Offense and Defense in Granulomatous Inflammation Disease

ZNFX1 promotes AMPK-mediated autophagy against Mycobacterium tuberculosis by stabilizing Prkaa2 mRNA

Tuberculosis has the highest mortality rate worldwide for a chronic infectious disease caused by a single pathogen. RNA-binding proteins (RBPs) are involved in autophagy - a key defense mechanism against Mycobacterium tuberculosis (M. tuberculosis) infection - by modulating RNA stability and forming intricate regulatory networks. However, the functions of host RBPs during M. tuberculosis infection remain relatively unexplored. Zinc finger NFX1-type containing 1 (ZNFX1), a conserved RBP critically involved in immune deficiency diseases and mycobacterial infections, is significantly upregulated in M. tuberculosis-infected macrophages. Here, we aimed to explore the immunoregulatory functions of ZNFX1 during M. tuberculosis infection. We observed that Znfx1 knockout markedly compromised the multifaceted immune responses mediated by macrophages. This compromise resulted in reduced phagocytosis, suppressed macrophage activation, increased M. tuberculosis burden, progressive lung tissue injury, and chronic inflammation in M. tuberculosis-infected mice. Mechanistic investigations revealed that the absence of ZNFX1 inhibited autophagy, consequently mediating immune suppression. ZNFX1 critically maintained AMPK-regulated autophagic flux by stabilizing protein kinase AMP-activated catalytic subunit alpha 2 mRNA, which encodes a key catalytic α subunit of AMPK, through its zinc finger region. This process contributed to M. tuberculosis growth suppression. These findings reveal a function of ZNFX1 in establishing anti-M. tuberculosis immune responses, enhancing our understanding of the roles of RBPs in tuberculosis immunity and providing a promising approach to bolster antituberculosis immunotherapy.

The diagnostic performance of endobronchial ultrasound with Xpert MTB/RIF Ultra in smear-negative pulmonary tuberculosis

OBJECTIVE

This study investigated the diagnostic performance of endobronchial ultrasound with Xpert MTB/RIF Ultra (Ultra) for detecting smear-negative pulmonary tuberculosis (TB).

METHODS

143 patients suspected of sputum smear-negative pulmonary tuberculosis were enrolled in this study in Shanghai Pulmonary Hospital, China. These patients underwent endobronchial ultrasound with a guide sheath (EBUS-GS) or endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) based on their chest CT manifestations. We assessed the sensitivity and specificity of tissue specimens with Ultra in the TB group and non-TB group. Culture and clinical diagnosis were used as gold-standard for TB.

RESULTS

Among these 143 patients, 11 patients were culture-positive TB, 85 patients were diagnosed with culture-negative TB and 47 were with the non-TB diseases. Direct testing with microscopy (Acid-Fast Bacilli smear, AFB), liquid culture, pathology, Xpert MTB/RIF(Xpert) test and Ultra had a sensitivity of 8.3%, 11.5%, 42.7%, 64.6%, and 78.1% individually among all the TB patients. Ultra had a higher sensitivity than Xpert (P = 0.011). But Ultra had a specificity of 59.6% (95% CI 44.3-73.3), lower than that of Xpert (89.4%, 95% CI 76.1-96.0, P = 0.001). Ultra had the same sensitivity on specimens from EBUS-TBNA and EBUS-GS (P = 0.975). Ultra's positive predictive value and negative predictive value were 79.8% and 57.1% respectively.

CONCLUSIONS

Tissue specimens from interventional bronchoscopy combined with Ultra provide a sensitive method for diagnosing smear-negative pulmonary tuberculosis, but its specificity was lower than Xpert.

Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning

Background

Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment.

Methods

A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group.

Results

The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644.

Conclusion

We used ML methods to screen out the blood-specific factors-PDW, LYM, AST/ALT, BUN, and Na⁺-of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.

Granulomatous inflammation in inborn errors of immunity

Granulomas have been defined as inflammatory infiltrates formed by recruitment of macrophages and T cells. The three-dimensional spherical structure typically consists of a central core of tissue resident macrophages which may merge into multinucleated giant cells surrounded by T cells at the periphery. Granulomas may be triggered by infectious and non-infectious antigens. Cutaneous and visceral granulomas are common in inborn errors of immunity (IEI), particularly among patients with chronic granulomatous disease (CGD), combined immunodeficiency (CID), and common variable immunodeficiency (CVID). The estimated prevalence of granulomas in IEI ranges from 1%-4%. Infectious agents causing granulomas such Mycobacteria and Coccidioides presenting atypically may be 'sentinel' presentations for possible underlying immunodeficiency. Deep sequencing of granulomas in IEI has revealed non-classical antigens such as wild-type and RA27/3 vaccine-strain Rubella virus. Granulomas in IEI are associated with significant morbidity and mortality. The heterogeneity of granuloma presentation in IEI presents challenges for mechanistic approaches to treatment. In this review, we discuss the main infectious triggers for granulomas in IEI and the major forms of IEI presenting with 'idiopathic' non-infectious granulomas. We also discuss models to study granulomatous inflammation and the impact of deep-sequencing technology while searching for infectious triggers of granulomatous inflammation. We summarize the overarching goals of management and highlight the therapeutic options reported for specific granuloma presentations in IEI.