1 H NMR-Based Fecal Metabolomics Reveals Changes in Gastrointestinal Function of Aging Rats Induced by d -Galactose

Abstract

d-galactose (d-gal) is widely used to induce aging. However, it is still unclear whether long-term injection of d-gal affects the gastrointestinal functions of aging rats, and how. In this study, we investigated the effects of d-gal on the gastrointestinal functions of aging rats, especially from the perspective of fecal metabolomics. Biochemical and behavioral analyses were performed. Besides, a ¹H NMR-based metabolomics approach was built and applied in combination with multivariate data analysis including principal components analysis (PCA) and orthogonal partial least squares-discriminate analysis (OPLS-DA). Regarding gastrointestinal functions, d-gal significantly decreased the small intestine propulsion rates and prolonged gastrointestinal transit time. In addition, d-gal significantly increased the oxidative damages. PCA results showed that d-gal interrupted the metabolic profiles of endogenous small molecules in aging rats. Furthermore, OPLS-DA showed that 40 metabolites were screened and identified to be involved in the disruption of gastrointestinal functions in aging rats. Accordingly, seven metabolic pathways were recognized as the most influenced pathways associated with gastrointestinal functions of aging rats induced by d-gal, including amino acid metabolism, energy metabolism, intestinal flora metabolism, and metabolism of short chain fatty acids. It is the first report to investigate the effects and underlying mechanisms of d-gal on gastrointestinal functions of aging rats from the perspective of fecal metabolomics. The current results are conducive to further comprehensively understand d-gal-induced aging and will expand the applications of d-gal in pharmacological researches.

Introduction

Aging is a natural and inevitable physiological alterations characterized by a gradual decline in both cellular functions leading to oxidative stress and senescence and pathophysiological declines of most organs and metabolic homeostasis.^1,2 Particularly, there is a decline of the gastrointestinal system,³ which definitely causes other diseases, consequently greatly influencing life quality.

Animal models have been normally and widely used to simulate pathological states related to human diseases under well-controlled experimental conditions.⁴ Since the damage of d-galactose (d-gal) to neurogenesis in the dentate gyrus is similar to the natural aging process of mice or rats, it has been widely used for aging-related researches.⁵ It has been previously reported that d-gal caused degenerative changes in physiological and functional organs, such as heart senescence,⁶ liver and kidney damages,^6,7 and premature ovarian failure⁸. However, it is still unclear whether injection of d-gal affects gastrointestinal functions of aging animals, and how. For one thing, biochemical parameters reflect the changes of gastrointestinal functions. For another, metabolism is also important with referring to all biological processes and pathways occurring in an organism. In this regard, a detailed understanding of the changes of gastrointestinal metabolism caused by d-gal in aging rats will provide more comprehensive insights on aging, especially from the perspective of fecal metabolites and metabolic pathways.

Metabolomics studies the metabolic responses of living systems to genetic and environmental stimuli, in particular small molecular weight metabolites and their changes in biological systems.⁹ As the physiological product of the gastrointestinal tract, a fecal sample is one of the best biomaterials providing information of physiological changes of intestine.^10,11 Fecal metabolomics-based analysis indisputably constitutes a very useful approach to reveal the complicated and potential metabolic interactions of digestion and their absorption of the gastrointestinal system. Metabolite profiling obtained can bring in a series of information on gut diseases.¹² The obtained metabolite map can provide a series of information about intestinal diseases

The aim of this study was to assess the influences of d-gal on the gastrointestinal functions of aging rats, especially from the perspective of fecal metabolomics. First, gastric emptying and gastrointestinal ink push were used to evaluate the gastrointestinal motility of rats. Meanwhile, the levels of oxidative stress parameters were also determined. In addition, a ¹H NMR metabolomics approach in combination with multivariate data (MVD) analysis was utilized for characterizing and analyzing the disruption of fecal metabolism of d-gal-induced aging rats, as well as metabolite contents and metabolic pathways. Collectively, the current findings will contribute to further understand the aging models induced by d-gal in depth and expand applications of d-gal-induced aging models.

Materials and Methods

Materials and reagents

d-gal (batch No. # WXBC6583V) was obtained from the Sigma Chemical Co. Ltd. (purity ≥98%; St. Louis), which was dissolved in 0.9% saline at 300 mg/mL. Deuterium oxide (D₂O) and sodium 3-trimethlysilyl (2,2,3,3-d₄) propionate (TSP) were separately purchased from the Norell (Landisville) and the Cambridge Isotope Laboratories, Inc., (Andover, MA). Phosphate buffer solution (PBS, K₂HPO₄/NaH₂PO₄, 0.1 M, pH = 7.4) was purchased from the Boster Biological Technology Co. Ltd. (Wuhan, China). In addition, the detection kits including superoxide dismutase (SOD), malonyldialdehyde (MDA), and catalase (CAT) were obtained from Nanjing Jiancheng Bioengineering Institute (Nanjing, China).

Animals and sample collection

Twelve 8-week-old male Sprague-Dawley rats (230 ± 10 g) were bought from the Experimental Animal Center of the Chinese Military Medical Sciences Academy (License number SCXK 2014-0013). The rats were housed in transparent rearing cages under standard conditions with room temperature (23°C ± 2°C), relative humidity (50% ± 10%), and a 12/12 hours light–dark cycle. The experimental rats were allowed free access to food and water. Then they were randomly placed into two groups, namely, the negative control (n = 6) and the aging model group (n = 6). Rats in the aging model group were daily subcutaneously injected with d-gal at a dose of 300 mg/kg³¹ for 11 weeks, whereas rats in the control group received the same volume of physiological saline. During the experiments, body weight and food intake were recorded once a week. According to dynamic behavioral observations, 11-week fecal samples as the final analysis sample were collected from individual animals by metabolism cages at 9 o'clock a.m. All fecal samples were placed into centrifuge tubes and stored at −80°C until further study. All animal experiments were performed in accordance with and approved by the committee on the ethics of animal experiments of Shanxi University (SXULL 20180052). We did our best to reduce the pain of animals suffering and the number of animals needed for the capture of reliable data.

The gastrointestinal transit trial

The gastrointestinal transit trial was conducted to test the defecation ability of the experimental rats.¹³ Specifically, all rats were fasted overnight at the 0^th, 9^th, and 11^th week, and in the next morning, all rats were gavaged with 1 mL of self-configured ink (containing 5% activated carbon powder and 10% Arabic gum) as the indicator. Each rat was separately placed in a metabolic cage with compartments, as well as water and foods were freely available. The time from the beginning of providing rats with self-configured ink to the first defecation of black feces of each rat was recorded.

Gastric and small intestinal movement trials

Semisolid nutritional paste was given to rats so that gastric emptying rates and gastrointestinal ink push rates could be measured at the same time. Rats were given a semisolid nutrient paste containing activated carbon (1 mL/100 g). After 30 minutes, all the animals were humanely sacrificed. The gastric cardia and gastric pylorus were immediately ligated. The whole stomach was removed and the total gastric weight (X) was obtained. After gastric contents were washed and the filter paper dries the water on the stomach surface, the net gastric weight (Y) was measured. The residue amount of semisolid nutritional paste in the stomach was calculated according to Formula A. The intestinal transport distance was determined by measuring the migration distance of black charcoal in the small intestine (from the pylorus to the cecum). The gastrointestinal ink push rates were calculated using Formula B.^13,14 $G a s t r i c e m p t y i n g r a t e (%) = \frac{X - Y}{Z} \times 100 %,$ A

where X and Y are the total gastric weight and the net weight of stomach, respectively, and Z is the weight of the semisolid nutrient paste. $\begin{matrix} G a s t r o i n t e s t i n a l i n k p u s h r a t e s (%) = \\ \frac{T h e m o v e m e n t d i s t a n c e o f c a r m i n e}{W h o l e l e n g t h o f s m a l l i n t e s t i n e} \times 100 % . \end{matrix}$ B

Biochemistry assays

The levels of SOD, MDA, and CAT in serum samples were measured according to instructions of manufacturers through commercial kits.

Fecal metabolomics analysis

Fecal sample collection and preparation

Fecal metabolites were extracted as described with minor modifications.¹⁵ In brief, fecal extraction was performed on ice, individually, by adding 1000 μL PBS buffer into 100 mg thawed fecal samples. After vortex for 30 seconds, the mixed slurry was dealt with ultrasonication cycles for 10 minutes. The ultrasound treatment was performed in an ice bath with an interval of 10 seconds for every 10 seconds of ultrasound. Afterward, vortex mixing was carried out again. The vortex samples were centrifuged for 15 minutes (4°C, 13,000 g). In total, 100 μL of D₂O containing 0.01% (w/v) TSP, which provides a field lock for the NMR and as chemical shift reference, was added into 500 μL supernatant. The sample solutions were transferred to NMR tubes with a 5 mm outer diameter for further analysis.

¹H NMR acquisition

¹H NMR spectra of the feces were measured by a Bruker Avance 600 MHz NMR spectrometer (Bruker, Germany) at a ¹H frequency of 600.13 MHz and a temperature of 25°C. Carr–Purcell–Meiboom–Gill spin-echo pulse sequence was used to inhibit signals from proteins and lipids, thereby increasing the signal of small metabolites.¹⁶ The acquisition parameters were as follows: acquisition time, 5 minutes; number of scans, 64; spectral size, 65,536 points; spectral width, 12019.2 Hz; pulse width, 14; and relaxation delay, 1.0 seconds.

¹H NMR data preprocessing and MVD analysis

The ¹H NMR spectra were phased and baseline corrected by the MestReNova software 8.1.0 (Santiago de Compostella, Spain). TSP (0.00 ppm) was calibrated. The region of 5.17–4.68 ppm was obliterated to eliminate the influences of water suppression. The spectra were integrated into 0.01 ppm bins for the whole region of 0.61–9.11 ppm. Subsequently, the total integrated area of the spectra was used to scale the remaining regions to compensate the concentration differences.

To identify variations in the fecal metabolic phenotypes induced by d-gal, NMR data were subjected to Simca-P 13.0 software (Umetrics, Sweden) for principal components analysis (PCA) and orthogonal partial least squares-discriminate analysis (OPLS-DA). PCA was used to obtain a visualization of the global differences between the control and aging groups. OPLS-DA can preset information unrelated to classification, which makes the sample separation between groups more effective as well as weakens the intragroup differences. The S-plot not only evaluates and identifies the contribution of the metabolites to the separation of metabolic profiles, but also obtains the change in the relative content of markers in the two groups. In total, 200 permutation tests and CV-ANOVA tests were used to verify the reliability of the model. The mass of the models constructed was assessed by the parameters of model fitness (R²) and predictive ability (Q²).

Identification of potential biomarkers and construction of metabolic pathways

Resonance assignments were performed through referring to previous studies^17
–19 as well as with online databases including the Human Metabolome Data Base and the Biological Magnetic Resonance Data Bank, as well as our in-house databases. Metabolites were further screened by the variable importance for projection (VIP) values and the S-plot. In the S-plot, the further the distance away from the center, the stronger the contribution to separation is. Fecal metabolites that were exceeded the threshold of VIP values at 1.0 were regarded as potential biomarkers. As for the naming of metabolites, we provide not only the generic name of the metabolite, but also the InChi Key ID.

On the basis of MetaboAnalyst 4.0 platform, metabolomic pathway analysis (MetPA) was used to construct and analyze the metabolic pathways to visualize the global networks of fecal metabolism and to confirm the targeted pathways involved. The impact values >1.0 were filtered out as the most important metabolic pathways.

Correlation analysis

Furthermore, Pearson's correlation analysis was performed to classify two kinds of relationships. One was the correlations among different metabolites, biochemical results, and behavioral parameters. The other was the correlation analysis among the 25 differential metabolites. These correlation coefficient values are a statistical indicator of the degree of linear correlation between variables.

Statistical analysis

All data of behavioral and biochemical results are given as mean ± standard error. Two-tailed unpaired t-tests were applied to analyze significance of behavioral data and relative metabolites' concentrations between the control and the aging groups. The statistical analysis was performed by SPSS 25.0 software (IBM, NY). The broken line graphs, column bar graphs, and scatter plot were drawn using GraphPad Prism 7.0 (San Diego, CA). The value of p < 0.05 was considered statistically significant.

Results

No effects of d-gal on the body weight gains and food intakes of rats

Although treatment with d-gal had no effects on body weight gain or food consumption, it had very significant effects on behavior (see Supplementary Fig. S1 for further consideration of these parameters).

d-gal disturbed gastrointestinal motility and small intestinal motility of rats

The gastrointestinal transit trial and gastric and small intestinal movement trial were further performed to detect the defecation status of rats. There was no significant difference in the initial defecating time of the first black feces between the control group and aging group. After 9-week treatment of d-gal, the defecation time of the first black stool in the aging group was still longer than that of the control group, but not significant. On week 11, the defecation time of rats was significantly longer than that of the control rats (Fig. 1A).

FIG. 1.

Defecation parameters were obtained from the control (NC) and aging groups (AG). (A) Gastrointestinal transit time, (B) gastric emptying rate, (C) gastrointestinal ink push rate. Data are presented as mean ± SE (n = 6). *p < 0.05, compared with NC. NC, the control group; SE, standard error.

The gastric emptying rates of the aging group (42.5% ± 11.0%) were significantly smaller than that of the control group (70.3% ± 5.5%; p < 0.05) (Fig. 1B). The gastrointestinal ink push rates were distinctly shorter in the aging group (64.3 ± 6.8 minutes) than those of the control group (83.5% ± 4.3%; p < 0.05) (Fig. 1C). Taken together, the present results suggested that gastrointestinal motility and small intestinal motility of aging rats were significantly disturbed by d-gal.

Biochemical index

To verify the level of oxidative stress injury in rats, we determined the contents of antioxidant enzymes (SOD, MDA, and CAT) in the serum samples. The activities of SOD and CAT were decreased in the serum of aging rats in comparison with those in control rats, whereas the opposite was true for the MDA level (Table 1). The underlying mechanisms of aging induced by d-gal may relate to stimulating/enhancing oxidative stress in rats.

Table 1.

Biochemical Parameters in the Serum of the Control and Aging Groups (n = 6, Mean ± Standard Error)

Group	SOD (U/mL)	MDA (nmol/mL)	CAT (U/mL)
NC	615.90 ± 16.39	5.63 ± 0.45	14.69 ± 1.86
AG	527.80 ± 14.64^**	7.96 ± 0.79^*	5.50 ± 0.75^*

p < 0.05, ^** p < 0.01, compared with NC.

AG, aging group; CAT, catalase; MDA, malonyldialdehyde; NC, the control group; SOD, superoxide dismutase.

Metabolite assignments

A typical ¹H NMR spectra of feces labeled are shown in Figure 2. Overall, 40 metabolites were identified as shown in Table 1, which could be classified into organic acids, amino acids, intermediates of tricarboxylic acid (TCA) cycle, and some other metabolites.

FIG. 2.

Representation of the 600 MHz ¹H NMR spectra of the feces from the control group (A) and aging group (B). The assignments of major metabolites are given in Table 2.

Table 2.

¹H NMR Assignments of Major Metabolites in Fecal Sample of Rats

No.	Metabolites	InChI keys	δ¹H (multiplicity ^a )
1	Butyrate	FERIUCNNQQJTOY-UHFFFAOYSA-M	0.90(t), 1.56(m), 2.16(t)
2	Isoleucine	AGPKZVBTJJNPAG-WHFBIAKZSA-N	0.96(t), 1.01(d), 1.28(m), 3.65(d)
3	Leucine	ROHFNLRQFUQHCH-YFKPBYRVSA-N	0.97(t), 1.69(m), 3.72(t)
4	Valine	KZSNJWFQEVHDMF-BYPYZUCNSA-N	0.99(d), 1.04(d), 2.27(m), 3.62(d)
5	Propionate	XBDQKXXYIPTUBI-UHFFFAOYSA-M	1.06(t), 2.17(q)
6	Ethanol	LFQSCWFLJHTTHZ-UHFFFAOYSA-N	1.20(t), 3.66(q)
7	Lactate	JVTAAEKCZFNVCJ-UHFFFAOYSA-N	1.33(d), 4.13(q)
8	Threonine	AYFVYJQAPQTCCC-GBXIJSLDSA-N	1.34(d), 3.59(d), 4.26(m)
9	Lysine	KDXKERNSBIXSRK-YFKPBYRVSA-N	1.47(m), 1.72(m), 1.89(m), 3.01(t), 3.76(t)
10	Alanine	QNAYBMKLOCPYGJ-REOHCLBHSA-N	1.48(d), 3.81(q)
11	Arginine	ODKSFYDXXFIFQN-BYPYZUCNSA-N	1.90(m), 1.72(m), 3.25(m), 3.76(m)
12	Acetate	QTBSBXVTEAMEQO-UHFFFAOYSA-M	1.93(s)
13	Proline	ONIBWKKTOPOVIA-BYPYZUCNSA-N	2.05(m), 2.36(m), 3.35(t), 3.42(t), 4.12(m)
14	Glutamate	WHUUTDBJXJRKMK-VKHMYHEASA-N	2.06(m), 2.36(m), 3.78(m)
15	Methionine	FFEARJCKVFRZRR-UHFFFAOYSA-N	2.12(s), 2.17(m), 2.63(t), 3.85(m)
16	Glutamine	ZDXPYRJPNDTMRX-VKHMYHEASA-N	2.17(m), 2.46(m), 3.77(m)
17	Acetone	CSCPPACGZOOCGX-UHFFFAOYSA-N	2.21(s)
18	Succinate	KDYFGRWQOYBRFD-UHFFFAOYSA-L	2.39(s)
19	2-Oxoglutarate	KPGXRSRHYNQIFN-UHFFFAOYSA-N	2.45(t), 3.00(t)
20	Citrate	KRKNYBCHXYNGOX-UHFFFAOYSA-N	2.52(d), 2.65(d)
21	Methylamine	BAVYZALUXZFZLV-UHFFFAOYSA-N	2.61(s)
22	Asparagine	DCXYFEDJOCDNAF-REOHCLBHSA-N	2.86(m), 3.03(m), 4.00(m)
23	Trimethylamine	GETQZCLCWQTVFV-UHFFFAOYSA-N	2.88(s)
24	Creatinine	DDRJAANPRJIHGJ-UHFFFAOYSA-N	3.04(s), 3.96(s), 4.05(s)
25	Phenylalanine	COLNVLDHVKWLRT-QMMMGPOBSA-N	3.17(dd), 3.33(m), 3.51(s), 3.99(dd), 7.32(t), 7.43(t), 7.38(m)
26	Choline	OEYIOHPDSNJKLS-UHFFFAOYSA-N	3.22(s)
27	Glucose	WQZGKKKJIJFFOK-GASJEMHNSA-N	3.25(t), 3.42(dd), 3.47(dd), 3.75(m), 3.54(dd), 3.75(m), 3.83(m), 3.88(m), 3.91(dd), 4.65(d), 5.25(d)
28	TMAO^b	UYPYRKYUKCHHIB-UHFFFAOYSA-N	3.26(s)
29	Betaine	KWIUHFFTVRNATP-UHFFFAOYSA-N	3.26(s), 3.90(s)
30	Taurine	XOAAWQZATWQOTB-UHFFFAOYSA-N	3.29(t), 3.41(t)
31	Scyllo-Inositol	CDAISMWEOUEBRE-UHFFFAOYSA-N	3.36(s)
32	Glycine	DHMQDGOQFOQNFH-UHFFFAOYSA-N	3.54(s)
33	Mannitol	FBPFZTCFMRRESA-KVTDHHQDSA-N	3.77(dd)
34	Glycolate	AEMRFAOFKBGASW-UHFFFAOYSA-N	3.93(s)
35	α-Xylose	SRBFZHDQGSBBOR-LECHCGJUSA-N	3.55(dd), 3.67(m), 5.21(t)
36	Uracil	ISAKRJDGNUQOIC-UHFFFAOYSA-N	5.81(d), 7.54(d)
37	Fumarate	VZCYOOQTPOCHFL-OWOJBTEDSA-L	6.53(s)
38	Tyrosine	OUYCCCASQSFEME-QMMMGPOBSA-N	6.89(d), 7.2(d)
39	Hypoxanthine	FDGQSTZJBFJUBT-UHFFFAOYSA-N	8.2(s), 8.22(s)
40	Formate	BDAGIHXWWSANSR-UHFFFAOYSA-M	8.46(s)

s, singlet; t, triple; q, quartet; m, multiple b: trimethylamine-N-oxide.

TMAO, trimethylamine oxide.

MVD analysis

In the PCA score plot, the control group and the aging group were separated (Fig. 3A), indicating that d-gal intervened the metabolic profiles of aging rats. The OPLS-DA score plot showed an evident separation between the control group and aging group, confirming the PCA results (Fig. 3B). The R²Y values were 0.965, and Q² values were 0.721, indicating a good fit and high predictability of the constructed model (Fig. 3C).

FIG. 3.

Multivariate data analysis of fecal ¹H NMR data. (A) Score plot of PCA model, (B) score plot of OPLS model, (C) permutation test (permutation no.: 200), (D) the corresponding S-plot and VIP value. OPLS-DA, orthogonal partial least squares-discriminate analysis; PCA, principal components analysis; VIP, variable importance for projection.

A total of 25 differential biomarkers were screened by the values of the S-plot and the VIP, involving in 9 metabolic pathways (Fig. 4). Changes in the concentrations of differential biomarkers are detailed in Supplementary Figure S3.

FIG. 4.

Schematic diagram of the pathway analysis of differential metabolites with MetPA. The sizes and colors of circles built on p-values and pathway impact values, respectively. The impact values >1.0 were filtered out as the most important metabolic pathways. MetPA, metabolomic pathway analysis.

Correlation analysis

To interpret the metabolic profiles based on the NMR, a correlation matrix based on Pearson's correlation analysis among biochemical and behavioral parameters was generated. Concerning the relationships among the metabolites, biochemical and behavioral parameters, the plot reveals a range of correlation coefficients ranging from 0.88 (maximum positive correlation) to −0.88 (maximum negative correlation), with 0 indicating no correlation. Besides sugars, phenylalanine, and butyrate, the remaining differential metabolites were significantly correlated with at least one oxidative stress biomarker (SOD, MDA, and CAT) (Fig. 5). There was no significant correlation among body weight gain and gastric emptying rate and 25 differential metabolites. The gastrointestinal ink push rates and gastrointestinal transit time, reflecting the ability of gastrointestinal motility, significantly correlated with the contents of six differential metabolites. Results showed that the main metabolic abnormalities caused by d-gal include TCA cycle, gut microbiota metabolism, amino acid metabolism, choline degradation pathway, and short chain fatty acids (SCFAs) metabolism (Fig. 6).

FIG. 5.

Heatmap generated by Pearson's correlation analysis among differential metabolites, biochemical parameters (SOD, MDA, and CAT), and behavioral parameters (body weight gain, gastrointestinal transit time, gastrointestinal ink push rate, gastric emptying rate, crossing time, incubation period). * Means p < 0.05. CAT, catalase; MDA, malonyldialdehyde; SOD, superoxide dismutase.

FIG. 6.

Potential metabolic pathways disturbed by d-gal. The identified metabolite markers are highlighted in bold. “↑” and “↓” indicate upregulation and downregulation with d-gal treatment, respectively. d-gal, d-galactose; DMA, dimethylamine oxide; GIS, gastrointestinal system; TCA, tricarboxylic acid; TMA, trimethylamine; TMAO, trimethylamine oxide.

Discussion

In this study, d-gal was subcutaneously injected to rats for 11 weeks to establish the aging model to explore how d-gal influences the gastrointestinal system of aging rats. The results of Morris water maze (Supplementary Fig. S2) and biochemical tests (Table 1) showed that the aging model was successfully constructed. And then, not only gastrointestinal behavioral indicators related, but also fecal metabolites, were measured to provide a new perspective. Of importance, d-gal significantly disrupted fecal metabolomics of aging rats, including but not limiting metabolic profiles of feces, metabolite contents, and corresponding metabolic pathways.

Gastrointestinal dysfunction of aging rats by d-gal

There is no doubt that gastrointestinal dysfunction will affect the life quality. Few studies have proved the relationships between d-gal and gastrointestinal functions. For instance, it has been said that d-gal can cause diarrhea or constipation, which, however, needs to be proved by experimental pieces of evidence. Previous reports have also indicated that the intestinal motility may decrease with age, leading to longer transit times.²⁰ Parameters, including gastric emptying rates, gastrointestinal ink push rates, and gastrointestinal transit time, have been used to detect the effects of laxative agents on constipation.²¹ In this study, d-gal significantly reduced the gastric emptying rates and gastrointestinal ink push rates, while significantly prolonged the gastrointestinal transit time, suggesting that the gastrointestinal function was destroyed or partially destroyed by d-gal (Fig. 2). This may relate to the direct influences of d-gal on intestinal smooth muscle and inhibition of peristalsis.¹⁴

Oxidative stress abnormality of aging rats induced by d-gal

Oxidative stress has been deemed to be a hazard factor for various diseases. CAT plays a crucial role in reducing lipid and hydrogen peroxide.^22,23 SOD is indispensable to the balance of antioxidation and oxidation in the body.²⁴ As the final product of lipid peroxidation, MDA indirectly reflects the extent of cellular injury.²⁵ In this study, after 11-week treatment of d-gal, the serum levels of SOD and CAT of aging rats significantly decreased as compared with control rats, while the MDA level increased. The significant changes of these oxidative parameters suggested an occurrence of serious oxidation in the aging rats induced by d-gal.

Increased oxidative stresses have been testified to disrupt mucosal barrier function of intestinal epithelial cells characterized by increased intestinal permeability.^9,26,27 In addition, various pathological conditions of gastrointestinal tract, that is, gastroduodenal ulcers, gastrointestinal malignancies, and inflammatory bowel disease, are partially related to oxidative stresses.²⁸ The results of the increase level of oxidative stress in aging rats suggested that d-gal may damage intestinal epithelial cells, affecting the barrier function of intestinal mucosa.

Perturbations in the energy metabolism of aging rats by d-gal

The TCA cycle is the end metabolic pathway of three major nutrients, that is, carbohydrates, amino acids, and lipids.^13,24 In our study, the accumulation of two TCA cycle intermediates, that is, 2-oxoglutarate and succinate, in the aging rats revealed that more nutrient substances were utilized for energy supply. The view is also supported by a significant reduction in glucose and α-xylose. Such an accumulation also explains why the glucose contents of the aging rats are significantly lower than that of control rats. Lactate, as the end product of glycolysis, can reflect the changes of energy metabolism in the body.²⁹ In our study, the concentrations of lactate increased in aging rats, possibly due to enhancing anaerobic glycolysis.³⁰ The results were consistent with previous results that energy metabolism in rats was disturbed by d-gal.^31,32

Altogether, the mentioned results suggest that d-gal induces not only the abnormality of physiological activities but also the disorder of energy metabolism. Furthermore, the results herein suggest that there is an internal causal relationship among gastrointestinal function, energy metabolism, and d-gal.

Perturbations in the liver functions of aging rats by d-gal

Creatine is mainly found in muscle, which is in the form of creatine phosphate.²⁴ Clinically, an elevated urinary creatinine suggests a possible kidney injury, which may be related to toxin accumulation.³³ The increase of creatine content in the aging rats indicates that the creatinine metabolism was promoted and the renal function has been impaired.

Taurine, the second most abundant amino acid of mammals, is an important index of the degree of liver injury.³¹ In this study, d-gal can significantly decrease the content of taurine in the feces of aging group. Such a decrease indicates that taurine accumulates in the body, enhancing antioxidant effects of aging rats.

Perturbations in the amino acids metabolism of aging rats by d-gal

In our study, amino acids were significantly altered by d-gal. Most of these amino acids are essential amino acids. In aging rats, the concentrations of six amino acids including leucine, phenylalanine, threonine, arginine, valine, and proline increased dramatically (Supplementary Fig. S3). Leucine, together with valine, plays important functions on repairing muscle, controlling blood sugar, and providing energy for body tissues.³⁴ Consequently, high levels of leucine and valine of aging rats possibly resulted from malabsorption due to epithelial inflammation and injury. The significant increase of phenylalanine indicates an abnormal metabolism caused by liver dysfunction.³⁵

Arginine protects ibuprofen-induced mucosal damage from oxidative stress.³⁶ The sharp increase of arginine in aging rats indicated that it might be involved in the oxidative stress, and then alleviating and repairing liver damages. Similar to arginine, the increase of proline indicated again that d-gal could cause oxidative stress in rats.³⁴ In addition, we found the significant negative correlations between the fecal concentrations of arginine and SOD, as well as between proline and SOD (Fig. 5), suggesting that the two amino acids and their corresponding pathways take part in the pathogenesis process of oxidative stresses.

For the aging rats, the contents of glycine, methionine, and isoleucine significantly declined (Supplementary Fig. S3). In this study, the abnormal metabolisms of amino acids indicated that d-gal damaged the functions of liver tissues in aging rats mainly through aggravating oxidative stress, which is consistent with previous results.^8,31,32

Perturbations in the gut bacteria metabolism of aging rats by d-gal

Gut microbial host mutualism endows the host with a fitness advantage including nutritional, immune, and intestinal health aspects.³⁷ Changes of gut microbiota run through the whole life stage, especially in the early birth and old age. It has been shown that the diversity of gut microbiota reduced by d-gal in aging rats.³⁸ In addition to participating in the formation of biofilm, choline can also involve in the conversion to TMA by gut microflora, which is a precursor of trimethylamine oxide (TMAO).³⁹ It is reported that TMAO and betaine can disrupt the balance of intestinal flora.⁴⁰ The increases of TMAO and betaine in the feces of the aging rats suggest that the balance of intestinal flora was disturbed by d-gal.

Perturbations in the SCFAs metabolism of aging rats by d-gal

SCFAs modulate health through providing energy to the host and regulating metabolic syndrome.⁴¹ More than 90% of SCFAs are produced and absorbed within the colon, mainly making up of acetate, propionate, and butyrate.⁴² The increased production of acetate may mediate an important positive feedback loop between the gut microbiota and central nervous system,⁴³ which promotes hyperphagia and increases energy storage in rats.⁴⁴ It may also explain why the weights of aging rats increase, but not decrease as natural aging rats do (Supplementary Fig. S1). We then found a significant negative correlation between propionate and intestinal propulsive rate (Fig. 5), suggesting that slight intestinal inflammatory response may occur in the aging rats.⁴⁵ The level of butyrate was significantly downregulated by d-gal in aging rats, as well as has a positive correlation with gastrointestinal propulsive rate (Fig. 5). This may be due to the thinning of colonic mucosa caused by d-gal, accompanied by mild inflammatory response.

One limitation of this study is the use of only male rats. In a further study, it is necessary to perform sexually dimorphic microbiome/metabolome. In addition, we will take the dynamic analysis of different time points into our further study.

Conclusion

In summary, the current findings comprehensively demonstrated that d-gal could induce gastrointestinal dysfunctions of aging rats from the point of views of behaviors, biochemical index, and fecal metabolomics. First, the gastrointestinal motility and functions of aging rats were significantly weakened, induced by d-gal. Besides, oxidative damages were enhanced in the aging rats. More importantly, for the first time, a ¹H NMR-based metabolomics approach was established to characterize the alternation in fecal metabolomics of gastrointestinal functions in aging rats, including changes in metabolic profiles, metabolite levels, and metabolic pathways.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

This study was supported by the Scientific and Technological Innovation Project of Higher Education Institutions in Shanxi Province (Grant No. 2019L0072), the Key Project of Innovation and Entrepreneurship Training Program for College Students in Colleges and Universities in Shanxi Province (Grant No. 2019017), and the Youth Science and Technology Research Fund of Shanxi Province (Grant No. 201901D211140).

Supplementary Material

Supplementary Figure S1

Supplementary Figure S2

Supplementary Figure S3

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Supplementary Material

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1 H NMR-Based Fecal Metabolomics Reveals Changes in Gastrointestinal Function of Aging Rats Induced by d -Galactose

Abstract

Introduction

Materials and Methods

Materials and reagents

Animals and sample collection

The gastrointestinal transit trial

Gastric and small intestinal movement trials

Biochemistry assays

Fecal metabolomics analysis

Fecal sample collection and preparation

1 H NMR acquisition

1 H NMR data preprocessing and MVD analysis

Identification of potential biomarkers and construction of metabolic pathways

Correlation analysis

Statistical analysis

Results

No effects of d-gal on the body weight gains and food intakes of rats

d-gal disturbed gastrointestinal motility and small intestinal motility of rats

Biochemical index

Metabolite assignments

MVD analysis

Correlation analysis

Discussion

Gastrointestinal dysfunction of aging rats by d-gal

Oxidative stress abnormality of aging rats induced by d-gal

Perturbations in the energy metabolism of aging rats by d-gal

Perturbations in the liver functions of aging rats by d-gal

Perturbations in the amino acids metabolism of aging rats by d-gal

Perturbations in the gut bacteria metabolism of aging rats by d-gal

Perturbations in the SCFAs metabolism of aging rats by d-gal

Conclusion

Footnotes

Author Disclosure Statement

Funding Information

Supplementary Material

References

Supplementary Material

¹H NMR acquisition

¹H NMR data preprocessing and MVD analysis