Elsevier

Analytica Chimica Acta

Volume 1251, 22 April 2023, 341039
Analytica Chimica Acta

Development of a simultaneous quantification method for the gut microbiota-derived core nutrient metabolome in mice and its application in studying host-microbiota interaction

https://doi.org/10.1016/j.aca.2023.341039Get rights and content

Highlights

  • A microbiota-derived core nutrient metabolome was proposed, covering 43 metabolites.

  • A 3-NPH derivatization-based LC‒MS quantitative method was established.

  • Leucine, isoleucine, glycine, methionine, tyrosine, and glutamine were obesity-resistant microbiota-derived metabolites.

  • Lactobacillus and Dubosiella were inversely associated with bodyweight and positively correlated with the fecal amino acids.

Abstract

The gut microbiota interacts with the host via production of various metabolites of dietary nutrients. Herein, we proposed the concept of the gut microbiota-derived core nutrient metabolome, which covers 43 metabolites in carbohydrate metabolism, glycolysis, tricarboxylic acid cycle and amino acid metabolism, and established a quantitative UPLC-Q/TOF-MS method through 3-nitrophenylhydrazine derivatization to investigate the influence of obesity on the gut microbiota in mice. All metabolites could be simultaneously analyzed via separation on a BEH C18 column within 18 min. The lower limits of quantification of most analytes were less than 1 μM. Validation results demonstrated suitability for the analysis of mouse fecal samples. The method was then applied to detect the gut microbiota-derived nutrient metabolome in the feces of high-fat diet induced obese (DIO) and ob/ob (leptin-deficient) mice, as well as obesity-prone (OP) and obesity-resistant (OR) mice. Compared to the control groups, there were 13, 23 and 10 differentially abundant metabolites detected in ob/ob, DIO and OP groups, respectively. Among them, amino acids including leucine, isoleucine, glycine, methionine, tyrosine and glutamine were co-downregulated in the obese or OP mice and exhibited inverse association with body weight. 16S rDNA analysis revealed that the genera Lactobacillus and Dubosiella were also inversely associated with body weight and positively correlated with fecal amino acids. Collectively, our work provides an effective and simplified method for simultaneous quantifying the gut microbiota-derived core nutrient metabolome in mouse feces, which could assist various future studies on host-microbiota metabolic interaction.

Introduction

A combination of genetic and environmental factors has led to an epidemic of obesity, which causes multiple complications [1,2]. Genes related to leptin or functional leptin receptors are considered as the most important genetic factors and leptin-deficient (ob/ob) mice is a commonly used genetic obesity model [3]. High fat diet (HFD) is one of the well-studied environment factors of obesity, which has undoubtedly exacerbated the development of obesity through metabolic disturbance and more recently recognized gut dysbiosis [4,5]. Even with the same genetic background, some mice are obesity-prone (OP), whereas others remain lean (obesity-resistant, OR) in response to HFD feeding [6]. The gut microbiota plays a significant role in the etiology of obesity and metabolic disorders [7]. Accumulating evidences suggested that the gut microbiota may function as another environmental factor for both genetic and diet-related obesity [8]. Hundreds of bacteria in the intestine contribute to the host metabolism via the fermentation of dietary fibers into carbohydrate metabolites, such as short-chain fatty acids (SCFAs), or hydrolysis of dietary proteins or peptides into amino acids [[9], [10], [11]]. The intestinal metabolites originated from both the gut microbiota and the host participate in a variety of physiological processes, including energy metabolism, intercellular signaling, and host immunity [[12], [13], [14]].

Although the gut microbiota plays an important role in the etiology of obesity by bio-transforming nutrients to metabolites to disturb the host's energy balance [[15], [16], [17], [18], [19], [20], [21]], a comprehensive quantitative method for the gut microbiota-derived nutrient metabolome is still lacking. Thus, the development of a robust and targeted quantification method for the gut microbiota-derived core nutrient metabolome is imperative for elucidating the role of gut microbiome in obesity-associated disorders, including diabetes and fatty liver disease, as well as cardiovascular disease, Parkinson's disease and Alzheimer's disease, which have also been closely linked to gut microbiota dysbiosis.

There are a few studies reporting the use of liquid chromatography-tandem mass spectrometry (LC-MS) to investigate the metabolic function of gut microbiota, but most of them were focused on SCFA [[22], [23], [24]]. The commonly used method of SCFA quantification includes acidification after protein precipitation, and then determination by gas chromatography-mass spectrometry [25,26]. In addition to SCFA, our preliminary study showed that HFD feeding significantly influenced the fecal levels of amino acids and carbohydrate metabolites. However, the majority of the gut microbiota-derived nutrient metabolites contain hydroxy-, keto-, and carboxyl groups, which are not suitable to be analyzed by regular reversed-phase liquid chromatography due to their high polarity, weak interaction with the stationary phase, poor retention and separation on columns [27,28]. Traditional analysis method of these metabolites is carried out by ion-exchange chromatography, which causes high background signals or high ion suppression effects in MS detection, resulting in compromised detection sensitivity. Moreover, some carbohydrates are difficult to be ionized in mass spectrometer due to the absence of ionizable function groups. Alternatively, chemical derivatization has been proven to be an excellent method to improve compound detection characteristics in LC-MS [[29], [30], [31], [32], [33]]. Among the various derivatization reagents, 3-nitrophenylhydrazine (3-NPH) has been proven to successfully derivatize carboxylic acid, aldehydes, and ketones for LC-MS analysis and the resulted derivatives achieve improved chromatographic separation capability and detection sensitivity [[30], [31], [32], [33]]. Therefore, we aim to establish a suitable LC-MS method with high coverage for the analysis of the gut microbiota-derived core nutrient metabolites via 3-NPH derivatization, which can be used to study the correlation between gut microbiota metabolism and obesity.

Herein, we firstly used 3-NPH derivatization method to profile the fecal metabolome in the HFD-fed mice and conceptualized a gut microbiota-derived core nutrient metabolome, containing 43 metabolites involved in carbohydrate metabolism, glycolysis, the TCA cycle and amino acid metabolism. Then, a quantitative 3-NPH derivatization-based UHPLC-Q/-TOF-/MS method was established and applied to determine the metabolites in the feces of HFD induced obese (DIO), ob/ob and OP mice. This 3-NPH derivatization-based method enables robust analysis of various classes of gut microbiota-derived nutrient metabolites in a single LC-MS run. Meanwhile, combined with the 16S rDNA sequencing, correlations between the obesity-induced change of the microbiota and the nutrient metabolome were investigated, which allowed validation of feasibility and accuracy of the current method. Therefore, our method achieves better separation, higher sensitivity, and broader metabolite coverage, which facilitate the rapid and precise analysis of the gut microbiota-derived core nutrient metabolome and can be adopted to investigate the impact of metabolic disease on microbiota nutrient metabolism.

Section snippets

Materials

Citrate, isocitrate, α-ketoglutarate, succinate, malate, aconitic acid, fumarate, fructose, pyruvate, rhamnose, galacturonic acid, itaconic acid, glyceric acid, ribose, arabinose, nicotinic acid, mevalonate, α-hydroxyglutaric acid, 3-indolepropionic acid, lactate, phosphoenolpyruvate, acetic acid, d3-aspartate, d3-methionine, d4-acetic acid, 13C5-hydroxyglutaric acid and HPLC-grade methanol were purchased from Sigma-Aldrich (St. Louis, MO, USA). Propionic acid, butyric acid, isobutyric acid,

Microbiome-focused metabolomics analysis in obese mice feces

To investigate the impact of obesity on the gut microbial metabolism, a comprehensive list containing 1496 metabolites, 753 originated from microbiota and 743 shared by microbiota and host, was constructed based on the previously reported host-microbiota metabolic reaction network [[38], [39], [40]]. Among them, 1242 metabolites containing carbonyl and carboxyl groups accounted for a large proportion of the metabolome. Therefore, we firstly used the 3-NPH derivatization method to conduct

Discussion

Obesity-related metabolic disorders are associated with alterations in the composition and function of the gut microbiota and the microbial metabolites are key factors in host-microbiota crosstalk. To profile the microbial metabolome, we constructed a list of microbiota-related metabolites, and 83% of metabolites contained carbonyl, and carboxyl groups that could be derivatized with 3-NPH to enhance ionization efficiency and improve LC behavior [32,33]. Hence, in this study, we firstly applied

Conclusion

In this work, we provided a novel quantification strategy for the microbiota-derived core nutrient metabolome including 43 key metabolites based on a UPLC-Q/TOF-MS system using 3-NPH derivatization reagent with good linearity, precision, accuracy, stability, and recovery. This metabolite assay strategy was applied to the different obesity models and discovered that amino acids (leucine, isoleucine, glycine, methionine, tyrosine, glutamine) are obesity-flavored metabolites. Meanwhile, 16S rDNA

Funding

This study was supported by the National Key Research and Development Program of China (2021YFA1301200), Strategic Priority Research Program of the Chinese Academy of Sciences (XDB39020600), National Natural Science Foundation of China (82222071, 82104253), and Shanghai Municipal Science and Technology Major Project.

CRediT authorship contribution statement

Hualing Xu: performed the experiments, completed the method validation experiments, completed the 16S rDNA sequencing data analysis, completed the animal experiments, wrote the manuscript. Jiawen Wang: performed the experiments, completed the method validation experiments, completed the animal experiments. Yameng Liu: performed the experiments, completed the 16S rDNA sequencing data analysis. Yangyang Wang: performed the experiments, completed the animal experiments. Xianchun Zhong: performed

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.

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    These authors contributed equally to this work.

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