Biology subjects

Fenner, L.

Publications and source records attributed to Fenner, L..

7 recordsLinked to original sources

bioRxiv2020

Local adaptation in populations of Mycobacterium tuberculosis endemic to the Indian Ocean Rim

Lineage 1 (L1) and 3 (L3) are two lineages of the Mycobacterium tuberculosis complex (MTBC), causing tuberculosis (TB) in humans. L1 and L3 are endemic to the Rim of the Indian Ocean, the region that accounts for most of the worlds new TB cases. Despite their relevance for this region, L1 and L3 remain understudied. Here we analyzed 2,938 L1 and 2,030 L3 whole genome sequences originating from 69 countries. We show that South Asia played a central role in the dispersion of these two lineages to neighboring regions. Moreover, we found that L1 exhibits signatures of local adaptation at the esxH locus, a gene coding for a secreted effector that targets the human endosomal sorting complex, and is included in several vaccine candidates. Our study highlights the importance of genetic diversity in the MTBC, and sheds new light on two of the most important MTBC lineages affecting humans.

genomics↗

bioRxiv2020

HIV coinfection is associated with low fitness rpoB variants in rifampicin-resistant Mycobacterium tuberculosis

We analysed 312 drug-resistant genomes of Mycobacterium tuberculosis (Mtb) collected from HIV coinfected and HIV negative TB patients from nine countries with a high tuberculosis burden. We found that rifampicin-resistant Mtb strains isolated from HIV coinfected patients carried disproportionally more resistance-conferring mutations in rpoB that are associated with a low fitness in the absence of the drug, suggesting these low fitness rpoB variants can thrive in the context of reduced host immunity.

evolutionary biology↗

bioRxiv2020

Phylogenomics of Mycobacterium africanum reveals a new lineage and a complex evolutionary history

Human tuberculosis is caused by members of the Mycobacterium tuberculosis Complex (MTBC). The MTBC comprises several human-adapted lineages known as M. tuberculosis sensu stricto as well as two lineages (L5 and L6) traditionally referred to as M. africanum. Strains of L5 and L6 are largely limited to West Africa for reasons unknown, and little is known on their genomic diversity, phylogeography and evolution. Here, we analyzed the genomes of 365 L5 and 326 L6 strains, plus five related genomes that had not been classified into any of the known MTBC lineages, isolated from patients from 21 African countries. Our population genomic and phylogeographical analyses show that the unclassified genomes belonged to a new group that we propose to name MTBC Lineage 9 (L9). While the most likely ancestral distribution of L9 was predicted to be East Africa, the most likely ancestral distribution for both L5 and L6 was the Eastern part of West Africa. Moreover, we found important differences between L5 and L6 strains with respect to their phylogeographical substructure, genetic diversity and association with drug resistance. In conclusion, our study sheds new light onto the genomic diversity and evolutionary history of M. africanum, and highlights the need to consider the particularities of each MTBC lineage for understanding the ecology and epidemiology of tuberculosis in Africa and globally.

genomics↗

bioRxiv2018

Insights into the genetic diversity of Mycobacterium tuberculosis in Tanzania

BackgroundHuman tuberculosis (TB) is caused by seven phylogenetic lineages of the Mycobacterium tuberculosis complex (MTBC), Lineage 1-7. Recent advances in rapid genotyping of MTBC based on single nucleotide polymorphisms (SNP), allow for rapid and phylogenetically robust strain classification, paving the way for defining genotype-phenotype relationships in clinical settings. Such studies have revealed that, in addition to host and environmental factors, different strains of the MTBC influence the outcome of TB infection and disease. In Tanzania, such molecular epidemiological studies of TB however are scarce in spite of a high TB burden.\n\nMethods and FindingsHere we used a SNP-typing method to genotype a nationwide collection of 2,039 MTBC clinical isolates obtained from new and retreatment TB cases diagnosed in 2012 and 2013. Four lineages, namely Lineage 1-4 were identified. The distribution and frequency of these lineages varied across the regions but overall, Lineage 4 was the most frequent (n=866, 42.5%), followed by Lineage 3 (n=681, 33.4%) and 1 (n=336, 16.5%), with Lineage 2 being the least frequent (n=92, 4.5%). A total of 64 (3.1%) isolates could not be assigned to any lineage. We found Lineage 2 to be associated with female sex (adjusted odds ratio [aOR] 2.25; 95% confidence interval [95% CI] 1.38 - 3.70, p<0.001) and retreatment (aOR 1.78; 95% CI 1.00 - 3.02, p=0.040). We found no associations between MTBC lineage and patient age or HIV status. Our sublineage typing based on spacer oligotyping revealed the presence of mainly EAI, CAS and LAM families. Finally, we detected low levels of multidrug resistant isolates among a subset of retreatment cases\n\nConclusionsThis study provides novel insights into the influence of pathogen-related factors on the TB epidemic in Tanzania.

epidemiology↗

bioRxiv2018

Multiple Introductions of the Mycobacterium tuberculosis Lineage 2 Beijing into Africa over centuries

The Lineage 2-Beijing (L2-Beijing) sub-lineage of Mycobacterium tuberculosis has received much attention due to its high virulence, fast disease progression, and association with antibiotic resistance. Despite several reports of the recent emergence of L2-Beijing in Africa, no study has investigated the evolutionary history of this sub-lineage on the continent. In this study, we used whole genome sequences of 817 L2 clinical strains from 14 geographical regions globally distributed to investigate the origins and onward spread of this lineage in Africa. Our results reveal multiple introductions of L2-Beijing into Africa linked to independent bacterial populations from East-and Southeast Asia. Bayesian analyses further indicate that these introductions occurred during the past 300 years, with most of these events pre-dating the antibiotic era. Hence, the success of L2-Beijing in Africa is most likely due to its hypervirulence and high transmissibility rather than drug resistance.

evolutionary biology↗

bioRxiv2018

Drug susceptibility testing and mortality in patients treated for tuberculosis in high-burden countries

BackgroundDrug resistance and HIV co-infection are challenges for the global control of tuberculosis.\n\nMethodsWe collected Mycobacterium tuberculosis isolates from adult patients in Cote dIvoire, Democratic Republic of the Congo, Kenya, Nigeria, South Africa, Peru, and Thailand, stratified by HIV status and tuberculosis drug resistance. Molecular or phenotypic drug susceptibility testing (DST) was done locally and at the Swiss tuberculosis reference laboratory. We examined mortality during treatment according to DST results and treatment adequacy in logistic regression models adjusting for sex, age, sputum microscopy and HIV status.\n\nFindings634 tuberculosis patients were included; median age was 33.2 years, 239 (37.7%) were female, 272 (42.9%) HIV-positive and 69 (10.9%) patients died. Based on the reference laboratory DST, 394 (62.2%) strains were pan-susceptible, 45 (7.1%) mono-resistant, 163 (25.7%) multidrug-resistant (MDR-TB), and 30 (4.7%) had pre-extensive or extensive drug resistance (pre-XDR/XDR-TB). Results of reference and local laboratories were discordant in 121 (19.1%) cases, corresponding to a sensitivity of 84.3% and a specificity of 90.8%. In patients with drug-resistant tuberculosis, discordant results were associated with increased mortality (risk ratio 1.81; 95% CI 1.07-3.07). In logistic regression, compared to adequately treated patients with pan-susceptible strains, the adjusted odds ratio for death was 4.23 (95% CI 2.16-8.29) for adequately treated patients with drug-resistant strains and 21.54 (95% CI 3.36-138.1) for inadequately treated patients with drug-resistant strains. HIV status was not associated with mortality.\n\nInterpretationUsing a reference laboratory standard, inaccurate DST leading to inappropriate treatment of drug-resistant tuberculosis, but not HIV infection, contributed to mortality.

epidemiology↗

bioRxiv2017

Tuberculosis outbreak investigation using phylodynamic analysis

The fast evolution of pathogenic viruses has allowed for the development of phylodynamic approaches that extract information about the epidemiological characteristics of viral genomes. Thanks to advances in whole genome sequencing, they can be applied to slowly evolving bacterial pathogens like Mycobacterium tuberculosis.\n\nIn this study, we investigate the epidemiological dynamics underlying two M. tuberculosis outbreaks using phylodynamic methods. The first outbreak occurred in the Swiss city of Bern (1993-2012) and was caused by a drug-susceptible strain belonging to the phylogenetic M. tuberculosis Lineage 4. The second outbreak was caused by a multidrug-resistant (MDR) strain of Lineage 2, imported from the Wat Tham Krabok (WTK) refugee camp in Thailand into California.\n\nThere is little temporal signal in the Bern data set and moderate temporal signal in the WTK data set. We estimate an evolutionary rate of 0.0039 per single nucleotide polymorphism (SNP) per year for Bern and 0.0024 per SNP per year for WTK. Nevertheless, due to its high sampling proportion (90%) the Bern outbreak allows robust estimation of epidemiological parameters despite the poor temporal signal. Conversely, theres much uncertainty in the epidemiological estimates concerning the WTK outbreak, which has a small sampling proportion (9%). Our results suggest that both outbreaks peaked around 1990, although the Bernese outbreak was only detected in 1993, and the WTK outbreak around 2004. Furthermore, individuals were infected for a significantly longer period (around 9 years) in the WTK outbreak than in the Bern outbreak (4-5 years).\n\nOur work highlights both the limitations and opportunities of phylodynamic analysis of outbreaks involving slowly evolving pathogens: (i) estimation of the evolutionary rate is difficult on outbreak time scales and (ii) a high sampling proportion allows quantification of the age of the outbreak based on the sampling times, and thus allows for robust estimation of epidemiological parameters.

epidemiology↗