Challenges of Diagnosing Mendelian Susceptibility to Mycobacterial Diseases in South Africa.

Inborn errors of immunity (IEI) are genetic disorders with extensive clinical presentations. They can range from increased susceptibility to infections to significant immune dysregulation that results in immune impairment. While IEI cases are individually rare, they collectively represent a significant burden of disease, especially in developing countries such as South Africa, where infectious diseases like tuberculosis (TB) are endemic. This is particularly alarming considering that certain high penetrance mutations that cause IEI, such as Mendelian Susceptibility to Mycobacterial Disease (MSMD), put individuals at higher risk for developing TB and other mycobacterial diseases. MSMD patients in South Africa often present with different clinical phenotypes than those from the developed world, therefore complicating the identification of disease-associated variants in this setting with a high burden of infectious diseases. The lack of available data, limited resources, as well as variability in clinical phenotype are the reasons many MSMD cases remain undetected or misdiagnosed. This article highlights the challenges in diagnosing MSMD in South Africa and proposes the use of transcriptomic analysis as a means of potentially identifying dysregulated pathways in affected African populations.

Utility of a three-gene transcriptomic signature in the diagnosis of tuberculosis in a low-endemic hospital setting.

BACKGROUND: Host transcriptomic blood signatures have demonstrated diagnostic potential for tuberculosis (TB), requiring further validation across different geographical settings. Discriminating TB from other diseases with similar clinical manifestations is crucial for the development of an accurate immunodiagnostic tool. In this exploratory cohort study, we evaluated the performance of potential blood-based transcriptomic signatures in distinguishing TB disease from non-TB lower respiratory tract infections in hospitalised patients in a TB low-endemic country. METHOD: Quantitative real-time polymerase chain reaction qPCR) was used to evaluate 26 previously published genes in blood from 31 patients (14 TB and 17 lower respiratory tract infection cases) admitted to Oslo University Hospital in Norway. The diagnostic accuracies of differentially expressed genes were determined by receiver operating characteristic curves. RESULTS: A significant difference (p < .01) in the age distribution was observed between patients with TB (mean age, 40 ± 15 years) and lower respiratory tract infection (mean age 59 ± 12 years). Following adjustment for age, ETV7, GBP1, GBP5, P2RY14 and BLK were significantly differentially expressed between patients with TB and those with LRI. A general discriminant analysis generated a three-gene signature (BAFT2, ETV7 and CD1C), which diagnosed TB with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI, 0.69 - 1.00), sensitivity of 69.23% (95% CI, 38.57%-90.91%) and specificity of 94.12% (95% CI, 71.31%-99.85%). CONCLUSION: The three-genes signature may have potential to improve diagnosis of TB in a hospitalised low-burden setting. However, the influence of confounding variables or covariates such as age requires further evaluation in larger studies.

Human whole genome sequencing in South Africa.

The advent and evolution of next generation sequencing has considerably impacted genomic research. Until recently, South African researchers were unable to access affordable platforms capable of human whole genome sequencing locally and DNA samples had to be exported. Here we report the whole genome sequences of the first six human DNA samples sequenced and analysed at the South African Medical Research Council's Genomics Centre. We demonstrate that the data obtained is of high quality, with an average sequencing depth of 36.41, and that the output is comparable to data generated internationally on a similar platform. The Genomics Centre creates an environment where African researchers are able to access world class facilities, increasing local capacity to sequence whole genomes as well as store and analyse the data.

Identification of potential biomarkers for predicting the early onset of diabetic cardiomyopathy in a mouse model.

Type 2 diabetes (T2D) is characterized by metabolic derangements that cause a shift in substrate preference, inducing cardiac interstitial fibrosis. Interstitial fibrosis plays a key role in aggravating left ventricular diastolic dysfunction (LVDD), which has previously been associated with the asymptomatic onset of heart failure. The latter is responsible for 80% of deaths among diabetic patients and has been termed diabetic cardiomyopathy (DCM). Through in silico prediction and subsequent detection in a leptin receptor-deficient db/db mice model (db/db), we confirmed the presence of previously identified potential biomarkers to detect the early onset of DCM. Differential expression of Lysyl Oxidase Like 2 (LOXL2) and Electron Transfer Flavoprotein Beta Subunit (ETFß), in both serum and heart tissue of 6-16-week-old db/db mice, correlated with a reduced left-ventricular diastolic dysfunction as assessed by high-resolution Doppler echocardiography. Principal component analysis of the combined biomarkers, LOXL2 and ETFß, further displayed a significant difference between wild type and db/db mice from as early as 9 weeks of age. Knockdown in H9c2 cells, utilising siRNA of either LOXL2 or ETFß, revealed a decrease in the expression of Collagen Type I Alpha1 (COL1A1), a marker known to contribute to enhanced myocardial fibrosis. Additionally, receiver-operating curve (ROC) analysis of the proposed diagnostic profile showed that the combination of LOXL2 and ETFß resulted in an area under the curve (AUC) of 0.813, with a cut-off point of 0.824, thus suggesting the favorable positive predictive power of the model and further supporting the use of LOXL2 and ETFß as possible early predictive DCM biomarkers.