Evidence map›Paper›PMID 30665429›Full record

ArticleJournal of translational medicine2019

An integrative genomic analysis of transcriptional profiles identifies characteristic genes and patterns in HIV-infected long-term non-progressors and elite controllers.

Jiwei Ding, Ling Ma, Jianyuan Zhao, Yongli Xie, Jinming Zhou, Xiaoyu Li, Shan Cen

Abstract read
In one paragraph

Article in Journal of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

7 authors.

Jiwei DingInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Beijing, 100050, China.
Ling MaInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Beijing, 100050, China.
Jianyuan ZhaoInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Beijing, 100050, China.
Yongli XieInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Beijing, 100050, China.
Jinming ZhouKey Laboratory of the Ministry of Education for Advanced Catalysis Materials, Department of Chemistry, Zhejiang Normal University, Jinhua, Zhejiang, 321004, China.
Xiaoyu LiInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Beijing, 100050, China. lixiaoyu@imb.pumc.edu.cn.
Shan CenInstitute of Medicinal Biotechnology, Chinese Academy of Medical Sciences, Beijing, 100050, China. shancen@imb.pumc.edu.cn.

Funding

CAMS innovation fund for Medical Sciences 2016-I2M-2-002CAMS innovation fund for Medical Sciences 2018-I2M-3-004The National Key Research and Development program of China 2016YFD0500307The National Mega-Project for Infectious Disease 2018ZX10301408The National Natural Science Foundation of China 81772205
6 · The paper itself

Abstract

backgroundDespite that most HIV-infected individuals experience progressive CD4+ T cell loss and develop AIDS, a minority of HIV-infected individuals remain asymptomatic and maintain high level CD4+ T cell counts several years after seroconversion. Efforts have been made to understand the determinants of the nonprogressive status, exemplified by the clinical course of elite controllers (ECs) who maintain an undetectable viremia and viremic nonprogressors (VNPs) who have a normal CD4+ count in spite of circulating viral load. However, the intrinsic mechanism underlying nonprogression remained elusive. In this study, we performed an integrative analysis of transcriptional profiles to pinpoint the underlying mechanism for a naturally occurring viral control.

methodsThree microarray datasets, reporting mRNA expression of the LTNPs or ECs in HIV-infected patients, were retrieved from Gene Expression Ominbus (GEO) or Arrayexpress databases. These datasets, profiled on the same type of microarray chip, were selected and merged by a bioinformatic approach to build a meta-analysis derived transcriptome (MADNT). In addition, we investigated the different transcriptional pathways and potential biomarkers in CD4+ and CD8+ cells in ECs and whole blood in VNPs compared to HIV progressors. The combined transcriptome and each subgroup was subject to gene set enrichment analysis and weighted co-expression network analysis to search potential transcription patterns related to the non-progressive status.

results30 up-regulated genes and 83 down-regulated genes were identified in lymphocytes from integrative meta-analysis of expression data. The interferon response and innate immune activation was reduced in both CD4+ and CD8+ T cells from ECs. Several characteristic genes including CMPK1, CBX7, EIF3L, EIF4A and ZNF395 were indicated to be highly correlated with viremic control. Besides that, we indicated that the reduction of ribosome components and blockade of translation facilitated AIDS disease progression. Most interestingly, among VNPs who have a relatively high viral load, we detected a two gene-interaction networks which showed a strong correlation to immune control even with a rigorous statistical threshold (p value = 2-e4 and p value = 0.004, respectively) by WGCNA.

conclusionsWe have identified differentially expressed genes and transcriptional patterns in ECs and VNPs compared to normal chronic HIV-infected individuals. Our study provides new insights into the pathogenesis of HIV and AIDS and clues for the therapeutic strategies for anti-retroviral administration.

Indexed as

Gene Expression ProfilingGenomicsTranscription, GeneticDatabases, GeneticGene OntologyGene Regulatory NetworksHIV InfectionsHIV Long-Term SurvivorsHumansMolecular Sequence AnnotationTime FactorsTranscriptomeViremiaElite controllerGSEALong-term nonprogressorMeta-analysisViremic nonprogressorWGCNA

Identifiers

PMID30665429
PMCPMC6341564

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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.