Zoonotic Transmission of Chlamydia spp.: Known for 140 Years, but Still Underestimated

Borel, Nicole; Sachse, Konrad

doi:10.1007/978-3-030-85877-3_53-1

Cited by 2 publications

(3 citation statements)

References 128 publications

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“…We next applied our computational viability framework to distinguish viable from dead Chlamydia abortus , one of the most common causes of infectious abortion in small ruminants worldwide [43, 44], and a zoonotic pathogen causing pneumonia and miscarriage in humans [45]. C. abortus are obligate intracellular bacterial species with a complex biphasic life cycle that form intracellular vacuoles termed inclusions.…”

Section: Resultsmentioning

confidence: 99%

Nanopore- and AI-empowered metagenomic viability inference

Urel,

Benassou,

Reska

et al. 2024

Preprint

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The ability to differentiate between viable and dead microorganisms in metagenomic samples is crucial for various microbial inferences, ranging from assessing ecosystem functions of environmental microbiomes to inferring the virulence of potential pathogens. While established viability-resolved metagenomic approaches are labor-intensive as well as biased and lacking in sensitivity, we here introduce a new fully computational framework that leverages nanopore sequencing technology to assess microbial viability directly from freely available nanopore signal data. Our approach utilizes deep neural networks to learn features from such raw nanopore signal data that can distinguish DNA from viable and dead microorganisms in a controlled experimental setting. The application of explainable AI tools then allows us to robustly pinpoint the signal patterns in the nanopore raw data that allow the model to make viability predictions at high accuracy. Using the model predictions as well as efficient explainable AI-based rules, we show that our framework can be leveraged in a real-world application to estimate the viability of pathogenicChlamydia, where traditional culture-based methods suffer from inherently high false negative rates. This application shows that our viability model captures predictive patterns in the nanopore signal that can in principle be utilized to predict viability across taxonomic boundaries and indendent of the killing method used to induce bacterial cell death. While the generalizability of our computational framework needs to be assessed in more detail, we here demonstrate for the first time the potential of analyzing freely available nanopore signal data to infer the viability of microorganisms, with many applications in environmental, veterinary, and clinical settings.Author summaryMetagenomics investigates the entirety of DNA isolated from an environment or a sample to holistically understand microbial diversity in terms of known and newly discovered microorganisms and their ecosystem functions. Unlike traditional culturing of microorganisms, metagenomics is not able to differentiate between viable and dead microorganisms since DNA might readily persist under different environmental circumstances. The viability of microorganisms is, however, of importance when making inferences about a microorganism’s metabolic potential, a pathogen’s virulence, or an entire microbiome’s impact on its environment. As existing viability-resolved metagenomic approaches are labor-intensive, expensive, and lack sensitivity, we here investigate our hypothesis if freely available nanopore sequencing signal data, which captures DNA molecule information beyond the DNA sequence, might be leveraged to infer such viability. This hypothesis assumes that DNA from dead microorganisms accumulates certain damage signatures that reflect microbial viability and can be read from nanopore signal data using fully computational frameworks. We here show first evidence that such a computational framework might be feasible by training a deep model on controlled experimental data to predict viability at high accuracy, exploring what the model has learned, and applying it to an independent real-world dataset of an infectious pathogen. While the generalizability of this computational framework needs to be assessed in much more detail, we demonstrate that freely available data might be usable for relevant viability inferences in environmental, veterinary, and clinical settings.

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Section: Resultsmentioning

confidence: 99%

Nanopore- and AI-empowered metagenomic viability inference

Urel,

Benassou,

Reska

et al. 2024

Preprint

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“…4 Ernst-Ruska Centre 3 / Structural Biology, Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52425 Jülich, Germany. 5…”

Section: Supplementary Informationunclassified

“…Although the big outbreaks of "parrot fever" following large shipments of exotic birds from South America to Europe and North America in the period from 1892 to 1929 are now history, the agent still deserves permanent attention. The zoonotic potential of C. psittaci is well documented in the literature [3][4][5]. Typically, individuals with previous contact to birds are affected, but fulminant manifestations in humans usually occur only when efficacious antimicrobials are not administered in time.…”

Section: Introductionmentioning

confidence: 99%

Genomic analysis of 61 Chlamydia psittaci strains reveals extensive divergence associated with host preference

et al. 2023

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Background Chlamydia (C.) psittaci, the causative agent of avian chlamydiosis and human psittacosis, is a genetically heterogeneous species. Its broad host range includes parrots and many other birds, but occasionally also humans (via zoonotic transmission), ruminants, horses, swine and rodents. To assess whether there are genetic markers associated with host tropism we comparatively analyzed whole-genome sequences of 61 C. psittaci strains, 47 of which carrying a 7.6-kbp plasmid. Results Following clean-up, reassembly and polishing of poorly assembled genomes from public databases, phylogenetic analyses using C. psittaci whole-genome sequence alignment revealed four major clades within this species. Clade 1 represents the most recent lineage comprising 40/61 strains and contains 9/10 of the psittacine strains, including type strain 6BC, and 10/13 of human isolates. Strains from different non-psittacine hosts clustered in Clades 2– 4. We found that clade membership correlates with typing schemes based on SNP types, ompA genotypes, multilocus sequence types as well as plasticity zone (PZ) structure and host preference. Genome analysis also revealed that i) sequence variation in the major outer membrane porin MOMP can result in 3D structural changes of immunogenic domains, ii) past host change of Clade 3 and 4 strains could be associated with loss of MAC/perforin in the PZ, rather than the large cytotoxin, iii) the distinct phylogeny of atypical strains (Clades 3 and 4) is also reflected in their repertoire of inclusion proteins (Inc family) and polymorphic membrane proteins (Pmps). Conclusions Our study identified a number of genomic features that can be correlated with the phylogeny and host preference of C. psittaci strains. Our data show that intra-species genomic divergence is associated with past host change and includes deletions in the plasticity zone, structural variations in immunogenic domains and distinct repertoires of virulence factors.

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Zoonotic Transmission of Chlamydia spp.: Known for 140 Years, but Still Underestimated

Cited by 2 publications

References 128 publications

Nanopore- and AI-empowered metagenomic viability inference

Nanopore- and AI-empowered metagenomic viability inference

Genomic analysis of 61 Chlamydia psittaci strains reveals extensive divergence associated with host preference

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