Fish-derived Probiotic Improves Growth Performance and Pathogen Resistance of Juvenile Grass Carp (Ctenopharyngodon idella) by Modulating Keystone Taxa and Enhancing Microbial Interactions

doi:10.21203/rs.3.rs-2349158/v1

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Fish-derived Probiotic Improves Growth Performance and Pathogen Resistance of Juvenile Grass Carp (Ctenopharyngodon idella) by Modulating Keystone Taxa and Enhancing Microbial Interactions

https://doi.org/10.21203/rs.3.rs-2349158/v1

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Host-derived probiotics are bacteria isolated from the host's gut to improve the host’s growth and health. With more stringent antibiotic prohibitions in animal production, in-feed probiotics are becoming an appealing alternative to antibiotics in aquaculture. To explore the effects of Pseudomonas monteilii JK-1 on grass carp growth performance and disease resistance, 180 apparently healthy juvenile grass carp (3 ± 0.05 g) were randomly separated into 2 groups, each of which had 3 duplicates and 30 grass carp per replicate. Fish were fed with basal diets (control) or basal diets supplemented with P. monteilii JK-1 (1 × 10⁷ CFU/g diet, PM-JK1) for 8 weeks. Results showed that P. monteilii JK-1 significantly increased the weight gain (WG), specific growth rate (SGR), survival rate and significantly reduced the pathogen load in grass carp (P < 0.05). Moreover, P. monteilii JK-1 supplementation significantly improved the expression of interleukin 1β (IL-1β), interleukin 10 (IL10), tumor necrosis factor alpha (TNF-α), and transforming growth factor beta (TGF-β) in head kidney and gut (P < 0.05). Furthermore, dietary supplementation with P. monteilii JK-1 significantly enhanced the activities of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx) in liver and gut (P < 0.05). The results of high-throughput sequencing revealed that P. monteilii JK-1 showed no significant influence on the composition of gut bacteria, but prevented changes of microbial community caused by A. hydrophila infection. Additionally, a network-based approach was used to analyze the influence of P. monteilii JK-1 on the interspecies relationships among the gut microbiome. The results showed that P. monteilii JK-1 supplementation improved the complexity of the gut ecological network. Further analysis found that Cetobacterium was the keystone microbiota in maintaining interspecies interactions and the stability of the gut network. Finally, we used a partial least squares path model to prove that P. monteilii JK-1 did not directly improve growth performance and pathogen resistance but rather by regulating keystone taxa and gut network stability. These results suggest that P. monteilii JK-1 could be used as a feed supplement to improve the growth performance and pathogen resistance of grass carp.

Pseudomonas monteilii JK-1

Grass carp

Growth performance

Pathogen resistance

Gut microbiota network

As a major source of animal protein, aquaculture is crucial for ensuring the safety of the global food supply. and is expected to grow even more due to increased customer needs [15]. However, with the growth of modern aquafarms, disease outbreaks for aquaculture have become more frequent and severe [29]. Grass carp (Ctenopharyngodon Idella) is a freshwater species that are commonly cultivated in China. The overall output of grass carp exceeded 5.75 million tons, with a total industrial output value of almost 70 billion yuan in 2022 according to the China Fishery Industry Report (http://www.yyj.moa.gov.cn). Nevertheless, under extremely intensive culture conditions, the grass carp is repeatedly infected with bacterial, viral, and parasite diseases, which causes huge annual economic losses in aquaculture. Aeromonas hydrophila-induced bacterial enteritis is one of the most typical illnesses found in aquaculture. Previous studies demonstrated that A. hydrophila gets into the body through the gut, causing damage to gut barrier function and resulting in the gut barrier being less effective against this pathogen [9, 26, 46].

The traditional method now being used in the prevention of bacterial disease is mostly focused on the use of antibiotics. However, it is well recognized that the improper use of these compounds can lead to a number of side effects, including a disruption of the gut flora and the buildup of antibiotics in foods [5]. Additionally, there is widespread worry regarding the rise in antibiotic-resistant microbes in the environment [23]. One option is to apply probiotic bacteria. Dietary supplementation with probiotics could decrease the incidence of pathogen-induced infections in aquaculture [51]. Previous studies revealed that certain probiotic strains could boost immunity and increase growth rate [28]. Moreover, probiotics supplementation could improve the beneficial intestinal microbial populations, promote the growth performance and protect the host against pathogen infection [11, 13].

The gut is an important immunity organ and digestive organ, as well as an essential barrier against disease entrance [54]. Inflammation and excessive oxidative stress are important factors in intestinal disease progression. Published works have proven that probiotics could benefit gut health by increasing the resistance of the fish gut to oxidation [7, 56]. In addition, the gut is home to a complex microbial community, consisting of bacteria, fungi, and viruses [43]. Gut microbial communities are pivotal fitness-enhancing factors for the benefit of the host [34]. Meanwhile, there are complex and relatively stable host-microbial and microbial-microbial connections in the intestinal microecosystem, which contribute to the digestion, development, immunity, and disease resistance of the host [33, 40]. Interspecies interactions among the gut microbes play a crucial function in driving the distribution patterns of gut microbial communities [19]. Meanwhile, interspecies interactions link microbes together to create a complex ecological network, which is crucial for preserving the balance of the microbial communities and enhancing the disease resistance of host [25, 37]. Previous researches have concentrated on the changes in the abundance of microbial genera as well as the effects of probiotics on the antioxidant capacity and immune response of the host [56].

In this research, P. monteilii JK-1, extracted from healthy grass carp gut content [42], was used to assess the impact on growth performance and pathogen resistance in grass carp. Our published work showed that this strain was effective at counteracting A. hydrophila in vitro, but it is unclear if it has a probiotic effect in vivo [42]. The present study comprehensively evaluates the effects of P. monteilii JK-1 on growth, immune response, antioxidant statue, pathogen resistance, gut microbiome and the gut microbiome network, providing a strong theoretical foundation for commercialization and application of this potential probiotic strain in aquaculture.

Animal ethics

This study was approved and conducted in accordance with the Animal Ethical and Welfare Committee of Northwest A&F University.

Experimental Fish And Bacterial Strain

Healthy juvenile grass carp (3 ± 0.05 g) were bought from a nearby fish farm (Ankang, Shaanxi, China). Grass carp were kept in the laboratory environment for 2 weeks for acclimatization. A commercial diet was given to the grass carp twice a day. Each tank containing dechlorinated and aerated water (pH 7.0 ± 0.5) at 28 ± 1^oC. More than 6.0 mg/L of dissolved oxygen was present in the tank. The P. monteilii JK-1 (MN855414.1) was separated from intestinal contents of grass carp [41]. A. hydrophila (2WCL-103) was provided by the Institute of Hydrobiology Chinese Academy of Sciences (Wuhan, China).

Diet Preparation

The preparation of experimental diet was performed following a published work [38]. In brief, basal diets was added 10⁷ CFU/g P. monteilii JK-1 under sterilized conditions, and stored at -20°C until use. Fresh meals were prepared every 7 days to maintain the levels of bacteria in diets. Supplementary Table S1 listed the ingredients of the basal diet used in this study.

Experimental Design And Sample Collection

A total of 180 healthy grass carp were allocated into two groups at random, each group containing three replicates, and each replicate was stocked with 30 grass carp (Fig. 1). The groups were set as follows: control group (control), P. monteilii JK-1-supplemented group (PM-JK1). The basal diet was fed to the grass carp in the control group, while the P. monteilii JK-1 enriched diet was provided to the fish in PM-JK1. The feeding experiment continued for 8 weeks. Fish were fed at 4% of body weight twice a day (9:00 and 17:00), and the feeding amount was fine-tuned weekly according to the growth and feed intake of the fish. The fish were weighed and counted following the feeding experiment to assess growth performance. The liver, kidney, and intestine content were collected and stored in -80^oC until further analysis.

The rest of grass carp (28 × 3 = 84 per group) were bath infected with A. hydrophila (1 × 10⁸ CFU/mL) for 10 days after the feeding trial. The mortality was recorded every day and the water and bacteria were changed every 2 days [60]. The samples collected after the infection in control and PM-JK1 were recorded as control-T and PM-JK1-T, respectively (Fig. 1). At the end of experiment, the rest of fish were sampled and collected the intestine content for 16S rRNA analysis. The following calculations were made for the growth performance parameters:

Percentage of weight gain (WG) = 100 × (final weight – initial weight)/ initial weight

Specific growth rate (SGR, %/day) = 100 × (ln final weight – ln initial weight)/ test period (day)

Figure 1 The experimental design. The grass carp were fed a basic diet (control) or a Pseudomonas monteilii JK-1 supplemented diet (PM-JK1) (1×10⁷ CFU/g diet per day) for 8 weeks, and then bath infected with A. hydrophila strain (1×10⁸ CFU/Ml) (Control-T/PM-JK1-T).

Genes Expression Analysis

The total RNA was extracted from the collected tissues using the procedure described in previous studies [16]. The cDNA was then synthesized using the PrimeScriptTM RT reagent Kit with gDNA Eraser (Perfect Real Time, Takara, Dalian, China). The mRNA levels of IL-1β, IL10, TNF-α, and TGF-β were measured with kits (ChamQ SYBR qPCR Master Mix, Vazyme, China) on a BIO-RAD CFX96 Real-Time PCR Detection System (BIO-RAD, USA). Supplementary Table S2 lists the primers used in this study. The 2^−△△Ct method was utilized to calculate the results [58].

Determination Of Liver And Gut Antioxidant Indices

The activity of antioxidant enzymes (SOD, CAT, GPx) in the liver and gut were assessed using kits from Nanjing Jiancheng Bioengineering Institute (NJJC bio, Nanjing, China) and following the instructions.

16s Rrna Sequencing And Gut Microbial Analysis

The entire gut of was anatomized using sterile tools and washed in 70% ethanol to prevent transient bacteria. The gut contents were thoroughly mixed after being squeezed out, and immediately frozen at liquid nitrogen. The DNA of gut microbes was extracted using a Stool DNA Kit (OMEGA, USA) according to the instructions. The V3-V4 regions of the bacterial 16S rRNA gene were amplified using the 341F-806R primers (341F: 5’ - CCTAYGGGRBGCASCAG − 3’; 806R: 5’ - GGACTACNNGGGTATCTAAT − 3’) [32]. High-throughput sequencing was performed on the Illumina MiSeq platform. The low-quality scores (Q score, 20) were eliminated using the FASTX-Toolkit (Hannon Lab, USA). Using QIIME, all sequences were categorized into operational taxonomic units (OTUs) with a 97% similarity. Each OTU was given a classification hierarchy using uclust. Later, using the R tool phyloseq, the low-abundance OTUs were eliminated from the raw OTU data. Alpha diversity was analyzed in phyloseq (v. 1.34.0). Beta diversity (PCoA) was analyzed using the vegan package of R [14].

Gut Microbiota Network Analysis Based On Bacterial Relative Abundance

Co-occurrence networks were built using the abundance of OTUs. The approach based on random matrix theory was used for network creation. Only OTUs found in at least half of data were used for network construction to prevent spurious linkages brought about by low-abundant OTUs (Deng et al., 2012). The Molecular Ecological Network Analyses (MENA) Pipeline (http://ieg4.rccc.ou.edu/mena) was used for network building and analysis [12]. The networks in group Control, Control-T, PM-JK1 and PM-JK1-T were constructed by 6 samples, respectively. The networks were then visualized in Cytoscape version 3.8.2.

Determination of Aeromonas load

The liver and gut were homogenized individually with 1 mL physiological saline followed by serial dilution. Then, apply 0.1 ml of dilution on an LB agar plate. Each plate's bacterial colony was enumerated after 24 hours of incubation.

Statistical Analyses

All data analysis were completed in R (v. 4.1.1), and all graphics were made using ggplot2 and GraphPad Prism. The estimates of path coefficients and the coefficients of determination was validated by R (v. 4.1.1) with the packages plspm. The results were presented as the mean ± SD. The significance level was set at P < 0.05. Detailed statistical analysis was described in the figure legends.

Effects of P. monteilii JK-1 on grass carp growth performance

The effects of P. monteilii JK-1 on the growth performance of grass carp are shown in Table 1. After an 8-week feeding trial, fish fed with P. monteilii JK-1 supplemented diet had significantly higher final body weight, weight gain, and specific growth rates than that in control (P < 0.05).

Table 1

Effects of dietary *P. monteilii* JK-1 supplemented diet on growth performance of juvenile grass carp
Group	Initial body weight (g)	Final body weight (g)	Weight gain rate (%)	Specific growth rate (%/day)
Control	3 ± 0.05	10.76 ± 0.38^a	259.54 ± 14.02^a	2.28 ± 0.07^a
PM-JK1	3 ± 0.05	15.62 ± 0.74^b	416.88 ± 24.67^b	2.93 ± 0.08^b
Note: All data were expressed as mean ± SE. Different superscript letters in each column indicate significant differences between groups (P < 0.05).

The protective effect of P. monteilii JK-1 on grass carp

We assessed the protected effects of P. monteilii JK-1 on the pathogen resistance of grass carp (Fig. 2). The results showed that grass carp in PM-JK1-T had considerably lower liver Aeromonas load than that in control-T (Fig. 2a). Notably, there was a significant decrease in the load of Aeromonas in the gut of grass carp after feeding P. monteilii JK-1. Also, grass carp in PM-JK1-T had a significant lower level of Aeromonas load in gut than that in the control-T after infection with Aeromonas (Fig. 2a). Meanwhile, the results of survival rate showed that grass carp in PM-JK1-T had higher survival rate than that in control-T (Fig. 2b), suggesting that P. monteilii JK-1 supplementation protect grass carp against the A. hydrophila infection.

Figure 2P. monteilii JK-1 protects grass carp against pathogen infection. a Aeromonas load (CFU/g) in liver and gut feeding with basal diet and P. monteilii JK-1 supplementation diet. The difference among the four group was constructed with Kruskal-Wallis test, significant differences (P < 0.05) among the four groups are denoted by different lowercase letters. b Kaplan-Meier graph of the grass carp survival after bath infection with A. hydrophila. One representative experiment of three independent biological repeats is shown. *P < 0.05, Gehan–Breslow–Wilcoxon test.

Effects of P. monteilii JK-1 on the immune response and antioxidant enzymes activity of grass carp

We evaluated the effects of fish-derived probiotics on the expression of immune-related genes in grass carp (Supplementary Fig. S1a, b). Compared to control, the expression of IL-1β, IL10, TNF-α, and TGF-β in head kidney was significantly improved in PM-JK1 (P < 0.05). Likewise, IL-1β, IL10, TNF-α, and TGF-β in gut were also recorded significantly higher in PM-JK1 than that in control.

Meanwhile, lower activities of SOD, CAT, and GPx in the liver were observed in control when compared to PM-JK1 (P < 0.05). Again, the PM-JK1 group displayed higher activities of SOD, CAT, and GPx in gut compared to control (Supplementary Fig. S1c, d).

Effects of P. monteilii JK-1 on gut microbiome

A total of 264,702 high-quality reads were obtained from 24 samples, after removing low quality reads, 61,785 ± 2916, 62,562 ± 2524, 71,744 ± 1272 and 66,123 ± 2259 were obtained for Control, group Control-T, group PM-JK1 and group PM-JK1-T, respectively. 4,041 OTUs with sequencing depths covering more than 99% were discovered in all samples, demonstrating significant sequencing coverage and the representativeness of the OTUs found in the samples. As shown in Fig. 5, compared to control, the Shannon index in PM-JK1 showed no significant difference, while in control-T showed a significant increase (Supplementary Fig. S2a). Meanwhile, there was no significant difference was observed between PM-JK1 and PM-JK1-T. The Richness also showed no significant change among the four groups (Supplementary Fig. S2b). PCoA based on Bray_curtis distance revealed there were significant differences among the groups in the distribution pattern of microbial communities (Supplementary Fig. S2c). There was no significant clear limit among the control, PM-JK1 and PM-JK1-T. However, the microbial communities in control-T were significantly different from the other three groups (control-T vs control: p = 0.006; control-T vs PM-JK1: p = 0.006; PM-JK1 vs PM-JK1-T: p = 0.006).

Analysis at the phylum level indicated that the most abundant phyla were Fusobacteria, Proteobacteria, and Actinobacteria, which accumulated abundance was higher than 90% (Fig. 3a). Fusobacteria was the most dominant phyla in control (81.42%), PM-JK1 (85.5%), and PM-JK1-T (69.37%), while Proteobacteria took dominance in control-T (48.7%) (Fig. 3a, b). At genus level, a similar bacterial composition was observed in control, PM-JK1, and PM-JK1-T, and the most abundance of genus was Cetobacterium (mean 81.2%, 85.4%, and 69.3%, respectively) (Fig. 3c). Of interest, the abundance of Bosea, Nocardia, Rhodobacteraceae_unclassified, Rhodococcus, and Phyllobacteriaceae_unclassified was significantly increased in control-T. The changes in bacterial composition among the four groups were then assessed using the linear discriminant analysis effect size (LEfSe) to pinpoint specific bacterial genera that were distinctive of the different treatments (Fig. 3d). The results revealed that Clostridiales_unclassified, Lachnospiraceae_unclassified, Propionibacterium, and Rhodobacter were enriched in control. A significant increase in Bosea, Nocardia, Rhodobacteraceae_unclassified, Rhodococcus, Phyllobacteriaceae_unclassified, Bradyrhizobiaceae_unclassified, Rhizobiaceae_unclassified, Alsobacter, Mycobacterium, and Microbacteriaceae_unclassified was observed in control-T. Furthermore, the abundance of Cetobacterium, Reyranells_unclassified, and Ruminococcaceae_unclassified was enriched in PM-JK1, while Romboutsia and Akkermansia were enriched in PM-JK1-T. Meanwhile, we used the STAMP to determine the significant differences in the abundance of microbiota between control and P. monteilii JK-1. As shown in Supplementary Fig. S3, dietary administration of P. monteilii JK-1 significantly improved the abundance of Akkermansia, Tessaracoccus, Ruminococcaceae_unclassified and Microbacterium, while decreasing the relative abundance of Firmicutes_unclassified and Thermus.

Figure 3 Bacterial compositions of the gut in different groups. a Relative abundance of the top 6 phyla in the four different groups. b Box plots showing the relative abundances of Proteobacteria and Fusobacteria in the four different groups. c Relative abundance of the top 20 genera in the gut samples from the four different groups. d Liner discriminant analysis effect size (LEfSe) analysis among the four groups, The LDA value threshold was set at 3.5.

Effects of P. monteilii JK-1 on gut ecological network

In this study, the phylogenetic molecular ecology networks (pMENs) were built using an RMT-based approach. There was significant difference between empirical networks and random networks (Table 2, Fig. 4). These four ecological networks may have exhibited typical small-word features because the average clustering coefficient and average path distance in empirical networks were much higher than those in random networks. In addition, the density, average clustering coefficient and average degree in PM-JK1 were significantly higher than that in control, while the average path distance was significantly lower, suggesting that dietary administration of P. monteilii JK-1 had significant effects on the gut microbiome ecological networks of grass carp. It's noteworthy that PM-JK1 has the greatest average degree, suggesting that the gut bacteria in PM-JK1 have the most complex network (Fig. 4).

Table 2

The topological properties of the gut microbiota co-occurrence networks in different groups and their respective identically sized random networks
Topological properties	Control	PM-JK1	Control-T	PM-JK1-T
Empirical Networks
Nodes	88	57	88	72
Total links	189	281	256	259
Density	0.049	0.176	0.067	0.101
Average clustering coefficient (ACC)	0.303^a	0.418^c	0.379^b	0.440^c
Average degree (AD)	4.295^a	9.860^c	5.818^b	7.194^c
Average path distance (GD)	4.626^c	2.416^a	3.782^b	3.656^b
Modularity	0.595^b	0.320^a	0.549^b	0.399^a
Random Networks
Average path distance	3.207 ± 0.058	2.163 ± 0.041	2.797 ± 0.042	2.472 ± 0.043
Average clustering coefficient	0.052 ± 0.014	0.315 ± 0.026	0.091 ± 0.018	0.193 ± 0.022
Modularity	0.426 ± 0.015	0.184 ± 0.010	0.334 ± 0.011	0.261 ± 0.011
Note: Random networks were generated by resetting all of the links of a matching empirical network with the same nodes and links. Data were generated from 100 random runs and SD indicates the standard deviation from the 100 runs. Different superscript letters in each row indicate significant differences between different groups (P < 0.05).

Figure 4 The interspecific interactions among the gut bacterial community of grass carp are described by an ecological network. A link defined as a significant (P-value < 0.05) and strong (Spearman’s r > 0.6) relationship. Blue edges stand for negative correlations, while red edges stand for positive correlations. a The gut microbiota network in control. b The gut microbiota network in PM-JK1. c The gut microbiota network in control-T. d The gut microbiota network in PM-JK1-T.

The change in network complexity could cause changes in the role of individual nodes. To understand the ecological roles of the genera that were altered in the network, the within-module connectivity (Zi) and among-module connectivity (Pi) were calculated [20, 21] (Fig. 5). We noticed that the roles that the species play in the gut network in control were different from those in PM-JK1 (Fig. 5a). Meanwhile, the nodes in control were almost generalized to non-hubs, which means that the genera belonged to within-module connectivity (Fig. 5b). However, there were 20% of nodes in PM-JK1 were generalized to hubs, which means that the genera belonged to intra-module connectivity (Fig. 5b). Most importantly, the genus Cetobacterium was the only species that had the ability to connector hubs in PM-JK1, indicating that Cetobacterium played a key role in maintaining interactions between modules.

Figure 5 Node structural roles in networks within various groups were used to identify potential keystone taxa. a Nodes were partitioned into ultra-peripheral (Pi ≈ 0, Zi < 0), peripheral (Pi < 0.625, Zi < 0), non-hub connectors (0.625 < Pi < 0.8, Zi < 0), non-hub kinless nodes (Pi > 0.8, Zi < 0), provincial hubs (0 < Pi < 0.3, Zi > 0), connector hubs (0.3 < Pi < 0.75, Zi > 0), or kinless hubs (Pi > 0.75, Zi > 0). Red dots represent the nodes that in Control; Blue dots represent the nodes that in PM-JK1. b Relative abundance (%) of Ultra-peripheral node, Peripheral node, Connector hubs, Provincial hubs, Non-hub connector, and Non-hub kinless node within each group.

The closeness centrality, which is based on the distances from the node to all other nodes, is an importance of a vertex within a given complex network. A node with high closeness centrality plays an important role in ecological networks. We calculated the closeness centrality of each genus in different groups to further evaluate the effect of P. monteilii JK-1 on the gut ecological network (Supplementary Table S3). The results showed significant changes in the role of the bacteria in the ecological network of the gut before and after the infection. Microbacteriaceae_unclassified had the highest connectivity in control, while Cetobacterium had the highest connectivity in control-T, suggesting that Cetobacterium plays an important role within the ecological network of the gut after the infection (Supplementary Table S3). Moreover, dietary administration of P. monteilii JK-1 significantly increased the importance of Cetobacterium in the gut ecological network and suggested a role for Cetobacterium in the stability of gut ecological network.

Correlation Between The Gut Microbiota And Growth Performance And Disease Resistance

As shown in Supplementary Fig. S4, we used the Spearman’s correlation heatmap to demonstrate the relationship between the relative abundance of gut microbiota and growth performance and pathogen resistance. The results showed that Cetobacterium had significantly positive correlations with weight gain and specific growth rate, but Clostridiales_unclassified, Bradyrhizobiaceae_unclassified and Candidatus_Saccharibacteria_unclassified were opposite, suggesting that that Cetobacterium may contribute to the growth of grass carp. Meanwhile, Rhodobacteriaceae_unclassified and Akkemansia had positive relationships with the gut antioxidant response, and Akkemansia had a significant positive correlation with gut immune response, suggesting that Akkemansia may activate the intestinal immune response and antioxidant enzyme system.

To better understand the relationship among the P. monteilii JK-1 supplementation, keystone taxa, gut network, grass carp growth performance and infection level, a partial least squares path model (PLS-PM) was constructed (Fig. 6). The results proved that P. monteilii JK-1 supplementation had significant positive correlation with keystone taxa (0.73) and gut network (0.50), but had no significant correlation with growth performance and infection level, suggesting that P. monteilii JK-1 affect the growth performance and disease resistance of grass carp mainly through an indirect way (Fig. 6a, b). The keystone taxa (Cetobacterium and Akkemansia) positively correlated with gut work (0.52) and growth performance (0.40) but not significantly correlated with the infection level, indicating that Cetobacterium and Akkemansia had significant impacts on the gut network structure and growth performance of grass carp. Moreover, the gut network showed significant impacts on the growth performance (1.01) and the infection level (-0.80), suggesting that a stable gut network is conducive to improving growth performance and reducing infection levels in grass carp.

Figure 6 Effects of the major factors on the growth performance and pathogen resistance as determined by the partial least squares path model (PLS-PM) analysis. a PLS-PM showing the cascading relationships of different factors. The dashed rectangles represent the loading for the keystone taxa, the gut network, growth performance, and infection level, which generate the latent variables. After 1,000 bootstraps, path coefficients are calculated and represented by the width of the arrow (red stands for positive relationship, green stands for negative relationship). The dashed arrow indicates a coefficient that did not differ substantially from 0 (P > 0.05). The GoF statistic was used to evaluate the model, and the GoF value was 0.83. b Standardized effects of each factor on grass carp growth performance and pathogen resistance profiles calculated from the results of partial least squares path modeling.

Probiotic use in aquaculture has received large attention in recent years because of mounting evidence that it is environmentally friendly and has positive benefits for aquatic animal growth and health, reducing the use of antibiotics [8, 49]. Most studies have focused their attention on the effects of probiotics on the changes in the abundance of microbial species [52]. However, little attention has been paid on the potential beneficial effects of probiotics on interspecific interactions of gut microbiota and the relationship between growth performance, immune response and gut microbiome.

The growth of juvenile grass carp was improved by supplementation of P. monteilii JK-1. Similar to the results of this study, grass carp growth performance has been improved by the use of Bacillus subtilis Ch9 [55]. Moreover, a higher growth performance and lower feed conversion ratio was found in Bacillus natto NT fed group than the other groups [31]. Additionally, our findings are consistent with several studies in other animals, like abalone and shrimp, which have demonstrated that probiotic-supplemented diets enhance growth performance [18, 45]. The improved growth performance of grass carp may be due to probiotics regulating other microorganisms in the gut to improve the digestibility of the feed [27]. Since the intestine is an important immunological organ and a barrier against pathogen infection, preserving proper intestinal function is crucial for host’s health [54]. It has been discovered that probiotics can improve immune function and prevent disease, which is mostly evidenced by an increase in the production of pro-inflammatory cytokines [35]. Immune cells release cytokines, which promote inflammation at the site of infection and hasten the entry of phagocytic cells to annihilate the invading pathogen [24]. In this work, we proved that P. monteilii JK-1, a fish- derived probiotic, could increase the relative expression of immune-related genes in head kidney and gut. Similar results were reported by El-Kady et al. (2022) who found that probiotics could involve the upregulated gene expression of cytokines in Nile tilapia. The supplementation of lactic acid bacteria (LAB) in the diets of rainbow trout caused a significant upregulation of cytokines such as IL-1β, IL-10, TNF-α, and TGF-β [39, 40]. Our study also displayed that P. monteilii JK-1 supplementation significantly increased the IL-1β, IL-10, TNF-α, and TGF-β expression in kidney and gut, indicating that a diet supplemented with P. monteilii JK-1 could stimulate non-specific immunity, which would strengthen fish's defenses against invasive infections. Antioxidant enzyme activities in liver and gut have been shown to play an important role in protecting host organisms against pathogenic agents [17]. According to the previous reports, SOD catalyzes the conversion of reactive O₂⁻ to H₂O₂, GSH-Px leads to a reduction of reactive oxygen species, and CAT aids in the hydrolysis of H₂O₂ into H₂O and O₂ [30, 53]. Our study demonstrated that P. monteilii JK-1 supplementation significantly enhanced the SOD, CAT and GSH-Px activity in liver and gut. This result was similar to the results of previous studies, which had proved that probiotics, including B. cereus, L. plantarum, and P. hypophthalmus, could improve the antioxidant capacity of crucian carp, grass carp, and common carp [1, 7, 48].

The gut microbiome had a direct impact on metabolism, immune, and physiology of fish [47]. In this study, P. monteilii JK-1 supplementation had no significant influence on the diversity and bacterial community structure of gut microbiome, while had significant impact on the composition. Interestingly, there was a significant difference between the microbial forms of the gut microbiome in grass carp after A. hydrophila infection. However, Grass carp previously fed P. monteilii JK-1 did not show significant changes in gut microbial forms after A. hydrophila infection. These indicated that P. monteilii JK-1 could reduce gut microbial disorders caused by A. hydrophila infection. As previous mentioned, probiotics played an important role in maintaining the balance of intestinal microbiomes and preventing the dysbiosis of intestinal flora [36]. Earlier research indicated that the use of antibiotics affected the gut microbiota of fish, but the use of probiotics could restore the balance of the microbiota after the use of antibiotics [59]. In addition, probiotics could also maintain the balance of the gut microbiome when the host was in an environment contaminated with other compounds. For example, probiotics efficiently prevented zebrafish gut microbial dysbiosis caused by perfluorobutanesulfonate [22].

Interspecific interactions among the gut microbiota can create a complex ecological network that helps keep the host gut in a dynamic balance [10]. A stable gut ecological network indicated a close relationship between intestinal microbial species [57]. To describe network stability, three generally used complimentary network indices can be utilized [3]. For example, average degree is the most frequently used to describe the topological characteristic of a network node, average path distance is the shortest distance between two nodes, and the clustering coefficient is used to express how well a node is linked to its neighbors. The results from this study proved a higher average degree and clustering coefficient of ecological network in PM-JK1, suggesting that P. monteilii JK-1 could improve the stability of network. Similar results were obtained from the study of Yang et al. (2019), who demonstrated that Bacillus cereus had significant positive impacts on gut ecological network homeostasis. A published work also showed that Haematococcus pluvialis stimulates red swamp crayfish growth by positively regulating the gut microbial co-occurrence network [6]. Furthermore, published works suggested that gut ecological network had important impacts on the host disease resistance [21, 50]. The results from present study demonstrated that grass carp with a more stable and complex gut ecological network had a higher survival rate after infection with pathogen. A similar study demonstrated that stress altered the structure of the skin microbial network leading to an increase in the number of pathogenic bacteria, causing fish mortality [4]. These results suggested that P. monteilii JK-1 enhanced interspecies relationships to improve the resistance against infection.

We suggested an additional hypothesis that Cetobacterium may play a role in maintaining the stability of the gut ecological network as an interspecies regulator. Interspecies interactions may be more important than microbiome diversity and species richness in maintaining the function of ecosystems [12, 44]. The present study showed that the types of nodes in control and PM-JK1 were significantly different. Connector nodes represent the species that have many links to most of the other modules [20], and play a key role in bridging intimate interactions between microbial–microbial relationships and host–microbial relationships. In the present work, no connector hubs were found in control, in contrast, it had a number of non-hub nodes. These results mean that the within-module interaction was stronger than the among-module interaction in the gut ecological network in control group. Interestingly, Cetobacterium was the only connector hub in the ecological network in PM-JK1. These results mean that Cetobacterium acted as a connector in the ecological network and may play a key role in bridging intimate interactions between the microbiome.

Large-scale researches had shown that probiotics could stimulate growth performance, pathogen resistance, as well as affect the compositions of gut microbiota [2, 7, 16], however, these studies have analyzed each factor separately. We used the PLS-PM model to explore the relationship among the P. monteilii JK-1, keystone taxa, gut network, growth performance, and infection level to explore the way in which probiotics enhance host growth performance and pathogen resistance. The PLS-PM model showed that P. monteilii JK-1 improved the growth performance and pathogen resistance of grass carp through indirect effects, while the gut network had greater direct impact on the growth performance and pathogen resistance of grass carp. Based on these results, we suggested that the probiotics improve growth performance and resistance to pathogenic bacterial infections in grass carp mainly by affecting intestinal commensal bacteria and ecological networks. Future research still needs to address how gut ecological networks promote growth and resistance to disease and how they work with the host to build an immune barrier against pathogen infections.

Taken together, this study demonstrates that dietary P. monteilii JK-1 have a potential beneficial effect on the growth performance and pathogen resistance in grass carp. Dietary administration of P. monteilii JK-1 significantly improve growth performance, immune and antioxidant responses, and reduce mortality in grass carp after infection with pathogenic bacteria. These positive effects may be due to the regulation of Cetobacterium and Akkemansia by probiotics increasing the stability and complexity of the gut ecological network. Therefore, this strain could be utilized in grass carp aquaculture to increase production and control disease. This study provides a nuanced understanding of how probiotics maintain the stability of the gut ecological network. And also provides a new perspective on probiotics to maintain host health and a scientific foundation for creating a functional feed.

Author’s Contribution

Xiaozhou Qi: Data Curation, Microbial analysis, Writing - original draft preparation, Formal analysis. Mingyang Xue: Methodology, Investigation, Data curation, Conducted the animal experiments. Gaoxue Wang: Resources. Fei Ling: Conceptualization, Methodology, Resources, Supervision, Project administration, Funding acquisition. All authors read and approved the final manuscript.

Conflict of interest

The authors declared that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Acknowledgments

The authors appreciate the support of Technology Foundation for Selected Overseas Scholar.

Funding

This research was supported by Technology Foundation for Selected Overseas Scholar (NO. A279021802).

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Fish-derived Probiotic Improves Growth Performance and Pathogen Resistance of Juvenile Grass Carp (Ctenopharyngodon idella) by Modulating Keystone Taxa and Enhancing Microbial Interactions

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Abstract

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Introduction

Materials And Methods

Animal ethics

Experimental Fish And Bacterial Strain

Diet Preparation

Experimental Design And Sample Collection

Genes Expression Analysis

Determination Of Liver And Gut Antioxidant Indices

16s Rrna Sequencing And Gut Microbial Analysis

Gut Microbiota Network Analysis Based On Bacterial Relative Abundance

Statistical Analyses

Results

Correlation Between The Gut Microbiota And Growth Performance And Disease Resistance

Discussion

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