Impact of industrial production system parameters on chicken microbiomes: mechanisms to improve performance and reduce Campylobacter

Abstract

Background The factors affecting host-pathogen ecology in terms of the microbiome remain poorly studied. Chickens are a key source of protein with gut health heavily dependent on the complex microbiome which has key roles in nutrient assimilation and vitamin and amino acid biosynthesis. The chicken gut microbiome may be influenced by extrinsic production system parameters such as Placement Birds/m2 (stocking density), feed type and additives. Such parameters, in addition to on-farm biosecurity may influence performance and also pathogenic bacterial numbers such as Campylobacter. In this study, three different production systems ‘Normal’ (N), ‘Higher Welfare’ (HW) and ‘Omega-3 Higher Welfare’ (O) were investigated in an industrial farm environment at day 7 and day 30 with a range of extrinsic parameters correlating performance with microbial dynamics and Campylobacter presence. Results Our data identified production system N as significantly dissimilar from production systems HW and O when comparing the prevalence of genera. An increase in Placement Birds/m2 density led to a decrease in environmental pressure influencing the microbial community structure. Prevalence of genera, such as Eisenbergiella within HW and O, and likewise Alistipes within N were representative. These genera have roles directly relating to energy metabolism, amino acid, nucleotide and short chain fatty acid (SCFA) utilisation. Thus, an association exists between consistent and differentiating parameters of the production systems that affect feed utilisation, leading to competitive exclusion of genera based on competition for nutrients and other factors. Campylobacter was identified within specific production system and presence was linked with the increased diversity and increased environmental pressure on microbial community structure. Addition of Omega-3 though did alter prevalence of specific genera, in our analysis did not differentiate itself from HW production system. However, Omega-3 was linked with a positive impact on weight gain. Conclusions Overall, our results show that microbial communities in different industrial production systems are deterministic in elucidating the underlying biological confounders, and these recommendations are transferable to farm practices and diet manipulation leading to improved performance and better intervention strategies against Campylobacter within the food chain.

Japanese quail (Coturnix japonica) as a novel model to study the relationship between the avian microbiome and microbial endocrinology-based host-microbe interactions

“…Indeed, common sources of stress such as extreme temperatures [ 122 ] and overcrowding [ 123 ] are routinely minimized in poultry production, yet handling stress is an understudied area. In addition, stocking density and different poultry production systems were recently demonstrated to affect the chicken microbiome and reduction of C. jejuni [ 124 ]. While the present study did not include an infection component, one route in poultry by which stress is known to affect bacterial pathogenicity is through exposure to host stress-related neurochemicals.…”

Temporal changes in the gut microbiota in farmed Atlantic cod (Gadus morhua) outweigh the response to diet supplementation with macroalgae

“…The OTUs identified in sPLS-DA were subsequently used in differential tree analysis to show the taxonomic shifts in microbial community structure occurring over time. The complete methodology is outlined in [ 124 ] and uses the DESeqDataSetFromMatric() function from the DESeq2 package [ 125 ] Metacoder package to generate the tree visualisations [ 126 ] in R.…”

“…We followed a method for identifying the core microbiome as outlined McKenna et al [ 124 ], and Shetty et al [ 127 ] using R’s microbiome package [ 128 ] adjustable parameters for percentage relative abundance and percentage prevalence within samples. For the samples in this study, the core microbiome was defined as prevalent in 85% of samples [ 129 ].…”

Revisiting Campylobacter jejuni Virulence and Fitness Factors: Role in Sensing, Adapting, and Competing

“…C. jejuni colonizes the gastrointestinal (GI) tract of a wide variety of food-producing animals such as poultry, cattle, sheep and swine ( Figure 1 ). However, poultry, particularly chickens are the major source of human infection ( Humphrey et al., 2014 ; Ijaz et al, 2018 ; McKenna et al, 2020 ). Outbreaks of C. jejuni infections are also associated with exposure to contaminated soil, unpasteurized milk and untreated water sources ( Korlath et al., 1985 ; Hudson et al., 1999 ; Bronowski et al., 2014 ; Artursson et al., 2018 ).…”