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Tick-borne pathogen detection: what's new?

  • Cabezas-Cruz A UMR BIPAR, INRA, ANSES, Ecole Nationale Vétérinaire d'Alfort, Université Paris-Est, Maisons-Alfort, 94700, France; Faculty of Science, University of South Bohemia, 37005, České Budějovice, Czech Republic; Institute of Parasitology, Biology Center, Czech Academy of Sciences, 37005, České Budějovice, Czech Republic.
  • Vayssier-Taussat M UMR BIPAR, INRA, ANSES, Ecole Nationale Vétérinaire d'Alfort, Université Paris-Est, Maisons-Alfort, 94700, France.
  • Greub G Center for Research on Intracellular Bacteria, Institute of Microbiology, Faculty of Biology and Medicine, University of Lausanne and University Hospital, 1011, Lausanne, Switzerland; Infectious Disease Service, University Hospital, 1011, Lausanne, Switzerland. Electronic address: gilbert.greub@chuv.ch.
  • 2018-01-14
Published in:
  • Microbes and infection. - 2018
Network analysis
Next generation sequencing
Pathogen detection
Ticks
Animals
Bacteria
Coinfection
High-Throughput Nucleotide Sequencing
Host-Pathogen Interactions
Humans
Microbial Interactions
Microbiota
Tick-Borne Diseases
Ticks
English Ticks and the pathogens they transmit constitute a growing burden for human and animal health worldwide. Traditionally, tick-borne pathogen detection has been carried out using PCR-based methods that rely in known sequences for specific primers design. This approach matches with the view of a 'single-pathogen' epidemiology. Recent results, however, have stressed the importance of coinfections in pathogen ecology and evolution with impact in pathogen transmission and disease severity. New approaches, including high-throughput technologies, were then used to detect multiple pathogens, but they all need a priori information on the pathogens to search. Thus, those approaches are biased, limited and conceal the complexity of pathogen ecology. Currently, next generation sequencing (NGS) is applied to tick-borne pathogen detection as well as to study the interactions between pathogenic and non-pathogenic microorganisms associated to ticks, the pathobiome. The use of NGS technologies have surfaced two major points: (i) ticks are associated to complex microbial communities and (ii) the relation between pathogens and microbiota is bidirectional. Notably, a new challenge emerges from NGS experiments, data analysis. Discovering associations among a high number of microorganisms is not trivial and therefore most current NGS studies report lists of microorganisms without further insights. An alternative to this is the combination of NGS with analytical tools such as network analysis to unravel the structure of microbial communities associated to ticks in different ecosystems.
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  • English
Open access status
green
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https://sonar.ch/global/documents/248468
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