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Historically, the first documented cases of infections by chlamydiae involved humans with contact to psittacine birds. While birds have remained the main source of zoonotic transmission until now, the spectrum of chlamydial zoonoses has broadened in recent decades. In the present chapter, we summarize current knowledge on etiology, pathology, epide...
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Citations
... 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. The course of the human disease ranges from asymptomatic to flu-like to severe systemic illness, with the latter manifesting as pneumonia, myocarditis, encephalitis or sepsis. ...
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.
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 pathogenic Chlamydia, 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.
