Since the dawn of probiotic concept in aquaculture (Gatesoupe 1999), its definition has been expanded as such “A probiotic can be seen as a live, dead or component of a microbial cell, which is administered via the feed or to the rearing water, benefiting the host by improving disease resistance, health status, growth performance, feed utilization, stress response or general vigour, which is achieved via improving the hosts microbial balance or the microbial balance of the ambient environment” (Hai et al. 2015; Merrifield et al. 2010). In line with this definition, it is clear that La. salivarius strain GZPH2 and Bdellovibrionales strain BDN-1F2 PL can be recognized as probiotics as they have demonstrated beneficial effects by significantly improving white-leg shrimp postlarvae growth performances, albeit at different aspects and degrees between them (Table 2). Compared to control, with the exception of SR that GZPH2 showed no improvement, it had significantly better effects in all other aspects related to growth performance, i.e., PTLG, TLG, PTWG, TWG, SGR, with the latter being 2.82 times higher (p < 0.05). However, when compared to BDN-1F2, GZPH2 showed a better effect only in PTLG and TLG, while in all other aspects, including PTWG, TWG, SGR and SR, BDN-1F2 performed significantly better (p < 0.05, Table 2). Consistent with our current findings, Nguyen et al. (2018) revealed that Lactobacillus strain has positive effects on the growth and resistance of Li. vannamei against V. parahaemolyticus which causes acute hepatopancreatic necrosis disease (AHPND). Another study also showed that the combination of La. salivarius BGHO1/La. reuteri BGGO6-55 had a positive effect on juvenile pike-perch (Sander lucioperca) growth, while improved survival (Ljubobratovic et al. 2017). On the other hand, some studies reveled that BALOs also showed better effect on shrimp growth performance. A previous study was done by Li et al. (2014) where, BDHSH06 had been applied to an 85-day rearing of black tiger shrimp (Penaeus monodon) and was found to significantly enhance its growth and survival and alter bacterial community structures in its rearing water. With the addition of BDHSH06, total bacterial and Vibrio numbers were significantly reduced (P < 0.05) by 1.3 to 4.5 log CFU/mL and CFU/g in both water and shrimp intestines, respectively, compared to those in the control. Similarly, Wen et al. (2014) shown that Bacteriovorax DA5 has positive effect on white shrimp (Li. vannamei) and it significantly improved the survival rate and metamorphic rates by controlling vibriosis.
Biodiversity is generally recognized as a main determinant of ecosystem functioning (Johnke et al. 2020), which could be better reflected by Shannon index values (Chen et al. 2015). It is generally recognized that a healthier and more robust microbial community has a higher biodiversity (and thus Shannon index) than an unhealthy one (Rungrassamee et al. 2016; Chen et al. 2017). To distinguish between healthy and unhealthy shrimp, the gut microbiota of shrimp at PL7-15, a value above 2.0 for a healthy state, was tentatively proposed, while below this value, it was considered an unhealthy state (Cao et al. 2020). Based on this criterion, it is obvious that at the start of the test, PL in all groups were in a healthy state, as their Shannon index values were well above 2.0 (Table 5). However, at day 3, this value in group C dropped to 1.99 ± 1.24, indicating a not-so-healthy state even though it recovered to 3.10 ± 0.28 at day 7 (Table 5). In groups L and F, PL gut microbiota stayed in a healthy state throughout the test period, albeit with the highest values at day 3. Within these two groups, group F had higher Shannon index values than group L, implying higher diversities, and more robust and healthier gut microbiota (Table 5). The reduction of Shannon index in control at day 3 could be due to the effect of salinity changes Cao et al. (2020) as PL were maintained at 30‰ seawater in the shrimp hatchery and its salinity was lowered to 15‰ prior to packing. Meanwhile, the rise of Shannon index in groups L and F at day 3 once again demonstrated the protective effect of BDN-1F2 and GZPH2 in counteracting the unfavorable impacts brought about by the salinity changes. As gut microbiota serves as a virtual endocrine organ (Clarke et al. 2014) and are known to promote juvenile growth, development and survival in Drosophila melanogaster (Erkosar et al. 2017), an unhealthy state would certainly undermine its growth. This is exactly the case in this study as PL grew most slowly in control (SGR at 0.11 ± 0.10%/day), and fastest in group F (SGR at 0.53 ± 0.40%/day), with group L in between (SGR at 0.42 ± 0.21% /day) (Table 2).
It is generally recognized that the growth of host is strongly associated with the gut microbiota (Tarnecki et al. 2017). Previous studies have proven that the ratio of Bacteroidetes vs. Firmicutes (B/F ratio) is a growth indicator, the higher growth rate with the lower ratio (Jia 2017; Wang et al. 2020 ). Here, it is evident that PL in groups F and L would grow faster than those in control as B/F ratios at days 0 and 3 in groups L and F were 1.04 and 1.70; and 1.12 and 1.31; respectively, both were lower than those in control (days 0 and 3, ratios of 2.63 and 19.14, respectively), albeit at day 7, B/F ratio in control (1.22) was lower than those in groups L (6.68) and F (8.39) (Fig. 1c).
Similar to others’ findings Cao et al. (2020), Proteobacteria was a major component in PL gut microbiota in all groups, ranging from 69.35 ± 8.91% to 85.92 ± 3.17% (Fig. 1c). Nevertheless, if we look into the two dominant and mutually antagonistic groupings in Proteobacteria, viz., Gammaproteobacteria and Alphaproteobacteria, the effects of BDN-1F2 and GZPH2 are clear albeit different. BDN-1F2 in group F reduced Gammaproteobacteria relative abundance by 32.21% while increased Alphaproteobacteria relative abundance by 1.11 times, GZPH2 in group L simultaneously increased the relative abundances of both Gammaproteobacteria and Alphaproteobacteria, by 22.30% and by 28.86% respectively; in control, the relative abundance of Gammaproteobacteria first increased by 46.71% then decreased by 25.70% while its Alphaproteobacteria relative abundance was first reduced by 50.80% and then increased by 44.54%. As Gammaproteobacteria is generally recognized to be associated with diseased or retarded growth organisms, including shrimps (Xiong et al. 2015), and Alphaproteobacteria with healthy or normal/faster growth shrimps (Chen et al. 2017), it is once again natural that PL in control grew much slower than those in groups F and L. Meanwhile, it seems that while BDN-1F2 possess healthy capabilities to reduce Gammaproteobacteria while promoting Alphaproteobacteria, the ability of GZPH2 to contain Gammaproteobacteria and to promote Alphaproteobacteria is both weaker.
Within the class Gammaproteobacteria, Vibrionales, Pseudomonadalesand and Betaproteobacteriales, were the dominant orders (a relative abundance ≥ 5% at a time) (Fig. 4). Once again, the effects of BDN-1F2 and GZPH2 on these orders were evident. With regard to order Vibrionales, especially genera Vibrio (Figs. 2 and 3), it seems that GZPH2 did not exert reduction effect on it, as Vibrio increment was higher in group L than in groups C and F at day 3, and reaching higher relative abundance at the end of the test. On the other hand TCBS plate counting results also showed, TCVC in PL was lower in group F than in groups C and L (Table 3), whereas green Vibrio counts in PL was significantly lower in group F than in groups C and L (p < 0.05, Table 4). It previously confirmed by Chen et al. (2019) that BDN-1F2, a mutant of wild type BDN-1 after Co60 mutagenesi could lyse 27 out of 30 tested bacteria, including 9 strains of V. alginolyticus, 3 strains of V. parahaemolyticus, 4 strains of V. cholerae, 11 strains of Pseudomonas sp., with 93.3% lysis rate on 16 strains of vibrio. We can conclude that because of lysis ability of BDN-1F2, the number of Vibrio in group BDN-1F2 was lower than in groups control and GZPH2. Recently, Yang et al. (2023) reveled that Bdellovibrio sp. exhibited a certain lysis effect on the selected aquatic pathogens including Vibrio fluvialis, Vibrio anguillarum, Vibrio cholerae and significantly reduce the mortality rate of Carassius auratus caused by the infections with A. vironii. Even we also found significantly negative correlation between survival rate with Vibrio (Fig. 7).
It is generally recognized that gut microbes are highly diverse and their interactions is very complex, which basically depend on microbial biodiversity and environmental factor in gut. However, explaining microbial interactions is challenging and largely dependent on correlation-based network analysis (Milici et al. 2016). In microbial interactions network has three types of centrality metrics viz. degree centrality, closeness centrality and betweenness centrality. Among the centrality metrics, betweenness centrality measures the extent to which a given nodes/species is located within the shortest paths between other pairs of nodes/species in a network (Brandes 2001) which specifically used to explore organisms relation with broad host or partner ranges (Toju et al. 2017). The genus with higher betweenness values were considered as keystone species in the genus network diagram (Vinothkumar et al. 2021). In this study, the microbial interactions network (Figs. 5a-c; Table S2) data revealed that betweenness value of Vibrio was highest in group L (0.05056) than group C (0.00914) and F (0.00401). The microbial correlation network (Figs. 5a-c; Table S2) data also showed that Vibrio was positively correlated (coefficient ≥ 0.5) to Yangia and unclassified_f__Rhodobacteraceae and negatively correlated (coefficient ≤ − 0.5) to unclassified_o__Chitinophagales, Bacillus, Flavobacterium, Staphylococcus and Acinetobacter (Fig. 5a-c). On the other hand, the relative abundance of Yangia increased in group L than group F, whereas unclassified_o__Chitinophagales, Bacillus, Flavobacterium, Staphylococcus and Acinetobacter decreased. As a result, from day 0 to day 7 in both group L, where Acinetobacter along with Staphylococcus decreased but Vibrio and Yangia increased. This result reveled that BDN-1F2 was better than GZPH2 to control Vibrio species in PL rearing. Several result support our this report that BALOs can control vibrio such as Vibrio sp. (Li et al. 2014), V. Cholerae (Cao et al. 2015), V. parahaemolyticus (Cheng et al. 2008).
Regarding Pseudomonadales where Acinetobacter and Pseudomonas were the dominant genera (Figs. 2 and 3), still, BDN-1F2 showed better reduction effect (77.16% reduction) than GZPH2 (50.15%) as compared to control (55.64% reduction), leaving the relative abundance of Pseudomonadales at 7.63 ± 0.75%, 12.07 ± 7.47% and 8.49 ± 4.39%, respectively, at the end of the test. While the relative abundance of Betaproteobacteriales was reduced in groups L (30.97%) and F (23.16%); in control, it first increased by 36.01% and then again decreased by 14.79% at the end of the test. Within the order Betaproteobacteriales, Ralstonia, found as a dominant genera in control (Fig. 3). The microbial interactions network (Fig. 5a-c; Table S2) showed that Ralstonia was positively correlated (coefficient ≥ 0.5) to Staphylococcus, Acinetobacter and negatively correlated (coefficient ≤ − 0.5) to Pseudomonas. The highest betweenness value of Ralstonia was found in group C (0.05591) than group L (0.0329) where in group F betweenness value of Ralstonia was zero. The highest betweenness value of Staphylococcus and Pseudomonas, was found in group F (0.04167 and 0.05361, respectively) than group L ( 0 and 0.02513 respectively) and C (0.007 and 0.01992, respectively) where, betweenness value of Acinetobacter was the highest in group L than in groups F and C (0.1201, 0.01216 and 0.00325, respectively). We can conclude that this may be one reason why the relative abundance of Ralstonia was higher in the control group whereas Pseudomonas was higher in the GZPH2 group.
The networks data also showed the higher ratios of positive correlations in group C (87%) and L (66%) than F (57%) and oppositely the higher ratios of negative correlations were shown in group F (43%) than in groups L (34%) and C (13%). This result reveled that BDN-1F2 helped to build up a balance positive and negative microbial interactions ratio which helped to reduce the potential pathogenic species Vibrio, Pseudomonas, Staphylococcus and Ralstonia for shrimp PL cultivation.
A possible explanation could be attributed to BALOs are obligate predatory bacteria that selectively prey on a broad range of Gram-negative bacteria, including Pseudomonas, Staphylococcus and Vibrio (Najnine et al. 2020; Saralegui et al. 2022). In the free-living attack phase BALOs enter into a prey cell periplasm, and there by grow forming a structure known as the bdelloplast, after completing the growth stage of new progeny’s they lysed prey cell and released new progeny to attack new prey again (Sockett and Lambert 2004). The Bugbase phenotypic function prediction bar plots (Fig. 8d) data showed that the relative abundance of gram negative bacteria Acinetobacter decreased from day 0 to day 3 in all PL groups, simultaneously the relative abundance of another gram negative bacteria Pseudomonas increased at day 3. On day 7, the relative abundance of Pseudomonas increased again in group L but decreased in groups F and C where the reduction rate was higher in group F. The pair Acinetobacter vs. Pseudomonas showed strongly negative correlatatio (coefficient ≤ − 0.5) (Fig. 5c) as a result when Acinetobacter decreased, Pseudomonas increaesd. Previous study was done by Saralegui et al. (2022) showed that Bdellovibrio bacteriovorus has ability to prey on pathogenic bacteria Pseudomonas aeruginosa (Cai et al. 2009). So, we could predict that the reduction rate of Gram negative bacteria Pseudomonas in BDN-1F2 was higher because of lysis ability of BALOs.
As to the reason(s) why GZPH2 was not effective in this occasion even though in vitro testing demonstrated its anti-vibrio effect (Guo et al. 2015), it could be due to the much higher contents of nitrate (4.92–6.68 times), nitrite (3.67–5.5 times) and ammonia (2.03–4.08 times) in the rearing water when compared to both control and group F (Table 1). Such high contents of these nitrogen salts could aid the growth of Gammaproteobacteria and Vibrionales/Vibrionaceae, as well as Pseudoalteromonadaceae (Huang et al. 2020) thus making the reduction less effective, although these nitrogen salts were around the safety levels as proposed by Valencia-Casta et al. (2018), viz., for salinities of 1 and 3 g/L, 0.54 and 0.81 mg/L for total ammonia-N, 0.17 and 0.25 mg/L for NO2-N, and 5.6 and 21.5 mg/L for NO3-N, respectively. This tempts us to suggest that the beneficial effects of La. salivarius strain GZPH2 could be even better if these nitrogen salts are lower.
Bugbase phenotypic function prediction analysis (Fig. 8h) at genus level among top 25 genera data showed that stress-related all genera Ralstonia, Pseudomonas, Vibrio, Achromobacter, Alcaligenes, Alcanivorax, Stenotrophomonas, norank_f__Beggiatoaceae, and Haliea were belonged to Gammaproteobacteria among all PL groups. Bugbase phenotypic function data also showed that potentially pathogenic (Fig. 8f) maximum genera were belonged to Proteobacteria/Gammaproteobacteria viz. Ralstonia, Pseudomonas, Vibrio, Achromobacter, and Burkholderia-Caballeronia-Paraburkholderia, and only one genera Proteobacteria/Alphaproteobacteria viz. Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, one genera was belonged to Firmicutes viz. Staphylococcus and one genera was belonged to Bacteroidetes viz. norank_f__NS9_marine_group. We could be concluded that Gammaproteobacteria were the dominant class of stress-related and potentially pathogenic genera of white leg shrimp (Litopenaeus vannamei) post larvae.
Visualizations of Bugbase phenotypic function prediction bar plots (Fig. 8f) further showed some differences in the relative abundance of stress-related and opportunistic pathogen taxa among three PL groups. The relative abundances of stress-related and potentially pathogenic genera Ralstonia, Pseudomonas, and Vibrio were lower in group F than in groups L and C. However, the relative abundances of stress-related four genera were higher in group F than groups C and L viz. Achromobacter (0.77 ± 0.45%, 0.01 ± 0.01% and 0.01 ± 0.01%, respectively), Alcaligenes (0.094 ± 0.042%, 0.01 ± 0.01%, and 0, respectively), Alcanivorax (0.94 ± 0.84%, 0%, and 0%, respectively) and Haliea (0.73 ± 0.39%, 0.12 ± 0.05%, and 0%, respectively) (Fig. 3). Here, GZPH2 showed better effect than BDN-1F2 to decrease Achromobacter,Alcaligenes, Alcanivorax, and Haliea.
Although, Pseudomonas, Alcaligenes, and Alcanivorax are stress-related genera, but there are still some evidence of their beneficial effects such as Alcanivorax helped in hydrocarbon degrading (Zadjelovic et al. 2020), Pseudomonas helped in denitrification (Tran et al. 2019; He et al. 2019) and Alcaligenes also helped in denitrification (Joo et al. 2005b). Previous studies also demonstrated that a healthier and more robust microbial community has a higher biodiversity than an unhealthy one (Rungrassamee et al. 2016; Chen et al. 2017). The overall trend indicated that the relative abundance of potentially pathogenic genera was higher in the group C as well as the microbial community structure was unstable. Furthermore, the biodiversity diversity of potentially pathogenic taxa was balanced as well as the relative abundances of potentially pathogenic genera was lower in group F than in groups L and C (Fig. 8f). Although, several studies have shown that Ralstonia is a potentially beneficial genera in fish gut microbial community and play a crucial role to change the intestinal microbial structure (Wu et al. 2021). The most surprising thing is, sometimes the absence of some bacteria can be pathogenic. Yu et al. (2022) found that, the abundance of Ralstonia in translucent diseased shrimp PL gut was significantly lower than healthy Shrimp PL while Vibrio and Mycoplasmataceae were higher in number and mentioned it might be one of the reason for the occurrence of translucent diseased in host. In this current experiment, we also found similar results that Ralstonia abundance suppresses the abundance of other bacteria in control group. As a result the microbial biodiversity of control group was lower than that of the BDN-1F2 and GZPH2 groups.
Within the class Alphaproteobacteria, Rhodobacterales, Rhizobiales and Sphingomonadales were the dominant orders (Fig. 2). With respect to Rhodobacterales (Fig. 2), both GZPH2 and BDN-1F2 showed positive promotion effects. While the relative abundance of Rhodobacterales in control decreased 14.03% in the first 3 days, and increased 40.47% later on; the trends of changes in groups L and F were just the opposite, viz., increased 48.80% and 9.85%, respectively. Though it decreased again 18.68% in group L at the end of test, it increased 1.33 times, to 27.07 ± 0.49% at day 7 in group F. Again, data here showed that BDN-1F2 had better promotion effect on Rhodobacterales than GZPH2 could do.
Regarding Rhizobiales (Fig. 2), though its relative abundance in three groups all initially decreased, the extents were different. In control, it decreased 93.98% throughout the test period. In group L, it first decreased 83.60% after that again increased 57.53% at day 7. Similarly, in group F, it first decreased but only by 38.71%, then increased 36.18% at day 7. Regarding Sphingomonadales (Fig. 2), the pattern of changes in three groups is similar to Rhizobiales, but with different extents. That is, in control, it decreased 76.86% and then increased 67.44% at the end of the test. In group L, it initially decreased 76.58% and then increased 27.04 times. In group F, it deceased 28.26% at first and then increased 3.42 times.
As both Rhodobacterales and Rhizobiales are shown to be beneficial (Xiong et al. 2017; Chen et al. 2017), and Sphingomonadales is aerobic anoxygenic phototrophs (photoheterotrophs), with a variety of physiological features and carotenoid pigments, including astaxanthin (Siddaramappa et al. 2018). Some could even detoxify a fungal toxin, fumonisin (Li et al. 2021). Therefore, their increments in groups F and L should be beneficial. According to Bugbase phenotypic function analysis (Fig. 8g) data, biofilms producer all genera Paracoccus, unclassified_f__Rhodobacteraceae, Yangia, Nautella, Gemmobacter, Rhodovulum, Roseovarius belonged to Rhodobacterales family, and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium and Nitratireductor belonged to Rhizobiaceae family. Initially, the relative abundances of biofilms former genera Paracoccus and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium were higher in the groups C and L than in group F. On the other hand, the relative abundances of Nautella, Nitratireductor, and Pseudochrobactrum were higher in the group F than in groups L and C. On the day 3, the relative abundances of Paracoccus decreased in groups F and C but increased in group L. Although, the relative abundances of Paracoccus decreased in group L where slightly increased in group C and F. On the day 7, the relative abundance of unclassified_f__Rhodobacteraceae, Nitratireductor, and Roseovarius significantly increased in group F than group L and group C (Fig. 4, Fig. 8g). Overall data reveled that enhancement of biofilms producing bacteria BDN-1F2 showed better performance than GZPH2.
Bacterial biofilms are complex communities of bacteria held together by self-generated extracellular polymeric matrix as well as increase survival rate by improving the defense system, increase nutrients availability and cellular communication and transfer of genetic material (Tremblay et al. 2014). In our experiment, we found most biofilm-producing genera under the Alphaproteobacteria class. The so far discovered, Alphaproteobacteria class in particularly the Rhodobacteraceae family is the dominant taxa for biofilm community formation than the Gammaproteobacteria class, which support our findings (Elifantz et al. 2013). A retrospective 16S rRNA gene study conducted by Elifantz et al. (2013) identified primary colonies of biofilms and the results showed that the Alphaproteobacteria class represented 30–70% of the bacterial community whereas Gammaproteobacteria accounted for only up to 10% of the community. Another study has reported that Rhodobacteraceae is a major core gut microbial taxa which help to create a stable microbial community in shrimp gut (Dong et al. 2023). In addition, some taxa belong to Alphaproteobacteria, help to denitrification Paracoccus (Zhao et al. 2020), Nitratireductor (Ye et al. 2020) and sulphur-oxidizing Paracoccus (Jaffer et al. 2019a) which were significantly higher in group F than group L and C. Possibility it could be the reason of the lower contents of nitrate, nitrite and ammonia in rearing water of group F compared to both control and group L (Table 1). Again, data here showed that BDN-1F2 had a better promotion effect on building healthy microbial community structures in the PL gut than GZPH2. The application of BDN-1F2 in the rearing of shrimp PL might help solve the existing so-called translucent disease in China (Zou et al. 2020) albeit more tests at the production level should be carried out.
Actinobacteria are well known as a group of secondary metabolites producers and tend to be playing beneficial roles (Binda et al. 2018). However, its relative abundance decreased in group L by 41.23%, while increased 26.84% in control and 22.40% in group F (Fig. 1c). With in phylum Actinobacteria, the relative abundance of Microbacterium was significantly changed among three PL groups and the abundance rate was higher in group F than in groups C and L. Similar patents of changes also occurred to Bacillales of phylum Firmicutes (Fig. 2). That is, their relative abundance decreased 92.24% and 85.87% in groups L and F, respectively, while in control, it first decreased by 91.15% and then increased 4.09 times. Though it is generally recognized to be beneficial, Bacilli (from the class to the family level) has been shown to be dominant in slow-growing shrimp intestines while Vibrio was dominant in the intestine of the fast-growing shrimp in outdoor ponds (Duan et al. 2020). In line with the suggestion of Duan et al. (2020), higher abundance of Actinobacteria and/or Bacillales could divert more energy for defence and leave less for growth, thus leading to poorer growth performance. This is exactly the case here as shrimp PL grew slower in control as compared to group L and F even though the latter had higher abundance of Vibrionales/Vibrio and lower abundance of Actinobacteria and/or Bacillales. Flavobacteriales belonged to phylum Bacteroidetes. In control, its relative abundance first increased 23.05% and then decreased 20.44%, whereas it decreased 48.73% in group L and increased 25.57% in group F (Fig. 2). As Flavobacteriales was identified as a shrimp gut keystone taxon associated with diseased shrimp, and its infectious disease causing potential was significantly and positively associated with its relative abundance (Dai et al. 2020), therefore, its increase would naturally endanger shrimp PL health. In this sense, it seems that GZPH2 could do better than BDN-1F2 here.
Over the 7-day test period, the differential enrichment of bacterial taxa in groups C, L and F was analyzed by LEfSe (Figs. 6a-c) and found four taxa were enriched in control, viz., f__Leuconostocaceae and g__Weissella, f__Dysgonomonadaceae, and f__Corynebacteriaceae. While the first two belong to LABs, obviously being beneficial to shrimp PL; the third taxon has been shown capable of degrading various polysaccharides (Murakami et al. 2018), and the last taxon has been found to be symbionts in the guts of salmon (Hartviksen et al. 2014). Two taxa were enriched in group L, viz., g_Martelella and g_Muricauda. Both could be considered beneficial as the former is within Rhizobiales (Xiong et al. 2017; Chen et al. 2017), while the latter could produce antioxidant pigments like zeaxanthin (Prabhu et al. 2014). In group F, we could classify the enriched taxa into 3 types, viz., beneficial, potential pathogenic, and functionally neutral or unknowns. The beneficial taxa enriched included f__Microbacteriaceae and g__Microbacterium (proposed as probiotics (Hipólito-Morales et al. 2009), f__Demequinaceae and g__Demequina (bioactive producers (Subramani and Sipkema 2019), f__norank_o__Saccharimonadales and g__norank_f__norank_o__Saccharimonadales (epibiotic living (He et al. 2014), f__Family_XII_o__Bacillales (known probiotics), g__Nitratireductor (denitrification (Sánchez et al. 2013), g__Exiguobacterium (proposed as probiotics (Subramani and Sipkema 2019), and g__Alcaligenes nitrification (Sánchez et al. 2013), g__Corynebacterium (symbiont in the guts of salmon (Hartviksen et al. 2014). The potentially pathogenic taxa enriched included f__Shewanellaceae and g__Shewanella (Prachumwat et al. 2020), f__Pseudoalteromonadaceae and g__Pseudoalteromonas (Zheng et al. 2016), g__Nautella (Zheng et al. 2016), g__Roseovarius (Travers et al. 2015) g__Myroides (opportunistic pathogens for human (Schroettner et al. 2014), and even g__Haloferula (higher in abundance associated with lower shrimp body weight (Fan et al. 2019). The functionally neutral or unknowns included f__Alcanivoracaceae and g__Alcanivorax (hydrocarbon-degrading bacteria (Yakimov et al. 2019), f__Halieaceae and g__Haliea (alkene and ethylene-assimilating bacteria (Suzuki et al. 2019) f__Rubritaleaceae (associated with live feed in yellowtail kingfish (Walburn et al. 2019), g__Phenylobacterium (prevalent in water of mixed fish culture (Zeng et al. 2020) and g__Maritalea.
If we look into the details of the relative abundances of these potentially pathogenic taxa enriched in three groups (Figs. 6a-c), they are as follows: f__Shewanellaceae and g__Shewanella, both with 0%, 0.01 ± 0.01% and 1.28 ± 1.00% in control, groups L and F, respectively; f__Pseudoalteromonadaceae and g__Pseudoalteromonas, both with 0%, 0%, 0.01 ± 0.01% in control, groups L and F, respectively; g__Nautella with 0%, 0%, 0.03 ± 0.01% in control, groups L and F, respectively; g__Roseovarius with 0.36 ± 0.08%, 0.14 ± 0.01%, 2.07 ± 0.93% in control, groups L and F, respectively; g__Myroides with 0%, 0%, 0.33 ± 0.24% in control, groups L and F, respectively; g__Haloferula with 0.09 ± 0.05%, 0.01 ± 0.01%, 0.24 ± 0.11% in control, groups L and F, respectively. Hereby, it is quite clear that as beneficial bacteria, GZPH2 and BDN-1F2 they didn’t just raise the abundances of other beneficial bacteria (as in beneficial taxa enriched), but also some of (potential) pathogenic bacteria (as in pathogenic taxa enriched, like Shewanellaceae and Shewanella), while keeping others in check (taxa with little changes, like Ralstonia) and reducing the overall abundances of (potential) pathogenic bacteria as a whole at the community level (Fig. 3). The rises in abundance of (potential) pathogens may be favourable to the hosts and their environment in some instances as they could provide complementary and/or necessary ecological functions for the ecosystem. In group F, over the 7-day test period, the relative abundance of Shewanella was rising, from 0.37 ± 0.12% at day 0 to 0.45 ± 0.09% at day 3, then further up to 1.28 ± 0.22%, while that of norank_f__NS9_marine_group was from 0.61 ± 0.06% at day 3 to 0.92 ± 0.09% at day 7, also rising albeit slightly (Fig. 3). Therefore, the rise of abundance of fermentative bacteria genus Shewanella or norank_f__NS9_marine_group should help lower NH3-N and NO2-N concentrations in the environment (Yoon et al. 2015; Zhang et al. 2022; Shi et al. 2023) and thus create better conditions for the PL to grow. This is exactly the case here as NH3-N and NO2-N in group F was 1.21 ± 0.49 mg/L and 0.03 ± 0.01 mg/L, much lower than that in group L (Table 1). As changes of a structure would bring on changes of functions. Here, the addition of BDN-1F2 and GZPH2 to the rearing of PL has strengthened various ecological functions in its gut microbiota, as predicted when compared to control, even though most functional changes were with no statistical significance.
The gut microbiota plays a potential role in host nutrition by producing numerous metabolites, such as free fatty acids, amino acids, and vitamins, are found in the host intestine which are equally vital for host intestinal homeostasis and gut microbial community structure (Postler et al. 2017). Postler et al. (2017) reveled that microorganisms produce three basic types of metabolites namely metabolites that are produced by gut microbes from dietary components, metabolites that are produced by the host and biochemically modified by gut microbes, and metabolites that are synthesized de novo by gut microbes. Hence, the PL gut microbial composition obtained by 16S rRNA gene sequencing was used to predict microbial function using COG and KEGG metabolic pathways that were involved, and the differences between different samples and groups were analyzed. Based on exploring the proportions of each COG function (Level 2) (Fig. 9a) and KEGG metabolic pathway (level 1/2/3) (Fig. 9b-d), we found some discrepancies among three PL groups C, L and F. Overall microbial function pathway analysis result showed that the relative abundances of functional genes involving metabolism were very high in all PL groups including amino acid metabolism, carbohydrate metabolism and so on (Fig. 9a-c). According to the relative abundances of functional genes involving metabolism data, we found amino acid metabolism and carbohydrate metabolism increased in group F (13.86% and 22.61%, respectively) where, decreased in groups C (0.69% and 0.56%, respectively) and L (17.56% and 16.39%, respectively) from the beginning to end of experiment (Fig. 9c).
According to Rosas et al. (2000), shrimp have a limited ability to metabolize carbohydrates; alternatively, shrimp use protein as a source of energy and growth. Chuntapa et al. (1999) reported that protein is important nutrients for optimal growth and survival of juvenile tiger shrimp and found the optimal protein:energy (P:E) ratios 150 and 146 mg protein/kcal, respectively. Amino acids are organic molecules that form a protein when combined together with other amino acids. At the end of our experiment, amino acids metabolism related functional genes was higher in group F on day 7 than groups C and L (Fig. 9c). Amino acids play an important role in the structure and metabolism of all living organisms. Shrimp cannot synthesize all amino acids but they need several amino acid to increase their immune function, survival rate as well as growth performance (Simon et al. 2021). Specifically, arginine, proline and glutamate have been demonstrated to regulate immune defense (Shao et al. 2023). The essential amino acids (EAAs): arginine, methionine, valine, threonine, isoleucine, leucine, lysine, histidine, phenylalanine, and tryptophan are must acquire through shrimp diet, all of which are not synthesized de novo by eukaryotic cells (NRC 2011). The gut microbiota can de novo synthesize some essential amino acids that contribute to the host's amino acid homeostasis (Metges 2000). In this study, we found PL gut microbiome had large enrichment of genes involved in the metabolism and biosynthesis of the essential amino acids and other amino acids viz. pyruvate family (valine, leucine, and isoleucine), aspartate family (lysine, threonine, methionine), aromatic family (phenylalanine, tyrosine and tryptophan), serine family (serine, glycine, cysteine) and histidine (Fig. 9d). Some findings are supported our result that the gut microbiome had large enrichment of genes involved in the metabolism and biosynthesis of amino acids. Gill et al. (2006) found that the gut microbiome had large enrichment of genes involved in the biosynthesis of leucine, isoleucine, lysine, phenylalanine, tyrosine, tryptophan, and valine as well as enrichment in genes associated with the metabolism of alanine, aspartate, glutamate, histidine, methionine, glycine, serine and threonine. Another findings showed that gut microbiome had large enrichment of genes involved in pathways such as the biosynthesis of lysine, phenylalanine, tyrosine, tryptophan, valine, leucine and isoleucine compared to the host genome (Qin et al. 2010). We also found that the essential amino acids and other amino acids metabolism and biosynthesis had significantly positive correlation with PL gut microbiota Paracoccus, Flavobacterium, Brevundimonas, Microbacterium, Nitratireductor and norank_f__Mycoplasmataceae and pyruvate family and lysine degradation had significant negative correlation with Vibrio and Yangia (Fig. 9d). In this test we found, the relative abundance of Paracoccus, Brevundimonas, Microbacterium (p < 0.05) and Nitratireductor (p < 0.01) were significantly higher in group F on day 7 than other samples (Fig. 4) and also found higher amino acids metabolism in this sample (Fig. 9d). Oppositely, in group L, the relative abundance of Vibrio and Yangia was significantly higher (p < 0.05) and Microbacterium was significantly lower (p < 0.05) than in groups F and L as well as Brevundimonas and Nitratireductor were absent on the day 7 (Fig. 4) which negatively affected the amino acids metabolism. As a result, we can conclude that the presence or absence of certain gut microbiota has been shown to significantly change amino acids metabolism and biosynthesis in PL groups: BDN-1F2, GZPH2 and control.
Lipid, an important group of nutrients are essential component of living beings including aquatic animals. It is recognized that gut microbiota improved the accumulation of lipids in the host gut by enhancing lipid metabolism (Ringø et al. 2022). In this study, we found, the lipid metabolism related functional genes was slightly higher in group C than in groups F and L (1390422, 1386260, and 1346231, respectively) at end of test (Fig. 9c). Instead, the enrichment of relative abundances of lipid metabolism-related functional genes was higher in group L than in groups F and C (77.32%, 10.29% and 6.52%, respectively) from the beginning to end of experiment (Fig. 9c). We observed that the presence of specific gut bacteria and their relative abundance strongly affected the lipid metabolism in host intestine. We found that Yangia, Vibrio, Algoriphagus, and Roseovarius had a significantly negative correlation with lipid metabolism and biosynthesis especially, fatty acid metabolism, synthesis and degradation of ketone bodies, linoleic acid metabolism, ether lipid metabolism, steroid biosynthesis, primary bile acid biosynthesis and secondary bile acid biosynthesis, in contrast, Acinetobacter, unclassified_o__Chitinophagales, and Staphylococcusthey had a significantly positive correlation (Fig. 9d). Furthermore, fatty acid metabolism, linoleic acid metabolism, ether lipid metabolism and steroid biosynthesis showed significantly positive correlation with Brevundimonas, Paracoccus, Rhodococcus and Taeseokella but negative correlation with Roseovarius (Fig. 9d). Furthermore, primary bile acid biosynthesis and secondary bile acid biosynthesis had significantly negative correlation with Unclassified__f__Rhodobacteraceae (p < 0.05) and Sphingolipid metabolism had significantly negative correlation with unclassified_o__Chitinophagales (p < 0.001) and Acinetobacter (p < 0.05) (Fig. 9d).. We observe that the relative abundance of Acinetobacter, unclassified_o__Chitinophagales (P < 0.01), Unclassified__f__Rhodobacteraceae, Brevundimonas, Roseovarius and Paracoccus (P < 0.05) was significantly higher in group F than in groups L and C (Fig. 4). Conversely, the relative abundance of Vibrio and Yangia (P < 0.05) was significantly increased in group L than in groups F and C from day 0 to day 7 (Fig. 4). We found in this test, the change of lipid metabolism and biosynthesis significantly correlated the relative abundance of PL microbiome (Fig. 9d). Although, we found the relative abundance of lipid metabolism functional genes was relatively higher in BDN-1F2 group (2.97%) than GZPH2 but the relative abundance enrichment percentage was 7.51 times higher in group GZPH2 than group BDN-1F2 and 11.85 times higher from control (Fig. 9c). Similar results was reported by Salas-Leiva et al. (2020) that lipid metabolism of longfin yellowtail (Seriola rivoliana) juvenile was strongly correlated with the gut microbiotal metabolic contribution and found biosynthesis of fatty acids, glycerolipid, glycerophospholipid, secondary bile acid, and sphingolipid were affected by gut microbial community. Several previous studies reveled that the regulation of lipid metabolism helps to increase the host body length and weight (Joyce et al. 2015; Zhou et al. 2017). Similar result we also found in this experiment, lipid metabolism along with the body length, body weight and survival rate of PL was the higher in groups BDN-1F2 and GZPH2 than control.
In this study, we found significantly positive correlations between the PL gut microbiota such as viz. Brevundimonas, Flavobacterium, Microbacterium and Paracoccus with carbohydrate metabolism especially glycolysis or gluconeogenesis, glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions and inositol phosphate metabolism (Fig. 9d). It has been previously shown that the intestinal microbiota of longfin yellowtail juveniles, mainly dominated by Proteobacteria, Firmicutes, Bacteroides, Cyanobacteria, and Actinobacteria, exhibited a contribution to carbohydrate and amino acid metabolism (Salas-Leiva et al. 2020). Another experimental result also reveled that the gut bacteria of Nile tilapia were positively correlated with carbohydrate metabolism (Wu et al. 2021). In this study, the abundance rate of Brevundimonas, Flavobacterium, Microbacterium and Paracoccus was significantly (p < 0.05) higher in group F than in groups L and C which strongly increased the carbohydrate metabolism in group F than in groups C and L. Carbohydrates occupy an important place in metabolism due to the energy source of various biosynthetic pathways. The pitiful scenario is that the utilization of carbohydrates is very poor in most of the fish species and crustaceans including shrimp. Several studies have shown that high levels of dietary carbohydrates can cause metabolic diseases in fish due to poor carbohydrate metabolism, especially in carnivorous fish species (Stone 2003). Recently, many aquaculture scientists have tried to improve carbohydrate utilization and explore new technologies to prevent metabolic diseases related to fish carbohydrate metabolism, one of them is the application of functional intestinal microorganisms or the use of probiotics, which is a new emerging technology (Serra et al. 2019). Here, we found between BDN-1F2 and GZPH2, BDN-1F2 showed a effect result than GZPH2 on Carbohydrate metabolism by changing the gut microbial community structure. On the basis of our finding, we can suggest that BDN-1F2 as a probiotic of shrimp PL rearing for enhancing carbohydrate utilization. It will be possible to spare dietary protein which can be an achievement to decrease the amount of nitrogen waste.