Host biomarkers for monitoring therapeutic response in extrapulmonary tuberculosis

Plasma immune profiling combined with machine learning contributes to diagnosis and prognosis of active pulmonary tuberculosis

Tuberculosis (TB) remains one of the deadliest chronic infectious diseases globally. Early diagnosis not only prevents the spread of TB but also ensures effective treatment. However, the absence of non-sputum-based diagnostic tests often leads to delayed TB diagnoses. Inflammation is a hallmark of TB, we aimed to identify biomarkers associated with TB based on immune profiling. We collected 222 plasma samples from healthy controls (HCs), disease controls (non-TB pneumonia; PN), patients with TB (TB), and cured TB cases (RxTB). A high-throughput protein detection technology, multiplex proximity extension assays (PEA), was applied to measure the levels of 92 immune proteins. Based on differential analysis and the correlation with TB severity, we selected 9 biomarkers (CXCL9, PDL1, CDCP1, CCL28, CCL23, CCL19, MMP1, IFNγ and TRANCE) and explored their diagnostic capabilities through 7 machine learning methods. We identified combination of these 9 biomarkers that distinguish TB cases from controls with an area under the receiver operating characteristic curve (AUROC) of 0.89-0.99, with a sensitivity of 82-93% at a specificity of 88-92%. Moreover, the model excels in distinguishing severe TB cases, achieving AUROC exceeding 0.95, sensitivities and specificities exceeding 93.3%. In summary, utilizing targeted proteomics and machine learning, we identified a 9 plasma proteins signature that demonstrates significant potential for accurate TB diagnosis and clinical outcome prediction.

Therapeutic Drug Monitoring and Biomarkers; towards Better Dosing of Antimicrobial Therapy

Due to variability in pharmacokinetics and pharmacodynamics, clinical outcomes of antimicrobial drug therapy vary between patients. As such, personalised medication management, considering both pharmacokinetics and pharmacodynamics, is a growing concept of interest in the field of infectious diseases. Therapeutic drug monitoring is used to adjust and individualise drug regimens until predefined pharmacokinetic exposure targets are achieved. Minimum inhibitory concentration (drug susceptibility) is the best available pharmacodynamic parameter but is associated with many limitations. Identification of other pharmacodynamic parameters is necessary. Repurposing diagnostic biomarkers as pharmacodynamic parameters to evaluate treatment response is attractive. When combined with therapeutic drug monitoring, it could facilitate making more informed dosing decisions. We believe the approach has potential and justifies further research.

Next-Gen Dual Transcriptomics for Adult Extrapulmonary Tuberculosis Biomarkers and Host-Pathogen Interplay in Human Cells: A Strategic Review

Tuberculosis (TB) is a major public health concern that results in significant morbidity and mortality, particularly in middle- to low-income countries. Extra-pulmonary tuberculosis (EPTB) in adults is a form of TB that affects organs other than the lungs and is challenging to diagnose and treat due to a lack of accurate early diagnostic markers and inadequate knowledge of host immunity. Next-generation sequencing-based approaches have shown potential for identifying diagnostic biomarkers and host immune responses related to EPTB. This strategic review discusses on the significance using primary human cells and cell lines for in vitro transcriptomic studies on common forms of EPTB, such as lymph node TB, brain TB, bone TB, and endometrial TB to derive potential insights. While organoids have shown promise as a model system, primary cell lines still remain a valuable tool for studying host-pathogen interplay due to their conserved immune system, non-iPSC origin, and lack of heterogeneity in cell population. This review outlines a basic workflow for researchers interested in performing transcriptomics studies in EPTB, and also discusses the potential of cell-line based dual RNA-Seq technology for deciphering comprehensive transcriptomic signatures, host-pathogen interplay, and biomarkers from the host and Mycobacterium tuberculosis. Thus, emphasizing the implementation of this technique which can significantly contribute to the global anti-TB effort and advance our understanding of EPTB.

Graphical Abstract

Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning

The diagnosis of tuberculosis depends on detecting Mycobacterium tuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration.

Identification of host biomarkers from dried blood spots for monitoring treatment response in extrapulmonary tuberculosis

There is a lack of objective tools for monitoring treatment response in extrapulmonary tuberculosis (EPTB). This study aimed to explore the utility of inflammatory biomarkers from the dry blood spots (DBS) as a tool for monitoring treatment response in EPTB. In a prospective cohort study, 40 inflammatory biomarkers were investigated in DBS samples from 105 EPTB cases using a Luminex platform. The samples were taken before, and, at the end of the 2nd and 6th months of treatment. A total of 11 inflammatory host biomarkers changed significantly with treatment in all EPTB patients. CXCL9/MIG, CCL20, CCL23, CXCL10/IP-10, CXCL1, CXCL2, and CXCL8 significantly declined in our cohort of EPTB (48 TB pleuritis and 57 TB lymphadenitis) patients at both time points. A biosignature consisting of MIG, CCL23, and CXCL2, corresponded with the treatment response in 81% of patients in the 2nd month and 79% of patients at the end of treatment. MIG, CCL23, IP-10, and CXCL2 changed significantly with treatment in all patients including those showing partial clinical response at the 2nd month of treatment. The changes in the levels of inflammatory biomarkers in the DBS correspond with the treatment success and can be developed as a routine test in low-resource settings.

Evaluation of host biomarkers for monitoring treatment response in spinal tuberculosis: A 12-month cohort study

BACKGROUND

Monitoring treatment response is an important precaution in spinal tuberculosis (TB), particularly when the condition was clinically diagnosed rather than bacteriologically confirmed and when drug susceptibility testing was not performed. Conventional monitoring measures have limitations and there is a need for favourable alternatives. Therefore, this study aimed to investigate changes in immune biomarkers over the course of treatment for spinal TB and to compare these responses to the conventional monitoring measure, erythrocyte sedimentation rate (ESR).

METHODS

Patients with spinal TB were recruited from a tertiary hospital in the Western Cape, South Africa, and provided blood samples at 0, 3, 6, 9 and 12 months of TB treatment. Blood samples were analysed for ESR, using standard techniques, and for 19 cytokines, using a multiplex platform. Changes in ESR and cytokine levels were investigated using a mixed model ANOVA and Least Significant Difference post-hoc testing.

RESULTS

Twenty-six patients with spinal TB were included in the study although only fifteen remained in follow-up at 12 months. Seven biomarkers changed significantly over the course of treatment (CRP, Fibrinogen, IFN-γ, Ferritin, VEGF-A, ApoA1 and NCAM, p < 0.01) with a further three showing a strong trend towards change (CCL1, CXCL9 and GDF-15, 0.05 ≥ p ≤ 0.06). Responsive biomarkers could be approximately grouped according to patterns of progressive, initial or delayed change. ESR performed similarly to CRP, Fibrinogen and IFN-γ with all showing significant decreases between 0, 6 and 12- months of treatment. Individual ESR responses were variable.

DISCUSSION

Individual ESR responses may be unreliable and support the investigation of multi-marker approaches to evaluating treatment response in spinal TB. Biomarkers of treatment response identified in the current study require validation in a larger study, which may also incorporate aspects such as evaluating biomarkers within the first week of treatment and the inclusion of a healthy control group.