Development of broiler intestinal microbiota with either probiotics or antibiotics as feed supplement
A total of 270 1-day-old Cobb 500 broilers were first randomly divided into three groups: they were either fed a base diet (i.e., the control group), the base diet plus the antibiotics of chlortetracycline and salinomycin at 500 g/ton-of-feed each (the antibiotic group), or the base diet plus LP-8 in drinking water (the probiotic group; “Methods”). Every group (i.e., 90 broilers) was then equally divided into six pens randomly. Each such 15-broiler pen thus served as a biological replicate and was tracked for physiological, immunological, and intestinal microbiome structure for 42 days (i.e., from birth to slaughter). To evaluate the performance in growth promotion, average daily gain (ADG; per broiler), average daily feed intake (ADFI; per pen), and FCR (per pen) were recorded. To assess the immunological response, immune indices including immune organ indices, serum IgG, and intestinal secretory IgA were measured for specific organs and tissues [21]. To probe the development of intestinal microbiota, 16S ribosomal RNA (rRNA) amplicon sequencing was performed for feces collected on day 7, 28, and 42 from 12 randomly picked broilers from each group, plus those from 12 randomly picked broilers on day 0 (i.e., before any treatments; “Methods”). Furthermore, 10 fecal samples were collected from each of the three groups on day 42 for total metagenome sequencing (“Methods”), for functional comparison of the intestinal microbiota.
Probiotics and antibiotics both conveyed growth benefits yet only probiotics activated protective host immune responses
All chickens were healthy throughout the feeding trial period. During the first 22 days, no significant ADFI change (P > 0.05) was detected among the three groups, but the antibiotic group exhibit 10.6 and 5.9% higher ADG than control and probiotic group, as for FCR, the antibiotic group is 13.4 and 9.1% lower than the control and probiotic group (Fig. 1a–c). In the next 21 days, both the antibiotic and the probiotic groups produced a 9.8 and 10.4% higher ADG than the control; however, the probiotic group consumed less food and consequently exhibit a 5.9% lower FCR than the antibiotic group. Over the complete birth-to-shelf process of 42 days, probiotic feeding produced a level of weight gain (i.e., ADG) that is identical to antibiotic feeding (both 10.3 and 6.7% higher than the control) and a significantly lower FCR, suggesting that P-8 provided equivalent or greater benefits in weight gain, feed intake, and feed efficiency as antibiotics did (Additional file 1: Table S1).
Induction and maintenance of an appropriate level of immunological activity is crucial for healthy broiler growth in poultry farms [22]. For the broilers, a number of key immunological indices were compared among the regimens of feed additives. Firstly, immune organ indices, referred to as immune organ weights and commonly used in poultry industry as a measurement for immunity [23, 24], were measured for each of the thymus gland, bursa, and spleen (“Methods”). Thymus is a central immune organ that plays an important role in inducing T lymphocytes differentiation and maturation, while bursa is a bird-specific humoral immune organ. Spleen, as the biggest peripheral immune organ, is involved in immune reaction of chicken. The immune organ indices of the thymus gland, bursa, and spleen on day 42 were 29.3, 36.5, and 28.0% higher in the probiotic group than the control, and immune organ index of the thymus gland was 14.7% higher in the probiotic group than the antibiotic group, indicating a most enhanced immunity in the probiotic group (Fig. 1d).
Secondly, serum IgG and intestinal secretory IgA were compared among the groups (“Methods”), as serum IgG reflects the system immune state, while intestinal secretory IgA reflects the intestinal immunity state [25]. On day 14, the probiotic group exhibit 63.7 and 48.0% higher expression level of serum IgG respectively than the control and the antibiotic groups and, moreover, 4.2 and 4.6% higher intestinal secretory IgA than the other two groups. On day 42, the probiotic group exhibited 19.5% higher expression level of serum IgG than the control and moreover 11.2 and 12.4% higher intestinal secretory IgA respectively than the other two groups (Fig. 1e). The highest level of IgG and IgA expression as detected in the probiotic group indicated a boosted immunity after probiotic feeding.
Oral administration of LP-8 elevated relative abundance of a wide range of indigenous Lactobacillus species in intestinal microbiota
Lactobacillus spp. are widely considered as beneficial to both humans and animals, thus high content of Lactobacillus spp. is linked to the wellbeing of chicken [26]. For example, L. paracasei reportedly enhances the phagocytic activity of the gut cells of poultry (including chicken [27]) and L. plantarum also exerted strong stimulation effect on chicken gut cells [28].
To test the ability of L. plantarum strain LP-8 to access the gut and the impact of LP-8 feeding on the intestinal Lactobacillus species, abundance of LP-8 and nine other Lactobacillus species from fecal samples were compared on day 7, 28, and 42 among the groups using RT-PCR (Additional file 2: Table S6), which is able to distinguish microbiota at the strain level. In the probiotic group, LP-8 reached 6.03 ± 0.18 Log10CFU/g on day 7, was reduced to 4.84 ± 0.10 Log10CFU/g on day 28 and then 4.67 ± 0.09 Log10CFU/g on day 42 (Fig. 2a). LP-8 was not detected in the other two groups. Thus, throughout the feeding period, LP-8 has survived in the digestive system and reached the broiler intestine.
Interestingly, oral administration of LP-8 resulted in remarkable enrichment of non-LP-8 Lactobacillus spp. in intestinal microbiota. In the probiotic group, on day 7, 28, and 42, nine species of Lactobacillus beyond LP-8 that included L. acidophilus, L. brevis, L. casei, L. gasseri, L. paracasei, L. plantarum, L. reutei, L. ruminis, L. sakei, and L. salivarius were detected. By day 7, in the probiotic group, L. acidophilus, L. casei, L. paracasei, L. plantarum, L. reutei, L. ruminis, and L. salivarius were all significantly elevated (by 11.1, 29.8, 1.1, 3.4, 26.6, 7.6, and 6.4% respectively), yet abundance of L. brevis, L. gasseri, and L. sakei did not respond to LP-8 supplementation. In the antibiotic group, however, L. acidophilus, L. casei, L. gasseri, L. paracasei, L. reutei, and L. ruminis all decreased (by 2.9, 2.4, 8.7, 1.9, 26.8, and 4.9% respectively), although L. plantarum and L. salivarius slightly increased (by 1.4 and 7.1%; Additional file 3: Fig. S1a). At day 28, in the probiotic group, the levels of L. plantarum (9.6%), L. ruminis (13.7%), and L. salivarius (2.7%) are higher than those in the control group, while those of L. acidophilus (3.7%), L. casei (6.4%), and L. paracasei (3.0%) are lower than those in the control; however, in the antibiotic group, all the Lactobacillus strains were reduced as compared to those in the control (except that L. gasseri increased by 4.0%; Additional file 3: Fig. S1b). At day 42, probiotic intake elevated the abundance of L. acidophilus (by 1.4%), L. brevis (by 24.1%), L. gasseri (by 3.8%), L. paracasei (by 6.6%), L. plantarum (by 3.4%), L. ruminis (by 24.7%), and L. salivarius (by 4.1%), whereas L. casei and L. sakei reduced by 5.9 and 6.0%; on the other hand, antibiotic intake resulted in the reduction of L. acidophilus (by 29.9%), L. reutei (by 13.4%), L. ruminis (by 21.4%), and L. salivarius (by 22.7%), as well as the elevation of L. brevis (by 4.9%), L. gasseri (by 6.4%), and L. paracasei (by 3.9%; Additional file 3: Fig. S1c).
Therefore, over the full course of 42 days, Lactobacillus spp. abundance was the highest in the probiotic group while the lowest in the antibiotic group (Fig. 2b). Moreover, based on their antibiotic/probiotic sensitivity, the nine Lactobacillus spp. can be grouped into (i) the insensitive cluster, including L. gasseri, L. paracasei, and L. sakei (Fig. 2f–k), (ii) the slightly sensitive cluster, including L. brevis and L. plantarum which differed with those in the control group only at selected time points (Fig. 2d, h), and (iii) the highly sensitive cluster, including L. acidophilus, L. casei, L. reutei, L. ruminis, and L. salivarius, which mostly were inhibited by antibiotics yet stimulated by probiotics (Fig. 2c–l).
LP-8 accelerated, yet antibiotics delayed, the maturation process of broiler intestinal microbiota
Administration of LP-8 and antibiotics also induced a significant change to broiler intestinal microbiota. PERMANOVA test based on Meta-Storm distance revealed that both time point and feed additive have a significant effect on the fecal microbiome structure (“Methods”, Additional file 4: Table S2). Feed additive (LP-8 or antibiotics) is the most important contributor of microbiota variation (F = 3.83, p = 0.002), as difference between LP-8 and antibiotics is consistently larger than the time point (i.e., age; F = 2.01, p = 0.048) or the variation among animal individuals (Fig. 3a). Thus, pinpointing the discriminating microbial features among feed additives would first require identification of the age-dependent microbiota features.
To probe the age-dependent development of broiler gut microbiota, age-discriminatory taxa were identified by respectively regressing the relative abundance of the entire list of genera against the corresponding chronologic age of chicken in the control group (“Methods”). In this way, 29 age-discriminatory taxa were identified. Among them, a short list of top genera were used for the subsequent construction of the microbiota-based model for discriminating different developmental stages, i.e., degree of microbiota maturity, as inclusion of any taxa beyond these top taxa produced only minimal improvement in model performance (Fig. 3b). This model which consists of 16 genera is able to distinguish the maturity of intestinal microbiota during the 42 days (56.68% variation explained; Fig. 3c).
To probe the effect of feed additive on microbiota maturation, development of microbiota in the probiotic and the antibiotic groups as defined by the age-discriminatory taxa identified above were monitored. Specifically, the Random Forest model was trained on the control group to identify age-discriminant taxa and then modeling of the microbiota age was performed on those same taxa across all three groups. Intriguingly, the patterns of microbiota development were highly distinct. The natural development of microbiota (i.e., in the control group when neither antibiotics nor probiotics were supplemented) exhibited a smooth curve that gradually grows until reaching plateau at day 30 (Fig. 3d). However, the curve in the antibiotic group featured a late-maturing pattern that does not reach the plateau until day 40, suggesting a delay of approximately 10 days in microbiota development as compared to the control group (Fig. 3e). In contrast, in the probiotic group, the curve exhibited an early-maturing pattern, which reaches plateau in as early as day 15, indicating an acceleration of intestinal microbiota maturation by approximately 15 days (Fig. 3f). The apparent early maturation of intestinal microbiota is consistent with the early development of immunity in the probiotic group (Fig. 2b). Thus apparently, probiotic and antibiotic administrations generated opposite effects on the age-dependent maturation of intestinal microbiota, with the former accelerating the process whereas the latter delaying it (the relative abundance change of the 16 age-discriminatory taxa are shown in Additional file 5: Fig. S2). In addition, from day 1 to day 42, the beta diversity of intestinal microbiota changed more heavily in the antibiotic group (F = 0.164, p = 0.003; ANOSIM) than in either the control group (F = 0.136, p = 0.003) or the probiotic group (F = 0.149, p = 0.003).
To quantitatively define the speed of microbiota maturity and thus compare the impact of feed additives (and diet in general) on microbiota development, we propose an index called “intestinal microbiota maturation index” (IMMI), which is defined as “time required to reach the full maturity of gut microbiota as defined by the additive-free group” (“Methods”). Interestingly, for the control group, the developmental pattern of broiler intestinal microbiota revealed that the timing of microbiota reaching the plateau (i.e., “full maturity”) actually coincided with the start of the finishing phase, i.e., when the chicken start to rapidly gain body weight (Fig. 3d). This suggests a link between intestinal microbiota and growth performance in broiler farming. On the other hand, administration of LP-8 and that of the antibiotics carry a IMMI of 15 and 40 respectively, as compared to a IMMI of 30 for the control group.
Organismal features of intestinal microbiota in the probiotic and the antibiotic treatments
To further probe how the distinct feed additives drive intestinal microbiota change, we compared the 16S gene-based profiles of bacterial phylogeny at the genus level at each of sampling times across the three groups. In total, eight genera were found changed significantly during the regimens by Kruskal Test (Additional file 6: Table S3). Among them, three abundant genera that include Blautia, Roseburia, and SMB53 (representing 1.2, 0.9, and 0.8% of normal microbiota respectively) have changed significantly on day 7 (Fig. 4a). Eubacterium, Roseburia, Clostridium, Clo_02d06, Tyzzerella, and Turicibacter which respectively represent 0.2, 0.9, 14.0, 0.6, 0.2, and 0.5% of normal microbiota were significantly changed on day 28 (Fig. 4a); however, no genera were found significantly different in relative abundance across the three regimens on day 42 (Fig. 4a).
Further analysis revealed that, at day 7, there was no difference in microbiota beta diversity among the regimens (F = 2.08, p = 0.112). However, at day 28 such difference emerged (F = 4.72, p = 0.001) and then at day 42 it disappeared again (F = 0.58, p = 0.700). To test the functional distinction of microbiota at day 42, whole-metagenome sequencing of 10 fecal samples from each of the three groups at day 42 revealed a significant alteration of microbial functional profile among the three groups. Principle component analysis (PCA) based on KOs showed statistically significant discrimination between the antibiotic and the probiotic groups (PC1, 70.4%, p = 0.004; PC2, 12%, p = 0.563; Student’s t test; Fig. 4b), while neither the antibiotic group (PC1, p = 0.179; PC2, p = 0.115) nor the probiotic group (PC1, p = 0.111; PC2, p = 0.987) was distinguishable from the control group. Totally 1054 KOs were identified as functional markers associated with treatments (adjusted p < 0.1; Additional file 7: Table S4), which were then assigned to specific functional pathways (Additional file 8: Fig. S3; Additional file 9: Table S5A, B; “Methods”). Probiotics influenced as many pathways as antibiotics did; however, five pathways were altered only by antibiotics but not probiotics, including cell cycle–Caulobacter (ko04112), pentose and glucuronate interconversions (ko00040), synthesis and degradation of ketone bodies (ko00072), d-glutamine and d-glutamate metabolism (ko00471), and drug metabolism—other enzymes (ko00983). This might indicate disturbed energy metabolism and cell cycle under antibiotics. Thus, the distinct gut microbiota maturity rate between the antibiotic and the probiotic groups can lead to profound alteration of microbiota function.
The key role of LP-8 in formation and development of bacterial correlation network in intestinal microbiota
To probe the potential mechanism underlying the distinct temporal patterns of gut microbiota maturation among the three regimens, we compared the corresponding co-occurrence networks among the bacterial genera. For each group, Spearman’s correlation coefficient was used to describe the adjacency relationship among genera. Intriguingly, in each of the three groups, the Lactobacillus spp. participated in the core interaction network (i.e., the largest sub-network; Fig. 5).
Compared to the control group (network density = 0.329; Fig. 5a), the inter-genera correlation in the antibiotic group was weaker (network density = 0.213; Fig. 5b), while in the probiotic group the correlation was stronger (network density = 0.355; Fig. 5c). On the other hand, the number of genera that were directly correlated with Lactobacillus spp. were very different. In the control groups, six genera were negatively correlated with Lactobacillus while this number was reduced to four in the antibiotic group but increased to 14 in the probiotic group. Furthermore, compared with the control group (n = 33), the number of genera participating in the core interaction network decreased in the antibiotic group (n = 27) and the probiotic group (n = 25). Interestingly, the inhibition of many intestinal non-Lactobacillus genera by the intestinal Lactobacillus spp. appeared to be the most prominent change taken place, suggesting enrichment of intestinal Lactobacillus spp. as induced by LP-8 feeding was one major driving force of the distinct global microbiota change in the probiotic group (Additional file 10: Fig. S4). Thus antibiotic feeding greatly disturbed and weakened the bacterial interacting network of the chicken gut microbiota while LP-8 feeding led to a strong interacting network where Lactobacillus spp. dominate. Consistently, the bacteriostasis effect by these enriched Lactobacillus spp. (due to administration of LP-8) against other intestinal bacterial genera might reduce nutrient consumption by the intestinal microbiota, which could have underlie the decline of FCR in the probiotic group (Fig. 1c).
The distinct impacts of antibiotics and probiotics on bacterial correlation network appeared to take place at an early phase. In the first period (day 1 to 7), the correlations among genera in the antibiotic group have already been weakened as compared to the controls (network density of 0.244 and 0.277 respectively), whereas LP-8 feeding produced an opposite effect (network density of 0.373). In the next period (day 7 to day 28), such patterns were largely maintained, with the network density for control, antibiotics, and probiotics being 0.274, 0.341, and 0.361 respectively. Interestingly, in the final period (during day 28 to day 42), both antibiotics and LP-8 feeding reduced the mean correlation value of the networks, to 0.255 and 0.260 respectively (Fig. 6a). As for the centralization of network, the probiotic group always featured the highest concentrations of interacting genera during the whole trial, followed by the control and the antibiotic group (Fig. 6b). Thus both LP-8 and antibiotics, as feed additive, play a key role in formation and development of the web of bacterial interactions in broiler intestinal microbiota.