Comprehensive plasma proteomic profiling reveals biomarkers for active tuberculosis

Evaluation of serological assays for the diagnosis of childhood tuberculosis disease: a study protocol

BACKGROUND

Tuberculosis (TB) poses a major public health challenge, particularly in children. A substantial proportion of children with TB disease remain undetected and unconfirmed. Therefore, there is an urgent need for a highly sensitive point-of-care test. This study aims to assess the performance of serological assays based on various antigen targets and antibody properties in distinguishing children (0-18 years) with TB disease (1) from healthy TB-exposed children, (2) children with non-TB lower respiratory tract infections, and (3) from children with TB infection.

METHODS

The study will use biobanked plasma samples collected from three prospective multicentric diagnostic observational studies: the Childhood TB in Switzerland (CITRUS) study, the Pediatric TB Research Network in Spain (pTBred), and the Procalcitonin guidance to reduce antibiotic treatment of lower respiratory tract infections in children and adolescents (ProPAED) study. Included are children diagnosed with TB disease or infection, healthy TB-exposed children, and sick children with non-TB lower respiratory tract infection. Serological multiplex assays will be performed to identify M. tuberculosis antigen-specific antibody features, including isotypes, subclasses, Fc receptor (FcR) binding, and IgG glycosylation.

DISCUSSION

The findings from this study will help to design serological assays for diagnosing TB disease in children. Importantly, those assays could easily be developed as low-cost point-of-care tests, thereby offering a potential solution for resource-constrained settings.

CLINICALTRIALS

GOV IDENTIFIER

NCT03044509.

Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers

BACKGROUNDNovel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections.METHODSWe applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts. Bioinformatic analysis using linear modeling and network correlation analyses identified 118 differentially expressed proteins, significant through 3 complementary analytical pipelines. Candidate biomarkers were subsequently analyzed in 2 validation cohorts of differing ethnicity using antibody-based proximity extension assays.RESULTSTB-specific host biomarkers were confirmed. A 6-protein diagnostic panel, comprising FETUB, FCGR3B, LRG1, SELL, CD14, and ADA2, differentiated patients with pulmonary TB from healthy controls and patients with other respiratory infections with high sensitivity and specificity in both cohorts.CONCLUSIONThis biomarker panel exceeds the World Health Organization Target Product Profile specificity criteria for a triage test for TB. The new biomarkers have potential for further development as near-patient TB screening assays, thereby helping to close the case-detection gap that fuels the global pandemic.FUNDINGMedical Research Council (MRC) (MR/R001065/1, MR/S024220/1, MR/P023754/1, and MR/W025728/1); the MRC and the UK Foreign Commonwealth and Development Office; the UK National Institute for Health Research (NIHR); the Wellcome Trust (094000, 203135, and CC2112); Starter Grant for Clinical Lecturers (Academy of Medical Sciences UK); the British Infection Association; the Program for Advanced Research Capacities for AIDS in Peru at Universidad Peruana Cayetano Heredia (D43TW00976301) from the Fogarty International Center at the US NIH; the UK Technology Strategy Board/Innovate UK (101556); the Francis Crick Institute, which receives funding from UKRI-MRC (CC2112); Cancer Research UK (CC2112); and the NIHR Biomedical Research Centre of Imperial College NHS.

From simple to complex: Protein-based biomarker discovery in tuberculosis

Tuberculosis (TB) is a deadly infectious disease that affects millions of people globally. TB proteomics signature discovery has been a rapidly growing area of research that aims to identify protein biomarkers for the early detection, diagnosis, and treatment monitoring of TB. In this review, we have highlighted recent advances in this field and how it is moving from the study of single proteins to high-throughput profiling and from only using proteomics to include additional types of data in multi-omics studies. We have further covered the different sample types and experimental technologies used in TB proteomics signature discovery, focusing on studies of HIV-negative adults. The published signatures were defined as either coming from hypothesis-based protein targeting or from unbiased discovery approaches. The methodological approaches influenced the type of proteins identified and were associated with the circulating protein abundance. However, both approaches largely identified proteins involved in similar biological pathways, including acute-phase responses and T-helper type 1 and type 17 responses. By analysing the frequency of proteins in the different signatures, we could also highlight potential robust biomarker candidates. Finally, we discuss the potential value of integration of multi-omics data and the importance of control cohorts and signature validation.

Advances in proteomics: characterization of the innate immune system after birth and during inflammation

Proteomics is the characterization of the protein composition, the proteome, of a biological sample. It involves the large-scale identification and quantification of proteins, peptides, and post-translational modifications. This review focuses on recent developments in mass spectrometry-based proteomics and provides an overview of available methods for sample preparation to study the innate immune system. Recent advancements in the proteomics workflows, including sample preparation, have significantly improved the sensitivity and proteome coverage of biological samples including the technically difficult blood plasma. Proteomics is often applied in immunology and has been used to characterize the levels of innate immune system components after perturbations such as birth or during chronic inflammatory diseases like rheumatoid arthritis (RA) and inflammatory bowel disease (IBD). In cancers, the tumor microenvironment may generate chronic inflammation and release cytokines to the circulation. In these situations, the innate immune system undergoes profound and long-lasting changes, the large-scale characterization of which may increase our biological understanding and help identify components with translational potential for guiding diagnosis and treatment decisions. With the ongoing technical development, proteomics will likely continue to provide increasing insights into complex biological processes and their implications for health and disease. Integrating proteomics with other omics data and utilizing multi-omics approaches have been demonstrated to give additional valuable insights into biological systems.

Proteomic-based identification of APCS as candidate protein for diagnosis of patients exhibiting anti-tubercular drug induced liver injury

Traditional markers evaluate anti-tubercular drug-induced liver injury (AT-DILI). However, these markers have certain limitations and studies are in progress to characterize AT-DILI at an early stage. In the present study, 40 patients were categorized and equally distributed into healthy controls, newly diagnosed tuberculosis (TB), TB without hepatotoxicity and TB with hepatotoxicity groups based on their conventional liver function tests. Relative protein quantification was performed on depleted pooled serum samples of each representative group by LC-MS/MS, and validation of shortlisted protein was done by ELISA. Levels of all analysed biochemical parameters showed a statistical increment in the hepatotoxicity group compared to the other three groups, representing AT-DILI. Comparative proteomic analysis between TB with hepatotoxicity versus TB without hepatotoxicity groups highlighted 24 significant differentially expressed proteins, including PROS1, KNG1, CFH, LCAT, APCS and ADIPOQ. Identified proteins were involved in complement activation, triglyceride-rich lipoprotein particle remodelling and pathways comprising complement, coagulation cascades and cholesterol metabolism. Based on functional relevance, the serum amyloid P component (APCS) was shortlisted for validation, and it showed a similar trend as observed in the discovery phase with 100% sensitivity and 87% specificity; however, findings need exploration in larger cohorts.

Network analysis of plasma proteomes in affective disorders

The conventional differentiation of affective disorders into major depressive disorder (MDD) and bipolar disorder (BD) has insufficient biological evidence. Utilizing multiple proteins quantified in plasma may provide critical insight into these limitations. In this study, the plasma proteomes of 299 patients with MDD or BD (aged 19-65 years old) were quantified using multiple reaction monitoring. Based on 420 protein expression levels, a weighted correlation network analysis was performed. Significant clinical traits with protein modules were determined using correlation analysis. Top hub proteins were determined using intermodular connectivity, and significant functional pathways were identified. Weighted correlation network analysis revealed six protein modules. The eigenprotein of a protein module with 68 proteins, including complement components as hub proteins, was associated with the total Childhood Trauma Questionnaire score (r = -0.15, p = 0.009). Another eigenprotein of a protein module of 100 proteins, including apolipoproteins as hub proteins, was associated with the overeating item of the Symptom Checklist-90-Revised (r = 0.16, p = 0.006). Functional analysis revealed immune responses and lipid metabolism as significant pathways for each module, respectively. No significant protein module was associated with the differentiation between MDD and BD. In conclusion, childhood trauma and overeating symptoms were significantly associated with plasma protein networks and should be considered important endophenotypes in affective disorders.

Convolutional neural network based on T-SPOT.TB assay promoting the discrimination between active tuberculosis and latent tuberculosis infection

OBJECTIVES

The study aims to investigate the potential of convolutional neural network (CNN) based on spot image of T-SPOT assay for distinguishing active tuberculosis (ATB) from latent tuberculosis infection (LTBI).

METHODS

CNN was applied to recognize and classify T-SPOT spot image. Logistic regression was used to establish prediction model based on CNN.

RESULTS

Areas under the receiver operating characteristic curve (AUCs) of early secreted antigenic target 6 (ESAT-6) CNN, culture filtrate protein 10 (CFP-10) CNN, and phytohemagglutinin (PHA) CNN were more than 0.7 in differentiating ATB from LTBI, while the performance of these indicators was significantly better than that of spot number. Furthermore, prediction model based on the combination of CNNs yielded an AUC of 0.898. The model presented a sensitivity of 85.76% and a specificity of 90.23%.

CONCLUSIONS

The current study identified CNN based on T-SPOT spot image with the potential to serve as a tool for TB diagnostics.

Coronin-1A serves as a serum biomarker for supportive diagnosis of Mycobacterium tuberculosis infection

Introduction

The severity and spread of tuberculosis, a major burden, can be prevented by more rapid and accurate laboratory diagnosis. The purpose of this study is to systematically explore candidate serum proteins in patients with Mycobacterium tuberculosis infection for further application as novel biomarkers.

Methods

Our study was performed in two major steps: screening of the literature for potential biomarkers, and then validation of their levels in patients and controls. Many serum/plasma proteins previously reported to be abnormally expressed in patients with tuberculosis between 2012 and 2021 were comprehensively assembled. The biological role in tuberculosis was also predicted for each using the bioinformatics tool STRING. Candidate proteins found to have the same expression in other related diseases were excluded. Subsequently, the serum level of the candidate serum/plasma protein that met the aforementioned criteria was validated by sandwich ELISA; diagnostic performance was analysed by the area under the curve (AUC) of the receiver operating characteristic (ROC).

Results

From 103 collected serum/plasma proteins, coronin-1A was found to have abnormal expression only in patients with tuberculosis and was associated with tuberculosis. In addition, the validation of coronin-1A in the serum of patients with pulmonary tuberculosis revealed a higher level than in that of healthy individuals. Furthermore, the area under the ROC curve for diagnostic power of coronin-1A was 0.866, with high sensitivity and specificity at a cut-point of approximately 52.7 ng/mL.

Conclusions

We concluded that the level of serum coronin-1A might serve as a novel biomarker for alternative laboratory examination to effectively distinguish patients with tuberculosis from those with other related diseases and healthy individuals.

Commercially available CD4 + and CD8 + IFN-γ release assays combined with an HBHA-induced IGRA improve the characterization of the tuberculosis spectrum and monitoring of treatment in children

Commercially available Interferon-γ release assays (IGRAs), including the last-generation QuantiFERON TB-Plus (QFT-Plus), are effective in aiding the diagnosis of tuberculosis (TB) infection but cannot distinguish latent TB subjects from active TB patients. The aim of this study was to prospectively evaluate the performance of an HBHA-based IGRA, combined with commercially available IGRAs, to assess their usefulness as a prognostic biomarkers and aid in the monitoring of TB treatment in children. Following clinical, microbiological, and radiological assessment, children younger than 18 years of age classified as either LTBI or active TB were tested at baseline and during treatment by the QuantiFERON TB-Plus (QFT) assay and an aliquot of whole-blood was stimulated with HBHA. Among the 655 children evaluated, 559 (85.3%) were classified as "Non TB", 44 patients (6.7%) with active TB, and 52 (7.9%) with LTBI. The median HBHA-IGRA IFN-gamma responses were able to discriminate active TB from LTBI (0.13 IU/ml vs 1.995, (p < 0,0001), those with asymptomatic TB from those with symptomatic TB (1.01 IU/ml vs 0.115 IU/ml, p 0.017), or more severe TB (p 0.022), and significantly raised during successful TB treatment (p < 0.0001). Conversely, CD4 + and CD8 + responses were similar in all groups of patients, although active TB patients had higher CD4 + responses and LTBI higher CD8 + responses.  Conclusion: HBHA-based IGRA, combined with CD4 + and CD8 + responses assessed by commercially available IGRAs, is a useful support in the characterization of the TB spectrum in children and monitoring of TB-therapy. What is Known: • Current immune diagnostics are not able to discriminate active and latent Ttuberculosis, including the recently approved QFT-PLUS.. • New immunological assays with prognostic value are highly needed. What is New: • HBHA-based IGRA, combined with CD4+ and CD8+ responses assessed by commercially available IGRAs, is a useful support for the differentiation of active and latent TB in children.. • HBHA-based IGRA, combined with CD4+ and CD8+ responses assessed by commercially available IGRAs, is a useful support in the monitoring of TBtherapy in children..

Diagnosis and biomarkers for ocular tuberculosis: From the present into the future

Tuberculosis is an airborne disease caused by Mycobacterium tuberculosis (Mtb) and can manifest both pulmonary and extrapulmonary disease, including ocular tuberculosis (OTB). Accurate diagnosis and swift optimal treatment initiation for OTB is faced by many challenges combined with the lack of standardized treatment regimens this results in uncertain OTB outcomes. The purpose of this study is to summarize existing diagnostic approaches and recently discovered biomarkers that may contribute to establishing OTB diagnosis, choice of anti-tubercular therapy (ATT) regimen, and treatment monitoring. The keywords ocular tuberculosis, tuberculosis, Mycobacterium, biomarkers, molecular diagnosis, multi-omics, proteomics, genomics, transcriptomics, metabolomics, T-lymphocytes profiling were searched on PubMed and MEDLINE databases. Articles and books published with at least one of the keywords were included and screened for relevance. There was no time limit for study inclusion. More emphasis was placed on recent publications that contributed new information about the pathogenesis, diagnosis, or treatment of OTB. We excluded abstracts and articles that were not written in the English language. References cited within the identified articles were used to further supplement the search. We found 10 studies evaluating the sensitivity and specificity of interferon-gamma release assay (IGRA), and 6 studies evaluating that of tuberculin skin test (TST) in OTB patients. IGRA (Sp = 71-100%, Se = 36-100%) achieves overall better sensitivity and specificity than TST (Sp = 51.1-85.7%; Se = 70.9-98.5%). For nuclear acid amplification tests (NAAT), we found 7 studies on uniplex polymerase chain reaction (PCR) with different Mtb targets, 7 studies on DNA-based multiplex PCR, 1 study on mRNA-based multiplex PCR, 4 studies on loop-mediated isothermal amplification (LAMP) assay with different Mtb targets, 3 studies on GeneXpert assay, 1 study on GeneXpert Ultra assay and 1 study for MTBDRplus assay for OTB. Specificity is overall improved but sensitivity is highly variable for NAATs (excluding uniplex PCR, Sp = 50-100%; Se = 10.5-98%) as compared to IGRA. We also found 3 transcriptomic studies, 6 proteomic studies, 2 studies on stimulation assays, 1 study on intraocular protein analysis and 1 study on T-lymphocyte profiling in OTB patients. All except 1 study evaluated novel, previously undiscovered biomarkers. Only 1 study has been externally validated by a large independent cohort. Future theranostic marker discovery by a multi-omics approach is essential to deepen pathophysiological understanding of OTB. Combined these might result in swift, optimal and personalized treatment regimens to modulate the heterogeneous mechanisms of OTB. Eventually, these studies could improve the current cumbersome diagnosis and management of OTB.

Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

BACKGROUND

The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status.

METHODS

T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms.

RESULTS

A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted antigenic target 6 (ESAT-6) and culture filtrate protein 10 (CFP-10) spot-forming cell (SFC) in T-SPOT assay, as well as global adaptive immunity assessed by CD4 cell IFN-γ secretion, CD8 cell IFN-γ secretion, and CD4 cell number, were found to contribute greatly to the cforest model. Superior performance obtained in the discovery cohort was further confirmed in the validation cohort. The sensitivity and specificity of cforest model in validation set were 92.80% and 89.86%, respectively.

CONCLUSIONS

Cforest model developed upon machine learning could serve as a valuable and prospective tool for identifying Mtb infection status. The present study provided a novel and viable idea for realizing the clinical diagnostic application of the combination of machine learning and laboratory findings.

Proteomics approaches: A review regarding an importance of proteome analyses in understanding the pathogens and diseases

Proteomics is playing an increasingly important role in identifying pathogens, emerging and re-emerging infectious agents, understanding pathogenesis, and diagnosis of diseases. Recently, more advanced and sophisticated proteomics technologies have transformed disease diagnostics and vaccines development. The detection of pathogens is made possible by more accurate and time-constrained technologies, resulting in an early diagnosis. More detailed and comprehensive information regarding the proteome of any noxious agent is made possible by combining mass spectrometry with various gel-based or short-gun proteomics approaches recently. MALDI-ToF has been proved quite useful in identifying and distinguishing bacterial pathogens. Other quantitative approaches are doing their best to investigate bacterial virulent factors, diagnostic markers and vaccine candidates. Proteomics is also helping in the identification of secreted proteins and their virulence-related functions. This review aims to highlight the role of cutting-edge proteomics approaches in better understanding the functional genomics of pathogens. This also underlines the limitations of proteomics in bacterial secretome research.

A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis

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.

Diagnostic biomarkers for active tuberculosis: progress and challenges

Tuberculosis (TB) is a leading cause of morbidity and mortality from a single infectious agent, despite being preventable and curable. Early and accurate diagnosis of active TB is critical to both enhance patient care, improve patient outcomes, and break Mycobacterium tuberculosis (Mtb) transmission cycles. In 2020 an estimated 9.9 million people fell ill from Mtb, but only a little over half (5.8 million) received an active TB diagnosis and treatment. The World Health Organization has proposed target product profiles for biomarker- or biosignature-based diagnostics using point-of-care tests from easily accessible specimens such as urine or blood. Here we review and summarize progress made in the development of pathogen- and host-based biomarkers for active TB diagnosis. We describe several unique patient populations that have posed challenges to development of a universal diagnostic TB biomarker, such as people living with HIV, extrapulmonary TB, and children. We also review additional limitations to widespread validation and utilization of published biomarkers. We conclude with proposed solutions to enhance TB diagnostic biomarker validation and uptake.

Pediatric Tuberculosis Diagnostics: Present and Future

The current diagnostic abilities for the detection of pediatric tuberculosis are suboptimal. Multiple factors contribute to the under-diagnosis of intrathoracic tuberculosis in children, namely the absence of pathognomonic features of the disease, low bacillary loads in respiratory specimens, challenges in sample collection, and inadequate access to diagnostic tools in high-burden settings. Nonetheless, the 2020s have witnessed encouraging progress in the area of novel diagnostics. Recent WHO-endorsed rapid molecular assays hold promise for use in service decentralization strategies, and new policy recommendations include stools as an alternative, child-friendly specimen for testing with the GeneXpert assay. The pipeline of promising assays in mid/late-stage development is expanding, and novel pediatric candidate biomarkers based on the host immune response are being identified for use in diagnostic and triage tests. For a new test to meet the pediatric target product profiles prioritized by the WHO, it is key that the peculiarities and needs of the hard-to-reach pediatric population are considered in the early planning phases of discovery, validation, and implementation studies.

Evaluation of Host Protein Biomarkers by ELISA From Whole Lysed Peripheral Blood for Development of Diagnostic Tests for Active Tuberculosis

Tuberculosis (TB) remains a significant global health crisis and the number one cause of death for an infectious disease. The health consequences in high-burden countries are significant. Barriers to TB control and eradication are in part caused by difficulties in diagnosis. Improvements in diagnosis are required for organisations like the World Health Organisation (WHO) to meet their ambitious target of reducing the incidence of TB by 50% by the year 2025, which has become hard to reach due to the COVID-19 pandemic. Development of new tests for TB are key priorities of the WHO, as defined in their 2014 report for target product profiles (TPPs). Rapid triage and biomarker-based confirmatory tests would greatly enhance the diagnostic capability for identifying and diagnosing TB-infected individuals. Protein-based test methods e.g. lateral flow devices (LFDs) have a significant advantage over other technologies with regard to assay turnaround time (minutes as opposed to hours) field-ability, ease of use by relatively untrained staff and without the need for supporting laboratory infrastructure. Here we evaluate the diagnostic performance of nine biomarkers from our previously published biomarker qPCR validation study; CALCOCO2, CD274, CD52, GBP1, IFIT3, IFITM3, SAMD9L, SNX10 and TMEM49, as protein targets assayed by ELISA. This preliminary evaluation study was conducted to quantify the level of biomarker protein expression across latent, extra-pulmonary or pulmonary TB groups and negative controls, collected across the UK and India, in whole lysed blood samples (WLB). We also investigated associative correlations between the biomarkers and assessed their suitability for ongoing diagnostic test development, using receiver operating characteristic/area under the curve (ROC) analyses, singly and in panel combinations. The top performing single biomarkers for pulmonary TB versus controls were CALCOCO2, SAMD9L, GBP1, IFITM3, IFIT3 and SNX10. TMEM49 was also significantly differentially expressed but downregulated in TB groups. CD52 expression was not highly differentially expressed across most of the groups but may provide additional patient stratification information and some limited use for incipient latent TB infection. These show therefore great potential for diagnostic test development either in minimal configuration panels for rapid triage or more complex formulations to capture the diversity of disease presentations.

Proteomics in Biomarker Discovery for Tuberculosis: Current Status and Future Perspectives

Tuberculosis (TB) continues to threaten many peoples' health worldwide, regardless of their country of residence or age. The current diagnosis of TB still uses mainly traditional, time-consuming, and/or culture-based techniques. Efforts have focused on discovering new biomarkers with higher efficiency and accuracy for TB diagnosis. Proteomics-the systematic study of protein diversity-is being applied to the discovery of novel protein biomarkers for different types of diseases. Mass spectrometry (MS) technology plays a revolutionary role in proteomics, and its applicability benefits from the development of other technologies, such as matrix-based and immune-based methods. MS and derivative strategies continuously contribute to disease-related discoveries, and some promising proteomic biomarkers for efficient TB diagnosis have been identified, but challenges still exist. For example, there are discrepancies in the biomarkers identified among different reports and the diagnostic accuracy of clinically applied proteomic biomarkers. The present review summarizes the current status and future perspectives of proteomics in the field of TB biomarker discovery and aims to elicit more promising findings for rapid and accurate TB diagnosis.

Machine learning based on routine laboratory indicators promoting the discrimination between active tuberculosis and latent tuberculosis infection

BACKGROUND

Discriminating active tuberculosis (ATB) from latent tuberculosis infection (LTBI) remains challenging. The present study aims to evaluate the performance of diagnostic models established using machine learning based on routine laboratory indicators in differentiating ATB from LTBI.

METHODS

Participants were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Diagnostic models were established based on routine laboratory indicators using machine learning.

RESULTS

A total of 2619 participants (1025 ATB and 1594 LTBI) were enrolled in discovery cohort and another 942 subjects (388 ATB and 554 LTBI) were recruited in validation cohort. ATB patients had significantly higher levels of tuberculosis-specific antigen/phytohemagglutinin ratio and coefficient variation of red blood cell volume distribution width, and lower levels of albumin and lymphocyte count than those of LTBI individuals. Six models were built and the optimal performance was obtained from GBM model. GBM model derived from training set (n = 1965) differentiated ATB from LTBI in the test set (n = 654) with a sensitivity of 84.38% (95% CI, 79.42%-88.31%) and a specificity of 92.71% (95% CI, 89.73%-94.88%). Further validation by an independent cohort confirmed its encouraging value with a sensitivity of 87.63% (95% CI, 83.98%-90.54%) and specificity of 91.34% (95% CI, 88.70%-93.40%), respectively.

CONCLUSIONS

We successfully developed a model with promising diagnostic value based on machine learning for the first time. Our study proposed that GBM model may be of great benefit served as a tool for the accurate identification of ATB.

Research Progress on Leucine-Rich Alpha-2 Glycoprotein 1: A Review

Leucine-rich alpha⁃2 glycoprotein 1 (LRG1) is an important member of the leucine-rich repetitive sequence protein family. LRG1 was mainly involved in normal physiological activities of the nervous system, such as synapse formation, synapse growth, the development of nerve processes, neurotransmitter transfer and release, and cell adhesion molecules or ligand-binding proteins. Also, LRG1 affected the development of respiratory diseases, hematological diseases, endocrine diseases, tumor diseases, eye diseases, cardiovascular diseases, rheumatic immune diseases, infectious diseases, etc. LRG1 was a newly discovered important upstream signaling molecule of transforming growth factor⁃β (TGF⁃β) that affected various pathological processes through the TGF⁃β signaling pathway. However, research on LRG1 and its involvement in the occurrence and development of diseases was still in its infancy and the current studies were mainly focused on proteomic detection and basic animal experimental reports. We could reasonably predict that LRG1 might act as a new direction and strategy for the treatment of many diseases.

Combination of HLA-DR on Mycobacterium tuberculosis-Specific Cells and Tuberculosis Antigen/Phytohemagglutinin Ratio for Discriminating Active Tuberculosis From Latent Tuberculosis Infection

Background

Novel approaches for tuberculosis (TB) diagnosis, especially for distinguishing active TB (ATB) from latent TB infection (LTBI), are urgently warranted. The present study aims to determine whether the combination of HLA-DR on Mycobacterium tuberculosis (MTB)-specific cells and TB antigen/phytohemagglutinin (TBAg/PHA) ratio could facilitate MTB infection status discrimination.

Methods

Between June 2020 and June 2021, participants with ATB and LTBI were recruited from Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort), respectively. The detection of HLA-DR on MTB-specific cells upon TB antigen stimulation and T-SPOT assay were simultaneously performed on all subjects.

Results

A total of 116 (54 ATB and 62 LTBI) and another 84 (43 ATB and 41 LTBI) cases were respectively enrolled from Qiaokou cohort and Caidian cohort. Both HLA-DR on IFN-γ⁺TNF-α⁺ cells and TBAg/PHA ratio showed discriminatory value in distinguishing between ATB and LTBI. Receiver operator characteristic (ROC) curve analysis showed that HLA-DR on IFN-γ⁺TNF-α⁺ cells produced an area under the ROC curve (AUC) of 0.886. Besides, TBAg/PHA ratio yield an AUC of 0.736. Furthermore, the combination of these two indicators resulted in the accurate discrimination with an AUC of 0.937. When the threshold was set as 0.36, the diagnostic model could differentiate ATB from LTBI with a sensitivity of 92.00% and a specificity of 81.82%. The performance obtained in Qiaokou cohort was further validated in Caidian cohort.

Conclusions

The combination of HLA-DR on MTB-specific cells and TBAg/PHA ratio could serve as a robust tool to determine TB disease states.

Diagnostic Dilemma of Cutaneous Tuberculosis: Opening of the Pandora's Box

ABSTRACT

Tuberculosis in all forms, that is, pulmonary (PTB) or extrapulmonary (EPTB), is a universal health problem. Cutaneous tuberculosis (CTb) remains one of the least studied and often under-reported variants of EPTB because of its wide and protean clinical presentation. The diagnosis of CTb remains challenging because of lack of sensitive and specific investigations for its diagnosis. The sensitivity of some of the traditional tests is low because of low concentration of mycobacteria in paucibacillary lesions. Besides it is difficult to distinguish between M. tuberculosis (MTb) and other mycobacterial species in skin biopsies morphologically. Molecular methods may target either MTb DNA or RNA, and serve as promising tools in the diagnosis of various forms of CTb, with high sensitivity and rapidity. This review is focused on diagnostic challenges of CTb and to discuss various methods and newer technologies for diagnosing CTb. This will help the dermatologists and dermatopathologists to elucidate and accurately diagnose CTb from other infectious granulomatous dermatitis for appropriate timely treatment of the patient.

Clinical Features and Risk Factors for Active Tuberculosis in Takayasu Arteritis: A Single-Center Case-Control Study

Backgrounds

Takayasu arteritis (TAK) is a chronic, granulomatous vasculitis correlated with tuberculosis (TB). The two diseases share similar pathological characteristics and clinical manifestations which increase the difficulty to diagnose. Active tuberculosis (ATB) has implications for treatment strategies in TAK patients. Therefore, the investigation of clinical features and potential risk factors of ATB in TAK patients is vital.

Methods

The study reviewed hospitalized patients diagnosed with TAK in our hospital from 2008, to 2021. TAK patients with ATB were enrolled as the case group. The control group was randomly selected in a 3:1 ratio. The clinical characteristics of TAK patients with and without ATB were compared. Multivariate logistic regression analysis was performed to determine risk factors for ATB in TAK patients.

Results

We reviewed 1,789 patients and ultimately identified 30 (1.7%) ATB cases. TAK patients with ATB were more prone to develop symptoms including fever (p=0.001), fatigue (p=0.003), cough (p=0.037), expectoration (p<0.001), weight loss (p=0.003), and night sweating (p<0.001). Increased level of hypersensitive C reactive protein (hsCRP, p=0.001), decreased level of albumin (p=0.031), and higher positive rate of T-SPOT.TB test (p<0.001) were observed in the case group. Multivariate logistic regression analysis revealed that hsCRP >8 mg/L (OR 9.108; 95% CI, 1.096-75.711; p=0.041) and positive T-SPOT.TB result (OR 68.669; 95% CI, 7.291-646.738; p<0.001) were risk factors for ATB in TAK patients. The proportion of patients undergoing subsequent surgery for Takayasu arteritis was lower in patients with ATB (p<0.001).

Conclusion

Our study suggested that the diagnosis of ATB should be considered when TAK patients experienced symptoms including fever, fatigue, weight loss, etc. hsCRP >8 mg/L and positive T-SPOT.TB result were identified as independent risk factors for ATB in TAK patients.

Dissecting Host-Pathogen Interactions in TB Using Systems-Based Omic Approaches

Tuberculosis (TB) is a devastating infectious disease that kills over a million people every year. There is an increasing burden of multi drug resistance (MDR) and extensively drug resistance (XDR) TB. New and improved therapies are urgently needed to overcome the limitations of current treatment. The causative agent, Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens that can manipulate host cell environment for adaptation, evading immune defences, virulence, and pathogenesis of TB infection. Host-pathogen interaction is important to establish infection and it involves a complex set of processes. Metabolic cross talk between the host and pathogen is a facet of TB infection and has been an important topic of research where there is growing interest in developing therapies and drugs that target these interactions and metabolism of the pathogen in the host. Mtb scavenges multiple nutrient sources from the host and has adapted its metabolism to survive in the intracellular niche. Advancements in systems-based omic technologies have been successful to unravel host-pathogen interactions in TB. In this review we discuss the application and usefulness of omics in TB research that provides promising interventions for developing anti-TB therapies.

HBFP: a new repository for human body fluid proteome

Body fluid proteome has been intensively studied as a primary source for disease biomarker discovery. Using advanced proteomics technologies, early research success has resulted in increasingly accumulated proteins detected in different body fluids, among which many are promising biomarkers. However, despite a handful of small-scale and specific data resources, current research is clearly lacking effort compiling published body fluid proteins into a centralized and sustainable repository that can provide users with systematic analytic tools. In this study, we developed a new database of human body fluid proteome (HBFP) that focuses on experimentally validated proteome in 17 types of human body fluids. The current database archives 11 827 unique proteins reported by 164 scientific publications, with a maximal false discovery rate of 0.01 on both the peptide and protein levels since 2001, and enables users to query, analyze and download protein entries with respect to each body fluid. Three unique features of this new system include the following: (i) the protein annotation page includes detailed abundance information based on relative qualitative measures of peptides reported in the original references, (ii) a new score is calculated on each reported protein to indicate the discovery confidence and (iii) HBFP catalogs 7354 proteins with at least two non-nested uniquely mapping peptides of nine amino acids according to the Human Proteome Project Data Interpretation Guidelines, while the remaining 4473 proteins have more than two unique peptides without given sequence information. As an important resource for human protein secretome, we anticipate that this new HBFP database can be a powerful tool that facilitates research in clinical proteomics and biomarker discovery.

Database URL:https://bmbl.bmi.osumc.edu/HBFP/

Activation Phenotype of Mycobacterium tuberculosis-Specific CD4+ T Cells Promoting the Discrimination Between Active Tuberculosis and Latent Tuberculosis Infection

Background

Rapid and effective discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains a challenge. There is an urgent need for developing practical and affordable approaches targeting this issue.

Methods

Participants with ATB and LTBI were recruited at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort) based on positive T-SPOT results from June 2020 to January 2021. The expression of activation markers including HLA-DR, CD38, CD69, and CD25 was examined on Mycobacterium tuberculosis (MTB)-specific CD4⁺ T cells defined by IFN-γ, TNF-α, and IL-2 expression upon MTB antigen stimulation.

Results

A total of 90 (40 ATB and 50 LTBI) and another 64 (29 ATB and 35 LTBI) subjects were recruited from the Qiaokou cohort and Caidian cohort, respectively. The expression patterns of Th1 cytokines including IFN-γ, TNF-α, and IL-2 upon MTB antigen stimulation could not differentiate ATB patients from LTBI individuals well. However, both HLA-DR and CD38 on MTB-specific cells showed discriminatory value in distinguishing between ATB patients and LTBI individuals. As for developing a single candidate biomarker, HLA-DR had the advantage over CD38. Moreover, HLA-DR on TNF-α⁺ or IL-2⁺ cells had superiority over that on IFN-γ⁺ cells in differentiating ATB patients from LTBI individuals. Besides, HLA-DR on MTB-specific cells defined by multiple cytokine co-expression had a higher ability to discriminate patients with ATB from LTBI individuals than that of MTB-specific cells defined by one kind of cytokine expression. Specially, HLA-DR on TNF-α⁺IL-2⁺ cells produced an AUC of 0.901 (95% CI, 0.833–0.969), with a sensitivity of 93.75% (95% CI, 79.85–98.27%) and specificity of 72.97% (95% CI, 57.02–84.60%) as a threshold of 44% was used. Furthermore, the performance of HLA-DR on TNF-α⁺IL-2⁺ cells for differential diagnosis was obtained with validation cohort data: 90.91% (95% CI, 72.19–97.47%) sensitivity and 68.97% (95% CI, 50.77–82.73%) specificity.

Conclusions

We demonstrated that HLA-DR on MTB-specific cells was a potentially useful biomarker for accurate discrimination between ATB and LTBI.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion

Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Lymphocyte-Related Immunological Indicators for Stratifying Mycobacterium tuberculosis Infection

Background

Easily accessible tools that reliably stratify Mycobacterium tuberculosis (MTB) infection are needed to facilitate the improvement of clinical management. The current study attempts to reveal lymphocyte-related immune characteristics of active tuberculosis (ATB) patients and establish immunodiagnostic model for discriminating ATB from latent tuberculosis infection (LTBI) and healthy controls (HC).

Methods

A total of 171 subjects consisted of 54 ATB, 57 LTBI, and 60 HC were consecutively recruited at Tongji hospital from January 2019 to January 2021. All participants were tested for lymphocyte subsets, phenotype, and function. Other examination including T-SPOT and microbiological detection for MTB were performed simultaneously.

Results

Compared with LTBI and HC, ATB patients exhibited significantly lower number and function of lymphocytes including CD4⁺ T cells, CD8⁺ T cells and NK cells, and significantly higher T cell activation represented by HLA-DR and proportion of immunosuppressive cells represented by Treg. An immunodiagnostic model based on the combination of NK cell number, HLA-DR⁺CD3⁺ T cells, Treg, CD4⁺ T cell function, and NK cell function was built using logistic regression. Based on receiver operating characteristic curve analysis, the area under the curve (AUC) of the diagnostic model was 0.920 (95% CI, 0.867-0.973) in distinguishing ATB from LTBI, while the cut-off value of 0.676 produced a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and specificity of 91.23% (95% CI, 81.06%-96.20%). Meanwhile, AUC analysis between ATB and HC according to the diagnostic model was 0.911 (95% CI, 0.855-0.967), with a sensitivity of 81.48% (95% CI, 69.16%-89.62%) and a specificity of 90.00% (95% CI, 79.85%-95.34%).

Conclusions

Our study demonstrated that the immunodiagnostic model established by the combination of lymphocyte-related indicators could facilitate the status differentiation of MTB infection.

Quantitative Proteomic Approach for Discriminating Major Depressive Disorder and Bipolar Disorder by Multiple Reaction Monitoring-Mass Spectrometry

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Lymphocyte Non-Specific Function Detection Facilitating the Stratification of Mycobacterium tuberculosis Infection

Background

Inadequate tuberculosis (TB) diagnostics, especially for discrimination between active TB (ATB) and latent TB infection (LTBI), are major hurdle in the reduction of the disease burden. The present study aims to investigate the role of lymphocyte non-specific function detection for TB diagnosis in clinical practice.

Methods

A total of 208 participants including 49 ATB patients, 64 LTBI individuals, and 95 healthy controls were recruited at Tongji hospital from January 2019 to October 2020. All subjects were tested with lymphocyte non-specific function detection and T-SPOT assay.

Results

Significantly positive correlation existed between lymphocyte non-specific function and phytohemagglutinin (PHA) spot number. CD4⁺ T cell non-specific function showed the potential for differentiating patients with negative T-SPOT results from those with positive T-SPOT results with an area under the curve (AUC) of 0.732 (95% CI, 0.572-0.893). The non-specific function of CD4⁺ T cells, CD8⁺ T cells, and NK cells was found significantly lower in ATB patients than in LTBI individuals. The AUCs presented by CD4⁺ T cell non-specific function, CD8⁺ T cell non-specific function, and NK cell non-specific function for discriminating ATB patients from LTBI individuals were 0.845 (95% CI, 0.767-0.925), 0.770 (95% CI, 0.683-0.857), and 0.691 (95% CI, 0.593-0.789), respectively. Application of multivariable logistic regression resulted in the combination of CD4⁺ T cell non-specific function, NK cell non-specific function, and culture filtrate protein-10 (CFP-10) spot number as the optimally diagnostic model for differentiating ATB from LTBI. The AUC of the model in distinguishing between ATB and LTBI was 0.939 (95% CI, 0.898-0.981). The sensitivity and specificity were 83.67% (95% CI, 70.96%-91.49%) and 90.63% (95% CI, 81.02%-95.63%) with the threshold as 0.57. Our established model showed superior performance to TB-specific antigen (TBAg)/PHA ratio in stratifying TB infection status.

Conclusions

Lymphocyte non-specific function detection offers an attractive alternative to facilitate TB diagnosis. The three-index diagnostic model was proved to be a potent tool for the identification of different events involved in TB infection, which is helpful for the treatment and management of patients.