Effect of a high-collagen peptide diet on the gut microbiota and short-chain fatty acid metabolism

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Fengfeng Mei, Zhouwei Duan, Muxue Chen, Jinfeng Lu, Meihui Zhao, Laihao Li, Xuanri Shen, Guanghua Xia, Shengjun Chen,
Effect of a high-collagen peptide diet on the gut microbiota and short-chain fatty acid metabolism,
Journal of Functional Foods, Volume 75, 2020, 104278, ISSN 1756-4646,
https://doi.org/10.1016/j.jff.2020.104278.

Highlights

  • •A high-dose intake of the collagen peptides increased body weights and caused liver dysfunction.
  • •A high-dose intake of the collagen peptides could profile the profiles of SCFA metabolism.
  • •A high-dose intake of the collagen peptides could alter of the gut microbiota community.

Abstract

In this study, a total of 24 male Sprague Dawley rats were randomly divided into 3 group (Collagen peptide of Salmon salar skin group, Ss-SCP; Collagen peptide of Tilapia nilotica skin group, Tn-SCP and Model control group, MC) to investigate the impact of a high-collagen peptide diet on the gut microbiota and host health. After 16 days intervention, the body weights of the Ss-SCP and Tn-SCP intervention groups were significantly increased and the liver index was also remarkably higher than that of the MC group. The acetic acid and propionic acid levels in feces were both significantly increased in the diet high-collagen peptide groups and valerate acid level was lower than that in the MC group. With the intervention of a high-dose collagen peptide diet, the gut microbiota of the groups was shifted with increased abundance of Lactobacillus, Unidentified-Prevotellaceae, Allobaculum, and Parasutterella, whereas the Tn-SCP administration have caused low abundance of Anaerostipes, Blautia, and Fusicatenibacter. The relative abundance of Allobaculum as well as Parasutterella was positively correlated with propionic acid and acetic acid levels, respectively. In addition, Allobaculum abundance was negatively correlated with valerate acid level. The serum valerate acid content was potentially harmful to rat health and significantly increased in the groups intervened with collagen peptide. All together, these results showed that administration of diet high-collagen peptide shifts the gut microbiota in rats and induced a disturbance in short-chain fatty acid metabolism which is potentially harmful to health.

Graphical abstract

Keywords

Collagen peptide

Gut microbiota

SCFA metabolism

Valerate acid

1. Introduction

Collagen peptide as a hydrolysate of collagen has various beneficial effects, such as protecting skin from aging, promoting wound healing (Mei et al., 2020, Zhang et al., 2011b), improving bone health (Song, Zhang, Zhang, & Li, 2019), increasing muscle strength (Hong, Fan, Chalamaiah, & Wu, 2019) reducing obesity (Lee et al., 2017), maintaining blood pressure (O’Keeffe, Norris, Alashi, Aluko, & FitzGerald, 2017), preventing atherosclerosis (Mallavia et al., 2013, McCabe et al., 2013), and modifying lipid metabolism (Blachier et al., 2019, Lin et al., 2012). These positive effects of collagen peptide oral administration on human health have motivated people to consume a high-collagen peptide diet in some areas of the world such as china. Specially, for the people who want to keep skin young and healthy, lose weight or the athletes who want to increase muscle content on a high-protein diet, protein intake is usually two to three times more than the recommended amount (Blachier et al., 2019, Eissa et al., 2019; L. Zhang et al., 2018). However, according to the current recommendation of protein amount recommended by the FAO/WHO at 2007 (FAO/WHO, 2007), the safe complete protein intake level is 0.83 g/kg·bw per day for both men and women, which means that the administration of 49.8 g protein satisfies the daily nutritional requirements of a 60-kg man.

According to the dietary protein consumption beyond the FAO/WHO-recommended amount is increasingly being associated with potentially harmful effects, including kidney diseases (Juraschek, Appel, Anderson, & Miller, 2013), bowel diseases, and hepatic dysfunction (Sun et al., 2019). Low-molecular-weight peptides have better bioavailability and are preferred over the larger peptides and parent proteins (Wang et al., 2019, Wang et al., 2019). In addition, low-molecular-weight collagen peptides, but not the complete proteins, from fish skin possess high immunomodulatory and antioxidant activity and better absorption of collagen than proteins from other sources (Xu, Hong, Wu, & Yan, 2019). Previous studies provided insight into the function of low-molecular-weight collagen peptides from fish skin in assist with clinical problems such as chronic wounds (Yang, Zhang, Li, & Hou, 2018). For better efficacy, 2 g/kg is often chosen as the treatment dose in a murine model (Yang et al., 2018, Zhang et al., 2011b), which is a high-dose collagen peptide intervention. Therefore, we need to better understand the detrimental of a high-dose collagen peptide diet.

Besides the host physiological condition, the gut microbiota also plays a key role in protein metabolism (Blachier et al., 2019, De Sordi et al., 2018, Han et al., 2018, Sun et al., 2019). Protein digestion is a very efficient process that depends on the nature of the protein and the proteins or peptides that undergo catalysis by bacterial proteases and peptidases to release shorter peptides and amino acids (Portune et al., 2016). Amino acid synthesis and metabolism in vivo largely remain to be determined, along with management of the effect of gut microbiota on the body nitrogen metabolism (Neis, Dejong, & Rensen, 2015). The increased transfer of nitrogenous compounds from high dietary protein liable to modify the gut microbiota, metabolic activity, and production of metabolites (Blachier et al., 2019, Fan et al., 2017, Portune et al., 2016). Short chain fatty acids (SCFA), including acetate, propionate, butyrate, and valerate acid, are some of the major metabolites of intestinal microorganisms, which are mainly produced by the fermentation of diet fiber and resistant starch that is not digested and absorbed by the host (Bloemen et al., 2010). Recent studies showed that dietary protein could increase SCFA production via increasing gut bacterial relative abundance, which is associated with SCFA metabolism (Han et al., 2018). Following production, acetate, propionate and butyrate are used for energy metabolism after transportation to the liver (Bloemen et al., 2010). Valerate acid, as a potential deleterious SCFA, is excreted in the feces instead of being absorbed into the blood. The remainder of the SCFAs are oxidized by the liver to prevent systemic, highly toxic concentrations.

Despite several crucial studies investigating the relationship of dietary protein within the recommended amount and the gut microbiota axis(Fan et al., 2017); (Han et al., 2018), the link between overload dietary protein, the gut microbiota, and SCFA metabolism remains unclear, and the potential deleterious effects on a body with excess protein remain unevaluated.

In this study, we administered rats with a high-collagen peptide diet composed of the hydrolysis products from Salmon salar (deep-sea fish, Ss-SCP) and Tilapia nilotica (fresh water fish, Tn-SCP) skin collagen. We analyzed the gut microbiota by 16S rDNA high-throughput sequencing, investigated SCFA metabolism, the liver index, cardiac index, spleen index and altered body weights of rats.

2. Materials and methods

2.1. Preparation of collagen peptides

Salmon salar skin was purchased from the market (Hainan, China). Tilapia nilotica collagen peptide (Tn-SCP) was donated by Hainan SEMNL Biotechnology Co., Ltd. (Hainan, China). All chemicals and reagents used were of analytical grade.

Salmon salar collagen peptide (Ss-SCP) was derived from Salmon salar skin by enzymatic hydrolysis according to the method of Mei et al. (2020). Briefly, for purification, the Salmon salar skin was mixed with 20 times the volume of 0.1 mol/L NaOH for 10 min. Second, at pH 9.5, a complex of 0.5% lipase and protease was added and incubated for 4 h before being mixed with 0.05 mol/L acetic acid pH 4 for 3.5 h at 64 °C. Lastly, the mixture was dried into a powder by spray-drying to Ss-SCP powder (Mei et al., 2020, Zhang et al., 2011a). In addition, Tilapia nilotica collagen peptide (Tn-SCP) was donated by Hainan SEMNL Biotechnology Co., Ltd. (Hainan, China). The collagen peptide samples were purified and the amino acid composition as well as the molecular weight distribution was analyzed by the Analysis & Testing Center Jiangnan University (Jiangsu, China).

2.2. Animals and diets

A total of 24 male Sprague Dawley (SD) rats (160±10g) were obtained from TianQin Biotechnology Company of Changsha. Rats were housed in the same conditions, including a constant temperature of 23 ± 1 °C and a 12-hour light–dark cycle (Xia, Yu, et al., 2015). All animals were handled according to the guidelines of the principle of Laboratory Animal Care of China Animal Health and Epidemiology Center (www.cahec.cn).

Before experiments, all rats were accepted a seven day’s acclimation period (Han et al., 2018) with a standard isonitrogeneous full-value chow (Changsha TianQin Biotechnology Co. Ltd., China). After that, SD rats were randomly assigned to three groups (n = 8), i.e. the model control group (MC), a Salmon salar skin collagen peptide (Ss-SCP) administration group, and a Tilapia nilotica skin collagen peptide (Tn-SCP) administration group. Rats in the collagen peptide groups were intragastric administered Ss-SCP and Tn-SCP with the dose of 2 g/kg body weight, while rats in MC were intragastric administered the same dose of physiological saline solution as vehicle. All rats were maintained on standard full-value chow (Changsha TianQin Biotechnology Co. Ltd., China) with 18% protein at the same time and weighted every day. Rats received a medium and long term daily oral administration of collagen peptide until they were sacrificed on day 16. The liver index, cardiac index and spleen index were recorded immediately after sacrificed.

2.3. Collection of blood and fecal samples

On day 16, blood samples were collected after the rats were sacrificed and centrifuged (3000 r/min for 20 min at 4 °C) to separate the serum (Mei et al., 2020). Fresh rat fecal samples were collected with sterile forceps aseptically immediately after defecation. The fresh feces were dropped into liquid nitrogen immediately after collection and stored at −80 °C for microbial diversity and metabolic profiling analysis (Xia, Zhao, et al., 2015).

2.4. Genomic DNA extraction, PCR amplification, high throughput sequencing and sequence data bioinformatics analysis

To investigate the relationship among the diet collagen peptide and gut microbiota, 16S rDNA high-throughput sequencing was used to investigate the change in gut bacteria.

The genomic DNA of the feces was extracted with the Qiagen QIAamp DNA Stool Mini Kit (Qiagen, Germany) according to previous study (Zhang & Zhang, 2018), and then the purity and concentration of DNA were detected by agar-gel electrophoresis. An appropriate amount of sample DNA was taken and diluted to 1 ng/ L with sterile water for subsequent 16S rRNA genes.

The S5-16SV45 hypervariable region of the bacterial 16S rRNA gene was amplified with the 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 907R (5′-CCGT CAATTCCTTTGAGTTT-3′) primers. The PCR products were verified by 2% agarose gel electrophoresis. The amplicon was extracted from 2% agarose gel and purified using QIAquick PCR purification kit (QIAGEN, Hilden, Germany).

16S rDNA amplicon sequencing was used to target the variable regions, and general primers were designed for PCR amplification of the variable region; then, sequencing analysis and species identification were carried out for the hypervariable regions. According to the characteristics of the amplified region, the IonS5TMXL sequencing platform was used to construct a small fragment library for single-end sequencing. To study the species composition of each sample, the Effective Tags of all samples were clustered into Operational Taxonomic Units with 97% consistency (Identity), and the representative sequences of OTUs were annotated with species. The alpha and beta diversity analysis was used to determine the differences among samples (Mei et al., 2020). The UPGMA-tree and Shanon-Weiner index was created using the Qiime analysis software. The raw data of the high-throughput sequencing can be found at the Novogene data platform (https://magic.novogene.com).

Cutadapt (V1.9.1, http://cutadapt.readthedocs.io/en/stable/) was used at first to identify low-quality partly sheared reads, and according to the Barcode these reads were split from the sample data reads. The barcode was removed and the preliminary quality of the reads was assessed to obtain the original data sequence (raw reads). Then, chimeric sequences were identified by searching the annotation database (https://github.com/torognes/vsearch/) for matching chimeric sequences and species, finally the chimeric sequences were removed to obtain the final validated data.

Next, the uparse software was used (Uparse v7.0.1001, http://www.drive5.com/uparse/) to cluster all clean reads with a default of 97% consistency resulting in clustering of the OTUs sequence. At the same time, based on the principles of the algorithm, this program will select the typical sequence of the OTUs. The sequences with the highest frequency were selected as the OTU representative sequences. The Mothur method and SILVA132 SSU rRNA database (http://www.arb-silva.de/) were used on the OTU sequences to conduct the species annotation analysis (setting threshold of 0.8–1), acquire taxonomy information and the classification level community composition of each sample: kingdom (community), phylum, class, order, family, genus, and species. The MUSCLE (Version 3.8.31, http://www.drive5.com/muscle/) software was used for multiple sequence alignment. After that, the predicted functional alterations of the gut microbiota in each group were evaluated using the SILVA SSU Ref NR database. Finally, the data from each sample was homogenized and the least amount of data in the sample was used to standardize the homogenization. The subsequent alpha and beta diversity analysis were all based on the homogenized data. The metagenome data are available at NCBI Sequence Read Archive (SRA) data base and Biosample under accession number PRJNA648676.

2.5. Determination of SCFAs in feces and serum

Serum samples (100 µL) were mixed with 25 µL methanol and 50 µL ethanol for deproteinization. The fecal samples were dispersed in distilled water (170 mg/mL), and mixed for 15 min. The supernatant of the mixture was collected after centrifugation (3000 r/min for 15 min at 4 °C) to further analyze the SCFAs. SCFAs (acetate, propionate, butyrate and Valerate acid in feces and serum) were studied with the Agilent 7890A gas chromatograph (Agilent, C) equipped with a flame ionization detector and a HP-FFAP column (30 mm × 0.32 mm × 0.25 µm, Agilent, USA) to evaluate the SCFA content of samples. The standard acetic acid, propionic acid, and Valerate acid (>99%) for gas chromatograph analysis were purchased from Aladdin Bio-Chem Technology Co., Ltd., China (Shanghai, China). All samples were prepared for SCFA analysis according to the following program: an initial temperature of 80 °C for 0.5 min, 80–150 °C at 4 °C/min, and finally 150–230 °C at 20 °C/min for 10 min. The detector temperature was 250 °C.

2.6. Statistical analysis

Data are presented as the mean ± standard error of the mean. The sequence data bioinformatics analysis was used Source Tracker analyses and micro PITA analyses. T-test and Wilcox rank sum test were performed for 2 groups. R software (Version 2.15.3) was used to draw PCA, PCoA, and NMDS diagrams. Other distribution and statistical comparison analyses of each group were performed using an ANOVA test in the Statistical Product and Service Solutions software. The analysis probability values of P < 0.05 were accepted as statistically significant.

3. Results

3.1. Composition and characteristics of collagen peptides

Our previous study has found that the average molecular weight distribution of Tn-SCP was 460 Da and the average molecular weight of Ss-SCP was consistent with Tn-SCP (Mei et al., 2020).

As presented in Table 1, the amino acid composition of Tn-SCP and Ss-SCP were different. The Tn-SCP was rich in Gly > Glu > Pro > Ala > Hyp > Arg > Asp and the Ss-SCP was rich in Gly > Glu > Pro > Hyp > Asp > Ala > Arg. In addition, the essential amino acids (EAA) of Tn-SCP and Ss-SCP accounted for 15.81% and 22.20% of the total amino acids (EAA/TAA), respectively. The ratio of essential amino acids to non-essential amino acids (EAA/NEAA) of Tn-SCP and Ss-SCP were 18.77% and 28.55%, respectively. These results indicated that the Tn-SCP and Ss-SCP conformed to the characteristics of collagen peptides. However, Ss-SCP and Tn-SCP were composed of incomplete proteins according to the FAO/WHO, which defines incomplete proteins as EAA/TAA ≥ 0.4 and high-quality proteins as EAA/NEAA ≥ 0.6.

Table 1. Amino acid composition.

Amino AcidAmino Acid composition of Tn-SCP (g/100 g)Amino Acid composition of Ss-SCP (g/100 g)
Asp5.447.29
Glu11.2212.22
Ser2.834.23
His0.751.61
Gly23.6723.77
Thr2.772.53
Arg8.346.08
Ala10.246.59
Tyr0.320.03
Val2.342.94
Met1.060.03
Phe1.902.51
Ile1.302.57
Leu2.624.61
Lys3.405.66
Pro10.429.79
Hyp8.727.51
EAA15.3920.85
NEAA81.9573.04
TAA97.3493.89
EAA/NEAA (%)18.7728.55
EAA/TAA (%)15.8122.20

TAA: total amino acids, EAA: essential amino acids, NEAA: non-essential amino acids

3.2. Diet high-dose of collagen peptide directly influenced host health

To investigate the impact of the high-collagen peptide diet on rats, the general information of each group of rats were recorded. During the days of feeding, the daily food intake per rat was slightly different. Approximately, per rat in each group, 7.2 g crude protein from full-value chow was consumed (Table 2).

Table 2. Full-value chow daily intake information.

Empty CellFull-value chow (g/100 g)Total daily intake (per rat)
Moisture104
Crude protein %187.2
Crude fat %41.6
Fiber52
Ash83.2

The general full-value chow daily intake information was recorded to investigate the average daily basic nutrient intake.

The average initial weight of all rats was 160 g ±10 g (Fig. 1A) and the final body weight of rats in the Tn-SCP and Ss-SCP groups was significantly higher than the MC group (Fig. 1A-B) (P < 0.05). In addition, the viscera index of the cardiac, liver (Sun et al., 2019) and spleen, which is directly correlated with amino acid metabolism (Blachier et al., 2019, Juraschek et al., 2013), was recorded immediately after sacrifice. The cardiac index was not significantly different between each group (Fig. 1C) (P > 0.05). In addition, compared with the MC, the spleen index in the Tn-SCP group was significantly increased but not significantly different from the Ss-SCP group, which might be associated with the amino acid composition of the peptides. The liver weight in the collagen peptide groups was significantly higher than in the MC (Fig. 1D) (Ss-SCP vs MC, P < 0.01; Tn-SCP vs MC, P < 0.001). Histologically, the liver histological image showed that compared with the MC group the arrangement of hepatocytes in the collagen group was relatively loose, and large number of cells were swollen near the portal area, which showed obvious hepatocyte injury. Thus, these data provide compelling evidence that intervention with a high collagen peptide diet directly influenced host health and might induce weight gain of the host.

3.3. Diet high-dose collagen peptide induced a credible change of gut microbiota transfer

After 16S rDNA high-throughput sequencing, an average of 85,231 reads were detected per sample, and 80,095 effective reads were obtained through quality control on average, with the quality control effective rate reaching 94.04%. Sequence clustering was conducted to transform the read data into OTUs with 97% consistency, and a total of 612 OTUs were obtained (Table 3).

Table 3. Information of 16S rDNA high-throughput sequencing.

Empty CellRaw readsClean readsBase (nt)Avglen (nt)Q20GC%Effective (%)
MC84,64380,09929,865372845394.68
Ss-SCP85,73180,070298,265372825393.48
Tn-SCP85,31780,1152,961,458369775399.95
PS85,23180,09529,688,47337075.8353.2994.04

Data preprocessing and quality control. The OTU levels of the large intestine reactor are different. Samples were collected from the rats immediately after sacrifice (n = 21). Raw reads were filtered for sequences of low-quality. Clean reads refer to the sequence that is finally used for subsequent analysis after filtering chimeras. Base refers to the Base number of final Clean reads. AvgLen refers to the average length of the Clean reads. Q20 refers to the percentage of bases whose base mass value in Clean reads is greater than 20 (sequencing error rate is less than 1%). GC (%) represents GC base content in the Clean reads. Effective (%) represents the percentage of the number of Clean reads versus the number of Raw reads. MC, model control group. Ss-SCP, Salmon salar skin collagen peptide group. Tn-SCP, Tilapia nilotica skin collagen peptide group. PS, Average value per sample in each group.

Rarefaction curves (Fig. 2A) and rank abundance (Fig. 2B) showed that the sample size and sequencing depth were increased and no additional OTUs were detected indicating that the gut bacteria sequencing results, which reflect sample diversity and richness of the microbiota, were credible.

Principal coordinate analysis (PCoA) (Fig. 2C) revealed that the principal components of PC1 and PC2 contributed about 67.54% and 13.35%, respectively. The PCoA indicated that there was a slight difference between the MC and Ss-SCP groups, however, the PC1 and PC2 in the Tn-SCP group were significantly different in comparison to the MC. The Tn-SCP group was mainly concentrated in the third and fourth quadrants, while the Ss-SCP and MC groups were mainly concentrated in the first and second quadrants. Nonmetric multidimensional (Fig. 2D) analysis further demonstrated that there was a total separation between the gut microbiota profiles of the MC and Tn-SCP groups, but there was similarity between the MC and Ss-SCP groups. Observed_species index and Shannon index (Fig. 2E–F) was significantly different between Mc VS Tn-SCP (P < 0.05) and showed no statistical difference between MC VS Ss-SCP and Ss-SCP VS Tn-SCP (P > 0.05). Collectively, the overall results indicated that the gut microbiota sequencing results in the groups with the high-collagen peptide diet, which reflect sample diversity and richness of microbiota, were credible and the extent of the gut microbiota transformation might be related to the amino acid composition of the collagen peptides.

3.4. Composition of gut microbiota

The gut microbiota at different classification levels under a high-collagen peptide diet was profiled using 16S rDNA high-throughput sequencing. Bacterial DNA profiles (obtained using the weighted Unifrac method with an arithmetic mean dendrogram) displayed a clear segregation between the Tn-SCP and MC and a slight difference between the Ss-SCP and MC (Fig. 3A). The relative abundance of the gut microbiota at the phylum level showed a significant alteration of microbiota in the MC versus Tn-SCP, including Firmicutes (61% in MC vs. 39% in Tn-SCP) (P < 0.01), Bacteroidetes (37% in MC vs. 58.7% in Tn-SCP) (P < 0.01) and Proteobacteria (1.34% in MC vs. 1.12% in Tn-SCP) (P < 0.05), but was insignificant between the MC and Ss-SCP (Fig. 3B–C). At the genus level, the gut microbiota showed a striking difference in those three groups, especially in Lactobacillus, Unidentified-Prevotellaceae, Blautia, Fusicatenibacter, Allobaculum and Anaerostipes (Fig. 3D, Table 4). Despite the fact that there were six bacteria altered in the collagen peptide group compared with the MC, only Allobaculum, Anaerostipes and Fusicatenibacter were significantly changed in both the Tn-SCP and Ss-SCP groups (Table 4) (P < 0.01). Interestingly, the Lactobacillus and Unidentified-Prevotellaceae were sharply changed in the Tn-SCP group but only had a slight difference in the Ss-SCP group compared to the MC (Table 4). All in all, these results indicated that the gut microbiota changes were significantly associated with the composition and characteristics of the collagen peptides.

Table 4. Relative abundance at the genus level of top nine bacteria.

Empty CellMCSs-SCPTn-SCP
Lactobacillus0.281307 ± 0.1825480.2478 ± 0.1299650.132685 ± 0.114587*
Unidentified-Prevotellaceae0.151382 ± 0.1449750.103146 ± 0.0780580.253448 ± 0.096791*
Blautia0.092052 ± 0.0626710.109049 ± 0.1005740.035863 ± 0.032427*
Fusicatenibacter0.043249 ± 0.0393420.027526 ± 0.019437*0.018091 ± 0.019437**
Alloprevotella0.055212 ± 0.0347870.057975 ± 0.0418950.052572 ± 0.030562
Allobaculum0.010166 ± 0.0104260.03051 ± 0.036844*0.036738 ± 0.02502*
Anaerostipes0.01548 ± 0.0330490.002695 ± 0.002277*0.003661 ± 0.008809*
Phascolarctobacterium0.048534 ± 0.0178690.052403 ± 0.0266390.049421 ± 0.023741
Bacteroides0.021055 ± 0.00850.019016 ± 0.0150150.031242 ± 0.018069

Data are the mean ± standard error of the mean. *P < 0.05, **P < 0.01, compared with the MC group.

3.5. The altered gut microbiota was linked to the concentration of SCFAs

To investigate the impact of a high-collagen peptide diet on the gut microbiota and a direct link with metabolism, we further analyzed the SCFA content and screened out the most remarkable data for analysis. In fecal samples, acetate was the most abundant SCFAs followed by propionate, and the concentration of acetate (Fig. 4B) or propionate (Fig. 4D) were higher in the collagen peptide intervention groups than in the MC group (acetate, P < 0.001 for Ss-SCP and Tn-SCP; propionate, P < 0.05 for Ss-SCP, P < 0.01 for Tn-SCP). The butyrate concentration (Fig. 4E) in Ss-SCP were significantly higher than MC group (P < 0.01) but there was no statistic difference between MC and Tn-SCP groups. It is worth noting that the valerate acid levels in the Tn-SCP and Ss-SCP groups were both significantly decreased in comparison with that in the MC group (Fig. 4C) (P < 0.05). We next detected valerate acid in serum; however, the valerate acid levels in the collagen peptide intervention groups were higher than those in the MC group (Fig. 4F) (P < 0.01 for Ss-SCP, P < 0.001 for Tn-SCP). To illustrate the change in the gut microbiota, a correlation heatmap is shown in Fig. 4A and Fig. S1. As shown in Fig. 4, at the genus level, the significantly altered bacteria were Allobaculum, Faecalibacterium, unidentified-Prevotellaceae, Parabacteroides, Sellimonas, Lachnoclostridium, Roseburia, unidentified-Lachnospiraceae and Parasutterella (relative abundance Ss-SCP > Tn-SCP > MC, P < 0.01 for Tn-SCP and Ss-SC in comparison to MC). We next addressed the potential contribution of the altered gut microbiota to the variety of SCFAs in rats fed the high-collagen peptide diet (Table 5). Using Pearson’s correlation, we analyzed the correlation between the concentrations of the significantly changed SCFAs and the relative abundance of the main gut microbiota at the genus level. Allobaculum, which was highly abundant in both the Tn-SCP and Ss-SCP groups (Table 4), was significantly positively correlated with propionic acid level (correlation coefficient was 0.953, P < 0.05) and butyrate acid level (correlation coefficient was 0.835, P < 0.05) (Table 5)and negatively correlated with valerate acid level (correlation coefficient was −0.977) (Table 5) (P < 0.05). In addition, Parasutterella, with a high abundance in the collagen peptide intervention groups was positively correlated with acetate level (correlation coefficient was 0.954).

Table 5. Pearson’s correlation coefficient between the relative abundance of bacteria and levels of SCFAs.

Bacteria CeneraPearson’s correlation coefficient
AcetatePropionicValerateButyrate
Allobaculum0.9360650.953*−0.977*0.835*
Parasutterella0.954*0.56377−0.901370.648
Faecalibacterium−0.268460.3858740.127487−0.41
unidentified_Prevotellaceae0.0703240.673053−0.2127−0.239
Parabacteroides0.5302120.941413−0.646360.17
Sellimonas0.411707−0.24122−0.276710.33
Lachnoclostridium−0.09859−0.693760.2403310.229
Roseburia−0.26642−0.806150.40194−0.122
unidentified_Lachnospiraceae0.082919−0.552140.060904−0.259

*P < 0.05, **P < 0.01, compared with the MC group.

3.6. Functional prediction changes in the gut microbiota

The predicted microbial functions including genetic-information processing, human diseases, metabolism, organismal systems, cellular processes and environmental information processing were analyzed in the present study (Fig. 5A). The functional pathways of the Ss-SCP group were similar to the MC group and both of those groups showed a high abundance in the cellular process pathway and environmental information processing pathways. The gut microbiota functions in the Tn-SCP group were totally different than the other groups. The Tn-SCP group showed increases in genetic information processing, human diseases, metabolism and organismal system metabolic pathways and decreases in the cellular processes and environmental information processing pathway compared with the MC group (P < 0.05). Collectively, the overall gut microbiota predicted function indicated whether the gut microbiota function would be altered and may be related to the composition of collagen peptide or the molecular weight.

4. Discussion

The investigation of the impacts on gut health with an intervention of protein diets has shown a surge of interest. Previous studies were focused on the benefits of a dietary protein within an appropriate dose (Fan et al., 2017, Han et al., 2018, Zhu et al., 2015), especially the modulation of the gut microbiota and production of SCFAs. The role of high content of protein in the diet and how that would modulate the gut microbiota and levels of SCFAs remains unclear.

In this study, the interaction of a high-collagen peptide diet with the gut microbiota and SCFA metabolism were investigated by giving rats a diet with high Tn-SCP and Ss-SCP collagen peptides and then evaluating the influence on the body’s metabolism and gut bacteria diversity. From a nutritional perspective, the Tn-SCP and Ss-SCP collagen peptides were not complete proteins according the FAO/WHO, and the composition of the Ss-SCP were closer to a complete protein composition than the Tn-SCP. The average molecular weight of the Tn-SCP and Ss-SCP were less than 500 Da, indicating that both peptides were low molecular weight and would facilitate digestion and absorption. During the experiment, the rats were all healthy and had no clinical symptoms such as diarrhea. On average, the rats of each group took in equal amounts of chow, but the rats in the Tn-SCP and Ss-SCP group gained significantly more weight compared to the MC. However, the collagen peptide diet with an advisable dose has been shown to have positive effects on inhibition of preadipocyte differentiation and has ameliorated obesity in rats with a high fat diet (Lee et al., 2017). In vivo, proteins can be converted to lipids via amino acid metabolism; the liver (Li et al., 2018) and spleen (Gong et al., 2014) are the main sites of lipid anabolism. Interestingly, the spleen index showed that the spleen weight of rats in the Tn-SCP group were higher than in the MC (P < 0.01), but the spleen index in the Ss-SCP group, which had a closer amino acid composition with complete protein, was only slightly different compared to the MC (P > 0.05). These results confirmed that the amino acid composition of the collagen peptides might have a direct link with the shifted spleen lipid metabolism. The liver index was significantly higher in the diet high-collagen peptide intervention groups (P < 0.001 for Tn-SCP, P < 0.01 for Ss-SCP) than in the MC group and the H&E staining showed that the arrangement of hepatocytes in the collagen group was relatively loose, which agrees with recently study from Islam et al (Islam, Ravandi, & Aukema, 2018) that a high-protein diet is associated with a physiologically enlarged liver. Thus, in the high-collagen peptide intervention condition, excessive amino acid metabolism in the liver can lead to liver enlargement, which can lead to liver dysfunction.

We speculated that the liver function may be directly linked to the exchange of SCFAs. Previous studies reported the correlation between liver function and SCFA metabolism (Bloemen et al., 2009, Bloemen et al., 2010) SCFAs are the major metabolites produced by gut microbiota and are key signaling molecules in various biochemical reaction processes (Koh, De Vadder, Kovatcheva-Datchary, & Backhed, 2016). SCFAs are absorbed and metabolized by the colonic epithelium, peripheral muscle tissue, and liver following their production (Blachier et al., 2019, Bloemen et al., 2010). SCFAs enter the portal circulation of the liver, directly regulating various pathways involved in the metabolism of fatty acids and cholesterol. In our present study, compared with MC group, the butyrate level of feces in Ss-SCP was significantly higher but in Tn-SCP was no statistical difference. Butyrate as an energy source is largely utilized by the colonic epithelium (Blachier et al., 2019, Han et al., 2018, Lin et al., 2012). Previous study showed that the butyrate could suppress diet-induced obesity in Free fatty acid receptor gene lacked mice(Lin et al., 2012) and regulated the energy homeostasis(Byrne, Chambers, Morrison, & Frost, 2015), which indicated that the butyrate is a beneficial SCFAs. As the Ss-SCP were closer to a complete protein composition, we suspect that composition of consumed protein is tightly relative with the butyrate produce. In addition, significantly high acetate and propionate levels, but low valerate acid levels in the feces of the Tn-SCP and Ss-SCP intervention groups were observed.

In this experiment, due to the low content of acetic acid and propionic acid in the serum, we did not detect acetate and propionic acid under the existing experimental conditions, but we observed a significant difference in valerate acid levels in contrast to that in the serum propionate levels in the diet collagen peptide groups compared to the MC group. Acetate and propionate, as the beneficial acids, may be in a dynamic equilibrium due to their hilar circulation, oxidization, and use as an energy source in vivo. The remaining propionate is used in gluconeogenesis and inhibits the synthesis of cholesterol, whereas acetate is used to synthesize the long-chain fatty acids, glutamine and beta hydroxybutyrate (Bloemen et al., 2009, Bloemen et al., 2010). The absorption mechanism of valerate acid in vivo remains unclear, as the high systemic concentration of valerate acid is toxic and may exert an adverse influence on the patient. Previous studies observed that propionate and acetate play an important role in appetite and energy regulation (Byrne et al., 2015), by regulating the free fatty acid receptor 3 (Lin et al., 2012) and reducing appetite via a homeostatic mechanism (Byrne et al., 2015). Thus, we hypothesize that the increased body weight gain in the collagen peptide intervention groups with significantly high levels of propionate and acetate were caused by liver dysfunction related to the high systemic concentration of valerate acid, which is worthy of further investigation.

The profiles of the SCFAs are tightly correlated with the gut microbiota diversity (Fan et al., 2017, Guardia-Escote et al., 2019, Wu et al., 2019). Despite the SCFAs showing a uniform change in the diet high-collagen peptide groups, the UPGMA analysis displayed a clear segregation between the Tn-SCP group and the MC and a slight difference between the Ss-SCP group and the MC. These data suggested that the intervention diet containing Tn-SCP strongly altered the structure of the gut bacterial communities while the Ss-SCP group may have a composition of gut microbiota similar to that of the MC group at the phylum level. Regarding the different results observed in the collagen peptide groups, the composition of amino acids might have greatly contributed to the change in the gut microbiota. The extent of the change in the gut microbiota would be less in the Ss-SCP group, which had an amino acid composition similar to that of the complete protein, according to the FAO/WHO.

For the major bacteria, Allobaculum, a member of the phyla Firmicutes, was highly abundant in both the Tn-SCP and Ss-SCP intervention groups and has been confirmed as a bacteria linked to the butyrate content in vivo (Han et al., 2018, McLeod et al., 2019). In the present study, the abundance of Allobaculum presented a remarkable positive correlation with the levels of propionic acid (P < 0.05) and butyrate acid (P < 0.05) and a negative correlation with the levels of valerate acid (P < 0.05). Parasutterella, which is presented in the heatmap graph as the most differentially abundant bacteria in the high-collagen peptide groups was also significantly positively correlated with the production of acetic acid. To our knowledge, previous studies have suggested that Parasutterella is a potential harmful bacteria (Wan et al., 2019), but little is known about its role in SCFAs metabolism. Thus, we hypothesized that the levels of SCFAs in the collagen peptide groups could be ascribed to the shifted Parasutterella and Allobaculum bacterial metabolism caused by the interaction with the high-collagen peptide diet.

Several previous studies have shown that the Allobaculum are potentially beneficial to the host and are found to have richer abundance in fat rats with a high fat diet (Bai et al., 2018, Everard et al., 2014) or rats with a high food intake (Han et al., 2018). However, Allobaculum were in high abundance in the collagen peptide groups with an overweight tendency. The predicted function of the gut microbiota in the Tn-SCP group was totally different than the MC group, and the Ss-SCP group also increased the unclassified pathway in comparison to the MC group. Furthermore, we hypothesized that the body weight result might be linked with the interaction of Allobaculum and other gut bacteria. For the gut bacteria profile, it was notable that the Tn-SCP and Ss-SCP groups both showed a significant abundance of Fusicatenibacter and Anaerostipes compared to the MC group, while only the Tn-SCP intervention group presented a lower abundance of Lactobacillus and Blautia. Fusicatenibacter and Anaerostipes, as the neutral bacteria, were increased via the intervention of the collagen peptide. Lachnospiraceae, which belongs to the Firmicutes, is a typical inflammatory bacterium that exists in the intestinal tract. In the obese mouse model, the relative abundance of Lachnospiraceae is increased with early subtherapeutic antibiotics, and is associated with metabolic shifts including diabetes (Kameyama & Itoh, 2014), obesity (Meehan & Beiko, 2014), and colitis (Zeng, Ishaq, Zhao, & Wright, 2016). Abundantstudies indicate that the Blautia are considered a health-promoting bacteria, which are associated with obesity and improved abundance in the high fat diet-induced obese rats (Bai et al., 2018). Furthermore, we speculate the altered abundance of Lachnospiraceae and Blautia correlated with the changes in the functional pathways altered in the Tn-SCP group in comparison with the MC and Ss-SCP groups including genetic information processing, human diseases, metabolism and organismal system metabolic pathways and decreases in the cellular processes and environmental information processing.

Our main finding was that that high-collagen peptide diet had a negative effect on the gut microbiota and SCFA metabolism. Briefly, the intestinal microbiota, as well as the SCFA metabolism of rats with excessive peptide, were shifted, and increased the risk of obesity to some extent.

5. Conclusion

In conclusion, these studies demonstrated that a high intake of the collagen peptides Tn-SCP and Ss-SCP could shift the profiles of SCFA metabolism via altering the gut microbiota. The amino acid composition tightly relative with the profiles of gut microbiota and SCFAs. In addition, the physical condition of the rats revealed that the high-collagen peptide diet induced rat weight gain and might cause liver dysfunction. This suggests that the daily peptide intake should be within the national limit and choose the complete protein. The long-term excessive consumption of peptide can cause dysfunction of metabolism and the intestinal flora.

6. Ethics statements

The study was performed in accordance with the Experimental Animal Administration regulations issued by the State Committee of Science and Technology of the People’s Republic of China and the Guidelines for Care and Use of Laboratory Animals of Hainan Medical College. The ethical approval reference number of the study is HYXK 20190035. All the procedures for the care of the rats were in accordance with the institutional guidelines for animal use in research.

CRediT authorship contribution statement

Fengfeng Mei: Methodology, Software, Writing – original draft. Zhouwei Duan: Project administration, Visualization, Writing – review & editing. Muxue Chen: Writing – review & editing. Jinfeng Lu: Writing – review & editing. Meihui Zhao: Formal analysis, Investigation. Laihao Li: Supervision, Funding acquisition. Xuanri Shen: Supervision, Funding acquisition. Guanghua Xia: Conceptualization, Investigation, Funding acquisition. Shengjun Chen: Conceptualization, Investigation, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by Fund of Key Laboratory of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs, China (no. NYJG202001), China Agriculture Research System (no. CARS-46), Special Scientific Research Funds for Central Non-profit Institutes, Chinese Academy of Fishery Sciences (no. 2020TD73), Marine and Fisheries Bureau of Haikou (no. HHCL201804), Innovative Research Projects of Graduate Students of Hainan Province (no. hys2019-73) and the Scientific Research Foundation of Hainan University (no. kyqd1662).

Appendix A. Supplementary material

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Supplementary data 1.

References