TB or NTM: Can a new multiplex PCR assay be the answer?

Rapid lateral flow test for Mycobacterium tuberculosis complex and non-tuberculous mycobacteria differentiation

The diagnosis of mycobacterial infections, including both the Mycobacterium tuberculosis complex (MTBC) and non-tuberculous mycobacteria (NTM), poses a significant global medical challenge. This study proposes a novel approach using immunochromatographic (IC) strip tests for the simultaneous detection of MTBC and NTM. Traditional methods for identifying mycobacteria, such as culture techniques, are hindered by delays in distinguishing between MTBC and NTM, which can affect patient care and disease control. Molecular methods, while sensitive, are resource-intensive and unable to differentiate between live and dead bacteria. In this research, we developed unique monoclonal antibodies (mAbs) against Ag85B, a mycobacterial secretory protein, and successfully implemented IC strip tests named 8B and 9B. These strips demonstrated high concordance rates with conventional methods for detecting MTBC, with positivity rates of 93.9% and 85.9%, respectively. For NTM detection, the IC strip tests achieved a 63.2% detection rate compared to culture methods, considering variations in growth rates among different NTM species. Furthermore, this study highlights a significant finding regarding the potential of MPT64 and Ag85B proteins as markers for MTBC detection. In conclusion, our breakthrough method enables rapid and accurate detection of both MTBC and NTM bacteria within the BACTEC MGIT system. This approach represents a valuable tool in clinical settings for distinguishing between MTBC and NTM infections, thereby enhancing the management and control of mycobacterial diseases. KEY POINTS: • Panel of mAbs for differentiating MTB versus NTM • IC strips for diagnosing MTBC and NTM after the BACTEC MGIT • Combined detection of MTP64 and Ag85B enhances diagnostic accuracy.

Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its overlapping clinical symptoms with tuberculosis (TB), leading to inappropriate treatment. Herein, we employed noninvasive metabolic phenotyping coupled with comprehensive statistical modeling to discover potential biomarkers for the differential diagnosis of NTM infection versus TB. Urine samples from 19 NTM and 35 TB patients were collected, and untargeted metabolomics was performed using rapid liquid chromatography-mass spectrometry. The urine metabolome was analyzed using a combination of univariate and multivariate statistical approaches, incorporating machine learning. Univariate analysis revealed significant alterations in amino acids, especially tryptophan metabolism, in NTM infection compared to TB. Specifically, NTM infection was associated with upregulated levels of methionine but downregulated levels of glutarate, valine, 3-hydroxyanthranilate, and tryptophan. Five machine learning models were used to classify NTM and TB. Notably, the random forest model demonstrated excellent performance [area under the receiver operating characteristic (ROC) curve greater than 0.8] in distinguishing NTM from TB. Six potential biomarkers for NTM infection diagnosis, including methionine, valine, glutarate, 3-hydroxyanthranilate, corticosterone, and indole-3-carboxyaldehyde, were revealed from univariate ROC analysis and machine learning models. Altogether, our study suggested new noninvasive biomarkers and laid a foundation for applying machine learning to NTM differential diagnosis.

First Case Reports of Nontuberculous Mycobacterial (NTM) Lung Disease in Ecuador: Important Lessons to Learn

Nontuberculous mycobacteria (NTM) lung infections are often misdiagnosed as tuberculosis, which can lead to ineffective antibiotic treatments. In this report, we present three cases of NTM lung infections in Ecuador that were initially diagnosed and treated as tuberculosis based on the results of sputum smear microscopy. The patients, all male, included two immunocompetent individuals and one HIV-positive subject. Unfortunately, sputum culture was not initiated until late in the course of the disease and the cause of the lung infection, Mycobacterium avium complex (MAC), was only identified after the patients had either passed away or were lost to follow-up. These cases are the first documented cases of NTM lung infections in the English medical literature from Ecuador. We emphasize the importance of accurate diagnosis of NTM infections by culture and identification to species level. Sputum smear staining alone cannot differentiate between mycobacterial species, which can lead to misidentification and ineffective treatments. Additionally, reporting NTM pulmonary disease as a notifiable disease to national TB control programs is recommended to obtain accurate prevalence data. These data are critical in determining the importance of this public health problem and the necessary actions needed to address it.

Beyond antibiotics: recent developments in the diagnosis and management of nontuberculous mycobacterial infection

Nontuberculous mycobacteria (NTM) pulmonary disease represents a significant clinical challenge with suboptimal therapy and increasing prevalence globally. Although clinical practice guidelines seek to standardise the approach to diagnosis and treatment of NTM disease, a lack of robust evidence limits their utility and significant variability exists in clinical practice. Here we walk through some novel approaches in diagnosis and therapy that are under development to tackle a disease where traditional strategies are failing.

Prevalence of NTM disease is rising globally, yet current diagnostic and therapeutic strategies are lacking. This review describes some burgeoning diagnostic and therapeutic approaches, but it is clear that real progress will need more focused attention.https://bit.ly/3O0K2SP