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A novel sustainable role of compost as a universal protective substitute for fish, chicken, pig, and cattle, and its estimation by structural equation modeling

Natural decomposition of organic matter is essential in food systems, and compost is used worldwide as an organic fermented fertilizer. However, as a feature of the ecosystem, its effects on the animals are poorly understood. Here we show that oral administration of compost and/or its derived thermophilic Bacillaceae, i.e., Caldibacillus hisashii and Weizmannia coagulans, can modulate the prophylactic activities of various industrial animals. The fecal omics analyses in the modulatory process showed an improving trend dependent upon animal species, environmental conditions, and administration. However, structural equation modeling (SEM) estimated the grouping candidates of bacteria and metabolites as standard key components beyond the animal species. In particular, the SEM model implied a strong relationship among partly digesting fecal amino acids, increasing genus Lactobacillus as inhabitant beneficial bacteria and 2-aminoisobutyric acid involved in lantibiotics. These results highlight the potential role of compost for sustainable protective control in agriculture, fishery, and livestock industries.

the functional thermophiles in the compost. Two types of thermophiles, strains closely related to the 16SrRNA sequences of Bacillus thermoamylovous and Bacillus coagulans, were isolated from the compost. These strains raised immune activities in the conventional mice 33 . Then, the strain closely related to Bacillus thermoamylovorans 33 was registered as B. hisashii 25 , and recently reclassified to Caldibacillus hisashii 26 . The strain closely related to Bacillus coagulans also reclassified to Weizmannia coagulans 26 . The effects of these single bacteria on livestock animals have also been evaluated, and the administration has obtained remarkable discoveries for cattle. The oral administration of C.hisashii to calves enhances their growth together with a rise of the phylum Bacteroidetes 27 . It reduces the fecal population of the genus Metanobrevibacter sp. involved in the generation of methane gas, which has a high global warming coefficient (approximately 25 times worse than CO2). These observations suggest that this compost and its derived thermophiles consistently exert various functions valuable for animal and plant health and environmental conservation. However, its mechanism of action is not well understood. Here, the aim of our study is to assess host-microbiota interactions in animals after exposure to the compost and/or the thermophile itself. Therefore, the productivities and qualities of several animals after applying the compost were analyzed. In fish, chickens, pigs, and cattle, the effects of oral administration of the compost exposure and/or their derived Bacillaceae and their productivity, physiological outputs, and fecal bacterial flora were surveyed. An interesting trend was observed with utilizing a thermophile as a feed additive: changes in the diversity of intestinal flora; reduced relative death rates; reduced antibiotic use, etc. Furthermore, the relationships between the protective effects of this compost, fecal microbiota, and metabolites were estimated by structural equation modeling according to association analysis. This study is the first report to evaluate the importance of compost quality in the environmental cycle across animal species to the best of our knowledge. Furthermore, the compost and the derived thermophiles used in this study is expected as the substitute of antibiotics, which will contribute to biodiversity, and as a feed additive efficiently digest excess feed, which reduce the nitrogen load in the external environment. Thus, this study provide a novel perspective for sustainable food production, recycling, and ecosystem. thermostable bacteria in the feces showed that Caldibacillus thermoamylovoras and/or C. hisashii (synonym: Bacillus thermoamylovoras and C.hisashii) 26 and Weizmannia coagulans (synonym: B. coagulans ) 26 were abundant in the compost groups (Extended fig.1h). Thus such dynamics in the gut environment of fish exposed to the compost could aid in fish survival against E. tarda infection. In particular, C.hisashii and W.coagulans may be key bacteria in the compost.

Evaluation in the chicken model
The growth effects of the compost extract on chicks were examined (CC-Test in Fig. S3a). The growth rates significantly increased after 9 weeks by the continuous compost exposure (Extended fig. 2a). Randomly selected seven healthy chicks were fed the compost for 13 weeks, the untreated and treated chicks were injected with Salmonella sp., respectively (CC Test in Fig. S3a). In the ovary tract of chicks with compost extract, Salmonella sp. was rarely detected, although it was seen in the cecum (Extended fig. 2b). The diversities of the fecal microbiota slightly appeared to alter one another (Fig.S3bc). Especially the phylum Actinobacteria had an increased tendency (p=0. 16), and phylum Firmicutes, the genera Enterococcus and Kurthia markedly decreased (p=0.041, p=0.091, and p=0.008, respectively) (Extended fig.  2c and Fig. S3de). On the other farm, which was always maintained as a production farm, the effects of the compost extract with a minimum concentration of C.hisashii N11 strain and W.coagulans N16 strain as probiotic candidates 24 , which were also identified by sequencing in feces of red seabream in this study, were investigated (CW Test in Fig.S3a). Two groups were prepared in a newly built henhouse: One hen group was fed the treatment with C.hisashii and W.coagulans, while a second hen group was not treated, respectively. At 3 months with feeding, the fecal bacterial diversities of the healthy hens showed a significant difference within the same newly house (Fig.S3fg). In particular, genus Kurthia had a decreased tendency (p=0.133), and genus Lactobacillus was significantly increased (p=0.018) (Extended fig. 2d): and these results were similar to those of the CC test. Although all the short-chain fatty acids (SCFAs), which modulate gut physiological function [36][37][38] , did not always increase, the fecal propionate level had an increased tendency at 1 month after the compost exposure (p < 0.2) (Extended fig. 2e), consistent with the slight rise of fecal IgA content (p=0.25)(Extended fig. 2f). Since the conditions of CW Test did not cause infectious disease, new tests were conducted at the hen houses where infectious disease, necrotizing enterocolitis with Clostridium and the related bacteria, sometimes occurred (CCR test in Fig.S3a). In these houses, necrotizing enterocolitis occurs in the summer. When the temperature in the hen houses was extremely high, the death rate in the compost administered hen house was similar to that in the negative control hen house maintained under moderate temperature (Extended fig.2fg). The correlation of temperature in the summer and the death rate per week yielded the difference by the compost exposure. However, the fecal bacterial diversities were significantly different (Fig.S4de), the increment of genus Lactobacillus was monitored as described in the chicken tests above (Extended fig.2h). The population of the genus Veillonella, which enhances athletic performance by studies of the human gut microbiome 39 , increased in the minor bacterial communities (Fig.S4f). The possibility that chicken with necrotizing enterocolitis caused by the genus Clostridium and that the compost extract administration reduced the related microbes was speculated. These effects can be explained by changes in intestinal flora caused by the administration of compost and concomitant changes in intestinal metabolites such as SCFAs, carbohydrates, and amino acids.

Evaluation in the pig model
Two tests were utilized to explore the key probiotic bacteria in the compost. In the tests of two swine farms (PK and PF Test in Fig.S5a), C. hisashii as a probiotic candidate, which was more efficient than the other isolates in the previous gnotobiote test 24 , was used. The bacterial diversities in the pigs' feces differed between the two environments (Fig.S5bc). The diversities in the administered pigs were weakly different (Fig.S5d). In both short-term tests, a decrease in the population of the genus Streptococcus was commonly observed after the administration (Extended fig.3a). Based on these observations in the farm controlled for the experiment, the experiments in the other two swine production farms were prepared, and the effects of oral administration of C.hisashii with the compost extract (PI-Test) and without the compost (PM-Test) on the fecundity of pigs were estimated (Fig.S5a). In PI-Test, the death rate of growing pigs decreased to approximately one-fifth after continuous administration, despite the highly reduced usage rates of four antibiotics per individual pig (Extended fig.3b). Under these conditions, the levels of IgA and some SCFAs at the final stage of the growing stage increased (Fig.S6ab). The difference in fecal bacterial diversities and phyla was insignificant (Fig.S6cde). However, the genus Lactobacillus significantly increased after administration, and Streptococcus decreased (Fig.S6f). In the PM-Test, the fecal IgA in the piglets was significantly increased by the C.hisashii administration, but only one exception was observed (piglets from sow No.1808) (Fig.S6g). After administration, the death rates of growing pigs were significantly at low levels (3.56%, 3.455, and 1.86%) (Extended fig.3d). The cost of the drugs used during the same period also substantially decreased (307, 283, and 254 yen per individual pig) (Extended fig.3e). To clarify the effects of the C.hisashii on the fecal bacterial population, the difference between males and females was examined on another occasion. An increment of fecal IgA (Extended fig.3f and Fig.S7a) was associated with the production of SCFAs (Extended fig.3g and Fig.S7b). However, the increase in IgA in the females appeared to be remarkable (Fig.S7a). Under these conditions, an increase in the phyla Bacteroidetes and Actinobacteria and a decrease in Firmicutes were observed (Extended fig.3h), although the fecal bacterial diversities in male and female pigs differed slightly. Thus, the gut flora of pigs could be improved by the compost-derived thermophile, reducing the use of synthetic drugs.

Evaluation in the cattle model
Since animal proteins derived from marine animals as raw materials before fermentation may be included in compost, they cannot be legally used as feed additives for cattle. Therefore, only C.hisashii was implemented in these tests. In addition, since a mother cow lays only one calf, which is expensive, it is impossible to set up various test systems, such as in pigs. Therefore, we analyzed some of the fecal conditions of the little six calves (Fig. S4ab). In young calves with diarrhea (Extended fig.4a), the recovery tendency of diarrhea was confirmed after the administration of C.hisashii (Extended fig.4b). The H2O ratio appeared to be decreased (Fig. S8c). The fecal IgA contents increased after the administration (Extended fig.4c), concomitant with increased SCFA levels (Extended fig.4de). The fecal H2O content was inversely correlated with the SCFA content (Extended fig.4f). Under such conditions, fecal bacterial diversities did not differ before and after the administration. However, genus Fusobacterium had a decreasing tendency after administration (Extended figs.4g and 4h). The detection of pathogenic genes of Escherichia coli clarified that some of the tested calves were positive for sequences of Shiga toxin genes (No.1294, No.1293, 1295) (Fig.  S8g). However, the causes of diarrhea in the other individuals were not clarified. Thus, these observations suggest that calves' pathogenic and/or non-pathogenic diarrhea could be improved through alteration of the fecal bacterial population by the specific thermophile administration.

Prediction of features beyond the animal species
Here, it was confirmed that the administration of compost and/or thermophile activated physiological protection in sea and land animals, even though the changes in intestinal flora were specific to each animal species. These observations have assumed an association between changes in fecal bacterial communities, their metabolites, and protective action. However, the manner of this change is not constant and varies depending on the animal species. Therefore, comprehensive metabolome analyses were performed on typical animal species in which samples exist ( Fig.S9-S10). As a result, the correlation of metabolites beyond the animal species was confirmed (Fig. S11), and the relative differences dependent upon the animal species were occurred by administration of compost and/or a thermophile (Fig.S12). Pathway enrichment analysis identified the metabolic pathways in common among the animals and those that were different. An association analysis, as an unsupervised machine learning method, was conducted to explore universal factors that administered compost across animal species (Fig. S13). Interactive components standard in the animals treated with compost and/or thermophile were clarified by association analysis. Of the associated metabolites, AIB, butyrate, and Lactbacillus was selected as increased components (Fig. S13). The free amino acids and amino acid-related metabolites in the feces were selected as decreased components. These increases and decreases for each animal species are shown in Fig. S14. Further energy landscape analysis, as an unsupervised machine learning method, examined the effects of composting across species. As shown in Fig. 2a, the macroscopic effects of composting were not discernible. On the other hand, it was suggested that there are differences at the factor and module levels (Fig. S15). Therefore, association analysis and energy landscape analysis were used to narrow down the factors assumed to be effective for composting, and structural equations including AIB, butyrate, and Lactobacillus were calculated, focusing especially on the factors that increase by administration of compost or Caldibacillus. After evaluating the regression equations in Tables 1 and 2, the optimal structure models (SEM) were estimated (Figs. 2b and 2c). Of these components, glutamic acid and tryptamine were the factors that tended to decrease with composting (Fig. 2d). In addition, candidate factors causally related to these increasing AIB, butyrate, and Lactobacillus were evaluated by Markov networks (Fig. S16). It is well known that Lactobacillus and butyrate are effective in the intestine. However, little effective information on the intestine is known about AIB. Therefore, based on the previous reports 40,41 on AIB, we were able to assume the effect of AIB on pathogenic microorganisms. First, the compost and the supernatant of the Caldibacillus culture contained trace amounts of AIB. Next, the direct effect of compost itself on pathogenic microorganisms was investigated. The results showed no antibiotic effect (data not shown). Therefore, its effects on viruses were investigated (Fig. 3). The results showed that it had an inhibitory effect on infectivity against RNA viruses: Alphacoronavirus, Betacoronavirus, and Influenzavirus (Fig. 3a). These effects were also confirmed in the experiment with Caldibacillus, a thermophile that is the core of the compost (Fig. 3b). Interestingly, the effect was suggested to have an increasing trend in culture supernatants. Caldibacillus was administered to germ free mice and the concentration of AIB in the cecum was detected. The results showed that the AIB concentration tended to increase when vegetative cell and culture supernatant to the mice were included (Fig. 3c), although at low levels in the mice administered the spore of Caldibacillus (Fig. 3d) and similar trends on changes in other fecal metabolites (Fig. S17).

Discussion
Here, it was speculated that physiological protection of fish, chickens, pigs, and cattle commonly tends to improve by oral administration of compost and/or thermophile, despite alteration of their gut flora depending on the animal species and their habitat. As a result of pathway analysis to find a common mechanism beyond the animal species, some metabolic pathways common to animal species were observed. Further statistical structure equation modeling (SEM) based on selected data using machine learning as association analysis and energy landscape analysis estimated Lactobacillus, butyrate, and 2-aminoisobutyrate (AIB) as component candidates optimally linked with the compost beyond species. Lactobacillus is widely known to play a role in the intestine as one of the lactic acid bacteria 42,43 . Butyrate are produced by commensal bacteria and promote peripheral regulatory T cell generation 44 . AIB is a molecule known to be involved in the synthesis of alamethicin and/or lantibiotic, which may have antibacterial activities 45,46 . These factors may play a role in the effect of the compost, although the molecular mechanism of the compost and/or thermophile that improves the intestinal flora is unknown. As one related piece of information, a rise in butyrate under the moderate temperature in artificial anaerobic conditions associated with the compost administration 47 is noted. The improvements of the animal's healthy balances by the compost and/or thermophile may be related to the physiological roles of SCFAs, which have been shown to protect the gut function in mouse models 36,38 . Furthermore, it has recently been suggested that AIBs can synthesize lantibiotic, which play a role in increasing cell permeability 40 . Papain is involved in these synthesis 41 . Interestingly, Caldibacillus had papain-like protease and lantibiotic Sun A-related gene in the genome. Also, some of the lantibiotic and cyclic peptide are known to be effectively disrupt against viral infectivity 48-50 . Therefore, the suppression effect against viral infectivity was evaluated and the effects was confirmed (Fig. 3). These data may point to the need for further research on RNA virus control in the gut. The involvement of bacteriophages and intestinal flora is obvious 51,52 . However, the study of RNA bacteriophages in the gut is still unexplored. In this study, a trend toward less incidence of diarrhea due to PED infection was observed in compost-added swine housing (data not shown). In addition, a trend toward an increase in Lactococcus in fish trials has been observed in compost-treated groups (Extended fig. 1g). Interestingly, some RNA bacteriophages are known to target Lactococcus 53,54 . Although these relationship between intestinal bacteria and the RNA bacteriophage were not examined in this study, it would be an interesting hypothesis if RNA bacteriophages in the gut of fish were responsible for the increase in Lactococcus caused after oral administration of the compost. Based on these results, in addition to Fig. 1c, the effect of compost is hypothesized in Fig.  4. Compost-derived Caldibacillus hisasshii is involved in the synthesis of AIB and lantibiotic including AIB in the gut. It is hypothesized that these and other factors are intertwined to improve the intestinal microbiota. One factor supporting this hypothesis is that AIB actually tends to increase in germ free mice after oral administration of Caldibacillus hisasshii (Fig.  3c). Although papain like protease is present in the genome sequence, the results of structural analysis were not always suitable. The predicted peptide structure (Fig. S18) was not calculated as a structure with high accuracy. On the other hand, there were known cases where the RNA virus itself uses its own papain like protease for infection 55 . As one of the possibilities, these facts should be considered in relation to the gut microbiota, such as the effects of competitive inhibition due to the presence of enzymes similar to papain. In addition, quorum sensing by the genus Bacillus has been shown to depress the population of staphylococci 56 . The possibility of a similar mechanism and an additional mechanism is speculated in this study. Previous data indicate that one mechanism may involve quorum sensing by a strain of B. subtilis, but it is often already included as a standard feed additive in the fishery and livestock industries of Japan. Therefore, the results of this study may indicate the independent effects of C.hisashii, although the sensing mechanism was not observed here. The molecular mechanism should be investigated in future studies. Generally, there are several types of compost, and compost is considered an organic fertilizer that only affects crops. In all the environments, namely, those of soil and sea, the bacterial diversity is important for the health of humans and animals 6,7 because the bacteria in these environments are indirectly administered orally to various animals. Therefore, the kind of compost fermentation microorganisms should be an essential consideration. This study is the first report to evaluate the importance of compost quality in the environmental cycle across animal species such as sea and land animals to the best of our knowledge. Since these microorganisms can circulate in the environment, research of this sort is necessary for sustainability efforts and for improving nitrogen cycle 3,4 and biodiversity 5-8 with the ultimate goal of establishing high-quality, society-level recycling strategies.

Materials and Methods
These information are described later as the Supplementary Methods.
Animal ethics All treatments of the animals were conducted in accordance with the institutional animal care guidelines for the farms. The management was according to individual farm-specific guidelines for most efficiently producing animals.  Fig. 1 (a) the experimental design in this study. The compost fermented with unutilized marine animal resources and/or its derived probiotic candidate thermophile itself cultivated by bioreactor were administrated to fish, chicken (hen), pig, and cattle. Each animal species is evaluated under different conditions. For each group, fish were estimated at n = 33-88 (individual data), chickens at n = 27-58 (hen houses with more than 10,000 hens per henhouse), pigs at n = 4-62 (swine houses with more than 300 individuals per swine breeding facility), and cattle at n = 4 (individual data as an improvement evaluation for diarrhea cattle). Details in the method section is described. (b) shows the effects of a compost fermented with unutilized marine animals resources and/or their-derived thermophile on the relative accident rates of the various animals. (c) shows a hypothesis of the molecular mechanism of gut environments altered by compost and/or its derived thermophile, which were predicted by omics evaluation for each animal.  Table S2. (e) Relative abundance of genus Lactobacillus and relative ratio of 2-aminoisobutyric acid (AIB), Butyrate, and Glu to the control data was assessed for each animal.  it is likely that Caldibacillus hisashii itself acts positively in the biosynthesis of AIB. It also possesses a gene related to papain, which is required for AIB to be metabolized into the lantibiotic. In addition, it also possesses other lantibiotic genes in its genome. It is assumed that these may function and affect bacteriophages/viruses in the gut. It is possible that the gut microbiome could be affected after acting on bacteriophages/viruses.

Extended figure legends
Extended fig.1. (a) The physiological conditions of red breams in the FS-test in Shizuoka (Fig.S.2a) were shown. (b) Photographs of sea breams administration with FS-control (condition without administration of compost extract), FS-1% compost extract, FS-5% compost extract at one month after the injection of Edwadsiella tarda. (c) shows survival rates of FS-test (n=10; p=0.07 in long rank test). (d) shows the complement activities before and after the injection (n=5). (e) shows the survival rates in Edwadsiella tarda exposed test as the repetitive test in Ehime (FE-test), In sea breams administration with blank (normal condition without the injection of Edwadsiella tarda), FE-control (condition without administration of compost extract), FE-1% compost extract, E-1% compost, and FE-5% compost, death rates of the fish were shown after the injection of Edwadsiella tarda (Blank, n=11; FE-control, n=30; FE-extract 1%, n=30; FE-1% compost, n=27; FE-5% compost, n=30; p=0.007 in long rank test). (f) shows α-diversity of the fecal bacterial community of fish tested in the FE-test. (g) shows the characteristic bacterial population (n=6; > 1% as average of relative abundance of OTU assigned sequences; p < 0.05 as FE-1% compost vs FE-control). The percentages show the identities. OTU00027 shows proteobacterium symbiont of Nilaparvata lugens. (h) shows characteristic thermostable bacterial species of the fecal microbiota of FE-control (fish without the compost exposure and with the injection of Edwadsiella tarda) and FE-5% compost (fish with the compost exposure and with the injection of Edwadsiella tarda). 18 colonies from the feces in FE-control and 21 colonies from the feces in FE-5% compost were isolated. The ratio of bacteria identified on the sequence of 16Sr RNA is shown in each group. B. shows the genus Bacillus containing a common synonym. B.coagulans, B.thermoamylovorans/hisashii, and B.megaterium, a common synonym and recently recategorized as Weizmannia coagulans, Caldibacillus thermoamylovorans/hisashii, and Priestia megaterium, respectively 26 . The marks shows as follows: #, p < 0.1;*, p < 0.05; **, p < 0.01 conditions (around 20-25 ℃ as average temperature). CCRcontrol 1 (Negative control 1) and CCR-control 2 (Negative control 2) show data of two hen houses in summer (around 25-30 ℃ as average temperature). CCR-compost ex shows data of hen house with compost exposure in the same summer. (g) shows the scatter plot for the relationship between the averages of temperature and death rates of hen (per week) in the CCR-test (n=73-77 as data of each hen house). (h) shows the relative population of a genus of the fecal bacterial community of hen in the tested hen house in the CCR farm (n=5; >1% as bacterial population). The data in (a), (b), and (e) were shown as Means ± SE. The mark of asterisks * shows p < 0.05. (e) shows the scatter plot for the relation between IgA and SCFAs in the KC farm. (f) shows the scatter plots for the relationship between SCFA contents and H2O in the feces. (g) shows fecal bacterial population before the administration (Before) and thereafter (After). All the calves before the administration were with diarrhea. (h) shows the bacterial population of genus Fusobacteirum before the administration (Before) and thereafter (After). (i) shows the bacterial population of close related species, which showed significant differences between before the administration (Before) and thereafter (After) (p<0.05). Table S1. A model and its fit indices inconcluded with amino acids were shown in the No.1 column. The No.  shows the inferior models and their fit indices.     shows OPG values in the tested hen house (n=4-5/month per group). *, p<0.05; #, p<0.2 (c) the contents of organic acids (SCFA and lactate) in feces of hen in the summer before and after the compost extract test in the CCR farms were shown. ***, p<0.001; **, p<0.01;*, p<0.05; #, p<0.1; $, p<0.2. (d) α-diversity, (e) β-diversity, and (f) population of species in the fecal bacterial community of chicken in the CCR farm were shown (p<0.05). The feces of chickens over 6 months with and without the administration of the compost extract in the same henhouse were detected.  The mark of asterisks * shows p < 0.05, and asterisks *** shows p < 0.001.       (a) Interactive systemic networks represented by association analysis using whole phyla, genera (> 0.1% as detection rate), and all the metabolites detected in all the animals (n=4). The intensely interactive components, targets associated directly by the compost-and thermophile-administrated condition as a source data (> lift value 1.2), were selected, and the representative networks with the components were shown by Gephi. Two colors discriminate between modularity classes. The difference in the color of nodes indicates the strength of degree, whose value sums up the weights of the adjacent edges for each node. (b) interactive systemic networks represented by association analysis using whole phyla, genera (> 0.1% as detection rate), and all the metabolites detected in all the animals (n=4 per each group). The intensely interactive components, targets associated directly by the compost-administrated condition as a source data (> lift value 1.2), were selected, and the representative networks with the components were shown by Gephi. Modularity classes are discriminated by three colors. The difference in the color of nodes indicates the strength of degree, whose value sums up the weights of the adjacent edges for each node. (c) Relative abundance of selected fecal bacteria was assessed for each animal.     Structure prediction of papain like-protease of Caldibacillus hisashii calculated by AlphaFold2. It contains stop codons on the genome sequence, and therefore, the stop codon was removed and predicted as a process, taking into account and discounting the splicing. Based on the (a) Predicted Aligned Error (PAE) and (b) predicted local distance difference test (pLDDT), five prediction data (rank_1 -rank_5) were calculated. (c) The predicted three-dimensional structure is visualized with an interpretation based on the calculation results of pLDDT. Barbieri

Supplementary Methods
Title: A novel sustainable role of compost as a universal protective substitute for fish, chicken, pig, and cattle.

Preparation of the compost as a feed additive and the compost-derived Bacillaceare
The compost fermented of the marine animal resources (MAR compost) was prepared using an aerobic repeated fed-batch fermentation system at high temperatures (approximately 75°C) (fermentationassociated self-heating) as previously described 1 (Miroku Co. Ltd. and Keiyo Gas Energy Solution Co. Ltd., Japan). The powdered compost and/or its extract were used as appropriate for feeding conditions of experimental animals. The extract was prepared to be diluted 1/100 with potable water (as vol./vol.) and incubated under aerobic conditions at 60°C for at least 10 h (this solution was used as the compost extract) 2-7 . The two thermophiles, compost-derived Bacillaceae, were prepared by Sermas Co., ltd., Japan. Firstly, C.hisashii, which was first isolated from the MAR compost as the Bacillus thermoamylovorans N11 strain 7 , registered as B. hisashii (Type stain N-11 T =NRBC 110226 T =LMG 28201 T ) 8 , registered as the accession number NITE BP-863 given by the international depositary authority in National Institute of Technology and Evolution (NITE) in Japan ( Sep 26, 2014), and recently reclassified to C. hisashii 9 , were cultivated as previously described 7 . Secondly, N16 strain, which was first isolated from the MAR compost as the B. coagulans N16 strain 7 , registered as the accession number NITE BP-02066 given by the international depositary authority in NITE (July 1, 2015), and recently reclassified to Weizmannia coagulans 9 . In CW and CCR test (Fig.S.3de), the compost extract concentration was more reduced to at least 1/1000 so far (diluted compost extract), and the amount of the probiotic candidates C.hisashii N-11 stain and W.coagulans N-16 stain were adjusted.
A compost extract comprising a 1:1 mixture of two stains and adjusted to 10 2-3 CFUml -1 in portable water was orally administered ad libitum. In the PK and PF swine tests (Fig.S.3gh), C. hisashii was adjusted to 10 2-3 CFUg -1 in total feed in the compost group. Only C. hisashii strain in the TB group of the swine test was adjusted to 10 2-3 CFUg -1 in total feed. As an additional excipient, defatted rice gran in the PK test and skim milk in PF test was used as vehicle at 0.1 % or 0.2% total feed, respectively. In the PI test (Fig.S.3i), C. hisashii in the diluted compost extract was adjusted to 10 2-3 CFUg -1 as a material in liquid feed until first Day 57 of the test ; thereafter, only the C. hisashii N-11 strain (0.1%) with defatted rice gran as an additional excipient was administered. In the PM test of swine test (Fig.S.3j), only the C. hisashii strain, adjusted to 10 2-3 CFUg -1 in total feed, with defatted rice gran as an additional excipient (0.1%) was administered. Similarly, in the KC cattle test (Fig.S.3k), only the C. hisashii strain (10 2-3 CFUg -1 as total feed) with potato starch as an additional excipient was administered.

Fish management
Artificial infection tests with Edwadsiella tarda were performed with the red sea bream Pagrus major as a fish model in the laboratory water tanks circulating in one way with sea water from two regions, Shizuoka (Chubushiryo, Japan) and Ehime (Ehime Univ., Japan). In the Shizuoka test (FS-Test in administration. This duration was set according to our observational data: calves were recovered from diarrhoea within 4 days following C.hisashii administration. Rectal feces were collected using long gloves immediately from the administered and non-administered calves on the first day and last day of the supply period.

Detection of infected bacteria and parasite
In the FS-Test (Fig.S.3a), the presence of Edwadsiella tarda in the fish after the infection test was determined by the proliferation of the colonies on generally known agar media. In the FE-Test, the presence was checked by PCR with the primer sets as previously described 13 . To investigated the infection test of the Salmonella for hens in the CC-Test, the fresh caecal feces excreted on the early morning of the 4 th and 7 th days after infection were collected individually from all the birds and diluted to a 1/10 volume with sterile physiological saline. Then, 0.1 ml of these diluted solutions was cultivated on DHL agar containing 50 μg/ml rifampicin (rif DHL) for 24 hr at 37 ℃, and thereafter, the number of resistant viable colonies and rif-resistant Salmonella colonies were calculated as previously described 12 . The liver and ovary tract were diluted to 1/10 volume with sterile triptic-soy-broth (TSB) (Nissui, Japan), and thereafter the number of the rif-resistant Salmonella colonies in organ solution were calculated by cultivation on rif DHL as the procedure for cecal feces. In order to evaluate the presence of coccidium, the numbers of oocysts per gram of feces (OPG values) were detected as previously described 14 .
In order to estimate the cause occurring diarrhea of cattle, pathogenic genes of Escherichia coli and Salmonera enteriridis were detected. Stx1, The primer sets of Sta 15 , LT 15 , F17 15 , SAA 15 , Vtcom 16 , VT1 16 , and VT2 16 , as targeted genes for the siga toxin of E.coli, were used as previously described 15,16 . As targetted gene for S.enteridis Salmonera (invA gene) One Shot PCR kit (Takara Co., Ltd., Japan)

Preparation of fecal samples
All the fecal samples obtained for the following omics analyses were transported at -20 °C and thereafter stored at −60 °C -−80 °C until analyses. The frozen feces were utilized for the following omics analyses; the sequencing of bacterial 16S RNA gene and metabolome analyses as the following procedure. As the metabolome analyses, HPLC Prominence (Shimazu, Japan) and chromatography-tandem mass spectrometry (GC/MS/MS), GCMS-TQ8030 triple quadrupole mass spectrometer (Shimazu, Japan), were basically performed. In addition, the excreted feces except cecal feces in the CC-Test, PS-Test, PK-Test, PF-Test, and PM-Test were analyzed their organic acids as SCFA and lactate by a capillary electrophoresis method as previously described 4,5 (9,10).

Detection of thermostable bacteria from feces
In order to detect the bacteria from the compost in FE Test, the colonies were cultivated on nutrient agar (Nissui co., ltd.). The colonies were directly amplified by a primer set (341f-907r), and the resultant PCR fragments were sequenced as previously described 1 .

Detection of immune response and the related defensive response
In order to speculate the defensive response, the extent of recovery against anesthesia and immune response were determined. 2-methylquinoline, quinaldine as an anesthesia, was prepared in Chubushiryo based on the manufactured protocol. The recovery time of fish administrated with it were detected to speculate recovery function of the liver against anesthesia. The sera of the red breams were obtained, in order to speculate the immune activities, and the complement activities of the sera were determined by the previously described methods 17 . In the other animals, fecal immunoglobulin A (IgA), a mucosal immune activity as non-invasive method, was determined using an enzyme-linked immunosorbent assay (ELISA) kit according to the manufacturer's instructions (Bethyl Laboratories Inc., Montgomery, TX, USA) 8 .

DNA preparation from fecal samples
Total DNA was prepared from the feces according to the previous report 18 with some modification.
Approximately 100 mg of lyophilized feces were disrupted with zirconia beads using Micro Smash MS-100 (Tomy Seiko, Tokyo, Japan), and suspended into 600 μL of 10 mM Tris-HCl, pH 8.0, and 1 mM EDTA (TE). We transferred 475 μL fecal suspension into a 1.5-mL microcentrifuge tube. This suspension was incubated with 15 mg/mL lysozyme for 1 h at 37°C. Achromopeptidase (FUJIFILM Wako Pure Chemical Corp., Osaka, Japan) was added at a final concentration of 2,000 units/mL, and further incubated for 30 min at 37°C. Furthermore, and finally 25.7 μL of proteinase K and sodium dodecyl sulfate were added at a final concentration of 1mg/mL and 1%, respectively, and then the mixed solution was incubated at 55°C for 1 h. These enzymatic treatments were carried out with shaking (1,000 rpm). The resulting lysate was treated with phenol/chloroform/isamyl alcohol, and DNA was precipitated with ethanol. DNA pellet was rinsed with 70% ethanol, dried, and dissolved in 300 μL TE.
Then, RNase was added at a final concentration of 0.1 mg/mL and incubated at 37°C for 30 min, and then an equal volume of a 20% polyethylene glycol 6000 solution containing 2.5 M NaCl was added.
After incubation for 30 min on ice, DNA was pelleted by centrifugation, rinsed and then dissolved in TE.

Meta sequence analysis of bacterial 16S rRNA gene
The V1-2 region of bacterial 16S rRNA gene (27fmod-338r) was sequenced according to the previous report 18 . The amplified fragments were sequenced on an Illumina Miseq according to the manufacturer's instructions. The paired-end reads were merged using the fastq-join program based on overlapping sequences. Reads with an average quality value of <25 and inexact matches to both universal primers were filtered out. Filter-passed reads were used for further analysis after trimming off both primer sequences. For each sample, quality filter-passed reads were rearranged in descending order according to the quality value and then clustered into OTUs with a 97% pairwise-identity cutoff using the UCLUST program version 5.2.32 (https://www.drive5.com). Taxonomic assignment of each OTU was made by similarity search against the Ribosomal Database Project (RDP) and the National Center for Biotechnology Information (NCBI) genome database using the GLSEARCH program. Indices for αdiversity, namely community richness (Chao1) and diversity (Shannon and Simpson), were calculated, and indices for β-diversity were also estimated using UniFrac analysis with weighted and unweighted principal coordinate analysis (PCoA). All 16S rRNA gene datasets were deposited in GenBank Sequencing Read Achive data base.

Analysis of fecal metabolites
All the fecal samples obtained were transported at -20 °C and thereafter stored at −60 °C -−80 °C until analysis. To determine the concentrations of SCFAs (acetate, propionate, butyrate), lactate, and succinate, the contents of frozen fresh fecal samples were determined by using an HPLC Prominence instrument equipped with an electric conductivity detector (CDD-10AVP) (Shimazu, Japan) 19

Correlation analyses
The relative values of fecal metabolites dependent upon animal species were analyzed through construction of a correlation heatmap after Spearman correlation coefficient was calculated among fecal metabolites. The fecal metabolites showing significant differences (p<0.1) between targeted animal species were selected if with the difference in at least one species. Correlation network analysis were performed as previously described 20 . As the conditions of this study, the metabolites showing high correlation coefficients (r > |0.4|) between metabolites were discriminated from each cluster. The data were sorted to 0 (r<|0.4| or 1 (r >|0.4|) by using the function "ifelse" of R software (ver. 4.0.5) (https://www.r-project.org/). The plus and minus data as the r value were prepared for visualization of positive and negative correlation network, respectively. The calculated data (.dl file) were rendered as correlation networks by Force Atlas with Noverlap in Gephi 0.9.2 (http://gephi.org).

Association analyses
Association analysis, a elementary way of unsupervised learning, is a technically established method, which is also used for market research as market basket analysis, and is applied to understand beyond the logic of numbers, using relative numbers [21][22][23] . It is easy to apply when there are missing values as a characteristic of association analysis. Therefore, it can be suitable when a condition is a set such that the identity for classification is different for each layer, such as microbial structure and metabolomic data. It is able to obtain and classify predictive associated components by applying it to conditions that are difficult to make horizontal comparisons. In order to predict the components associated with compost and/or thermophile, an association analysis was performed as previously reported [21][22][23] . In brief, the association analysis is a elementary way to infer the effect, "target", from the cause, "source". In the case, "source" and "target" are represented as the x and y, respectively, calculation factors of association analysis were defined as follows: support (x ⇒ y) = P(x ∩ y) "support" is P(xy), joint probability (P) of co-occurance confidence (x ⇒ y) = P(x ∩ y) / P(x) "confidence" is P(xy) / P(x), conditional probability (P) of occurence of y after x is occurred.
lift (x ⇒ y) = P(x ∩ y)/P(x) P(y) "lift" is P(xy) / P(x)P(y), measure of association / independence Value of > 1 represents positive association (If the value represents independent, value with < 1 represents as negative association).
Here association rules were determined by using criterion values of support, confidence, and lift ( "support = 0.063, confidence = 0.25, maxlen = 2" and "lift > 1.2"). Data jointed with all information such as animal species, phyla, genera, metabolites, with compost and thermophile and without them were used. To avoid the difference dependent upon animal species, all data were calculated based on the median value (M) among the data of the same animal and sorted to 0 ( < M) and 1 ( > M). Therefore, potential associated components should be explored by this analysis. These data set was ruled by the package "arules" of R software. The systemic network was rendered by Force Atlas with Noverlap in Gephi 0.9.2.

Energy landscape analysis
Energy landscape analysis (ELA) was performed as a machine learning method as previously described 24 . ELA is a data-driven method for constructing landscapes that explain the stability of communities compositions across environmental gradients. Here, ELA is based on an extended pairwise maximum entropy model that explains the probability for the occurrence of the ecological state of sample , (") given the environmental condition (") ; as the ecological state, we combined the presence/absence status of selected taxa and levels of physiological factors, with 51 components, which accompanies the environmental condition (") represents the status of the distance from the aquaculture facility and the seasonal stage.

Structural Equation Modeling
Structural equation modeling (SEM) for confirmatory factor analysis (CFA) was conducted using the package "lavaan" 26,27 of R software. The analysis codes were referred to the website (https://lavaan.ugent.be). Since CFA needs a hypothesis, the groups selected by association analysis were utilized as factors for a latent construct of metabolites and microbiota. The models as hypotheses were statistically estimated using maximum likelihood (ML) parameter estimation with bootstrapping (n = 1000) by the functions 'lavaan' and 'sem'. Model fit was assessed by the chi-squared p-value (p >0.05, nonsignificant), comparative fit index (cfi) (>0.9), Tucker-Lewis index (tli) (>0.9), goodnessof-fit index (gfi) (>0.95), adjusted goodness-of-fit index (agfi) (>0.95), and root mean square error of approximation (rsmea) (<0.05) as indices of good model fit. 28 The path diagrams of the good model were visualized with layout="spring" using the package "semPlot" of R software 29 .

In vitro virus infection test
Virus infectivity control tests were conducted with reference to previous reports 30, 31 . In brief, as preliminary test, the effect of the test material on cultured cells (cytotoxicity) was investigated. As the materials, 1/100 diluted compost extract, the vegetative cell of Caldibacillus hisashii itself (at least 10 6 cfu/ml) and the derived culture solution. The test materials were inoculated into cultured cells after 10fold step dilution in phosphate buffer solution, and the highest concentration at which the cells showed normal condition after incubation was confirmed to determine the virus concentration to be used for the test. As a result, no cytotoxicity was confirmed in the 10-fold dilution. Therefore, the detection limit in this study was set at 101.5 TCID50 / mL. Ten mL of each of the test materials and phosphate buffer

Germ free mouse experiment
Oral administration test with germ free mice were conducted by the methods on the basis of previous reports 8, 32 . In brief, BALB/c germfree male mice were used in accordance with the guidelines for the care and use of laboratory animals at Yokohama City University. The germ-free mice (n = 5)(8-week-old) received either autoclaved water with or without the vegetative cells (including spore) of C. hisashii. The mice received water ad libitum for the experiment.

Protein structure prediction
After converting the target gene sequence into an amino acid sequence in the genome sequence data of Caldibacillus hisashii N11 strain, prediction of peptide structure was performed by Alphafold 2 according to the developed procedure (https://alphafold.ebi.ac.uk/faq) 33,34 .