Integrative analysis of microbiome and metabolome in rats with Gest-Aid Plus Oral Liquid supplementation reveals mechanism of its healthcare function

Objective: This study aimed to elucidate the possible mechanism of Gest-Aid Plus Oral Liquid (GAP) on healthcare function. Method: Ultrahigh-performance liquid chromatography–quadrupole time-of-flight mass spectrometry-based metabolomics and 16S rDNA sequencing of gut microbiota were performed on serum and fecal samples of GAP and control rats. Additionally, short-chain fatty acids (SCFAs) and inflammatory cytokines in fecal samples were determined through gas chromatography–mass spectrometry and enzyme-linked immunosorbent assay kits. Result: Metabolomics discovered 41 metabolites, which mainly involved amino acid metabolism, lipid metabolism, coenzyme factors, and vitamin metabolism. Administration of GAP increased abundance of Prevotella_9 , Alloprevotella , Blautia , Phascolarctobacterium , Parabacteroides , and Fusicatenibacter , and six SCFAs were increased in the GAP group. Measurement of inflammatory cytokines showed that GAP had an anti-inflammatory effect in rats. Conclusion: Administration of GAP greatly affects the aspartate metabolism and microecology of rats, enhances intestinal motility and gut barrier integrity and anti-inflammation. These findings not only have possible implications for further application of GAP, but also provide a link between the gut microbiome, SCFAs, inflammation and serum metabolites in rats.


Introduction
Gest-Aid Plus Oral Liquid (GAP) is made from varieties of herbaceous plants, including Radix codonopsis, Rhizoma Atractylodis Macrocephalae, Poria cocos and a polysaccharide extract of Hericium erinaceus, which is used to strengthen the spleen and stomach. GAP is mainly suitable for people with intestinal dysfunction and mild gastric mucosal damage. Yang et al. (2006) proved that GAP can increase the content of beneficial bacteria (Bifidobacterium and Lactobacillus) in the intestine while inhibiting the growth of harmful bacteria. It has been found that GAP has protective effects on diverse gastric mucosal injury, including reserpine-induced gastric mucosal injury, stress-induced gastric mucosal injury and ethanolinduced mucosal injury (Ma et al., 2010). However, the mechanisms by which GAP improves intestinal function are still unclear.
The gut microbiome emerged as a critical factor regulating the human physiological state. Previous research showed that Lactobacillus plantarum DSM 2648 was a potential probiotic that enhanced intestinal barrier function by upregulating tight junction proteins and maintaining intestinal integrity (Anderson et al., 2010). Besides, species such as Lactobacillus casei and Bifidobacteria longum were anti-inflammatory, while Helicobacter, Clostridium, and Enterococcus species were proinflammatory (Round and Mazmanian, 2009). Metabolites of intestinal flora affected brain function through transmitting signals to the vagus nerve, and maintained intestinal function via blood circulation, which included short-chain fatty acids (SCFAs), intestinal peptides, tryptophan, and cytokines (O'Mahony et al., 2015;Sherwin et al., 2016Sherwin et al., , 2018. Many factors affected the composition and structure of intestinal microorganisms, including diet, medications, and diseases (Tang et al., 2018;Liu et al., 2019;Nie et al., 2019). For example, it was reported GAP increases the content of Bifidobacterium and Lactobacillus (Yang et al., 2006).
The metabolome refers to the overall view of endogenous metabolites in the body, which directly reflects the physiological state of the body (Psychogios et al., 2011). Studies showed that intestinal dysfunction leads to abnormal metabolism (Li Z. Q. et al., 2019). In addition, there is an interactive relationship between the intestinal flora and the metabolome (Holmes et al., 2011). Therefore, the integrated analysis of microbiomics and metabolomics is an important approach to understand how to maintain intestinal homeostasis.
Based on ultrahigh-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF MS) and 16S rDNA sequencing, metabolomics and microbiome of the control and GAP groups were performed in this study. Then, the difference in metabolism and intestinal flora between the control and GAP groups were described. The quantitative analysis of short-chain fatty acid and inflammatory cytokines between the control and GAP groups was conducted by gas chromatography-mass spectrometry (GC-MS) and enzyme-linked immunosorbent assay (ELISA) kits. The correlation relationship between intestinal flora and SCFAs was obtained through Pearson correlation analysis, which provided clues for explaining the role of intestinal flora and its metabolites in enhancing intestinal function. The goal of this study was to reveal the mechanism of GAP improving intestinal function via regulating intestinal flora and metabolism. This would provide a scientific basis for clarifying the nutritional value of GAP and its further studies.

Materials and Methods
Animals, diets, and sample collection All animal operations involved in this study have been reviewed by the Animal Ethics Committee of Guangdong Provincial Hospital of Traditional Chinese Medicine. Twenty male Sprague Dawley rats (6-week-old, (200±20) g) were provided by Southern Medical University Laboratory Animal Center (Guangdong, China; animal license SCXK2016-0041). Rats were maintained in a specific-pathogenfree (SPF) environment with controlled conditions (12 h light/dark cycle at temperature 20-30 °C and humidity 40-60 per cent). Rats were fed for 5 days' adaption period after they were brought from the Animal Center. Rats had free access to standard chow diet and water.
Then, the animals were randomly assigned to the GAP group and the control group. Rats in the GAP group were given GAP (Infinitus (China) Co., Ltd., Guangzhou, China) at a dose of 1 mL/100 g by intragastric administration for 8 weeks. Rats in the control group were given an equal volume of water. After 4 weeks of treatment, fresh feces samples were collected and immediately stored at -80 °C for a maximum of 4 weeks. At the end of the trial, all of the animals were anesthetized after an overnight fast. Blood was collected from the abdominal aorta. Sera were separated via centrifugation at 3000 r/min for 15 min at 4 °C after standing for 30 min. The supernatants serum was stored at -80 °C for a maximum of 4 weeks.

Sample preparation
Serum samples were thawed on ice prior to analysis. A quality control (QC) sample was prepared by pooling 10 μL from all the serum samples. Proteins were precipitated from the serum samples (200 μL) by adding 800 μL acetonitrile (high-performance liquid chromatography (HPLC) grade; Merck KGaA, Darmstadt, Germany) in 1.5-mL micro tubes at room temperature. The mixture was vortexed for 1 min and centrifuged at 15 000 r/min for 10 min at 4 °C. Two hundred microliters of the supernatant was decanted into a vial with an inner cannula.
The mass spectrometry (MS) scan ranged from m/z 50 to 1500 in positive ion mode and negative ion mode. The cone and capillary voltages were set at 35 V and 5 kV for positive ionization mode, respectively, and 35 V and 4.5 kV, respectively, for negative ionization mode. The collision energy was set at 10 V for time-of-flight mass spectrometry (TOF MS) and 35 V for Product Ion. A desolvation gas flow rate of 50 mL/min at a temperature of 500 °C was used. The data acquisition rate was set to 0.1 s. Data acquisition was performed using Analyst TF 1.4 software (version 4.1; SCIEX Now™, Framingham, MA, USA).

Sample preparation
Microbial DNA was extracted using the HiPure Soil DNA Kits (Magen, Guangzhou, China) according to the manufacturer's protocols. The 16S rDNA V3-V4 region of the ribosomal RNA gene were amplified by polymerase chain reaction (PCR; 95 °C for 2 min, followed by 27 cycles at 98 °C for 10 s, 62 °C for 30 s, and 68 °C for 30 s and a final extension at 68 °C for 10 min) using primers 341F: CCTACGGGNGGCWGCAG; 806R: GGACTACHVGGGTATCTAAT, where the barcode is an eight-base sequence unique to each sample. PCR reactions were performed in triplicate 50 μL mixtures containing 5 μL of 10×KOD buffer, 5 μL of 2.5 mmol/L dNTPs, 1.5 μL of each primer (5 mol/L), 1 μL of KOD polymerase, and 100 ng of template DNA.

16S rDNA sequencing
Amplicons were extracted from 2 per cent agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer's instructions and quantified using ABI StepOnePlus Real-Time PCR System (Life Technologies, Foster City, CA, USA). Purified amplicons were pooled in equimolar and paired-end sequenced (2250) on an Illumina platform according to the standard protocols. The raw reads were deposited into the NCBI Sequence Read Archive database (accession number: PRJNA746631).

Sample preparation
Fecal samples were thawed on ice prior to analysis. Briefly, 150 μg fecal samples were added to 1350 μL 0.5 per cent phosphoric acid (HPLC grade; Merck KGaA, Darmstadt, Germany) in 2-mL micro tubes at room temperature. The mixture was vortexed for 5 min and centrifuged at 7500 r/min for 10 min. Supernatant (1100 μL) was extracted by adding equal amounts of ethyl acetate (HPLC grade; Merck KGaA, Darmstadt, Germany), vortexed for 2 min and centrifuged at 4500 r/min for 15 min. Eight hundred microliters of the supernatant was added to anhydrous sodium sulfate to remove water and then decanted into a vial with an inner cannula.

GC-MS analysis
Each of the samples was injected into an Agilent 7890A/5975C GC-MS system (Agilent, Santa Clara, CA, USA) equipped with a Agilent DB-WAX capillary column (30 mm×0.32 mm, i.d. 0.25 μm). Helium was used as the carrier gas with a constant flow rate of 1.0 mL/min. The column temperature was initially kept at 50 °C for 3 min, then elevated to 100°C at an increasing rate of 25 °C per min, followed by 15 °C per min to 200 °C and finally reached 220 °C at a rate of 25 °C per min for 3 min. The interface and ion source temperature were 220 °C and 230 °C, respectively. MS was conducted in an electron impact ionization mode at 70 eV. Mass data were obtained in a full scan mode from m/z 30 to 550. Total ion chromatograms and fragmentation patterns of GC were acquired using the GC/MSD ChemStation Software (Agilent, Santa Clara, CA, USA).

Data processing
Mass spectrometry data were analyzed using Markerview software (Waters, Milford, MA, USA). The data collection range was 0.5-30 min. The noise threshold and the minimum peak width were set to 0.005‰ and 0.05‰, respectively. Six mass spectral peaks were detected simultaneously at the same retention time, with a retention time deviation of 0.2 min and a mass-to-charge ratio deviation of 0.05‰. Markerview preprocessed data were imported into SIMCA13.0 software (version 14.1; Umetrics, Umea, Sweden) for principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). According to the variable importance in projection (VIP) value in PCA and PLS-DA, combined with the result of a t-test, the differential metabolites were screened.
PeakView ® Software 1.2 (AB Sciex Pte. Ltd., Framingham, MA, USA) was used for qualitative analysis of metabolites. According to the relative ratio of isotopic peaks, the elemental composition of compounds were determined. The structure of differential metabolites was determined through the ionization fragmentation of two mass spectrometry, chemical bond-breaking rules, HMDB library (http://www.hmdb.ca/) and the Chemspider website (http:// www.chemspider.com/). Pathway enrichment analysis of differential metabolites was performed by MetaboAnalyst 4.0 (https://www. metaboanalyst.ca/). Statistical analysis were performed using SPSS software (version 19.0; IBM Corp., Armonk, NY, USA). Student's t-test and Welch's t-test were performed for comparing the relative abundance of metabolites and bacteria, respectively. Differences in metabolites and bacteria were considered to be significant with P<0.05.

Gest-Aid Plus Oral Liquid affected the metabolism of rats
In this experiment, a total of 20 samples were analyzed in a random order. The QC sample was inserted into the analysis sequence to monitor and correct changes in the instrument response. A blank sample was inserted for every 10 samples, and was used to identify and remove the characteristic peaks caused by source pollutants, tube components, or solvent impurities. Analyses were conducted in both positive and negative ion modes on serum samples ( Figure 1A).
In order to intuitively reflect the differences between the metabolomes of the control rats and the GAP rats, a PCA model was established for multivariate identification analysis ( Figure 1B). There was an obvious classification between the clustering of the control and GAP groups, which indicated that long-term intake of Gest-Aid Plus Oral Liquid had an effect on the metabolism of rats. Subsequently, a PLS-DA model was established ( Figure 1C). According to VIP>1 and P<0.05, 41 differential metabolites were screened (17 differential metabolites were found in positive mode and 24 differential metabolites were found in negative mode). See Table 1 for details.
Metaboanalyst 4.0 was used to obtain the related metabolic pathways of 41 potential biomarkers. The obvious separation between the control and GAP groups suggests that significant metabolic change occurred in the GAP group. As shown in Figure 1D, GAP improved metabolism by regulating multiple metabolic pathways, including serine and threonine metabolism, alanine, aspartate and glutamate metabolism, pantothenate and coenzyme A biosynthesis, β-alanine metabolism and sphingolipid metabolism, which were concentrated in amino acid metabolism, lipid metabolism, coenzyme factors and vitamin metabolism.
Gest-Aid Plus Oral Liquid reconstructed the gut microbiome of rats Figure 2A shows that the Shannon-Wiener curve reached saturation in the sequencing volume of this study, indicating that the amount of sequencing data was large enough to reflect most of the microbial  information in the sample. α Diversity refers to the diversity of species within a community or habitat. It mainly focuses on the number of species in a uniform local habitat, so it is also called within-habitat diversity. The result of α diversity analysis ( Figure 2B) showed that the α diversity of the intestinal flora of GAP rats was lower than that of control rats (P<0.05). Moreover, the results of weighted UniFrac principal coordinates analysis (PCoA) ( Figure 2C) showed that the beta diversity of intestinal flora in the GAP and control groups was obviously separated. The distance between samples was calculated based on the weighted UniFrac method. The results suggested that the beta diversity of intestinal flora of GAP rats was more abundant ( Figure 2D). The abundance of intestinal flora of the GAP rats and the control rats at the family level is shown in Figure 2E. It can be seen that compared to control rats, the abundance of Lactobacillaceae, Bacteroidales_S24-7_group, Ruminococcaceae, and Bacteroides Bacteroidaceae had decreased in the GAP rats, while the abundance of Prevotellaceae, Lachnospiraceae, Acidaminococcaceae, Peptostreptococcaceae, Erysipelotrichaceae, and Coriobacteriaceae was relatively increased. Welch's t-test was performed to screen out the differential bacteria of the GAP rats based on P<0.05. At the family level, the abundance of Bacteroidales_S24-7_group, Acidaminococcaceae, and Porphyromonadaceae changed significantly; at the genus level, compared with the control group, the abundance of Prevotella_9, Alloprevotella, Blautia, Phascolarctobacterium, Parabacteroides and Fusicatenibacter significantly increased, while that of Gram-negative_bacterium_cTPY-13, Anaerotruncus and Ruminiclostridium decreased in the GAP group ( Figure 2F and 2G).

Gest-Aid Plus Oral Liquid increased the content of SCFAs and cytokines
Measurement of fecal SCFAs of the control and GAP rats based on GC-MS showed that the contents of acetic acid, propionic acid, butyric acid, isobutyric acid were higher in the GAP rats (P<0.05), and the content of valeric acid and isovaleric acid increased with no significance, as shown in Figure 3A.
The anti-inflammatory and proinflammatory factors after GAP intervention were changed by determining the IL-10, TNFα, IL-6, IL-12 and CRP in serum. As shown in Figure 3B, the decreased production of CRP in the GAP group indicates that the inflammatory response is weakening in rats. In addition, significant increased production of IL-10 and decrease of TNF-α, IL-6 and IL-12 were observed in the GAP group compared to the control group (P<0.05). This result suggested that the anti-inflammatory response of rats was significantly enhanced after GAP intervention.

Correlation analysis between the altered gut microbiota and SCFAs
Redundancy analysis (RDA) reflects the relationship between the gut microbiota and environmental factors based on a linear model. This study used RDA redundancy analysis to characterize the correlation between intestinal flora and SCFAs, as shown in Figure 3D. The correlation heatmap was drawn to reveal a close relationship between intestinal flora and SCFAs ( Figure 3C). Then a co-occurrence network between intestinal flora and SCFAs was obtained ( Figure 3E). The figure described that propionic acid was positively correlated with Alloprevotella and Phascolarctobacterium; correlation coefficient is 0.7, and blue indicates the correlation coefficient is -0.53; *P value less than 0.05, **P value less than 0.01). (D) RDA model score chart of intestinal flora and SCFAs (the quadrant in which it is located indicates the positive and negative correlation between the environmental factor and the sorting axis; arrows indicate environmental factors; the length of the arrow line represents the degree of correlation between an environmental factor and the distribution of the research objects: the longer the line, the more the environmental factor represents the research object and the greater the influence of the distribution; the angle between the arrow line and the sort axis represents the correlation between this environmental factor and the sort axis; the smaller the angle, the higher the correlation). (E) Co-occurrence network between gut microbiota and SCFAs (the solid line indicates positive correlation, the dashed line indicates negative correlation, and the thickness of the line is drawn according to the size of the correlation. The larger the size of the node, the larger the degree value). CRP, C-reactive protein; IL-6, interleukin-6; IL-12, interleukin-12; RDA, redundancy analysis; SCFAs, short-chain fatty acids.
isobutyric acid was positively correlated with Blautia and Fusicatenibacter; and isovaleric acid was positively correlated with Blautia and Fusicatenibacter.

Discussion
Metabolomics revealed that intake of GAP significantly influenced the metabolism status of rats. Forty-one biomarkers discovered by PLS-DA and its enrichment pathways were associated to GAP function, including serine and threonine metabolism, alanine, aspartate and glutamate metabolism, β-alanine metabolism, sphingolipids metabolism, pantothenate and coenzyme A biosynthesis. Serine and threonine metabolism, alanine, aspartate and glutamate metabolism, β-alanine metabolism were subclasses of amino acid metabolism, and the map of these metabolism pathways indicated that serine, threonine, alanine and glutamate were produced from aspartate catalyzed by different enzymes. Therefore, a high content of l-aspartic acid in the GAP rats resulted from the regulation of aspartate metabolism. GAP modulated sphingolipid metabolism to upregulate the content of sphingosine, which is a downstream product of aspartate metabolism. The study exposited that changes in sphingolipid composition affect the contraction of gastric smooth muscle (Choi et al., 2015). Wang et al. (2017) revealed that Fructus Aurantii flavonoids regulated sphingosine and plant sphingosine and modulated the sphingomyelin metabolism pathway to affect intestinal motility. The generation of coenzyme A (CoA) from pantothenate produced from β-alanine and catalyzed by phosphopantetheine adenylyltransferase/ dephospho-CoA kinase (Daugherty et al., 2002). CoA played a crucial role affecting the beta-oxidation of fatty acids and protecting mitochondria from free radicals-induced oxidative stress (Koves et al., 2008). Previous studies indicated that hyperphosphorylation of SIRT3 deteriorated lipopolysaccharide (LPS)-induced decrease of gut tight junction proteins (such as zonula occludens-1 and occludin) expression and disrupted gut barrier integrity, which could be inhibited by acetyl CoA (Kwon et al., 2017;Chen et al., 2019). The results and literature analysis found that intake of GAP upregulated aspartate metabolism and its downstream sphingolipid metabolism and pantothenate and CoA biosynthesis, to maintain intestinal motility and gut barrier integrity.
Our study revealed that microecology of rats was greatly changed by intake of GAP. At the family level, the Bacteroidales_S24-7_group was considered to be intestinal probiotics with a negative correlation with intestinal inflammation . Acidaminococcaceae belonged to the Negativicutes class, producing propionic acid through the succinic acid pathway (Boga et al., 2007). Studies by Paulsen and Hu have shown that fiber and polysaccharide supplementation resulted in a significant increase in Porphyromonadaceae abundance and SCFAs (Ma et al., 2017;Gamage et al., 2018). More specifically, at the genus level, administration of GAP increased the abundance of Prevotella_9, Alloprevotella, Blautia, Phascolarctobacterium, Parabacteroides and Fusicatenibacter, and reduced that of Gram-negative_bacterium_cTPY-13, Anaerotruncus and Ruminiclostridium. The genus Prevotella_9 belongs to the family Prevotellaceae and phylum Bacteroidetes. Prevotella_9 contained a set of bacterial genes for cellulose hydrolysis, and it was a major dietary fiber fermenter producing SCFAs (Jadoon et al., 2018). SCFAs maintained the gut barrier function by reducing gut content pH, increasing mucin formation, providing energy for intestinal epithelial cells and advancing calcium absorption (Chen et al., 2020). Previous studies described that Prevotella_9 inhibited LPS-induced intestinal inflammation (Xu et al., 2020). Alloprevotella of family Prevotellaceae also produced SCFAs (Downes et al., 2013). Phascolarctobacterium within the family Acidaminococcaceae utilized succinate and pyruvate to produce propionate and acetate (Shigeno et al., 2019). The membranous fraction of Parabacteroides decreased the production of proinflammatory cytokines including TNF-α, which was induced by LPS-activated macrophages through a possible direct effect on innate cell immunity (Shigeno et al., 2019). As the occurrence of intestinal inflammation lead to disruption of the intestinal mucosa, this anti-inflammatory activity of Parabacteroides was beneficial to maintain intestinal barrier integrity (Wang et al., 2005). Parabacteroides stabilized the microbiota composition by improving epithelial barrier function or regulating the mucosal immune system (Shigeno et al., 2019). From the correlation between intestinal flora and SCFAs, the high expression of Blautia and Fusicatenibacter were positively correlated with isobutyric acid and isovaleric acid, respectively.
The result of the measurement of SCFAs indicated that the content of acetic acid, propionic acid, butyric acid, isobutyric acid (P<0.05), valeric acid and isovaleric acid was higher in GAP rats. Acetic acid, propionic acid and butyric acid were the main products of dietary fiber fermentation in the colony (Miller and Wolin, 1996), while valeric acid, isobutyric acid and isovaleric acid were mainly produced by intestinal flora fermentation protein. Studies have shown that SCFAs play important roles in maintaining the intestinal barrier, inhibiting intestinal inflammation, and enhancing intestinal motility (Nilsson et al., 2008). Acetic acid and propionic acid protected intestinal cells by stimulating mucus secretion in the intestine (Barcelo et al., 2000). Gaudier revealed that propionic acid affected Mucin 2 (MUC2) gene expression and induced mucus secretion by intestinal cells (Gaudier et al., 2009). Jing Lin suggested that butyrate improved intestinal barrier function by increasing tight junction protein expression through the adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) pathway (Peng et al., 2009). Colgan said that butyrate promoted epithelial barrier integrity through IL-10 receptor-dependent inhibition of Claudin-2 (a cation-selective pore) expression (Zheng et al., 2017). Propionic acid and butyric acid inhibited histone deacetylase to induce histone hyperacetylation (Arpaia et al., 2013), thereby regulating gene expression and cell differentiation, and downregulating proinflammatory factors IL-6 and IL-12 to exert anti-inflammatory effects (Hamer et al., 2008). Moreover, propionate and butyrate promoted the differentiation of regulatory T cells and induced the expression of the transcription factors Foxp3, which was a core element to limit intestinal inflammation (Josefowicz et al., 2012). Butyric acid also promoted T cell differentiation and increased IL-10 content through binding to GPR109A (Josefowicz et al., 2012). Preclinical rodent studies indicated that SCFAs stimulated the release of intestinal hormone peptide YY or serotonin to regulate bowel movements (Cherbut et al., 1998;Jadoon et al., 2018). There was a study that showed that SCFAs controlled the rhythm of Ffar3 expression in the colonic myenteric plexus, then regulated the rhythm of colonic movement (Segers et al., 2019). Blakeney et al. (2019) elaborated that isovaleric acid in the colon induced intestinal smooth muscle relaxation through the cAMP/PKA pathway. Combining the results of species diversity analysis at the genus level, it can be inferred that intake of GAP promoted the growth of Prevotella_9, Alloprevotella, Blautia, Phascolarctobacterium, Parabacteroides and Fusicatenibacter, which were associated with the production of SCFAs and inflammation to enhance intestinal function (Figure 4).

Conclusion
The study, for the first time, combined the metabolome and the microbiome to gain an insight into the mechanism of GAP enhancing intestinal function. Intake of GAP upregulated aspartate metabolism and its downstream sphingolipid metabolism and pantothenate and CoA biosynthesis, to maintain intestinal motility and gut barrier integrity. Besides, GAP administration promoted the growth of Prevotella_9, Alloprevotella, Blautia, Phascolarctobacterium, Parabacteroides and Fusicatenibacter, which were associated with production of SCFAs and anti-inflammatory effects to enhance intestinal function. These results have provided the basic information for understanding the mechanisms of the healthcare function of GAP through metabolism and intestinal flora, which provides a useful guide for its further application and development.