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Article

Metabolomic Analysis of Feces vs. Cecum Content in Animals: A Comparative Study Investigated by 1H-NMR

by
Xiexin Li
1,†,
Yang Li
1,†,
Xin Nie
1,*,
Chenglin Zhu
2,*,
Qiqi Luo
3,
Luca Laghi
4 and
Gianfranco Picone
4
1
College of Culinary and Food Science Engineering, Sichuan Tourism University, Chengdu 610100, China
2
College of Pharmacy and Food, Southwest Minzu University, Chengdu 610041, China
3
School of Laboratory Medicine, Chengdu Medical College, Chengdu 610500, China
4
Department of Agricultural and Food Sciences, University of Bologna, 47521 Cesena, Italy
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Metabolites 2025, 15(9), 565; https://doi.org/10.3390/metabo15090565
Submission received: 7 July 2025 / Revised: 18 August 2025 / Accepted: 20 August 2025 / Published: 22 August 2025
(This article belongs to the Section Animal Metabolism)
Browse Figures
Versions Notes

Abstract

Background: Feces and cecum content are commonly involved in metabolomic analysis to understand the gut metabolic profile of the host, while, in fact, they are different. Feces represent the terminal excretory product after extensive host enzymatic digestion, absorption, and significant modification by the distal gut microbiota. In contrast, cecum content reflects the localized, in situ metabolic microenvironment at that specific site. However, it is worth noting that feces are the most accessible sample type for non-invasive studies, which could be considered proxies for cecum content in some specific cases. Unfortunately, the validity of fecal samples as an alternative to cecum content has rarely been assessed. Methods: The current study attempted to illustrate the distinct metabolomic and microbiota features of feces and cecum content in eight animals (mouse, pig, chicken, duck, rabbit, Gansu yak, Sichuan yak, and sheep) by means of 1H-NMR and 16S rRNA, respectively. Results: A total of 116 molecules were characterized in feces and cecum content samples. Among them, 22 molecules were shared in all groups. Taking advantage of the univariate analysis, twenty-seven of the quantified molecules were significantly different between feces and cecum content, mainly pertaining to amino acids and organic acids. Moreover, in terms of mammals and non-mammals, short-chain fatty acids could be considered the main factor discriminating the metabolomic profiles between feces and cecum content. Furthermore, to better understand the mechanism of their metabolomic differences, 16S rRNA sequencing analysis was performed on feces and cecum content samples of mice, which is the most widely used animal model. The result showed that the Ace, Shannon, and Sobs indexes in feces were significantly higher than those of cecum content (p < 0.05). At the phylum and genus levels, the microbiota structures of feces and cecum content were similar, while the relative abundances of their microbiota exhibited distinct features. Conclusions: The present study could reduce this gap in information by characterizing, for the first time, the metabolomic differences between feces and cecum content using 1H-NMR. Moreover, this study is meant as a reference guide for researchers wishing to apply a metabolomics approach to the gut of the host.

1. Introduction

Metabolomics is widely utilized to unveil the metabolic characteristics of biofluids in distinct physiological states, thereby shedding light on their physiological and pathological processes. In the realm of animal research, the application of metabolomics has yielded noteworthy outcomes [1,2,3]. Feces and cecum content serve as pivotal indicators of gut microbial metabolism, and their metabolomic profiles are influenced by various factors, primarily pertaining to physiological states [4]. For instance, our previous study indicated that the concentrations of sixteen molecules in feces were found to be significantly different between mastitic and healthy cows [5]. Another previous study showed that a high-fat diet could significantly elevate the concentrations of acetone and methionine while decreasing the levels of methanol, arabinose, acetate, and 3-hydroxyphenylacetate in the cecum contents of mice [6].
Fecal metabolomics is increasingly employed to examine the composition and dynamic patterns of gut microbial metabolism in animal models by taking advantage of its simple and non-invasive features. Fecal sample collection is not harmful to animals, so it is suitable for monitoring metabolomic changes in the host in a longitudinal study [7]. Zhang et al. found that major changes in fecal metabolites were confirmed for HFID-fed mice, including those related to entero-hepatic circulation (i.e., bile acids), tryptophan metabolism (e.g., indole derivatives), and lipid metabolism (e.g., lipoic acid), as well as increased antioxidants, including isorhapontigenin [8]. Kang et al. found that the contents of malic acid, malonic acid, succinic acid, and fumaric acid in the normal mice’s feces were 2–10 times higher than those of obese mice [9]. In addition, gut content, being a biological sample in contact with the gut microbiota, offers a direct insight into the gut microbiota metabolism [10,11,12,13]. Zhou et al. found that 0.4% of Ampelopsis grossedentata extract supplementation significantly downregulated the levels of p-cresol sulfate and cholesterol sulfate in broilers’ gut content, through a UPLC-MS-based metabolomic approach [14].
However, the metabolomic point of view obtained from feces and cecum content is distinct in several cases. Therefore, the proper selection of feces and cecum contents for metabolomic analyses within the same animal model under identical treatments is still a question mark. For example, Van Hul et al. attempted to compare the effects of soluble corn fiber and fructooligosaccharides on the metabolism of high-fat-diet-fed mice. The results showed that the levels of isobutyric acid, isovaleric acid, and valeric acid in cecum content were significantly different among the groups investigated. In parallel, the same molecules were found to be non-significantly different in feces, even if other short-chain fatty acids, like acetic acid and butyric acid, were [15].
Currently, there is a prevalence of metabolomics analyses focusing on either cecum contents or feces in animal models. However, representative studies comparing the similarities and differences of metabolomic profiles between feces and cecum content are relatively limited. In order to fill such gaps, the present comparative study attempted to investigate by 1H-NMR the metabolomic differences between feces and cecum content in several commonly observed animals, namely mouse, pig, chicken, duck, rabbit, Gansu yak, Sichuan yak, and sheep. The findings of this study will provide a reference guide for researchers seeking to apply a metabolomics approach to the feces and cecum content and provide new lines of evidence for researchers to select the best-suited samples for their investigations.

2. Materials and Methods

2.1. Sampling

The experimental designs and protocols of the current study received approval from the Southwest Minzu University Animal Ethics Committee (Protocol NO. SWUN-A-0060) and adhered to the recommendations outlined in the academy’s guidelines for animal research.
All feces and cecum content samples, involving pig, chicken, duck, rabbit, Gansu yak, Sichuan yak, and sheep, were randomly collected from a slaughterhouse in Chengdu, Sichuan. Mice were housed in the laboratory for one week. Then, feces and cecum content samples were collected right after sacrifice. A total of 80 samples, belonging to eight animals, were considered for the present study (5 feces and 5 cecum content samples per animal). All samples were promptly transported to the laboratory with ice and stored at −80 °C for the subsequent analysis.

2.2. Metabolomic Analysis

NMR solutions were prepared in accordance with previous works of some of the authors [16], as indicated in Figure 1.
As shown in Figure 1a, we added 80 mg of each stool/cecum content sample to 1 mL of deionized water in an Eppendorf tube and vortexed for 5 min, followed by centrifugation for 15 min at 18,630× g and 4 °C. Subsequently, we moved 0.7 mL of supernatant to a new Eppendorf tube, together with 0.2 mL of NMR analysis solution described above. After a final centrifugation, the supernatant was transferred to an NMR tube. As previously reported [17], the 1H-NMR spectra were recorded at 298 K using an AVANCE III spectrometer (Bruker, Wuhan, China) at 600.13 MHz.
As shown in Figure 1b, the baseline 1H-NMR spectrum was adjusted by peak detection according to the “rolling ball” principle [18] implemented in the baseline R package 4.4.1 [19]. Differences in water and fiber content among samples were taken into consideration by probabilistic quotient normalization [20] applied to the entire spectra array.
As shown in Figure 1c, the broad resonance signal from the macromolecule was suppressed by a CPMG filter consisting of 400 echoes with 400 μs of τ, 24 μs of 180° pulses, and 330 ms of total filter. The HOD residual signal was suppressed by a prediction method. This was accomplished by using cpmgpr1d sequences from a library of standard pulse sequences. Each spectrum was acquired to summarize 256 transients using 32 K data points in a 7184 Hz spectral window with an acquisition time of 2.28 s. The acquisition time was 2.28 s.
In order to apply NMR as a quantitative technique [21], the cycle delay was set to 5 s, keeping in mind the relaxation time of the proton under investigation. These signals were assigned by calculating their chemical shifts and diversity using the Chenomx software library (Chenomx Incorporation, Edmonton, AL, Canada, version 8.4). Molecule quantification was carried out by means of signal integration.

2.3. Microbiome Analysis

Total genomic DNA of the microbial community was extracted from feces and cecum contents according to the instructions of the E.Z.N.A.® soil DNA kit (Omega Bio-tek, Norcross, GA, USA). As shown in Figure 2, the quality of the DNA was checked by agarose gel electrophoresis, and the concentration and purity of DNA were determined by NanoDrop2000 (Thermo Fisher Scientific, Waltham, MA, USA). The hypervariable region V3-V4 of the bacterial 16S rRNA gene was amplified with primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [22] by T100 Thermal Cycler PCR thermocycler (BIO-RAD, Hercules, CA, USA).
PCR amplification was performed, and the recovered products were detected and quantified using Qubit 4.0 (Thermo Fisher Scientific, USA). The purified PCR products were constructed using a library with the NEXTFLEX® Rapid DNA-Seq Kit. High-throughput sequencing data were analyzed for species taxonomy of ASVs based on the Silva 16S rRNA gene database (v 138) using the Naive Bayes (or Vsearch 2.30.0, or Blast) classifier in Qiime2 [23], and 16S function prediction analysis was performed using PICRUSt2 [24] (version 2.2.0) software.

2.4. Statistical Analysis

Statistical analysis on metabolomic data was conducted in the R computational language [25]. Molecules whose concentration varied between groups were assessed using a t-test. For this purpose, a cut-off p-value of 0.05 was accepted. Prior to the t-test, variables that were not normally distributed were normally distributed by following Box and Cox [26]. To highlight the trends underlying the structure of the samples, we relied on PLS-DA models on the molecules accepted by the above-described univariate analysis [27]. Microbiota data analysis was performed on the Meggie BioCloud platform (https://cloud.majorbio.com, accessed on 15 December 2024) [28]. A clustering correlation heatmap was generated using the OmicStudio tools (https://www.omicstudio.cn, accessed on 15 December 2024) [29].

3. Results

3.1. Metabolomic Characterizations of Feces and Cecum Content

Taking advantage of 1H-NMR, a total of 116 molecules were identified and quantified in feces and cecum content samples from all the involved animals. Information on the above molecules is provided in Supplementary Materials Table S1. A representative spectrum, which contains all the molecules identified from all samples, is shown in Figure 3. As the molecules characterized from feces and cecum content of the same animal are identical, in order to visually identify the metabolomic differences among distinct animals, an upset plot was generated, as shown in Supplementary Materials Figure S1.

3.2. Comparative Metabolomic Analysis of Feces and Cecum Content in Mammals and Non-Mammals

As shown in Figure 4, PLS-DA analysis indicated that the main differences between feces and cecum content regarded short-chain fatty acids (SCFAs), namely acetate, butyrate, and propionate. However, it is worth noting that the trend of SCFAs in each mammal model was peculiar. In detail, mouse, pig, and Sichuan yak had higher levels of SCFAs in feces, while rabbit, sheep, and Gansu yak showed an opposite trend.
Figure 5 shows PLS-DA models built on feces and cecum content of non-mammals. It is remarkable to notice that, similarly to the mammal models studied, the main differences between feces and cecum content were again SCFAs, with opposite overall trends between chicken and duck.

3.3. Microbiome Characterizations of Feces and Cecum Content in Mouse

In order to underline the biological mechanisms leading to the metabolomic differences observed between feces and cecum content, the samples from the mouse, which is the most commonly used animal model in experiments, were subjected to 16S rRNA analysis. Furthermore, correlations between metabolomic and microbiome profiles of feces and cecum content were investigated.
As shown in Figure 6, the Ace, Shannon, and Sobs indexes in feces were significantly higher than those of cecum content (p < 0.05). In addition, the PCA plot of β-diversity double confirmed the differences of microbiome features between feces and cecum content.
In order to address the difference in microorganisms’ composition between feces and cecum content, a stacking diagram was created at the phylum and genus levels, as shown in Figure 7. At the phylum level, Firmicutes and Bacteroidota were the dominant microorganisms, followed by Desulfobacterota and Campilobacterota in cecum content and feces, respectively. At the genus level, Lactobacillus and norank_f_Muribaculaceae could be considered as the dominant microorganisms, followed by others and Bacteroides in cecum content and feces, respectively.
As shown in Figure 8a, the level of glucose was positively correlated with the abundance of Prevotellaceae_UCG-001. Moreover, the amounts of lactate and xylose were positively correlated with the abundance of Lachnospiraceae_NK4A136_group. Referring to feces, the concentration of fructose was positively related to the abundance of Bacillus and unclassified_c_Bacilli, while negatively related to the amount of Enterorhabdus. In addition, the concentration of succinate was positively correlated with the amounts of Lachnospiraceae_NK4A136_group and unclassified_f_Lachnospiraceae, as shown in Figure 8b.

4. Discussion

In recent years, numerous studies have been conducted to analyze the metabolome of biofluids in animal models, attempting, for example, to investigate the features connected to health and disease [30]. Fecal metabolomics is widely utilized to provide insights into gut–microbial co-metabolism, taking advantage of its non-invasiveness and ease of sample collection. In parallel, cecum content can allow for a more direct observation of the connections between gut flora and host metabolism, which counterbalances the complex sample collection procedures [31]. However, it is noteworthy that the trends highlighted in feces and cecum content have been found to be different in some cases. For example, in a metabolomics study on cecum content, Zhou et al. found that green tea catechin epigallocatechin gallate (EGCG) could alleviate high-fat-diet-induced obesity in mice, by increasing the gut microbial abundance and reducing short-chain fatty acids (SCFAs) [32]. However, another metabolomics study on the feces of the same animal found that administration of tea water extracts could improve glucose tolerance and significantly increase the production of SCFAs [33]. Therefore, investigating the metabolomic differences between feces and cecum content appears of the utmost importance. On one hand, these studies can assist a researcher in choosing the best samples for his/her experiments. On the other hand, he/she can understand whether a certain feature is connected to the performed experiment or the selected matrix.
The number of molecules quantified in the present study is higher than in previous studies based on the same technique and involving the same animals and biofluids. Examples can be found for mouse feces (89 vs. 67) [34]; mouse cecum content (89 vs. 33) [35]; pig feces (52 vs. 39) [36]; rabbit cecum content (59 vs. 29) [37]; and sheep feces (43 vs. 28) [38].
In mammals, dietary carbohydrates are mainly fermented into SCFAs, such as acetate, propionate, and butyrate. As one of the most abundant SCFAs, acetate could be formed from pyruvate by acetogenic bacteria [39]. Propionate was reported to be mainly produced from succinate by Bacteroidetes [40]. In meat rabbits, Fang et al. found that fecal SCFAs levels were potentially correlated with high and low fattening body weights. In detail, the level of butyrate in feces was significantly positively related to finishing weight, while the concentrations of acetate and propionate showed a positive and negative association with finishing weight, respectively [41]. As suggested by Zhao et al., propionate and butyrate are mainly produced in the hindgut of pigs [42]. Butyrate plays a crucial role in intestinal homeostasis as a major energy source for colonocytes, and it is important for maintaining tissue barrier function [43]. Moreover, large quantities of acetate and lactate could facilitate the synthesis of butyrate in the gut by butyrate-producing bacteria [44,45]. Xylooligosaccharide (XOS) administration could significantly increase the levels of SCFAs, both in feces and cecum content, of high-fat-diet-induced obese mice, which could be linked to its modulation by gut microorganisms like Prevotella and Paraprevotella [46,47]. In sheep, the trends of SCFAs have been found to differ partially between feces and cecum content under N-carbamoylglutamate supplementation. In detail, Zhang et al. found that N-carbamoylglutamate supplementation elevated the levels of acetate, butyrate, and propionate in the colon of lambs [48], while it reduced the level of acetate in feces, as assessed by Ma et al. [49].
In the gut, the host-derived and gut microbiota-derived proteases and peptidases break down dietary protein to amino acids, which are absorbed by the host or reach the colon, where they are further metabolized to SCFAs, including acetate, butyrate, propionate, and BCFAs by colonic microbes [50,51]. In the present study, compared to feces, higher levels of amino acids in cecum content could be linked to their reabsorption and metabolism in the colon. Pyruvate is a crucial intermediate for propionate production through gluconeogenesis and glycolysis [52,53]. Xylose is mainly absorbed in the small intestine at a rate slower than that of galactose and glucose [54,55], which could promote the growth and reproduction of anaerobic bacteria, such as Bacteroidota [56].
In order to further underline the reasons for the metabolomic differences we found between cecum content and feces, as the most commonly used animal model, mouse feces and cecum content were collected specifically to obtain their microbiome’s features by means of 16S rRNA analysis. At the phylum level, we found that Firmicutes and Bacteroidota were the dominant microorganisms in feces and cecum contents, which could be attributed to the digestion of dietary fiber and polysaccharides [57,58]. The distinct ratio between Firmicutes and Bacteroidota could result in the different concentrations of SCFAs between feces and cecum content [59]. Unclassified_f_Lachnospiraceae and Lachnospiracae_NK4A136_group were shown to contribute to the production of butyrate [60,61]. Bacillus has the ability to digest fructose and is of interest as a potential probiotic for the treatment of metabolic disorders [62,63,64], which has been highlighted by correlation analysis in the present study. Similar to Bacillus, Prevotellaceae_UCG-001 is a bacterium that has the ability to produce SCFAs. Decreased quantities of Prevotellaceae_UCG-001 in feces and cecum content have been confirmed to be related to several diseases, such as ulcerative colitis [65], chemical poisoning [66], and inflammatory bowel disease [67].

5. Conclusions

The objective of the present study was to investigate the metabolomic differences between feces and cecum content using 1H-NMR. A total of 116 molecules were characterized in feces and cecum content samples of various animals, a number higher than any previous study based on 1H-NMR. Taking advantage of uni- and multivariate analyses, twenty-seven of the quantified molecules were found significantly different between feces and cecum content, mainly pertaining to amino acids and organic acids groups. Moreover, both in mammals and non-mammals, SCFAs could be considered the main factor discriminating the metabolomic profiles of feces and cecum content, probably in connection with the action of the microbiome. It is noteworthy that this study has several limitations for future investigation. As samples were collected randomly from a slaughterhouse, future research should account for influential factors, such as dietary regimens and feeding environments. Additionally, while only murine microbiota was analyzed, incorporating microbiota data from other animal species would strengthen the reference value of findings. Finally, future studies should evaluate interspecies variation in both metabolomic profiles and microbiota characteristics across animals.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/metabo15090565/s1, Figure S1: Upset plot showing metabolites in common and specific to the eight animals. Table S1: Information of molecules characterized by 1H-NMR.

Author Contributions

Conceptualization, X.N. and C.Z.; methodology, C.Z. and L.L.; formal analysis, X.L.; investigation, Y.L.; writing—original draft preparation, X.L., Y.L., C.Z., and L.L.; writing—review and editing, X.L., Y.L., C.Z., Q.L., L.L., and G.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Project of Doctoral Training Camp of Sichuan Tourism University (2023SCTUBSZD05), the Innovative Team of Sichuan Tourism University (22SCTUTP01), High Level Research Project of Sichuan Tourism University (2022PY03), and the Southwest Minzu University Research Startup Funds (Grant No. RQD2021079).

Institutional Review Board Statement

The study was approved by the Ethics Committee of Southwest Minzu University (protocol code SWUN-A-0060 and date of approval 15 February 2023).

Informed Consent Statement

Not applicable.

Data Availability Statement

Data from this study are available upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. (a) Sample preparation of feces and cecum contents; (b) applied to the entire spectral array using probabilistic quotient normalization; (c) 1H-NMR main conditions.
Figure 1. (a) Sample preparation of feces and cecum contents; (b) applied to the entire spectral array using probabilistic quotient normalization; (c) 1H-NMR main conditions.
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Figure 2. Pictorial representation of (a) DNA extraction, (b) PCR amplification, Illumina PE300 sequencing, and amplicon sequence processing and analysis.
Figure 2. Pictorial representation of (a) DNA extraction, (b) PCR amplification, Illumina PE300 sequencing, and amplicon sequence processing and analysis.
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Figure 3. A representative 1H-NMR spectrum of feces and cecum content from all samples.
Figure 3. A representative 1H-NMR spectrum of feces and cecum content from all samples.
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Figure 4. PLS-DA model built on the basis of molecular concentrations characterized by 1H-NMR for each mammal model. (a,b) Mouse; (c,d) Pig; (e,f) Rabbit; (g,h) Sheep; (i,j) Sichuan yak; (k,I) Gansu yak.
Figure 4. PLS-DA model built on the basis of molecular concentrations characterized by 1H-NMR for each mammal model. (a,b) Mouse; (c,d) Pig; (e,f) Rabbit; (g,h) Sheep; (i,j) Sichuan yak; (k,I) Gansu yak.
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Figure 5. PLS-DA model built on the concentrations of molecules characterized by 1H-NMR in each non-mammal. (a,b) Chicken; (c,d) Duck.
Figure 5. PLS-DA model built on the concentrations of molecules characterized by 1H-NMR in each non-mammal. (a,b) Chicken; (c,d) Duck.
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Figure 6. α-Diversity and β-diversity of feces and cecum content: Ace index (a), Shannon index (b), Sobs index (c), and PCA plot of β-diversity (d); * denotes significant differences between the two groups (p < 0.05).
Figure 6. α-Diversity and β-diversity of feces and cecum content: Ace index (a), Shannon index (b), Sobs index (c), and PCA plot of β-diversity (d); * denotes significant differences between the two groups (p < 0.05).
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Figure 7. The composition of different microorganisms between feces and cecum content samples at the phylum level (a) and the genus level (b) (n = 5).
Figure 7. The composition of different microorganisms between feces and cecum content samples at the phylum level (a) and the genus level (b) (n = 5).
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Figure 8. Heatmap of Spearman correlation between VIP molecules and the TOP 15 microorganisms at the genus level: (a) cecum content; (b) feces.
Figure 8. Heatmap of Spearman correlation between VIP molecules and the TOP 15 microorganisms at the genus level: (a) cecum content; (b) feces.
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Li, X.; Li, Y.; Nie, X.; Zhu, C.; Luo, Q.; Laghi, L.; Picone, G. Metabolomic Analysis of Feces vs. Cecum Content in Animals: A Comparative Study Investigated by 1H-NMR. Metabolites 2025, 15, 565. https://doi.org/10.3390/metabo15090565

AMA Style

Li X, Li Y, Nie X, Zhu C, Luo Q, Laghi L, Picone G. Metabolomic Analysis of Feces vs. Cecum Content in Animals: A Comparative Study Investigated by 1H-NMR. Metabolites. 2025; 15(9):565. https://doi.org/10.3390/metabo15090565

Chicago/Turabian Style

Li, Xiexin, Yang Li, Xin Nie, Chenglin Zhu, Qiqi Luo, Luca Laghi, and Gianfranco Picone. 2025. "Metabolomic Analysis of Feces vs. Cecum Content in Animals: A Comparative Study Investigated by 1H-NMR" Metabolites 15, no. 9: 565. https://doi.org/10.3390/metabo15090565

APA Style

Li, X., Li, Y., Nie, X., Zhu, C., Luo, Q., Laghi, L., & Picone, G. (2025). Metabolomic Analysis of Feces vs. Cecum Content in Animals: A Comparative Study Investigated by 1H-NMR. Metabolites, 15(9), 565. https://doi.org/10.3390/metabo15090565

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