S'abonner

Comprehensive identification of immuno-related transcriptional signature for active pulmonary tuberculosis by integrated analysis of array and single cell RNA-seq - 13/10/22

Doi : 10.1016/j.jinf.2022.08.017 
Yuzhong Xu a, Yaoju Tan b, Xianyi Zhang a, Minggang Cheng a, Jinxing Hu b, Jianxiong Liu b, Xinchun Chen c, Jialou Zhu b, c,
a Department of Clinical Laboratory, Shenzhen Baoan Hospital, The Second Affiliated Hospital of Shenzhen University, Shenzhen 518101, China 
b Department of Clinical Laboratory, Guangzhou Chest Hospital, Guangzhou/State Key Laboratory of Respiratory Disease, Hengzhigang Road 1066, Guangzhou 510095, China 
c Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathogen Biology, Shenzhen University School of Medicine, Shenzhen 518060, China 

Corresponding author.

Highlights

Comprehensive illustration of immuno-related transcriptional signature for active pulmonary TB.
Immune transcriptional profiling could effectively differ TB with LTBI and HC.
First scRNA-seq-based description of ADM expression abundance in TB Myeloid cells.
ADM could be used as a novel biomarker for distinguishing patients with TB from LTBI and HC.

Le texte complet de cet article est disponible en PDF.

Abstract

Background

Tuberculosis (TB) continues to be a major cause of morbidity and mortality worldwide. However, the molecular mechanism underlying immune response to human infection with Mycobacterium tuberculosis (Mtb) remains unclear. Assessing changes in transcript abundance in blood between health and disease on a genome-wide scale affords a comprehensive view of the impact of Mtb infection on the host defense and a reliable way to identify novel TB biomarkers.

Methods

We combined expression profiling by array and single cell RNA-sequencing (scRNA-seq) via 10X Genomics platform to better illustrate the immuno-related transcriptional signature of TB and explore potential diagnostic markers for differentiating TB from latent tuberculosis infection (LTBI) and healthy control (HC).

Findings

Pathway analysis based on differential expressed genes (DEGs) revealed that immune transcriptional profiling could effectively differ TB with LTBI and HC. Following WGCNA and PPI network analysis based on DEGs, we screened out three key immuno-related hub genes (ADM, IFIT3 and SERPING1) highly associated with TB. Further validation found only ADM expression significantly increased in TB patients in both adult and children's datasets. By comparing the scRNA-seq datasets from TB, LTBI and HC, we observed a remarkable elevated expression level and proportion of ADM in TB Myeloid cells, further supporting that ADM expression changes could distinguish patients with TB from LTBI and HC. Besides, the hsa-miR-24–3p-NEAT1-ADM-CEBPB regulation pathway might be one of the critical networks regulating the pathogenesis of TB. Although further investigation in a larger cohort is warranted, we provide useful and novel insight to explore the potential candidate genes for TB diagnosis and intervention.

Interpretation

We propose that the expression of ADM in peripheral blood could be used as a novel biomarker for differentiating TB with LTBI and HC.

Le texte complet de cet article est disponible en PDF.

Keywords : Tuberculosis, Single-cell RNA sequencing, Transcriptional signature, Immuno-related hub gene, diagnostic biomarker, ADM


Plan


© 2022  Publié par Elsevier Masson SAS.
Ajouter à ma bibliothèque Retirer de ma bibliothèque Imprimer
Export

    Export citations

  • Fichier

  • Contenu

Vol 85 - N° 5

P. 534-544 - novembre 2022 Retour au numéro
Article précédent Article précédent
  • Next generation sequencing reveals miR-431–3p/miR-1303 as immune-regulating microRNAs for active tuberculosis
  • Yung-Che Chen, Chang-Chun Hsiao, Chao-Chien Wu, Tung-Ying Chao, Sum-Yee Leung, Yu-Ping Chang, Chia-Cheng Tseng, Chiu-Ping Lee, Po-Yuan Hsu, Ting-Ya Wang, Po-Wen Wang, Ting-Wen Chen, Meng-Chih Lin
| Article suivant Article suivant
  • Antibody correlates of protection from SARS-CoV-2 reinfection prior to vaccination: A nested case-control within the SIREN study
  • Ana Atti, Ferdinando Insalata, Edward J Carr, Ashley D Otter, Javier Castillo-Olivares, Mary Wu, Ruth Harvey, Michael Howell, Andrew Chan, Jonathan Lyall, Nigel Temperton, Diego Cantoni, Kelly da Costa, Angalee Nadesalingam, Andrew Taylor-Kerr, Nipunadi Hettiarachchi, Caio Tranquillini, Jacqueline Hewson, Michelle J Cole, Sarah Foulkes, Katie Munro, Edward J M Monk, Iain D Milligan, Ezra Linley, Meera A Chand, Colin S Brown, Jasmin Islam, Amanda Semper, Andre Charlett, Jonathan L Heeney, Rupert Beale, Maria Zambon, Susan Hopkins, Tim Brooks, Victoria Hall, the SIREN Study Group and the Crick COVID Immunity Pipeline Consortium

Bienvenue sur EM-consulte, la référence des professionnels de santé.
L’accès au texte intégral de cet article nécessite un abonnement.

Déjà abonné à cette revue ?

Mon compte


Plateformes Elsevier Masson

Déclaration CNIL

EM-CONSULTE.COM est déclaré à la CNIL, déclaration n° 1286925.

En application de la loi nº78-17 du 6 janvier 1978 relative à l'informatique, aux fichiers et aux libertés, vous disposez des droits d'opposition (art.26 de la loi), d'accès (art.34 à 38 de la loi), et de rectification (art.36 de la loi) des données vous concernant. Ainsi, vous pouvez exiger que soient rectifiées, complétées, clarifiées, mises à jour ou effacées les informations vous concernant qui sont inexactes, incomplètes, équivoques, périmées ou dont la collecte ou l'utilisation ou la conservation est interdite.
Les informations personnelles concernant les visiteurs de notre site, y compris leur identité, sont confidentielles.
Le responsable du site s'engage sur l'honneur à respecter les conditions légales de confidentialité applicables en France et à ne pas divulguer ces informations à des tiers.


Tout le contenu de ce site: Copyright © 2024 Elsevier, ses concédants de licence et ses contributeurs. Tout les droits sont réservés, y compris ceux relatifs à l'exploration de textes et de données, a la formation en IA et aux technologies similaires. Pour tout contenu en libre accès, les conditions de licence Creative Commons s'appliquent.