Abstract
Evaluating therapy efficacy is crucial for patients with tuberculosis (TB), especially those with drug-resistant tuberculosis (DR-TB). The World Health Organization currently recommends sputum smear and culture as the standard methods for evaluating pulmonary tuberculosis (PTB) therapy efficacy. However, these approaches have limitations including low sensitivity, lengthy culture periods, and susceptibility to contamination. There is an urgent need for dependable biomarkers to evaluate therapy efficacy in patients with PTB. Numerous new biomarkers of Mycobacterium tuberculosis (MTB) and the host have been used in recent studies to evaluate PTB therapy efficacy. A systematic review and update of these biomarkers can facilitate the discovery of novel biomarkers and assessment models, as well as provide a solid scientific basis for alternative indicators of evaluating therapy efficacy. In this review we summarize the recent advancements and limitations of biomarkers used to monitor therapy efficacy, highlighting the importance of utilizing a combination of biomarkers. Although some biomarkers have potential in evaluating the efficacy of therapy in patients with PTB, they also have some limitations. Further research, validation, and optimization are required to identify the most reliable and effective alternative biomarkers and apply them to clinical practice.
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Avoid common mistakes on your manuscript.
Evaluating the efficacy of pulmonary tuberculosis (PTB) therapy is vital for treatment decision-making, improving the cure rate, and preventing the transmission of tuberculosis. |
The conventional methods for evaluating efficacy of therapy for PTB have limitations, including low sensitivity and a long culture period. |
Host biomarkers, especially a combination of biomarkers, serve as valuable tools for evaluating the efficacy of PTB therapy, although certain biomarkers may have inherent limitations. |
The changes in certain host immune markers vary between patients with drug-sensitive PTB and patients with drug-resistant PTB, and even within the same patient group, the same type of immune marker may exhibit different changes under different Mycobacterium tuberculosis (MTB) antigen stimuli. |
Further studies are needed to identify the most reliable and effective alternative biomarkers and apply them to clinical practice. |
Introduction
The COVID-19 pandemic has adversely affected the progress of tuberculosis (TB) prevention and control programs [1]. The World Health Organization (WHO) 2022 report estimated that about 10.6 million people were living with PTB in 2021, a 4.5% increase from 10.1 million in 2020, and that the total number of deaths among HIV-negative and HIV-positive people rose to 1.6 million, up from 1.5 million in 2020. The period from 2015 to 2021 is only halfway to the first milestone of the End TB strategy. Moreover, the burden of drug-resistant tuberculosis (DR-TB) has also increased between 2020 and 2021 and has become a major public health concern [2]. Therefore, there is an urgent need for more effective interventions to prevent the further development of TB into DR-TB in the prevention and control of TB.
Conventional treatment for drug-sensitive TB (DS-TB) requires 6 months of therapy (2 months of isoniazid, rifampicin, pyrazinamide, and ethambutol followed by 4 months of isoniazid and rifampicin), while treatment for DR-TB is much longer [3]. Prolonged therapy imposes a heavy burden on patients and health workers and increases the risk of poor adherence or treatment failure [4]. Poor adherence or treatment failure can lead to incomplete clearance of Mycobacterium tuberculosis (MTB), resulting in replication, transmission, and drug resistance mutations [5]. Therefore, early evaluation of TB therapy efficacy is beneficial for timely adjustment of therapy and improvement of cure rates, which can help control the spread of TB. Evaluating treatment outcomes, determining therapy efficacy, and adjusting treatment plans earlier are important steps in the course of therapy to prevent disease progression.
Currently, the conventional methods used to evaluate the efficacy of anti-tuberculosis (ATTB) treatment rely on monitoring the tubercle bacilli within sputum specimens by smear microscopy and mycobacterial culture. Sputum smear is a quick and cost-effective technique, but its sensitivity is limited to detecting 104 bacteria per milliliter of sputum, and it cannot differentiate between live and dead bacteria [6, 7]. Sputum culture is considered the gold standard for evaluating ATTB therapy efficacy. Two consecutive negative sputum culture results in patients previously testing positive for MTB after intensive treatment are indicative of a favorable treatment outcome. However, sputum culture is prone to contamination, has a long culture time for MTB, and may take longer to determine a positive culture, especially for samples that are negative on sputum smear [8]. Traditional evaluation methods have certain drawbacks, which have led to the investigation of new biomarkers and imaging techniques for monitoring the efficacy of ATTB treatment. These include studies focused on MTB and host biomarkers and imaging changes [9,10,11]. These novel biomarkers and imaging techniques can predict ATTB therapy efficacy earlier and faster, thereby improving clinical outcomes and controlling pulmonary tuberculosis (PTB) outbreaks.
In this review, we discuss and summarize the latest advancements in the application of novel biomarkers for evaluating the efficacy of ATTB therapy. This article does not require ethical approval because it is based on published research and does not involve any research conducted by the authors on humans or laboratory animals.
Bacteriological Biomarkers
Surveillance of MTB
Direct bacteriological test is the most reliable evidence to evaluate the efficacy of PTB therapy. The traditional monitoring techniques are sputum smear and sputum culture. Sputum smear monitoring is based on the conversion of acid-fast stain of MTB in sputum, indicating the clearance of PTB bacilli and predicting good treatment outcomes [12, 13]. However, sputum smear has low sensitivity and requires a high concentration of bacilli in sputum (> 104 bacilli/ml) to achieve a positive result [14,15,16]. For patients with poorly treated PTB, sputum culture is more predictive of treatment outcomes than sputum smear [17]. Sputum culture includes solid culture and liquid culture. Solid culture evaluates the efficacy by observing the change of colony numbers on the medium, while liquid culture evaluates the efficacy by measuring the time of conversion, which can predict the colony numbers in solid culture [18, 19]. It has been found that the sputum culture status at month 2 can predict treatment outcomes well [20]. For patients with multidrug-resistant tuberculosis (MDR-TB), some studies suggest that sputum culture conversion at month 2 of treatment can predict treatment success [21,22,23]. However, other studies show that the predictive effect of sputum culture conversion at different time points varies in different populations [24], which may be related to the composition of MDR-TB treatment regimen [25]. Nevertheless, the results of sputum culture conversion after 3, 6, and 24 months of treatment have a good predictive effect on the cure of MDR-TB [22, 26]. The drawback of sputum culture is that it has a long cycle and is prone to contamination [27], so it cannot reflect the treatment effect of PTB in a timely and rapid manner, and cannot prevent the further development and transmission of PTB effectively. To overcome the limitations of traditional sputum smear and sputum culture, fluorescence microscopy has been developed to detect MTB in sputum [28, 29], but its sensitivity is still very low [30], and its detection rate in patients with PTB and positive culture is only 68.7% [31]. Recent studies have shown that direct and rapid quantitative observation of viable MTB through fluorescent staining can be used to reflect the treatment effect of PTB [32], suggesting that this method has great potential for evaluating the treatment effect of PTB by detecting live MTB, but its accuracy needs to be further verified.
Early bactericidal activity (EBA) is the value of the average rate of decline of colony forming unit (CFU) per milliliter of sputum in the first 2 days of ATTB treatment, which is used to evaluate the efficacy of new ATTB drugs [33]. The current extension of EBA measurements to 14 days is mainly because some ATTB drugs, such as pyrazinamide, ethambutol, and bedaquiline, exhibit their bactericidal activity only after longer periods of administration [34,35,36]. EBA testing also relies on solid and liquid cultures of MTB, and studies have shown that early sustained rapid bactericidal activity is associated with treatment outcomes [37]. However, EBA has many limitations, such as frequent sampling, complex operation, high contamination risk, and low sensitivity for drugs with delayed bactericidal activity.
Surveillance of Nucleic Acid of MTB
DNA
DNA detection of MTB is a rapid method to reflect the effect of PTB treatment. The Xpert MTB/RIF technique recommended by WHO guidelines is a semi-nested real-time fluorescent PCR in vitro diagnostic technique that reflects the MTB load by outputting cycle threshold (Ct) value [38,39,40]. Recent studies have shown that Xpert MTB/RIF technology can be used to monitor the efficacy of PTB therapy [41,42,43], and that the Ct value of Xpert MTB/RIF technology output has a strong correlation with the sputum culture time of PTB treatment [44], with the correlation reaching 86.0% and 90.2% at week 8 and week 24, respectively [45]. It has been shown that sputum conversion at month 2 and month 6 of treatment has a good predictive effect on the success of PTB treatment [20, 26]. Therefore, Ct value can be used as a marker to predict treatment success and cure. Compared to Xpert MTB/RIF, the recently developed Xpert MTB/RIF Ultra offers further improvements in the detection sensitivity of PTB [46], However, it is important to note that Xpert MTB/RIF Ultra has a higher false positive rate compared to the gold standard sputum culture. This higher false positive rate can be attributed to the inclusion of DNA from dead MTB in the Xpert MTB/RIF Ultra detection [47], as well as the high sensitivity of the probe-based Taqman real-time quantitative PCR (RT-qPCR) in detecting MTB DNA [48]. In addition, the transrenal DNA (trDNA) of MTB in urine can also be detected by RT-qPCR to reflect the treatment effect. During the course of treatment, MTB trDNA will gradually decrease and become almost undetectable after 2 months of treatment. This biomarker has potential to be used as a prognostic marker for patients with PTB, especially for those with low bacterial load and extrapulmonary PTB [49]. Although DNA testing can quickly indicate MTB clearance and predict treatment effects, it cannot distinguish between dead and live MTB [50, 51], which may lead to false positive results in cured patients due to residual DNA.
RNA
RNA detection from sputum can be used as an alternative marker to reflect the viability of MTB [52, 53], as mycobacterial RNA can rapidly respond to bacterial cell death [54, 55]. Studies have shown that the mRNA detection of MTB antigen 85B after ATTB treatment has 87.1% agreement with sputum culture, indicating that mRNA detection can quickly indicate the clearance of active MTB during ATTB treatment [56]. In addition, Stephen et al. used molecular bacterial load assay (MBLA) to detect 16S rRNA of MTB, which can quickly and accurately quantify the MTB load in sputum during treatment [57]. Compared with sputum culture, MBLA performed better in quantifying live MTB during treatment than GeneXpert and microscopy. It can be used to monitor bacterial load during PTB treatment, facilitating early detection of treatment failure and improving treatment outcomes [58, 59]. Many studies have used this method to evaluate the changes in viable bacterial counts during PTB treatment and the early efficacy of PTB therapy [59,60,61,62], and MBLA can also monitor the efficacy of PTB treatment by testing the stool of patients with sputum-negative PTB [63]. Recent studies have optimized RNA detection methods for MTB, including RNA extraction protocols and 16S rRNA primers, which have greatly improved the detection efficiency of MTB [64]. Although 16S rRNA detection can quickly reflect the effect of ATTB treatment, whether the measurement of 16S rRNA in the early stage of treatment can be used to predict the prognosis of PTB needs to be confirmed by more studies [57].
Surveillance of Antigen Components of MTB
Detection of the cell wall lipoarabinomannan (LAM) component of MTB in urine can indicate the presence of MTB and has been commercialized. The LAM level in the urine of patients with culture-positive PTB decreases gradually after ATTB treatment, and the survival probability of patients with rapid LAM decline within 2 months of ATTB treatment is higher [65]. In addition, LAM can be used not only to predict the co-infection of PTB and HIV, with a positive predictive value of up to 80% for co-infected patients, but also to predict mortality, especially for patients with advanced HIV [66]. However, the sensitivity of LAM detection is very low (13–93%) [67, 68], and more studies are needed to improve the sensitivity of LAM detection. Moreover, detection of MTB Ag85 antigen in sputum can reflect the early bactericidal effect of ATTB drugs, and its continuous presence in sputum indicates treatment failure or relapse [69,70,71]. Although the diagnostic accuracy of PTB is high [72, 73], the sensitivity of detecting Ag85 antigen in filtrate after 2 weeks of culture of MTB is only 80%. Interestingly, MPT64, a secreted protein of MTB, was found to be only secreted by viable MTB, with a sensitivity and specificity of 86.9% and 92.0%, respectively, which was similar to the diagnostic performance of GeneXpert for PTB [74] and was consistent with the 1+ positive predictive value of sputum smear [75]. Therefore, it is very promising to detect the expression of MPT64 secreted protein of MTB to monitor drug efficacy and ATTB therapy effect. Reduced or undetectable levels of serum culture filtrate protein-10 (CFP-10) and early secretory antigen target-6 (ESAT-6) concentrations after treatment can be used to monitor the efficacy of PTB therapy [76]. Studies have shown that the sensitivity and specificity of detection of MTB antigens CFP-10 and ESAT-6 in serum can be improved by using antibody-labeled and energy-focused porous disk silicon nanoparticles (nanodisks) and high-throughput mass spectrometry during ATTB treatment. However, this method is only preliminary at present, and more large-scale prospective studies are needed to verify and simplify the experimental process. Table 1 provides a summary of MTB pathogen and its components as biomarkers for evaluating the efficacy of PTB therapy.
MTB-Specific Host Biomarkers
Cytokines
Host biomarkers for assessing the response to ATTB treatment largely rely on cytokine balance [77], with cytokines being the most extensively studied biomarkers for monitoring treatment response. The most commonly used cytokine-specific assay for assessing the response to ATTB treatment is interferon gamma (IFNγ), which can be detected through T cell-based interferon-gamma release assays (IGRAs) using CFP-10 or ESAT-6 as stimulants. The number of spots produced by IFNγ-secreting T cells reflects the response of MTB-specific T cells to these antigens, and this number decreases after 2 months of ATTB treatment [78]. There are two forms of IGRA, including the ELISA-based QuantiFERON TB Gold test and the ELISPOT-based T-SPOT test [79]. While the number of spots produced by CFP-10- and ESAT-6-stimulated IFNγ-secreting T cells can be used to monitor the efficacy of ATTB treatment [80,81,82,83], and ELISPOT has shown potential as a surrogate marker of PTB treatment outcome [84], the utility of IGRA as a monitoring tool for therapy efficacy is limited. Most patients still test positive for IGRA even 6 months after they finish their treatment [85,86,87,88], primarily because MTB-specific T cells induced by MTB infection persist in the body after successful treatment of patients with active PTB, and these T cells can produce IFNγ upon stimulation with CFP-10 and ESAT-6. Owing to differences in study design, the results regarding MTB-specific IFNγ after ATTB treatment are inconsistent. Some studies have shown that CFP-10- and ESAT-6-stimulated whole blood IFNγ and interleukin-4 (IL-4) increase, while tumor necrosis factor-α (TNFα), IL-6, and IL-10 decrease after 2 months of ATTB treatment, with significant changes in IFNγ [89]. Moreover, IFNγ also increases under recombinant 32-kDa Mycobacterium bovis stimulation after 6 months of ATTB treatment [90]. However, other studies have shown that peripheral blood mononuclear cells (PBMCs) stimulated by CFP-10/ESAT-6 [91, 92] or CFP-10 [93] produce less IFNγ after 6 months of ATTB treatment. Some studies have also indicated that CFP-10/ESAT-6-stimulated whole blood IFNγ does not change after 3 months [94] and 6 months [95] of ATTB treatment, but IFNγ decreases under selected RD1 peptide stimulation [95]. Therefore, further investigation is needed to evaluate the use of extracellular MTB-specific IFNγ as a sole biomarker for assessing the efficacy of ATTB treatment.
Other MTB-specific cytokines, such as TNFα [91, 96], IL-1 receptor antagonist (IL-1ra) [97], IL-4 [89], IL-6 [91], IL-10, ratios of IFNγ to IL-10 [90], and IL-2/IFNγ ratios [98], have been associated with PTB treatment response. However, the results regarding certain cytokines after ATTB treatment are inconsistent. For example, MTB-specific TNFα and IL-10 have shown varying results. Some studies have reported an increase in TNFα and IL-10 after ATTB treatment in patients with MDR-TB stimulated with CFP [93], while other studies have shown a decrease in TNFα and IL-10 after ATTB treatment stimulated with CFP-10/ESAT-6 [89, 91, 97]. Additionally, stimulation with Bacillus Calmette–Guérin (BCG) or its recombinant 32-kDa antigen has been found to reduce IL-10 and increase IL-12 [99]. In cases of adverse treatment outcomes, low levels of TNFα, IL-1β, and IL-7 have been observed in whole blood stimulated with H37Rv [100]. MTB-specific IL-1ra (stimulated with CFP-10 and purified protein derivative (PPD)) [97] and IL-6 (stimulated with CFP-10 and ESAT-6) [89] have been shown to decrease after ATTB treatment, while MTB-specific IL-4 (stimulated with CFP-10 and ESAT-6) has been found to increase.
Chemokines
MTB-specific chemokines have not been extensively studied for monitoring PTB treatment. One of the most commonly studied chemokines is IFNγ inducible protein 10 (IP-10), which belongs to the CXC class of chemokines. IP-10 has been found to decrease in whole blood stimulated with QuantiFERON TB Gold In-Tube (QFT-GIT) antigens after ATTB treatment [101]. Some studies have shown that IP-10 decreases in whole blood stimulated with CFP-10 or ESAT-6 after 9 months of ATTB treatment [97], and with QFT-GIT antigens after 6 months of ATTB treatment, although the results were not statistically significant. However, IP-10 has been found to decrease significantly when stimulated with selected RD1 peptide [95]. Therefore, further studies are needed to determine whether extracellular MTB-specific IP-10 is associated with the outcome of ATTB treatment.
T Cell Features
The differentiation of T cell subsets upon completion of PTB treatment has been associated with a cure [102]. A study on PBMCs from patients with PTB undergoing ATTB treatment found that IFNγ+CD4+ T cells (stimulated with CFP10/ESAT-6) increased after 2 weeks of treatment, while IFNγ+CD8+ T cells (stimulated with CFP10/ESAT-6) [103] and total TNFα+CD8+ T cells (stimulated with Ag85) decreased. IFNγ+TNFα+CD4+ T cells (stimulated with CFP10/ESAT-6) also decreased. Additionally, regulatory T cell (Treg) subsets CD25hiCD127low+, CD25hiCD147++, and CD25hiCD27lowCD161+ expanded significantly after in vitro PTB antigen stimulation, while CD25hiCD127lowCD39+ Treg remained unchanged [104]. After 8 weeks of treatment, the proportion of CD4+CD25hiCD127low Treg cells and CD4+CD25hiCD127lowCD147+ Treg cells increased [105]. At 9 weeks of treatment, PPD stimulation-induced IFNγ+CD4 + T cells decreased in HLA-DR+, CD38+, and Ki-67+ subsets [106]. Moreover, 1 month into treatment, the expression of CD45RA−CCR7+ central memory T cells (TCM) in PBMCs of patients with PTB increased and correlated with sputum conversion [107]. After 2 months of treatment, the expression levels of CD27+CD38+CD4+, CD27+HLA-DR+CD4+, and CD27−HLA-DR+CD4+ T cells increased, while the expression levels of CD27-IFNγ+CD8+, CD27−TNFα+CD8+, and CD27−Ki-67+CD4+ T cells decreased. CD27−IFNγ+CD4+ T cells (stimulated with PPD) increased in patients with a rapid response to ATTB treatment [108]. Therefore, changes in these T cell subpopulations can serve as early and rapid indicators for monitoring PTB treatment. Another study by Young et al. showed that stimulation of PBMCs from patients’ peripheral blood with CFP-10/ESAT-6 and PPD after completing PTB treatment led to a decrease in the expression of TNFα+CD4+/CD8+ T cells (stimulated with PPD) and CD107a+CD4+/CD8+ T cells (stimulated with CFP-10/ESAT-6 or PPD) [109]. Additionally, after stimulation of PBMCs with PPD, the expression of the CD25 marker and the percentage of T cell subsets CD4+CD25+ and CD4+CD25+CD39+ were significantly reduced [110]. The study also found that a significant decline in CD4+CD25+FoxP3+ Treg cells is a biomarker of a good outcome after treatment of PTB with extensive lung damage. Other studies have shown that a decline in MTB-specific CD38+IFNγ+, HLA-DR+IFNγ+, and Ki-67+IFNγ+CD4+ T cells is a characteristic of treatment success [111]. Analysis of CD8+ T cells showed a decrease in CD95+IFNγ+, Ki-67+IFNγ+, and CD127+IFNγ+CD8+ T cells, while an increase was observed in other subsets [112]. Additionally, the co-expression of HLA-DR/CD38 and PD-1/CD38 on CD4+ and CD8+ T cells decreased in patients with successful treatment [105]. Studies have also shown that patients with cured PTB exhibit high expression of killer cell lectin-like receptor G1 (KLRG1), PD-1, and cytotoxic T lymphocyte (CTLA-4) in CD4+ T cells after in vitro BCG stimulation of PBMCs [113]. These changes and expression levels of biomarkers have the potential to serve as indicators of a cure.
Antibodies
The plasma of patients with PTB contains several MTB-specific antibodies, including anti-ESAT-6, Rv2626c, 38 kDa antigen, LAM and FdxA antibodies. Studies have demonstrated that during ATTB treatment, levels of anti-ESAT-6 and Rv2626c antibodies decrease, while levels of anti-38 kDa antigen and LAM antibodies increase. However, levels of anti-FdxA antibodies remain unchanged. These antibody levels are also correlated with disease severity and lung lesions [114]. Additionally, the study found that alanine dehydrogenase and malate synthetase antibodies were higher in patients who failed treatment compared to those who were cured at the start of treatment. After 6 months of ATTB treatment, co-incubation of CFP-10/ESAT-6 with patient serum revealed a decrease in serum IgG antibodies [91], while patients with cured PTB exhibited high expression of serum IgG antibodies in response to PTB antigen Ag85 [115]. Therefore, these antibody titers have the potential to serve as biomarkers for monitoring treatment efficacy.
Other Biomarkers
The neutrophil to lymphocyte ratio (NLR) has been observed to decrease after ATTB treatment, which can be used to assess the effectiveness of drug treatment [98]. Furthermore, MMP-8 levels at 2 months after ATTB treatment have been found to be associated with persistent positive sputum culture in patients, making it a potential biomarker for predicting poor response to ATTB treatment [116].
The results of these in vitro experiments suggest that monitoring changes in MTB-specific immune factors, intracellular immune factors, and T cell subsets during ATTB treatment can be useful for assessing therapy efficacy and predicting prognosis in the early stages. However, it should be noted that these in vitro PTB stimulation experiments require meticulous operations and some flow experiments can be expensive. Furthermore, the existing research findings have certain limitations. Therefore, further investigations with larger sample sizes are needed to gain a more comprehensive understanding.
MTB-Nonspecific Host Biomarkers
Cytokines
IFNγ is widely used as a non-specific cytokine to assess the effectiveness of ATTB treatment. IFNγ levels decrease significantly in patients who experience sputum conversion [117,118,119,120], and these levels are also correlated with treatment success [121]. Other cytokines have also shown promising results in studies. For instance, soluble TNF (solTNF) and IL-12 levels decrease in patients with drug-sensitive PTB, while transforming growth factor-beta 1 (TGFβ1) and IL-35 levels increase in patients with DR-TB [120]. After 6 months of ATTB treatment, IL-1β, IL-9, IL-10, and IL-15 levels decrease in the saliva of patients with DR-TB [122]. IL-6 levels decrease and IL-1β levels remain unchanged after treatment, but IL-1β levels can differentiate between smear-positive and smear-negative patients after 6 months of treatment [123]. Furthermore, high levels of IL-10 and low levels of IL-8 after ATTB treatment indicate poor PTB treatment response and suggest a possible recurrence of the disease [124].
Chemokines
Serum levels of CXCR3 ligands (CXCL9, CXCL10 (IP-10), CXCL11) significantly decrease in patients who achieve sputum conversion to negative after 2 months of ATTB treatment [125]. Studies have also demonstrated that plasma levels of CXCL8, CXCL9, CXCL10, and CCL5 decrease after 6 months of ATTB treatment [126]. Among these, IP-10 has been extensively studied as a biomarker and has been found to decrease in plasma [127, 128] and urine [129, 130] after treatment. Furthermore, significantly lower levels of IP-10 have been observed in the plasma of patients with successfully cured PTB [131]. In patients with DR-TB, baseline plasma IP-10 levels have been positively associated with delayed sputum culture conversion [132]. Therefore, IP-10 is closely linked to PTB treatment success [121] and serves as a reliable biomarker for predicting treatment outcomes. In cohort studies, pre-treatment levels of CCL2, CCL3, CCL4, CXCL8, CXCL10, and CX3CL1 have been identified as risk factors for poor PTB treatment, while CXCL1 has shown the opposite effect [133]. Validation cohorts have also shown that CCL3, CXCL8, and CXCL10 are associated with an increased risk of adverse treatment outcomes [133]. Additionally, serum overexpression of eotaxin has been significantly observed in well-treated patients [134], indicating its importance as an efficacy monitoring indicator. Therefore, the combined use of chemokines may provide a better prediction of PTB treatment outcomes.
T Cell Features
After 6 months of ATTB treatment, there is an increase in the number of CD3+ cells and CD4+ cells, while the number of CD8+ cells decreases. Further analysis reveals that the number of IFNγ+ and IL-2+CD4+ T cells also increases [123], whereas the number of IL-10+CD4+ T cells decreases. A high Th1/Th2 ratio is identified as a biomarker for poor treatment outcomes [135]. Additional studies demonstrate a decrease in the frequency of CD25+ markers and CD4+CD25+, CD4+CD25+FoxP3−, and CD4+CD25+CD39+ Treg cells in patients with PTB who have completed treatment, indicating their potential for monitoring treatment success. However, in the treatment failure group, there is a sharp increase in the frequency of the CD4+CD25+FoxP3+ Treg subgroup, suggesting its potential for predicting treatment failure [136]. Other studies indicate that low PD-1 expression on CD25−CD4+ T cells and CD25+Foxp3−CD4+ T cells during treatment is a positive indicator of ATTB treatment [137]. Additionally, studies show differences in the changes of T cell subsets between patients with drug-sensitive PTB and drug-resistant PTB after ATTB treatment. The expression of conventional Treg cells (cTreg), transmembrane form (tm) TNFR1+, and tmTNFR2+CD4+ T cells decreases in both patients with drug-sensitive and patients with drug-resistant PTB after ATTB treatment. However, the decrease in unconventional tmTNFR2+ Treg cells is observed only in patients with drug-sensitive PTB, and the decrease in tmTNFR2+ activated CD4+ (actCD4+) cells is observed only in patients with drug-resistant PTB [120]. Therefore, the changes in T cell immune characteristics after ATTB treatment differ between patients with drug-sensitive and drug-resistant PTB, and further exploration is needed to study biomarkers for predicting therapy efficacy separately.
Other Biomarkers
Serum C-reactive protein (CRP), intracellular adhesion molecule-1 (sICAM-1), urokinase-type plasminogen activator receptor (suPAR), and pentraxin 3 (PTX3) all decreased after treatment [138]. Additionally, Heslop’s team discovered that high levels of fibroblast growth factor (FGF) in sputum after treatment were negatively correlated with reduced bacterial load [139], and a low expression of the antioxidant enzyme heme oxygenase-1 (HO-1) is also indicative of successful treatment [140]. Furthermore, plasma levels of extracellular matrix protein 1 (ECM1) are associated with rapid conversion of sputum to negative after 2 months of treatment, while levels of L-selectin (SELL) and CD14 decline after completion of treatment. These biomarkers can be used as indicators for therapeutic monitoring [141]. The failure of serum globulin levels to return to normal after 2 months of treatment can be used as a biomarker to predict the need for prolonged PTB treatment [131, 142].
Some immune molecules in other immune cells (monocytes and B cells) also change during ATTB treatment. For example, the high expression of PD-L1 in monocytes of patients with PTB after treatment is associated with worse treatment outcomes [143], while the increase of activated B cells is related to the success of PTB treatment, as indicated by the high expression of a proliferation-inducing ligand (APRIL), Fas-ligand (FASLG), IL-5 receptor alpha (IL5RA), and CD19 mRNA in B cells after ATTB treatment [144]. Moreover, the high expression of FASLG and IL5RA mRNA in B cells is a marker of successful ATTB treatment. Although there are few studies on the changes of immune molecules in other immune cells after ATTB treatment, these studies suggest that there are some immune molecules in other immune cells that can be used to predict the efficacy of PTB therapy. Table 2 provides a summary of the host biomarkers for evaluating the efficacy of PTB therapy, and Supplementary Table S1 presents a summary of the changes and monitoring outcomes of each host biomarker after ATTB treatment.
Host-nonspecific immune factor changes after ATTB treatment are widespread, so recent studies have begun to conduct multi-immune factor screening to identify representative biomarkers. Vladyslav et al. conducted a multicenter cohort study and discovered significant changes in Toll-like receptor (TLR) and aptamer gene expression, along with cytokine and chemokine levels, in patients with PTB following 2 months of ATTB treatment [145]. It was found that the changes of Toll interaction protein (TOLLIP), TLR9, TLR7, Toll-like receptor adaptor molecule 1 (TICAM1), IL-1 receptor-associated kinase 4 (IRAK4), CD14, and cytokines vascular endothelial growth factor (VEGF), macrophage inflammatory protein (MIP)-1β, IL-7, IFNγ, and granulocyte colony-stimulating factor (G-CSF) after 2 months of ATTB treatment were statistically significant. TOLLIP, lymphocyte antigen 96 (LY96), and nine cytokines, including TNFα, MIP-1α, monocyte chemoattractant protein-1 (MCP-1/CCL2), IL-8, IL-5, IL-15, IFNα, granulocyte–macrophage colony-stimulating factor (GM-CSF), and G-CSF, were found to be associated with successful treatment outcomes. Serum TLR2 levels decreased in successful patients, while failed patients showed the opposite trend. Another clinical trial screening 70 host biomarkers associated with PTB severity and treatment response demonstrated that most biomarkers decreased after 8 weeks of treatment, with serum amyloid A1 (SAA1), procalcitonin (PCT), IL-1β, IL-6, CRP, PTX3, and MMP-8 showing strong associations with disease severity [146]. In the case of patients with DR-TB, plasma levels of CRP, SAA, VEGF-A, soluble interleukin-2 receptor alpha (sIL-2Rα), and IP-10 at baseline treatment were positively correlated with delayed sputum culture conversion and a combination of MCP-1, sIL-2Rα, and SAA could distinguish patients with PTB with a fast response to ATTB treatment [132]. Additionally, significant changes were observed in the concentrations of granzyme A, MCP-1, IL-1β, IL-9, IL-10, IL-15, MIP-1β, ferritin, and serum amyloid A in the saliva of patients with drug-resistant PTB after 2 or 6 months of treatment [122]. Among these, only MCP-1 increased after treatment, while the others decreased. These findings suggest that a combination of multiple immune factors may be useful for monitoring PTB treatment, but further research is needed to determine the optimal combination for predictive purposes.
Host Biomarkers for Omics Analysis
Recent study has summarized biomarkers in omics for monitoring PTB treatment [147]. Transcriptomics revealed that complement C1q, C2, BF, and serpin in whole blood, as well as G1, UCP2 (involved in fatty acid metabolism), IFNα signaling pathway-related molecules IL15RA, UBE2L6 (a member of the ubiquitin family), guanylate binding protein 4 (GBP4), GBP5, dual-specific phosphatase 3 (DUSP3), and kruppel-like transcription factor 2 (KLF2), decreased after treatment. Serum miRNAs, including miR-21-5p, miR-92a-3p, and miR-148b-3p, also decreased after treatment, and high expression of Pragmin (Src kinase regulator) after treatment was a risk factor for relapse. Proteomics showed that the serum complement C7 and angiotensinogen, innate and adaptive immune-related proteins, such as coagulation factor V and serum amyloid protein, decreased after treatment, while the phosphoserine-tRNA kinase of infected primary human leukocytes increased after treatment. In addition, metabolomics showed that pyridoxine and bradykinin decreased after treatment, and the combination of l-histidine, arachidonic acid, biliverdin, and cysteine glutathione disulfide could be used as markers to cure PTB. Exploring the biomarkers in the host after ATTB treatment by omics methods has great application potential, but the sample size of the current omics studies is limited. It is necessary to further increase the sample size and reduce the biological differences of individuals to verify whether they are suitable for the monitoring of clinical efficacy. In addition, the Opti-4TB study, a “proof of concept” method, may predict the prognosis of PTB based on multi-omics combined detection of host immune markers [148]. Although it has a broad clinical application potential, one still needs to obtain more accurate biomarkers that reflect the effect of ATTB treatment and develop a simple prognostic prediction and evaluation system to achieve clinical translation.
Conclusions
Despite the limitations of traditional biomarkers, they are still widely used owing to their simplicity, cost-effectiveness, and suitability for resource-limited settings. However, the lack of timeliness and low sensitivity of these biomarkers has led to the further spread of PTB, resulting in significant health risks and economic losses. Therefore, there is an urgent need for improved biomarkers that can accurately reflect therapy efficacy and guide treatment decisions to control PTB outbreaks. The emergence of new biomarkers, including those derived from MTB itself and its host, as well as advancements in detection methods, offer the possibility of rapid, accurate, and efficient monitoring of PTB therapy efficacy. However, as a result of the diversity of these biomarkers, variations in study designs, small sample sizes, and inconsistent reports of certain biomarkers [149], it is necessary to expand the sample size to identify important biomarkers for monitoring ATTB therapy efficacy.
Cytokines and chemokines play a crucial role in evaluating the efficacy of PTB therapy [150]. Zimmer et al. conducted a meta-analysis of biomarkers associated with treatment response to active PTB, summarizing 81 biomarkers from 77 relevant studies. Despite the heterogeneity in the design of surveillance studies for PTB treatment, studies involving a large number of biomarkers have shown that cytokines (IL-6 and TNFα), chemokine (IP-10), and non-specific inflammatory marker (CRP) can serve as biomarkers for early monitoring of ATTB treatment [151]. Moreover, the assessment of therapy efficacy in patients with PTB through the use of MTB-specific and nonspecific host biomarkers encompasses a wide range of cytokines and chemokines. During ATTB treatment, changes in MTB-specific biomarkers produced through in vitro stimulation can reflect the therapeutic efficacy in patients with PTB. These biomarkers can improve the specificity of prediction, although they may involve complex procedures and have high requirements. Monitoring MTB-nonspecific host biomarkers during ATTB treatment, including those identified through omics analysis, is a promising approach. Although the changes in MTB-nonspecific biomarkers of ATTB treatment are complex, more accurate prediction models can be developed by combining multiple immune factors [152]. Additionally, the alterations in host immune markers differ between patients with drug-sensitive PTB and patients with drug-resistant PTB, and even within the same patient group, the same type of immune marker may display distinct changes in response to different MTB antigen stimuli. Hence, it is crucial to investigate and analyze the biomarkers of these two patient groups separately.
Certain biomarker changes during PTB treatment can reflect the efficacy of ATTB treatment (Fig. 1). This review provides an updated summary of biomarkers for monitoring ATTB therapy efficacy and suggests new research directions for further validation studies. It is hoped that through these biomarkers, a simple and feasible tool for evaluating therapy efficacy can be established, thereby reducing the incidence and mortality of PTB and achieving the goal of ending tuberculosis as soon as possible.
Data Availability
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
References
Alene KA, Wangdi K, Clements ACA. Impact of the COVID-19 pandemic on tuberculosis control: an overview. Tropical Med Infect Dis. 2020;5(3):123.
World Health Organization. Global tuberculosis report 2022. https://www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022. 2022. Accessed 27 Oct 2022.
WHO Guidelines Approved by the Guidelines Review Committee. WHO consolidated guidelines on drug-resistant tuberculosis treatment. Geneva: World Health Organization World Health Organization 2019;2019.
Lienhardt C, Raviglione M, Spigelman M, et al. New drugs for the treatment of tuberculosis: needs, challenges, promise, and prospects for the future. J Infect Dis. 2012;205(S2):S241–9.
Muture BN, Keraka MN, Kimuu PK, Kabiru EW, Ombeka VO, Oguya F. Factors associated with default from treatment among tuberculosis patients in Nairobi province, Kenya: a case control study. BMC Public Health. 2011;11:696.
Hobby GL, Holman AP, Iseman MD, Jones JM. Enumeration of tubercle bacilli in sputum of patients with pulmonary tuberculosis. Antimicrob Agents Chemother. 1973;4(2):94–104.
Das PK, Ganguly SB, Mandal BH. Sputum smear microscopy in tuberculosis: it is still relevant in the era of molecular diagnosis when seen from the public health perspective. Biomed Biotechnol Res J. 2019;3(2):77–9.
van Zyl-Smit RN, Binder A, Meldau R, et al. Comparison of quantitative techniques including Xpert MTB/RIF to evaluate mycobacterial burden. PLoS ONE. 2011;6(12): e28815.
Goletti D, Arlehamn CSL, Scriba TJ, et al. Can we predict tuberculosis cure? What tools are available? Eur Respirat J 2018;52(5):1801089
Yong YK, Tan HY, Saeidi A, et al. Immune biomarkers for diagnosis and treatment monitoring of tuberculosis: current developments and future prospects. Front Microbiol. 2019;10:2789.
Heyckendorf J, Georghiou SB, Frahm N, et al. Tuberculosis treatment monitoring and outcome measures: new interest and new strategies. Clin Microbiol Rev. 2022;35(3): e0022721.
Gilpin C, Kim SJ, Lumb R, Rieder HL, Van Deun A. Critical appraisal of current recommendations and practices for tuberculosis sputum smear microscopy. Int J Tuberculosis Lung Disease. 2007;11(9):946–52.
Maher D, Chaulet P, Spinaci S, Harries A, Stop TB Partnership. Treatment of tuberculosis: guidelines for national programmes. Centers for Disease Control and Prevention; 2003.
Horne DJ, Royce SE, Gooze L, et al. Sputum monitoring during tuberculosis treatment for predicting outcome: systematic review and meta-analysis. Lancet Infect Dis. 2010;10(6):387–94.
Davies PD, Pai M. The diagnosis and misdiagnosis of tuberculosis. Int J Tuberculosis Lung Dis. 2008;12(11):1226–34.
Desikan P. Sputum smear microscopy in tuberculosis: is it still relevant? Indian J Med Res. 2013;137(3):442–4.
Bahrmand AR, Velayati AA, Bakayev VV. Treatment monitoring and prevalence of drug resistance in tuberculosis patients in Tehran. Int J Tuberculosis Lung Dis. 2000;4(6):544–9.
Diacon AH, Maritz JS, Venter A, van Helden PD, Dawson R, Donald PR. Time to liquid culture positivity can substitute for colony counting on agar plates in early bactericidal activity studies of antituberculosis agents. Clin Microbiol Infect. 2012;18(7):711–7.
Diacon AH, van der Merwe L, Demers AM, von Groote-Bidlingmaier F, Venter A, Donald PR. Time to positivity in liquid culture predicts colony forming unit counts of Mycobacterium tuberculosis in sputum specimens. Tuberculosis (Edinb). 2014;94(2):148–51.
Wallis RS, Peppard T, Hermann D. Month 2 culture status and treatment duration as predictors of recurrence in pulmonary tuberculosis: model validation and update. PLoS ONE. 2015;10(4):e0125403.
Ige OM, Oladokun RE. Time to sputum culture conversion and treatment outcome among the first cohort of multidrug resistant tuberculosis patients in a high burden country. Indian J Tuberc. 2018;65(4):322–8.
Kurbatova EV, Cegielski JP, Lienhardt C, et al. Sputum culture conversion as a prognostic marker for end-of-treatment outcome in patients with multidrug-resistant tuberculosis: a secondary analysis of data from two observational cohort studies. Lancet Respir Med. 2015;3(3):201–9.
Lv L, Li T, Xu K, et al. Sputum bacteriology conversion and treatment outcome of patients with multidrug-resistant tuberculosis. Infect Drug Resist. 2018;11:147–54.
Phillips PP, Fielding K, Nunn AJ. An evaluation of culture results during treatment for tuberculosis as surrogate endpoints for treatment failure and relapse. PLoS ONE. 2013;8(5):e63840.
Tierney DB, Franke MF, Becerra MC, et al. Time to culture conversion and regimen composition in multidrug-resistant tuberculosis treatment. PLoS ONE. 2014;9(9):e108035.
Lu P, Liu Q, Martinez L, et al. Time to sputum culture conversion and treatment outcome of patients with multidrug-resistant tuberculosis: a prospective cohort study from urban China. Eur Respirat J. 2017;49(3):1601558.
Chihota VN, Grant AD, Fielding K, et al. Liquid vs. solid culture for tuberculosis: performance and cost in a resource-constrained setting. Int J Tuberculosis Lung Dis. 2010;14(8):1024–31.
Panicker RO, Kalmady KS, Rajan J, Sabu MK. Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods. Biocybernetics Biomed Eng. 2018;38(3):691–9.
Chang J, Arbeláez P, Switz N, et al. Automated tuberculosis diagnosis using fluorescence images from a mobile microscope. Med Image Comput Comput-Assisted Intervention. 2012;15(Pt 3):345–52.
Steingart KR, Henry M, Ng V, et al. Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review. Lancet Infect Dis. 2006;6(9):570–81.
Cuevas LE, Al-Sonboli N, Lawson L, et al. LED fluorescence microscopy for the diagnosis of pulmonary tuberculosis: a multi-country cross-sectional evaluation. PLoS Med. 2011;8(7):e1001057.
Datta S, Sherman JM, Bravard MA, Valencia T, Gilman RH, Evans CA. Clinical evaluation of tuberculosis viability microscopy for assessing treatment response. Clin Infect Dis. 2015;60(8):1186–95.
Jindani A, Aber VR, Edwards EA, Mitchison DA. The early bactericidal activity of drugs in patients with pulmonary tuberculosis. Am Rev Respir Dis. 1980;121(6):939–49.
Paramasivan CN, Herbert D, Umapathy KC, Rahman F, Krishnamurthy PV, Prabhakar R. Early bactericidal action of pulsed exposure to rifampicin, ethambutol, isoniazid & pyrazinamide in pulmonary tuberculosis patients. Indian J Med Res. 1994;100:1–4.
Diacon AH, Pym A, Grobusch M, et al. The diarylquinoline TMC207 for multidrug-resistant tuberculosis. N Engl J Med. 2009;360(23):2397–405.
Rustomjee R, Diacon AH, Allen J, et al. Early bactericidal activity and pharmacokinetics of the diarylquinoline TMC207 in treatment of pulmonary tuberculosis. Antimicrob Agents Chemother. 2008;52(8):2831–5.
Brindle R, Odhiambo J, Mitchison D. Serial counts of Mycobacterium tuberculosis in sputum as surrogate markers of the sterilising activity of rifampicin and pyrazinamide in treating pulmonary tuberculosis. BMC Pulm Med. 2001;1:2.
Hanrahan CF, Theron G, Bassett J, et al. Xpert MTB/RIF as a measure of sputum bacillary burden. Variation by HIV status and immunosuppression. Am J Respirat Crit Care Med. 2014;189(11):1426–34.
Beynon F, Theron G, Respeito D, et al. Correlation of Xpert MTB/RIF with measures to assess Mycobacterium tuberculosis bacillary burden in high HIV burden areas of Southern Africa. Sci Rep. 2018;8(1):5201.
Friedrich SO, Rachow A, Saathoff E, et al. Assessment of the sensitivity and specificity of Xpert MTB/RIF assay as an early sputum biomarker of response to tuberculosis treatment. Lancet Respir Med. 2013;1(6):462–70.
Pires MM, Pereira GR, Barbosa MS, et al. Association of Xpert MTB/RIF cycle threshold values with tuberculosis treatment outcomes. Lung. 2020;198(6):985–9.
Miotto P, Bigoni S, Migliori GB, Matteelli A, Cirillo DM. Early tuberculosis treatment monitoring by Xpert(R) MTB/RIF. Eur Respir J. 2012;39(5):1269–71.
Jayakumar A, Savic RM, Everett CK, et al. Xpert MTB/RIF assay shows faster clearance of Mycobacterium tuberculosis DNA with higher levels of rifapentine exposure. J Clin Microbiol. 2016;54(12):3028–33.
Tang T, Liu F, Lu X, Huang Q. Evaluation of GeneXpert MTB/RIF for detecting Mycobacterium tuberculosis in a hospital in China. J Int Med Res. 2017;45(2):816–22.
Shenai S, Ronacher K, Malherbe S, et al. Bacterial loads measured by the Xpert MTB/RIF assay as markers of culture conversion and bacteriological cure in pulmonary TB. PLoS ONE. 2016;11(8): e0160062.
Dorman SE, Schumacher SG, Alland D, et al. Xpert MTB/RIF ultra for detection of Mycobacterium tuberculosis and rifampicin resistance: a prospective multicentre diagnostic accuracy study. Lancet Infect Dis. 2018;18(1):76–84.
Arend SM, van Soolingen D. Performance of Xpert MTB/RIF ultra: a matter of dead or alive. Lancet Infect Dis. 2018;18(1):8–10.
Wang HY, Lu JJ, Chang CY, et al. Development of a high sensitivity TaqMan-based PCR assay for the specific detection of Mycobacterium tuberculosis complex in both pulmonary and extrapulmonary specimens. Sci Rep. 2019;9(1):113.
Labugger I, Heyckendorf J, Dees S, et al. Detection of transrenal DNA for the diagnosis of pulmonary tuberculosis and treatment monitoring. Infection. 2017;45(3):269–76.
Kayigire XA, Friedrich SO, Karinja MN, van der Merwe L, Martinson NA, Diacon AH. Propidium monoazide and Xpert MTB/RIF to quantify Mycobacterium tuberculosis cells. Tuberculosis (Edinb). 2016;101:79–84.
Gaikwad UN, Gaikwad NR. Modalities to monitor the treatment response in tuberculosis. Indian J Tuberc. 2018;65(2):109–17.
Desjardin LE, Perkins MD, Wolski K, et al. Measurement of sputum Mycobacterium tuberculosis messenger RNA as a surrogate for response to chemotherapy. Am J Respir Crit Care Med. 1999;160(1):203–10.
Li L, Mahan CS, Palaci M, et al. Sputum Mycobacterium tuberculosis mRNA as a marker of bacteriologic clearance in response to antituberculosis therapy. J Clin Microbiol. 2010;48(1):46–51.
Moore DF, Curry JI, Knott CA, Jonas V. Amplification of rRNA for assessment of treatment response of pulmonary tuberculosis patients during antimicrobial therapy. J Clin Microbiol. 1996;34(7):1745–9.
van der Vliet GM, Schepers P, Schukkink RA, van Gemen B, Klatser PR. Assessment of mycobacterial viability by RNA amplification. Antimicrob Agents Chemother. 1994;38(9):1959–65.
Mdivani N, Li H, Akhalaia M, et al. Monitoring therapeutic efficacy by real-time detection of Mycobacterium tuberculosis mRNA in sputum. Clin Chem. 2009;55(9):1694–700.
Honeyborne I, McHugh TD, Phillips PP, et al. Molecular bacterial load assay, a culture-free biomarker for rapid and accurate quantification of sputum Mycobacterium tuberculosis bacillary load during treatment. J Clin Microbiol. 2011;49(11):3905–11.
Hai HT, Vinh DN, Thu DDA, et al. Comparison of the Mycobacterium tuberculosis molecular bacterial load assay, microscopy and GeneXpert versus liquid culture for viable bacterial load quantification before and after starting pulmonary tuberculosis treatment. Tuberculosis (Edinb). 2019;119: 101864.
Said B, Charlie L, Getachew E, Wanjiru CL, Abebe M, Manyazewal T. Molecular bacterial load assay versus culture for monitoring treatment response in adults with tuberculosis. SAGE Open Med. 2021;9:20503121211033470.
Honeyborne I, Mtafya B, Phillips PP, et al. The molecular bacterial load assay replaces solid culture for measuring early bactericidal response to antituberculosis treatment. J Clin Microbiol. 2014;52(8):3064–7.
Sabiiti W, Azam K, Farmer ECW, et al. Tuberculosis bacillary load, an early marker of disease severity: the utility of tuberculosis molecular bacterial load assay. Thorax. 2020;75(7):606–8.
Mbelele PM, Mpolya EA, Sauli E, et al. Mycobactericidal effects of different regimens measured by molecular bacterial load assay among people treated for multidrug-resistant tuberculosis in Tanzania. J Clin Microbiol. 2021;59(4):e02927-20.
Musisi E, Sessolo A, Kaswabuli S, et al. High Mycobacterium tuberculosis bacillary loads detected by tuberculosis molecular bacterial load assay in patient stool: a potential alternative for nonsputum diagnosis and treatment response monitoring of tuberculosis. Microbiol Spectr. 2022;10(1): e0210021.
Zainabadi K, Lee MH, Walsh KF, et al. An optimized method for purifying, detecting and quantifying Mycobacterium tuberculosis RNA from sputum for monitoring treatment response in TB patients. Sci Rep. 2022;12(1):17382.
Drain PK, Gounder L, Grobler A, Sahid F, Bassett IV, Moosa MY. Urine lipoarabinomannan to monitor antituberculosis therapy response and predict mortality in an HIV-endemic region: a prospective cohort study. BMJ Open. 2015;5(4): e006833.
Suwanpimolkul G, Kawkitinarong K, Manosuthi W, et al. Utility of urine lipoarabinomannan (LAM) in diagnosing tuberculosis and predicting mortality with and without HIV: prospective TB cohort from the Thailand Big City TB research network. Int J Infectious Dis. 2017;59:96–102.
Hamasur B, Bruchfeld J, van Helden P, Källenius G, Svenson S. A sensitive urinary lipoarabinomannan test for tuberculosis. PLoS ONE. 2015;10(4): e0123457.
Minion J, Leung E, Talbot E, Dheda K, Pai M, Menzies D. Diagnosing tuberculosis with urine lipoarabinomannan: systematic review and meta-analysis. Eur Respir J. 2011;38(6):1398–405.
Wallis RS, Perkins M, Phillips M, et al. Induction of the antigen 85 complex of Mycobacterium tuberculosis in sputum: a determinant of outcome in pulmonary tuberculosis treatment. J Infect Dis. 1998;178(4):1115–21.
Wallis RS, Perkins MD, Phillips M, et al. Predicting the outcome of therapy for pulmonary tuberculosis. Am J Respir Crit Care Med. 2000;161(4 Pt 1):1076–80.
Wallis RS, Phillips M, Johnson JL, et al. Inhibition of isoniazid-induced expression of Mycobacterium tuberculosis antigen 85 in sputum: potential surrogate marker in tuberculosis chemotherapy trials. Antimicrob Agents Chemother. 2001;45(4):1302–4.
Zadeh Babaki MK, Soleimanpour S, Rezaee SA. Antigen 85 complex as a powerful Mycobacterium tuberculosis immunogene: biology, immune-pathogenicity, applications in diagnosis, and vaccine design. Microb Pathog. 2017;112:20–9.
Phunpae P, Chanwong S, Tayapiwatana C, Apiratmateekul N, Makeudom A, Kasinrerk W. Rapid diagnosis of tuberculosis by identification of Antigen 85 in mycobacterial culture system. Diagn Microbiol Infect Dis. 2014;78(3):242–8.
Heyckendorf J, Gillespie SH, Ruhwald M. Culture-free proof of Mycobacterium tuberculosis—a new assay for viable bacteria. EBioMedicine. 2020;62: 103117.
Wang WH, Takeuchi R, Jain SH, et al. A novel, rapid (within hours) culture-free diagnostic method for detecting live Mycobacterium tuberculosis with high sensitivity. EBioMedicine. 2020;60: 103007.
Liu C, Zhao Z, Fan J, et al. Quantification of circulating Mycobacterium tuberculosis antigen peptides allows rapid diagnosis of active disease and treatment monitoring. Proc Natl Acad Sci USA. 2017;114(15):3969–74.
Ritter K, Rousseau J, Hölscher C. The role of gp130 cytokines in tuberculosis. Cells. 2020;9(12):2695.
Nafae RM, Mohammad MA, El-Gammal MS, Abdullah MAM. Use of enzyme linked immunospot assay (ELISpot) for monitoring treatment response of pulmonary tuberculosis patients. Egypt J Chest Dis Tuberculosis. 2013;62(3):409–17.
Amlerová J, Hrabák J. [IGRA methods in the routine operation - QuantiFERON®-TB Gold or T-SPOT.TB?]. Epidemiologie, mikrobiologie, imunologie : casopis Spolecnosti pro epidemiologii a mikrobiologii Ceske lekarske spolecnosti JE Purkyne. 2016;65(4):246–8.
Carrara S, Vincenti D, Petrosillo N, Amicosante M, Girardi E, Goletti D. Use of a T cell-based assay for monitoring efficacy of antituberculosis therapy. Clin Infect Dis. 2004;38(5):754–6.
Kobashi Y, Mouri K, Yagi S, Obase Y, Miyashita N, Oka M. Transitional changes in T-cell responses to Mycobacterium tuberculosis-specific antigens during treatment. J Infect. 2009;58(3):197–204.
Feng JY, Pan SW, Huang SF, Chen YY, Lin YY, Su WJ. Depressed gamma interferon responses and treatment outcomes in tuberculosis patients: a prospective cohort study. J Clin Microbiol. 2018;56(10):e00664-18.
Chedid C, Kokhreidze E, Tukvadze N, et al. Relevance of QuantiFERON-TB gold plus and heparin-binding hemagglutinin interferon-γ release assays for monitoring of pulmonary tuberculosis clearance: a multicentered study. Front Immunol. 2020;11: 616450.
Aiken AM, Hill PC, Fox A, et al. Reversion of the ELISPOT test after treatment in Gambian tuberculosis cases. BMC Infect Dis. 2006;6:66.
Chee CB, KhinMar KW, Gan SH, et al. Tuberculosis treatment effect on T-cell interferon-gamma responses to Mycobacterium tuberculosis-specific antigens. Eur Respir J. 2010;36(2):355–61.
Yang Q, Ruan Q, Liu X, et al. Preventive tuberculosis treatment effect on QuantiFERON TB-Gold in-tube testing in a high tuberculosis-endemic country: a clinical trial. Int J Infect Dis. 2020;91:182–7.
Mthiyane T, Rustomjee R, Pym A, et al. Impact of tuberculosis treatment and antiretroviral therapy on serial RD-1-specific quantitative T-cell readouts (QuantiFERON-TB Gold In-Tube), and relationship to treatment-related outcomes and bacterial burden. Int J Infect Dis. 2015;36:46–53.
Bocchino M, Chairadonna P, Matarese A, et al. Limited usefulness of QuantiFERON-TB gold in-tube for monitoring anti-tuberculosis therapy. Respir Med. 2010;104(10):1551–6.
Mvungi HC, Mbelele PM, Buza JJ, Mpagama SG, Sauli E. Blood cytokine responses to early secreted protein antigen-6/culture filtrate protein-10 tuberculosis antigens 2 months after antituberculosis treatment among patients with drug-susceptible pulmonary tuberculosis. Int J Mycobacteriol. 2019;8(1):53–9.
Sai Priya VH, Latha GS, Hasnain SE, Murthy KJ, Valluri VL. Enhanced T cell responsiveness to Mycobacterium bovis BCG r32-kDa Ag correlates with successful anti-tuberculosis treatment in humans. Cytokine. 2010;52(3):190–3.
Mattos AM, Almeida Cde S, Franken KL, et al. Increased IgG1, IFN-gamma, TNF-alpha and IL-6 responses to Mycobacterium tuberculosis antigens in patients with tuberculosis are lower after chemotherapy. Int Immunol. 2010;22(9):775–82.
Kassa D, de Jager W, Gebremichael G, et al. The effect of HIV coinfection, HAART and TB treatment on cytokine/chemokine responses to Mycobacterium tuberculosis (Mtb) antigens in active TB patients and latently Mtb infected individuals. Tuberculosis (Edinb). 2016;96:131–40.
Eum SY, Lee YJ, Min JH, et al. Association of antigen-stimulated release of tumor necrosis factor-alpha in whole blood with response to chemotherapy in patients with pulmonary multidrug-resistant tuberculosis. Respirat Int Rev Thoracic Dis. 2010;80(4):275–84.
Mamishi S, Marjani M, Pourakbari B, Sadeghi RH, Mahmoudi S. Evaluation of treatment response in active tuberculosis using QuantiFERON-TB gold plus. Eur Cytokine Netw. 2020;31(4):129–33.
Kabeer BS, Raja A, Raman B, et al. IP-10 response to RD1 antigens might be a useful biomarker for monitoring tuberculosis therapy. BMC Infect Dis. 2011;11:135.
Lee JS, Song CH, Lim JH, et al. The production of tumour necrosis factor-alpha is decreased in peripheral blood mononuclear cells from multidrug-resistant tuberculosis patients following stimulation with the 30-kDa antigen of Mycobacterium tuberculosis. Clin Exp Immunol. 2003;132(3):443–9.
Clifford V, Tebruegge M, Zufferey C, et al. Mycobacteria-specific cytokine responses as correlates of treatment response in active and latent tuberculosis. J Infect. 2017;75(2):132–45.
Leem AY, Song JH, Lee EH, et al. Changes in cytokine responses to TB antigens ESAT-6, CFP-10 and TB 7.7 and inflammatory markers in peripheral blood during therapy. Sci Rep. 2018;8(1):1159.
Sai Priya VH, Anuradha B, Latha Gaddam S, Hasnain SE, Murthy KJ, Valluri VL. In vitro levels of interleukin 10 (IL-10) and IL-12 in response to a recombinant 32-kilodalton antigen of Mycobacterium bovis BCG after treatment for tuberculosis. Clin Vaccine Immunol. 2009;16(1):111–5.
Waitt CJ, Banda P, Glennie S, et al. Monocyte unresponsiveness and impaired IL1β, TNFα and IL7 production are associated with a poor outcome in Malawian adults with pulmonary tuberculosis. BMC Infect Dis. 2015;15:513.
Hong JY, Lee HJ, Kim SY, et al. Efficacy of IP-10 as a biomarker for monitoring tuberculosis treatment. J Infect. 2014;68(3):252–8.
Chedid C, Andrieu T, Kokhreidze E, et al. In-depth immunophenotyping with mass cytometry during TB treatment reveals new T-cell subsets associated with culture conversion. Front Immunol. 2022;13: 853572.
Mensah GI, Addo KK, Tetteh JA, et al. Cytokine response to selected MTB antigens in Ghanaian TB patients, before and at 2 weeks of anti-TB therapy is characterized by high expression of IFN-γ and Granzyme B and inter- individual variation. BMC Infect Dis. 2014;14:495.
Feruglio SL, Tonby K, Kvale D, Dyrhol-Riise AM. Early dynamics of T helper cell cytokines and T regulatory cells in response to treatment of active Mycobacterium tuberculosis infection. Clin Exp Immunol. 2015;179(3):454–65.
Feruglio SL, Kvale D, Dyrhol-Riise AM. T cell responses and regulation and the impact of in vitro IL-10 and TGF-β modulation during treatment of active tuberculosis. Scand J Immunol. 2017;85(2):138–46.
Ahmed MIM, Ntinginya NE, Kibiki G, et al. Phenotypic changes on Mycobacterium tuberculosis-specific CD4 T cells as surrogate markers for tuberculosis treatment efficacy. Front Immunol. 2018;9:2247.
Wang X, Cao Z, Jiang J, et al. Association of mycobacterial antigen-specific CD4(+) memory T cell subsets with outcome of pulmonary tuberculosis. J Infect. 2010;60(2):133–9.
Vickers MA, Darboe F, Muefong CN, et al. Monitoring anti-tuberculosis treatment response using analysis of whole blood Mycobacterium tuberculosis specific T cell activation and functional markers. Front Immunol. 2020;11: 572620.
Young JM, Adetifa IM, Ota MO, Sutherland JS. Expanded polyfunctional T cell response to mycobacterial antigens in TB disease and contraction post-treatment. PLoS ONE. 2010;5(6): e11237.
Agrawal S, Parkash O, Palaniappan AN, et al. Efficacy of T regulatory cells, Th17 cells and the associated markers in monitoring tuberculosis treatment response. Front Immunol. 2018;9:157.
Adekambi T, Ibegbu CC, Cagle S, et al. Biomarkers on patient T cells diagnose active tuberculosis and monitor treatment response. J Clin Investig. 2015;125(9):3723.
Day CL, Moshi ND, Abrahams DA, et al. Patients with tuberculosis disease have Mycobacterium tuberculosis-specific CD8 T cells with a pro-apoptotic phenotype and impaired proliferative capacity, which is not restored following treatment. PLoS ONE. 2014;9(4): e94949.
Boer MC, van Meijgaarden KE, Goletti D, et al. KLRG1 and PD-1 expression are increased on T-cells following tuberculosis-treatment and identify cells with different proliferative capacities in BCG-vaccinated adults. Tuberculosis (Edinb). 2016;97:163–71.
Azzurri A, Kanaujia GV, Sow OY, et al. Serological markers of pulmonary tuberculosis and of response to anti-tuberculosis treatment in a patient population in Guinea. Int J Immunopathol Pharmacol. 2006;19(1):199–208.
Sánchez-Rodríguez C, Estrada-Chávez C, García-Vigil J, et al. An IgG antibody response to the antigen 85 complex is associated with good outcome in Mexican Totonaca Indians with pulmonary tuberculosis. Int J Tuberculosis Lung Dis. 2002;6(8):706–12.
Lee MR, Tsai CJ, Wang WJ, et al. Plasma biomarkers can predict treatment response in tuberculosis patients: a prospective observational study. Medicine. 2015;94(39): e1628.
Ribeiro-Rodrigues R, Resende Co T, Johnson JL, et al. Sputum cytokine levels in patients with pulmonary tuberculosis as early markers of mycobacterial clearance. Clin Diagn Lab Immunol. 2002;9(4):818–23.
Chung W, Lee K, Jung Y, et al. Serum CXCR3 ligands as biomarkers for the diagnosis and treatment monitoring of tuberculosis. Int J Tuberculosis Lung Dis. 2015;19(12):1476–84.
Abdul-Aziz AA, Elhassan MM, Yousuf AM, et al. Changes of th1 and th2 cytokines levels among sudanese tuberculosis patients during treatment. Int J Mycobacteriol. 2022;11(1):70–4.
Téllez-Navarrete NA, Ramon-Luing LA, Muñoz-Torrico M, et al. Anti-tuberculosis chemotherapy alters TNFR2 expression on CD4+ lymphocytes in both drug-sensitive and -resistant tuberculosis: however, only drug-resistant tuberculosis maintains a pro-inflammatory profile after a long time. Mol Med (Cambridge, Mass). 2021;27(1):76.
Chavez K, Ravindran R, Dehnad A, Khan IH. Gender biased immune-biomarkers in active tuberculosis and correlation of their profiles to efficacy of therapy. Tuberculosis (Edinb). 2016;99:17–24.
Jacobs R, Tshehla E, Malherbe S, et al. Host biomarkers detected in saliva show promise as markers for the diagnosis of pulmonary tuberculosis disease and monitoring of the response to tuberculosis treatment. Cytokine. 2016;81:50–6.
Li G, Yang F, He X, et al. Anti-tuberculosis (TB) chemotherapy dynamically rescues Th1 and CD8+ T effector levels in Han Chinese pulmonary TB patients. Microbes Infect. 2020;22(3):119–26.
Waghmare PJ, Lende T, Goswami K, et al. Immunological host responses as surveillance and prognostic markers in tubercular infections. Int J Mycobacteriol. 2019;8(2):190–5.
Chung WY, Yoon D, Lee KS, et al. The usefulness of serum CXCR3 ligands for evaluating the early treatment response in tuberculosis: a longitudinal cohort study. Medicine. 2016;95(17): e3575.
Almeida Cde S, Abramo C, Alves CC, Mazzoccoli L, Ferreira AP, Teixeira HC. Anti-mycobacterial treatment reduces high plasma levels of CXC-chemokines detected in active tuberculosis by cytometric bead array. Mem Inst Oswaldo Cruz. 2009;104(7):1039–41.
Wergeland I, Pullar N, Assmus J, et al. IP-10 differentiates between active and latent tuberculosis irrespective of HIV status and declines during therapy. J Infect. 2015;70(4):381–91.
Zhao Y, Yang X, Zhang X, et al. IP-10 and RANTES as biomarkers for pulmonary tuberculosis diagnosis and monitoring. Tuberculosis (Edinb). 2018;111:45–53.
Petrone L, Bondet V, Vanini V, et al. First description of agonist and antagonist IP-10 in urine of patients with active TB. Int J Infect Dis. 2019;78:15–21.
Kim SY, Kim J, Kim DR, et al. Urine IP-10 as a biomarker of therapeutic response in patients with active pulmonary tuberculosis. BMC Infect Dis. 2018;18(1):240.
Azzurri A, Sow OY, Amedei A, et al. IFN-gamma-inducible protein 10 and pentraxin 3 plasma levels are tools for monitoring inflammation and disease activity in Mycobacterium tuberculosis infection. Microbes Infect. 2005;7(1):1–8.
Ferrian S, Manca C, Lubbe S, et al. A combination of baseline plasma immune markers can predict therapeutic response in multidrug resistant tuberculosis. PLoS ONE. 2017;12(5): e0176660.
Kumar NP, Moideen K, Nancy A, et al. Plasma chemokines are baseline predictors of unfavorable treatment outcomes in pulmonary tuberculosis. Clin Infect Dis. 2021;73(9):e3419–27.
Choi R, Kim K, Kim MJ, et al. Serum inflammatory profiles in pulmonary tuberculosis and their association with treatment response. J Proteomics. 2016;149:23–30.
Lienhardt C, Azzurri A, Amedei A, et al. Active tuberculosis in Africa is associated with reduced Th1 and increased Th2 activity in vivo. Eur J Immunol. 2002;32(6):1605–13.
Agrawal S, Parkash O, Palaniappan AN, et al. The role of T regulatory cell-associated markers in monitoring tuberculosis treatment completion and failure. Immunol Res. 2018;66(5):620–31.
Shen L, Shi H, Gao Y, et al. The characteristic profiles of PD-1 and PD-L1 expressions and dynamic changes during treatment in active tuberculosis. Tuberculosis (Edinb). 2016;101:146–50.
Musteikienė G, Miliauskas S, Zaveckienė J, et al. Is analysis of inflammatory biomarkers and lymphocyte subpopulations useful in prediction of tuberculosis treatment outcomes? J Clin Tuberculosis Other Mycobacterial Dis. 2021;25: 100275.
Heslop R, Bojang AL, Jarju S, et al. Changes in host cytokine patterns of TB patients with different bacterial loads detected using 16S rRNA analysis. PLoS ONE. 2016;11(12): e0168272.
Rockwood N, Costa DL, Amaral EP, et al. Mycobacterium tuberculosis induction of heme oxygenase-1 expression is dependent on oxidative stress and reflects treatment outcomes. Front Immunol. 2017;8:542.
Ndiaye MDB, Ranaivomanana P, Rasoloharimanana LT, et al. Plasma host protein signatures correlating with Mycobacterium tuberculosis activity prior to and during antituberculosis treatment. Sci Rep. 2022;12(1):20640.
Singanayagam A, Manalan K, Connell DW, et al. Evaluation of serum inflammatory biomarkers as predictors of treatment outcome in pulmonary tuberculosis. Int J Tuberculosis Lung Dis. 2016;20(12):1653–60.
Pan SW, Shu CC, Huang JR, et al. PD-L1 expression in monocytes correlates with bacterial burden and treatment outcomes in active pulmonary tuberculosis. Int J Mol Sci. 2022;23(3):1619.
van Rensburg IC, Wagman C, Stanley K, et al. Successful TB treatment induces B-cells expressing FASL and IL5RA mRNA. Oncotarget. 2017;8(2):2037–43.
Nikolayevskyy V, Balabanova Y, Kontsevaya I, et al. Biomarkers of treatment success in fully sensitive pulmonary tuberculosis patients: a multicenter longitudinal study. Biomark Med. 2020;14(15):1439–52.
Sigal GB, Segal MR, Mathew A, et al. Biomarkers of tuberculosis severity and treatment effect: a directed screen of 70 host markers in a randomized clinical trial. EBioMedicine. 2017;25:112–21.
Pitaloka DAE, Syamsunarno M, Abdulah R, Chaidir L. Omics biomarkers for monitoring tuberculosis treatment: a mini-review of recent insights and future approaches. Infect Drug Resist. 2022;15:2703–11.
Bahuaud O, Genestet C, Hoffmann J, Dumitrescu O, Ader F. Opti-4TB: a protocol for a prospective cohort study evaluating the performance of new biomarkers for active tuberculosis outcome prediction. Front Med. 2022;9: 998972.
Clifford V, Zufferey C, Street A, Denholm J, Tebruegge M, Curtis N. Cytokines for monitoring anti-tuberculous therapy: a systematic review. Tuberculosis (Edinb). 2015;95(3):217–28.
Rambaran S, Naidoo K, Lewis L, et al. Effect of inflammatory cytokines/chemokines on pulmonary tuberculosis culture conversion and disease severity in HIV-infected and -uninfected individuals from South Africa. Front Immunol. 2021;12: 641065.
Zimmer AJ, Lainati F, Aguilera Vasquez N, et al. Biomarkers that correlate with active pulmonary tuberculosis treatment response: a systematic review and meta-analysis. J Clin Microbiol. 2022;60(2): e0185921.
Cilliers K, Menezes A, Webber T, et al. Mycobacterium tuberculosis-stimulated whole blood culture to detect host biosignatures for tuberculosis treatment response. Tuberculosis (Edinb). 2021;128:102082.
Funding
This work was supported by the Beijing Hospitals Authority Ascent Plan from Yu Pang (DFL20191601) and Liang Li (DFL20221401) and Beijing Hospitals Authority Clinical Medicine Development of Special Funding from Yu Pang (ZYLX202122), and Yu Pang funded the journal’s Rapid Service fee.
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The initial draft of the manuscript was prepared by Fuzhen Zhang and Yu Pang. Fuzhen Zhang, Fan Zhang, Yu Dong, Liang Li and Yu Pang contributed to the writing of the manuscript. Fuzhen Zhang, Fan Zhang, Yu Dong, Liang Li and Yu Pang have reviewed and approved the manuscript's results and conclusions. All authors have read and confirmed that they meet the criteria for authorship as outlined by the International Committee of Medical Journal Editors (ICMJE).
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Zhang, F., Zhang, F., Dong, Y. et al. New Insights into Biomarkers for Evaluating Therapy Efficacy in Pulmonary Tuberculosis: A Narrative Review. Infect Dis Ther 12, 2665–2689 (2023). https://doi.org/10.1007/s40121-023-00887-x
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DOI: https://doi.org/10.1007/s40121-023-00887-x