Abubakar, I. | Search bioRxiv

bioRxiv2019

Transmission analysis of a large TB outbreak in London: mathematical modelling study using genomic data

Outbreaks of tuberculosis- such as the large isoniazid-resistant outbreak centered on London, United Kingdom, which originated in 1995- provide excellent opportunities to model transmission of this devastating disease. Transmission chains for tuberculosis are notoriously difficult to ascertain, but mathematical modelling approaches, combined with whole-genome sequencing (WGS) data, have strong potential to contribute to transmission analyses. Using such data, we aimed to reconstruct transmission histories for the outbreak using a Bayesian approach, and to use machine learning techniques with patient-level data to identify the key covariates associated with transmission. By using our transmission reconstruction method that accounts for phylogenetic uncertainty, we are able to identify 24 transmission events with reasonable confidence, 11 of which have zero single nucleotide polymorphism (SNP) distance, and as maximum distance of 3. Patient age, alcohol abuse and history of homelessness were found to be the most important predictors of being credible tuberculosis transmitters.

genomics↗

bioRxiv2019

Concise whole blood transcriptional signatures for incipient tuberculosis: A systematic review and patient-level pooled meta-analysis

Blood transcriptional signatures may predict risk of tuberculosis (TB). We compared the performance of 17 mRNA signatures in a pooled dataset comprising 1,026 samples, including 183 samples from 127 incipient TB cases, from four studies conducted in South Africa, Ethiopia, The Gambia and the UK. We show that eight signatures (comprising 1-25 transcripts) that predominantly reflect interferon inducible gene expression, have equivalent diagnostic accuracy for incipient TB over a two-year period with areas under the receiver operating characteristic curves ranging from 0.70 (95% confidence interval 0.64-0.76) to 0.77 (0.71-0.82). The sensitivity of all eight signatures declined with increasing disease-free time interval. Using a threshold derived from two standard deviations above the mean of uninfected controls giving specificities of >90%, the eight signatures achieved sensitivities ranging 24.7-39.9% over a 24 month interval, rising to 47.1-81.0% over 3 months. Based on pre-test probability of 2%, the eight signatures achieved positive predictive value ranging from 6.8-9.4% over 24 months, rising to 11.1-14.3% over 3 months. When using biomarker thresholds maximising sensitivity and specificity with equal weighting to both, no signature met the minimum World Health Organization (WHO) Target Product Profile parameters for incipient TB biomarkers over a two-year period. Blood transcriptional biomarkers reflect short-term risk of TB and only exceed WHO benchmarks if applied to 3-6 month intervals.

microbiology↗