Detection and Quantification of Clostridium perfringens and Eimeria spp. in Poultry Dust Using Real-Time PCR Under Experimental and Field Conditions

Centennial Review: Factors affecting the chicken gastrointestinal microbial composition and their association with gut health and productive performance

Maintenance of "gut health" is considered a priority in commercial chicken farms, although a precise definition of what constitutes gut health and how to evaluate it is still lacking. In research settings, monitoring of gut microbiota has gained great attention as shifts in microbial community composition have been associated with gut health and productive performance. However, microbial signatures associated with productivity remain elusive because of the high variability of the microbiota of individual birds resulting in multiple and sometimes contradictory profiles associated with poor or high performance. The high costs associated with the testing and the need for the terminal sampling of a large number of birds for the collection of gut contents also make this tool of limited use in commercial settings. This review highlights the existing literature on the chicken digestive system and associated microbiota; factors affecting the gut microbiota and emergence of the major chicken enteric diseases coccidiosis and necrotic enteritis; methods to evaluate gut health and their association with performance; main issues in investigating chicken microbial populations; and the relationship of microbial profiles and production outcomes. Emphasis is given to emerging noninvasive and easy-to-collect sampling methods that could be used to monitor gut health and microbiological changes in commercial flocks.

Microbial communities of poultry house dust, excreta and litter are partially representative of microbiota of chicken caecum and ileum

Traditional sampling methods for the study of poultry gut microbiota preclude longitudinal studies as they require euthanasia of birds for the collection of caecal and ileal contents. Some recent research has investigated alternative sampling methods to overcome this issue. The main goal of this study was to assess to what extent the microbial composition of non-invasive samples (excreta, litter and poultry dust) are representative of invasive samples (caecal and ileal contents). The microbiota of excreta, dust, litter, caecal and ileal contents (n = 110) was assessed using 16S ribosomal RNA gene amplicon sequencing. Of the operational taxonomic units (OTUs) detected in caecal contents, 99.7% were also detected in dust, 98.6% in litter and 100% in excreta. Of the OTUs detected in ileal contents, 99.8% were detected in dust, 99.3% in litter and 95.3% in excreta. Although the majority of the OTUs found in invasive samples were detected in non-invasive samples, the relative abundance of members of the microbial communities of these groups were different, as shown by beta diversity measures. Under the conditions of this study, correlation analysis showed that dust could be used as a proxy for ileal and caecal contents to detect the abundance of the phylum Firmicutes, and excreta as a proxy of caecal contents for the detection of Tenericutes. Similarly, litter could be used as a proxy for caecal contents to detect the abundance of Firmicutes and Tenericutes. However, none of the non-invasive samples could be used to infer the overall abundance of OTUs observed in invasive samples. In conclusion, non-invasive samples could be used to detect the presence and absence of the majority of the OTUs found in invasive samples, but could not accurately reflect the microbial community structure of invasive samples.

Characterization of poultry house dust using chemometrics and scanning electron microscopy imaging

Poultry house dust is composed of fine particles which likely originate from a diverse range of materials such as feed, litter, excreta, and feathers. Little is known about the contribution of these sources to broiler house airborne dust so the present study was designed to identify the relative contributions of these sources. Samples of feed, excreta, feather, and bedding, known mixtures of these and settled dust from 28 broiler chicken flocks were tested for the concentration of 18 chemical elements. A chemometrics approach (the application of multivariate statistical techniques to chemical analysis data) was used to identify the primary source material in broiler chicken house dust samples. Scanning electron microscopy (SEM) was also used to analyze dust sample particulates based on examination of source materials. Excreta was found to be the main component of broiler chicken house dust, both by SEM and chemometric analysis. SEM of experimental flock dust between 7 and 35 days of age (d) revealed that the contribution of excreta to dust increased with age from 60% at 7 d to 95% at 28 d (P < 0.001). The proportion of bedding and feed in dust declined with age while the contribution of feather material remained low throughout. This study demonstrates that excreta provides the bulk of the material in poultry dust samples with bedding material, feed and feather material providing lower proportions. The relative contributions of these materials to dust varies with age of birds at dust collection. Additional research is required to determine the health and diagnostic implications of this variation.

Molecular detection of Eimeria species and Clostridium perfringens in poultry dust and pooled excreta of commercial broiler chicken flocks differing in productive performance

Necrotic enteritis and coccidiosis are the most economically detrimental enteric diseases of broiler chickens. This study aimed to investigate the association of DNA load of Clostridium perfringens, netB, and five Eimeria species (E. brunetti, E. maxima, E. necatrix, E. acervulina and E. tenella) in poultry house dust and pooled excreta with flock productive performance. The dust and pooled excreta from the floor were collected weekly at days 7, 14, 21, 28 and 35 of chicken age from 16 flocks of eight farms from two Australian integrator companies. The farms were ranked as high or low performers by each integrator according to the production performance of studied flocks. Eimeria tenella and necatrix were not detected in any farm while E. brunetti was detected in a low-performance farm and netB was detected in a high-performance farm. C. perfringens, E. acervulina and E. maxima DNA were detected on all farms with no significant differences in DNA load between high and low-performance farms or companies. The lack of association of pathogen DNA load and farm performance is possibly due to overall low to moderate pathogen DNA load detected in this study. Further studies on a larger number of farms are needed to determine whether these population level measurements of key pathogens based on PCR detection of nucleic acids are correlated with performance variables.