Effect of early gut microbiota intervention using pre-designed poultry microbiota substitute on broiler health and performance

Introduction

The poultry industry, a vital agricultural sector in any country, plays a significant role in the Australian economy, with over 800 commercial farms contributing to food production and meat processing (Poultry Hub Australia 2023). The research and development efforts relating to chicken industry focus on enhancing productivity and bird welfare, as well as increasing the yield of poultry products (Poultry Hub Australia 2023). The current developments are also concentrating on developing farming practices and promoting the usage of innovative technologies to increase the sustainability and efficiency of the industry.

However, the industry faces challenges such as disease outbreaks, affecting animal welfare and production efficiency (Food and Agriculture Organization – The AVIS Consortium 2020). Necrotic enteritis-, spotty liver- and leaky gut-related mortality is a major concern for the chicken industry (De Meyer et al. 2019; Hussein et al. 2020). These are often addressed using antibiotics (Hafez and Attia 2020). Overcrowding at the farms increases stress levels, increasing the susceptibility of the birds to these infections (Gomes et al. 2014). Promoting free-range and open-range production systems while employing other sustainable farming practices, such as reducing antibiotics usage, is essential to improve the living conditions for the birds and enhance bird welfare.

Healthy gut microbiota in chickens significantly contributes to overall health and well-being. The gut microbiota is a diverse community of microorganisms colonising the gastrointestinal tract that alters the chemical environment and morphology (Stanley et al. 2014), and assists in the breakdown and absorption of nutrients, including complex carbohydrates and fibres (Pan and Yu 2014). Microbial communities can influence feed efficiency and contribute to the development of the immune system (Stanley et al. 2014) and assist by acting as a protective barrier and preventing colonisation by pathogens via competitive exclusion (La Ragione and Woodward 2003).

Unnatural industrial hatching practices separating the newly hatched chicks from the mother hens can lead to significantly variable gut microbiota despite using the same food and providing the birds with the same environment. Maternal influence provides exposure to maternal faecal matter, which can help colonise the gut by native maternal chicken microbiota (Stanley et al. 2013). Faecal microbiota transplant has been shown to have a positive effect on the gut strength, morphology and short-chain fatty acid (SCFA) profile (Metzler-Zebeli et al. 2019). Exposure of newly hatched chicks to desired Lactobacillus strains at a very high level can also result in vigorous gut colonisation and cause a reduction in richness and diversity by affecting the maturation of gut microbiota (Wilkinson et al. 2020). One study has reported rapid colonisation and maturation of microbiota in four commercial flocks of layer chickens from day 1 to 70 weeks of age (Joat et al. 2023), whereas others (Baldwin et al. 2018; Wilkinson et al. 2020) have reported a small window for permanent microbiota modulation.

In this study, we exposed the newly hatched birds to a predesigned commercial microbial inoculum, developed as a predefined mix of common commensal bacteria from healthy and well-performing birds, by spraying the inoculum on newly hatched chicks similarly to standard vaccine spray delivery. After spraying with the microbiota inoculum product, we studied differences in performance and cloacal swab microbial community between treated and untreated birds.

Materials and methods

Results

Animal health and performance

The average bodyweight of the birds in the aviguard (AVG) group was significantly higher than in control (CTR) (2.39%, CTR = 1085 g, AVG = 1111 g, P = 0.0173) on Day 21 (Fig. 1). On Day 28, 1.6% increase in bodyweight in AVG group was recorded, but this was not statistically significant (CTR = 1680 g, AVG = 1707 g). The FCR for the AVG group did not show any significant change compared with the control group (Supplementary Fig. S1) but was numerically lower than that for the control group by 0.015 and 0.006 points on Day 21 and Day 28 respectively. The coefficient of variation (%) showed that from Week 2 to Week 4, the Aviguard birds were more similar in bodyweight than were those in the control group (Table 1).

Fig. 1.

Bodyweight data for Aviguard and control groups collected through the trial. (a) shows bodyweight data for day 7. (b), (c) and (d) show bodyweight data for day 14, day 21 and day 28 respectively. *P = 0. 0173.

Table 1.Coefficient of variation in weight (expressed in %) observed between the groups.

Week	Control	Aviguard
Week 1	9.878	12.96
Week 2	11.07	9.151
Week 3	9.602	7.801
Week 4	11.68	9.483

The core microbiota analysis of the cloacal swab samples showed a high prevalence of Escherichia–Shigella in the overall flock (Fig. S2). The other genera that were abundant in the flock included Enterococcus, Corynebacterium, Paenibacillus, Olsenella, Acinetobacter, Lactobacillus, Romboutsia, Bifidobacterium, Clostridia UCG-014 and Collinsella (Fig. S2).

Alpha and beta diversity modulation

There was a significant (P < 0.001) reduction in cloacal swab microbiota richness and diversity in the Aviguard-treated group (Fig. 2a, b).

Fig. 2.

Alpha diversity plots showing the richness and diversity using (a) the Chao1 matrix and (b) the Simpson entropy matrix.

Permutational multivariate analysis of variance (PERMANOVA) showed that there was a significant (P < 0.001) difference between the microbiota observed in the control birds as compared to the treated birds by using both weighted and unweighted UniFrac distances. There was a significantly lower sample-to-sample distance between AVG-treated samples than between CTR samples by using unweighted (Fig. 3a) and weighted (Fig. 3b) UniFrac. However, the differences between CTR and AVG groups were more prominent when using weighted Unifrac distance; thus, abundance played a higher role than the taxonomy membership. The increased uniformity of AVG samples can also be observed using NMDS plot (Fig. 4).

Fig. 3.

Beta diversity plots generated using (a) unweighted UniFrac and (b) weighted UniFrac distances. Each box represents the distribution of sample distances in a particular group, indicating that samples are more similar (less distant) in AVG group.

Fig. 4.

NMDS plot shows sample-to-sample distances between the treatments. The ellipse presents 90% confidence interval.

Differences in microbiota

Taxonomic level differences were evaluated using Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT), a robust statistical modelling and biomarker extraction tool (Fig. 5). A machine-learning model was constructed from the genus-level data, and the LASSO regression model was trained after normalisation and cross-validation. The model interpretation plot in Fig. 5 shows the top selected genera for the control and AVG group with model weights and their robustness and a heatmap with feature z-score.

Fig. 5.

SIAMCAT interpretation plot shows the signature in microbiota present between the treatments at the genus level. The area under the ROC curve value is 0.664.

Discussion

The colonisation of the chicken intestine, like in other animals, starts immediately after hatch or birth. Ignoring very minimal microbial load in ovo, the chick is immediately exposed to a heavy microbial load post-hatch. It has been reported that the first few days of life determine intestinal membership, because the species that arrive into the gut first shape the gut environment to suit their own needs and show competitive behaviour, preventing colonisation of other species. The dynamics of this process is not yet fully understood because of the complexity and variation of microbial communities at a species and even at the strain level. Interestingly, these first few days of a chick’s life shape bird health, behaviour and productivity.

Considering that poultry production practices entirely remove maternal influence on the chick, including access to maternal microbiota, early post-hatch intervention opens a window of opportunity to shape bird microbiota towards increasing proportion of beneficial and non-pathogenic commensal membership. Aviguard is, to our knowledge, the only product on the poultry market that replaces maternal microbiota to chicks, offering very diverse yet non-pathogenic species originating from the guts of healthy chickens.

In this study, we report improved weight and FCR in birds treated with AVG, indicating the benefits of colonisation control. Control birds showed higher weight only during the first week, after which AVG birds were consistently heavier, significantly at the end of Week 3. FCR did not show any noticeable difference in the first 2 weeks, but it has shown marginal improvement at the end of Weeks 3 and 4. Considering comparable mortality, this improvement in performance and bird weight, which became apparent after Week 2, is encouraging and could play a significant role in large commercial production systems. Several studies reported significant improvements in performance in animal trial facilities (Dame-Korevaar et al. 2020) and commercial production systems with an immense number of birds (Kayal et al. 2022).

Aviguard significantly affected the alpha diversity in the cloacal microbial community. Glendinning et al. (2022) showed that cecal microbiota transplant can increase the alpha diversity in the treated birds. However, this study collected samples from Day 1 to Day 7, whereas our study included sample collection on Day 28. One study has shown that Aviguard increased alpha diversity in broilers (Dame-Korevaar et al. 2020).

Whereas most other studies reported AVG-induced increases in diversity, our trial shows a reduction in cloacal swab samples. We can argue that the reduced diversity observed in the cloacal swabs is not necessarily observed or comparable with other gut sections. Previous studies have shown that diversity can increase in one section while reducing in another, demonstrating the complexity of gut microbiota dynamics (Kayal et al. 2022; Joat et al. 2023). A comprehensive study by Bajagai et al. (2024, p. 13) concluded, ‘The microbial richness and diversity tended to decrease with age in the upper gut, remained constant in the small intestine, and increased with age in the lower gut’.

It is likely that early administration of diverse microbial inoculum restricted the colonisation by the environmental bacteria through competitive exclusion, helping create a balanced and beneficial designer gut microbiota. Colonisation could prove challenging to reproduce in different poultry sheds having distinct pathogenic and environmental microbiota. At-hatch administration of a diverse microbial inoculum could be particularly helpful in sheds with higher pathogen levels.

While it is a commonly held notion that increased richness and diversity can benefit the host, this concept is being challenged as a common misconception in microbiota studies (Shade 2017). According to Shade (2017), diversity does not provide an accurate picture of the health of the microbial community while ignoring the stability and productivity of a community with lower diversity. Others have argued that diversity should be considered as just one of many factors, among stability, structure and function of the community (Johnson and Burnet 2016). An increase in the richness and diversity, if occurring as a result of gut colonisation by opportunistic pathogens, may lead to infections and inflammation of the gut.

Diverse microbial inoculum could be used to secure the colonisation and maturation of the gut, assuring a more homogenous development between sheds and flocks. Our data presented in Fig. 5 support this argument, showing that using either microbiota membership (unweighted UniFrac) or microbiota abundance data, the application of AVG at-hatch reduced the distance (difference) between microbial communities in treated birds, thus increasing the uniformity. This trend of AVG-reduced bird-to-bird variation is confirmed in the weight data presented in Table 1. More uniform flocks are preferred in the poultry industry, particularly processing facilities where the outlier birds often get discarded.

The sequencing also showed a high presence of Escherichia in the flock, including both control and AVG groups, and this was significantly higher in the AVG group. The high relative abundance of Escherichia–Shigella detected in the birds at 4 weeks (Day 28) did not result in unusual mortality during the trial or any mortality in Week 4. Despite a higher abundance of Escherichia–Shigella genera in the treatment group, the AVG group showed better performance across all of the parameters measured, than did the control. AVG is a product containing non-pathogenic strains typically present in poultry gut, and it is highly probable that the species representing Escherichia–Shigella genera in this study were non-pathogenic. A large-scale study, observing regularly collected data from hatch to over 70 weeks in layers showed high temporal variation in microbiota, where dominant genera were shifting on a weekly basis (Joat et al. 2023), and another study confirmed this trend across all gut sections in layer chickens during the period from Day 0 to 70 weeks (Bajagai et al. 2024).

Conclusions

The present study focused on the differences in microbiota maturation owing to Aviguard intervention by using cloacal swabs. Because of the continual movement of the intestinal content via peristalsis, cloacal swabs sample microbiota representing a mix of the communities inhabiting all upstream gut sections, and, together with caecum, are consistently the most diverse sampled intestinal origin (Joat et al. 2023). However, as opposed to caecum, cloacal swabs represent the best sampling origin for identifying a range of pathogens present in upstream gut sections (Joat et al. 2023). Thus, we used high-powered swab sequencing (n = 320) to pinpoint commercially relevant species alterations emerging from predesigned microbiota seeding intervention. Investigating changes in all gut sections would be beneficial in identifying modifications in the hotspots of pathogenesis for each commercially significant pathogen. Finally, to understand 16S amplicon-based microbiota data, we must understand the limitations and benefits of the methodology. Although 16S-based microbial community data provide reliable insights into microbial community structure, it cannot reliably resolve and identify taxa at the species level. This methodology detects a majority of the microbial community that responds to primers selected in the study. Thus, this methodology can be considered as an estimation of the major part of intestinal microbiota and does not distinguish the taxa at the species level. So, to better understand the effects of any treatment aiming to improve intestinal microbiota, a large number of trials under a range of diets and environmental conditions are needed to identify reproducible trends and benefits.

Supplementary material

Supplementary material is available online.