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.

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.

Molecular-based testing of poultry dust has been used as a fast, sensitive and specific way to monitor viruses in chicken flocks but it provides no information on viral viability. Differentiation of viable and nonviable virus would expand the usefulness of PCR-based detection. This study tested three treatments (1. DNAse, 2. propidium monoazide [PMA], 3. immunomagnetic separation [IMS]) applied to dust or virus stock prior to nucleic acid extraction for their ability to exclude nonviable virus from PCR amplification. Infectious laryngotracheitis virus (ILTV) was used as a model. These treatments assume loss of viral viability due to damage to the capsid or to denaturation of epitope proteins. DNAse and PMA assess the integrity of the capsid to penetration by enzyme or intercalating dye, while IMS assesses the integrity of epitope proteins. Treatments were evaluated for their ability to reduce PCR signal, measured as ILTV log 10 genomic copies (ILTV GC), of heat and chemically inactivated ILTV in poultry dust and virus stock. Compared to untreated dust samples, there was an overall reduction of 1.7 ILTV GC after IMS treatment (p<0.01), and a reduction of 2.0 ILTV GC after PMA treatment (p<0.0001). DNAse treatment did not reduce ILTV GC in dust (p = 0.68). Compared to untreated virus stocks, there was an overall reduction of 0.5 ILTV GC after DNAse treatment (p = 0.04), a reduction of 1.8 ILTV GC after IMS treatment (p<0.001) and a reduction of 1.4 ILTV GC after PMA treatment (p<0.0001). None of the treatments completely suppressed the detection of inactivated ILTV GC. In conclusion, treatments that use capsid integrity or protein epitope denaturation as markers to assess ILTV infectivity are unsuitable to accurately estimate proportions of viable virus in poultry dust and virus stocks.

Background A major focus of research on the gut microbiota of poultry has been to define signatures of a healthy gut and identify microbiota components that correlate with feed conversion. However, there is a high variation in individual gut microbiota profiles and their association with performance. Population level samples such as dust and pooled excreta could be useful to investigate bacterial signatures associated with productivity at the flock-level. This study was designed to investigate the bacterial signatures of high and low-performing commercial meat chicken farms in dust and pooled excreta samples. Poultry house dust and fresh pooled excreta were collected at days 7, 14, 21, 28 and 35 of age from 8 farms of two Australian integrator companies and 389 samples assessed by 16S ribosomal RNA gene amplicon sequencing. The farms were ranked as low (n = 4) or high performers (n = 4) based on feed conversion rate corrected by body weight. Results Permutational analysis of variance based on Bray–Curtis dissimilarities using abundance data for bacterial community structure results showed that company explained the highest variation in the bacterial community structure in excreta (R2 = 0.21, p = 0.001) while age explained the highest variation in the bacterial community structure in dust (R2 = 0.13, p = 0.001). Farm performance explained the least variation in the bacterial community structure in both dust (R2 = 0.03, p = 0.001) and excreta (R2 = 0.01, p = 0.001) samples. However, specific bacterial taxa were found to be associated with high and low performance in both dust and excreta. The bacteria taxa associated with high-performing farms in dust or excreta found in this study were Enterococcus and Candidatus Arthromitus whereas bacterial taxa associated with low-performing farms included Nocardia, Lapillococcus, Brachybacterium, Ruania, Dietzia, Brevibacterium, Jeotgalicoccus, Corynebacterium and Aerococcus. Conclusions Dust and excreta could be useful for investigating bacterial signatures associated with high and low performance in commercial poultry farms. Further studies on a larger number of farms are needed to determine if the bacterial signatures found in this study are reproducible.

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