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Ansari A, Singh GP, Singh M, Singh H. Identification of host immune-related biomarkers in active tuberculosis: A comprehensive analysis of differentially expressed genes. Tuberculosis (Edinb) 2024; 148:102538. [PMID: 38954895 DOI: 10.1016/j.tube.2024.102538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/25/2024] [Accepted: 06/28/2024] [Indexed: 07/04/2024]
Abstract
Tuberculosis (TB) is a serious public health issue in India. Numerous molecular mechanisms and immunological responses play significant roles in the pathogenesis of tuberculosis. This study aimed to identify host immune-related biomarkers that are significantly differentially expressed in active TB and that play a vital role in disease progression. The methodology employed in this study included data collection, pre-processing, analysis, and interpretation of the results. Six microarray datasets were used to identify differentially expressed genes (DEGs), and only the common DEGs were used for further downstream analysis, such as hub gene identification, gene ontology, pathway enrichment analysis, and drug-gene interaction analysis. The study identified 1728 DEGs, including 906 upregulated and 822 downregulated genes. Five hub genes were identified that were: STAT1, GBP5, GBP1, FCGR1A, and BATF2. Gene ontology and pathway enrichment revealed that most of the genes were involved in interferon-gamma signaling. In addition, through drug-gene interactions, known drugs have been identified for STAT1, FCGR1A and GBP1. The findings of this study may contribute to early detection and treatment of active TB.
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Affiliation(s)
- Alisha Ansari
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, Delhi, 110067, India
| | - Gajendra Pratap Singh
- School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, Delhi, 110067, India.
| | - Mamtesh Singh
- Department of Zoology, Gargi College, University of Delhi, New Delhi, Delhi, 110049, India
| | - Harpreet Singh
- Department of Bioinformatics, Division of Biomedical Informatics, Indian Council of Medical Research, Delhi, 110029, India
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Arya R, Shakya H, Chaurasia R, Kumar S, Vinetz JM, Kim JJ. Computational reassessment of RNA-seq data reveals key genes in active tuberculosis. PLoS One 2024; 19:e0305582. [PMID: 38935691 PMCID: PMC11210783 DOI: 10.1371/journal.pone.0305582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 05/31/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Tuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance. METHODS Two human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment. RESULTS A total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity. CONCLUSIONS The study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB.
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Affiliation(s)
- Rakesh Arya
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
| | - Hemlata Shakya
- Department of Biomedical Engineering, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India
| | - Reetika Chaurasia
- Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, United States of America
| | - Surendra Kumar
- Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Joseph M. Vinetz
- Department of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, United States of America
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea
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Chang A, Loy CJ, Eweis-LaBolle D, Lenz JS, Steadman A, Andgrama A, Nhung NV, Yu C, Worodria W, Denkinger CM, Nahid P, Cattamanchi A, De Vlaminck I. Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis. Nat Commun 2024; 15:4949. [PMID: 38858368 PMCID: PMC11164910 DOI: 10.1038/s41467-024-49245-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/29/2024] [Indexed: 06/12/2024] Open
Abstract
Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide, partly due to a lack of effective strategies to screen and triage individuals with potential TB. Whole blood RNA signatures have been tested as biomarkers for TB, but have failed to meet the World Health Organization's (WHO) optimal target product profiles (TPP). Here, we use RNA sequencing and machine-learning to investigate the utility of plasma cell-free RNA (cfRNA) as a host-response biomarker for TB in cohorts from Uganda, Vietnam and Philippines. We report a 6-gene cfRNA signature, which differentiates TB-positive and TB-negative individuals with AUC = 0.95, 0.92, and 0.95 in test, training and validation, respectively. This signature meets WHO TPPs (sensitivity: 97.1% [95% CI: 80.9-100%], specificity: 85.2% [95% CI: 72.4-100%]) regardless of geographic location, sample collection method and HIV status. Overall, our results identify plasma cfRNA as a promising host response biomarker to diagnose TB.
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Affiliation(s)
- Adrienne Chang
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Conor J Loy
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | | | - Joan S Lenz
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | | | - Alfred Andgrama
- World Alliance for Lung and Intensive Care Medicine in Uganda, Kampala, Uganda
| | | | - Charles Yu
- De La Salle Medical and Health Sciences Institute, Dasmarinas, Philippines
| | - William Worodria
- World Alliance for Lung and Intensive Care Medicine in Uganda, Kampala, Uganda
| | - Claudia M Denkinger
- University Hospital Heidelberg & German Center of Infection Research, Heidelberg, Germany
| | - Payam Nahid
- UCSF Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
| | - Adithya Cattamanchi
- UCSF Center for Tuberculosis, University of California San Francisco, San Francisco, CA, USA
- Division of Pulmonary and Critical Care Medicine, University of California Irvine, Orange, CA, USA
| | - Iwijn De Vlaminck
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
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Mousavian Z, Källenius G, Sundling C. From simple to complex: Protein-based biomarker discovery in tuberculosis. Eur J Immunol 2023; 53:e2350485. [PMID: 37740950 DOI: 10.1002/eji.202350485] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/15/2023] [Accepted: 09/22/2023] [Indexed: 09/25/2023]
Abstract
Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.
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Affiliation(s)
- Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Källenius
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Christopher Sundling
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
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Chang A, Loy CJ, Lenz JS, Steadman A, Andama A, Nhung NV, Yu C, Worodria W, Denkinger CM, Nahid P, Cattamanchi A, De Vlaminck I. Circulating Cell-Free RNA in Blood as a Host Response Biomarker for the Detection of Tuberculosis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.11.23284433. [PMID: 36711999 PMCID: PMC9882491 DOI: 10.1101/2023.01.11.23284433] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Tuberculosis (TB) remains a leading cause of death from an infectious disease worldwide. This is partly due to a lack of tools to effectively screen and triage individuals with potential TB. Whole blood RNA signatures have been extensively studied as potential biomarkers for TB, but they have failed to meet the World Health Organization's (WHOs) target product profiles (TPPs) for a non-sputum triage or diagnostic test. In this study, we investigated the utility of plasma cell-free RNA (cfRNA) as a host response biomarker for TB. We used RNA profiling by sequencing to analyze plasma samples from 182 individuals with a cough lasting at least two weeks, who were seen at outpatient clinics in Uganda, Vietnam, and the Philippines. Of these individuals, 100 were diagnosed with microbiologically-confirmed TB. Our analysis of the plasma cfRNA transcriptome revealed 541 differentially abundant genes, the top 150 of which were used to train 15 machine learning models. The highest performing model led to a 9-gene signature that had a diagnostic accuracy of 89.1% (95% CI: 83.6-93.4%) and an area under the curve of 0.934 (95% CI: 0.8674-1) for microbiologically-confirmed TB. This 9-gene signature exceeds the optimal WHO TPPs for a TB triage test (sensitivity: 96.2% [95% CI: 80.9-100%], specificity: 89.7% [95% CI: 72.4-100%]) and was robust to differences in sample collection, geographic location, and HIV status. Overall, our results demonstrate the utility of plasma cfRNA for the detection of TB and suggest the potential for a point-of-care, gene expression-based assay to aid in early detection of TB.
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Affiliation(s)
- Adrienne Chang
- Meinig School of Biomedical Engineering, Cornell University; Ithaca, NY, USA
| | - Conor J. Loy
- Meinig School of Biomedical Engineering, Cornell University; Ithaca, NY, USA
| | - Joan S. Lenz
- Meinig School of Biomedical Engineering, Cornell University; Ithaca, NY, USA
| | | | | | | | - Charles Yu
- De La Salle Medical and Health Sciences Institute; Dasmarinas, Philippines
| | | | - Claudia M. Denkinger
- University Hospital Heidelberg & German Center of Infection Research; Heidelberg, Germany
| | - Payam Nahid
- UCSF Center for Tuberculosis, University of California San Francisco; San Francisco, CA, USA
| | - Adithya Cattamanchi
- UCSF Center for Tuberculosis, University of California San Francisco; San Francisco, CA, USA
- Division of Pulmonary and Critical Care Medicine, University of California Irvine; Orange, CA, USA
| | - Iwijn De Vlaminck
- Meinig School of Biomedical Engineering, Cornell University; Ithaca, NY, USA
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Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis. DISEASE MARKERS 2022; 2022:2090560. [PMID: 36411825 PMCID: PMC9674975 DOI: 10.1155/2022/2090560] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Accepted: 10/25/2022] [Indexed: 11/13/2022]
Abstract
Methods Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test dataset (GSE152532). Gene expression-based immune cell infiltration and the relationship between such infiltration and hub gene expression were further assessed via a single-sample gene set enrichment analysis (ssGSEA). Results In total, 485 differentially expressed genes were analyzed, with the WGCNA approach yielding 8 coexpression models. Of these, the black module was the most closely correlated with ATB. In total, five hub genes (FBXO6, ATF3, GBP1, GBP4, and GBP5) were identified as potential biomarkers associated with LTBI progression to ATB based on a combination of differential expression and LASSO analyses. The area under the ROC curve values for these five genes ranged from 0.8 to 0.9 in the test dataset, and ssGSEA revealed the expression of these genes to be negatively correlated with lymphocyte activity but positively correlated with myeloid and inflammatory cell activity. Conclusion The five hub genes identified in this study may play a novel role in tuberculosis-related immunopathology and offer value as novel biomarkers differentiating LTBI from ATB.
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