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Biomedical subjects

Xavier Didelot

Publications and source records attributed to Xavier Didelot.

4 recordsLinked to original sources

Comparative genomics and phenotypic divergence of ERIC I and ERIC II genotypes of Paenibacillus larvae, the causative agent of American Foulbrood disease.

Honeybees of the species Apis mellifera are important pollinators of crops and wild plants. Paenibacillus larvae, a spore-forming bacterium, is a problematic pathogen that causes American foulbrood (AFB) in honeybee larvae worldwide. In many countries, AFB is a notifiable disease, requiring the destruction of diseased colonies, resulting in economic loss that impacts beekeeping and agriculture. Disease onset starts with larval ingestion of P. larvae spores, which germinate into growing cells that proliferate in the larval gut, leading to larval death and eventually bee colony collapse. As infection progresses, P. larvae produce spores, reinitiating the disease cycle. Thus, growth, sporulation and germination underlie AFB. In this study, using various microbiological assays, quantitative cell biology methods, transmission electron microscopy and genomics, we sought to identify genetic and phenotypic characteristics associated with the predominant ERIC I and ERIC II genotypes of P. larvae during growth, sporulation and germination. Extending previous findings, our data identify genetic differences between ERIC I and ERIC II strains and some genetic variation between strains of the same ERIC type. Furthermore, we describe significant differences in cellular morphology during growth, differences in spore envelope structure and differences in germination efficiency between ERIC I and ERIC II genotypes. Collectively, our findings improve understanding of P. larvae biology and provide a foundation for developing genotype-specific disease management strategies for AFB.

Animals

Macrolide-resistant Mycoplasma pneumoniae resurgence in Chinese children in 2023: a longitudinal, cross-sectional, genomic epidemiology study.

BACKGROUND: After a prolonged period of low detection rates, Mycoplasma pneumoniae resurged in China, during September to November, 2023, raising global concern. This study aims to gain a better understanding of the genetic mechanisms underlying the 2023 increase in cases and the evolutionary dynamics of the epidemic populations, which has been previously hampered due to limited genomic data of this pathogen. METHODS: We sequenced 685 M pneumoniae isolates, including 248 isolates from 11 Chinese provinces and municipalities in 2023 and 437 isolates from Beijing (2013-22). By analysing these isolates and 436 publicly global sequences, we reconstructed the pathogen's evolutionary history using time-calibrated phylogenies and effective population size inference. We investigated potential genomic variations contributing to the 2023 resurgence through genome-wide association study and conducted phylogeographic analysis of the 2023 isolates across China. FINDINGS: Two macrolide-resistant epidemic clusters (T1-2-EC1 and T2-2-EC2) were responsible for the 2023 resurgence in China. Both clusters, having acquired the 23S ribosomal RNA A2063G mutation conferring macrolide resistance, emerged in approximately 1997 and 2014, respectively, and subsequently outcompeted their predecessor populations. This coincided with China's large-scale adoption of azithromycin for paediatric community-acquired pneumonia around the early 2000s. Aside from macrolide resistance, T1-2-EC1 independently acquired 17 clade-specific mutations and T2-2-EC2 four clade-specific mutations, which could further explain their increased competitiveness. Whole-genome analysis revealed no resurgence-specific mutations in the 2023 isolates. Phylogeographic analysis showed rapid mixing of T1-2-EC1 isolates between different sampled regions within China. INTERPRETATION: Our study provides evidence that the 2023 resurgence in China is a continuation of the pre-COVID epidemic, rather than emergence of novel variants. The high prevalence of macrolide resistance and rapid intranational spread emphasise the urgent need for enhanced global surveillance of this pathogen. FUNDING: National Key Research and Development Program of China, National Natural Science Foundation of China for Key Programs of China Grants, and Beijing High-Level Public Health Technical Talent Project.

Humans

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

Bayes Theorem

Incorporating Epidemiological Data into the Genomic Analysis of Partially Sampled Infectious Disease Outbreaks.

Pathogen genomic data are increasingly being used to investigate transmission dynamics in infectious disease outbreaks. Combining genomic data with epidemiological data should substantially increase our understanding of outbreaks, but this is highly challenging when the outbreak under study is only partially sampled, so that both genomic and epidemiological data are missing for intermediate links in the transmission chains. Here, we present a new dynamic programming algorithm to perform this task efficiently. We implement this methodology into the well-established TransPhylo framework to reconstruct partially sampled outbreaks using a combination of genomic and epidemiological data. We use simulated datasets to show that including epidemiological data can improve the accuracy of the inferred transmission links compared with inference based on genomic data only. This also allows us to estimate parameters specific to the epidemiological data (such as transmission rates between particular groups), which would otherwise not be possible. We then apply these methods to two real-world examples. First, we use genomic data from an outbreak of tuberculosis in Argentina, for which data was also available on the HIV status of sampled individuals, in order to investigate the role of HIV coinfection in the spread of this tuberculosis outbreak. Second, we use genomic and geographical data from the 2003 epidemic of avian influenza H7N7 in the Netherlands to reconstruct its spatial epidemiology. In both cases, we show that incorporating epidemiological data into the genomic analysis allows us to investigate the role of epidemiological properties in the spread of infectious diseases.

Humans