Dynamic response of gut microbiota mediates the adaptation of Cipangopaludina chinensis to Pomacea canaliculata invasion

Abstract

Background

As an invasive species, Pomacea canaliculata exerts significant adverse effects on aquatic ecosystems. It can infect native freshwater snails, such as Cipangopaludina chinensis, by secreting pathogens, leading to increased stress and mortality. Gut microbiota play a crucial role in the survival and adaptation of gastropods, significantly influencing their health and resistance to environmental stressors. By comparing the gut microbiota composition and metabolic profiles between resistant (RE) and sensitive (SE) populations of C. chinensis, this study aims to elucidate the role of the gut microbiota in enhancing the survival of C. chinensis under the invasion pressure from P. canaliculata. And the mechanisms were further explored through gut microbiota transplantation, horizontal and vertical transmission experiments, and field studies.

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Video Abstract

Results

Our findings reveal that RE individuals exhibit greater gut microbiota diversity and a higher abundance of core microbiota, including Psychrobacter, Comamonas, and Pseudomonas, which are correlated with enhanced host survival in the presence of pathogen infections. Analysis of metabolite composition demonstrate that antibiotics and immunological enhancers are the main metabolites, which significantly enhance the host’s resistance to pathogen infections. Notably, these core gut microbiota can be transmitted both horizontally and vertically, allowing C. chinensis populations to acquire resistance to the invasion of P. canaliculata. The SE group is enriched in pathogens, such as Mycoplasma. Following the transplantation of RE gut microbiota, SE individuals exhibited improved survival rates and core microbiota abundance. The vital role of core microbiota in maintaining the survival rate of C. chinensis was further confirmed in the field studies.

Conclusion

This study highlights the crucial interactions between the gut microbiota and the host's adaptability, offering valuable insights for native species in response of invasive species pressure.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40168-025-02160-4.

Introduction

Pomacea canaliculata is a widely invasive and highly adaptive freshwater snail worldwide [1, 2]. Its intestine contains a considerable abundance of pathogenic microorganisms which can be excreted or secreted into aquatic ecosystems, leading to infections and mortality among other species [3–6]. Additionally, the metabolic byproducts and excretions of P. canaliculata may contribute to eutrophication in aquatic ecosystems, negatively affecting the viability of native species [7, 8]. Cipangopaludina chinensis, a native herbivorous snail in China, exhibits considerable adaptability and an extensive distribution range, functioning as a significant ecological competitor that coexists with P. canaliculata [9]. Nonetheless, its feeding efficiency and resistance to environmental contaminants are inferior to those of P. canaliculata. Consequently, in the presence of P. canaliculata, C. chinensis has to develop adaptations to maintain the population.

Currently, studies on environmental adaptation predominantly emphasize host genomic and phenotypic variations, while the contributions of gut microbiota to the host's adaptation remain underexplored [10–12]. Gut microbiota play a crucial role in nutrition absorption, metabolic activities, immunological responses and environmental adaptation [13–18]. Alterations in the abundance and composition of gut microbiota can significantly influence the survival and reproduction of the host within ecosystems [19, 20]. The gut microbiota of aquatic organisms is highly sensitive to external environment [21, 22], and changes in environment may lead to alterations in both the composition and abundance of gut microbiota, influencing the adaptability of the host [23]. The process of adaptation resembles natural selection, aging and weakened individuals prone to perish, while robust individuals exhibit similar dynamic response in their gut microbiota to endure unfavorable environments. It has been demonstrated that the gut microbiota of various freshwater snails is directly correlated to their surrounding environment, with core microbial communities being vertically transmitted to offspring or horizontally transmitted among populations, thereby enhancing host survival and facilitating population growth under adverse conditions [24]. This phenomenon suggests that environmental alterations might trigger co-evolution between gut microbiota and their host, thereby enhancing the competitive advantage within their habitats.

The invasion of P. canaliculata has changed the structure and function of ecosystems, resulting in a scarcity of critical resources such as habitat, food, and dissolved oxygen [25]. Our previous research has demonstrated that P. canaliculata can cause widespread infections in populations of C. chinensis by releasing its own intestinal pathogenic microorganisms, resulting in a significantly increased mortality rates among native snails. In areas invaded by P. canaliculata, populations of C. chinensis exhibited stronger resistance to pathogens, which may have adapted to the altered environmental conditions resulting from the invasion. Nonetheless, it remains unclear how the gut microbiota of C. chinensis dynamically interacts with its host to adapt to the invasion of P. canaliculata and maintain population reproduction.

In this study, C. chinensis populations that are either resistance (RE) or sensitive (SE) to the invasion of P. canaliculata were selected. The differences in gut microbiota and metabolic composition among the groups were investigated, and the pathways of core microbiota were further elucidated. Besides, gut microbiota transplantation experiments were performed to clarify the mechanisms by which gut microbiota enhance tolerance to the pressures from P. canaliculata invasion. Furthermore, the gut microbiota composition of C. chinensis in both invaded and non-invaded regions within natural ecosystems was investigated to evaluate whether the gut microbiota can assist C. chinensis withstand the invasion of P. canaliculata. The dynamic response mechanisms of C. chinensis gut microbiota and their adaptive regulation in the host will be clarified, providing novel insights and strategies for the conservation of native ecosystem.

Results

The abundances of Psychrobacter, Pseudomonas and Comamonas significantly increased in the gut of C. chinensis that can resist the invasion of P. canaliculata

The C. chinensis survival rate of the sensitive (SE) group was significantly lower than that of the resistance (RE) group under the invasion pressure of P. canaliculata (Fig. 1a, P < 0.05) (Table S1). The SE group contained 726 core operational taxonomic units (OTUs), while the RE group had 634 OTUs, with unique OTUs accounting for 44.63% and 36.59%, respectively (Fig. 1b, Data sheets S1–S2). The Shannon Index of gut microbiota in the RE group was significantly higher than that in the SE group (Fig. 1c, Data sheet S3). Principal Coordinates Analysis (PCoA) revealed significant differences in the gut microbial community structure between the RE and SE groups, with a remarkable clustering for the RE group (Fig. 1d), suggesting a trend towards homogenization. A total of 487 genera were identified in the gut microbiota of both RE and SE groups, among the top 10 genera, Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter were notably prominent (Data sheets S4–S5, Fig. S1–S2). At the level of species, Citrobacter freundii, unclassified Mycoplasma, Comamonas, Pseudomonas fragi, and Pseudomonas putida was the five predominant species in RE and SE group (Fig. 1e, Data sheet S6). Besides, Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter were identified as significantly discriminative genera between the RE and SE groups of C. chinensis by Linear Discriminant Analysis and Effect Size (LEfSe) (Fig. S3). This differentiation was confirmed by PCoA, which clearly separated these genera between the groups (Fig. S4). Random Forest analysisis a supervised machine learning algorithm that evaluates feature importance by decision trees, and Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter was identified as top predictors differentiating resistant (RE) and sensitive (SE) groups (within the top 100 features) by the analysis,, with Mycoplasma, Comamonas and Psychrobacter ranked in the top 30 (Fig. S5, Data sheet S7). Network analysis revealed that Mycoplasma was significantly negatively correlated with Comamonas, Pseudomonas and Psychrobacter (R < − 0.6, P < 0.01), while the latter three showed strong positive correlations (R > 0.6, P < 0.01), indicating functional antagonism between pathogens and beneficial microbiota (Fig. S6, Data sheet S8).Therefore, these four genera can serve as biomarkers for distinguishing between the RE and SE snails. Among them, the relative abundance of Mycoplasma was significantly higher in the SE group than that in the RE group (P < 0.05), but the other three genera exhibited significantly higher abundances in the RE group (Fig. 1f, Fig. S2, Data sheet S9, P < 0.05).

Fig. 1.

The survival rates and gut microbiota composition in Cipangopaludina chinensis between the resistance (RE) and sensitive (SE) groups. a Comparative analysis of survival rates between SE and RE. b Composition of core operational taxonomic units (OTUs) in SE and RE. c The Shannon Index assessment of gut microbiota in SE and RE. d The Principal Coordinates Analysis (PCoA) of core gut microbiota in SE and RE. e Identification of the ten most abundance species in SE and RE groups. f Abundance assessment of Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter

The gut microbiota of C. chinensis in RE group synthesizes antibiotics to withstand the invasion of P. canaliculata

Although differences in the composition of the gut microbiota between the SE and RE groups have been identified, the mechanism by which these microbiota participate in assisting RE snails to resist P. canaliculata invasion remains unknown. A partial least squares discriminant analysis (PLS-DA) model was constructed to discriminate metabolite profiles between resistant (RE) and sensitive (SE) groups of C. chinensis. Our findings manifested distinct variations in metabolic characteristics between the two groups (Fig. 2a, Data sheet S10–S11), with particular expression patterns of metabolites identified across the different groups (Fig. 2b, c, Data sheet S11). Differential expression analysis disclosed 31 metabolites that were significantly difference between the SE and RE groups (Fig. 2d, Data sheet S12, P < 0.05). The metabolites comprised numerous antibiotics, including Moxifloxacin, Norfloxacin, Kanamycin X and Geneticin. Moreover, vitamin B2 metabolites, including reduced riboflavin and its intermediate 7-hydroxy-6-methyl-8-ribityllumazine, are involved in the modulation of the immune system. The relationships between metabolites and the four core differential genera of RE and SE group were further analysis. Among them, Mycoplasma was the pathogen highly enriched in SE group, while the other three genera mainly enriched in RE group. These metabolites had a negative correlation with Mycoplasma, which is the primary differential genus between the RE and SE groups, while exhibiting a positive correlation with Comamonas, Pseudomonas, and Psychrobacter (Fig. 2e, Data sheet S13). The KEGG enrichment analysis of these metabolites mainly involved in 20 metabolic pathways, including Neomycin, kanamycin and gentamicin biosynthesis (ko00524)、Riboflavin metabolism (ko00740), and ABC transporters (ko02010) (Fig. S7). Consequently, it can be speculated that Comamonas, Pseudomonas and Psychrobacter in RE snails produces amount of antibiotics and immunological enhancers. These metabolites assist in resisting pathogens, such as Mycoplasma released by P. canaliculata. Moreover, these genera are involved in metabolic pathways related to ABC transporters, as well as the biosynthesis of Neomycin, Kanamycin, and Gentamicin, and riboflavin metabolism, these processes jointly alleviate the toxicity of various pathogens and their metabolic products, thereby enhancing the overall survival rate of the population.

Fig. 2.

Comparative of gut metabolites between the SE and RE groups of C. chinensis. a Differentiation of metabolites between SE and RE groups as assessed by Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA). b Permutation analysis of metabolites in the SE and RE groups via OPLS-DA c Expression patterns of metabolites in SE and RE. d Heatmap of core differential metabolites between SE and RE. e Co-analysis of differential microbes and metabolites in the SE and RE groups

The transplantation of gut microbiota from RE individuals increases the resistance ability of SE C. chinensis to the invasion pressure from P. canaliculata

To further confirm the role of specific gut microbiota in C. chinensis survival under the invasion pressure of P. canaliculata, gut microbiota transplantation from RE individuals to SE individuals (designated as SRE group) was conducted, and the control groups transplanted the filtered RE gut microbiota (SLR group) and PBS (SPBS group) were set. Our results indicate that the transplantation of gut microbiota from RE significantly enhances the survival rate of SE individuals under the stress of P. canaliculata (Fig. 3a, P < 0.05). After transplantation, the Shannon Index of gut microbiota in the SRE group was significantly increased compared to SLR and SPBS group (Fig. 3b, P < 0.05, Data sheets S14). PCoA revealed significant differences in the gut microbial community structure among the three groups, with distinct clustering observed in the SRE group (Fig. 3c). The primary core difference genera between the SE and RE groups (Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter) demonstrated significant enrichment following gut microbiota transplantation. Mycoplasma was mainly enriched in the SLR and SPBS groups, while the other three genera exhibited enrichment in the SRE group (Fig. 3d, Data sheets S15). The relative abundance of Mycoplasma in the SRE group was significantly reduced, while the relative abundances of Comamonas, Pseudomonas, and Psychrobacter were significantly increased (Fig. 3e-f, Data sheets S15). The capacity of these genera to metabolize and produce antibiotics and immune boosters may be the primary reason for the elevated survival rate of SRE individuals under the invasion pressure of P. canaliculata.

Fig. 3.

The survival rates and gut microbiota composition in C. chinensis among the SPBS, SLR, and SRE groups. a Comparison of survival rates among the SPBS, SLR, and SRE groups. b The Shannon Index assessment of gut microbiota in the SPBS, SLR, and SRE groups. c PCoA of core gut microbiota across the SPBS, SLR, and SRE groups. d Triangular analysis of core differential genera (Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter) among the SPBS, SLR, and SRE groups. e Abundance assessment of core differential genera across samples. f Relative abundance differences analysis of core differential genera among the SPBS, SLR, and SRE groups

The gut microbiota of RE individuals can provide resistance to SE individuals via horizontal transmission

To clarify the role of horizontal transmission of gut microbiota in the acquisition of resistance ability by C. chinensis against the invasion of P. canaliculata, SE and RE individuals were coexisted without contaction (RS), and SE and SE individuals cohabitation system was set as control group (SS). Our results indicate that the survival rate of SE snails in the RS group was significantly higher than that of the SS group under the P. canaliculata invasion (Fig. 4a, P < 0.05). Additionally, analysis of the gut microbiota in the two groups disclosed that the Shannon Index in the RS group was significantly higher (Fig. 4b, P < 0.05, Data sheets S16). PCoA revealed significant differences in the gut microbial composition between the two groups, with obviously clustered in the RS group (Fig. 4c). This suggests that the gut microbiota in RE individuals might be horizontally transferred and subsequently amplified within the gut of SE individuals, resulting in a tendency of microbiota homogenization. The abundances of the core differential genera Comamonas, Psychrobacter, and Mycoplasma were significantly increased in the RS group compared with the SS group (Fig. 4d, e, Data sheets S17, P < 0.05). This suggests that Comamonas and Psychrobacter can proliferate within the gut of SE individuals through horizontal transmission, providing SE individuals with a core microbiome capable of withstand the invasion of P. canaliculata.

Fig. 4.

The survival rates and gut microbiota composition in C. chinensis between the RS and SS groups. a Comparison of survival rates between the RS and SS groups. b The Shannon Index assessment of gut microbiota in the RS and SS groups. c PCoA of core gut microbiota in the RS and SS groups. d Abundance analysis of core differential genera (Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter) across samples. e Relative abundance differences analysis of core differential genera between the RS and SS groups

RE individuals enable their offspring to acquire the ability to resist the invasion of P. canaliculata through vertical transmission of gut microbiota

To explore whether the gut microbiota that enable host with the ability to resist the invasion pressure of P. canaliculata can be vertical transmitted to offspring, F1 and F2 offspring (F1IC and F2IC) from individuals exposed to the invasion pressure of P. canaliculata (IC) was collected, along with control offspring (F1CC and F2CC) from unexposed individuals the transmission dynamics of gut microbiota could be analyzed. In contrast to IC, CC shares more OTUs between the F1 and F2 generations, suggesting that C. chinensis had the capacity for vertical transmission of gut microbiota, while the capacity has weakened due to the invasion of P. canaliculata (Fig. 5a, Data sheet S18). In the F1 generation, the Shannon Index of gut microbiota in the CC group was significantly higher than that in the IC group, while no significant differences were observed in the F2 generation. Furthermore, both CC and IC groups in the F2 generation had a much greater Shannon Index compared to that in the F1 generation (Fig. 5b, Data sheets S19, P < 0.05). PCoA analysis disclosed a significant difference in the gut microbial composition between CC and IC in the F1 generation, while the difference was weakened in the F2 generation (Fig. 5c). Mycoplasma displayed an extremely high abundance in the F1IC group, establishing itself as the dominant gut microbe, while its abundance significantly decreased in the F2 generation (Fig. 5d, e, P < 0.05, Data sheet S19–20). In contrast, Pseudomonas and Psychrobacter manifested significantly greater abundances in the F2 generation than in F1, with elevated abundances in the IC group compared to the CC group (Fig. 5d, e, P < 0.05, Data sheet S20–S21). This indicates that pathogens rapidly colonize the gut of IC individuals at the initial stage, and the offspring can resist pathogen infections by proliferating a considerable antibiotic-producing core microbiota, such as Pseudomonas and Psychrobacter.

Fig. 5.

Comparative of the gut microbiota composition in C. chinensis among the F1CC, F2CC, F1IC, and F2IC groups. a Composition of OTUs. b The Shannon Index assessment of gut microbiota. c PCoA of core gut microbiota. d Abundance assessment of core differential genera (Mycoplasma, Comamonas, Pseudomonas, and Psychrobacter) across samples. e Relative abundance differences analysis of core differential genera

Comamonas, Pseudomonas, and Psychrobacter participated in the resistance of C. chinensis to P. canaliculata invasion in natural ecosystems

Consistent results were obtained in snails from natural ecosystems. Unlike C. chinensis in non-invaded regions (WN), individuals from invaded regions (WP) and those transplanted with WP gut microbiota (WT) exhibited enhanced resistance to P. canaliculata invasion, along with the enrichment of core microbiota similar to those in the RE group in the laboratory experiment. Under laboratory conditions, following non-contact co-exposure to P. canaliculata, WT and WP groups had significantly higher survival rates compared to WN group (Fig. 6a, P < 0.05). Moreover, the gut of WT group exhibits higher diversity, the Shannon Index of gut microbiota in WT group was significantly higher than that in WP and WN groups, and the WP group presented a higher Shannon Index than WN group (Fig. 6b, Data sheet S22, P < 0.05). Among the three groups, significant differences in gut microbial community patterns were observed through PCoA analysis (Fig. 6c). Cetobacterium, Comamonas, Pseudomonas, Psychrobacter, and Mycoplasma were demonstrated have the relatively high abundances (Fig. 6d, Data sheet S23–S24). Subsequent investigation revealed that Mycoplasma and Cetobacterium were mainly enriched in WN group, while Mycoplasma, Pseudomonas, and Psychrobacter were enriched in WP group, and Comamonas, Pseudomonas, and Psychrobacter were enriched in WT group (Fig. 6e–g). Consistent with the results from laboratory experiments, the dynamic responses of Comamonas, Pseudomonas, and Psychrobacter serve as one of the crucial factors enabling C. chinensis to survive when facing P. canaliculata invasion in natural ecosystems.

Fig. 6.

The survival rates and gut microbiota composition in C. chinensis among the WN, WT, and WP groups. a Survival rates of C. chinensis among the three groups. b The Shannon Index assessment of gut microbiota across the three groups. c PCoA of core gut microbiota among the three groups. d Abundance composition of the ten most abundance genera in the gut microbiota across all groups. e Ternary enrichment analysis of core differential genera (Mycoplasma, Comamonas, Pseudomonas, Psychrobacter) and the predominant genus (Cetobacterium) across the three groups. f Relative abundance differences analysis of core differential genera and the predominant genus among the three groups. g Heatmap depicting abundance analysis for core differential genera and the predominant genus among samples from the three groups

Discussion

The presence of invasive species exert continuous pressure on native species, driving them to undergo adaptive regulatory mechanisms for survival. Biological invasion often involves co-evolution between invasive and native species. It had been reported that the microbes and their genes can be spread from the host’s gut to the environment and be transferred among different species, leading to symbiotic microbial convergence across animal hosts [26–28]. Native populations are susceptible to the gut microorganisms of invasive species due to the lack of a long-term co-evolutionary history. The disruption of microbial homeostasis may lead to the death of vulnerable individuals within native populations, and even cause regional extinctions [29]. Many invasive species have the capability to cause mortality among native species by secreting or excreting pathogenic microorganisms [30]. For instance, the primary strategy employed by the invasive ladybird Harmonia axyridis is to release microbiota that infect native ladybirds, while these microbiota are harmless to H. axyridis itself [31]. Similarly, the gut of Pomacea canaliculata contains a great abundance of pathogenic bacteria [3–5], and our previous research found that the pathogenic bacteria can be released into environment, causing infections in native species, which might be a crucial invasion strategy for the invasion of P. canaliculata.

The gut microbiota is essential for host survival, influencing immune response, metabolism and detoxification [32–34], as well as development and environmental adaptability [18]. The composition of gut microbiota among different species within the same region is affected by both environmental microbial communities and interspecies interactions [35]. Interactions between natural ecosystem and species are regarded as stronger driving forces than host genotype, facilitating similar gut microbiota composition within population [36]. The increased selective pressure from the natural environment may lead to the disappearance of individuals with low adaptability and resistance, leaving only the most robust individuals in the population that can adapt to such changes. These individuals are commonly have a more diverse microbiome and a greater abundance of beneficial bacteria in their gut, which enhances their resistance to external challenges and increases the survival rates in variable conditions [37, 38]. Our research reveals that approximately 15% of C. chinensis (RE) can survive under the long-term invasion of P. canaliculata, these individuals exhibit greater gut microbiota diversity and lower intraspecific variation, which also suggested that the invasion pressure from P. canaliculata results in the homogenization of gut microbiota within the C. chinensis community. The homogenization has been also reported in both vertebrates and invertebrates in response to environmental pressures [39]. In the SE group, Mycoplasma is the most abundant microorganism, a common pathogen in the gut of P. canaliculata, which that can trigger significant inflammatory reactions and mortality in native species [40]. The RE group exhibited an enrichment of Psychrobacter, Comamonas, and Pseudomonas. Although Pseudomonas is often classified as a pathogenic bacteria [41], certain species are demonstrated for providing survival benefits to their hosts. Notably, Pseudomonas putida has the ability to eliminate pathogenic bacteria within the host through its secretions, thereby enhancing host survival [42]. It has been reported that Pseudomonas fragi can synthesize biosurfactants, enhancing the competitive advantage of specific bacteria and potentially reducing the pathogenic capacity [43]. Thus, Pseudomonas is likely to play a vital role in assisting RE group individuals resist infections from other pathogens. Moreover, Psychrobacter enhances the gut barrier function and activates the host immune system, including the expression of antimicrobial proteins, thereby suppressing the growth of pathogenic bacteria and inhibiting pathogen infection [44]. Comamonas has demonstrated the ability to eliminate pathogenic factors at the transcriptional level [45]. These bacteria may cooperate from multiple aspects to significantly enhance the resistance of RE against invasion pressure. Furthermore, metabolomic analysis revealed that these bacteria are capable of synthesizing numerous antibiotics, including Raffinose, Cytidine, and Kanamycin. The antibiotic metabolites synthesized by host gut microbiota are regarded as one of the primary means to regulate the intestinal environment and eliminate excessive pathogens in response to environmental stress [46]. Comamonas, Pseudomonas, and Psychrobacter are capable of generating vitamin B2-related metabolites, such as reduced riboflavin and the intermediate 7-Hydroxy-6-methyl-8-ribityllumazine, which assist in regulating the host's inflammatory response, maintaining microbial homeostasis, and enhancing intestinal immunity [47]. This ability enables C. chinensis to survive in adverse conditions. Therefore, P. canaliculata invasion stimulates the abundance of Comamonas, Pseudomonas, and Psychrobacter, which are regarded as the core microbiota contributing to the resistance of C. chinensis. These core microbiota synthesize antibiotics and vitamin B2-related antimicrobial compounds that help regulating the host's internal immunological environment. Following the transplantation of microbiota from RE, the abundances of Psychrobacter, Comamonas, and Pseudomonas were increased in the gut of SE, correlating with an enhanced survival rate of C. chinensis. It has been demonstrated that these core microbiota enhance the activity of host immune system, resisting the stress from P. canaliculata invasion and promoting the health and survival for the host.

Under persistent environmental stress, the gut microbiota of gastropods tend to undergo a similar dynamic response, enabling specific microbial compositions dominant in the population. And the similar dynamic response is mainly driven by horizontal transmission of microbiota [24]. The gut microbiota of gastropods can be spread not only through horizontal transmission but also through vertical transmission, helping offspring acquire similar gut microbiota compositions and enhancing their resistance to environmental pressures [24]. Vertical transmission of gut microbiota is commonly observed in various animals, including insects [48–50], freshwater snails [24], and mice [51], contributing to the health and survival of offspring and improving their capacity to withstand adverse environments. In present study, gut microbiota of RE individuals cause convergent to the gut microbiota of RS individuals through horizontal transmission, enriching RS individuals with the core microbiota of Psychrobacter, Comamonas, and Pseudomonas, which enhances the survival rate of C. chinensis in response to the invasion pressure from P. canaliculata. Furthermore, the gut microbiota of RE adults can maintain the abundance of core bacteria at high levels through vertical transmission for at least two generations. Thus, under prolonged stress from P. canaliculata, gut microbiota may co-evolve with the host through horizontal and vertical transmission, resulting in homogenization among different individuals. The increased α-diversity and the enrichment of core microbiota such as Psychrobacter, Comamonas, and Pseudomonas, which play critical roles in maintaining the population dynamics of C. chinensis.

In natural ecosystems, the gut microbiota of gastropods varies among individuals, while it tend to homogenize to withstand adverse conditions (such as parasites or pathogens) [52]. In our study, Psychrobacter, Comamonas and Pseudomonas were maintained at high levels in WT individuals following gut microbiota transplantation from WP. This is consistent with previous laboratory findings that Psychrobacter, Comamonas, and Pseudomonas were involved in the resistance of C. chinensis to P. canaliculata. Apart from the enrichment of Mycoplasma in WN, Cetobacterium as the core microbiota in many gastropods was also had a high abundance in WN, which has been identified to enhance host resistance to diseases [24, 53]. C. chinensis enriched itself with both Psychrobacter, Comamonas Pseudomonas, and other beneficial microorganisms from the wild environment, to better counter the environment with P. canaliculata invasion.

Conclusion

A combined laboratory and field investigation was carried out to clarify the dynamic response of gut microbiota in C. chinensis hosts and its resistance to the invasion of P. canaliculata. Several key insights was revealed by the study:

1. The abundances of Psychrobacter, Comamonas and Pseudomonas are significantly increased in gut of C. chinensis that can resist P. canaliculata invasion, while Mycoplasma is mainly enriched in C. chinensis that is not resistant to the invasion.

2. Synthesize antibiotic and vitamin B2 metabolites by Psychrobacter, Comamonas and Pseudomonas enhances host immunity and eliminates excess pathogenic bacteria within the gut, which may be the strategy of RE individuals in combating pathogen infections to P. canaliculata invasion.

3. Both horizontal and vertical transmission of Psychrobacter, Comamonas and Pseudomonas play a crucial role in enhancing the survival rates of C. chinensis populations faced to the invasion pressure of P. canaliculata. The core microbiota had a higher relative abundance in the C. chinensis that was affected by the wild invasion of P. canaliculata, which is consistent with the conclusions from the laboratory experiments.

This study offers novel insights into the function of gut microbiota in host adaptability to environmental stressors and biological invasions, highlighting the importance of microbial communities in the context of invasive species pressure. These findings provide a scientific basis for regulating P. canaliculata invasions and protecting native species, while also enhancing the understanding of species adaptation to environmental stressors, with significant ecological and evolutionary implications.

Materials and methods

Construction of the RE and SE groups

C. chinensis were purchased from a farm in Suzhou (Jiangsu Province, China), no contact with P. canaliculata. A total of 3986 individuals were obtained and reproduced in the laboratory, and the offspring were used for subsequent experiments. P. canaliculata were collected from West Lake in Hangzhou (Zhejiang Province, China), with a total of 688 individuals, which were promptly transported to the laboratory and acclimatized for 10 days before the experiment. The snails were reared with 70 L of aerated sterile water, which was replaced every 5 days. The temperature was maintained at 25–27 °C, and the light cycle was set to 12:12 h (L:D).

Three offspring of C. chinensis (average weight 3.5 ± 0.5 g) were randomly selected and placed four P. canaliculata (average weight 7.5 ± 0.5 g) in 4 L of aerated sterile water cultivated for 96 h. After 96 h, the status was evaluated. The individuals of C. chinensis remaining at the bottom without movement were categorized as the SE (Sensitive C. chinensis) group, while those with active feeding and exploratory behaviors were classified as the RE (Resistance C. chinensis) group. The system was repeated 90 times to ensure sufficient samples for the subsequent experiments. After recovery in aerated sterile water for 96 h, 12 individuals of C. chinensis from both RE and SE group and four P. canaliculata were co-cultivated in a 4-L systems for 10 days, repeated 4 times. During the experiment, 3.5 g of sterilized animal feed (Tetra, Tianjin) was provided daily and renewed water every 2 days. The survival rates of RE and SE groups were recorded after 10 days, and gut samples from each group were obtained for the analysis of gut microbiota composition. Subsequent laboratory experimental setups were consistent with this methodology.

Transplantation of the gut microbiota from RE to SE snails

To determine the role of gut microbiota from RE individuals in resisting the invasion of P. canaliculata, gut microbiota transplantation from RE to SE was carried out according to the previous researches [54, 55]. The intestines of 12 individual RE snails were promptly dissected and homogenized in 600 mL of sterile PBS. After centrifugation (800 rpm, 1 min), the supernatant was served as the gut microbiota solution from RE snails, and 5 µL solution was injected to SE snails (SRE). For the positive control group, the supernatant was filtered via a 0.22-μm membrane (SLR), while PBS was used for the negative control (SPBS). Then, these groups were co-cultivated with P. canaliculata for 10 days, the survival rates and gut microbiota composition were evaluated.

Horizontal transmission of the gut microbiota from RE to SE snails

To investigate whether gut microbiota from RE individuals can be horizontally transferred to SE individuals and confer resistance against the invasion of P. canaliculata. The horizontal transmission system consists of 12 SE snails and 4 P. canaliculata, and then 4 RE or 4 SE individuals are placed in separate small boxes and added to the system to avoid direct contact between the two sides of the animals, which are marked as the RS and SS groups, respectively. After cultivated for 10 days, the survival rates and gut microbiota compositions of RS and SS group snails were evaluated.

Vertical transmission of the gut microbiota in C. chinensis under the invasion pressure of P. canaliculata

To further explore whether the core gut microbiota and the resistance to P. canaliculata invasion can be vertically transmitted to the offspring of C. chinensis, C. chinensis sourced from a breeding farm was reared at a density of 10 individuals per liter in 12 L of aerated sterile water. The system was replicated 6 times, with three systems received no supplementary treatment, while the other three were augmented with 12 individuals of P. canaliculata (average weight 7.5 ± 0.5 g), designated as the, CC abd IC group, respectively. When the next two generations of C. chinensis was produced, the gut microbiota compositions of the F1CC, F1IC, F2CC, and F2IC juvenile snails was analyzed.

Exposure of C. chinensis from wild ecosystems to P. canaliculata

Six hundred eighty-eight individuals of C. chinensis from the P. canaliculata invaded region and 388 individuals from a non-invasive area in West Lake was collected and promptly transferred ti the laboratory on ice, the samples were labeled WP and WN, respectively.

To verify the findings from the laboratory that gut microbiota assist the host in resisting invasions by P. canaliculata. WT group was established by the gut microbiota transplantion from the WP group to the WN group. After stabilizing for 96 h, WN, WT and WP were exposed to P. canaliculata for 10 days. The survival rates and gut microbiota compositions of C. chinensis from the WN, WT and WP groups were subsequently assessed.

Detection of snail survival status

The survival rates of the adult snails and their offspring was assessed. Snails showing detachment or disintegration of soft tissues, edema without tactile reflex, or absence of operculum were classified as dead.

Collection of gut samples, high-throughput sequencing, and bacterial data analysis

Before gut collection, the animals were fasted for 24 h. The entire snail was washed in sterile PBS for 30 s and then rinsed twice more with sterile PBS. The shells were promptly separated on ice, and the soft tissues were washed twice in sterile PBS for 15 s each time. The gut was subsequently dissected in a new sterile petri dish. All dissections and sample collections were carried out under consistent sterile conditions. The collected samples were rapidly snap-frozen in liquid nitrogen and stored at − 80 °C. Each group had six biological replicates. To achieve species-level taxonomic resolution and enable integrated analysis with metabolomics between the RE and SE groups, full-length 16S rRNA sequencing was performed to identified the core differential microbiota, and the 16S V3-V4 hypervariable regions were sequenced for other groups.

Genomic DNA from C. chinensis was extracted using the TGuide S96 Magnetic Soil/Fecal DNA Kit according to the manufacturer’s instructions. The bacterial 16S rRNA gene was amplified using primers 515F-806R (F: CCTAYGGGRBGCASCAG, R: GGACTACNNGGGTATCTAAT). The amplicons were quantified, and equal molar concentrations were pooled before sequencing on the PacBio Sequel II platform. 16S rRNA gene sequencing was conducted at Biomarker Technologies using the Illumina NovaSeq 6000 platform (Illumina). To analyze the bacterial data, sequence assembly, data filtration, and chimera removal steps were performed [56–60]. For RE and SE groups, sequences with similarity ≥ 97% were clustered into the same operational taxonomic unit (OTU) by USEARCH (v10.0) [56], and the OTUs with reabundace < 0.005% were filtered. For other groups, clean reads then were conducted on feature classification to output an ASVs (amplicon sequence variants) by dada2 [57]. Taxonomy annotation of the OTUs was performed based on the Naive Bayes classifier in QIIME2 using the SILVA database (release 132) with a confidence threshold of 70% [58, 60], and the ASVs with reabundace < 0.005% were filtered. The Alpha diversity (Shannon Index) were calculated and displayed by the QIIME2 and R software, respectively. Beta diversity was determined to evaluate the degree of similarity of microbial communities from different samples using QIIME. Principal coordinate analysis (PCoA) based on Bray − Curtis was used to obtain principal coordinates. Furthermore, we employed Linear Discriminant Analysis (LDA) effect size (LEfSe) to test the significant taxonomic difference among group [59]. A logarithmic LDA score of 4.0 was set as the threshold for discriminative features. To explore the dissimilarities of the microbiome among different factors, a redundancy analysis (RDA) were performed in R using the package “vegan”. Network analysis (|R|> 0.6, P < 0.01) was performed in R (v3.6.1), while random forest models were developed using the R (v.3.1.1) to identify bacterial features. Microbiota sequencing data in this study are available from the National Center for Biotechnology Information (NCBI) under accession number PRJNA1190832.

Analysis of metabolite composition in C. chinensis based on LC/MS

A total of 50 mg of gut samples from C. chinensis were homogenized with 1000 μL of extraction solvent containing an internal standard (methanol:acetonitrile:water = 2:2:1, internal standard concentration at 20 mg/L). After vortexing for 30 s, steel beads were added, and the mixture was processed using a ball mill at 45 Hz for 10 min, followed by sonication in an ice bath for another 10 min. The samples were allowed to stand at – 20 °C for 1 h and then centrifuged at 12,000 rpm for 15 min at 4 °C. Carefully, 500 μL of the supernatant was transferred to an Eppendorf tube, concentrated under vacuum, and re-dissolved in 160 μL of extraction solvent (acetonitrile:water = 1:1). After vortexing for 30 s, the sample underwent further sonication in an ice bath for 10 min. The samples were again centrifuged at 12,000 rpm for 15 min at 4 °C, and 120 μL of the supernatant was transferred to a 2-mL injection vial. From each sample, 10 μL was pooled to prepare a QC sample for analysis.

The raw LC/MS data were converted to m/z format and processed using Progenesis QI software for peak extraction and alignment. Identifications were conducted using the online METLIN database, public databases, and a self-constructed library from BaiMaiKe, with theoretical fragment identification allowing for a mass deviation of ≤ 100 ppm for parent ions and ≤ 50 ppm for fragment ions. Compounds significantly different between groups were obtained at a variable influence on projection (VIP) > 1.5, and P value of t test statistics < 0.05 based on the peak intensities. The m/z values of these compounds were used to identify the metabolites corresponding to the featured peak in the Metlin metabolite database.

Statistical analysis

Data are expressed as mean ± standard error of the mean (SEM). All analyses were validated by Shapiro–Wilk test (normality, P > 0.05) and Levene’s test (homogeneity of variance, P > 0.05). Independent t-tests were used for two-group comparisons, while one-way ANOVA with LSD, Tukey’s b, and Waller-Duncan post hoc tests (adjusted by Benjamini–Hochberg FDR) were applied for multi-group comparisons. Statistical significance was set at P < 0.05 using SPSS 19.0 software (SPSS Inc., USA), and graphs were generated with GraphPad Prism (v. 5.0; GraphPad Software, USA).

Authors’ contributions

Mingyuan Liu, Qian Zhang and Ying Liu conceived the ideas and designed methodology; Mingyuan Liu, Changrun Sui, Wenyu Zhao and Chonghui Fan collected the data; Mingyuan Liu, Changrun Sui, Yao Zhang, Yuqing Wang and Zhujun Qiu analysed the data; Mingyuan Liu, Changrun Sui and Wenyu Zhao led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

Funding

This study was funded by Joint Fund of General Research Project of Liaoning Province (2023-MSLH-007), Basic scientific research project of Educational Department of Liaoning province (LJKMZ20221107), the earmarked found for China Agriculture Research System (CARS-49), the Overseas Training Program for Innovation Team, the Basic Scientific Research Project of Educational Department of Liaoning Province (2019RD12).

Data availability

The raw data of the 16S rRNA gene microbiome sequence has been uploaded to the SRA database (BioProject:PRJNA1190832 at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1190832.) All data generated or analyzed during this study are included in this published article and its supplementary information files.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.