Detection of Campylobacter in air samples from poultry houses using shot-gun metagenomics – a pilot study

Abstract

Background: Foodborne pathogens such as Campylobacter jejuni are responsible for a large fraction of the gastrointestinal infections worldwide associated with poultry meat. Campylobacter spp. can be found in the chicken fecal microbiome and can contaminate poultry meat during the slaughter process. The current standard methods to detect these pathogens at poultry farms use fecal dropping or boot swaps in combination with cultivation / PCR. In this study, we have used air filters in combination with shotgun metagenomics for the detection of Campylobacter in poultry houses and MOCK communities to test the applicability of this approach for the detection of foodborne pathogens. Results: The spiked MOCK communities showed that we could detect as little as 200 CFU Campylobacter per sample using our protocols. Since we were interested in detecting Campylobacter, a DNA extraction protocol for Gram negative bacteria was chosen, and as expected, we found that the DNA extraction protocol created a substantial bias affecting the community composition of the MOCK communities. It can be expected that the same bias is present for poultry house samples analyzed. We observed significant amounts of Campylobacter on the air filters using both real-time PCR as well as shotgun metagenomics, irrespective of the amount of spiked in Campylobacter cells, suggesting that the flocks in both houses harboured Campylobacter spp.. Interestingly, in both houses we find diverse microbial communities present in the indoor air. In addition, have we tested the Campylobacter detection rate using shotgun metagenomics by spiking with different levels of C. jejuni cells in both the mock and the house samples. This showed that even with limited sequencing Campylobacter is detectable in samples with low abundance. Conclusions: These results show that air sampling of poultry houses in combination with shotgun metagenomics can detect and identify Campylobacter spp. present at low levels. This is important since early detection of Campylobacter in food production can help to decrease the number of food-borne infections.

Defining a metagenomic threshold for detecting low abundances of Providencia alcalifaciens in canine faecal samples

“…Using receiver operating characteristic (ROC) curves, they found that the accuracy of organism detection was maximized when this ratio was 10. A study on Campylobacter in air of poultry houses used a threshold of 50 reads per sample based on the number of putative false Campylobacter reads in mock samples ( Haverkamp et al., 2021 ), whereas a study on Klebsiella in human faecal samples suggested an abundance threshold of 0.01% to accurately estimate its occurrence based on spiked microbiomes ( Lindstedt et al., 2022 ). We choose to use unique k -mer counts as parameter for metagenomic abundance threshold in place of read counts as the former have improved species-recall and better separation between negative and positive samples ( Breitwieser et al., 2018 ), increasing the robustness of the metagenomic assay.…”

“…These factors will vary between pathogenic targets as well as matrix under investigation. Further, misclassifications of sequences are commonly encountered, especially when using deep sequencing, as shown in several studies where metagenomic classifiers readily identify hundreds of species in known mock communities despite only a few being present ( Haverkamp et al., 2021 ). Additional sources of misclassifications include contamination, presence of kitome, index hopping and sample bleeding ( Mitra et al., 2015 ; van der Valk et al., 2020 ).…”