Multiple merger genealogies in outbreaks of Mycobacterium tuberculosis

The Kingman coalescent and its developments are often considered among the most important advances in population genetics of the last decades. Demographic inference based on coalescent theory has been used to reconstruct the population dynamics and evolutionary history of several species, including Mycobacterium tuberculosis (MTB), an important human pathogen causing tuberculosis. One key assumption of the Kingman coalescent is that the number of descendants of different individuals does not vary strongly, and violating this assumption could lead to severe biases caused by model misspecification. Individual lineages of MTB are expected to vary strongly in reproductive success because 1) MTB is potentially under constant selection due to the pressure of the host immune system and of antibiotic treatment, 2) MTB undergoes repeated population bottlenecks when it transmits from one host to the next, and 3) some hosts show much higher transmission rates compared to the average ("super-spreaders"). Here we used an Approximate Bayesian Computation approach to test whether multiple merger coalescents (MMC), a class of models that allow for large variation in reproductive success among lineages, are more appropriate models to study MTB populations. We considered eleven publicly available whole genome sequence data sets sampled from local MTB populations and outbreaks, and found that MMC had a better fit compared to the Kingman coalescent for ten of the eleven data sets. These results indicate that the null model for analyzing MTB outbreaks should be reassessed, and that past findings based on the Kingman coalescent need to be revisited

<i>Albugo candida</i> race diversity, ploidy and host-associated microbes revealed using DNA sequence capture on diseased plants in the field

• Physiological races of the oomycete Albugo candida are biotrophic pathogens of diverse plant species, primarily the Brassicaceae, and cause infections that suppress host immunity to other pathogens. However, A. candida race diversity and the consequences of host immunosuppression are poorly understood in the field. • We report a method that enables sequencing of DNA of plant pathogens and plant-associated microbes directly from field samples (Pathogen Enrichment Sequencing: PenSeq). We apply this method to explore race diversity in A. candida and to detect A. candida-associated microbes in the field (91 A. candida-infected plants).• We show with unprecedented resolution that each host plant species supports colonization by one of 17 distinct phylogenetic lineages, each with an unique repertoire of effector candidate alleles. These data reveal the crucial role of sexual and asexual reproduction, polyploidy and host domestication in A. candida specialization on distinct plant species. Our bait design also enabled phylogenetic assignment of DNA sequences from bacteria and fungi from plants in the field.• This paper shows that targeted sequencing has a great potential for the study of pathogen populations while they are colonizing their hosts. This method could be applied to other microbes, especially to those that cannot be cultured

Rifampicin mono-resistant tuberculosis is not the same as multidrug-resistant tuberculosis: a descriptive study from Khayelitsha, South Africa

Rifampicin mono-resistant TB (RMR-TB, rifampicin resistance and isoniazid susceptibility) constitutes 38% of all rifampicin-resistant TB (RR-TB) in South Africa and is increasing. We aimed to compare RMR-TB with multidrug-resistant TB (MDR-TB) within a high TB, RR-TB and HIV burden setting. Patient-level clinical data and stored RR-TB isolates from 2008-2017 with available whole genome sequencing (WGS) data were used to describe risk factors associated with RMR-TB and to compare rifampicin-resistance (RR) conferring mutations between RMR-TB and MDR-TB. A subset of isolates with particular RR-conferring mutations were subjected to semi-quantitative rifampicin phenotypic drug susceptibility testing. Among 2,041 routinely diagnosed RR-TB patients, 463 (22.7%) had RMR-TB. HIV-positive individuals (adjusted Odds Ratio 1.4, 95% CI 1.1-1.9) and diagnosis between 2013-2017 versus 2008-2012 (aOR 1.3, 1.1-1.7) were associated with RMR-TB. Among 1,119 (54.8%) patients with available WGS data showing RR-TB, significant differences in the distribution of rpoB RR-conferring mutations between RMR-TB and MDR-TB isolates were observed. Mutations associated with high-level RR were more commonly found among MDR-TB isolates (811/889, 90.2% versus 162/230, 70.4% among RMR-TB, p<0.0001). In particular, the rpoB L430P mutation, conferring low-level RR, was identified in 32/230 (13.9%) RMR-TB versus 10/889 (1.1%) in MDR-TB (p<0.0001). Among 10 isolates with an rpoB L430P mutation, 7 were phenotypically susceptible using the critical concentration of 0.5 mug/ml (range 0.125-1 mug/ml). The majority (215/230, 93.5%) of RMR-TB isolates showed susceptibility to all other TB drugs, highlighting the potential benefits of WGS for simplified treatment. These data suggest that the evolution of RMR-TB differs from MDR-TB with a potential contribution from HIV infection

Potential contribution of HIV during first-line tuberculosis treatment to subsequent rifampicin-monoresistant tuberculosis and acquired tuberculosis drug resistance in South Africa: a retrospective molecular epidemiology study

Background: South Africa has a high burden of rifampicin-resistant tuberculosis (including multidrug-resistant [MDR] tuberculosis), with increasing rifampicin-monoresistant (RMR) tuberculosis over time. Resistance acquisition during first-line tuberculosis treatment could be a key contributor to this burden, and HIV might increase the risk of acquiring rifampicin resistance. We assessed whether HIV during previous treatment was associated with RMR tuberculosis and resistance acquisition among a retrospective cohort of patients with MDR or rifampicin-resistant tuberculosis. Methods: In this retrospective cohort study, we included all patients routinely diagnosed with MDR or rifampicin-resistant tuberculosis in Khayelitsha, Cape Town, South Africa, between Jan 1, 2008, and Dec 31, 2017. Patient-level data were obtained from a prospective database, complemented by data on previous tuberculosis treatment and HIV from a provincial health data exchange. Stored MDR or rifampicin-resistant tuberculosis isolates from patients underwent whole-genome sequencing (WGS). WGS data were used to infer resistance acquisition versus transmission, by identifying genomically unique isolates (single nucleotide polymorphism threshold of five). Logistic regression analyses were used to assess factors associated with RMR tuberculosis and genomic uniqueness. Findings: The cohort included 2041 patients diagnosed with MDR or rifampicin-resistant tuberculosis between Jan 1, 2008, and Dec 31, 2017; of those, 463 (22.7%) with RMR tuberculosis and 1354 (66.3%) with previous tuberculosis treatment. In previously treated patients, HIV positivity during previous tuberculosis treatment versus HIV negativity (adjusted odds ratio [OR] 2.07, 95% CI 1.35-3.18), and three or more previous tuberculosis treatment episodes versus one (1.96, 1.21-3.17) were associated with RMR tuberculosis. WGS data showing MDR or rifampicin-resistant tuberculosis were available for 1169 patients; 360 (30.8%) isolates were identified as unique. In previously treated patients, RMR tuberculosis versus MDR tuberculosis (adjusted OR 4.96, 3.40-7.23), HIV positivity during previous tuberculosis treatment (1.71, 1.03-2.84), and diagnosis in 2013-17 (1.42, 1.02-1.99) versus 2008-12, were associated with uniqueness. In previously treated patients with RMR tuberculosis, HIV positivity during previous treatment (adjusted OR 5.13, 1.61-16.32) was associated with uniqueness as was female sex (2.50 [1.18-5.26]). Interpretation: These data suggest that HIV contributes to rifampicin-resistance acquisition during first-line tuberculosis treatment and that this might be driving increasing RMR tuberculosis over time. Large-scale prospective cohort studies are required to further quantify this risk. Funding: Swiss National Science Foundation, South African National Research Foundation, and Wellcome Trust