing for HIV-1 drug resistance genotyping. The platform’s enhanced sensitivity, particularly for low-frequency variants, and its simplified one-step amplification protocol make it well-suited for routine resistance testing, especially in low-viral-load samples. These findings support the broader clinical adoption of MGI HTS as a reliable and scalable tool for comprehensive resistance profiling and timely optimization of antiretroviral therapy.

Indexed as

Drug Resistance, ViralGenotyping TechniquesHigh-Throughput Nucleotide SequencingHIV-1HIV InfectionsAnti-HIV AgentsGenotypeHumansMutationViral LoadAnti-HIV AgentsDrug-resistance mutationHigh-throughput sequencingHIV-1

Identifiers

PMID41851630
PMCPMC13112854

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.