Kunming mice (SPF grade, 7 weeks old weighing 18–22 g) were obtained from Jiangsu Huachuang Xinnuo Pharmaceutical Technology Co., Ltd. (Jiangsu, China, SCXK (Su) 2020-0009). The mice were housed at the Animal Experiment Center of Yan’an University and given free access to food and water under temperature- and humidity-controlled conditions with a 12 h light/dark cycle. The animals were acclimatized to laboratory conditions for 7 days prior to the commencement of experimentation. All the animal experiments were approved by the Animal Ethics Committee of Yan’an University (No. 2020-009) and performed in accordance with the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. Cell lines including Raji cell (B cell), Jurkat cell (T cell) and Raw264.7 cell (mouse macrophage) were kindly donated by Prof. Ding Xiang from China West Normal University. The cell lines were preserved by the Yan’an Key Laboratory of Microbial Drug Innovation and Transformation. Fungus samples were collected from the wild locust trees on the Loess Plateau around Yan’an University. The total DNA of sporophore and mycelium was extracted and used to identify species through ITS amplification and sequencing, and the sample was named as P. igniarius YASH1. Biochemical reagents, molecular biology kits, and cell biology reagents for DNA exaction and amplification, cell culture, natural products extraction and identification, were obtained from Beijing Solarbio Science and Technology Co., Ltd. Primers used for species identification were synthesized by Sangon Biotech (Shanghai) Co., Ltd. Enzyme-linked immunosorbent assay (ELISA) kits for interleukin (IL)-2, IL-6, interferon (IFN)-γ, and tumor necrosis factor (TNF)-α and kits for antioxidant or hydroxyl radical scavenging capacity assay as well as Cell Counting Kit-8 for cell proliferation and cytotoxicity assays were all from Beijing Solarbio Science and Technology Co., Ltd.

After disinfection with a chemical disinfectant, the sporophore was cut into thin slices, and the slices were inoculated onto the potato dextrose agar in a Petridish and incubated in an incubator controlled at 28 °C for 10 days. A piece of mycelium was aseptically transferred to a new potato dextrose agar Petridish and incubated at 28°C for 30 days, and the mycelium was collected to extract the natural products. The total DNA of sporophore and mycelia was extracted by guanidine isothiocyanate/phenol extraction. ITS1 (5′TCC​GTG​AAC​CTG​CGG-3′) and ITS4 (5′TCC​CGT​TAT​TGA​TAT​GC-3′) primers were used to amplify the DNA of specific sequence regions and sequenced using the Sanger method. The fungus was named P. igniarius YASH1 after the DNA sequence was blasted by BlastN.

Polysaccharides were extracted, purified, and identified as described by Yuan et al. (2018), in which fresh mycelium or sporophore was extracted with double distilled H₂O (ddH₂O) at 85°C for 6 h. The extracts were centrifuged at 12,000 rpm for 20 min before being concentrated under vacuum. 95% ethanol (EtOH) was added into the supernatant to precipitate the crude polysaccharides. The crude polysaccharides were redissolved in ddH₂O, and then Sephacryl S-300 gel and DEAE-cellulose column were used to purify polysaccharides. High-performance gel permeation chromatography was employed to detect the molecular weight of the polysaccharides. Briefly, polysaccharides were dissolved in ddH₂O and filtered through a 0.22-µm membrane filter. The data were analyzed using GPC software (Millennium 32 software; Agilent Technologies, Inc., Santa Clara, CA, USA). For monosaccharide composition analysis, polysaccharides were hydrolyzed with TFA at 110°C for 6 h via acid-catalyzed hydrolysis, and the products were dissolved in ddH₂O for monosaccharide composition analysis by high-performance liquid chromatography (HPLC) in an evaporative light scattering detector. HPLC was performed under the following conditions: concentration of refined polysaccharides, 20 mg/mL; mobile phase, 75% acetonitrile at 1.4 mL/min; and column oven temperature, 35°C. D-glucose, D-mannose, D-fructose, D-galactose, L-rhamnose, L-arabinose, and D-xylose were used as standard sugars.

Flavonoids were extracted, purified, and identified as described by Shi et al. (2016; 2019), in which 100 g fresh mycelium or sporophore was extracted thrice (4, 2, and 1 h) with 1,000 mL (10 times volume) of 40% EtOH, and then filtered. The filtrate was evaporated under reduced pressure by using a rotary evaporator to obtain the crude extract solution. The crude extract solution was loaded in glass columns which were wet-packed with HPD722 macroporous resin and washed with ddH₂O, and eluted with 70% EtOH. The eluting solution was concentrated in the rotary evaporation apparatus and vacuum-dried for qualitative and quantitative analysis of total flavonoids using LC-MS/MS(Agilent 1100- triple quadrupole mass spectrometry API4000 with Agilent Poroshell 120 EC-C18 2.7 μm (3 × 50 mm)and Double-flavone of Cortex Platycladi and big posein as internal standard).

The hydroxyl radical scavenging capacity assay of polysaccharides and flavonoid was carried out by using the hydroxyl free radical scavenging capacity assay kit according to the principle that H₂O₂/Fe²⁺ can generate hydroxyl free radical through Fenton reaction (Lyngsie et al., 2018), and the experiment was carried out according to the instructions described in the kit manual (kit BC1325-100T/96S). The total antioxidant capacity of polysaccharides and flavonoid was determined using the total antioxidant capacity assay kit (kit BC1310-50T/48S) based on the reduction of Cu²⁺ to Cu⁺ (Madariaga et al., 2021), and the experiment was carried out according to the instructions described in the kit manual.

The effect of mycelium polysaccharide (JDPS), sporophore polysaccharide (ZDPS), mycelium flavonoids (JHFA), sporophore flavonoids (ZHFA) and zunyimycin C (Wang et al., 2022) on the proliferation rate of immune cells was analyzed as described by Li et al. (2020), in which RAW264.7, Raji and Jurkat cells were inoculated to 96-well plates and incubated with 5% CO₂ at 37°C. The polysaccharides were dissolved in LPS complete medium at a concentration of 1,000 mg/mL and diluted to final concentrations of 400, 200, 100, 50, 25, 12.5, 6.25, and 3.125 μg/mL. Flavonoids were dissolved in DMSO at a concentration of 1,600 μg/mL and diluted to final concentrations of 100, 50, 25, 12.5, 6.25, and 3.125 μg/mL. LPS was diluted to final concentrations of 5 μg/mL with complete medium as control. After the cells were incubated for 24 h, 100 µL of different concentrations of the drug were added, and RAW264.7 cells were incubated for 48 h, while the Raji and Jurkat cells were incubated for 72 h. Then, the OD450 absorbance values of each cell were detected, and the cell proliferation rate was calculated.

Macrophage phagocytosis assay was carried out as described by Djaldetti and Bessler, (2015), in which 4000 RAW264.7 cells were plated and inoculated with 5% CO₂ at 37°C for 48 h. After discarding the culture medium, 0.4%, 0.04%, 0.01%, and 0.004% trypan blue solutions were added, and the sample was incubated at 37°C for 15 min. The trypan blue solution was discarded, and 200 µL of PBS was added. The sample was rinsed thrice, PBS was discarded, and 100 µL of cell lysate was added to each well to lyse the samples for 2 h. The absorption values were detected at 540 nm, and phagocytosis was calculated.

After Jurkat cells and RAW264.7 were inoculated at 5% CO₂ and 37°C for 24 h, they were incubated for 72 h after the drug was added. The supernatant of cultured cells was collected and centrifuged at 1,000 rpm for 10 min at 4°C. TNF-α, IL-2, IFN-γand IL-6 levels were determined by ELISA. The following ELISAs were employed for the measurement of each parameter: mouse TNF-α (kit SEKM-0034-96T), mouse IL-2 (kit SEKM-0004-96T), mouse IL-6 (kit SEKM-0007-96T), and mouse IFN-γ(kit SEKM-0031-96T) (Avci et al., 2014).

Thirty-five mice were randomly divided into three groups, namely, the blank control (BLK) group, model group (Cyclophosphamide, CTX), ZDPS low-dose group (ZDPS50, 50 mg/kg/day), ZDPS high-dose group (ZDPS200, 200 mg/kg/day), ZHFA low-dose group (ZHFA50, 50 mg/kg/day), ZHFA high-dose group (ZHFA200, 200 mg/kg/day), and Zunyimycin C group (4.8 mg/kg/day). The mice were intraperitoneally injected with CTX (100 mg/kg) once a day for 3 days to build an immunosuppressive mouse model (Kong et al., 2005; Wang et al., 2022), while the mice in the BLK group were intraperitoneally injected with normal saline (NS) as control. From the fourth day, mice were given normal saline by gavage in the BLK and CTX groups, and other mice were administered with the ZDPS50, ZDPS200, ZHFA50, ZHFA200 and Zunyimycin C for 15 days respectively. Blood samples were obtained from the mice in each group from the orbit, and hemolysin, IL-2, IL-6, IFN-γ, and TNF-α were quantified using ELISA kits as per the manufacturer’s instruction.

On the fourth day, all mice were sensitized by intraperitoneal injection of 0.2 mL of 6% chicken red blood cells, and mice blood samples were collected from the orbit. After resting at room temperature for 1 h, blood samples were centrifuged at 2,000 rpm for 20 min at 4°C, and the supernatant was collected and incubated for 1 h at 37°C with 250 µL of 6% (v/v) chicken red blood cells and 250 µL of 10% guinea pig serum. The reaction was stopped at 15 min in ice, and the sample was centrifuged at 2,000 rpm for 20 min at 4°C. Approximately 200 µL of supernatant was collected in 96-well plate, and the OD value was measured at 540 nm.

The feces were collected with sterile centrifuge tubes and stored at −80°C. Fecal SCFA was determined by Shanghai LuMing Biotechnology Company via LC-MS-MS. SCIEX OS-MQ software (Sciex, USA) was employed to identify and integrate MRM transitions with default parameters (Liebisch et al., 2019). The abscissa of the extracted ion current (XIC) diagram of each substance was the retention time (RT) of metabolite detection, and the ordinate was the ion strength (count per second, cps) of ion detection, generating the map of each metabolite. The peak area of the metabolite was interpolated into the regression equation fitted by the standard curve to obtain the concentration of the metabolite. On the basis of the parameters, such as sampling and/or dilution ratio, the concentration of metabolites was calculated, and the absolute content of each metabolite in the actual sample was obtained.

Murine intestinal microbiota 16S rRNA amplicon sequencing and analysis were conducted by OE Biotech Co., Ltd. (Shanghai, China) (Hu et al., 2022). Total genomic DNA was extracted using a DNA extraction kit following the manufacturer’s instruction. The quality and quantity of DNA were verified with NanoDrop and agarose gel. Extracted DNA was diluted to a concentration of 1 ng/μL and stored at 20°C until further processing. The diluted DNA was used as a template for the PCR amplification of bacterial 16S rRNA genes with the barcoded primers and Takara Ex Taq (Takara). For bacterial diversity analysis, the V3-V4 variable regions of 16S rRNA genes were amplified with universal primers 343F (TACGGRAGGCAGCAG) and 798R (AGGGTATCTAATCCT). The amplicon quality was visualized by gel electrophoresis, purified with AMPure XP beads (Agencourt), and amplified for another round of PCR. After purifying with the AMPure XP beads again, the final amplicon was quantified using the Qubit dsDNA assay kit. Equal amounts of purified amplicon were pooled for subsequent sequencing. Raw sequencing data were stored in FASTQ format. Paired-end reads were then preprocessed using cutadapt software to detect and cut off the adapter. After trimming, paired-end reads were filtered for low-quality sequences, denoised, merged, and detected. The chimaera reads were cut off by using DADA2 with the default parameters of QIIME2 (2020.11). Finally, the software produced the representative reads and ASV abundance table. The representative read of each ASV was selected using the QIIME2 package. All representative reads were annotated and blasted against the Silva database Version 138 (or Unite; 16s rDNA) by using q2-feature-classifier with the default parameters.

The data of each group were expressed as the mean ± standard deviation. Statistical analysis was performed using GraphPad Prism 8 software. One-way ANOVA was used for comparison between multiple groups, and t-test was used for comparison between two groups. Statistical significance was considered at p < 0.05 (Wang et al., 2022).

Sequencing results of the PCR products of ITS were BLAST at the NCBI database, and the results showed that the nucleotide sequence similarity between the experimentally collected fungi and the wild P. igniarius NW518 in the database, which originated from the Loess Plateau in northern China, was 100%. Therefore, the isolate from this experiment was named P. igniarius YASH1.

LC-MS-MS was used to identify and quantify the composition of the total flavonoids from P. igniarius YASH1, and the result showed that the flavonoids in ZHFA from wild-type sporophore and laboratory-grown mycelium differed in terms of composition and content (Table 1). In comparison with the mycelium, flavonoids from the sporophore contain unique ingredients such as ihydroquercetin-7-0-rhamnoside, procyanidin B1, anthocyanin B2, p-coumaric acid 1, dglycyrrhizin 1, and cyanidin 2 and lack some flavonoids such as quercetin 1, 2,4,6-tribromophenol 2, vanillin 2 and trans-resveratrol 2. Licorice 1 (581 ng/mL), the largest flavonoid in the sporophore, was not detected in the total flavonoids of the mycelia cultured in the laboratory. By contrast, quercetin 1 (143 ng/mL), which has the highest content in the mycelia cultured in the laboratory, was not detected in the total flavonoids of the wild sporophore.

The molecular weights of polysaccharides from P. igniarius YASH1 were analyzed by high-performance gel permeation chromatography, and the data (Table 2) show that the number-averaged molecular weight (Mn) of ZDPS is 7,472 Da, the weight-averaged molecular weight (Mp) is 19,319 Da, the molecular weight (Mw) is 20,323, Z average molecular weight (Mz) is 40,689, and the molecular weight z+1-average (Mz+1) is 64,771 with corresponding values in JDPS of 9,795, 23,457, 21,270, 43,172, and 65,541, respectively.

The hydroxyl radical scavenging activity of polysaccharides and flavonoids extracted from sporophore or laboratory-grown mycelium of P. igniarius YASH1 was analyzed, and the result is shown in Figure 1. Results showed that the hydroxyl radical scavenging rate of flavonoids and polysaccharides increased with the increasing in concentration at a certain concentration range, indicating a concentration-dependent effect (Figures 1A–D). In the concentration range of 20–100 mg/mL, the hydroxyl radical scavenging rate of ZDPS (Figure 1A) increased with the increasing in concentration, which was concentration-dependent and statistically significant (p < 0.0001), while JDPS (Figure 1B) showed a concentration-dependent activity of hydroxyl radical scavenging rate in the range of 1–13 mg/mL (p < 0.0001). For flavonoids, in the concentration range of 14–30 mg/mL, the hydroxyl radical scavenging rate of ZHFA (Figure 1C) increased with the increasing in concentration, which was concentration-dependent and statistically significant (p < 0.0001), while JDFA (Figure 1D) showed a concentration-dependent activity of hydroxyl radical scavenging rate in the range of 30–190 mg/mL (p < 0.0001).

The antioxidant capacity of polysaccharides and flavonoids from the sporophore or laboratory-grown mycelium of P. igniarius YASH1 was detected. The results are shown in Figure 1, in which the ZDPS and JDPS of polysaccharides and the ZHFA and JHFA of flavonoids have antioxidant capacity (Figures 1E–H). The ZDPS (Figure 1E) and JDPS (Figure 1F) of polysaccharides were in the concentration ranges of 1–3 and 0.5–7 mg/mL, respectively, and the total antioxidant increased with the increasing in concentration. A certain significant concentration dependence (p < 0.0001) was observed. Both the ZHFA (Figure 1G) and JHFA (Figure 1H) flavonoids were in the concentration ranges of 0.125–1 and 0.5–2.5 mg/mL, respectively. The total antioxidant increased with the increasing in concentration, and a significant concentration dependence (p < 0.0001) was observed.

The effect of drugs on the proliferation rate of immune cells was determined, and results show (Figure 2) that both polysaccharides and flavonoids promote the cell proliferation of the three cell lines. For example, the sporophore polysaccharides ZDPS has a proliferative effect on Jurkat cells and showed the strongest stimulating effect (74.44%) at 50 μg/mL (p < 0.0001; Figure 2A). It also has a proliferative effect on Raji cells and showed the strongest stimulating effect (62.33%) at 12.5 μg/mL (p < 0.0001) (Figure 2E), while in Raw264.7 cells, the strongest stimulating effect (36.57%) was observed at 100 μg/mL (p < 0.001; Figure 2I), which was statistically significant compared with the blank control (BLK) group. JDPS, the mycelium polysaccharide, has a proliferative effect on Jurkat cells and showed the strongest stimulating effect (27.5%) at 250 μg/mL (p < 0.0001; Figure 2B). It also has a proliferative effect on Raji cells and showed the strongest stimulating effect (53.85%) at 100 μg/mL (p < 0.0001; Figure 2F). In Raw264.7 cells, the strongest stimulating effect (136.57%) was observed at 25 μg/mL (p < 0.0001; Figure 2J) which is statistically significant compared with the negative control. The sporophore flavonoids ZHFA has a proliferative effect on Jurkat cells, showed the strongest stimulating effect (88.85%) at 12.5 μg/mL (p < 0.001; Figure 2C), and had a proliferative effect on Raji cells, showing the strongest stimulating effect (56.14%) at 50 μg/mL (p < 0.0001; Figure 2G), while in Raw264.7 cells, the strongest stimulating effect (100.47%) was observed at 6.25 μg/mL (p < 0.001; Figure 2K), and the effect was statistically significant compared with the negative control. JHFA, the mycelium flavonoid with a proliferative effect on Jurkat cells and showed a strongest stimulating effect (37.5%) at 25 μg/mL (p < 0.005; Figure 2D), and had a proliferative effect on Raji cells that showed the strongest stimulating effect (54.29%) at 5 μg/mL (p < 0.0001; Figure 2H). In Raw264.7 cells, the strongest stimulating effect (48.36%) was observed at 3.125 μg/mL (p < 0.0001; Figure 2L), and the effect was statistically significant compared with the negative control.

The effect difference between drug combinations and drugs alone was compared using half the drug concentration of the best effect concentration of ZDPS, JDPS, ZHFA, and JHFA and half the concentration of the best effect concentration of zunyimycin C. The combination of ZDPS (50 μg/mL), JDPS (500 μg/mL), ZHFA (12.5 μg/mL), and JHFA (5 μg/mL) with zunyimycin C (1.25 μg/mL) was used to detect the effect of drug combination on the proliferation of Jurkat cells. The results in Figure 3 show that compared with the BLK group, the drugs ZDPS, ZDPS+Zun C, JDPS, JDPS+Zun C, ZHFA, ZHFA+Zun C, JHFA, and JHFA+Zun C promoted the proliferation of Jurkat cells with a statistical difference (p < 0.01). No significant difference was observed between ZDPS and ZDPS+Zun C. Similarly, no significant difference was observed between JDPS and JDPS+Zun C, ZHFA and ZHFA+Zun C, and JHFA and+Zun C (Figure 3A). ZDPS (12.5 μg/mL), JDPS (100 μg/mL), ZHFA (50 μg/mL), and JHFA (50 μg/mL) were used in combination with zunyimycin C (2.5 μg/mL). ZDPS, ZDPS+Zun C, JDPS, JDPS+Zun C, ZHFA, ZHFA+Zun C, JHFA, and JHFA+Zun C promoted Raji cell proliferation compared with the BLK group with a statistical difference (p < 0.05). No significant difference was observed between JDPS and ZDPS+Zun C. Similarly, no significant difference was observed between JDPS and JDPS+Zun C, ZHFA and ZHFA+Zun C (Figure 3B). ZDPS (100 μg/mL), JDPS (25 μg/mL), ZHFA (6.25 μg/mL), and JHFA (3.125 μg/mL) were used in combination with zunyimycin C (2.5 μg/mL). ZDPS, JDPS, JDPS+ZC, ZHFA, and JHFA promoted the proliferation of Raw 264.7 cells compared with the BLK group, with a statistical difference (p < 0.001), and JDPS+Zun C had a higher cell proliferation rate than JDPS. A statistical difference was observed (p < 0.05), but the cells in the drug combination were all dead, except for JDPS+Zun C (Figure 3C).

The effects of polysaccharides and flavonoids on the phagocytic ability of macrophages were determined using the trypan blue method to detect the phagocytic ability of RAW264.7 cell lines according to the change of OD540 after drug use. After stimulating macrophages by using various drugs and drug combinations, the phagocytic ability of macrophages was improved, which was statistically significant compared with the negative control (p < 0.05; Figure 3D).

The levels of cytokines in vivo and in vitro were measured, and the secretion of IL-2 and IFN-γ of Jurkat cells, as well as IL-6 and TNF-α of RAW264.7 cells were detected with cytokine detection kits. Figure 4 shows that the secretion levels of IL-2 increased from 0.29 pg/mL to 3.5, 3.82, 5.45, 1.56, and 2.36 pg/mL when Jurkat cells were stimulated with LPS, ZDPS, JDPS, ZHFA, and JHFA respectively, and the difference was statistically significant (p < 0.01; Figure 4A).

When Jurkat cells were stimulated with LPS, ZDPS, JDPS, ZHFA, and JHFA, the secretion levels of IFN-γ increased from 1.68 pg/mL to 2.28, 2.20, 2.05, 1.9, and 2.2 pg/mL respectively, and the difference was statistically significant (p < 0.01; Figure 4C). In RAW264.7 cells, the secretion levels of IL-6 increased from 0.276 pg/mL to 3.63 pg/m, 2.10, 0.65, 1.56, and 0.64 pg/mL when the cells were stimulated with LPS, ZDPS, JDPS, ZHFA, and JHFA, respectively, and the difference was statistically significant (p < 0.01; Figure 4B). The secretion levels of TNF-α increased from 0.154 pg/mL to 3.63, 2.10, 0.65, 1.56, and 0.64 pg/mL when the cells were stimulated with LPS, ZDPS, JDPS, ZHFA, and JHFA, respectively, and the difference was statistically significant (p < 0.01; Figure 4D). After treatment with polysaccharides or flavonoids, the expression level of cytokines in immunosuppressive animal models was detected using the ELISA kit. Results showed that the level of IL-2 in CTX significantly decreased (p < 0.01) and the levels of IL-2 in mice medicated with polysaccharide and flavone increased (p < 0.05; Figure 4E) compared with the BLK group. Similarly, the level of IL-6 in the CTX group was significantly lower than that in the BLK group (p < 0.05). After treatment with polysaccharides and flavonoids, the level of IL-6 in mice significantly increased compared with that in the CTX group (p < 0.05; Figure 4F). The expression level of TNF-α in the CTX group was significantly lower than that in the BLK group (p < 0.01), but the effect was different after drug treatment, in which only the level of TNF-α treated with ZDPS significantly increased (p < 0.001; Figure 4G). The level of IFN-γ, like IL-2 and IL-6, was significantly lower in the CTX group than in BLK group. The expression level of IFN-γ in mice interfered by polysaccharides or flavonoids increased, but the difference was not significant (Figure 4H).

After medication, the mice were sacrificed by necking, and the relevant organs were obtained to calculate the organ index. The effect of the drug on the recovery of the organ index of the immunocompromised mice was analyzed. Supplementary Figure S1 shows the effects of each drug on the organs of mice.

In comparison with the BLK group, the thymus and spleen index were significantly lower in the CTX group (p < 0.01), while flavonoids and polysaccharides showed a certain recovery ability to the thymus of CTX-treated mice at different concentrations (Supplementary Figure S1A). Higher concentrations of polysaccharides (p < 0.05) and lower concentrations of flavonoids (p < 0.01) showed a certain recovery ability to the spleen of CTX-treated mice (Supplementary Figure S1B). However, the liver (Supplementary Figure S1C), heart, lung, and kidney index in the CTX-, BLK- and polysaccharide- or flavonoid-medicated animals had no obvious difference in the final week of dosing.

The composition and content of SCFAs were calculated using the UPLC-ESI-MS/MS, and SCIEX OS-MQ software was used to analyze the difference of SCFAs in stools between the seven groups of mice. In comparison with the normal BLK group mice, the intestinal acid, butyrate, caproic acid, and isobutyric acid in the mice treated with CTX drugs and the contents of isovaleric acid, valic acid, and probionic acid decreased significantly by 74.79%, 82.83%, 79.90%, 86.14%, 76.41%, 77.01%, and 90.28% respectively (Table 3). However, compared with the model mice treated with CTX, the changes in SCFA contents in the intestinal tract of mice were not consistent after intervention with different drugs (Figure 5). Both high-dose (p < 0.05) and low-dose drug ZDPS (p < 0.01) and low-dose ZHFA (p < 0.05) can restore the ability of intestinal flora to produce acetic acid to a certain extent, while high-dose ZHFA and antibiotic zunyimycin C have no effect on the recovery of intestinal acetic acid in mice (Figure 5A). For the biosynthesis of propionic acid in the intestine, five drugs all showed positive intervention effects. Therefore, the ability of intestinal flora to produce propionic acid can be restored to a certain extent by using high-dose (p < 0.01) and low-dose drugs ZDPS (p < 0.05), as well as high-dose (p < 0.001) and low-dose ZHFA (p < 0.01). The antibiotic zunyimycin C also promoted the recovery of propionic acid production in the intestine of mice (p < 0.01; Figure 5B). For butyric acid, the effects of these five drugs are very different, of which high dose (p < 0.01) and low dose of ZHFA (p < 0.01) can promote the intestinal flora to produce butyric acid to a certain extent, while low dose of ZDPS did not affect the recovery of butyric acid in the intestinal tract of mice. However, high-dose ZDPS had a negative effect on butyric acid production in the intestinal tract of mice similar to zunyimycin (p < 0.01; Figure 5C). For valeric acid biosynthesis, except for the positive effect of low-dose ZDPS on the recovery of intestinal valeric acid production in mice (p < 0.01), high-dose ZHFA had no effect on the recovery of intestinal valeric acid production in mice. However, high dose of ZDPS, low dose of ZHFA, and zunyimycin had a negative effect on the production of valeric acid in the intestine of mice (p < 0.01; Figure 5D). For isovaleric acid, high dose of ZHFA and zunyimycin had no effect on the production of isovaleric acid in mouse intestine, whereas high (p < 0.01) and low dose of ZDPS (p < 0.01) and low dose of ZHFA (p < 0.01) promoted the production of isovaleric acid in mouse intestine (Figure 5E). The effect of the drug on the production of isobutyric acid is very similar to that of isovaleric acid, except that high doses of ZHFA and zunyimycin have no effect on the production of isobutyric acid in the intestine of mice, whereas high (p < 0.0001) and low doses of ZDPS (p < 0.001) and low doses of ZHFA (p < 0.0001) promote the production of isobutyric acid in the intestine of mice (Figure 5F). Notably, all drugs inhibited the production of caproic acid in the intestine of CTX model mice. Consequently, the presence of caproic acid in the sample could not be detected.

The effects of drug treatment on intestinal flora in immunocompromised mice were investigated by analyzing the 16S rDNA sequencing of the stools. The 16S rDNA gene amplicon sequencing method (V3–V4 region) was used, and 78,078–80,736 raw reads and 35,564–52,143 clean tags were generated for each sample, with clean tags that include 706 ASVs ranging from 212 to 351 for different drug treatments. Each group had specific ASVs (Figure 6A).

The seven groups of experimental animals shared 68 ASVs (Core ASVs: represents the ASVs common to all samples). Among the 68 common core ASVs, the largest is Muribaculateae with 32 ASVs, followed by Rikenellacaee with 8 ASVs (including 6 Alistipes), Lachnospiraceae, and Bacteroids with 4 ASVs. Prevotellelaceae, Oscillospiraceae, and Enterorhabdus have three ASVs, while Lactobacillus and Helicobacter have two ASVs. Negatinibacillus, Biophila, Odoribactor, Ruminococcus, Colorectobacter, Alloprevotella, Escherichia coli, and Escherichia Shigella have only one ASV. The ASVs among the groups were significantly different. For example, the CTX group has 320 ASVs, the BLK group has 259 ASVs, and Zunyimycin C, ZDPS200, ZDPS50, ZHFA200, and ZHFA50 have 268, 212, 310, 351, and 298ASVs, respectively. The relationship between circle samples and specifications was analyzed, and the data are shown in Figure 7B. Results show that among the 20 OTUs with the highest abundance (sorted from high to low and numbered according to abundance), Bacteroidote has the highest number of OTUs, including 15 OTUs (OTU1-5, OTU8, OTU10-12, OTU15-16, and OTU18-20), followed by firms, including OTU6, OTU13-14, and OTU17. Finally, Campilobacterota includes OTU7 and OTU9. OTU1 was used as an example. In comparison with the normal group of animals, the abundance of OTU1 in mice decreased by about 2/3 after using CTX. The drugs ZDPS50 and ZDPS200 can promote its proliferation and restore its abundance, but ZHFA50 had no effect, while the use of drugs ZHFA200 and zunyimycin resulted in the continued decline of OTU1 in the animal intestine. For OTU2, after the use of drug CTX, the animal OTU2 abundance decreased, and the drugs ZDPS50, ZHFA200, and zunyimycin could not restore their abundance, whereas ZDPS200 and ZHFA50 could significantly increase their abundance. For OTU3, ZDPS50 and ZHFA50 can improve its abundance, but other drugs cannot change the abundance of OTU3 in CTX-treated animals. OTU4 is different. CTX can reduce its abundance to some extent, but zunyimycin has no effect, whereas the other four drugs significantly increase its abundance. CTX can increase the abundance of OTU5. In addition to being irreversible by ZDPS50, the four other drugs can restore the abundance of OTU5 to near or even below the normal level to a certain extent. CTX can reduce the abundance of OTU8. This condition can be reversed by ZHFA50, and the four other drugs may not affect the recovery of the abundance of OTU8. ZDPS200 can aggravate the decrease in OTU10 abundance caused by CTX, but the four other drugs can increase its abundance. OTU11 was slightly affected by CTX. ZDPS200 can increase its abundance, whereas the other drugs only slightly affected it. Except CTX can reduce the abundance of OTU12 and ZDPS50 can increase its abundance, other drugs only have a slight impact. For OTU15, all treatments only slightly affect its abundance. Zunyimycin and ZHFA200 had little effect on the OTU16 abundance of mice treated with CTX, while ZDPS50, ZDPS200, and ZHFA50 could promote their recovery. ZHFA200 and ZDPS50 slightly affected the OTU18 abundance of mice treated with CTX, while ZDPS200 and ZHFA50 can promote their recovery, and zunyimycin can further increase the OTU18 abundance. All drugs can promote the reduction of the abundance of OTU19 and OTU20 caused by the drug CTX to return to normal. For bacteria such as OTU6, OTU13-14, and OTU17 belonging to Firmicutes, a different finding was obtained. CTX significantly reduced the abundance of OTU6. In addition to zunyimycin, other drugs cannot restore the abundance. CTX only slightly affected OTU13, which has a low abundance, but ZDPS200 and ZHFA50 had no effect, whereas the three other drugs can increase its abundance. Both drugs can increase the abundance of OTU14 with decreased CTX, and decrease the abundance of OTU17 with increased CTX. For OTU7 and OTU9 bacteria belonging to Campilobacterota, drugs can significantly reduce the abundance of OTU7, which has been greatly increased by CTX, while ZDPS50 and ZDPS200 have little effect on CTX, but the three other drugs can increase the abundance of OTU9.

The abundance of intestinal flora of animals in each group affected by drugs was ranked at the taxonomic level of different species, such as phyla, class, order, family, genus, and species, and the influence of drugs on the structure and composition of intestinal flora was analyzed according to their abundance changes. Figure 8 shows the ranking results of the top 30 species at the phyla and species levels. The species with the highest abundance in all treatment groups were classified into the eight main microflora and only a few other microflora species (others).

At the classification level of phyla (Figure 7A), the dominant intestinal microflora of BLK mice without drug treatment were mainly Bacteroidota, Firmicutes, Actinobaciota, Campilobacterota, Proteobateria, Desulfobacterota, and Pattescibateria, whereas Deferribacterota was not detected. After being treated with the drug CTX, the order of dominant bacteria was Bacteroidota, Firmicutes, Campilobacterota, Desulfobacterota, Deferribacterota, Actinobaciota, Proteobateria, and Pattescibacteria. Among them, the abundance of Bacteroidota decreased significantly, whereas the abundance of Firmicutes, Campilobacterota, and Desulfobacterota increased significantly. After the intervention of the drug ZDPS50, the order of dominant bacteria was Bacteroidota, Firmicutes, Campilobacterota, Actinobaciota, Proteobateria, Desulfobacterota, Deferribacterota, and Pattescibateria. The abundance of Bacteroidota and Proteobateria increased, whereas the abundance of Firmicutes and Campilobacterota decreased. After the intervention of the drug ZDPS200, the abundance of Bacteroidota and Actinobaciota further increased, while Firmicutes decreased. However, the extent of the decrease in Campilobacterota abundance was not as large as that in the intervention group of the drug ZDPS50, while Deferribacterota and Pattescibacteria were completely suppressed to a level that could not be detected. The overall change trend of the flora abundance after the intervention of the drug ZHFA50 was similar to that of the drug ZDPS200, but the abundance of Bacteroidota remarkably increased, and the abundance of Firmicute decreased. The abundance of Campilobacterota was higher than in the intervention groups ZDPS200 and ZDPS50. Notably, the high concentration of ZHFA200 drug intervention showed different effects from ZHFA50, and the order of abundance of the dominant bacteria in the ZHFA200 drug intervention group was Firmicutes, Bacteroidota, Campilobacterota, Actinobaciota, Desulfobacterota, Deferribacterota, Proteobateria, and Patescibacteria. In other words, ZHFA200 drug significantly decreased the abundance of Bacteroidota and Campilobacterota while increasing the abundance of Firmicutes and Actinobaciota. For the mice treated with the new antibiotic zunyimycin, the order of the abundance of intestinal flora is Bacteroidota, Firmicutes, Campilobacterota, Actinobaciota, Desulfobacterota, Proteobateria, and Deferribacterota, while the abundance of Pattescibateria decreased to a level that cannot be detected. In short, zunyimycin C increases the abundance of Bacteroidota, Actinobaciota, Desulfobacterota, Proteobacteria, Deferribacterota.

The effects of drugs on the structure and composition of intestinal flora at the species level are complex (Figure 7B). In the BLK group mice without drug treatment, the top 30 most abundant intestinal flora at the level of species were B acidifaciens, L. murinus, C. bacterium, L. reuteri, L. intestinalis, L. bacterium, B. caecimuris, P. gordonii, B. stercorirosoris, B. bacterium, Eubacterium sp., E. coli, Dorea sp., C. stationis, A. Colihominis, Clostridium sp., C inhibens, A. lwoffii, Clostridium sp., B. vulgatus, A. stercorihominis, A. muris, A. urinaeequi, and H. hepaticus. The abundance of finegoldii and other species cannot be detected. However, after the use of CTX drugs to reduce animal immunity, the abundance of its species changed remarkably, and the order of dominant bacteria abundance is C. bacterium, B. acidifaciens, L. bacterium, L. johnsonii, P. goldsteinii, L. intestinalis, L. reuteri, P. gordonii, P. distasonis, L. murinus, B. caecimuris, L. bacterium, B. stercorirosoris, B. bacterium, A. colihominis, Clostridium sp., A. finegoldii, E. coli, B. vulgatus, and Eubacterium sp. The abundance of muris decreased to an undetectable level. After the intervention of ZDPS50, the abundance of intestinal flora in mice changed, and the abundance of dominant flora is in the order of B acidifaciens, L. johnsonii, Lactobacillus, C. bacterium, B. caecimuris, L. murinus, L. reuteri, L. intestinalisus, B. bacterium, P. goldsteinii, P. gordonii, L. bacterium, Dorea sp., C. stationis, A. stercorihominis, A. colihominis, E. colia-Shigella. And P. distasonis, L. bacterium, B. stercorirosoris, Clostridium sp., A. finegoldii, B. vulgatus, Eubacterium sp., Lachnospiraceae FCS020, C. inhibens, A. lwoffii, Clostridium sp., A. muris, and A. urinaeequi. The abundance of hepaticus decreased to an undetectable level. Similarly, after the intervention of ZDPS200, the abundance of intestinal flora in mice changed, and the abundance of dominant flora is in the order of B. acidifaciens, L. murinus, P. gordonii, P. goldsteinii, B. caecimuris, B. stercorirosoris, C. bacterium, Clostridium sp., E. coli, H. hepaticus, A. colihominis, L. reuteri, B. bacterium, and L. bacterium. The abundance of urinaeequi decreased to an undetectable level. After the intervention of ZHFA50, the diversity of intestinal flora in mice was further reduced, and abundance of flora was changed, and the abundance of dominant flora were B. acidifaciens, L. johnsonii, L. reuteri, L. murinus, L. bacterium, P. gordonii, P. distasonis, Eubacterium sp., P. goldsteinii, L. bacterium, L. intestinalis, A. colihominis, B. vulgatus, and E. coli. However, the abundance of B. caecuris, B. stercorosoris, C. bacterium, Clostridium sp., H. hepaticus, Dora sp., C. stationis, A. stercorihominis, Clostridium sp., A. finegoldii, Lachnospirace FCS020. C. inhibens, A. lwoffii, A. muris and A. urinaequi decreased to an undetectable level. After the intervention of ZHFA200, the diversity of intestinal flora in mice was further reduced, the flora abundance was changed, and the dominant flora abundance was in the order of L murinus, L. johnsonii, B. acidifaciens, L. reuteri, L. intestinalis, L. bacterium, B. caecimuris, Eubacterium sp., L. bacterium, C. bacterium, P. gordonii, C. stationis, Dorea sp., P. goldsteinii, E. coli, A. lwoffii, B. vulgatus, and A. colihominis. However, the abundance of P. distasonis, B. bacterium, B. stereosoris, Clostridium sp., H. hyperpaticus, A. stereohominis, Clostridium sp., A. finegoldii, Lachnospirace FCS020, C. inhibens, A. muris and A. urinaequi decreased to an undetectable level. After the intervention of antibiotic zunyimycin, the diversity of intestinal flora in mice was further reduced, the abundance of flora was changed, and the abundance of dominant flora was in the order of B. acidifaciens, L. johnsonii, L. intestinalis, L. murinus, L. reuteri, L. bacterium, B. caecimuris, P. goldsteinii, P. gordonii, Lachnospiraceae FCS020, C. inhibens, Clostridium sp., E. coli, Escherichia-Shigella, A. lwoffii, A. urinaeequi, and A. stercorihominis. However, the abundance of Eubacterium sp., L. bacterium, C. bacterium, C. stationis, Dora sp., B. vulgatus, A. colihominis, P. distasonis, B. bacterium, B. stereosoris, Clostridium sp., H. hepaticus, A. finegoldii, and A. muris decreased to an undetectable level.