1
|
Moghaddam AH, Kerdabadi MN, Zhong C, Yao Z. Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2025; 2024:828-837. [PMID: 40417531 PMCID: PMC12099339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/27/2025]
Abstract
Gene expression profiles obtained through DNA microarray have proven successful in providing critical information for cancer detection classifiers. However, the limited number of samples in these datasets poses a challenge to employ complex methodologies such as deep neural networks for sophisticated analysis. To address this "small data" dilemma, Meta-Learning has been introduced as a solution to enhance the optimization of machine learning models by utilizing similar datasets, thereby facilitating a quicker adaptation to target datasets without the requirement of sufficient samples. In this study, we present a meta-learning-based approach for predicting lung cancer from gene expression profiles. We apply this framework to well-established deep learning methodologies and employ four distinct datasets for the meta-learning tasks, where one as the target dataset and the rest as source datasets. Our approach is evaluated against both traditional and deep learning methodologies, and the results show the superior performance of meta-learning on augmented source data compared to the baselines trained on single datasets. Moreover, we conduct the comparative analysis between meta-learning and transfer learning methodologies to highlight the efficiency of the proposed approach in addressing the challenges associated with limited sample sizes. Finally, we incorporate the explainability study to illustrate the distinctiveness of decisions made by meta-learning.
Collapse
Affiliation(s)
| | | | | | - Zijun Yao
- University of Kansas, Lawrence, KS, USA
| |
Collapse
|
2
|
Shen Y, Han D, Qu W, Yu F, Zhang D, Xu Y, Shen E, Chu Q, Timko MP, Fan L, Zheng S, Chen Y. Robust Diagnosis of Acute Bacterial and Viral Infections via Host Gene Expression Rank-Based Ensemble Machine Learning Algorithm: A Multi-Cohort Model Development and Validation Study. Clin Chem 2025; 71:497-509. [PMID: 39835348 DOI: 10.1093/clinchem/hvae220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2024] [Accepted: 10/15/2024] [Indexed: 01/22/2025]
Abstract
BACKGROUND The accurate and prompt diagnosis of infections is essential for improving patient outcomes and preventing bacterial drug resistance. Host gene expression profiling as an approach to infection diagnosis holds great potential in assisting early and accurate diagnosis of infection. METHODS To improve the precision of infection diagnosis, we developed InfectDiagno, a rank-based ensemble machine learning algorithm for infection diagnosis via host gene expression patterns. Eleven data sets were used as training data sets for the method development, and the InfectDiagno algorithm was optimized by multi-cohort training samples. Nine data sets were used as independent validation data sets for the method. We further validated the diagnostic capacity of InfectDiagno in a prospective clinical cohort. RESULTS After selecting 100 feature genes based on their gene expression ranks for infection prediction, we trained a classifier using both a noninfected-vs-infected area under the receiver-operating characteristic curve (area under the curve [AUC] 0.95 [95% CI, 0.93-0.97]) and a bacterial-vs-viral AUC 0.95 (95% CI, 0.93-0.97). We then used the noninfected/infected classifier together with the bacterial/viral classifier to build a discriminating infection diagnosis model. The sensitivity was 0.931 and 0.872, and specificity 0.963 and 0.929, for bacterial and viral infections, respectively. We then applied InfectDiagno to a prospective clinical cohort (n = 517), and found it classified 95% of the samples correctly. CONCLUSIONS Our study shows that the InfectDiagno algorithm is a powerful and robust tool to accurately identify infection in a real-world patient population, which has the potential to profoundly improve clinical care in the field of infection diagnosis.
Collapse
Affiliation(s)
- Yifei Shen
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Zhejiang University, Hangzhou, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
| | - Dongsheng Han
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Zhejiang University, Hangzhou, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
| | - Wenxin Qu
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Zhejiang University, Hangzhou, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
| | - Fei Yu
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Zhejiang University, Hangzhou, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
| | - Dan Zhang
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Zhejiang University, Hangzhou, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
| | - Yifan Xu
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Enhui Shen
- Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Qinjie Chu
- Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Michael P Timko
- Departments of Biology and Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Longjiang Fan
- Institute of Bioinformatics, Zhejiang University, Hangzhou, China
| | - Shufa Zheng
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Zhejiang University, Hangzhou, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Chen
- Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province, Zhejiang University, Hangzhou, China
- Institute of Laboratory Medicine, Zhejiang University, Hangzhou, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
3
|
O'Garra A. From Cytokines to Tuberculosis and Back: My Journey to Understanding the Immune Response to Infection. Annu Rev Immunol 2025; 43:1-28. [PMID: 40279305 DOI: 10.1146/annurev-immunol-010824-041601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2025]
Abstract
I felt honored by the invitation to write this autobiography, although it was an arduous task to describe my journey through science: first bacterial adhesion, then cytokine function, and then immune responses in tuberculosis. Since only seven women had been authors of autobiographies for the Annual Review of Immunology, I felt I couldn't refuse to contribute to Volume 43 of the journal. Moreover, this was a good occasion to record my appreciation to all the lab members and collaborators for their contributions over the last 40 years, to remember the exciting times, and to reflect on the obstacles we faced. I often reflect on this line that is commonly attributed to Winston Churchill: Success is not final; failure is not fatal: It is the courage to continue that counts. What kept me going was a burning desire to know how things work and find enjoyment in the discovery. This passion to understand immune responses to infection remains with me to this day. I thank all those I have interacted with for the support and friendship they provided.
Collapse
Affiliation(s)
- Anne O'Garra
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, United Kingdom;
| |
Collapse
|
4
|
Xia Z, Rong X, Chen Q, Fang M, Xiao J. A nomogram to predict lung cancer in pulmonary lesions for tuberculosis infection patients. Monaldi Arch Chest Dis 2025; 95. [PMID: 38497197 DOI: 10.4081/monaldi.2024.2847] [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: 11/14/2023] [Accepted: 03/01/2024] [Indexed: 03/19/2024] Open
Abstract
Similar clinical features make the differential diagnosis difficult, particularly between lung cancer and pulmonary tuberculosis (TB), without pathological evidence for patients with concomitant TB infection. Our study aimed to build a nomogram to predict malignant pulmonary lesions applicable to clinical practice. We retrospectively analyzed clinical characteristics, imaging features, and laboratory indicators of TB infection of patients diagnosed with lung cancer or active pulmonary TB at Xiangya Hospital of Central South University. A total of 158 cases from January 1, 2018, to May 30, 2019, were included in the training cohort. Predictive factors for lung cancer were screened by a multiple-stepwise logistic regression analysis. A nomogram model was established, and the discrimination, stability, and prediction performance of the model were analyzed. A total of 79 cases from June 1, 2019, to December 30, 2019, were used as the validation cohort to verify the predictive value of the model. Eight predictor variables, including age, pleural effusion, mediastinal lymph node, the number of positive tumor markers, the T cell spot test for TB, pulmonary lesion morphology, location, and distribution, were selected to construct the model. The corrected C-statistics and the Brier scores were 0.854 and 0.130 in the training cohort and 0.823 and 0.163 in the validation cohort. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit. In conclusion, the nomogram model provides an effective method to calculate the probability of lung cancer in TB infection patients, and it has excellent discrimination, stability, and prediction performance in detecting a malignant diagnosis of undiagnosed pulmonary lesions.
Collapse
Affiliation(s)
- Zhi Xia
- Department of Oncology, Hunan Provincial People's Hospital, Changsha; Key Laboratory of Small Molecule Targeted Drug Research and Creation in Hunan Province, Changsha; Hunan Provincial Clinical Medical Research Center for Hepatobiliary Pancreatic Tumors, Changsha
| | - Xueyao Rong
- Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha
| | - Qiong Chen
- Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha
| | - Min Fang
- Hunan Provincial Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, the "Double-First Class" Application Characteristic Discipline of Hunan Province (Pharmaceutical Science), Changsha Medical University; School of Pharmacy, Changsha Medical University
| | - Jian Xiao
- Department of Geriatric Respiratory and Critical Care Medicine, Xiangya Hospital, Central South University, Changsha; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha
| |
Collapse
|
5
|
Pullen KM, Finethy R, Ko SHB, Reames CJ, Sassetti CM, Lauffenburger DA. Cross-species transcriptomics translation reveals a role for the unfolded protein response in Mycobacterium tuberculosis infection. NPJ Syst Biol Appl 2025; 11:19. [PMID: 39955299 PMCID: PMC11830044 DOI: 10.1038/s41540-024-00487-6] [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: 09/07/2024] [Accepted: 12/25/2024] [Indexed: 02/17/2025] Open
Abstract
Numerous studies have identified similarities in blood transcriptomic signatures of tuberculosis (TB) phenotypes between mice and humans, including type 1 interferon production and innate immune cell activation. However, murine infection pathophysiology is distinct from human disease. We hypothesized that this is partly due to differences in the relative importance of biological pathways across species. To address this animal-to-human gap, we applied a systems modeling framework, Translatable Components Regression, to identify the axes of variation in the preclinical data most relevant to human TB disease state. Among the pathways our cross-species model pinpointed as highly predictive of human TB phenotype was the infection-induced unfolded protein response. To validate this mechanism, we confirmed that this cellular stress pathway modulates immune functions in Mycobacterium tuberculosis-infected mouse macrophages. Our work demonstrates how systems-level computational models enhance the value of animal studies for elucidating complex human pathophysiology.
Collapse
Affiliation(s)
- Krista M Pullen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ryan Finethy
- Department of Microbiology and Physiological Systems, UMass Chan Medical School, Worcester, MA, USA
| | - Seung-Hyun B Ko
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Charlotte J Reames
- Department of Microbiology and Physiological Systems, UMass Chan Medical School, Worcester, MA, USA
| | - Christopher M Sassetti
- Department of Microbiology and Physiological Systems, UMass Chan Medical School, Worcester, MA, USA.
| | - Douglas A Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
6
|
Painter H, Larsen SE, Williams BD, Abdelaal HFM, Baldwin SL, Fletcher HA, Fiore-Gartland A, Coler RN. Backtranslation of human RNA biosignatures of tuberculosis disease risk into the preclinical pipeline is condition dependent. mSphere 2025; 10:e0086424. [PMID: 39651886 PMCID: PMC11774039 DOI: 10.1128/msphere.00864-24] [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: 10/11/2024] [Accepted: 11/03/2024] [Indexed: 12/18/2024] Open
Abstract
It is unclear whether human progression to active tuberculosis disease (TB) risk signatures are viable endpoint criteria for evaluations of treatments in development. TB is the deadliest infectious disease globally and more efficacious vaccines are needed to reduce this mortality. However, the immune correlates of protection for either preventing infection with Mycobacterium tuberculosis or preventing TB disease have yet to be completely defined, making the advancement of candidate vaccines through the pipeline slow, costly, and fraught with risk. Human-derived correlate of risk (COR) gene signatures, which identify an individual's risk of progressing to active TB disease, provide an opportunity for evaluating new therapies for TB with clear and defined endpoints. Though prospective clinical trials with longitudinal sampling are prohibitively expensive, the characterization of COR gene signatures is practical with preclinical models. Using a 3Rs (replacement, reduction, and refinement) approach we reanalyzed heterogeneous publicly available transcriptional data sets to determine whether a specific set of COR signatures are viable endpoints in the preclinical pipeline. We selected RISK6, Sweeney3, and BATF2 human-derived blood-based RNA biosignatures because they require relatively few genes and have been carefully evaluated across several clinical cohorts. These data suggest that in certain experimental designs and in several tissue types, human COR signatures correlate with disease progression as measured by the bacterial burden in the preclinical TB model pipeline. We observed the best performance when the model most closely reflected human infection or disease conditions. Human-derived COR signatures offer an opportunity for high-throughput preclinical endpoint criteria of vaccine and drug therapy evaluations. IMPORTANCE Understanding the strengths or limitations of back-translating human-derived correlate of risk (COR) RNA signatures into the preclinical pipeline may help streamline down-selection of therapeutic vaccine and drug candidates and better align preclinical models with proposed clinical trial efficacy endpoints.
Collapse
Affiliation(s)
- Hannah Painter
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Sasha E. Larsen
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, USA
| | - Brittany D. Williams
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
| | - Hazem F. M. Abdelaal
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, USA
| | - Susan L. Baldwin
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, USA
| | - Helen A. Fletcher
- Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Andrew Fiore-Gartland
- Biostatistics, Bioinformatics and Epidemiology Program, Fred Hutch Cancer Center, Seattle, Washington, USA
| | - Rhea N. Coler
- Center for Global Infectious Disease Research, Seattle Children’s Research Institute, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, Washington, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA
| |
Collapse
|
7
|
Adeva-Andany MM, Carneiro-Freire N, Castro-Quintela E, Ameneiros-Rodriguez E, Adeva-Contreras L, Fernandez-Fernandez C. Interferon Upregulation Associates with Insulin Resistance in Humans. Curr Diabetes Rev 2025; 21:86-105. [PMID: 38500280 DOI: 10.2174/0115733998294022240309105112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/10/2024] [Accepted: 02/21/2024] [Indexed: 03/20/2024]
Abstract
In humans, insulin resistance is a physiological response to infections developed to supply sufficient energy to the activated immune system. This metabolic adaptation facilitates the immune response but usually persists after the recovery period of the infection and predisposes the hosts to type 2 diabetes and vascular injury. In patients with diabetes, superimposed insulin resistance worsens metabolic control and promotes diabetic ketoacidosis. Pathogenic mechanisms underlying insulin resistance during microbial invasions remain to be fully defined. However, interferons cause insulin resistance in healthy subjects and other population groups, and their production is increased during infections, suggesting that this group of molecules may contribute to reduced insulin sensitivity. In agreement with this notion, gene expression profiles (transcriptomes) from patients with insulin resistance show a robust overexpression of interferon- stimulated genes (interferon signature). In addition, serum levels of interferon and surrogates for interferon activity are elevated in patients with insulin resistance. Circulating levels of interferon- γ-inducible protein-10, neopterin, and apolipoprotein L1 correlate with insulin resistance manifestations, such as hypertriglyceridemia, reduced HDL-c, visceral fat, and homeostasis model assessment-insulin resistance. Furthermore, interferon downregulation improves insulin resistance. Antimalarials such as hydroxychloroquine reduce interferon production and improve insulin resistance, reducing the risk for type 2 diabetes and cardiovascular disease. In addition, diverse clinical conditions that feature interferon upregulation are associated with insulin resistance, suggesting that interferon may be a common factor promoting this adaptive response. Among these conditions are systemic lupus erythematosus, sarcoidosis, and infections with severe acute respiratory syndrome-coronavirus-2, human immunodeficiency virus, hepatitis C virus, and Mycobacterium tuberculosis.
Collapse
Affiliation(s)
- Maria M Adeva-Andany
- Internal Medicine Department, Hospital General Juan Cardona, c/ Pardo Bazán s/n, 15406 Ferrol, Spain
| | - Natalia Carneiro-Freire
- Internal Medicine Department, Hospital General Juan Cardona, c/ Pardo Bazán s/n, 15406 Ferrol, Spain
| | - Elvira Castro-Quintela
- Internal Medicine Department, Hospital General Juan Cardona, c/ Pardo Bazán s/n, 15406 Ferrol, Spain
| | - Eva Ameneiros-Rodriguez
- Internal Medicine Department, Hospital General Juan Cardona, c/ Pardo Bazán s/n, 15406 Ferrol, Spain
| | | | | |
Collapse
|
8
|
Li X, Li X, Qin J, Lei L, Guo H, Zheng X, Zeng X. Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms. Biofactors 2025; 51:e2129. [PMID: 39415336 DOI: 10.1002/biof.2129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2024] [Accepted: 09/30/2024] [Indexed: 10/18/2024]
Abstract
Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.
Collapse
Affiliation(s)
- Xiaohua Li
- Department of Respiratory and Critical Care Medicine, Sixth People's Hospital of Chengdu, Chengdu, Sichuan, China
| | - Xuebing Li
- Department of Respiratory and Critical Care Medicine, People's Hospital of Yaan, Yaan, Sichuan, China
| | - Jiangyue Qin
- Department of General Practice, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lei Lei
- Department of Oncology, Sixth People's Hospital of Chengdu, Chengdu, Sichuan, China
| | - Hua Guo
- Department of Respiratory and Critical Care Medicine, Sixth People's Hospital of Chengdu, Chengdu, Sichuan, China
| | - Xi Zheng
- Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xuefeng Zeng
- Department of Respiratory and Critical Care Medicine, Sixth People's Hospital of Chengdu, Chengdu, Sichuan, China
| |
Collapse
|
9
|
Leo S, Narasimhan M, Rathinam S, Banerjee A. Biomarkers in diagnosing and therapeutic monitoring of tuberculosis: a review. Ann Med 2024; 56:2386030. [PMID: 39097795 PMCID: PMC11299445 DOI: 10.1080/07853890.2024.2386030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 03/06/2024] [Accepted: 06/12/2024] [Indexed: 08/05/2024] Open
Abstract
Tuberculosis (TB) continues to pose a significant health challenge worldwide, emphasizing the importance of prompt diagnosis and efficient monitoring of treatment outcomes for effective disease control. Biomarkers have become increasingly important in the realm of TB diagnoses and treatment. The objective of this comprehensive review is to examine the present state of biomarkers employed in the diagnosis of TB, monitoring the response to treatment, and predicting treatment outcomes. In this study, we undertake a comprehensive examination of the diverse biomarkers utilized in TB diagnoses, spanning molecular, immunological, and other novel methodologies. Furthermore, we examine the potential of biomarkers in the context of therapeutic monitoring, assessment of treatment effectiveness, and anticipation of drug resistance. Additionally, this paper presents future prospects regarding the utilization of biomarkers in the therapy of tuberculosis.
Collapse
Affiliation(s)
- Sneha Leo
- Department of Respiratory Medicine, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, India
| | - Meenakshi Narasimhan
- Department of Respiratory Medicine, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, India
| | - Sridhar Rathinam
- Department of Respiratory Medicine, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, India
| | - Antara Banerjee
- Faculty of Allied Health Sciences, Chettinad Hospital and Research Institute, Chettinad Academy of Research and Education, Kelambakkam, Tamil Nadu, India
| |
Collapse
|
10
|
Nakiboneka R, Walbaum N, Musisi E, Nevels M, Nyirenda T, Nliwasa M, Msefula CL, Sloan D, Sabiiti W. Specific human gene expression in response to infection is an effective marker for diagnosis of latent and active tuberculosis. Sci Rep 2024; 14:26884. [PMID: 39505948 PMCID: PMC11541504 DOI: 10.1038/s41598-024-77164-5] [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: 08/12/2024] [Accepted: 10/21/2024] [Indexed: 11/08/2024] Open
Abstract
RNA sequencing and microarray analysis revealed transcriptional markers expressed in whole blood can differentiate active pulmonary TB (ATB) from other respiratory diseases (ORDs), and latent TB infection (LTBI) from healthy controls (HC). Here we describe a streamlined reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) assay that could be applied at near point-of-care for diagnosing and distinguishing ATB from ORDs and LTBI from HC. A literature review was undertaken to identify the most plausible host-gene markers (HGM) of TB infection. Primers, and dual labelled hydrolysis probes were designed and analytically evaluated for accuracy in an in-vitro model of infection using a lung fibroblast cell-line. Best performing genes were multiplexed into panels of 2-4 targets and taken forward for clinical evaluation. Mycobacteria Growth Indicator Tube and QuantiFERON-TB Gold Plus were used as reference tests for ATB and LTBI respectively. A total of 16 HGM were selected and incorporated into five multiplex RT-qPCR panels. PCR assay efficiency of all evaluated targets was ≥ 90% with a median analytical sensitivity of 292 copies/µl [IQR: 215.0-358.3 copies/µl], and a median limit of quantification of 61.7 copies/µl [IQR: 29.4-176.3 copies/µl]. Clinically, ATB was characterised by higher gene expression than ORDs, while LTBI was associated with lower gene expression than HC, Kruskal-Wallis p < 0.0001. Crucially, BATF2, CD64, GBP5, C1QB, GBP6, DUSP3, and GAS6 exhibited high differentiative ability for ATB from ORDs, LTBI or HC while KLF2, PTPRC, NEMF, ASUN, and ZNF296 independently discriminated LTBI from HC. Our results show that different HGM maybe required for ATB and LTBI differentiation from ORDs or HC respectively and demonstrate the feasibility of host gene-based RT-qPCR to diagnose ATB and LTBI at near point-of-care.
Collapse
Affiliation(s)
- Ritah Nakiboneka
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, KY16 9TF, UK
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
- Helse Nord Tuberculosis Initiative (HNTI), Pathology Department, Kamuzu University of Health Sciences, Blantyre, Malawi
- Africa Centre for Public Health and Herbal Medicines (ACEPHEM), Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Natasha Walbaum
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, KY16 9TF, UK
| | - Emmanuel Musisi
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, KY16 9TF, UK
- Adroit Biomedical and Bio-entrepreneurship Research Services (ABBRS), Kampala, Uganda
| | - Michael Nevels
- Biomedical Sciences Research Complex (BSRC), School of Biology, University of St Andrews, St Andrews, UK
| | - Tonney Nyirenda
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Marriott Nliwasa
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
- Helse Nord Tuberculosis Initiative (HNTI), Pathology Department, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Chisomo L Msefula
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
- Helse Nord Tuberculosis Initiative (HNTI), Pathology Department, Kamuzu University of Health Sciences, Blantyre, Malawi
- Africa Centre for Public Health and Herbal Medicines (ACEPHEM), Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Derek Sloan
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, KY16 9TF, UK
| | - Wilber Sabiiti
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, KY16 9TF, UK.
| |
Collapse
|
11
|
Abhimanyu, Longlax SC, Nishiguchi T, Ladki M, Sheikh D, Martinez AL, Mace EM, Grimm SL, Caldwell T, Portillo Varela A, Sekhar RV, Mandalakas AM, Mlotshwa M, Ginidza S, Cirillo JD, Wallis RS, Netea MG, van Crevel R, Coarfa C, DiNardo AR. TCA metabolism regulates DNA hypermethylation in LPS and Mycobacterium tuberculosis-induced immune tolerance. Proc Natl Acad Sci U S A 2024; 121:e2404841121. [PMID: 39348545 PMCID: PMC11474056 DOI: 10.1073/pnas.2404841121] [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: 03/20/2024] [Accepted: 08/28/2024] [Indexed: 10/02/2024] Open
Abstract
Severe and chronic infections, including pneumonia, sepsis, and tuberculosis (TB), induce long-lasting epigenetic changes that are associated with an increase in all-cause postinfectious morbidity and mortality. Oncology studies identified metabolic drivers of the epigenetic landscape, with the tricarboxylic acid (TCA) cycle acting as a central hub. It is unknown if the TCA cycle also regulates epigenetics, specifically DNA methylation, after infection-induced immune tolerance. The following studies demonstrate that lipopolysaccharide and Mycobacterium tuberculosis induce changes in DNA methylation that are mediated by the TCA cycle. Infection-induced DNA hypermethylation is mitigated by inhibitors of cellular metabolism (rapamycin, everolimus, metformin) and the TCA cycle (isocitrate dehydrogenase inhibitors). Conversely, exogenous supplementation with TCA metabolites (succinate and itaconate) induces DNA hypermethylation and immune tolerance. Finally, TB patients who received everolimus have less DNA hypermethylation demonstrating proof of concept that metabolic manipulation can mitigate epigenetic scars.
Collapse
Affiliation(s)
- Abhimanyu
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
| | - Santiago Carrero Longlax
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
| | - Tomoki Nishiguchi
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
| | - Malik Ladki
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
| | - Daanish Sheikh
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
| | - Amera L. Martinez
- Department of Pediatrics, Baylor College of Medicine, Houston, TX77030
| | - Emily M. Mace
- Department of Pediatrics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY10032
| | - Sandra L. Grimm
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX77030
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX77030
| | - Thaleia Caldwell
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
| | - Alexandra Portillo Varela
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
| | - Rajagopal V. Sekhar
- Translational Metabolism Unit, Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Baylor College of Medicine, Houston, TX77030
| | - Anna M. Mandalakas
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
- Epidemiology, Human Genetics & Environmental Sciences, University of Texas-UTHealth School of Public Health, Houston, TX77030
- Clinical Infectious Disease Group, German Center for Infectious Research (DZIF), Clinical tuberculosis (TB) Unit, Research Center Borstel, Borstel27246, Germany
| | - Mandla Mlotshwa
- The Aurum institute, Johannesburg2006, South Africa
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Johannesburg, Johannesburg 2006, South Africa
- Department of Medicine, Vanderbilt University, Nashville, TN37232
| | | | - Jeffrey D. Cirillo
- Center for Airborne Pathogen Research and Imaging, Texas A&M College of Medicine, Bryan, TX77843
| | - Robert S. Wallis
- The Aurum institute, Johannesburg2006, South Africa
- Department of Medicine, Case Western Reserve University, Cleveland, OH44106
- Vanderbilt Institute for Global Health, Vanderbilt University, Nashville, TN37232
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen6525, Netherlands
- Department of Immunology and Metabolism, Life and Medical Sciences Institute, University of Bonn, Bonn53113, Germany
| | - Reinout van Crevel
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen6525, Netherlands
- Nuffield Department of Medicine, Centre for Tropical Medicine and Global Health, University of Oxford, OxfordOX1 4BH, United Kingdom
| | - Cristian Coarfa
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX77030
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX77030
| | - Andrew R. DiNardo
- Department of Pediatrics, The Global TB Program, William T Shearer Center for Immunobiology, Texas Children’s Hospital, Baylor College of Medicine, Houston, TX77030
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen6525, Netherlands
| |
Collapse
|
12
|
Fraser SD, Thackray-Nocera S, Wright C, Flockton R, James SR, Crooks MG, Kaye PM, Hart SP. Effects of Azithromycin on Blood Inflammatory Gene Expression and Cytokine Production in Sarcoidosis. Lung 2024; 202:683-693. [PMID: 39284999 PMCID: PMC11427505 DOI: 10.1007/s00408-024-00743-w] [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: 07/01/2024] [Accepted: 08/26/2024] [Indexed: 09/27/2024]
Abstract
INTRODUCTION In sarcoidosis granulomas, monocyte-derived macrophages are activated by pro-inflammatory cytokines including TNF and IL-6. Current drug treatment for sarcoidosis aims to suppress inflammation but disabling side effects can ensue. The macrolide azithromycin may be anti-inflammatory. We aimed to determine whether treatment with azithromycin affects blood inflammatory gene expression and monocyte functions in sarcoidosis. METHODS Blood samples were collected from patients with chronic pulmonary sarcoidosis enrolled in a single arm, open label clinical trial who received oral azithromycin 250 mg once daily for 3 months. Whole blood inflammatory gene expression with or without LPS stimulation was measured using a 770-mRNA panel. Phenotypic analysis and cytokine production were conducted by flow cytometry and ELISA after 24h stimulation with growth factors and TLR ligands. mTOR activity was assessed by measuring phosphorylated S6RP. RESULTS Differential gene expression analysis indicated a state of heightened myeloid cell activation in sarcoidosis. Compared with controls, sarcoidosis patients showed increased LPS responses for several cytokines and chemokines. Treatment with azithromycin had minimal effect on blood gene expression overall, but supervised clustering analysis identified several chemokine genes that were upregulated. At the protein level, azithromycin treatment increased LPS-stimulated TNF and unstimulated IL-8 production. No other cytokines showed significant changes following azithromycin. Blood neutrophil counts fell during azithromycin treatment whereas mononuclear cells remained stable. Azithromycin had no detectable effects on mTOR activity or activation markers. CONCLUSION Blood myeloid cells are activated in sarcoidosis, but azithromycin therapy did not suppress inflammatory gene expression or cytokine production in blood. TRIAL REGISTRATION EudraCT 2019-000580-24 (17 May 2019).
Collapse
Affiliation(s)
- Simon D Fraser
- Respiratory Research Group, Hull York Medical School, Castle Hill Hospital, Cottingham, HU16 5JQ, UK
| | - Susannah Thackray-Nocera
- Respiratory Clinical Trials Unit, Hull University Teaching Hospitals NHS Trust, Castle Hill Hospital, Cottingham, HU16 5JQ, UK
| | - Caroline Wright
- Respiratory Clinical Trials Unit, Hull University Teaching Hospitals NHS Trust, Castle Hill Hospital, Cottingham, HU16 5JQ, UK
| | - Rachel Flockton
- Respiratory Clinical Trials Unit, Hull University Teaching Hospitals NHS Trust, Castle Hill Hospital, Cottingham, HU16 5JQ, UK
| | - Sally R James
- Biosciences Technology Facility, Dept. of Biology, University of York, York, UK
| | - Michael G Crooks
- Respiratory Research Group, Hull York Medical School, Castle Hill Hospital, Cottingham, HU16 5JQ, UK
| | - Paul M Kaye
- York Biomedical Research Institute, University of York, York, YO10 5DD, UK
| | - Simon P Hart
- Respiratory Research Group, Hull York Medical School, Castle Hill Hospital, Cottingham, HU16 5JQ, UK.
| |
Collapse
|
13
|
Luo T, Zhang S, Li X, Huang M. Challenges in the differential diagnosis of pulmonary tuberculosis vs. lung cancer: A case report. Oncol Lett 2024; 28:494. [PMID: 39211306 PMCID: PMC11358719 DOI: 10.3892/ol.2024.14627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 06/03/2024] [Indexed: 09/04/2024] Open
Abstract
Pulmonary tuberculosis (TB) and certain types of lung cancer (LC), such as lung adenocarcinoma, squamous cell carcinoma and small cell undifferentiated carcinoma, are prevalent diseases that share similar clinical symptoms and imaging characteristics, increasing the risk of misdiagnosis. The present report documents the case of a man with a history of close contact with TB who exhibited clinical symptoms and lung CT scan findings that strongly indicated pulmonary TB. However, the diagnosis was ultimately confirmed to be lung adenocarcinoma on endoscopic biopsy. The present report shows that clinicians should always consider the possibility of LC in patients with TB-related pulmonary pathological changes detected by imaging.
Collapse
Affiliation(s)
- Tao Luo
- Central Laboratory, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong 519000, P.R. China
| | - Shuiwang Zhang
- Department of Tuberculosis, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong 519000, P.R. China
| | - Xiaoliang Li
- Central Laboratory, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong 519000, P.R. China
| | - Mingxing Huang
- Central Laboratory, The Third People's Hospital of Zhuhai, Zhuhai, Guangdong 519000, P.R. China
| |
Collapse
|
14
|
Muwanga VM, Mendelsohn SC, Leukes V, Stanley K, Mbandi SK, Erasmus M, Flinn M, Fisher TL, Raphela R, Bilek N, Malherbe ST, Tromp G, Van Der Spuy G, Walzl G, Chegou NN, Scriba TJ. Blood transcriptomic signatures for symptomatic tuberculosis in an African multicohort study. Eur Respir J 2024; 64:2400153. [PMID: 38964778 PMCID: PMC11325265 DOI: 10.1183/13993003.00153-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/12/2024] [Indexed: 07/06/2024]
Abstract
BACKGROUND Multiple host blood transcriptional signatures have been developed as non-sputum triage tests for tuberculosis (TB). We aimed to compare the diagnostic performance of 20 blood transcriptomic TB signatures for differentiating between symptomatic patients who have TB versus other respiratory diseases (ORD). METHODS As part of a nested case-control study, individuals presenting with respiratory symptoms at primary healthcare clinics in Ethiopia, Malawi, Namibia, Uganda, South Africa and The Gambia were enrolled. TB was diagnosed based on clinical, microbiological and radiological findings. Transcriptomic signatures were measured in whole blood using microfluidic real-time quantitative PCR. Diagnostic performance was benchmarked against the World Health Organization Target Product Profile (TPP) for a non-sputum TB triage test. RESULTS Among 579 participants, 158 had definite, microbiologically confirmed TB, 32 had probable TB, while 389 participants had ORD. Nine signatures differentiated between ORD and TB with equivalent performance (Satproedprai7: area under the curve 0.83 (95% CI 0.79-0.87); Jacobsen3: 0.83 (95% CI 0.79-0.86); Suliman2: 0.82 (95% CI 0.78-0.86); Roe1: 0.82 (95% CI 0.78-0.86); Kaforou22: 0.82 (95% CI 0.78-0.86); Sambarey10: 0.81 (95% CI 0.77-0.85); Duffy9: 0.81 (95% CI 0.76-0.86); Gliddon3: 0.8 (95% CI 0.75-0.85); Suliman4 0.79 (95% CI 0.75-0.84)). Benchmarked against a 90% sensitivity, these signatures achieved specificities between 44% (95% CI 38-49%) and 54% (95% CI 49-59%), not meeting the TPP criteria. Signature scores significantly varied by HIV status and country. In country-specific analyses, several signatures, such as Satproedprai7 and Penn-Nicholson6, met the minimal TPP criteria for a triage test in Ethiopia, Malawi and South Africa. CONCLUSION No signatures met the TPP criteria in a pooled analysis of all countries, but several signatures met the minimum criteria for a non-sputum TB triage test in some countries.
Collapse
Affiliation(s)
- Vanessa Mwebaza Muwanga
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Simon C Mendelsohn
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Vinzeigh Leukes
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Kim Stanley
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Stanley Kimbung Mbandi
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Mzwandile Erasmus
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Marika Flinn
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Tarryn-Lee Fisher
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Rodney Raphela
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Nicole Bilek
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Stephanus T Malherbe
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Gerard Tromp
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Gian Van Der Spuy
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Gerhard Walzl
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Novel N Chegou
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Immunology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Thomas J Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| |
Collapse
|
15
|
Nakiboneka R, Margaritella N, Nyirenda T, Chaima D, Walbaum N, Musisi E, Tionge S, Msosa T, Nliwasa M, Msefula CL, Sloan D, Sabiiti W. Suppression of host gene expression is associated with latent TB infection: a possible diagnostic biomarker. Sci Rep 2024; 14:15621. [PMID: 38972907 PMCID: PMC11228037 DOI: 10.1038/s41598-024-66486-z] [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: 03/08/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024] Open
Abstract
The World Health Organization End TB strategy aims for a 90% reduction of tuberculosis (TB) incidence by 2035. Systematic testing and treatment of latent TB infection (LTBI) among contacts of active TB patients is recommended as one of the ways to curtail TB incidence. However, there is a shortage of tools to accurately diagnose LTBI. We assessed the appropriateness of whole blood host transcriptomic markers (TM) to diagnose LTBI among household contacts of bacteriologically confirmed index cases compared to HIV negative healthy controls (HC). QuantiFERON-TB Gold Plus Interferon gamma release assay (IGRA) and reverse-transcriptase quantitative PCR were used to determine LTBI and quantify TM expression respectively. Association between TM expression and LTBI was evaluated by logistic regression modelling. A total of 100 participants, 49 TB exposed (TBEx) household contacts and 51 HC, were enrolled. Twenty-five (51%) TBEx individuals tested positive by IGRA, and were denoted as LTBI individuals, and 37 (72.5%) HC were IGRA-negative. Expression of 11 evaluated TM was significantly suppressed among LTBI compared to HC. Out of the 11 TM, ZNF296 and KLF2 expression were strongly associated with LTBI and successfully differentiated LTBI from HC. Paradoxically, 21 (49%) TBEx participants who tested IGRA negative exhibited the same pattern of suppressed TM expression as IGRA positive (LTBI-confirmed individuals). Results suggest that suppression of gene expression underlies LTBI and may be a more sensitive diagnostic biomarker than standard-of-care IGRA.
Collapse
Grants
- Wellcome Trust
- 204821/Z/16/Z Wellcome Trust Institutional Strategic Support fund of the University of St Andrews
- Helse Nord Tuberculosis Initiative (HNTI), Pathology Department, Kamuzu University of Health Sciences
- Africa Centre for Public Health and Herbal Medicines (ACEPHEM), Kamuzu University of Health Sciences
- School of Medicine, University of St Andrews, UK
- Uganda Virus Research Institute, Entebbe, Uganda
- School of Mathematics and Statistics, University of St Andrews, UK
- Department of Pathology, Kamuzu University of Health Sciences
- Adroit Biomedical and Bioentrepreneurship Research Service
Collapse
Affiliation(s)
- Ritah Nakiboneka
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
- Pathology Department, Helse Nord Tuberculosis Initiative (HNTI), Kamuzu University of Health Sciences, Blantyre, Malawi
- Africa Centre for Public Health and Herbal Medicines (ACEPHEM), Kamuzu University of Health Sciences, Blantyre, Malawi
- Uganda Virus Research Institute, Entebbe, Uganda
| | - Nicolò Margaritella
- School of Mathematics and Statistics, University of St Andrews, St Andrews, UK
| | - Tonney Nyirenda
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - David Chaima
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Natasha Walbaum
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK
| | - Emmanuel Musisi
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK
- Adroit Biomedical and Bioentrepreneurship Research Service, Kampala, Uganda
| | - Sikwese Tionge
- Pathology Department, Helse Nord Tuberculosis Initiative (HNTI), Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Takondwa Msosa
- Pathology Department, Helse Nord Tuberculosis Initiative (HNTI), Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Marriott Nliwasa
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
- Pathology Department, Helse Nord Tuberculosis Initiative (HNTI), Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Chisomo L Msefula
- Department of Pathology, Kamuzu University of Health Sciences, Blantyre, Malawi
- Pathology Department, Helse Nord Tuberculosis Initiative (HNTI), Kamuzu University of Health Sciences, Blantyre, Malawi
- Africa Centre for Public Health and Herbal Medicines (ACEPHEM), Kamuzu University of Health Sciences, Blantyre, Malawi
| | - Derek Sloan
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK
| | - Wilber Sabiiti
- Division of Infection and Global Health, School of Medicine, University of St Andrews, St Andrews, UK.
| |
Collapse
|
16
|
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.
Collapse
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
| |
Collapse
|
17
|
Wang X, VanValkenberg A, Odom AR, Ellner JJ, Hochberg NS, Salgame P, Patil P, Johnson WE. Comparison of gene set scoring methods for reproducible evaluation of tuberculosis gene signatures. BMC Infect Dis 2024; 24:610. [PMID: 38902649 PMCID: PMC11191245 DOI: 10.1186/s12879-024-09457-z] [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: 07/29/2023] [Accepted: 05/31/2024] [Indexed: 06/22/2024] Open
Abstract
BACKGROUND Blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease. However, an unresolved issue is whether gene set enrichment analysis of the signature transcripts alone is sufficient for prediction and differentiation or whether it is necessary to use the original model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data and missing details about the original trained model. To facilitate the utilization of these signatures in TB research, comparisons between gene set scoring methods cross-data validation of original model implementations are needed. METHODS We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both rrebuilt original models and gene set scoring methods. Existing gene set scoring methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, were used as alternative approaches to obtain the profile scores. The area under the ROC curve (AUC) value was computed to measure performance. Correlation analysis and Wilcoxon paired tests were used to compare the performance of enrichment methods with the original models. RESULTS For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original models. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies. CONCLUSION Gene set enrichment scoring of existing gene sets can distinguish patients with active TB disease from other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.
Collapse
Affiliation(s)
- Xutao Wang
- Department of Biostatistics, Boston University, Boston, MA, USA
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Arthur VanValkenberg
- Division of Infectious Disease, Center for Data Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Aubrey R Odom
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Jerrold J Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Natasha S Hochberg
- Boston Medical Center, Boston, MA, USA
- Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Prasad Patil
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - W Evan Johnson
- Division of Infectious Disease, Center for Data Science, Rutgers New Jersey Medical School, Newark, NJ, USA.
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA.
| |
Collapse
|
18
|
van Wijck RTA, Sharma HS, Swagemakers SMA, Dik WA, IJspeert H, Dalm VASH, van Daele PLA, van Hagen PM, van der Spek PJ. Bioinformatic meta-analysis reveals novel differentially expressed genes and pathways in sarcoidosis. Front Med (Lausanne) 2024; 11:1381031. [PMID: 38938383 PMCID: PMC11208482 DOI: 10.3389/fmed.2024.1381031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/23/2024] [Indexed: 06/29/2024] Open
Abstract
Introduction Sarcoidosis is a multi-system inflammatory disease of unknown origin with heterogeneous clinical manifestations varying from a single organ non-caseating granuloma site to chronic systemic inflammation and fibrosis. Gene expression studies have suggested several genes and pathways implicated in the pathogenesis of sarcoidosis, however, due to differences in study design and variable statistical approaches, results were frequently not reproducible or concordant. Therefore, meta-analysis of sarcoidosis gene-expression datasets is of great importance to robustly establish differentially expressed genes and signalling pathways. Methods We performed meta-analysis on 22 published gene-expression studies on sarcoidosis. Datasets were analysed systematically using same statistical cut-offs. Differentially expressed genes were identified by pooling of p-values using Edgington's method and analysed for pathways using Ingenuity Pathway Analysis software. Results A consistent and significant signature of novel and well-known genes was identified, those collectively implicated both type I and type II interferon mediated signalling pathways in sarcoidosis. In silico functional analysis showed consistent downregulation of eukaryotic initiation factor 2 signalling, whereas cytokines like interferons and transcription factor STAT1 were upregulated. Furthermore, we analysed affected tissues to detect differentially expressed genes likely to be involved in granuloma biology. This revealed that matrix metallopeptidase 12 was exclusively upregulated in affected tissues, suggesting a crucial role in disease pathogenesis. Discussion Our analysis provides a concise gene signature in sarcoidosis and expands our knowledge about the pathogenesis. Our results are of importance to improve current diagnostic approaches and monitoring strategies as well as in the development of targeted therapeutics.
Collapse
Affiliation(s)
- Rogier T. A. van Wijck
- Department of Pathology & Clinical Bioinformatics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Hari S. Sharma
- Department of Pathology & Clinical Bioinformatics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Sigrid M. A. Swagemakers
- Department of Pathology & Clinical Bioinformatics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Willem A. Dik
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Hanna IJspeert
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Virgil A. S. H. Dalm
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Paul L. A. van Daele
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - P. Martin van Hagen
- Laboratory Medical Immunology, Department of Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Internal Medicine, Division of Allergy & Clinical Immunology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Peter J. van der Spek
- Department of Pathology & Clinical Bioinformatics, Erasmus MC University Medical Center, Rotterdam, Netherlands
| |
Collapse
|
19
|
Hendrix SV, Mreyoud Y, McNehlan ME, Smirnov A, Chavez SM, Hie B, Chamberland MM, Bradstreet TR, Webber AM, Kreamalmeyer D, Taneja R, Bryson BD, Edelson BT, Stallings CL. BHLHE40 Regulates Myeloid Cell Polarization through IL-10-Dependent and -Independent Mechanisms. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2024; 212:1766-1781. [PMID: 38683120 PMCID: PMC11105981 DOI: 10.4049/jimmunol.2200819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/16/2024] [Indexed: 05/01/2024]
Abstract
Better understanding of the host responses to Mycobacterium tuberculosis infections is required to prevent tuberculosis and develop new therapeutic interventions. The host transcription factor BHLHE40 is essential for controlling M. tuberculosis infection, in part by repressing Il10 expression, where excess IL-10 contributes to the early susceptibility of Bhlhe40-/- mice to M. tuberculosis infection. Deletion of Bhlhe40 in lung macrophages and dendritic cells is sufficient to increase the susceptibility of mice to M. tuberculosis infection, but how BHLHE40 impacts macrophage and dendritic cell responses to M. tuberculosis is unknown. In this study, we report that BHLHE40 is required in myeloid cells exposed to GM-CSF, an abundant cytokine in the lung, to promote the expression of genes associated with a proinflammatory state and better control of M. tuberculosis infection. Loss of Bhlhe40 expression in murine bone marrow-derived myeloid cells cultured in the presence of GM-CSF results in lower levels of proinflammatory associated signaling molecules IL-1β, IL-6, IL-12, TNF-α, inducible NO synthase, IL-2, KC, and RANTES, as well as higher levels of the anti-inflammatory-associated molecules MCP-1 and IL-10 following exposure to heat-killed M. tuberculosis. Deletion of Il10 in Bhlhe40-/- myeloid cells restored some, but not all, proinflammatory signals, demonstrating that BHLHE40 promotes proinflammatory responses via both IL-10-dependent and -independent mechanisms. In addition, we show that macrophages and neutrophils within the lungs of M. tuberculosis-infected Bhlhe40-/- mice exhibit defects in inducible NO synthase production compared with infected wild-type mice, supporting that BHLHE40 promotes proinflammatory responses in innate immune cells, which may contribute to the essential role for BHLHE40 during M. tuberculosis infection in vivo.
Collapse
Affiliation(s)
- Skyler V. Hendrix
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Yassin Mreyoud
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Michael E. McNehlan
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Asya Smirnov
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Sthefany M. Chavez
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Brian Hie
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Megan M. Chamberland
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tara R. Bradstreet
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Ashlee M. Webber
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Darren Kreamalmeyer
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Reshma Taneja
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Bryan D. Bryson
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian T. Edelson
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Christina L. Stallings
- Department of Molecular Microbiology, Center for Women’s Infectious Disease Research, Washington University School of Medicine, St. Louis, MO 63110, USA
| |
Collapse
|
20
|
Alonso-Rodríguez N, Vianello E, van Veen S, Jenum S, Tonby K, van Riessen R, Lai X, Mortensen R, Ottenhoff THM, Dyrhol-Riise AM. Whole blood RNA signatures in tuberculosis patients receiving H56:IC31 vaccine as adjunctive therapy. Front Immunol 2024; 15:1350593. [PMID: 38433842 PMCID: PMC10904528 DOI: 10.3389/fimmu.2024.1350593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 01/25/2024] [Indexed: 03/05/2024] Open
Abstract
Introduction Therapeutic vaccination in tuberculosis (TB) represents a Host Directed Therapy strategy which enhances immune responses in order to improve clinical outcomes and shorten TB treatment. Previously, we have shown that the subunit H56:IC31 vaccine induced both humoral and cellular immune responses when administered to TB patients adjunctive to standard TB treatment (TBCOX2 study, NCT02503839). Here we present the longitudinal whole blood gene expression patterns in H56:IC31 vaccinated TB patients compared to controls receiving standard TB treatment only. Methods The H56:IC31 group (N=11) and Control group (N=7) underwent first-line TB treatment for 182 days. The H56:IC31 group received 5 micrograms of the H56:IC31 vaccine (Statens Serum Institut; SSI, Valneva Austria GmbH) intramuscularly at day 84 and day 140. Total RNA was extracted from whole blood samples collected in PAXgene tubes on days 0, 84, 98, 140, 154, 182 and 238. The expression level of 183 immune-related genes was measured by high-throughput microfluidic qPCR (Biomark HD system, Standard BioTools). Results The targeted gene expression profiling unveiled the upregulation of modules such as interferon (IFN) signalling genes, pattern recognition receptors and small nucleotide guanosine triphosphate (GTP)-ases in the vaccinated group compared to controls two weeks after administration of the first H56:IC31 vaccine. Additionally, the longitudinal analysis of the Adolescent Cohort Study-Correlation of Risk (ACS-COR) signature showed a progressive downregulation in both study arms towards the end of TB treatment, in congruence with reported treatment responses and clinical improvements. Still, two months after the end of TB treatment, vaccinated patients, and especially those developing both cellular and humoral vaccine responses, showed a lower expression of the ACS-COR genes compared to controls. Discussion Our data report gene expression patterns following H56:IC31 vaccination which might be interpreted as a lower risk of relapse in therapeutically vaccinated patients. Further studies are needed to conclude if these gene expression patterns could be used as prognostic biosignatures for therapeutic TB vaccine responses.
Collapse
Affiliation(s)
| | - Eleonora Vianello
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | - Suzanne van Veen
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | - Synne Jenum
- Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | - Kristian Tonby
- Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rosalie van Riessen
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | - Xiaoran Lai
- Oslo Centre for Biostatistics and Epidemiology, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Rasmus Mortensen
- Deptartment of Infectious Disease Immunology, Statens Serum Institut, Copenhagen, Denmark
| | - Tom H. M. Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | - Anne Ma Dyrhol-Riise
- Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| |
Collapse
|
21
|
Weeratunga P, Moller DR, Ho LP. Immune mechanisms of granuloma formation in sarcoidosis and tuberculosis. J Clin Invest 2024; 134:e175264. [PMID: 38165044 PMCID: PMC10760966 DOI: 10.1172/jci175264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024] Open
Abstract
Sarcoidosis is a complex immune-mediated disease characterized by clusters of immune cells called granulomas. Despite major steps in understanding the cause of this disease, many questions remain. In this Review, we perform a mechanistic interrogation of the immune activities that contribute to granuloma formation in sarcoidosis and compare these processes with its closest mimic, tuberculosis, highlighting shared and divergent immune activities. We examine how Mycobacterium tuberculosis is sensed by the immune system; how the granuloma is initiated, formed, and perpetuated in tuberculosis compared with sarcoidosis; and the role of major innate and adaptive immune cells in shaping these processes. Finally, we draw these findings together around several recent high-resolution studies of the granuloma in situ that utilized the latest advances in single-cell technology combined with spatial methods to analyze plausible disease mechanisms. We conclude with an overall view of granuloma formation in sarcoidosis.
Collapse
Affiliation(s)
- Praveen Weeratunga
- MRC Translational Immunology Discovery Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Ling-Pei Ho
- MRC Translational Immunology Discovery Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
22
|
Ko ER, Reller ME, Tillekeratne LG, Bodinayake CK, Miller C, Burke TW, Henao R, McClain MT, Suchindran S, Nicholson B, Blatt A, Petzold E, Tsalik EL, Nagahawatte A, Devasiri V, Rubach MP, Maro VP, Lwezaula BF, Kodikara-Arachichi W, Kurukulasooriya R, De Silva AD, Clark DV, Schully KL, Madut D, Dumler JS, Kato C, Galloway R, Crump JA, Ginsburg GS, Minogue TD, Woods CW. Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance. Sci Rep 2023; 13:22554. [PMID: 38110534 PMCID: PMC10728077 DOI: 10.1038/s41598-023-49734-6] [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: 12/27/2022] [Accepted: 12/11/2023] [Indexed: 12/20/2023] Open
Abstract
Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.
Collapse
Affiliation(s)
- Emily R Ko
- Division of General Internal Medicine, Department of Medicine, Duke Regional Hospital, Duke University Health System, Duke University School of Medicine, 3643 N. Roxboro St., Durham, NC, 27704, USA.
| | - Megan E Reller
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - L Gayani Tillekeratne
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Champica K Bodinayake
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Cameron Miller
- Clinical Research Unit, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Thomas W Burke
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Ricardo Henao
- Department of Biostatistics and Informatics, Duke University, Durham, NC, USA
| | - Micah T McClain
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
| | - Sunil Suchindran
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | | | - Adam Blatt
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA
| | - Elizabeth Petzold
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Ephraim L Tsalik
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Danaher Diagnostics, Washington, DC, USA
| | - Ajith Nagahawatte
- Department of Microbiology, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Vasantha Devasiri
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
| | - Matthew P Rubach
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Programme in Emerging Infectious Diseases, Duke-National University of Singapore, Singapore, Singapore
- Kilimanjaro Christian Medical Center, Moshi, Tanzania
| | - Venance P Maro
- Kilimanjaro Christian Medical Center, Moshi, Tanzania
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
| | - Bingileki F Lwezaula
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
- Maswenzi Regional Referral Hospital, Moshi, Tanzania
| | | | | | - Aruna D De Silva
- General Sir John Kotelawala Defence University, Colombo, Sri Lanka
| | - Danielle V Clark
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
- Austere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Ft. Detrick, MD, USA
| | - Kevin L Schully
- Austere Environments Consortium for Enhanced Sepsis Outcomes (ACESO), Biological Defense Research Directorate, Naval Medical Research Center-Frederick, Ft. Detrick, MD, USA
| | - Deng Madut
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| | - J Stephen Dumler
- Joint Departments of Pathology, School of Medicine, Uniformed Services University, Bethesda, MD, USA
| | - Cecilia Kato
- Centers for Disease Control and Prevention, National Center for Emerging Zoonotic Infectious Diseases, Atlanta, USA
| | - Renee Galloway
- Centers for Disease Control and Prevention, National Center for Emerging Zoonotic Infectious Diseases, Atlanta, USA
| | - John A Crump
- Duke Global Health Institute, Duke University, Durham, NC, USA
- Department of Medicine, Faculty of Medicine, University of Ruhuna, Galle, Sri Lanka
- Kilimanjaro Christian Medical Center, Moshi, Tanzania
- Kilimanjaro Christian Medical University College, Moshi, Tanzania
- Centre for International Health, University of Otago, Dunedin, New Zealand
| | - Geoffrey S Ginsburg
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- National Institute of Health, Bethesda, MD, USA
| | - Timothy D Minogue
- Diagnostic Systems Division, USAMRIID, Fort Detrick, Frederick, MD, USA
| | - Christopher W Woods
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Global Health Institute, Duke University, Durham, NC, USA
| |
Collapse
|
23
|
Sodsri T, Baughman RP, Sriprasart T. Diagnosis of pulmonary sarcoidosis in tuberculosis endemic area-a narrative review. J Thorac Dis 2023; 15:5760-5772. [PMID: 37969315 PMCID: PMC10636435 DOI: 10.21037/jtd-23-192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/18/2023] [Indexed: 11/17/2023]
Abstract
Background and Objective Pulmonary sarcoidosis and tuberculosis (TB) are the most frequent tissue-confirmed granulomatous diseases. Due to its unknown etiology, pulmonary sarcoidosis is diagnosed by ruling out other granulomatous diseases and necessitating clinical, radiological, and pathological evidence. There are many factors that contribute to the diagnostic dilemma between these two diseases. Even though some aspects of both diseases, such as their pathological evidence and abnormal X-ray findings, are quite similar, the treatment options for each are entirely different. The standard treatment for sarcoidosis is immunosuppressive agents such as glucocorticoids, which can exacerbate TB. Consequently, the overlap between clinical and radiological features constitutes a significant challenge for many physicians in selecting the optimal treatment for each patient. Therefore, the exclusion of pulmonary TB is a mandatory step for the diagnosis of pulmonary sarcoidosis. This article reviews and summarizes basic science and clinical research on distinguishing these two disorders. Methods A systematic search of the MEDLINE and PubMed databases focusing on studies published within the last 35 years was conducted. The last search date is February 4, 2023. The authors used the following combinations of terms: tuberculosis, sarcoidosis, diagnosis, bronchoscopy, biomarkers, and radiography. All studies were reviewed, and 69 references from 1990 to 2023 were found to be relevant. Key Content and Findings Innovative laboratory tests are essential for distinguishing between pulmonary sarcoidosis and TB. The Xpert MTB/RIF assay diagnoses TB with 98% sensitivity and 89% specificity. Loop-mediated isothermal amplification (LAMP) and simultaneous amplification and testing method for Mycobacterium tuberculosis rRNA (SAT-TB) are also highly sensitive and specific for TB diagnosis. Several novel tests, such as the difference of immune complexes for the ESAT-6/SFP-10 antigen in vitro with dynamic light scattering (DLS), lung tissue-based molecular markers, and the blood transcriptome, are promising for differentiating TB from sarcoidosis. Conclusions Recent advancements in laboratory investigations, non-invasive procedures, and invasive procedures play an important role in the diagnosis of sarcoidosis in TB-endemic areas. However, further study is needed to evaluate the diagnostic performance of all tests in terms of their competency in distinguishing between TB and sarcoidosis.
Collapse
Affiliation(s)
- Tulaton Sodsri
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, Thailand
| | - Robert P. Baughman
- Department of Internal Medicine, University of Cincinnati Medical Center, Cincinnati, OH, USA
| | - Thitiwat Sriprasart
- Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| |
Collapse
|
24
|
Phat NK, Tien NTN, Anh NK, Yen NTH, Lee YA, Trinh HKT, Le KM, Ahn S, Cho YS, Park S, Kim DH, Long NP, Shin JG. Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers. Front Immunol 2023; 14:1210372. [PMID: 38022579 PMCID: PMC10644770 DOI: 10.3389/fimmu.2023.1210372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Background The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear. Methodology In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response. Results and discussion The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers. Conclusions Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.
Collapse
Affiliation(s)
- Nguyen Ky Phat
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Tran Nam Tien
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Ky Anh
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Thi Hai Yen
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Yoon Ah Lee
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul, Republic of Korea
| | - Hoang Kim Tu Trinh
- Center for Molecular Biomedicine, University of Medicine and Pharmacy at Ho Chi Minh, Ho Chi Minh, Vietnam
| | - Kieu-Minh Le
- Center for Molecular Biomedicine, University of Medicine and Pharmacy at Ho Chi Minh, Ho Chi Minh, Vietnam
| | - Sangzin Ahn
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Yong-Soon Cho
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Seongoh Park
- School of Mathematics, Statistics and Data Science, Sungshin Women’s University, Seoul, Republic of Korea
- Data Science Center, Sungshin Women’s University, Seoul, Republic of Korea
| | - Dong Hyun Kim
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
| | - Nguyen Phuoc Long
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| | - Jae-Gook Shin
- Department of Pharmacology and PharmacoGenomics Research Center, Inje University College of Medicine, Busan, Republic of Korea
- Center for Personalized Precision Medicine of Tuberculosis, Inje University College of Medicine, Busan, Republic of Korea
| |
Collapse
|
25
|
Celada SI, Lim CX, Carisey AF, Ochsner SA, Arce Deza CF, Rexie P, Poli De Frias F, Cardenas-Castillo R, Polverino F, Hengstschläger M, Tsoyi K, McKenna NJ, Kheradmand F, Weichhart T, Rosas IO, Van Kaer L, Celada LJ. SHP2 promotes sarcoidosis severity by inhibiting SKP2-targeted ubiquitination of TBET in CD8 + T cells. Sci Transl Med 2023; 15:eade2581. [PMID: 37703351 PMCID: PMC11126869 DOI: 10.1126/scitranslmed.ade2581] [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: 08/04/2022] [Accepted: 08/24/2023] [Indexed: 09/15/2023]
Abstract
Sarcoidosis is an interstitial lung disease (ILD) characterized by interferon-γ (IFN-γ) and T-box expressed in T cells (TBET) dysregulation. Although one-third of patients progress from granulomatous inflammation to severe lung damage, the molecular mechanisms underlying this process remain unclear. Here, we found that pharmacological inhibition of phosphorylated SH2-containing protein tyrosine phosphatase-2 (pSHP2), a facilitator of aberrant IFN-γ abundance, decreased large granuloma formation and macrophage infiltration in the lungs of mice with sarcoidosis-like disease. Positive treatment outcomes were dependent on the effective enhancement of TBET ubiquitination within CD8+ T cells. Mechanistically, we identified a posttranslational modification pathway in which the E3 F-box protein S-phase kinase-associated protein 2 (SKP2) targets TBET for ubiquitination in T cells under normal conditions. However, this pathway was disrupted by aberrant pSHP2 signaling in CD8+ T cells from patients with progressive pulmonary sarcoidosis and end-stage disease. Ex vivo inhibition of pSHP2 in CD8+ T cells from patients with end-stage sarcoidosis enhanced TBET ubiquitination and suppressed IFN-γ and collagen synthesis. Therefore, these studies provided new mechanistic insights into the SHP2-dependent posttranslational regulation of TBET and identified SHP2 inhibition as a potential therapeutic intervention against severe sarcoidosis. Furthermore, these studies also suggest that the small-molecule SHP2 inhibitor SHP099 might be used as a therapeutic measure against human diseases linked to TBET or ubiquitination.
Collapse
Affiliation(s)
- Sherly I. Celada
- Department of Biological Sciences, Tennessee State University, Nashville, TN 37209, USA
| | - Clarice X. Lim
- Center of Pathobiochemistry and Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria
| | - Alexandre F. Carisey
- William T. Shearer Center for Human Immunobiology, Texas Children’s Hospital, Houston, TX 77030, USA
- Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Cell and Molecular Biology, St. Jude Children’s Hospital, Memphis, TN 38105, USA
| | - Scott A. Ochsner
- Department of Molecular and Cellular Biology, Houston, TX 77030, USA
| | - Carlos F. Arce Deza
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Praveen Rexie
- Center of Pathobiochemistry and Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria
| | - Fernando Poli De Frias
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Mout Sinai Medical Center, Miami Beach, FL 33140, USA
| | - Rafael Cardenas-Castillo
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Francesca Polverino
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Markus Hengstschläger
- Center of Pathobiochemistry and Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria
| | - Konstantin Tsoyi
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Neil J. McKenna
- Department of Molecular and Cellular Biology, Houston, TX 77030, USA
| | - Farrah Kheradmand
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey, Houston, TX 77030, USA
| | - Thomas Weichhart
- Center of Pathobiochemistry and Genetics, Institute of Medical Genetics, Medical University of Vienna, Vienna, Austria
| | - Ivan O. Rosas
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Luc Van Kaer
- Department of Pathology, Microbiology and Immunology, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| | - Lindsay J. Celada
- Department of Medicine, Section of Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, TX 77030, USA
- Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN 37232 USA
| |
Collapse
|
26
|
Suliman S, Jaganath D, DiNardo A. Predicting Pediatric Tuberculosis: The Need for Age-Specific Host Biosignatures. Clin Infect Dis 2023; 77:450-452. [PMID: 37144361 PMCID: PMC10425193 DOI: 10.1093/cid/ciad270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/03/2023] [Indexed: 05/06/2023] Open
Affiliation(s)
- Sara Suliman
- Department of Medicine, Division of Experimental Medicine, Zuckerberg San Francisco General Hospital, University of California San Francisco, San Francisco, California, USA
- Chan Zuckerberg Biohub, San Francisco, California, USA
| | - Devan Jaganath
- Division of Pediatric Infectious Diseases, University of California San Francisco, San Francisco, California, USA
| | - Andrew DiNardo
- Global TB Program, Center for Human Immunbiology, Baylor College of Medicine, Houston, Texas, USA
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| |
Collapse
|
27
|
Vargas R, Abbott L, Bower D, Frahm N, Shaffer M, Yu WH. Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatment. PLoS Comput Biol 2023; 19:e1010770. [PMID: 37471455 PMCID: PMC10393163 DOI: 10.1371/journal.pcbi.1010770] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/15/2023] [Indexed: 07/22/2023] Open
Abstract
While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximates the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring.
Collapse
Affiliation(s)
- Roger Vargas
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
- Harvard University, Cambridge, Massachusetts, United States of America
| | - Liam Abbott
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Daniel Bower
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Nicole Frahm
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Mike Shaffer
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| | - Wen-Han Yu
- Bill & Melinda Gates Medical Research Institute, Cambridge, Massachusetts, United States of America
| |
Collapse
|
28
|
Xu R, Wang J, Zhu Q, Zou C, Wei Z, Wang H, Ding Z, Meng M, Wei H, Xia S, Wei D, Deng L, Zhang S. Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer. Biomark Res 2023; 11:71. [PMID: 37475010 PMCID: PMC10360339 DOI: 10.1186/s40364-023-00497-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/05/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme. METHODS TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set. RESULTS Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%. CONCLUSIONS Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.
Collapse
Affiliation(s)
- Runhao Xu
- Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- Department of Clinical Laboratory, Renji Hospital, Shanghai, 200001, China
| | - Jiongran Wang
- Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qingqing Zhu
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Chen Zou
- Department of Clinical Laboratory, Children's Hospital of Shanghai, Shanghai, 200040, China
| | - Zehao Wei
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Hao Wang
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Zian Ding
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430000, China
| | - Minjie Meng
- School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, 510006, China
| | - Huimin Wei
- Shanghai Cellsolution Biotech Co.,Ltd, Shanghai, 200444, China
| | - Shijin Xia
- Department of Geriatrics, Huadong Hospital, Shanghai Institute of Geriatrics, Fudan University, Shanghai, 200040, China
| | - Dongqing Wei
- Department of Bioinformatics, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Nanyang, 473006, Henan, China
| | - Li Deng
- Shanghai Cellsolution Biotech Co.,Ltd, Shanghai, 200444, China.
| | - Shulin Zhang
- Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Nanyang, 473006, Henan, China.
- Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
| |
Collapse
|
29
|
Ruprecht NA, Singhal S, Schaefer K, Gill JS, Bansal B, Sens D, Singhal SK. Establishing a genomic radiation-age association for space exploration supplements lung disease differentiation. Front Public Health 2023; 11:1161124. [PMID: 37250098 PMCID: PMC10213902 DOI: 10.3389/fpubh.2023.1161124] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/07/2023] [Indexed: 05/31/2023] Open
Abstract
Purpose One possible way to quantify each individual's response or damage from ionizing radiation is to estimate their accelerated biological age following exposure. Since there is currently no definitive way to know if biological age estimations are accurate, we aim to establish a rad-age association using genomics as its foundation. Methods Two datasets were combined and used to empirically find the age cutoff between young and old patients. With age as both a categorical and continuous variable, two other datasets that included radiation exposure are used to test the interaction between radiation and age. The gene lists are oriented in preranked lists for both pathway and diseases analysis. Finally, these genes are used to evaluate another dataset on the clinical relevance in differentiating lung disease given ethnicity and sex using both pairwise t-tests and linear models. Results Using 12 well-known genes associated with aging, a threshold of 29-years-old was found to be the difference between young and old patients. The two interaction tests yielded 234 unique genes such that pathway analysis flagged IL-1 signaling and PRPP biosynthesis as significant with high cell proliferation diseases and carcinomas being a common trend. LAPTM4B was the only gene with significant interaction among lung disease, ethnicity, and sex, with fold change greater than two. Conclusion The results corroborate an initial association between radiation and age, given inflammation and metabolic pathways and multiple genes emphasizing mitochondrial function, oxidation, and histone modification. Being able to tie rad-age genes to lung disease supplements future work for risk assessment following radiation exposure.
Collapse
Affiliation(s)
- Nathan A. Ruprecht
- Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND, United States
| | - Sonalika Singhal
- Department of Pathology, University of North Dakota, Grand Forks, ND, United States
| | - Kalli Schaefer
- Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND, United States
| | - Jappreet S. Gill
- Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND, United States
| | - Benu Bansal
- Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND, United States
| | - Donald Sens
- Department of Pathology, University of North Dakota, Grand Forks, ND, United States
| | - Sandeep K. Singhal
- Department of Biomedical Engineering, University of North Dakota, Grand Forks, ND, United States
- Department of Pathology, University of North Dakota, Grand Forks, ND, United States
| |
Collapse
|
30
|
Madamarandawala P, Rajapakse S, Gunasena B, Madegedara D, Magana-Arachchi D. A host blood transcriptional signature differentiates multi-drug/rifampin-resistant tuberculosis (MDR/RR-TB) from drug susceptible tuberculosis: a pilot study. Mol Biol Rep 2023; 50:3935-3943. [PMID: 36749527 DOI: 10.1007/s11033-023-08307-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 01/27/2023] [Indexed: 02/08/2023]
Abstract
BACKGROUND Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis is one of the top thirteen causes of death worldwide. The major challenge to control TB is the emergence of drug-resistant tuberculosis (DR-TB); specifically, multi-drug resistant TB which are resistant to the most potent drugs; rifampin and isoniazid. Owing to the inconsistencies of the current diagnostic methods, a single test cannot identify the whole spectrum of DR-TB associated mutations. Recently, host blood transcriptomics has gained attention as a promising technique that develops disease-specific RNA signatures/biomarkers. However, studies on host transcriptomics infected with DR-TB is limited. Herein, we intended to identify genes/pathways that are differentially expressed in multi-drug/rifampin resistant TB (MDR/RR-TB) in contrast to drug susceptible TB. METHOD AND RESULTS We conducted blood RNA sequencing of 10 pulmonary TB patients (4; drug susceptible and 6; DR-TB) and 55 genes that were differentially expressed in MDR/RR-TB from drug-susceptible/mono-resistant TB were identified. CD300LD, MYL9, VAMP5, CARD17, CLEC2B, GBP6, BATF2, ETV7, IFI27 and FCGR1CP were found to be upregulated in MDR/RR-TB in all comparisons, among which CLEC2B and CD300LD were not previously linked to TB. In comparison pathway analysis, interferon alpha/gamma response was upregulated while Wnt/beta catenin signaling, lysosome, microtubule nucleation and notch signaling were downregulated. CONCLUSION Up/down-regulation of immunity related genes/pathways speculate the collective effect of hosts' attempt to fight against continuously multiplying DR-TB bacteria and the bacterial factors to fight against the host defense. The identified genes/pathways could act as MDR/RR-TB biomarkers, hence, further research on their clinical use should be encouraged.
Collapse
Affiliation(s)
- Pavithra Madamarandawala
- Molecular Microbiology & Human Diseases Project, National Institute of Fundamental Studies, Hantana Road, Kandy, 20000, Sri Lanka
| | - Sanath Rajapakse
- Department of Molecular Biology and Biotechnology, Faculty of Science, University of Peradeniya, Peradeniya, 20400, Sri Lanka
| | - Bandu Gunasena
- National Hospital for Respiratory Diseases, Welisara, 11010, Sri Lanka
| | - Dushantha Madegedara
- Respiratory Diseases Treatment Unit, General Teaching Hospital, Kandy, 20000, Sri Lanka
| | - Dhammika Magana-Arachchi
- Molecular Microbiology & Human Diseases Project, National Institute of Fundamental Studies, Hantana Road, Kandy, 20000, Sri Lanka.
| |
Collapse
|
31
|
Wang X, VanValkenberg A, Odom-Mabey AR, Ellner JJ, Hochberg NS, Salgame P, Patil P, Johnson WE. Comparison of gene set scoring methods for reproducible evaluation of multiple tuberculosis gene signatures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.19.520627. [PMID: 36711818 PMCID: PMC9882404 DOI: 10.1101/2023.01.19.520627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Rationale Many blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed. Objectives We compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use. Methods We considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models. Measurement and Main Results For many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies. Conclusion Gene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.
Collapse
Affiliation(s)
- Xutao Wang
- Department of Biostatistics, Boston University, Boston, MA, USA
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Arthur VanValkenberg
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Aubrey R. Odom-Mabey
- Division of Computational Biomedicine and Bioinformatics Program, Boston University, Boston, MA, USA
| | - Jerrold J. Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Natasha S. Hochberg
- Boston Medical Center, Boston, MA, USA
- Section of Infectious Diseases, Boston University School of Medicine, Boston, MA, USA
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Prasad Patil
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - W. Evan Johnson
- Division of Infectious Disease, Center for Data Science, Rutgers New Jersey Medical School, Newark, NJ, USA
| |
Collapse
|
32
|
Chendi BH, Jooste T, Scriba TJ, Kidd M, Mendelsohn S, Tonby K, Walzl G, Dyrhol-Riise AM, Chegou NN. Utility of a three-gene transcriptomic signature in the diagnosis of tuberculosis in a low-endemic hospital setting. Infect Dis (Lond) 2023; 55:44-54. [PMID: 36214761 DOI: 10.1080/23744235.2022.2129779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Host transcriptomic blood signatures have demonstrated diagnostic potential for tuberculosis (TB), requiring further validation across different geographical settings. Discriminating TB from other diseases with similar clinical manifestations is crucial for the development of an accurate immunodiagnostic tool. In this exploratory cohort study, we evaluated the performance of potential blood-based transcriptomic signatures in distinguishing TB disease from non-TB lower respiratory tract infections in hospitalised patients in a TB low-endemic country. METHOD Quantitative real-time polymerase chain reaction qPCR) was used to evaluate 26 previously published genes in blood from 31 patients (14 TB and 17 lower respiratory tract infection cases) admitted to Oslo University Hospital in Norway. The diagnostic accuracies of differentially expressed genes were determined by receiver operating characteristic curves. RESULTS A significant difference (p < .01) in the age distribution was observed between patients with TB (mean age, 40 ± 15 years) and lower respiratory tract infection (mean age 59 ± 12 years). Following adjustment for age, ETV7, GBP1, GBP5, P2RY14 and BLK were significantly differentially expressed between patients with TB and those with LRI. A general discriminant analysis generated a three-gene signature (BAFT2, ETV7 and CD1C), which diagnosed TB with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, 0.69 - 1.00), sensitivity of 69.23% (95% CI, 38.57%-90.91%) and specificity of 94.12% (95% CI, 71.31%-99.85%). CONCLUSION The three-genes signature may have potential to improve diagnosis of TB in a hospitalised low-burden setting. However, the influence of confounding variables or covariates such as age requires further evaluation in larger studies.
Collapse
Affiliation(s)
- Bih Hycenta Chendi
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Tracey Jooste
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Thomas Jens Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Martin Kidd
- Department of Statistics and Actuarial Sciences, Centre for Statistical Consultation, Stellenbosch University, Cape Town, South Africa
| | - Simon Mendelsohn
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine, Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Kristian Tonby
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | - Gerhard Walzl
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Anne M Dyrhol-Riise
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.,Department of Infectious Diseases, Oslo University Hospital, Oslo, Norway
| | - Novel Njweipi Chegou
- DSI-NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| |
Collapse
|
33
|
Rao AM, Popper SJ, Gupta S, Davong V, Vaidya K, Chanthongthip A, Dittrich S, Robinson MT, Vongsouvath M, Mayxay M, Nawtaisong P, Karmacharya B, Thair SA, Bogoch I, Sweeney TE, Newton PN, Andrews JR, Relman DA, Khatri P. A robust host-response-based signature distinguishes bacterial and viral infections across diverse global populations. Cell Rep Med 2022; 3:100842. [PMID: 36543117 PMCID: PMC9797950 DOI: 10.1016/j.xcrm.2022.100842] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/12/2022] [Accepted: 11/09/2022] [Indexed: 12/24/2022]
Abstract
Limited sensitivity and specificity of current diagnostics lead to the erroneous prescription of antibiotics. Host-response-based diagnostics could address these challenges. However, using 4,200 samples across 69 blood transcriptome datasets from 20 countries from patients with bacterial or viral infections representing a broad spectrum of biological, clinical, and technical heterogeneity, we show current host-response-based gene signatures have lower accuracy to distinguish intracellular bacterial infections from viral infections than extracellular bacterial infections. Using these 69 datasets, we identify an 8-gene signature to distinguish intracellular or extracellular bacterial infections from viral infections with an area under the receiver operating characteristic curve (AUROC) > 0.91 (85.9% specificity and 90.2% sensitivity). In prospective cohorts from Nepal and Laos, the 8-gene classifier distinguished bacterial infections from viral infections with an AUROC of 0.94 (87.9% specificity and 91% sensitivity). The 8-gene signature meets the target product profile proposed by the World Health Organization and others for distinguishing bacterial and viral infections.
Collapse
Affiliation(s)
- Aditya M. Rao
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Immunology Graduate Program, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Stephen J. Popper
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Sanjana Gupta
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Viengmon Davong
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Krista Vaidya
- Dhulikhel Hospital, Kathmandu University Hospital, Kavrepalanchok, Nepal
| | - Anisone Chanthongthip
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Sabine Dittrich
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Matthew T. Robinson
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Manivanh Vongsouvath
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Mayfong Mayxay
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK,Institute of Research and Education Development (IRED), University of Health Sciences, Ministry of Health, Vientiane, Lao PDR
| | - Pruksa Nawtaisong
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
| | - Biraj Karmacharya
- Dhulikhel Hospital, Kathmandu University Hospital, Kavrepalanchok, Nepal
| | - Simone A. Thair
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Isaac Bogoch
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - Paul N. Newton
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Jason R. Andrews
- Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - David A. Relman
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University, Stanford, CA, USA,Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA,Infectious Diseases Section, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Purvesh Khatri
- Institute for Immunity, Transplantation, and Infection, Stanford University School of Medicine, 240 Pasteur Dr., Biomedical Innovation Building, Room 1553, Stanford, CA, USA,Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, CA, USA,Corresponding author
| |
Collapse
|
34
|
Ko ER, Tsalik EL. A New Era in Host Response Biomarkers to Guide Precision Medicine for Infectious Diseases. J Pediatric Infect Dis Soc 2022; 11:477-479. [PMID: 35964237 DOI: 10.1093/jpids/piac081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 07/21/2022] [Indexed: 11/14/2022]
Affiliation(s)
- Emily R Ko
- Section of Hospital Medicine, Division of General Internal Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Ephraim L Tsalik
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Danaher Diagnostics, Washington DC, USA
| |
Collapse
|
35
|
Li L, Konigsberg IR, Bhargava M, Liu S, MacPhail K, Mayer A, Davidson EJ, Liao SY, Lei Z, Mroz PM, Fingerlin TE, Yang IV, Maier LA. Multiomic Signatures of Chronic Beryllium Disease Bronchoalveolar Lavage Cells Relate to T-Cell Function and Innate Immunity. Am J Respir Cell Mol Biol 2022; 67:632-640. [PMID: 35972918 PMCID: PMC9743181 DOI: 10.1165/rcmb.2022-0077oc] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/16/2022] [Indexed: 12/15/2022] Open
Abstract
Chronic beryllium disease (CBD) is a Th1 granulomatous lung disease preceded by sensitization to beryllium (BeS). We profiled the methylome, transcriptome, and selected proteins in the lung to identify molecular signatures and networks associated with BeS and CBD. BAL cell DNA and RNA were profiled using microarrays from CBD (n = 30), BeS (n = 30), and control subjects (n = 12). BAL fluid proteins were measured using Olink Immune Response Panel proteins from CBD (n = 22) and BeS (n = 22) subjects. Linear models identified features associated with CBD, adjusting for covariation and batch effects. Multiomic integration methods identified correlated features between datasets. We identified 1,546 differentially expressed genes in CBD versus control subjects and 204 in CBD versus BeS. Of the 101 shared transcripts, 24 have significant cis relationships between gene expression and DNA methylation, assessed using expression quantitative trait methylation analysis, including genes not previously identified in CBD. A multiomic model of top DNA methylation and gene expression features demonstrated that the first component separated CBD from other samples and the second component separated control subjects from remaining samples. The top features on component one were enriched for T-lymphocyte function, and the top features on component two were enriched for innate immune signaling. We identified six differentially abundant proteins in CBD versus BeS, with two (SIT1 and SH2D1A) selected as important RNA features in the multiomic model. Our integrated analysis of DNA methylation, gene expression, and proteins in the lung identified multiomic signatures of CBD that differentiated it from BeS and control subjects.
Collapse
Affiliation(s)
- Li Li
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
- Division of Pulmonary and Critical Care Sciences
| | - Iain R. Konigsberg
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, School of Medicine
| | - Maneesh Bhargava
- Pulmonary, Allergy, Critical Care and Sleep, Department of Medicine, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Sucai Liu
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
| | - Kristyn MacPhail
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
| | - Annyce Mayer
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
- Department of Environmental and Occupational Health
| | - Elizabeth J. Davidson
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, School of Medicine
| | - Shu-Yi Liao
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
- Division of Pulmonary and Critical Care Sciences
- Department of Environmental and Occupational Health
| | - Zhe Lei
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
| | - Peggy M. Mroz
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
| | - Tasha E. Fingerlin
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
- Department of Biostatistics and Bioinformatics, and
| | - Ivana V. Yang
- Division of Pulmonary and Critical Care Sciences
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, School of Medicine
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, Colorado; and
| | - Lisa A. Maier
- Division of Environmental and Occupational Health Sciences, Department of Medicine, and
- Division of Pulmonary and Critical Care Sciences
- Department of Environmental and Occupational Health
| |
Collapse
|
36
|
Mousavian Z, Folkesson E, Fröberg G, Foroogh F, Correia-Neves M, Bruchfeld J, Källenius G, Sundling C. A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis. iScience 2022; 25:105652. [PMID: 36561889 PMCID: PMC9763869 DOI: 10.1016/j.isci.2022.105652] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/19/2022] [Accepted: 11/18/2022] [Indexed: 11/23/2022] Open
Abstract
Annually, approximately 10 million people are diagnosed with active tuberculosis (TB), and 1.4 million die of the disease. If left untreated, each person with active TB will infect 10-15 new individuals. The lack of non-sputum-based diagnostic tests leads to delayed diagnoses of active pulmonary TB cases, contributing to continued disease transmission. In this exploratory study, we aimed to identify biomarkers associated with active TB. We assessed the plasma levels of 92 proteins associated with inflammation in individuals with active TB (n = 20), latent TB (n = 14), or healthy controls (n = 10). Using co-expression network analysis, we identified one module of proteins with strong association with active TB. We removed proteins from the module that had low abundance or were associated with non-TB diseases in published transcriptomic datasets, resulting in a 12-protein plasma signature that was highly enriched in individuals with pulmonary and extrapulmonary TB and was further associated with disease severity.
Collapse
Affiliation(s)
- Zaynab Mousavian
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
| | - Elin Folkesson
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - Gabrielle Fröberg
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Microbiology, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
| | - Fariba Foroogh
- 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
| | - Margarida Correia-Neves
- Division of Infectious Diseases, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s, PT Government Associate Laboratory, Braga, Portugal
| | - Judith Bruchfeld
- Division of Infectious Diseases, Department of Medicine Solna, 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
- Corresponding author
| |
Collapse
|
37
|
Ascoli C, Schott CA, Huang Y, Turturice BA, Wang W, Ecanow N, Sweiss NJ, Perkins DL, Finn PW. Altered transcription factor targeting is associated with differential peripheral blood mononuclear cell proportions in sarcoidosis. Front Immunol 2022; 13:848759. [PMID: 36311769 PMCID: PMC9608777 DOI: 10.3389/fimmu.2022.848759] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
IntroductionIn sarcoidosis, peripheral lymphopenia and anergy have been associated with increased inflammation and maladaptive immune activity, likely promoting development of chronic and progressive disease. However, the molecular mechanisms that lead to reduced lymphocyte proportions, particularly CD4+ T-cells, have not been fully elucidated. We posit that paradoxical peripheral lymphopenia is characterized by a dysregulated transcriptomic network associated with cell function and fate that results from altered transcription factor targeting activity.MethodsMessenger RNA-sequencing (mRNA-seq) was performed on peripheral blood mononuclear cells (PBMCs) from ACCESS study subjects with sarcoidosis and matched controls and findings validated on a sarcoidosis case-control cohort and a sarcoidosis case series. Preserved PBMC transcriptomic networks between case-control cohorts were assessed to establish cellular associations with gene modules and define regulatory targeting involved in sarcoidosis immune dysregulation utilizing weighted gene co-expression network analysis and differential transcription factor involvement analysis. Network centrality measures identified master transcriptional regulators of subnetworks related to cell proliferation and death. Predictive models of differential PBMC proportions constructed from ACCESS target gene expression corroborated the relationship between aberrant transcription factor regulatory activity and imputed and clinical PBMC populations in the validation cohorts.ResultsWe identified two unique and preserved gene modules significantly associated with sarcoidosis immune dysregulation. Strikingly, increased expression of a monocyte-driven, and not a lymphocyte-driven, gene module related to innate immunity and cell death was the best predictor of peripheral CD4+ T-cell proportions. Within the gene network of this monocyte-driven module, TLE3 and CBX8 were determined to be master regulators of the cell death subnetwork. A core gene signature of differentially over-expressed target genes of TLE3 and CBX8 involved in cellular communication and immune response regulation accurately predicted imputed and clinical monocyte expansion and CD4+ T-cell depletion.ConclusionsAltered transcriptional regulation associated with aberrant gene expression of a monocyte-driven transcriptional network likely influences lymphocyte function and survival. Although further investigation is warranted, this indicates that crosstalk between hyperactive monocytes and lymphocytes may instigate peripheral lymphopenia and underlie sarcoidosis immune dysregulation and pathogenesis. Future therapies selectively targeting master regulators, or their targets, may mitigate dysregulated immune processes in sarcoidosis and disease progression.
Collapse
Affiliation(s)
- Christian Ascoli
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Cody A. Schott
- University of Illinois at Chicago College of Medicine, Chicago, IL, United States
| | - Yue Huang
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | | | - Wangfei Wang
- Department of Bioengineering, University of Illinois at Chicago College of Engineering and Medicine, Chicago, IL, United States
| | - Naomi Ecanow
- University of Illinois at Chicago College of Medicine, Chicago, IL, United States
| | - Nadera J. Sweiss
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
- Division of Rheumatology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - David L. Perkins
- Division of Nephrology, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Patricia W. Finn
- Division of Pulmonary, Critical Care, Sleep, and Allergy, Department of Medicine, University of Illinois at Chicago, Chicago, IL, United States
- *Correspondence: Patricia W. Finn,
| |
Collapse
|
38
|
Kaipilyawar V, Zhao Y, Wang X, Joseph NM, Knudsen S, Prakash Babu S, Muthaiah M, Hochberg NS, Sarkar S, Horsburgh CR, Ellner JJ, Johnson WE, Salgame P. Development and Validation of a Parsimonious Tuberculosis Gene Signature Using the digital NanoString nCounter Platform. Clin Infect Dis 2022; 75:1022-1030. [PMID: 35015839 PMCID: PMC9522394 DOI: 10.1093/cid/ciac010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. METHODS The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker. RESULTS Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB. CONCLUSIONS The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.
Collapse
Affiliation(s)
- Vaishnavi Kaipilyawar
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - Yue Zhao
- Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Xutao Wang
- Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Noyal M Joseph
- Department of Microbiology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | | | - Senbagavalli Prakash Babu
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Muthuraj Muthaiah
- Department of Microbiology, State TB Training and Demonstration Center, Government Hospital for Chest Disease, Gorimedu, Puducherry, India
| | - Natasha S Hochberg
- Boston Medical Center, Boston, Massachusetts, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Medicine, Section of Infectious Diseases, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Sonali Sarkar
- Department of Preventive and Social Medicine, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Charles R Horsburgh
- Department of Epidemiology, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Jerrold J Ellner
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| | - W Evan Johnson
- Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Padmini Salgame
- Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA
| |
Collapse
|
39
|
Kelly E, Whelan SO, Harriss E, Murphy S, Pollard AJ, O' Connor D. Systematic review of host genomic biomarkers of invasive bacterial disease: Distinguishing bacterial from non-bacterial causes of acute febrile illness. EBioMedicine 2022; 81:104110. [PMID: 35792524 PMCID: PMC9256842 DOI: 10.1016/j.ebiom.2022.104110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/27/2022] [Accepted: 05/28/2022] [Indexed: 12/03/2022] Open
Abstract
Background Infectious diseases play a significant role in the global burden of disease. The gold standard for the diagnosis of bacterial infection, bacterial culture, can lead to diagnostic delays and inappropriate antibiotic use. The advent of high- throughput technologies has led to the discovery of host-based genomic biomarkers of infection, capable of differentiating bacterial from other causes of infection, but few have achieved validation for use in a clinical setting. Methods A systematic review was performed. PubMed/Ovid Medline, Ovid Embase and Scopus databases were searched for relevant studies from inception up to 30/03/2022 with forward and backward citation searching of key references. Studies assessing the diagnostic performance of human host genomic biomarkers of bacterial infection were included. Study selection and assessment of quality were conducted by two independent reviewers. A meta-analysis was undertaken using a diagnostic random-effects model. The review was registered with PROSPERO (ID: CRD42021208462). Findings Seventy-two studies evaluating the performance of 116 biomarkers in 16,216 patients were included. Forty-six studies examined TB-specific biomarker performance and twenty-four studies assessed biomarker performance in a paediatric population. The results of pooled sensitivity, specificity, negative and positive likelihood ratio, and diagnostic odds ratio of genomic biomarkers of bacterial infection were 0.80 (95% CI 0.78 to 0.82), 0.86 (95% CI 0.84 to 0.88), 0.18 (95% CI 0.16 to 0.21), 5.5 (95% CI 4.9 to 6.3), 30.1 (95% CI 24 to 37), respectively. Significant between-study heterogeneity (I2 77%) was present. Interpretation Host derived genomic biomarkers show significant potential for clinical use as diagnostic tests of bacterial infection however, further validation and attention to test platform is warranted before clinical implementation can be achieved. Funding No funding received.
Collapse
Affiliation(s)
- Eimear Kelly
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford. UK; NIHR Oxford Biomedical Research Centre, Oxford, UK.
| | - Seán Olann Whelan
- Department of Clinical Microbiology, Galway University Hospital, Galway, Ireland
| | - Eli Harriss
- Bodleian Health Care Libraries, University of Oxford
| | - Sarah Murphy
- Department of Paediatrics, Cork University Maternity Hospital, Wilton, Cork, Ireland
| | - Andrew J Pollard
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford. UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Daniel O' Connor
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford. UK; NIHR Oxford Biomedical Research Centre, Oxford, UK
| |
Collapse
|
40
|
Role of GBP1 in innate immunity and potential as a tuberculosis biomarker. Sci Rep 2022; 12:11097. [PMID: 35773466 PMCID: PMC9247026 DOI: 10.1038/s41598-022-15482-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 06/24/2022] [Indexed: 01/19/2023] Open
Abstract
Tuberculosis (TB) is a global health problem of major concern. Identification of immune biomarkers may facilitate the early diagnosis and targeted treatment of TB. We used public RNA-sequencing datasets of patients with TB and healthy controls to identify differentially expressed genes and their associated functional networks. GBP1 expression was consistently significantly upregulated in TB, and 4492 differentially expressed genes were simultaneously associated with TB and high GBP1 expression. Weighted gene correlation analysis identified 12 functional modules. Modules positively correlated with TB and high GBP1 expression were associated with the innate immune response, neutrophil activation, neutrophil-mediated immunity, and NOD receptor signaling pathway. Eleven hub genes (GBP1, HLA-B, ELF4, HLA-E, IFITM2, TNFRSF14, CD274, AIM2, CFB, RHOG, and HORMAD1) were identified. The least absolute shrinkage and selection operator model based on hub genes accurately predicted the occurrence of TB (area under the receiver operating characteristic curve = 0.97). The GBP1-module-pathway network based on the STRING database showed that GBP1 expression correlated with the expression of interferon-stimulated genes (GBP5, BATF2, EPSTI1, RSAD2, IFI44L, IFIT3, and OAS3). Our study suggests GBP1 as an optimal diagnostic biomarker for TB, further indicating an association of the AIM2 inflammasome signaling pathway in TB pathology.
Collapse
|
41
|
Kalesinskas L, Gupta S, Khatri P. Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology. PLoS Comput Biol 2022; 18:e1010260. [PMID: 35759523 PMCID: PMC9269905 DOI: 10.1371/journal.pcbi.1010260] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 07/08/2022] [Accepted: 05/29/2022] [Indexed: 01/07/2023] Open
Abstract
A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.
Collapse
Affiliation(s)
- Laurynas Kalesinskas
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, United States of America
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, California, United States of America
| | - Sanjana Gupta
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, United States of America
| | - Purvesh Khatri
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, California, United States of America
- Center for Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California, United States of America
- * E-mail:
| |
Collapse
|
42
|
Martínez-Pérez A, Estévez O, González-Fernández Á. Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis. Front Microbiol 2022; 13:835620. [PMID: 35283833 PMCID: PMC8908424 DOI: 10.3389/fmicb.2022.835620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
While Tuberculosis (TB) infection remains a serious challenge worldwide, big data and “omic” approaches have greatly contributed to the understanding of the disease. Transcriptomics have been used to tackle a wide variety of queries including diagnosis, treatment evolution, latency and reactivation, novel target discovery, vaccine response or biomarkers of protection. Although a powerful tool, the elevated cost and difficulties in data interpretation may hinder transcriptomics complete potential. Technology evolution and collaborative efforts among multidisciplinary groups might be key in its exploitation. Here, we discuss the main fields explored in TB using transcriptomics, and identify the challenges that need to be addressed for a real implementation in TB diagnosis, prevention and therapy.
Collapse
Affiliation(s)
- Amparo Martínez-Pérez
- Biomedical Research Center (CINBIO), Universidade de Vigo, Vigo, Spain.,Hospital Álvaro Cunqueiro, Galicia Sur Health Research Institute (IIS-GS), Vigo, Spain
| | - Olivia Estévez
- Biomedical Research Center (CINBIO), Universidade de Vigo, Vigo, Spain.,Hospital Álvaro Cunqueiro, Galicia Sur Health Research Institute (IIS-GS), Vigo, Spain
| | - África González-Fernández
- Biomedical Research Center (CINBIO), Universidade de Vigo, Vigo, Spain.,Hospital Álvaro Cunqueiro, Galicia Sur Health Research Institute (IIS-GS), Vigo, Spain
| |
Collapse
|
43
|
Dirix V, Collart P, Van Praet A, Hites M, Dauby N, Allard S, Racapé J, Singh M, Locht C, Mascart F, Corbière V. Immuno-Diagnosis of Active Tuberculosis by a Combination of Cytokines/Chemokines Induced by Two Stage-Specific Mycobacterial Antigens: A Pilot Study in a Low TB Incidence Country. Front Immunol 2022; 13:842604. [PMID: 35359958 PMCID: PMC8960450 DOI: 10.3389/fimmu.2022.842604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/11/2022] [Indexed: 11/27/2022] Open
Abstract
Active tuberculosis (aTB) remains a major killer from infectious disease, partially due to delayed diagnosis and hence treatment. Classical microbiological methods are slow and lack sensitivity, molecular techniques are costly and often unavailable. Moreover, available immuno-diagnostic tests lack sensitivity and do not differentiate between aTB and latent TB infection (LTBI). Here, we evaluated the performance of the combined measurement of different chemokines/cytokines induced by two different stage-specific mycobacterial antigens, Early-secreted-antigenic target-6 (ESAT-6) and Heparin-binding-haemagglutinin (HBHA), after a short in vitro incubation of either peripheral blood mononuclear cells (PBMC) or whole blood (WB). Blood samples were collected from a training cohort comprising 22 aTB patients, 22 LTBI subjects and 17 non-infected controls. The concentrations of 13 cytokines were measured in the supernatants. Random forest analysis identified the best markers to differentiate M. tuberculosis-infected from non-infected subjects, and the most appropriate markers to differentiate aTB from LTBI. Logistic regression defined predictive abilities of selected combinations of cytokines, first on the training and then on a validation cohort (17 aTB, 27 LTBI, 25 controls). Combining HBHA- and ESAT-6-induced IFN-γ concentrations produced by PBMC was optimal to differentiate infected from non-infected individuals in the training cohort (100% correct classification), but 2/16 (13%) patients with aTB were misclassified in the validation cohort. ESAT-6-induced-IP-10 combined with HBHA-induced-IFN-γ concentrations was selected to differentiate aTB from LTBI, and correctly classified 82%/77% of infected subjects as aTB or LTBI in the training/validation cohorts, respectively. Results obtained on WB also selected ESAT-6- and HBHA-induced IFN-γ concentrations to provided discrimination between infected and non-infected subjects (89%/90% correct classification in the training/validation cohorts). Further identification of aTB patients among infected subjects was best achieved by combining ESAT-6-induced IP-10 with HBHA-induced IL-2 and GM-CSF. Among infected subjects, 90%/93% of the aTB patients were correctly identified in the training/validation cohorts. We therefore propose a two steps strategy performed on 1 mL WB for a rapid identification of patients with aTB. After elimination of most non-infected subjects by combining ESAT-6 and HBHA-induced IFN-γ, the combination of IP-10, IL-2 and GM-CSF released by either ESAT-6 or HBHA correctly identifies most patients with aTB.
Collapse
Affiliation(s)
- Violette Dirix
- Laboratory of Vaccinology and Mucosal Immunity, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Philippe Collart
- Biostatistiques du Pôle Santé (BIOPS), Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Anne Van Praet
- Laboratory of Vaccinology and Mucosal Immunity, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Maya Hites
- Clinique des maladies infectieuses et tropicales, Hôpital Erasme, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Nicolas Dauby
- Department of Infectious Diseases, Centre Hospitalier Universitaire Saint-Pierre, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
- Institute for Medical Immunology, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Sabine Allard
- Dienst Interne Geneeskunde - Infectiologie, Universitair Ziekenhuis Brussel (UZ Brussel), Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Judith Racapé
- Biomedical Research Center, Erasme Hospital, Brussels, Belgium
| | - Mahavir Singh
- Lionex Diagnostics and Therapeutics, Braunschweig, Germany
| | - Camille Locht
- Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 – UMR9017 – CIIL – Center for Infection and Immunity of Lille, Lille, France
| | - Françoise Mascart
- Laboratory of Vaccinology and Mucosal Immunity, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| | - Véronique Corbière
- Laboratory of Vaccinology and Mucosal Immunity, Université Libre de Bruxelles (U.L.B.), Brussels, Belgium
| |
Collapse
|
44
|
Bobak CA, Abhimanyu, Natarajan H, Gandhi T, Grimm SL, Nishiguchi T, Koster K, Longlax SC, Dlamini Q, Kahari J, Mtetwa G, Cirillo JD, O’Malley J, Hill JE, Coarfa C, DiNardo AR. Increased DNA methylation, cellular senescence and premature epigenetic aging in guinea pigs and humans with tuberculosis. Aging (Albany NY) 2022; 14:2174-2193. [PMID: 35256539 PMCID: PMC8954968 DOI: 10.18632/aging.203936] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 02/22/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND Tuberculosis (TB) is the archetypical chronic infection, with patients having months of symptoms before diagnosis. In the two years after successful therapy, survivors of TB have a three-fold increased risk of death. METHODS Guinea pigs were infected with Mycobacterium tuberculosis (Mtb) for 45 days, followed by RRBS DNA methylation analysis. In humans, network analysis of differentially expressed genes across three TB cohorts were visualized at the pathway-level. Serum levels of inflammation were measured by ELISA. Horvath (DNA methylation) and RNA-seq biological clocks were used to investigate shifts in chronological age among humans with TB. RESULTS Guinea pigs with TB demonstrated DNA hypermethylation and showed system-level similarity to humans with TB (p-value = 0.002). The transcriptome in TB in multiple cohorts was enriched for DNA methylation and cellular senescence. Senescence associated proteins CXCL9, CXCL10, and TNF were elevated in TB patients compared to healthy controls. Humans with TB demonstrate 12.7 years (95% CI: 7.5, 21.9) and 14.38 years (95% CI: 10.23-18.53) of cellular aging as measured by epigenetic and gene expression based cellular clocks, respectively. CONCLUSIONS In both guinea pigs and humans, TB perturbs epigenetic processes, promoting premature cellular aging and inflammation, a plausible means to explain the long-term detrimental health outcomes after TB.
Collapse
Affiliation(s)
- Carly A. Bobak
- Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - Abhimanyu
- The Global Tuberculosis Program, Baylor College of Medicine, Houston, TX 77030, USA
- William Shearer Center for Human Immunobiology, Texas Children's Hospital, Houston, TX 77030, USA
- Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Harini Natarajan
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH 03755, USA
| | - Tanmay Gandhi
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
- Molecular and Cellular Biology Department, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sandra L. Grimm
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
- Molecular and Cellular Biology Department, Baylor College of Medicine, Houston, TX 77030, USA
| | - Tomoki Nishiguchi
- The Global Tuberculosis Program, Baylor College of Medicine, Houston, TX 77030, USA
- William Shearer Center for Human Immunobiology, Texas Children's Hospital, Houston, TX 77030, USA
- Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Kent Koster
- Department of Microbial Pathogenesis and Immunology, Texas A&M University Health, Bryan, TX 77807, USA
| | - Santiago Carrero Longlax
- The Global Tuberculosis Program, Baylor College of Medicine, Houston, TX 77030, USA
- William Shearer Center for Human Immunobiology, Texas Children's Hospital, Houston, TX 77030, USA
- Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Qiniso Dlamini
- Baylor-Swaziland Children’s Foundation, Mbabane, Swaziland
| | | | - Godwin Mtetwa
- Baylor-Swaziland Children’s Foundation, Mbabane, Swaziland
| | - Jeffrey D. Cirillo
- Department of Microbial Pathogenesis and Immunology, Texas A&M University Health, Bryan, TX 77807, USA
| | - James O’Malley
- Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
- The Dartmouth Institute, Dartmouth College, Hanover, NH 03755, USA
| | - Jane E. Hill
- Department of Chemical and Biological Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Cristian Coarfa
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
- Molecular and Cellular Biology Department, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andrew R. DiNardo
- The Global Tuberculosis Program, Baylor College of Medicine, Houston, TX 77030, USA
- William Shearer Center for Human Immunobiology, Texas Children's Hospital, Houston, TX 77030, USA
- Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| |
Collapse
|
45
|
Thakur C, Tripathi A, Ravichandran S, Shivananjaiah A, Chakraborty A, Varadappa S, Chikkavenkatappa N, Nagarajan D, Lakshminarasimhaiah S, Singh A, Chandra N. A new blood-based RNA signature (R 9), for monitoring effectiveness of tuberculosis treatment in a South Indian longitudinal cohort. iScience 2022; 25:103745. [PMID: 35118358 PMCID: PMC8800112 DOI: 10.1016/j.isci.2022.103745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 03/31/2021] [Accepted: 01/06/2022] [Indexed: 11/17/2022] Open
Abstract
Tuberculosis (TB) treatment involves a multidrug regimen for six months, and until two months, it is unclear if treatment is effective. This delay can lead to the evolution of drug resistance, lung damage, disease spread, and transmission. We identify a blood-based 9-gene signature using a computational pipeline that constructs and interrogates a genome-wide transcriptome-integrated protein-interaction network. The identified signature is able to determine treatment response at week 1-2 in three independent public datasets. Signature-based R9-score correctly detected treatment response at individual timepoints (204 samples) from a newly developed South Indian longitudinal cohort involving 32 patients with pulmonary TB. These results are consistent with conventional clinical metrics and can discriminate good from poor treatment responders at week 2 (AUC 0.93(0.81-1.00)). In this work, we provide proof of concept that the R9-score can determine treatment effectiveness, making a case for designing a larger clinical study.
Collapse
Affiliation(s)
- Chandrani Thakur
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | - Ashutosh Tripathi
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
- Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | | | - Akshatha Shivananjaiah
- SDS Tuberculosis Research Centre and Rajiv Gandhi Institute of Chest Diseases, Bangalore, India
| | - Anushree Chakraborty
- SDS Tuberculosis Research Centre and Rajiv Gandhi Institute of Chest Diseases, Bangalore, India
| | - Sreekala Varadappa
- SDS Tuberculosis Research Centre and Rajiv Gandhi Institute of Chest Diseases, Bangalore, India
| | | | - Deepesh Nagarajan
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
| | | | - Amit Singh
- Department of Microbiology and Cell Biology, Indian Institute of Science, Bangalore, India
- Centre for Infectious Disease Research, Indian Institute of Science, Bangalore, India
| | - Nagasuma Chandra
- Department of Biochemistry, Indian Institute of Science, Bangalore, India
- National Mathematics Initiative, Indian Institute of Science, Bangalore, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, India
| |
Collapse
|
46
|
DiNardo AR, Gandhi T, Heyckendorf J, Grimm SL, Rajapakshe K, Nishiguchi T, Reimann M, Kirchner HL, Kahari J, Dlamini Q, Lange C, Goldmann T, Marwitz S, Abhimanyu, Cirillo JD, Kaufmann SH, Netea MG, van Crevel R, Mandalakas AM, Coarfa C. Gene expression signatures identify biologically and clinically distinct tuberculosis endotypes. Eur Respir J 2022; 60:13993003.02263-2021. [PMID: 35169026 PMCID: PMC9474892 DOI: 10.1183/13993003.02263-2021] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/27/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND In vitro, animal model, and clinical evidence suggests that tuberculosis is not a monomorphic disease, and that host response to tuberculosis is protean with multiple distinct molecular pathways and pathologies (endotypes). We applied unbiased clustering to identify separate tuberculosis endotypes with classifiable gene expression patterns and clinical outcomes. METHODS A cohort comprised of microarray gene expression data from microbiologically confirmed tuberculosis patients were used to identify putative endotypes. One microarray cohort with longitudinal clinical outcomes was reserved for validation, as was two RNA-seq cohorts. Finally, a separate cohort of tuberculosis patients with functional immune responses was evaluated to clarify stimulated from unstimulated immune responses. RESULTS A discovery cohort, including 435 tuberculosis patients and 533 asymptomatic controls, identified two tuberculosis endotypes. Endotype A is characterised by increased expression of genes related to inflammation and immunity and decreased metabolism and proliferation; in contrast, endotype B has increased activity of metabolism and proliferation pathways. An independent RNA-seq validation cohort, including 118 tuberculosis patients and 179 controls, validated the discovery results. Gene expression signatures for treatment failure were elevated in endotype A in the discovery cohort, and a separate validation cohort confirmed that endotype A patients had slower time to culture conversion, and a reduced cure rate. These observations suggest that endotypes reflect functional immunity, supported by the observation that tuberculosis patients with a hyperinflammatory endotype have less responsive cytokine production upon stimulation. CONCLUSION These findings provide evidence that metabolic and immune profiling could inform optimisation of endotype-specific host-directed therapies for tuberculosis.
Collapse
Affiliation(s)
- Andrew R DiNardo
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, WTS Center for Human Immunobiology, Department of Pediatrics, Baylor College of Medicine, Houston, USA .,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands.,Co-first authors contributing equally
| | - Tanmay Gandhi
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, USA.,Molecular and Cellular Biology Department, Baylor College of Medicine, Houston, USA.,Co-first authors contributing equally
| | - Jan Heyckendorf
- Division of Clinical Infectious Diseases, Research Center Borstel; German Center for Infection Research (DZIF) Clinical Tuberculosis Unit, Borstel, Germany.,Respiratory Medicine & International Health, University of Lübeck, Lübeck, Germany.,Co-first authors contributing equally
| | - Sandra L Grimm
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, USA.,Co-first authors contributing equally
| | - Kimal Rajapakshe
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, USA.,Molecular and Cellular Biology Department, Baylor College of Medicine, Houston, USA
| | - Tomoki Nishiguchi
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, WTS Center for Human Immunobiology, Department of Pediatrics, Baylor College of Medicine, Houston, USA
| | - Maja Reimann
- Division of Clinical Infectious Diseases, Research Center Borstel; German Center for Infection Research (DZIF) Clinical Tuberculosis Unit, Borstel, Germany
| | - H Lester Kirchner
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, WTS Center for Human Immunobiology, Department of Pediatrics, Baylor College of Medicine, Houston, USA
| | - Jaqueline Kahari
- Respiratory Medicine & International Health, University of Lübeck, Lübeck, Germany
| | - Qiniso Dlamini
- Respiratory Medicine & International Health, University of Lübeck, Lübeck, Germany
| | - Christoph Lange
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, WTS Center for Human Immunobiology, Department of Pediatrics, Baylor College of Medicine, Houston, USA.,Division of Clinical Infectious Diseases, Research Center Borstel; German Center for Infection Research (DZIF) Clinical Tuberculosis Unit, Borstel, Germany.,Respiratory Medicine & International Health, University of Lübeck, Lübeck, Germany
| | - Torsten Goldmann
- Division of Clinical Infectious Diseases, Research Center Borstel; German Center for Infection Research (DZIF) Clinical Tuberculosis Unit, Borstel, Germany
| | - Sebastian Marwitz
- Division of Clinical Infectious Diseases, Research Center Borstel; German Center for Infection Research (DZIF) Clinical Tuberculosis Unit, Borstel, Germany
| | | | - Abhimanyu
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, WTS Center for Human Immunobiology, Department of Pediatrics, Baylor College of Medicine, Houston, USA
| | - Jeffrey D Cirillo
- Department of Microbial Pathogenesis and Immunology, Texas A&M Health Science Center, Bryan, TX, USA
| | - Stefan He Kaufmann
- Max Planck Institute for Infection Biology, Berlin, Germany.,Hagler Institute for Advanced Study at Texas A&M University, College Station, TX, USA.,Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands.,Genomics and Immunoregulation, Life & Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Reinout van Crevel
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, Netherlands.,Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Anna M Mandalakas
- The Global Tuberculosis Program, Texas Children's Hospital, Immigrant and Global Health, WTS Center for Human Immunobiology, Department of Pediatrics, Baylor College of Medicine, Houston, USA.,Co-senior authors contributing equally
| | - Cristian Coarfa
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, USA.,Molecular and Cellular Biology Department, Baylor College of Medicine, Houston, USA.,Center for Precision Environmental Health, Baylor College of Medicine, Houston, USA.,Co-senior authors contributing equally
| |
Collapse
|
47
|
Wen G, Zhou T, Gu W. The potential of using blood circular RNA as liquid biopsy biomarker for human diseases. Protein Cell 2021; 12:911-946. [PMID: 33131025 PMCID: PMC8674396 DOI: 10.1007/s13238-020-00799-3] [Citation(s) in RCA: 106] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 10/09/2020] [Indexed: 12/14/2022] Open
Abstract
Circular RNA (circRNA) is a novel class of single-stranded RNAs with a closed loop structure. The majority of circRNAs are formed by a back-splicing process in pre-mRNA splicing. Their expression is dynamically regulated and shows spatiotemporal patterns among cell types, tissues and developmental stages. CircRNAs have important biological functions in many physiological processes, and their aberrant expression is implicated in many human diseases. Due to their high stability, circRNAs are becoming promising biomarkers in many human diseases, such as cardiovascular diseases, autoimmune diseases and human cancers. In this review, we focus on the translational potential of using human blood circRNAs as liquid biopsy biomarkers for human diseases. We highlight their abundant expression, essential biological functions and significant correlations to human diseases in various components of peripheral blood, including whole blood, blood cells and extracellular vesicles. In addition, we summarize the current knowledge of blood circRNA biomarkers for disease diagnosis or prognosis.
Collapse
Affiliation(s)
- Guoxia Wen
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tong Zhou
- Department of Physiology and Cell Biology, Reno School of Medicine, University of Nevada, Reno, NV, 89557, USA.
| | - Wanjun Gu
- State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, 210096, China.
| |
Collapse
|
48
|
de Araujo LS, Ribeiro-Alves M, Wipperman MF, Vorkas CK, Pessler F, Saad MHF. Transcriptomic Biomarkers for Tuberculosis: Validation of NPC2 as a Single mRNA Biomarker to Diagnose TB, Predict Disease Progression, and Monitor Treatment Response. Cells 2021; 10:cells10102704. [PMID: 34685683 PMCID: PMC8534371 DOI: 10.3390/cells10102704] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/13/2022] Open
Abstract
External validation in different cohorts is a key step in the translational development of new biomarkers. We previously described three host mRNA whose expression in peripheral blood is significantly higher (NPC2) or lower (DOCK9 and EPHA4) in individuals with TB compared to latent TB infection (LTBI) and controls. We have now conducted an independent validation of these genes by re-analyzing publicly available transcriptomic datasets from Brazil, China, Haiti, India, South Africa, and the United Kingdom. Comparisons between TB and control/LTBI showed significant differential expression of all three genes (NPC2high p < 0.01, DOCK9low p < 0.01, and EPHA4low p < 0.05). NPC2high had the highest mean area under the ROC curve (AUROC) for the differentiation of TB vs. controls (0.95) and LTBI (0.94). In addition, NPC2 accurately distinguished TB from the clinically similar conditions pneumonia (AUROC, 0.88), non-active sarcoidosis (0.87), and lung cancer (0.86), but not from active sarcoidosis (0.66). Interestingly, individuals progressing from LTBI to TB showed a constant increase in NPC2 expression with time when compared to non-progressors (p < 0.05), with a significant change closer to manifestation of active disease (≤3 months, p = 0.003). Moreover, NPC2 expression normalized with completion of anti-TB treatment. Taken together, these results validate NPC2 mRNA as a diagnostic host biomarker for active TB independent of host genetic background. Moreover, they reveal its potential to predict progression from latent to active infection and to indicate a response to anti-TB treatment.
Collapse
Affiliation(s)
- Leonardo S. de Araujo
- Cellular Microbiology Laboratory, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 20045-360, Brazil;
- Research Group Biomarkers for Infectious Diseases, TWINCORE Centre for Experimental and Clinical Infection Research, 30519 Hannover, Germany
| | - Marcelo Ribeiro-Alves
- National Institute of Infectology Evandro Chagas, Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 21040-360, Brazil;
| | - Matthew F. Wipperman
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA; (M.F.W.); (C.K.V.)
- Clinical and Translational Science Center, Weill Cornell Medicine, New York, NY 10021, USA
| | - Charles Kyriakos Vorkas
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA; (M.F.W.); (C.K.V.)
- Division of Infectious Diseases, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Frank Pessler
- Research Group Biomarkers for Infectious Diseases, TWINCORE Centre for Experimental and Clinical Infection Research, 30519 Hannover, Germany
- Centre for Individualised Infection Medicine, 30625 Hannover, Germany
- Helmholtz Center for Infection Research, 38124 Braunschweig, Germany
- Correspondence: or (F.P.); or (M.H.F.S.); Tel.: +49-511-220027167 (F.P.); +55-21-25621598 (M.H.F.S.)
| | - Maria Helena Féres Saad
- Cellular Microbiology Laboratory, Oswaldo Cruz Institute (IOC), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro 20045-360, Brazil;
- Correspondence: or (F.P.); or (M.H.F.S.); Tel.: +49-511-220027167 (F.P.); +55-21-25621598 (M.H.F.S.)
| |
Collapse
|
49
|
Tabone O, Verma R, Singhania A, Chakravarty P, Branchett WJ, Graham CM, Lee J, Trang T, Reynier F, Leissner P, Kaiser K, Rodrigue M, Woltmann G, Haldar P, O'Garra A. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis. J Exp Med 2021; 218:212624. [PMID: 34491266 PMCID: PMC8493863 DOI: 10.1084/jem.20210915] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/08/2021] [Accepted: 08/05/2021] [Indexed: 12/02/2022] Open
Abstract
Blood transcriptomics have revealed major characteristics of the immune response in active TB, but the signature early after infection is unknown. In a unique clinically and temporally well-defined cohort of household contacts of active TB patients that progressed to TB, we define minimal changes in gene expression in incipient TB increasing in subclinical and clinical TB. While increasing with time, changes in gene expression were highest at 30 d before diagnosis, with heterogeneity in the response in household TB contacts and in a published cohort of TB progressors as they progressed to TB, at a bulk cohort level and in individual progressors. Blood signatures from patients before and during anti-TB treatment robustly monitored the treatment response distinguishing early and late responders. Blood transcriptomics thus reveal the evolution and resolution of the immune response in TB, which may help in clinical management of the disease.
Collapse
Affiliation(s)
- Olivier Tabone
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK
| | - Raman Verma
- Department of Respiratory Sciences, National Institute for Health Research Respiratory Biomedical Research Centre, University of Leicester, UK
| | - Akul Singhania
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK
| | | | - William J Branchett
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK
| | - Christine M Graham
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK
| | - Jo Lee
- Department of Respiratory Sciences, National Institute for Health Research Respiratory Biomedical Research Centre, University of Leicester, UK
| | - Tran Trang
- Bioaster Microbiology Technology Institute, Lyon, France
| | | | | | - Karine Kaiser
- Medical Diagnostic Discovery Department, bioMérieux SA, Marcy l'Etoile, France
| | - Marc Rodrigue
- Global Medical Affairs, bioMérieux SA, Marcy l'Etoile, France
| | - Gerrit Woltmann
- Department of Respiratory Sciences, National Institute for Health Research Respiratory Biomedical Research Centre, University of Leicester, UK
| | - Pranabashis Haldar
- Department of Respiratory Sciences, National Institute for Health Research Respiratory Biomedical Research Centre, University of Leicester, UK
| | - Anne O'Garra
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK.,National Heart and Lung Institute, Imperial College London, London, UK
| |
Collapse
|
50
|
Heyckendorf J, Marwitz S, Reimann M, Avsar K, DiNardo AR, Günther G, Hoelscher M, Ibraim E, Kalsdorf B, Kaufmann SHE, Kontsevaya I, van Leth F, Mandalakas AM, Maurer FP, Müller M, Nitschkowski D, Olaru ID, Popa C, Rachow A, Rolling T, Rybniker J, Salzer HJF, Sanchez-Carballo P, Schuhmann M, Schaub D, Spinu V, Suárez I, Terhalle E, Unnewehr M, Weiner J, Goldmann T, Lange C. Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model. Eur Respir J 2021; 58:2003492. [PMID: 33574078 PMCID: PMC11967237 DOI: 10.1183/13993003.03492-2020] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 01/20/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND The World Health Organization recommends standardised treatment durations for patients with tuberculosis (TB). We identified and validated a host-RNA signature as a biomarker for individualised therapy durations for patients with drug-susceptible (DS)- and multidrug-resistant (MDR)-TB. METHODS Adult patients with pulmonary TB were prospectively enrolled into five independent cohorts in Germany and Romania. Clinical and microbiological data and whole blood for RNA transcriptomic analysis were collected at pre-defined time points throughout therapy. Treatment outcomes were ascertained by TBnet criteria (6-month culture status/1-year follow-up). A whole-blood RNA therapy-end model was developed in a multistep process involving a machine-learning algorithm to identify hypothetical individual end-of-treatment time points. RESULTS 50 patients with DS-TB and 30 patients with MDR-TB were recruited in the German identification cohorts (DS-GIC and MDR-GIC, respectively); 28 patients with DS-TB and 32 patients with MDR-TB in the German validation cohorts (DS-GVC and MDR-GVC, respectively); and 52 patients with MDR-TB in the Romanian validation cohort (MDR-RVC). A 22-gene RNA model (TB22) that defined cure-associated end-of-therapy time points was derived from the DS- and MDR-GIC data. The TB22 model was superior to other published signatures to accurately predict clinical outcomes for patients in the DS-GVC (area under the curve 0.94, 95% CI 0.9-0.98) and suggests that cure may be achieved with shorter treatment durations for TB patients in the MDR-GIC (mean reduction 218.0 days, 34.2%; p<0.001), the MDR-GVC (mean reduction 211.0 days, 32.9%; p<0.001) and the MDR-RVC (mean reduction of 161.0 days, 23.4%; p=0.001). CONCLUSION Biomarker-guided management may substantially shorten the duration of therapy for many patients with MDR-TB.
Collapse
Affiliation(s)
- Jan Heyckendorf
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
- Authors contributed equally
| | - Sebastian Marwitz
- Pathology of the Universal Medical Center Schleswig-Holstein (UKSH) and the Research Center Borstel, Campus Borstel, Airway Research Center North (ARCN), Borstel, Germany
- German Center for Lung Research (DZL), Germany
- Authors contributed equally
| | - Maja Reimann
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
- Authors contributed equally
| | - Korkut Avsar
- Asklepios Fachkliniken München-Gauting, Munich, Germany
| | - Andrew R DiNardo
- The Global TB Program, Dept of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA
| | - Gunar Günther
- Dept of Medicine, University of Namibia School of Medicine, Windhoek, Namibia
- Inselspital Bern, Dept of Pulmonology, Bern, Switzerland
| | - Michael Hoelscher
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Elmira Ibraim
- Institutul de Pneumoftiziologie "Marius Nasta", MDR-TB Research Department, Bucharest, Romania
| | - Barbara Kalsdorf
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Stefan H E Kaufmann
- Max Planck Institute for Infection Biology, Berlin, Germany
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
- Hagler Institute for Advanced Study, Texas A&M University, College Station, TX, USA
| | - Irina Kontsevaya
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Frank van Leth
- Dept of Global Health, Amsterdam University Medical Centres, Location AMC, Amsterdam, The Netherlands
- Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands
| | - Anna M Mandalakas
- The Global TB Program, Dept of Pediatrics, Baylor College of Medicine and Texas Children's Hospital, Houston, TX, USA
| | - Florian P Maurer
- National and WHO Supranational Reference Laboratory for Mycobacteria, Research Center Borstel, Borstel, Germany
- Institute of Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | | | - Dörte Nitschkowski
- Pathology of the Universal Medical Center Schleswig-Holstein (UKSH) and the Research Center Borstel, Campus Borstel, Airway Research Center North (ARCN), Borstel, Germany
- German Center for Lung Research (DZL), Germany
| | - Ioana D Olaru
- London School of Hygiene and Tropical Medicine, London, UK
- Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Cristina Popa
- Institutul de Pneumoftiziologie "Marius Nasta", MDR-TB Research Department, Bucharest, Romania
| | - Andrea Rachow
- Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Partner Site Munich, Munich, Germany
| | - Thierry Rolling
- German Center for Infection Research (DZIF), Germany
- Division of Infectious Diseases, I. Dept of Internal Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
- Dept of Clinical Immunology of Infectious Diseases, Bernhard-Nocht-Institute for Tropical Medicine, Hamburg, Germany
| | - Jan Rybniker
- Dept I of Internal Medicine, Division of Infectious Diseases, University of Cologne, Cologne, Germany
- German Center for Infection Research (DZIF), Partner Site Bonn-Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany
| | | | - Patricia Sanchez-Carballo
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | | | - Dagmar Schaub
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Victor Spinu
- Institutul de Pneumoftiziologie "Marius Nasta", MDR-TB Research Department, Bucharest, Romania
| | - Isabelle Suárez
- Dept I of Internal Medicine, Division of Infectious Diseases, University of Cologne, Cologne, Germany
| | - Elena Terhalle
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
| | - Markus Unnewehr
- Dept of Respiratory Medicine and Infectious Diseases, St. Barbara-Klinik, Hamm, Germany
- University of Witten-Herdecke, Witten, Germany
| | - January Weiner
- Berlin Institute of HealthCUBI (Core Unit Bioinformatics), Berlin, Germany
| | - Torsten Goldmann
- Pathology of the Universal Medical Center Schleswig-Holstein (UKSH) and the Research Center Borstel, Campus Borstel, Airway Research Center North (ARCN), Borstel, Germany
- German Center for Lung Research (DZL), Germany
- Authors contributed equally
| | - Christoph Lange
- Division of Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany
- German Center for Infection Research (DZIF), Germany
- International Health/Infectious Diseases, University of Lübeck, Lübeck, Germany
- Dept of Medicine, Karolinska Institute, Stockholm, Sweden
- Authors contributed equally
| |
Collapse
|