Biology subjects

Frahm, N.

Publications and source records attributed to Frahm, N..

3 recordsLinked to original sources

bioRxiv2026

Associations Between QuantiFERON-TB Gold Plus IFNγ Concentrations and Progression to Symptomatic Tuberculosis in Global High-Burden TB Settings

IntroductionPredictive biomarkers for symptomatic tuberculosis (TB) progression would transform targeted prevention efforts. Although interferon-gamma release assays (IGRAs), including QuantiFERON(R) TB-Gold Plus (QFT-Plus), have been studied for this purpose, systematic evaluation of the QFT-Plus TB1 and TB2 Interferon-Gamma (IFN{gamma}) concentrations remains limited, particularly in high-burden TB settings. MethodsBaseline TB1 and TB2 IFN{gamma} concentrations from 5,259 participants in TB-endemic regions were analyzed in relation to subsequent TB outcomes over a median of 525 days follow-up. Participants were categorized as controls (no TB), suspected TB (no microbiological confirmation), or laboratory-confirmed TB, including a subset meeting a stringent case definition ([≥]2 positive microbiologic tests). Associations between baseline IFN{gamma} concentrations and progression to symptomatic TB were assessed. ResultsIn the full cohort (IGRA+/-participants), baseline TB2 IFN{gamma} concentrations were significantly higher compared to controls among participants who developed suspected TB (p=0.01), laboratory-confirmed TB (p=0.01), or met the stringent case definition (p<0.0001). In IGRA+ participants, baseline TB2 concentrations were significantly higher than controls in suspected (p=0.01) and laboratory-confirmed (p=0.02) groups. Associations with baseline TB1 IFN{gamma} concentrations and TB progression were observed for participants meeting the stringent case definition within the full cohort (p=0.009). Among stringent definition cases, TB2 concentrations achieved an area under the Receiver Operating Characteristic curve of 0.84, with a sensitivity of 80% and specificity of 78%. ConclusionsQuantitative IFN{gamma} concentrations from QFT-Plus, particularly TB2, were associated with progression to symptomatic TB, met or exceeded WHO-recommended sensitivity and specificity thresholds for predictive biomarkers, and may support biomarker-based stratification in TB clinical research. SummaryIn an exploratory analysis from a global tuberculosis (TB) epidemiology study, higher QuantiFERON-TB Gold Plus IFN{gamma} concentrations were associated with progression to symptomatic TB and met WHO predictive biomarker thresholds, supporting their potential value for risk stratification in TB research.

immunology↗

bioRxiv2025

A Validated Method for Detection of Bifidobacterium infantis Bi-26™ DNA in Stool by Quantitative Real-Time PCR

BackgroundBifidobacterium longum subspecies infantis (B. infantis) is a commensal bacterium enriched in the microbiota of breastfed infants and associated with beneficial effects on infant gut health. Clinical studies evaluating B. infantis supplementation in infants have reported increased colonization, reduced inflammation, enhanced vaccine responsiveness, and improved growth outcomes. To support rigorous evaluation of these effects in further clinical trials, reliable and validated methods for quantifying B. infantis in stool are needed. MethodsWe validated a quantitative real-time PCR (qPCR) assay against a conserved B. infantis genomic target. Assay performance was evaluated using DNA from the B. infantis strain Bi-26TM, spiked infant stool pools, and matrix controls. Validation parameters included linearity, sensitivity, accuracy, within- and between-run reproducibility, recovery, and matrix interference, with acceptance criteria informed by bioanalytical best practices. ResultsThe assay demonstrated robust performance across validation parameters. Linearity was confirmed over at least six orders of magnitude (R2 > 0.99; mean efficiency 96.4%), with a lower limit of quantification of 37.36 genome equivalents / reaction. Analytical sensitivity was high, with a 95% limit of detection of 4.67 genome equivalents / reaction. Accuracy across multiple stool pools met acceptance criteria, with mean deviation from nominal values of 0.39 log10. Reproducibility was strong, with within-run variability of 0.37% and between-run variability of 1.78%. Recovery averaged 41.3%, exceeding the predefined minimum threshold, and matrix interference was minimal. ConclusionsThis study reports a validated qPCR assay suitable for quantifying B. infantis Bi-26 DNA in stool samples collected in stabilized formats. Its robust analytical performance supports its use in clinical and translational research evaluating B. infantis colonization and its health impacts in early infant life.

microbiology↗

bioRxiv2022

Common gene signature model discovery and systematic validation for TB prognosis and response to treatment

While blood gene signatures have shown promise in tuberculosis (TB) diagnosis and treatment monitoring, most signatures derived from a single cohort may be insufficient to capture TB heterogeneity in populations and individuals. Here we report a new generalized approach combining a network-based meta-analysis with machine-learning modeling to leverage the power of heterogeneity among studies. The transcriptome datasets from 57 studies (37 TB and 20 viral infections) across demographics and TB disease states were used for gene signature discovery and model training and validation. The network-based meta-analysis identified a common 45-gene signature specific to active TB disease across studies. Two optimized random forest regression models, using the full or partial 45-gene signature, were then established to model the continuum from Mycobacterium tuberculosis infection to disease and treatment response. In model validation, using pooled multi-cohort datasets to mimic the real-world setting, the model provides robust predictive performance for incipient to active TB risk over a 2.5-year period with an AUROC of 0.85, 74.2% sensitivity, and 78.3% specificity, which approximated the minimum criteria (>75% sensitivity and >75% specificity) within the WHO target product profile for prediction of progression to TB. Moreover, the model strongly discriminates active TB from viral infection (AUROC 0.93, 95% CI 0.91-0.94). For treatment monitoring, the TB scores generated by the model statistically correlate with treatment responses over time and were predictive, even before treatment initiation, of standard treatment clinical outcomes. We demonstrate an end-to-end gene signature model development scheme that considers heterogeneity for TB risk estimation and treatment monitoring. AUTHOR SUMMARYAn early diagnosis for incipient TB is a one of the key approaches to reduce global TB deaths and incidence, particularly in low and middle-income countries. However, in appreciation of TB heterogenicity at the population and individual level due to TB pathogenesis, host genetics, demographics, disease comorbidities and technical variations from sample collecting and gene profiling, the responses of the molecular gene signatures have showed to be associated with these diverse factors In this work, we develop a new computational approach that combines a network-based meta-analysis with machine-learning modeling to address the existing challenge of early incipient TB prediction against TB heterogenicity. With this new approach, we harness the power of TB heterogeneity in diverse populations and individuals during model construction by including massive datasets (57 studies in total) that allow us not only to consider different confounding variables inherited from each cohort while identifying the common gene set and building the predictive model, but also to systematically validate the model by pooling the datasets to mimic the real-world setting. This generalized predicting model provides a robust prediction of long-term TB risk estimation (>30 months to TB disease). In addition, this model also demonstrates the utility in TB treatment monitoring along with Mycobacterium tuberculosis elimination.

bioinformatics↗