Efficient Extraction from Mice Feces for NMR Metabolomics Measurements with Special Emphasis on SCFAs

Gut-Brain-axis: effect of basil oil on the gut microbiota and its contribution to the anticonvulsant properties

BACKGROUND

Epilepsy is a chronic neurological condition that disrupts the normal functioning of the brain and it is characterized by seizures. Research suggests the involvement of the Gut-Brain axis in epilepsy. This study seeks to determine the role of the gut microbiota in the anticonvulsant effect of basil oil (BO) using antibiotic-depleted and altered germ-free mice against naïve mice in Pentylenetetrazole (PTZ) induced seizure model. There is an ever growing interest in improvement of treatment outcomes in epilepsy and also in the development of newer therapeutic options, especially in the population of patients that do not attain seizure relief from available antiseizure medications (ASMs). According to research, gut microbiota can alter brain function and development. Increasing evidence suggests disrupting the delicate symbiotic balance existing between the gut and brain results in disease conditions. Also, the oil from Ocimum basilicum L., (BO) has been proven scientifically to significantly block clonic seizures induced by PTZ and picrotoxin in seizure models.

METHODS

The microbiota of mice were depleted or altered by administering cocktail antibiotics and individual antibiotics respectively. DNA was isolated from mouse stool, and then the 16S ribosomal ribonucleic acid (16S rRNA) gene was quantitatively amplified using reverse transcription-polymerase chain reaction (RT-PCR). Amplicons were sequenced to determine the phylogenetic make-up of the bacteria involved. Metabolic profiles of the serum and stool of mice were determined using Proton (1H) Nuclear Magnetic Resonance (NMR) spectroscopy.

RESULTS

Cocktail antibiotic pre-treatment significantly reversed the anticonvulsant effect of BO by increasing frequency and duration of seizures but did not affect latency to seizure. In mice pre-treated with single antibiotics, the anticonvulsant effect of BO was lost as latency to seizures, frequency and duration of seizures increased compared to mice that received only BO. Assessment of the phylogenetic make-up of the microbiota in antibiotic pre-treated mice showed a distorted composition of the microbiota compared to the control group.

CONCLUSION

Depletion of the microbiota significantly reversed the anticonvulsant actions of BO. The concentrations of short chain fatty acids (SCFAs) was higher in stool than in the serum of the mice. Administration of BO probably does not influence the microbial composition within the mouse microbiota. The elevated ratio of Firmicutes to Bacteroidetes in microbiota-depleted groups might have contributed to the reversal of anticonvulsant actions of BO.

Gut microbiota short-chain fatty acids and their impact on the host thyroid function and diseases

Thyroid disorders are clinically characterized by alterations of L-3,5,3',5'-tetraiodothyronine (T4), L-3,5,3'-triiodothyronine (T3), and/or thyroid-stimulating hormone (TSH) levels in the blood. The most frequent thyroid disorders are hypothyroidism, hyperthyroidism, and hypothyroxinemia. These conditions affect cell differentiation, function, and metabolism. It has been reported that 40% of the world's population suffers from some type of thyroid disorder and that several factors increase susceptibility to these diseases. Among them are iodine intake, environmental contamination, smoking, certain drugs, and genetic factors. Recently, the intestinal microbiota, composed of more than trillions of microbes, has emerged as a critical player in human health, and dysbiosis has been linked to thyroid diseases. The intestinal microbiota can affect host physiology by producing metabolites derived from dietary fiber, such as short-chain fatty acids (SCFAs). SCFAs have local actions in the intestine and can affect the central nervous system and immune system. Modulation of SCFAs-producing bacteria has also been connected to metabolic diseases, such as obesity and diabetes. In this review, we discuss how alterations in the production of SCFAs due to dysbiosis in patients could be related to thyroid disorders. The studies reviewed here may be of significant interest to endocrinology researchers and medical practitioners.

Metabolic phenotyping reveals a potential link between elevated faecal amino acids, diet and symptom severity in individuals with severe mental illness

The brain-gut axis is increasingly recognized as an important contributing factor in the onset and progression of severe mental illnesses such as schizophrenia spectrum disorders and bipolar disorder. This study investigates associations between levels of faecal metabolites identified using ¹H-NMR, clinical parameters, and dietary components of forty-two individuals diagnosed in a transdiagnostic approach to have severe mental illness. Faecal levels of the amino acids; alanine, leucine, and valine showed a significant positive correlation with psychiatric symptom severity as well as with dairy intake. Overall, this study proposes a diet-induced link between the brain-gut axis and the severity of psychiatric symptoms, which could be valuable in the design of novel dietary or therapeutic interventions to improve psychiatric symptoms.

Simultaneous quantification of eleven short-chain fatty acids by derivatization and solid phase microextraction - Gas chromatography tandem mass spectrometry

As metabolites of the gut microbiome, short-chain fatty acids (SCFAs) played an important role in the diagnosis of the metabolic diseases. Because of the high polarity, high volatility and complex matrix of biological samples, the highly sensitive, selective and accurate method to determine SCFAs remains a major challenge. Herein, a new method for simultaneous quantification of eleven SCFAs by derivatization combined with solid phase microextraction (SPME) and gas chromatography tandem mass spectrometry (GC-MS/MS) was developed. Isobutyl chloroformate coupled with isobutanol was used as the reaction reagent to derivatize SCFAs. The method validation data showed a satisfactory linearity with the linear regression coefficients (R) ranging from 0.9964 to 0.9996. The limit of detection (LOD) of all SCFAs ranges from 0.01 ng·mL^-1 to 0.72 ng·mL^-1 and the limit of quantification (LOQ) ranges from 0.04 ng·mL^-1 to 2.41 ng·mL^-1. The intra-day and inter-day precision (RSDs) ranged from 0.65% to 8.92% and 1.62% to 15.61%, respectively. The recovery ranged from 88.10% to 108.71%. Finally, the developed method was successfully used to determine SCFAs in mice fecal sample, and ten of the SCFAs were found in feces of mice, including formic acid.

Lactobacillus rhamnosus GG modifies the metabolome of pathobionts in gnotobiotic mice

Background

Lactobacillus rhamnosus GG (LGG) is the most widely used probiotic, but the mechanisms underlying its beneficial effects remain unresolved. Previous studies typically inoculated LGG in hosts with established gut microbiota, limiting the understanding of specific impacts of LGG on host due to numerous interactions among LGG, commensal microbes, and the host. There has been a scarcity of studies that used gnotobiotic animals to elucidate LGG-host interaction, in particular for gaining specific insights about how it modifies the metabolome. To evaluate whether LGG affects the metabolite output of pathobionts, we inoculated with LGG gnotobiotic mice containing Propionibacterium acnes, Turicibacter sanguinis, and Staphylococcus aureus (PTS).

Results

16S rRNA sequencing of fecal samples by Ion Torrent and MinION platforms showed colonization of germ-free mice by PTS or by PTS plus LGG (LTS). Although the body weights and feeding rates of mice remained similar between PTS and LTS groups, co-associating LGG with PTS led to a pronounced reduction in abundance of P. acnes in the gut. Addition of LGG or its secretome inhibited P. acnes growth in culture. After optimizing procedures for fecal metabolite extraction and metabolomic liquid chromatography-mass spectrometry analysis, unsupervised and supervised multivariate analyses revealed a distinct separation among fecal metabolites of PTS, LTS, and germ-free groups. Variables-important-in-projection scores showed that LGG colonization robustly diminished guanine, ornitihine, and sorbitol while significantly elevating acetylated amino acids, ribitol, indolelactic acid, and histamine. In addition, carnitine, betaine, and glutamate increased while thymidine, quinic acid and biotin were reduced in both PTS and LTS groups. Furthermore, LGG association reduced intestinal mucosal expression levels of inflammatory cytokines, such as IL-1α, IL-1β and TNF-α.

Conclusions

LGG co-association had a negative impact on colonization of P. acnes, and markedly altered the metabolic output and inflammatory response elicited by pathobionts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02178-2.

Sample Preparation and Data Analysis for NMR-Based Metabolomics

NMR spectroscopy has become one of the preferred analytical techniques for metabolomics studies due to its inherent nondestructive nature, ability to identify and quantify metabolites simultaneously in a complex mixture, minimal sample preparation requirement, and high degree of experimental reproducibility. NMR-based metabolomics studies involve the measurement and multivariate statistical analysis of metabolites present in biological samples such as biofluids, stool/feces, intestinal content, tissue, and cell extracts by high-resolution NMR spectroscopy-the goal then is to identify and quantify metabolites and evaluate changes of metabolite concentrations in response to some perturbation. Here we describe methodologies for NMR sample preparation of biofluids (serum, saliva, and urine) and stool/feces, intestinal content, and tissues for NMR experiments including extraction of polar metabolites and application of NMR in metabolomics studies. One dimensional (1D) ¹H NMR experiments with different variations such as pre-saturation, relaxation-edited, and diffusion-edited are routinely acquired for profiling and metabolite identification and quantification. 2D homonuclear ¹H-¹H TOCSY and COSY, 2D J-resolved, and heteronuclear ¹H-¹³C HSQC and HMBC are also performed to assist with metabolite identification and quantification. The NMR data are then subjected to targeted and/or untargeted multivariate statistical analysis for biomarker discovery, clinical diagnosis, toxicological studies, molecular phenotyping, and functional genomics.