Автоматизированная Разделение<em> С. Элеганс</em> Переменно колонизирована бактериальным патогеном

Automatic worm sorting is much like FACS, operating by measuring a fluorescent signal in a worm (typically provided by transgenic expression of reporter proteins) as it passes straightened in a tube, allowing redirection either to a collection tube/well or to a waste container according to gating parameters set by the researcher¹. The wormsorter can facilitate research in many ways; an example of employing it as an analytical tool is a study that followed spatiotemporal patterns of promoter activity for almost 1,000 genes².
However, the major use of the wormsorter is in genetic screens, following target gene expression levels or localization of fluorescent protein along the axis of the worm^3-5.

Here, we describe a new application for the wormsorter, in following colonization of the worm by a fluorescently tagged pathogen. With this as a tool we focused on the relationship between pathogen colonization/load and the immune response, to gain new insights into the mechanisms responsible for initiation of immune responses in the worm.

In virtually all organisms studied to date, initiation of innate immune responses to microbial pathogens depends on recognition of pathogen-associated molecular patterns (PAMPs), and/or danger/damage-associated molecular patterns (DAMPs)^6,7. The first are conserved microbial structures that include components of the microbial cell wall, its flagellum, or its lipid bilayer⁶; the second, include both released molecules (e.g. ATP⁸), altered proteins or other markers of altered cellular processes^9,10. Both types of signals are recognized by proteins designated as pattern recognition receptors (PRRs), which upon specific binding of a pattern molecule activate a chain of events leading to a protective response. C. elegans has been extremely useful as a tractable model to dissect various aspects of host-pathogen interactions, but one thing that is not well understood is how immune responses are initiated in the worm. None of the putative receptors that are orthologous to pattern recognition receptors (PRRs) in other organisms have been shown to bind PAMPs, and many of the orthologs of PRRs that are pivotal for immune responses in other organisms show a surprisingly limited contribution to worm pathogen responses and resistance. For example, the Drosophila Toll receptor, which is essential for resisting Gram positive pathogens, is represented in C. elegans by a sole homolog, tol-1, which contributes to protection from the Gram negative pathogen Salmonella Typhimurium¹¹, but not from other tested Gram-negative, or -positive pathogens^11,12 . These observations, combined with data indicating that immune responses could be induced by disrupting cellular protein translation has led some to suggest that C. elegans primarily detects DAMPs^9,13,14. Nevertheless, reports describing ability of dead pathogens to induce immune responses suggest that PAMP binding may be have an important role in pathogen recognition in C. elegans^15,16. Previous work focusing on immune responses in age-synchronized genetically identical C. elegans populations, demonstrated large individual variability in intestinal colonization by the bacterial Gram-negative pathogen Pseudomonas aeruginosa.

However, transcriptional profiling studies treated these variably-colonized populations as one entity^17,18. Taking advantage of this variability, we developed a protocol focusing an automated wormsorter to separate differentially colonized populations of Caenorhabditis elegans exposed to GFP-expressing P. aeruginosa. Examining gene expression in differently-colonized populations facilitated assessment of the relationship between pathogen load (and the associated damage) and immune responses and provided new insights about pathogen recognition in C. elegans¹⁹. Below we describe the protocol, which could be applied to sort worms infected with any fluorescently labeled pathogen.

To potential users it should be noted that the number of worms required to be sorted out depends on the nature of the subsequent analyses and protocols in use. For example, in the case of microarray gene expression analysis, >1,000 worms will be required to obtain enough RNA, if standard protocols are used, but ~100 worms would suffice if amplification is employed, allowing fast collection of material and thus minimizing stress to the worms.