ABSTRACT
Ruminants possess a specialized four-compartment forestomach, consisting of the reticulum, rumen, omasum, and abomasum. The rumen, the primary fermentative chamber, harbours a dynamic ecosystem comprising bacteria, protozoa, fungi, archaea, and bacteriophages. These microorganisms engage in diverse ecological interactions within the rumen microbiome, primarily benefiting the host animal by deriving energy from plant material breakdown. These interactions encompass symbiosis, such as mutualism and commensalism, as well as parasitism, predation, and competition. These ecological interactions are dependent on many factors, including the production of diverse molecules, such as those involved in quorum sensing (QS). QS is a density-dependent signalling mechanism involving the release of autoinducer (AIs) compounds, when cell density increases AIs bind to receptors causing the altered expression of certain genes. These AIs are classified as mainly being N-acyl-homoserine lactones (AHL; commonly used by Gram-negative bacteria) or autoinducer-2 based systems (AI-2; used by Gram-positive and Gram-negative bacteria); although other less common AI systems exist. Most of our understanding of QS at a gene-level comes from pure culture in vitro studies using bacterial pathogens, with much being unknown on a commensal bacterial and ecosystem level, especially in the context of the rumen microbiome. A small number of studies have explored QS in the rumen using 'omic' technologies, revealing a prevalence of AI-2 QS systems among rumen bacteria. Nevertheless, the implications of these signalling systems on gene regulation, rumen ecology, and ruminant characteristics are largely uncharted territory. Metatranscriptome data tracking the colonization of perennial ryegrass by rumen microbes suggest that these chemicals may influence transitions in bacterial diversity during colonization. The likelihood of undiscovered chemicals within the rumen microbial arsenal is high, with the identified chemicals representing only the tip of the iceberg. A comprehensive grasp of rumen microbial chemical signalling is crucial for addressing the challenges of food security and climate targets.
ABSTRACT
Pre-weaned dairy calves are very susceptible to disease in the first months of life due to having a naïve immune system and because of the numerous physiological stressors they face. Hygiene management is a key element in minimizing enteric disease risk in calves by reducing their exposure to pathogens. Samples of milk, concentrate feed and drinking water, boot swabs of bedding and swabs of feed equipment were collected from 66 dairy farms as part of a survey of calf rearing practice and housing design. All the samples were cultured to determine total viable counts (TVC), total coliforms (TCC) and Escherichia coli as indicators of hygiene. Target ranges for levels of TVC, TCC and E. coli were defined from the literature and the sample results compared against them. The TVC targets in milk, MR and water were <4.0 log10 CFU/mL. TCC and E. coli targets of <1.1 log10 CFU/mL (the detection limit) were used for milk, MR, concentrate feed and feeding equipment. For water, the TCC and E. coli targets were <1.0 log10 CFU/100 mL. The targets used for bedding boot swabs were <6.3 log10 TVC CFU/mL and <5.7 log10 TCC or E. coli CFU/mL. Farm management factors were included as fixed effects in a generalized linear mixed model to determine the probability of samples being within each hygiene indicator target range. Milk replacer samples obtained from automatic feeders were more likely to be within the TVC target range (0.63 probability) than those prepared manually (0.34) or milk samples taken from the bulk tank (0.23). Concentrate feed samples taken from buckets in single-calf pens were more likely to have E. coli detected (0.89) than samples taken from group pen troughs (0.97). A very small proportion of water samples were within the indicator targets (TVC 9.8%, TCC 6.0%, E. coli 10.2%). Water from self-fill drinkers had a lower likelihood of being within the TVC target (0.03) than manually filled buckets (0.14), and water samples from single pens were more likely to be within TCC target ranges (0.12) than those from group pens (0.03). However, all self-fill drinkers were located in group pens so these results are likely confounded. Where milk feeders were cleaned after every feed, there was a greater likelihood of being within the TVC target range (0.47, compared with 0.23 when not cleaned after every feed). Detection of coliforms in milk replacer mixing utensils was linked with reduced probability of TVC (0.17, compared with 0.43 when coliforms were not detected) and TCC (0.38, compared with 0.62), which was within target in feeders. Key factors related to increased probability of bedding samples being within TCC target range were use of group calf pens (0.96) rather than single-calf pens (0.80), use of solid floors (0.96, compared with 0.76 for permeable floors) and increased space allowance of calves (0.94 for pens with ≥2 m2/calf, compared with 0.79 for pens with <2 m2/calf). Bedding TVC was more likely to be within the target range in group (0.84) rather than in single pens (0.66). The results show that hygiene levels in the calf rearing environment vary across farms and that management and housing design impact hygiene.
ABSTRACT
Antimicrobial resistance (AMR) is a serious threat to public health globally; it is estimated that AMR bacteria caused 1.27 million deaths in 2019, and this is set to rise to 10 million deaths annually. Agricultural and soil environments act as antimicrobial resistance gene (ARG) reservoirs, operating as a link between different ecosystems and enabling the mixing and dissemination of resistance genes. Due to the close interactions between humans and agricultural environments, these AMR gene reservoirs are a major risk to both human and animal health. In this study, we aimed to identify the resistance gene reservoirs present in four microbiomes: poultry, ruminant, swine gastrointestinal (GI) tracts coupled with those from soil. This large study brings together every poultry, swine, ruminant, and soil shotgun metagenomic sequence available on the NCBI sequence read archive for the first time. We use the ResFinder database to identify acquired antimicrobial resistance genes in over 5,800 metagenomes. ARGs were diverse and widespread within the metagenomes, with 235, 101, 167, and 182 different resistance genes identified in the poultry, ruminant, swine, and soil microbiomes, respectively. The tetracycline resistance genes were the most widespread in the livestock GI microbiomes, including tet(W)_1, tet(Q)_1, tet(O)_1, and tet(44)_1. The tet(W)_1 resistance gene was found in 99% of livestock GI tract microbiomes, while tet(Q)_1 was identified in 93%, tet(O)_1 in 82%, and finally tet(44)_1 in 69%. Metatranscriptomic analysis confirmed these genes were "real" and expressed in one or more of the livestock GI tract microbiomes, with tet(40)_1 and tet(O)_1 expressed in all three livestock microbiomes. In soil, the most abundant ARG was the oleandomycin resistance gene, ole(B)_1. A total of 55 resistance genes were shared by the four microbiomes, with 11 ARGs actively expressed in two or more microbiomes. By using all available metagenomes we were able to mine a large number of samples and describe resistomes in 37 countries. This study provides a global insight into the diverse and abundant antimicrobial resistance gene reservoirs present in both livestock and soil microbiomes.
