Heterogeneity in the cytokine profile of tuberculosis - diabetes co-morbidity

Distinct TB-antigen stimulated cytokine profiles as predictive biomarkers for unfavorable treatment outcomes in pulmonary tuberculosis

Introduction

The assessment of tuberculosis (TB) treatment outcomes predominantly relies on sputum culture conversion status. To enhance treatment management, it is crucial to identify non-sputum-based biomarkers that can predict unfavorable outcomes. Cytokines are widely studied as diagnostic biomarkers for active TB. However, their potential as indicators for unfavorable treatment outcomes remains uncertain.

Methodology

This study was conducted within a well-characterized cohort comprising newly diagnosed patients with drug-sensitive pulmonary TB, confirmed through sputum smear and culture positivity. Our objective was to elucidate the TB antigen-stimulated cytokine profile at pre-treatment and at 2 months into anti-TB treatment (ATT) in patients with unfavorable treatment outcomes (cases, n = 27) in comparison to recurrence-free, microbiologically cured controls (n = 31). Whole blood was stimulated with TB antigens using the QuantiFERON In-tube gold method, and plasma supernatants were subjected to a panel of 14 cytokine measurements.

Results

In our study, pre-treatment analysis revealed that eight cytokines (IL-2, IFN-γ, TNF-α, IL-6, IL-10, IL-17A, IL-18, and GM-CSF) were significantly elevated at baseline in cases compared to cured controls, both in unstimulated conditions and following TB antigen (CFP10, ESAT6, and TB7.7) stimulation. A similar pattern was observed at the 2-month mark of ATT, with eight cytokines (IL-2, IL-10, IL-13, IFN-γ, IL-6, IL-12p70, IL-17A, and TNF-α) showing significant differences between the groups. Importantly, no variations were detected following mitogen stimulation, underscoring that these distinctive immune responses are primarily driven by TB-specific antigens.

Conclusion

Our findings indicate that individuals with unfavorable TB treatment outcomes display a characteristic cytokine profile distinct from TB-cured patients, even before commencing ATT. Therefore, the levels of specific cytokine pre-treatment and at the 2-month point in the course of treatment may serve as predictive immune markers for identifying individuals at risk of unfavorable TB treatment outcomes, with these responses being predominantly influenced by TB-specific antigens.

Advancing personalized medicine for tuberculosis through the application of immune profiling

While early and precise diagnosis is the key to eliminating tuberculosis (TB), conventional methods using culture conversion or sputum smear microscopy have failed to meet demand. This is especially true in high-epidemic developing countries and during pandemic-associated social restrictions. Suboptimal biomarkers have restricted the improvement of TB management and eradication strategies. Therefore, the research and development of new affordable and accessible methods are required. Following the emergence of many high-throughput quantification TB studies, immunomics has the advantages of directly targeting responsive immune molecules and significantly simplifying workloads. In particular, immune profiling has been demonstrated to be a versatile tool that potentially unlocks many options for application in TB management. Herein, we review the current approaches for TB control with regard to the potentials and limitations of immunomics. Multiple directions are also proposed to hopefully unleash immunomics' potential in TB research, not least in revealing representative immune biomarkers to correctly diagnose TB. The immune profiles of patients can be valuable covariates for model-informed precision dosing-based treatment monitoring, prediction of outcome, and the optimal dose prediction of anti-TB drugs.

The impact of pulmonary tuberculosis on immunological and metabolic features of diabetic patients

Impaired immune responses have been observed in patients with type-2 diabetes mellitus (T2DM), which increases susceptibility to tuberculosis infection. However, the effect of the tuberculosis infection on the immunological and metabolic features of T2DM is largely unknown. To investigate this question, age- and sex-matched patients with pulmonary tuberculosis (PTB), T2DM, or T2DM combined with PTB were recruited from the Infectious Disease Hospital of Heilongjiang Province between January and September 2020. Healthy subjects were used as controls. Cytokines and chemokines in fasting serum samples were determined using the Quantibody Inflammation Array. Compared with T2DM alone, patients with T2DM combined with PTB have higher fasting blood glucose levels and monocyte counts in circulation. Among the four groups, circulating IL-10 levels peaked in patients with T2DM and PTB (p<0.05). Univariate linear analysis showed that serum IL-10 levels were positively associated with myeloid cells but negatively correlated with lymphocyte counts in these patients (p<0.05). Serum IL-6 levels were 1.6-fold higher in patients with T2DM plus PTB than in those with T2DM alone. In conclusion, PTB infection in patients with T2DM had distinct inflammatory profiles and sustained hyperglycaemia compared with PTB or T2DM alone. IL-10 levels and elevated monocyte counts could be hallmarks of patients with T2DM infected with PTB.

Impact of Type 2 Diabetes on the capacity of human macrophages infected with Mycobacterium tuberculosis to modulate monocyte differentiation through a bystander effect

Type 2 diabetes mellitus (T2DM) is a risk factor for the development of tuberculosis (TB) through mechanisms poorly understood. Monocytes and macrophages are key effector cells to control TB, but they are also subverted by Mycobacterium tuberculosis (Mtb). Specifically, Mtb can induce a bystander effect that skews monocyte differentiation towards macrophages with a permissive phenotype to infection. Here, we evaluated whether T2DM impacts this TB aspect. Our approach was to differentiate monocytes from healthy control (HC) subjects and T2DM patients into macrophages (MDM), and then assess their response to Mtb infection, including their secretome content and bystander effect capacity. Through flow cytometry analyses, we found a lower level of activation markers in MDM from T2DM patients in comparison to those from HC in response to mock (HLA-DR, CD86, and CD163) or Mtb challenge (CD14 and CD80). In spite of high TGF-β1 levels in mock-infected MDM from T2DM patients, cytometric bead arrays indicated there were no major differences in the secretome cytokine content in these cells relative to HC-MDM, even in response to Mtb. Mimicking a bystander effect, the secretome of Mtb-infected HC-MDM drove HC monocytes towards MDM with a permissive phenotype for Mtb intracellular growth. However, the secretome from Mtb-infected T2DM-MDM did not exacerbate the Mtb load compared to cmMTB-HC, possibly due to the high IL-1β production relative to Mtb-infected HC-MDM. Collectively, despite T2DM affecting the basal MDM activation, our approach revealed it has no major consequence on their response to Mtb or capacity to generate a bystander effect influencing monocyte differentiation.

Mycobacterial Growth Inhibition Assay (MGIA) as a Host Directed Diagnostic Tool for the Evaluation of the Immune Response in Subjects Living With Type 2 Diabetes Mellitus

The lack of efficient and cost-effective diagnostic tools contributes to poor control of tuberculosis in endemic countries. Moreover, host biological processes influence susceptibility, and infection resolution. It is well known that comorbidities such as type 2 diabetes mellitus (DM2) affect the host immune response, making individuals more susceptible to Mycobacterium tuberculosis infection. Currently, there are no laboratory tools that can identify those subjects who have a higher risk of developing the disease. In this study, we used a whole blood mycobacterial growth inhibition assay to assess the immune response capacity to inhibit mycobacterial growth between healthy subjects and those living with DM2 with optimal and poor glycemic control. We also measured cytokine levels in the culture supernatant by cytokine bead arrays. We included 89 patients with DM2: 54 patients with optimal control (mean age 56.2 ± 11.75 years) and 35 patients with poor control (mean age 52.05 ± 9.94 years). We also included 44 healthy subjects as controls (mean age 42.12 ± 11.75 years). We compared the Δlog UFC (a value that represents the difference between mycobacterial growth in the control tube versus the subject’s blood) between each group. Our results demonstrate that patients with DM2 had a lower capacity to inhibit M. tuberculosis growth (Δlog UFC DM2 subjects 0.9581 (-0.3897 to 2.495) vs Δlog UFC healthy subjects 0.7190 (-0.2678 to 2.098); p=0.013). Comparing subjects living with DM2 (optimal and poor glycemic control) vs healthy subjects, we found only significant differences between healthy subjects and patients poorly controlled (Δlog UFC optimal control group 0.876 (-0.3897 to 2.495); Δlog UFC poor control group 1.078 (0.068 to 2.33); Δlog UFC healthy subjects 0.7190 (-0.2678 to 2.098); p= 0.022). Therefore, glycemic control assessed by glycosylated hemoglobin values influences the capacity of the host to control the infection. Our results confirm that the whole blood mycobacterial growth inhibition assay has potential utility as an in vitro marker of M. tuberculosis immunological control in vivo in subjects living with DM2. This assay can be used to evaluate the immune response of each individual against M. tuberculosis, allowing clinicians to choose a more specific host-directed therapy.

The Functional Significance of Endocrine-immune Interactions in Health and Disease

Hormones are known to influence various body systems that include skeletal, cardiac, digestive, excretory, and immune systems. Emerging investigations suggest the key role played by secretions of endocrine glands in immune cell differentiation, proliferation, activation, and memory attributes of the immune system. The link between steroid hormones such as glucocorticoids and inflammation is widely known. However, the role of peptide hormones and amino acid derivatives such as growth and thyroid hormones, prolactin, dopamine, and thymopoietin in regulating the functioning of the immune system remains unclear. Here, we reviewed the findings pertinent to the functional role of hormone-immune interactions in health and disease and proposed perspective directions for translational research in the field.