High-Resolution Metabolomics of 50 Neurotransmitters and Tryptophan Metabolites in Feces, Serum, and Brain Tissues Using UHPLC-ESI-Q Exactive Mass Spectrometry

Interplay between gut microbiota and tryptophan metabolism in type 2 diabetic mice treated with metformin

Tryptophan (TRP) metabolites have been identified as potent biomarkers for complications of type 2 diabetes mellitus (T2DM). However, it remains unclear whether the therapeutic effect of metformin in T2DM is related to the modulation of TRP metabolic pathway. This study aims to investigate whether metformin affects TRP metabolism in T2DM mice through the gut microbiota. A liquid chromatography-tandem mass spectrometry method was established to determine 16 TRP metabolites in the serum, colon content, urine, and feces of T2DM mice, and the correlations between metabolites and the T2DM mice gut microbiota were performed. The method demonstrated acceptable linearity (R2 > 0.996), with the limit of quantification ranging from 0.29 to 69.444 nmol/L for 16 analytes, and the limit of detection ranging from 0.087 to 20.833 nmol/L. In T2DM mice, metformin treatment effectively restored levels of indole-3-lactic acid (ILA), indole-3-propionic acid (IPA), and the ILA/IPA ratio, along with several aryl hydrocarbon receptor ligands in the serum, with a notable impact in the colon but not in the urine. This restoration was accompanied by a shift in the relative abundance of Dubosiella, Turicibacter, RF39, Clostridia_UCG-014, and Alistipes. Spearman's correlation analysis revealed positive correlations between Turicibacter and Alistipes with IPA and indole-3-acetic acid. Conversely, these genera displayed negative correlations with ILA and kynurenine. In addition, our study revealed the presence of endogenous indole pathway in germ-free mice, and the impact of metformin on endogenous TRP metabolism in T2DM mice cannot be disregarded. Further research is needed to investigate the regulation of TRP metabolism by metformin.

IMPORTANCE

This study provides valuable insights into the interrelationship between metformin administration, changes in the tryptophan (TRP) metabolome, and gut microbiota in type 2 diabetes mellitus (T2DM) mice. Indole-3-lactic acid (ILA)/indole-3-propionic acid (IPA) emerges as a potential biomarker for the development of T2DM and prediction of therapeutic response. While the indole metabolic pathway has long been associated exclusively with the gut microbiome, recent research has demonstrated the ability of host interleukin-4-induced-1 to metabolize TRP. The detection of indole derivatives in the serum of germ-free mice suggests the existence of inherent endogenous indole metabolic pathways. These findings deepen our understanding of metformin's efficacy in correcting TRP metabolic disorders and provide valuable directions for further investigation. Moreover, this knowledge may pave the way for the development of targeted treatment strategies for T2DM, focusing on the gut microbiome and restoration of associated TRP metabolism.

Fast Targeted Metabolomics for Analyzing Metabolic Diversity of Bacterial Indole Derivatives in ME/CFS Gut Microbiome

Disruptions in microbial metabolite interactions due to gut microbiome dysbiosis and metabolomic shifts may contribute to Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and other immune-related conditions. The aryl hydrocarbon receptor (AhR), activated upon binding various tryptophan metabolites, modulates host immune responses. This study investigates whether the metabolic diversity-the concentration distribution-of bacterial indole pathway metabolites can differentiate bacterial strains and classify ME/CFS samples. A fast targeted liquid chromatography-parallel reaction monitoring method at a rate of 4 minutes per sample was developed for large-scale analysis. This method revealed significant metabolic differences in indole derivatives among B. uniformis strains cultured from human isolates. Principal component analysis identified two major components (PC1, 68.9%; PC2, 18.7%), accounting for 87.6% of the variance and distinguishing two distinct B. uniformis clusters. The metabolic difference between clusters was particularly evident in the relative contributions of indole-3-acrylate and indole-3-aldehyde. We further measured concentration distributions of indole derivatives in ME/CFS by analyzing fecal samples from 10 patients and 10 healthy controls using the fast targeted metabolomics method. An AdaBoost-LOOCV model achieved moderate classification success with a mean LOOCV accuracy of 0.65 (Control: precision of 0.67, recall of 0.60, F1-score of 0.63; ME/CFS: precision of 0.64, recall of 0.7000, F1-score of 0.67). These results suggest that the metabolic diversity of indole derivatives from tryptophan degradation, facilitated by the fast targeted metabolomics and machine learning, is a potential biomarker for differentiating bacterial strains and classifying ME/CFS samples. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (ST002308, DOI: 10.21228/M8G13Q; ST003344, DOI: 10.21228/M8RJ9N; ST003346, DOI: 10.21228/M8RJ9N).

Automated Liquid Handling Extraction and Rapid Quantification of Underivatized Amino Acids and Tryptophan Metabolites from Human Serum and Plasma Using Dual-Column U(H)PLC-MRM-MS and Its Application to Prostate Cancer Study

Amino acids (AAs) and their metabolites are important building blocks, energy sources, and signaling molecules associated with various pathological phenotypes. The quantification of AA and tryptophan (TRP) metabolites in human serum and plasma is therefore of great diagnostic interest. Therefore, robust, reproducible sample extraction and processing workflows as well as rapid, sensitive absolute quantification are required to identify candidate biomarkers and to improve screening methods. We developed a validated semi-automated robotic liquid extraction and processing workflow and a rapid method for absolute quantification of 20 free, underivatized AAs and six TRP metabolites using dual-column U(H)PLC-MRM-MS. The extraction and sample preparation workflow in a 96-well plate was optimized for robust, reproducible high sample throughput allowing for transfer of samples to the U(H)PLC autosampler directly without additional cleanup steps. The U(H)PLC-MRM-MS method, using a mixed-mode reversed-phase anion exchange column with formic acid and a high-strength silica reversed-phase column with difluoro-acetic acid as mobile phase additive, provided absolute quantification with nanomolar lower limits of quantification within 7.9 min. The semi-automated extraction workflow and dual-column U(H)PLC-MRM-MS method was applied to a human prostate cancer study and was shown to discriminate between treatment regimens and to identify metabolites responsible for discriminating between healthy controls and patients on active surveillance.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

Differential Effects of Hypothermia and SZR72 on Cerebral Kynurenine and Kynurenic Acid in a Piglet Model of Hypoxic-Ischemic Encephalopathy

Kynurenic acid (KYNA), an endogenous neuroprotectant with antiexcitotoxic, antioxidant, and anti-inflammatory effects, is synthesized through the tryptophan-kynurenine (KYN) pathway. We investigated whether brain KYN or KYNA levels were affected by asphyxia in a translational piglet model of hypoxic-ischemic encephalopathy (HIE). We also studied brain levels of the putative blood-brain barrier (BBB) permeable neuroprotective KYNA analogue SZR72, and whether SZR72 or therapeutic hypothermia (TH) modified KYN or KYNA levels. KYN, KYNA, and SZR72 levels were determined using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry in five brain regions 24 h after 20 min of asphyxia in vehicle-, SZR72- and TH-treated newborn piglets (n = 6-6-6) and naive controls (n = 4). Endogenous brain KYN levels (median range 311.2-965.6 pmol/g) exceeded KYNA concentrations (4.5-6.0 pmol/g) ~100-fold. Asphyxia significantly increased cerebral KYN and KYNA levels in all regions (1512.0-3273.9 and 16.9-21.2 pmol/g, respectively), increasing the KYN/Tryptophan-, but retaining the KYNA/KYN ratio. SZR72 treatment resulted in very high cerebral SZR72 levels (13.2-33.2 nmol/g); however, KYN and KYNA levels remained similar to those of the vehicle-treated animals. However, TH virtually ameliorated asphyxia-induced elevations in brain KYN and KYNA levels. The present study reports for the first time that the KYN pathway is altered during HIE development in the piglet. SZR72 readily crosses the BBB in piglets but fails to affect cerebral KYNA levels. Beneficial effects of TH may include restoration of the tryptophan metabolism to pre-asphyxia levels.

Assisted Reductive Amination for Quantitation of Tryptophan, 5-Hydroxytryptophan, and Serotonin by Ultraperformance Liquid Chromatography Coupled with Tandem Mass Spectrometry

Herein, we used isotopic formaldehyde and sodium cyanoborohydride via reductive amination to label two methyl groups on primary amine to arrange the standards (h₂-formaldehyde-modified) and internal standards (ISs, d₂-formaldehyde-modified) of tryptophan and its metabolites, such as serotonin (5-hydroxytryptamine) and 5-hydroxytryptophan. These derivatized reactions with a high yield are very satisfactory for manufacturing standards and ISs. This strategy will generate one or two methyl groups on amine to create different mass unit shifts with 14 vs. 16 or 28 vs. 32 in individual compounds for biomolecules with amine groups. In other words, multiples of two mass units shift are created using this derivatized method with isotopic formaldehyde. Serotonin, 5-hydroxytryptophan, and tryptophan were used as examples to demonstrate isotopic formaldehyde-generating standards and ISs. h₂-formaldehyde-modified serotonin, 5-hydroxytryptophan, and tryptophan are standards to construct calibration curves, and d₂-formaldehyde-modified analogs such as ISs spike into samples to normalize the signal of each detection. We utilized multiple reaction monitoring modes and triple quadrupole mass spectrometry to demonstrate the derivatized method suitable for these three nervous biomolecules. The derivatized method demonstrated a linearity range of the coefficient of determinations between 0.9938 to 0.9969. The limits of detection and quantification ranged from 1.39 to 15.36 ng/mL.

Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics

BACKGROUND

The nasal mucosa, as a primary site of entry for inhaled substances, contains both inhaled xenobiotic and endogenous biomarkers. Nasal mucosa can be non-invasively sampled (nasal epithelial lining fluid "NELF") and analyzed for biological mediators. However, methods for untargeted analysis of compounds inhaled and/or retained in the nasal mucosa are needed.

OBJECTIVES

This study aimed to develop a high resolution LC-MS untargeted method to analyze collected NELF. Profiling of compounds in NELF samples will also provide baseline data for future comparative studies to reference.

METHODS

Extracted NELF analytes were injected to LC-ESI-MS. After spectrum processing, an in-house library provided annotations with high confidence, while more tentative annotation proposals were obtained via ChemSpider database matching.

RESULTS

The established method successfully detected unique molecular signatures within NELF. Baseline profiling of 27 samples detected 2002 unknown molecules, with 77 and 463 proposed structures by our in-house library and Chemspider matching. High confidence annotations revealed common metabolites and tentative annotations implied various environmental exposure biomarkers are also present in NELF.

SIGNIFICANCE

The experimental pipeline for analyzing NELF samples serves as simple and robust method applicable for future studies to characterize identities/effects of inhaled substances and metabolites retained in the nasal mucosa.

IMPACT STATEMENT

The nasal mucosa contains exogenous and endogenous compounds. The development of an untargeted analysis is necessary to characterize the nasal exposome by deciphering the identity and influence of inhaled compounds on nasal mucosal biology. This study established a high resolution LC-MS based untargeted analysis of non-invasively collected nasal epithelial lining fluid. Baseline profiling of the nasal mucosa (n = 27) suggests the presence of environmental pollutants, along with detection of endogenous metabolites. Our results show high potential for the analytical pipeline to facilitate future respiratory health studies involving inhaled pollutants or pharmaceutical compounds and their effects on respiratory biology.

Shenling Baizhu San ameliorates ulcerative colitis by regulating the gut microbiota and its tryptophan metabolites: A complementary medicine to mesalamine

ETHNOPHARMACOLOGICAL RELEVANCE

Shenling Baizhu San (SBS) is commonly employed to improve gastrointestinal dysfunction in patients with ulcerative colitis (UC) in China. SBS combined with mesalamine has been demonstrated to result in improve its curative effects without increasing any adverse reactions, but the underlying mechanism remains unclarified.

AIM OF THE STUDY

Our study aimed to illuminate the potential therapeutic effects and mechanisms of SBS, which is a medicine complementary to mesalamine, in the treatment of UC.

MATERIALS AND METHODS

A prospective cohort study was conducted to evaluate the efficacy of SBS as a complementary medicine to mesalamine for patients with UC (n = 48). The patients in the control group (n = 24) were given mesalamine alone, whereas those in the experimental group were administered mesalamine combined with SBS. The therapeutic outcome was assessed at 8 weeks. The structures of the gut microbiota (GMB) were characterized by 16S rRNA sequencing, and the microbial tryptophan metabolites were analyzed by UPLC-MS/MS to investigate the mechanism through which SBS achieves its effects.

RESULTS

Our results showed that the combination of SBS and mesalamine could significantly improve the clinical signs of UC by achieving mucosal healing and relieving colon damage. Interestingly, the combination of SBS and mesalamine could alter the GMB structures and increase the microbial levels of tryptophan metabolites, including indole-3-propionic acid and indole-3-acetic acid.

CONCLUSION

SBS combined with mesalamine is effective in improving the clinical and endoscopic outcomes of patients with UC. SBS, as a complementary therapy to conventional treatment, alleviates UC via the GMB-tryptophan metabolite axis.

Moving beyond descriptive studies: harnessing metabolomics to elucidate the molecular mechanisms underpinning host-microbiome phenotypes

Advances in technology and software have radically expanded the scope of metabolomics studies and allow us to monitor a broad transect of central carbon metabolism in routine studies. These increasingly sophisticated tools have shown that many human diseases are modulated by microbial metabolism. Despite this, it remains surprisingly difficult to move beyond these statistical associations and identify the specific molecular mechanisms that link dysbiosis to the progression of human disease. This difficulty stems from both the biological intricacies of host-microbiome dynamics as well as the analytical complexities inherent to microbiome metabolism research. The primary objective of this review is to examine the experimental and computational tools that can provide insights into the molecular mechanisms at work in host-microbiome interactions and to highlight the undeveloped frontiers that are currently holding back microbiome research from fully leveraging the benefits of modern metabolomics.