Agricultural intensification and the evolution of host specialism in the enteric pathogen Campylobacter jejuni

Abstract

Modern agriculture has dramatically changed the distribution of animal species on Earth. Changes to host ecology have a major impact on the microbiota, potentially increasing the risk of zoonotic pathogens being transmitted to humans, but the impact of intensive livestock production on host-associated bacteria has rarely been studied. Here, we use large isolate collections and comparative genomics techniques, linked to phenotype studies, to understand the timescale and genomic adaptations associated with the proliferation of the most common food-born bacterial pathogen (Campylobacter jejuni) in the most prolific agricultural mammal (cattle). Our findings reveal the emergence of cattle specialist C. jejuni lineages from a background of host generalist strains that coincided with the dramatic rise in cattle numbers in the 20th century. Cattle adaptation was associated with horizontal gene transfer and significant gene gain and loss. This may be related to differences in host diet, anatomy, and physiology, leading to the proliferation of globally disseminated cattle specialists of major public health importance. This work highlights how genomic plasticity can allow important zoonotic pathogens to exploit altered niches in the face of anthropogenic change and provides information for mitigating some of the risks posed by modern agricultural systems.

Genomic epidemiology ofCampylobacter jejuniassociated with asymptomatic pediatric infection in the Peruvian Amazon

“…This has previously been observed in another LMICs, with very few ST-21 complex isolates cultured in surveys from Africa, SE Asia and South America [84][85][86][87][88]. Ruminant specialist lineages were also rare among the Peruvian pediatric samples (6.1 %) and the most common cattle associated lineage (ST-61 complex [25]) was completely absent. This is clear evidence of different epidemiology in LMICs and potentially suggests different routes to human infection.…”

Genomic epidemiology of Campylobacter jejuni associated with asymptomatic pediatric infection in the Peruvian Amazon

“…The absence of ST-21 complex isolates has previously been observed in another LMICs, with very few isolates cultured in surveys from Africa, SE Asia and South America [ 85 – 89 ]. Ruminant specialist lineages were also rare among the Peruvian pediatric samples (6.1%) and the most common cattle associated lineage (ST-61 complex [ 25 ]) was completely absent. This is clear evidence of different epidemiology in LMICs and potentially suggests different routes to human infection.…”

“…This has allowed identification of the genotypes, and in some cases genes, linked with variation in disease symptoms and the source of infecting strains. For example, the identification of host-associated genetic variation [ 24 ] and the extent to which this segregates by host (host generalist and specialist genotypes) [ 25 – 27 ], means that human infection can be attributed to a specific reservoir source, when there is no human-to-human transmission [ 24 , 25 , 27 – 29 ]. Furthermore, in some cases it is possible to link particular genotypes to common disease sequelae [ 30 – 32 ] or severe infections [ 33 – 35 ], and identify locally [ 36 – 38 ] and globally distributed strains [ 39 , 40 ].…”

Quantifying bacterial evolution in the wild: a birthday problem forCampylobacterlineages

“…The uneven distribution of lineages within the population and the possibility that they differ in key evolutionary measures (r/m and dN/dS), means that our molecular clock estimate may not be applicable to all Campylobacter lineages (Didelot et al 2012;Croucher et al 2013;Everitt et al 2014). Perhaps this is best illustrated by considering two host-specialist C. jejuni lineages, one associated with chickens and the other with cattle (Sheppard et al 2011;Mourkas et al 2020). There are 19 billion chickens on earth compared to 1.3 billion cattle (Bar-On et al 2018) and C. jejuni colonizes up to 80% of chickens (Dhillon et al 2006) with much lower rates in cattle.…”

“…Theoretical models of a relatively constant rate of molecular change over time (Kimura 1968), the molecular clock, have become fundamental to explaining the evolution in bacteria (Kuo and Ochman 2009;Didelot et al 2016). Spurred by the increasing availability of population-scale genome datasets, it is now common for comparative genomic studies to describe not only the relatedness of isolates but also how long ago they diverged (Kidgell et al 2002;Mutreja et al 2011;Mcadam et al 2012;Cui et al 2013;Mourkas et al 2020). This can provide valuable information when combined with host, habitat or ecosystem data.…”