Development of a simultaneous quantification method for the gut microbiota-derived core nutrient metabolome in mice and its application in studying host-microbiota interaction
Graphical abstract
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.
References (58)
- et al.
Epigenetics in human obesity and type 2 diabetes
Cell Metabol.
(2019) Obesity wars: molecular progress confronts an expanding epidemic
Cell (Cambridge, MA, U. S.)
(2004)- et al.
From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites
Cell (Cambridge, MA, U. S.)
(2016) - et al.
Gut microbiota-produced succinate promotes C. difficile infection after antibiotic treatment or motility disturbance
Cell Host Microbe
(2014) - et al.
Specialized metabolites from the microbiome in health and disease
Cell Metabol.
(2014) - et al.
Microbiota and fatty liver disease-the known, the unknown, and the future
Cell Host Microbe
(2020) - et al.
Gut microbiota dysbiosis contributes to the development of hypertension
Microbiome
(2017) - et al.
Altered gut microbiota and short chain fatty acids in Chinese children with autism spectrum disorder
Sci. Rep.
(2019) - et al.
Gut microbiota induces DNA methylation via SCFAs predisposing obesity-prone individuals to diabetes
Pharmacol. Res.
(2022) - et al.
A sensitive GC/MS detection method for analyzing microbial metabolites short chain fatty acids in fecal and serum samples
Talanta
(2019)
Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications
J. Chromatogr. A
An isotope-labeled chemical derivatization method for the quantitation of short-chain fatty acids in human feces by liquid chromatography-tandem mass spectrometry
Anal. Chim. Acta
Variable influence on projection (VIP) for OPLS models and its applicability in multivariate time series analysis
Chemometr. Intell. Lab. Syst.
A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance
Cell Metabol.
Glycine supplementation during calorie restriction accelerates fat loss and protects against further muscle loss in obese mice
Clin. Nutr.
Probiotics Lactobacillus reuteri DSM 17938 and Lactobacillus casei CRL 431 modestly increase growth, but not iron and zinc status, among Indonesian children aged 1-6 years
J. Nutr.
Gut microbiota role in dietary protein metabolism and health-related outcomes: the two sides of the coin
Trends Food Sci. Technol.
The promise of new anti-obesity therapies arising from knowledge of genetic obesity traits
Nat. Rev. Endocrinol.
Long term but not short term exposure to obesity related microbiota promotes host insulin resistance
Nat. Commun.
Gut-associated IgA+ immune cells regulate obesity-related insulin resistance
Nat. Commun.
Metabolic and gut microbial characterization of obesity-prone mice under a high-fat diet
J. Proteome Res.
An obesity-associated gut microbiome with increased capacity for energy harvest
Nature (London, U. K.)
The gut microbiota as an environmental factor that regulates fat storage
Proc. Natl. Acad. Sci. U. S. A
Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes
Science (Washington, DC, U. S.)
Gut microbial metabolites as multi-kingdom intermediates
Nat. Rev. Microbiol.
Insights into the role of the microbiome in obesity and type 2 diabetes
Diabetes Care
Butyrate acts through HDAC inhibition to enhance aryl hydrocarbon receptor activation by gut microbiota-derived ligands
Gut Microb.
Gut microbiota, metabolites and host immunity
Nat. Rev. Immunol.
Intestinal plasticity and metabolism as regulators of organismal energy homeostasis
Nature Metabolism
Cited by (1)
- 1
These authors contributed equally to this work.